Improvement of empirical negative symptoms of schizophrenia in subjects using digital therapy with adaptive goal setting.

Abstract

A method is provided to bring about the necessary improvement in the user's empirical negative symptoms of schizophrenia. The calculation system can obtain a first metric associated with the user from multiple point in time. The calculation system can repeat the operation of identifying a configuration file selected based on the endpoint for improving empirical negative symptoms from multiple point in time. The calculation system can repeatedly present a set of content items identified by the configuration file to encourage the user to perform activities toward achieving the endpoint. The calculation system can obtain a second metric associated with the user from multiple point in time. If the second metric is lower than the first metric, the user is considered to have improved in the empirical negative symptoms of schizophrenia. The calculation system can enhance the effectiveness of medications the user is taking to cope with schizophrenia.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims priority to U.S. Provisional Patent Application No. 63 / 483,512, filed on February 6, 2023, entitled "Combination Therapy with Digital Therapeutics and Antipsychotics in Subjects with Experiential Negative Symptoms of Schizophrenia", the entire content of which is incorporated herein by reference.

Background Art

[0002] Schizophrenia is a leading cause of disability when adjusted for prevalence. Due to the chronic nature of schizophrenia, patients with schizophrenia often have low educational levels, reduced quality of life, difficulty living independently, and are frequently associated with impairments in social and vocational functioning. This disability is extremely severe, with only 10% to 20% of schizophrenia patients being employed full - time or part - time. Most patients require public funding, and the reduced productivity is a major factor contributing to high costs. In addition to the burden on patients, caregivers of schizophrenia patients also face a significant burden. Such disabilities and the resulting burdens on patients and caregivers typically tend to persist or worsen over time, and it has been confirmed that only 14% of those with schizophrenia achieved functional recovery in a recent meta - analysis.

[0003] In addition to the disability, schizophrenia imposes a heavy burden on the individual. The mortality rate is 2.5 times higher than that of the general population, and schizophrenia patients die 28.5 years earlier than the general population. This remarkable early mortality is mainly due to the high suicide rate. Schizophrenia patients have a 20 - fold higher suicide tendency compared to the general population. As a result, schizophrenia patients account for up to 23% of the people who die by suicide each year.

[0004] One of the major contributing factors to the severe burden described above is the presence of significant negative symptoms. Negative symptoms are often reported as the first symptoms of schizophrenia. Consequently, negative symptoms are present in 73% of patients before the onset of positive symptoms, in 90% of patients experiencing their first psychotic episode, and persist in 35-70% of patients with schizophrenia even after the initiation of treatment. Despite their early onset, negative symptoms cannot be effectively managed with standard of care (SOC) or other pharmacotherapy. Furthermore, these symptoms lead to further deterioration of function over time, making them a strong predictor of functional impairment.

[0005] Current guidelines for schizophrenia recommend antipsychotic medication and adjunctive psychosocial interventions. While antipsychotics are effective in treating positive symptoms, they have burdensome side effects, leading to a high rate of medication non-compliance. Furthermore, there are no pharmacological interventions available to treat negative symptoms. Therefore, adjunctive psychosocial interventions are recommended for negative symptoms and other symptoms that are not adequately improved by pharmacological interventions.

[0006] Significant barriers to treatment availability and adherence limit the effectiveness of standard of care (SOC) in addressing negative symptoms. First, there are currently no approved or authorized drugs, biological products, or medical devices for adjunctive therapy of experiential negative symptoms of schizophrenia. Second, while adjunctive psychosocial interventions are recommended to address negative symptoms, a shortage of psychiatrists and appropriately trained clinicians, coupled with a gap in evidence-based practice implementation among local healthcare providers, exacerbates this situation. As a result, only about 10% of schizophrenia patients receive evidence-based adjunctive psychosocial intervention for negative symptoms. Therefore, there is an unmet need for available therapies that target and improve negative symptoms.

[0007] Applications can be deployed and installed on computing devices to provide digital therapy aimed at improving a user's behavioral or psychological state. As part of the digital therapy, the application itself may include various elements displayed in a user interface and be configured to perform multiple actions when the user interacts with the elements of the user interface. After deployment, it may become difficult for the application to dynamically adapt to the user's responses, which may reduce the likelihood of the user interacting with the application. As a result, the efficacy of the digital therapy and the user's adherence to it may be significantly reduced.

[0008] In a network environment, an application on a client can display one or more content items received as part of a message sent from a server. These content items can be customized for a specific user on the client and can be selected based on multiple factors, such as user history, device type, location, and time. Content items can include scripts (e.g., event listeners or handlers) to specify their display functionality when loaded into the client application. For example, content items may be provided as part of a digital therapeutic platform and may contain text content related to user-specific conditions (e.g., smoking cessation, diet, exercise, psychological disorders, etc.). When an application on a client receives a content item, it can be displayed as a user interface element in the application's graphical user interface (GUI). When a content item is displayed through the application, the user can interact with one or more user interface elements to activate the functionality of the corresponding content item.

[0009] While providing content items in this manner may achieve the specified functionality, the range of available functionality and the selection of additional content items that the user can later choose from may be very limited. In the context of digital therapy, such selection and provision of content items may be insufficient to achieve endpoints associated with the user's medical condition. Firstly, the digital therapy application itself may have relatively static and fixed functionality after being deployed and installed on the client, without any application updates. For example, once the application is installed, the number of graphical user interface configurations for displaying content items may be limited. Furthermore, the user's state and behavior may change over time, and the information used to pre-select content items may not be sufficient to adapt to the user's relevant and constantly changing state.

[0010] Furthermore, content items themselves may lack the logic to properly reflect such changes, potentially resulting in generic and irrelevant content that doesn't conform to the user's state at the time of display, ultimately degrading the quality of human-computer interaction (HCI) between the application and the user. These can consume computational resources on the application for loading and rendering these content items, and lead to network bandwidth consumption due to round-trip communication between the client and server. From an HCI perspective, a lack of content item adaptability can lead to reduced user engagement with and compliance with the digital therapy provided through the display of content items. [Overview of the project]

[0011] To address these challenges, this specification presents a system and method for dynamically selecting application configuration files according to the Adaptive Goal Setting (AGS) framework. These configuration files can be initially selected and personalized based on the user's baseline measurements, and then updated in response to user feedback data. Delivering digital therapy through configuration files in this manner increases user engagement and adherence, ultimately improving the effectiveness of digital therapy. The AGS framework allows for continuous and dynamic adaptation of the application to the user and their endpoints by capturing user interests (aligning with the user's current situation), providing personalized content, and setting incremental goals based on baseline proficiency and metrics (aligning goals with the user's current capabilities). Furthermore, this framework allows users to engage in daily activities (e.g., multiple times a day) using the application on their personal mobile devices, potentially increasing engagement and therapeutic effectiveness (compared to traditional models where users only meet with their doctors once a week, or even less frequently, during clinic visits). The AGS framework also includes mood-altering strategies that address the user's condition and improve retention rates. Furthermore, the AGS framework can include personalization features such as daily status checks (check-in), self-assessment of pre- and post-activity evaluations, exploration of personal values, and "do it now" options.

[0012] A server (or computing device) can select a configuration file to provide to an application in order to present content. Each configuration file may contain automaton logic or modules (e.g., a finite state machine (FSM)) to specify one or more activities to be performed within a certain time period through a client-side application for one or more endpoints. In the case of a digital therapeutic application, the configuration file can identify a set of states and a set of transitions. Each state can correspond to a level associated with the activity of an endpoint, and can specify the activity to be performed through the application at that level. Each transition can correspond to a condition that must be met to move from one state to the next. For example, the condition could be success or failure, depending on whether the specified activity was performed within a certain period of time.

[0013] The selection and provision of configuration files, and their internal logic, may be carried out in accordance with an Adaptive Goal Setting (AGS) framework. The AGS framework can adaptively support the execution of activities toward endpoints (hereinafter also referred to as goals, targets, or objectives) in response to the user's state. This framework may include skill implementation, planning for endpoint achievement, tracking user progress, and adaptive changes to the activities to be included with respect to the endpoints.

[0014] To this end, during the implementation phase, a user profile can be built based on the user's status and responses to endpoint-related prompts (e.g., questionnaires). This user profile can be used to determine one or more endpoints as part of a plan to address the user's status, and to select configuration files for executing those endpoints. The AGS framework can be used to make adjustments such as: various goal categories (e.g., smoking cessation, dietary restrictions for weight loss, improving exercise habits, addressing symptoms of mental illness); various activity types (e.g., walking, running, swimming, hiking, dancing, climbing stairs); various activity difficulty levels (e.g., lead climbing, top-rope climbing, bouldering); activity duration (e.g., 5 minutes, 10 minutes, 1 hour); activity frequency (e.g., once a week, once a day, three times a day); and the time of day when the activity is performed (e.g., morning, afternoon, after meals).

[0015] Configuration files provide persistent state to the automaton logic, enabling the maintenance of user progress across usage sessions. The configuration file itself can be a plugin for the application and can be modified for specific endpoints (e.g., by a clinician) without relying on application reconfiguration. The automaton logic can be used to hold a set of variables, such as level, activity, endpoint, and frequency. A set of variables might correspond, for example, to a user performing Level 4 Activity A for 5 minutes every other morning. By creating a configuration file based on this information, it becomes possible to provide a set of content items that guide the user through the application on the client side, enabling them to perform and record the activity. As the user progresses along the automaton logic, the level, activity, endpoint, and frequency are updated, providing a customized experience for each state. This logic can adjust the content items up or down based on responses received through the application to match the user's current state. Furthermore, over time, different endpoints can be adaptively determined using response data, and various configuration files can be dynamically selected and provided to the application accordingly.

[0016] By providing configuration files in this manner, applications can be customized on demand, providing a more diverse user experience tailored to different endpoints, potentially improving human-computer interaction (HCI) between the user and the application. Since content items can be selected based on the user's associated state according to the configuration file, the degree of user engagement with the digital therapy application may increase. Increased engagement may lead to a greater effectiveness of the digital therapy provided by the application in improving the patient's condition and improving user adherence to the digital therapy. Furthermore, configuration files can reduce the need to update the application itself to provide additional functionality. Dynamically selecting and providing configuration files reduces the consumption of computing resources (e.g., processor, memory, network bandwidth) by providing and loading irrelevant content items on the application. Additionally, configuration files can reduce network bandwidth consumption associated with round-trip communication related to requesting and retrieving content items.

[0017] Aspects of this disclosure relate to a system, method, and non-temporary computer-readable medium for selecting an application configuration file. A computing system can maintain a number of configuration files readable by an application. Each of these configuration files can identify a corresponding set of content items that can prompt a user to perform at least one of several activities through the application toward achieving a corresponding endpoint among several endpoints. The computing system can determine from among several endpoints which endpoint addresses a user's state. The computing system can select one configuration file from among several configuration files, which identifies a set of content items for one of several activities that a user may perform through the application toward achieving a particular endpoint. The computing system provides the configuration file to the application and presents a set of content items that prompt a user to perform activities through the application.

[0018] In some embodiments, the computing system can receive response data from an application that identifies one or more interactions by the user with a set of content items presented through the application toward achieving an endpoint. Based on this response data relating to this endpoint, the computing system can determine a second endpoint from among several endpoints. Based on the second endpoint, the computing system can select a second configuration file from among several configuration files and provide it to the application.

[0019] In some embodiments, the computing system can identify a first level from multiple levels toward achieving an endpoint, based on the user's profile. The computing system can identify a transition from the first level to a second level based on response data that identifies one or more interactions between the user and a set of content items presented through the application. Based on the transition to the second level, the computing system can select a second configuration file from multiple configuration files. In some embodiments, the computing system can determine, based on the user's activity toward achieving the endpoint, whether to transition back to the same first level or to a second level toward achieving a second endpoint from multiple levels.

[0020] In some embodiments, each of the multiple configuration files may identify criteria that define a first measure about the user in order to select a corresponding configuration file that identifies a set of content items. These content items prompt the user to perform an activity. The calculation system may determine a second measure based on the user's profile. This measure may identify (i) the likelihood that the user will perform the activity, or (ii) the expected effect of this activity on the user toward achieving an endpoint. In some embodiments, the calculation system may identify a target activity from among multiple activities based on a comparison of the first and second measures.

[0021] In some embodiments, the computing system can receive responses from an application that identify the user's mood in response to prompts presented via the application at predetermined times. Furthermore, the computing system can identify an activity from among several based on the user's mood indicated in the response. The computing system can also receive responses from the application that identify several personal values ​​of the user. In some embodiments, the computing system can identify an activity from among several based on several values ​​associated with the user identified in the response. In some embodiments, the computing system can determine a progress metric based on the performance of activities toward achieving an endpoint. In some embodiments, the computing system can present the relationship between this progress metric and the personal values ​​associated with the user through the application.

[0022] In some embodiments, the computing system may prompt the user to provide a first evaluation associated with an activity before the activity is performed via the application. In some embodiments, the computing system may store the response from the application that identifies the first evaluation associated with the activity. In some embodiments, the computing system may prompt the user to provide a second evaluation associated with the activity after the activity is performed via the application. In some embodiments, the computing system may present a comparison of the first and second evaluations via the application. In some embodiments, the computing system may determine a target endpoint from among a plurality of endpoints based on at least one of (i) a baseline evaluation and (ii) instructions from the user requesting an activity toward achieving the endpoint.

[0023] In some embodiments, at least one of several configuration files defines a finite state machine. The finite state machine can identify multiple states, including at least a first and second state, each corresponding to an intensity level of the corresponding activity and specifying an output. This output can identify one or more content items to be presented through the application's user interface. The finite state machine can identify multiple transitions, each transition specifying an event detected through the application's user interface for transitioning from the first state to the second state, the event corresponding to an interaction performed through the application with respect to the corresponding activity.

[0024] In some embodiments, multiple endpoints can be associated with at least one of multiple classifications of endpoints. In some embodiments, a first subset of multiple endpoints can be associated with a first classification, and a second subset of multiple endpoints can be associated with a second classification. In some embodiments, a user may be taking medication to address a condition at least partially simultaneously when performing an activity through the application. In some embodiments, the condition may include a psychological disorder.

[0025] Aspects of this disclosure relate to a method for bringing about necessary improvements in a user's empirical negative symptoms of schizophrenia. One or more processors can acquire a first metric associated with a user prior to a plurality of time points. The one or more processors can repeat the operation of identifying one configuration file selected from a plurality of configuration files based on an endpoint for improving the empirical negative symptoms, and the one configuration file is provided to the application. The one configuration file can identify a set of content items relating to one of a plurality of activities toward achieving the endpoint. In response to providing the one configuration file to the application, the one or more processors can repeat the step of presenting the set of content items identified by the one configuration file through the application to prompt the user to perform the one activity toward achieving the endpoint. The one or more processors can repeatedly receive response data identifying one or more interactions by the user with the set of content items. The one or more processors can acquire a second metric associated with the user after at least one of the plurality of time points. The user may demonstrate improvement in empirical negative symptoms of schizophrenia when the second metric is statistically different from the first metric.

[0026] In some embodiments, the first metric and the second metric can include at least one score of the Clinical Assessment Interview for Negative Symptoms (CAINS) Motivation and Pleasure Scale, the Clinical Assessment Interview for Negative Symptoms - Expressivity Scale (CAINS-EXP), the Positive and Negative Syndrome Scale (PANSS), the Personal and Social Performance Scale (PSP), the Defeatist Beliefs Subscale of the Dysfunctional Attitudes Scale (DAS), the Global Improvement Impression Scale by the Patient (PGI-I), the Global Severity Impression Scale by the Patient (PGI-S), the Clinical Global Impression - Severity Scale (CGI-S), the World Health Organization Disability Assessment Scale 2nd Edition (WHODAS 2.0), or the Schizophrenia Quality of Life Scale Revised 4th Edition (SQLS-R4).

[0027] In some embodiments, the experiential negative symptoms of schizophrenia can include one or more of affective flattening, alogia (decrease in speech volume), avolition (decrease in goal-directed activity due to decreased motivation), asociality, and anhedonia (decrease in the experience of pleasure). In some embodiments, the user can be an adult or a late adolescent. In some embodiments, the user experiences at least moderate to severe severity of negative symptoms prior to the first activity. In some embodiments, the user may have a Motivation and Pleasure Scale (MAPS) score of 30 or less prior to the first activity.

[0028] In some embodiments, the user may have been taking a stable dose of antipsychotic medication for at least 12 weeks prior to the first activity. In some embodiments, the drug can also include, for example, risperidone, quetiapine, olanzapine, ziprasidone, paliperidone, aripiprazole, or iloperidone. In some embodiments, the user can be male, female, or non-binary.

[0029] In some embodiments, at least one of the plurality of configuration files can identify criteria for defining an indicator of the user for selecting other configuration files among the plurality of configuration files, and the indicator identifies (i) the likelihood that the user will perform the activity, or (ii) the predicted effect of the activity on the user towards achieving the endpoint. In some embodiments, the endpoint at the first time point of the plurality of time points can be selected from a plurality of endpoints based on the user's baseline evaluation regarding the experiential negative symptoms of schizophrenia.

[0030] In some embodiments, the endpoint can be selected from a plurality of endpoints based on the personal values indicated by the user. In some embodiments, the endpoint can be selected from a set of endpoints based on response data that identifies, via the application, the one or more interactions of the user with the set of content items presented from a previous time point. In some embodiments, the endpoint can be selected from a plurality of endpoints in response to a transition from a first level to a second level towards achieving the endpoint. The transition can be determined based on the response data.

[0031] In some embodiments, the endpoint may be selected from a plurality of endpoints based on the mood indicated by the user. In some embodiments, the plurality of endpoints may be associated with at least one of a plurality of domains. The plurality of domains may include a social domain, a leisure domain, and a productivity domain. The user may be presented with a comparison of a first evaluation made before performing the corresponding second activity and a second evaluation made after performing the corresponding second activity. In some embodiments, the configuration file may be selected based on changes in the endpoint determined using the response data from a previous time point. In some embodiments, one or more processors may decide to continue the repeating step over the plurality of time points based on a certain amount of time since the acquisition of the baseline metric. The repeating step for each of the plurality of time points may further include a step that repeats that time point in response to the decision to continue.

[0032] In some embodiments, the step repeated for each of the multiple time points may include, for one time point, updating the endpoint based on the response data that identifies the user's one or more interactions with the set of content items provided through the application from a previous time point. In some embodiments, the step repeated for each of the multiple time points may include translating the human-readable instructions in the configuration file to generate a package containing machine-executable instructions. In some embodiments, the step of obtaining the first and second metrics may include obtaining them from a source other than the application. [Brief explanation of the drawing]

[0033] The other purposes, aspects, features, and advantages of this disclosure described above will be made clearer and better understood by referring to the following description together with the accompanying drawings. [Figure 1]Figure 1 shows a block diagram of a system for selecting a configuration file for an application, according to an exemplary embodiment. [Figure 2] Figure 2A shows a block diagram of the process for maintaining a configuration file in a system for selecting a configuration file, according to an exemplary embodiment. Figure 2B shows a block diagram of the process for evaluating a user profile to provide a configuration file, in a system for selecting a configuration file, according to an exemplary embodiment. Figure 2C shows a block diagram of the process for processing a configuration package in a system for selecting a configuration file, according to an exemplary embodiment. Figure 2D shows a block diagram of the process for modifying a user interface in a system for selecting a configuration file, according to an exemplary embodiment. Figure 2E shows a block diagram of the process for evaluating response data to provide a configuration file, in a system for selecting a configuration file, according to an exemplary embodiment. [Figure 3] Figure 3 shows a flowchart illustrating a method for selecting a configuration file for an application according to an exemplary embodiment. [Figure 4] Figure 4 shows a block diagram of the architecture of an adaptive goal-setting system for selecting a configuration file, according to an exemplary embodiment. [Figure 5] Figure 5A shows a flowchart of a method for performing adaptive goal setting in the selection of a configuration file, according to an exemplary embodiment. Figure 5B shows a flowchart of a method for performing activities according to a configuration file, according to an exemplary embodiment. [Figure 6] Figures 6A-6C show block diagrams of configuration files for executing a routine, respectively, according to an exemplary embodiment. [Figure 7] Figures 7A-7F show examples of screenshots of prompts for accessing user physical activity according to an exemplary embodiment. [Figure 8]Figures 8A-L show example screenshots of prompts for building a user profile for an endpoint to pursue hobbies in the first stage of Level 0, according to an exemplary embodiment. [Figure 9] Figures 9A-L show example screenshots of prompts for the user to perform activities associated with an endpoint for pursuing hobbies in the second stage of Level 0, according to an exemplary embodiment. [Figure 10] Figures 10A-L show example screenshots of prompts for the user to perform activities associated with an endpoint for pursuing hobbies in the third stage of Level 0, according to an exemplary embodiment. [Figure 11] Figures 11A-G show example screenshots of prompts for a user to perform an activity associated with an endpoint for pursuing a hobby in Level 0, Stage 4, according to an exemplary embodiment. [Figure 12] Figures 12A-D show example screenshots of prompts for a user to perform an activity associated with an endpoint for pursuing a hobby in the first stage of Level 1, according to an exemplary embodiment. [Figure 13] Figures 13A-K show, respectively, example screenshots of prompts for the user to perform activities associated with an endpoint for pursuing a hobby in the second stage of Level 1, according to an exemplary embodiment. [Figure 14] Figures 14A-J show, respectively, example screenshots of prompts for the user to perform activities associated with an endpoint for pursuing a hobby in the third stage of Level 1, according to an exemplary embodiment. [Figure 15] Figures 15A-J show, respectively, example screenshots of prompts for the user to perform activities associated with an endpoint for pursuing a hobby in the third stage of Level 1, according to an exemplary embodiment. [Figure 16]Figures 16A-J show example screenshots of prompts for a user to perform an activity associated with an endpoint for pursuing a hobby in Level 1, Stage 4, according to an exemplary embodiment. [Figure 17] Figures 17A-G show, respectively, examples of screenshots of prompts in which the user indicates their mood, used to select the next endpoint, according to an exemplary embodiment. [Figure 18] Figures 18A-C show examples of screenshots of prompts for the user to input later personal values, according to an exemplary embodiment. [Figure 19] Figures 19A-D show examples of screenshots of prompts for presenting pre- and post-activity evaluations according to an exemplary embodiment. [Figure 20] Figure 20 illustrates a method, according to an exemplary embodiment, for providing the necessary improvement in the user's empirical negative symptoms of schizophrenia. [Figure 21] Figure 21 shows a timeline of a multicenter, exploratory, single-arm study evaluating the feasibility and acceptability of a shortened digital therapeutic application in adults diagnosed with schizophrenia. [Figure 22] Figure 22 shows a timeline of a study on the combination therapy of digital therapy and antipsychotics in subjects with empirical negative symptoms of schizophrenia. [Figure 23] Figures 23A and 23B show tables illustrating the schedules of participants' activities and evaluations. [Figure 24] Figure 24 is a block diagram of a server system and a client computer system according to an exemplary embodiment. [Modes for carrying out the invention]

[0034] The descriptions and their contents listed following each section in the specification should be helpful when reading the descriptions of the various embodiments below.

[0035] Section A describes embodiments of systems and methods for selecting application configuration files.

[0036] Section B describes embodiments of a method for achieving the necessary improvement in the subject's empirical negative symptoms of schizophrenia.

[0037] Section C describes network and computing environments that may be useful for carrying out the embodiments described herein.

[0038] A. System and method for selecting application configuration files Referring here to Figure 1, a block diagram of system 100 for selecting application configuration files is shown. In summary, system 100 includes at least one application configuration service 105, one or more user devices 110A-N (hereinafter collectively referred to as user devices 110), and at least one database 115, which are connected to each other so as to be able to communicate via at least one network 120. The application configuration service 105 includes at least one file indexer 125, at least one profile evaluator 130, at least one configuration selector 135, at least one configuration packager 140, at least one content manager 145, and at least one progress tracker 150, etc. The database 115 may store, maintain, or otherwise contain a set of configuration files 155A-N (hereinafter collectively referred to as configuration files 155) and a set of user profiles 160A-N (hereinafter collectively referred to as user profiles 160), etc. At least one of the user devices 110 may include at least one application 165. The application 165 may include at least one profiler 170, at least one behavior manager 175, at least one layout handler 180, and at least one event bus 185, etc. The application 165 may provide at least one user interface 190 which includes one or more user interface elements 195A-N (hereinafter collectively referred to as UI elements 195).

[0039] Each component of System 100 (for example, the Application Configuration Service 105 and its components, and each User Device 110 and its components) can be executed, processed, or otherwise implemented using hardware or a combination of hardware, as in System 1700, which is described in detail in Section B. In some embodiments, the Application Configuration Service 105 can be part of the User Device 110 (for example, part of Application 165). In some embodiments, at least some of the functions of the Application Configuration Service 105 (including the File Indexer 125, Profile Evaluator 130, Configuration Selector 135, Configuration Packager 140, Content Manager 145, and Progress Tracker 150) can be executed on the User Device 110. For example, the operation of the Profile Evaluator 130, Configuration Selector 135, and Configuration Packager 140 can be executed on the User Device 110.

[0040] More specifically, the application configuration service 105 (sometimes referred to as the service in this specification) is a computing device comprising one or more processors coupled with memory and software, and may be any computing device capable of performing the various processes and tasks described herein. The application configuration service 105 can communicate with one or more user devices 110 and database 115 via the network 120. The application configuration service 105 may be located in, deployed to, or otherwise associated with at least one server group. This server group may correspond to a data center, branch office, or site where one or more servers corresponding to the application configuration service 105 are located.

[0041] Within the application configuration service 105, the file indexer 125 can generate and store the configuration file 155 to be provided to the application 165. The profile evaluator 130 can create a user profile 160 for the user of application 165 on the user device 110. The endpoint selector 135 can determine the user's endpoint and select the configuration file 155 to be provided. The configuration packager 140 can provide the configuration file 155 to be loaded into application 165. The content manager 145 can identify and provide the content presented through the UI elements 195 of the user interface 190 of application 165. The progress tracker 150 can update the user profile 160 and manage the re-determination of the user's endpoint. The functions of the various components of the application configuration service 105 are performed by application 165 on the user device 110.

[0042] The user device 110 (which may also be referred to herein as a client, client device, or end-user computing device) is a computing device comprising one or more processors coupled with memory and software, and may be any computing device capable of performing the various processes and tasks described herein. The user device 110 can communicate with the application configuration service 105 and the database 115 via the network 120. The user device 110 may be a smartphone, another mobile phone, a tablet computer, a wearable computing device (e.g., a smartwatch, glasses), or a laptop computer. The application 165 can be accessed using the user device 110. In some embodiments, the application 165 can be downloaded (e.g., via a digital distribution platform) and installed on the user device 110. In some embodiments, the application 165 may be a web application with resources accessible via the network 120.

[0043] An application 165 running on the user device 110 may be a digital therapeutic application and may provide one or more sessions (sometimes referred to herein as therapeutic sessions) to address at least one of the user's medical conditions. End-user medical conditions may include, for example, habits (e.g., smoking, diet, or exercise), chronic pain (e.g., associated with or including arthritis, migraines, fibromyalgia, back pain, Lyme disease, endometriosis, recurrent stress injury, irritable bowel syndrome, inflammatory bowel disease, and cancer pain), skin lesions (e.g., atopic dermatitis, psoriasis, dermatophytosis, and eczema), mood disorders (e.g., depression, bipolar disorder, dysthymia), cognitive impairments (e.g., mild cognitive impairment (MCI), Alzheimer's disease, multiple sclerosis, schizophrenia, etc.), and other disorders (e.g., narcolepsy and tumors, etc.). Mood disorders and cognitive impairments may be classified as psychological (or psychiatric) illnesses or disorders.

[0044] Users may be taking medications to address their condition at least partially concurrently with sessions provided through Application 120. Application 120 can enhance the effectiveness of medications the user is taking to address their condition. For example, if the medication is for pain management, the end user may be taking acetaminophen; nonsteroidal anti-inflammatory compositions; antidepressants; anticonvulsants; or other compositions. In the case of skin lesions, the user may be taking steroids, antihistamines, or topical disinfectants. In the case of cognitive impairment, the user may be taking cholinesterase inhibitors such as memantine or icrepertin, or antipsychotics (such as risperidone, quetiapine, olanzapine, ziprasidone, paliperidone, or aripiprazole). In the case of neurological disorders, the user may be taking stimulants or antidepressants. In the case of mood disorders, the user may be taking antidepressants or mood stabilizers. Users of Application 120 may also be receiving other psychotherapies for these conditions.

[0045] Application 165 may, according to a configuration file 155 loaded onto Application 165, present, or otherwise provide to a user of user device 110 a user interface 190 having one or more UI elements 195. The UI elements 195 may correspond to visual components of the user interface 190, such as command buttons, text boxes, checkboxes, radio buttons, menu items, and sliders. Application 165 may be a digital therapeutic application that can provide a session (sometimes referred to herein as a therapeutic session) aimed at achieving one or more endpoints for a user (sometimes referred to herein as a patient, person, or subject) via the user interface 190.

[0046] Referring to Figure 2A, a block diagram of process 200, which maintains configuration files in system 100 for selection of configuration files, is shown. Process 200 may include or correspond to operations for storing and cataloging application configuration files in system 100. In process 200, a file indexer 125 running on application configuration service 105 may retrieve, identify, or otherwise receive at least one configuration file 155. Configuration files 155 may also be obtained from other sources, such as the computing device of the developer who created the configuration files 155. For example, a clinician may write a script that creates a set of configuration files 155 for providing digital therapy.

[0047] Configuration file 155 may contain instructions for configuring, defining, or otherwise specifying various functions of packages (or plugins) provided to application 165. Configuration file 155 may contain, or correspond to, one or more files containing instructions that define the layout of application 165 (e.g., the display of UI element 195 on user interface 190) and behavior (e.g., the functionality of UI element 195). The functions specified in configuration file 155 may be independent of the built-in logic and functions of application 165 (including the functions of application configuration service 105).

[0048] In some embodiments, the instructions contained in configuration file 155 can be in a human-readable format, such as another markup language (YAML), an extended markup language (XML), or JavaScript object notation (JSON). The format of the instructions in configuration file 155 can be a human-readable data serialization language for defining structured data. This format is different from a binary format that can be read and executed by one or more processors running application 165. Therefore, the effort required for developers to write instructions in a human-readable format in configuration file 155 may be less than the effort required to configure instructions in other formats (e.g., high-level programming languages ​​such as Java, C++, or Python). Furthermore, since configuration file 155 can specify custom functionality for application 165 without modifying the underlying code of application 165, configuration file 155 can be read by and replaced by application 165, thereby extending the functionality of application 165.

[0049] The configuration file 155 may specify, define, or otherwise identify at least one routine logic 202. In some embodiments, the routine logic 202 itself may be defined according to an automaton such as a finite state machine (FSM), a decision tree, or a pushdown automaton. The routine logic 202 may identify a set of content items that prompt the user to perform a set of activities toward achieving one or more endpoints (which may also be referred to herein as goals, targets, or objectives) in order to address a user's condition in application 165. For example, such conditions may include smoking, obesity, mental disorders (e.g., schizophrenia), cognitive impairment, or depression. Activities defined by the routine logic 202 may include those relating to the treatment or management of these conditions. These sets of activities may form stages in the user's progress toward achieving a target endpoint for a particular condition (e.g., smoking cessation).

[0050] The routine logic 202 of the configuration file 155 may identify or include a set of states 204A-N (hereinafter collectively referred to as states 204), a set of transitions 206A-N (hereinafter collectively referred to as transitions 206), and a set of levels 208A-N (hereinafter collectively referred to as levels 208). A set of states 204, a set of transitions 206, and a set of levels 208 can be used in combination to define or specify a set of activities that a user of application 165 performs to achieve one or more endpoints. Each state 204 may define, identify, or otherwise specify at least one output that is generated when the routine logic 202 is invoked. The output may relate to a particular activity of the set of activities and may include one or more actions performed by application 165. In some embodiments, the output may identify a user interface element 195 presented via the user interface 190 of application 165. For example, the output may include or specify a set of content items to be generated or retrieved (for example, using one or more identifiers (e.g., a universal resource locator (URL))), which are displayed in the user interface 190 as user interface elements 195. In some embodiments, the output may also identify another configuration file 155 to be loaded. For example, the output may include an identifier (e.g., a file name) of the configuration file 155 to be next loaded in application 165. At least one state 204 may correspond to a state 204 in which routine logic 202 starts (e.g., state 204A as shown).

[0051] Between a pair of states 204, the routine logic 202 may define, specify, or otherwise include at least one transition 206. Each transition 206 defines, identifies, or otherwise specifies an event detected via the application 165, causing the routine logic 202 to transition or update from one state 204 to another. This event may correspond to a user interaction received via the application 165 on a user device 110. For example, transition 206 may specify a transition from state 204A to the next state 204B, where the user records the completion of exercise A via the user interface 1685 of the application 165. In some embodiments, the routine logic 202 may include at least two transitions 206 from a particular state 204. For example, the routine logic 202 may identify one transition 206 corresponding to the successful completion of an activity associated with state 204. Conversely, the routine logic 202 may identify another transition 206 corresponding to the failure of an activity associated with state 204.

[0052] Between a set of states 204 and a set of transitions 206, routine logic 202 may define, specify, or otherwise include a level 208. In some embodiments, the level 208 may be defined, specified, or otherwise designated by the state 204 or transition 206 itself. Depending on the level 208, a set of activities designated by a set of corresponding states 204 may be defined based on the duration, frequency, and time of day of the activity. In some embodiments, the level 208 may correspond to the same endpoint or different endpoints. For example, the first level 208A may correspond to a lower level for a particular endpoint, and the second level 208B may correspond to a higher level for the same endpoint or to a different endpoint. Generally, if the activity in the previous state 204 is successfully completed, the higher the level 208, the more difficult or intense the activity associated with the particular state 204 may be in terms of duration, frequency, or both. For example, in a set of activities, a lower level 208A state 204A might specify that activity B be performed once a week in the evening, while a higher level 208B state 204B-1 might specify that activity C be performed five times a week in the morning. Conversely, if an activity in a previous state 204 fails, the higher the level 208, the lower the difficulty or intensity of the activity associated with that particular state 204 may be in terms of duration, frequency, or both. An activity associated with a particular state 204 may also differ from an activity associated with a previous state 204. For example, that activity might be part of a mitigation measure in case the previous activity was not successfully completed.

[0053] Based on the definitions of state 204, transition 206, and level 208, the routine logic 202 in configuration file 155 can adjust the type, frequency, and duration of activities specified for user execution via application 165. Furthermore, the routine logic 202 can adaptively generate outputs in response to user behavior in application 165. An example of routine logic 202, including state 204, transition 206, and level 208, is described in detail herein in relation to Figures 6A-C. An adaptive goal setting (AGS) framework can be implemented using routine logic 202 throughout a set of configuration files 155. For example, as shown in Figure 6A-C, a user can undergo a baseline assessment and select an aspiration. The user can then be presented with a one-day (1D) goal (or endpoint) to guide them. Depending on the outcome, the user can progress to a three-day (3D) goal at the same or a lower level. The user continues working towards the three-day goal until they reach the lowest or highest level. Users can choose and work on new aspirations (if they have enough time remaining).

[0054] Each configuration file 155 corresponds to one or more goals and is selected based on a baseline assessment of the user's capabilities, with the goal level and associated activities determined based on that assessment. Assessments of the user's interests and desires in that area can be used to identify the goal content and associated activities, as well as the configuration file 155. After implementation, the user can receive any number of goals as part of the configuration file 155. For example, if there are four aspects, the user may receive four goals, then four more, and so on, up to eight goals. Each goal can also be associated with, for example, four daily activities. Once a user completes all activities associated with a goal, they can move on to the next level goal within the same interest or desire. When a user completes a goal, application 165 can provide positive encouragement and give them the opportunity to enjoy the positive emotions associated with goal achievement. If the lowest or highest goal level within an interest or desire is achieved, a new goal may be set within a new interest or goal presented via application 165 at a level determined from the initial baseline assessment. Once a certain number of goals within the same interest or desire have been achieved, the user may be prompted to re-select a different interest or desire.

[0055] When provided to Application 165, the configuration file 155 can provide one or more activities related to endpoints or goals for the user of Application 165 to perform and complete. The configuration file 155 can provide the user with the option to perform the specified activities when prompted or to schedule them later (for example, via a user interface element 195 on the user interface 190 of Application 165). These activities can be configured to enhance the likelihood of reflecting and practicing the therapeutic skills acquired through the activities. The configuration file 155 and activities can be selected based on personal values ​​related to the classification of endpoints.

[0056] By selectively providing configuration file 155, application 165 can provide a series of daily activities within the endpoint. When a user completes an activity, application 165 can provide positive encouragement. If a user states during a daily status check that they did not complete the previous day's activity, application 165 can prompt the user to perform the same daily activity, encouraging them to continue towards achieving the endpoint or goal. If a user is inactive or has not completed a previous activity, new activities may be locked. For example, if a user is inactive or has not completed an activity for a certain period (e.g., 1-2 days), application 165 can prompt the user to continue the same daily activity. In the case of a longer period of inactivity or incomplete activity (e.g., 4 days or more), the user may be offered the option to repeat a previously presented activity through application 165 or to select an activity from a new endpoint.

[0057] Furthermore, in certain situations, if a user selects an activity from a new endpoint, that activity may be displayed through application 165 at a lower level within the same aspiration. If the user reaches the lowest level of the endpoint, previously presented activities may be presented again. For other configuration files 155, if a user selects an activity from a new endpoint, the user may be presented to re-select a goal or area. After a daily activity is presented to the user, application 165 can prompt the user to choose whether to start the activity immediately or set a time to complete the activity later that day. If the user chooses to set a reminder, application 165 can send a notification at the selected time. The user can choose to start the scheduled activity before or after the scheduled time via the home screen of application 165.

[0058] Furthermore, a configuration file 155 can identify, define, or specify at least one selection criterion 210 for selecting a corresponding configuration file 155. In some embodiments, the selection criterion 210 is separate from the configuration file 155 but can be associated with it. For example, the association between the selection criterion 210 and the configuration file 155 can be stored in the database 115 using one or more data structures, such as a linked list, tree, table, array, graph, heap, or hash table. The selection criterion 210 can specify one or more parameters associated with the user profile 160 for selecting a configuration file 155 to be provided to the application 165 on the user device 110. If the parameters specified in the selection criterion 210 match the parameters identified in the user profile 160, the configuration file 155 can be selected and provided to the application 165. In some embodiments, the selection criterion 210 can define one or more baseline measurements derived from the user profile 160 for selecting a configuration file 155. If the measurements derived from the parameters of user profile 160 meet (e.g., equal to or greater than) the baseline measurements specified in selection criterion 210, configuration file 155 can be selected and provided to application 165. Additional details regarding the parameters or baseline measurements of selection criterion 210 are described in detail below.

[0059] The file indexer 125 can store and maintain the configuration file 155 in the database 115 upon receipt. The database 115 can be part of the application configuration service 105 or accessible from the application configuration service 105. In some embodiments, the file indexer 125 can store a relationship between the configuration file 155 and the origin of the received configuration file 155. In some embodiments, the file indexer 125 can store a relationship between the configuration file 155 and the version identifier of that configuration file 155. The configuration file 155 is maintained in the database 115 for provision to instances of the application 165 on various clients 110. Because the configuration file 155 is separate from the application 165, the configuration file 155 is easily updated and replaceable.

[0060] Referring here to Figure 2B, a block diagram is shown of a process 220 in system 100 for selecting a configuration file, which evaluates the user profile to provide the configuration file. Process 220 may include, or correspond to, the operation of evaluating the parameters of the user profile in system 100, selecting a configuration file 155, and generating a package for delivery to application 165. In process 220, the profiler 170 of application 165 running on user device 110 generates, outputs, or otherwise creates a user profile 160 of user 222 of application 165. The user profile 160 may be generated based on one or more responses from user 222, which identify, define, or associate endpoints and conditions specified by user 222.

[0061] When generating a user profile 160, the profiler 170 may display, render, or otherwise present at least one prompt 224 to receive a response from the user 222. The prompt 224 may be presented to the user 222 using one or more user interface elements 195 on the user interface 190. In some embodiments, the prompt 224 may also be displayed when the application 165 is installed on the user device 110. In some embodiments, the prompt 224 may also be presented in response to an interaction with the user on the user interface 190 on the application 165. This interaction may respond to a request from the user 222 via the application 165 regarding another endpoint or a set of new activities to be performed. In some embodiments, the prompt 224 may be displayed according to a defined time interval, for example, once every 2-6 hours, once a day in the morning, or once a week. The prompt 224 may identify or include a set of questions for the user 222. The questions themselves may correspond to text, audio, or visual content on the user interface elements 195 of the user interface 190. The user 222 may provide or input their response to the question via other user interface elements 195, such as radio buttons, command buttons, text boxes, sliders, and checkboxes.

[0062] The profiler 170 may include one or more event listeners for detecting, retrieving, or otherwise receiving responses via prompts 224 displayed on the user interface 190. A set of questions displayed on prompts 224 can be created, set, or otherwise configured by the administrator of the application configuration service 105 or application 165. These questions may ask the user 222 to specify at least one endpoint (also referred to herein as “goal,” “objective,” or “target”) that they wish to achieve, and at least one medical condition (e.g., behavioral, psychological, or physical) that they wish to address. The questions may also ask the user 222 to identify at least one state of the user 222 (e.g., mood, emotional, behavioral, or physiological state). The questions may also ask the user 222 about their preferences regarding the performance of activities or routines (e.g., type of routine, frequency, duration, day of the week, time of day). Responses to the questions can be recorded, entered, or otherwise provided via user interface elements 195 on the user interface 190.

[0063] In some embodiments, the set of questions presented by the survey prompt 224 may conform to a survey policy. The survey policy may define rules for selecting questions in response to answers to previous questions within the set. The answers to the questions can be used to generate a user profile 160 of user 222. For example, if user 222 responds that they prefer hobby A as a type of activity performed and recorded through application 165, the survey may be specified to display some questions regarding the frequency, duration, and level of hobby A. In some embodiments, the survey may be a validated clinical assessment of physical or psychological (or mental) status, such as the Structured Interview for the Diagnostic and Statistical Manual of Mental Disorders (SCID-DSM); the Mini International Neuropsychiatric Interview (MINI); the Clinical Assessment Interview for Negative Symptoms (CAINS) scale, which assesses the motivational and pleasurable areas of negative symptoms; or the Hamilton Depression Rating Scale (HDRS). Examples of the prompt 224 and the set of questions presented therein are shown in Figures 7A–F (regarding the user's physical activity). In this illustrated example, the survey policy specifies that if the answer to the previous question about whether the user engaged in physical activity is "yes" (Figure 7A), prompt 224 should display a question about the amount of time the user spent engaging in physical activity (Figure 7B).

[0064] Based on the answers to the questions presented via prompt 224, the profiler 170 can identify or generate one or more parameters 226A-N (hereinafter collectively referred to as parameters 226). Parameters 226 can identify or include any number of factors used to select one or more configuration files 155 provided to application 165. Parameters 226 can identify the endpoint to be achieved and the user 222's medical condition to be addressed. Parameters 226 can identify the user 222's state (e.g., mood, emotions, behavior, physiological state). Parameters 226 can identify the type, frequency, duration, and time (e.g., day of the week or time of day) of routine activities indicated by user 222 via prompt 224. In some embodiments, parameters 226 can identify the identifier of user device 110, the device type of user device 110, the identifier of application 165, and the user 222's location. During generation, the profiler 170 can include one or more parameters 226 in the user profile 160. The user profile 160 can be incorporated according to a template. The template may include fields for incorporating or inserting values ​​for parameter 226. The profile creator 170 can send, transmit, or otherwise provide the user profile 160 to the application configuration service 105. The user profile 160 can be sent as part of a message to the application configuration service 105.

[0065] A profile evaluator 130 running on the application configuration service 105 can acquire, receive, or otherwise identify a user profile 160 from the user device 110. In some embodiments, the profile evaluator 130 runs on the user device 110 as part of application 165 and can evaluate, for example, a user profile 160 generated by a profile creator 170 on the user device 110. Once the profile evaluator 130 receives a user profile 160 from the user device 110, it can store and maintain it in the database 115. In some embodiments, the profile evaluator 130 can acquire, retrieve, or otherwise identify a user profile 160 from the database 115. A user profile 160 associated with user 222 of user device 110 can be stored and maintained in the database 115. The user profile 160 maintained in the database 115 can be updated using an instance of the user profile 160 received from the user device 110. After identification, the profile evaluator 130 can analyze the user profile 160 and extract or identify one or more parameters 226 within the user profile 160.

[0066] Using parameter 226, the profile evaluator 130 can calculate, determine, or generate one or more measurements for user 222 associated with user profile 160. The measurements derived from parameter 226 of user profile 160 are used to select a configuration file 155 to provide to application 165. The generation of measurements can be performed according to a function of parameter 226. For example, parameter 226 may include the user's answers to the questions shown in Figures 7A-D and be related to physical activity (e.g., whether user 222 performed physical activity or how long they spent that physical activity). In this example, the profile evaluator 130 generates measurements about user 222's physical activity, such as the level of physical activity.

[0067] In some embodiments, the profiler 130 can calculate, determine, or otherwise generate at least one measure for the user profile 160 based on the user's responses to clinical assessment questionnaires, such as SCID, MINI, CAINS, and HDRS. The generation of the measure is performed according to a function of these clinical assessments. The measure identifies characteristics of the condition, such as the severity of the condition. If identified before the performance of an activity, the measure is determined as the baseline assessment measure for the user 222. If identified after the performance of one or more activities, the measure is determined as the assessment measure for the user 222.

[0068] In some embodiments, the profiler 130 can generate measurements to identify the degree of the user 222's conditional features. These features may include, for example, the severity of behavioral, physical, or psychological (or mental) conditions. In some embodiments, the measurements identify the likelihood that the user 222 will perform that type of activity when prompted via the application 165. For example, a measurement might indicate the likelihood that the user 222 will engage in a hobby when prompted via the user interface 190 of the application 165, while another measurement might indicate the likelihood that the user 222 will engage in exercise. In some embodiments, the measurements identify the predicted effectiveness of this type of activity toward achieving one or more endpoints. For example, a measurement might identify how effective it would be for the user to engage in running to address a condition related to dietary improvement, which the user has identified as a desired endpoint.

[0069] Furthermore, the profile evaluator 130 can store and maintain the user profile 160, including the parameter 226, in the database 11. In some embodiments, the profile evaluator 130 can store and maintain the measured values ​​derived from the parameter 226 in the database 115. In some embodiments, the profile evaluator 130 can include the measured values ​​in the user profile 160. In some embodiments, the profile evaluator 130 can use one or more data structures to generate associations between the measured values ​​and the user profile 160. After generating the association information, the profile evaluator 130 can store and maintain the association information in the database 115. In some embodiments, the profile evaluator 130 can cooperate with the profile generator 170 to perform the functions described herein, and vice versa.

[0070] The configuration selector 135, which runs on the application configuration service 105, can select, identify, or otherwise determine one or more endpoints to address user 222's condition. Generally, endpoints can correspond to outcomes or measures associated with addressing user 222's condition. Endpoints can correspond to metrics of completion of a defined set of activities to address user 222's condition. For example, an endpoint could be associated with a predetermined number of activities within a defined time frame (e.g., four activities per day), and user 222's completion of these activities could correspond to the achievement of the endpoint. Alternatively, endpoints can correspond to measured improvements in user 222's condition. For example, an endpoint could be associated with a reduction in pain perception (or other metrics) associated with user 222's condition.

[0071] In some embodiments, endpoints may be categorized into one or more classifications (which may also be referred to herein as “domains”). For example, one set of endpoints may be associated with a first classification, and another set of endpoints may be associated with a different second classification. Each endpoint may be associated with a predetermined number of activities, such as four activities to be performed. Each domain may be associated with a predetermined number of endpoints, such as four endpoints. Each domain may be associated with a certain number of parameters indicated by the user 222, such as three desires. Endpoints may include, for example, a social domain, a leisure domain, or a productivity domain. These domains may be associated with addressing specific medical conditions. Some endpoints associated with the social domain may be aimed at improving the user’s social skills. Other endpoints associated with the leisure domain may identify activities for the user to engage in gentle exercise or tasks. Other endpoints associated with the productivity domain may identify activities to improve the user’s attention or work habits.

[0072] The configuration selector 135 can identify endpoints for user 222 based on the state that user 222 needs to address. For example, if the state indicated by user 222 is dieting, the configuration selector 135 can select one or more diet-related endpoints, such as eating a snack, walking, or jogging. In some embodiments, the configuration selector 135 can determine endpoints based on at least one activity (or type of activity) that user 222 has demonstrated toward achieving the endpoint. In some embodiments, the configuration selector 135 can determine endpoints based on parameters 226 of the user profile 160. For example, the configuration selector 135 can determine endpoints based on the type, frequency, duration, and time of activity specified by user 222. In some embodiments, the configuration selector 135 can determine endpoints based on measurements obtained from a clinical assessment (e.g., baseline assessment metrics for the clinical assessment, likelihood of the user performing the activity, or predicted effectiveness).

[0073] In some embodiments, the configuration selector 135 can select or identify one or more activities associated with an endpoint that a user 222 performs through the application 165. Activity identification can be performed based on parameters 226 provided by the user 222. For example, if the user 222 specifies the type of activity they want, the configuration selector 135 can identify activities such as activity D, activity E, or activity F. In some embodiments, the configuration selector 135 runs on the user device 110 as part of the application 165 and can identify, for example, an endpoint for user 222 on the user device 110. Endpoint identification is performed based on parameters 226 of the user profile 160 and selection criteria 210 for each endpoint. As previously mentioned, the selection criteria 210 can specify parameters 226 for selecting a configuration file 155 associated with an endpoint.

[0074] The configuration selector 135 can identify or select one or more configuration files 155 from a set of configuration files 155 and provide them to the application 165 on the user device 110. Based on an endpoint (or activity toward achieving an endpoint), the configuration selector 135 can select associated configuration files 155 to provide to the application 165. In some embodiments, the configuration selector 135 can select multiple configuration files 155 for at least one endpoint. For example, if the determined endpoint relates to smoking cessation support, the configuration selector 135 can identify a set of configuration files 155 marked as smoking cessation support in the selection criterion 210. On the other hand, for each routine identified as not included, the configuration selector 135 can refrain from selecting associated configuration files 155 to provide to the application 165.

[0075] To make a selection, the configuration selector 135 can identify selection criteria 210 for each configuration file 155 from the database 115. In some embodiments, the configuration selector 135 can parse each configuration file 155 to extract or identify selection criteria 210 for the corresponding endpoint. In some embodiments, the configuration selector 135 can identify selection criteria 210 for one or more specified activities. Once identified, the configuration selector 135 can compare the parameter 226 with the selection criteria 210 for the endpoint. If the parameter 226 matches the parameter specified by the selection criteria 210, the configuration selector 135 can select the configuration file 155. On the other hand, if the parameter 226 does not match the parameter specified by the selection criteria 210, the configuration selector 135 can refrain from selecting the configuration file 155.

[0076] In some embodiments, the routine selector 315 can compare a measured value derived from parameter 226 with the routine selection criterion 210 to determine whether to select a given configuration file 155. If the obtained measured value satisfies the measured value specified in the selection criterion 210 (e.g., equal to, greater than, or within the range), the routine selector 315 can select and provide the configuration file 155. On the other hand, if parameter 226 does not satisfy the parameter specified in the selection criterion 210 (e.g., less than or outside the range), the routine selector 315 will not select and provide the corresponding configuration file 155.

[0077] Once a selection is made, the configuration selector 135 can communicate, transmit, or otherwise provide to the profile evaluator 130 the identification information of the selected configuration file 155 (and therefore may be a routine) in order to set up or update the user profile 160. The user profile 160 stored in the database 115 is used to track the routines and configuration files 155 provided to user 222 and to track the progress of user 222 in each routine using the states 204 and levels 208 of the routine logic 202. When saving the user profile 160 as part of the initial selection of the configuration files 155, the profile evaluator 130 can identify the states 204 and levels 208 of each selected routine as the initial state (e.g., state 204A) and initial level (e.g., level 208A), respectively. The profile evaluator 130 can store and maintain the initial state 204 and initial level 208 for each of the selected configuration files 155 in the user profile 160 on the database 115.

[0078] A configuration packager 140, running on the application configuration service 105, can generate, output, or otherwise create at least one package 228 using a selected configuration file 155. In some embodiments, the configuration packager 140 can run on the user device 110 and generate a package 228 that will be loaded, for example, by application 165. The package 228 can contain instructions for configuring, defining, or otherwise specifying various functions to be executed in application 165. Generally, the instructions in package 228 can be in machine-readable code format. The instructions in package 228 can also define routine logic 202, which includes a set of states, transitions 206, and levels 208. Furthermore, the instructions in package 228 can also define user interface elements 195 with respect to the output of states in routine logic 202. Since package 228 is generated separately from application 165, the package 228 that application 165 will include is easily interchangeable depending on the state of the target user.

[0079] During package generation, the configuration packager 140 can create a separate file to store the specifications for package 228. The configuration packager 140 can parse one or more configuration files 155 to read and identify instructions in their original format. In some embodiments, when generating package 228, the configuration packager 140 can insert, append, or otherwise include information from the user profile 160 into package 228. For example, when generating package 228, the configuration packager 140 can store the state 204, level 208, and the name of user 222 in fields of package 228. The included information can be displayed through the user interface element 195 when package 228 is loaded into application 165.

[0080] Upon identifying the configuration file 155, the configuration packager 140 can generate or identify equivalent instructions in executable form for incorporation into the application 165. In some embodiments, the configuration packager 140 can compile the configuration file 155 to generate low-level language instructions. Once compiled, the instructions in package 228 can be in a low-level language format readable by the processor running the application 165, such as binary, bytecode, assembly, object code, or machine code. Unlike the human-readable instructions in the configuration file 155, the low-level language instructions in package 228 are in a format readable and processable by the processor running the application 165. In some embodiments, the configuration packager 140 can transpile the configuration file 155 to generate intermediate-form instructions, which can then be added to the application 165. For example, the instructions could be in a language at a similar level to the original in the configuration file 155, such as JavaScript or TypeScript. During generation, the configuration packager 140 can write equivalent instructions to a file in package 228, repeating this process until the end of the configuration file 155.

[0081] This generation allows the configuration packager 140 to provide instructions for package 228 to be added to or incorporated into application 165. In some embodiments, the configuration packager 140 inserts or injects package 228 into application 165 before providing it to user device 110. For example, the configuration packager 140 can inject package 228 into application 165 that already contains other components such as behavior manager 175, layout handler 180, and event bus 185. The configuration packager 140 can provide application 165, including the injected package 228, to user device 110 via a digital distribution platform (e.g., application marketplace or store). User device 110 can request to download or obtain application 165 for installation from application configuration service 105 (or digital distribution platform). After receipt, user device 110 can unpack and install application 165, including package 228.

[0082] The configuration packager 140 can send, transmit, or otherwise provide instructions for package 228 to add or include application 165 installed on user device 110. For example, user device 110 may have pre-installed application 165 received from application configuration service 105 (e.g., via a digital distribution platform). In some embodiments, user device 110 may later request an update to the configuration of application 165. In some embodiments, the configuration packager 140 can identify or determine that an update should be provided to application 165 via configuration file 155. For example, a system administrator of application configuration service 1605 may instruct that an instance of application 165 be updated. After identifying the update, the configuration packager 140 can provide instructions for package 228 without providing other components of application 165. Upon receipt, user device 110 (or application 165 itself) can update the already installed application 165 to include package 228. In some embodiments, the instructions in package 228 are received in an intermediate form, and application 165 can further compile the instructions to generate a low-level form for execution on user device 110.

[0083] Referring to Figure 2C, a block diagram of process 240, which processes the configuration package in system 100 for selecting a configuration file, is shown. Process 240 may include or correspond to actions performed in system 100 when it loads and executes the routine logic 202 defined in package 228. In process 240, application 165 (or application services of application 165) may perform initialization actions such as starting the execution of the behavior manager 175, layout handler 180, event bus 185, user interface 190, etc. Application 165 may execute various logic and actions defined for application 165, separate from package 228 received from application configuration service 105. Application 165 may obtain, identify, or otherwise receive package 228 from application configuration service 105.

[0084] The behavior manager 175 of application 165 running on user device 110 can parse package 228 to read, load, and execute routine logic 202. As described above, routine logic 202 can correspond to a set of activities performed, recorded, and logged on user device 110 via application 165 by user 222. In some embodiments, the behavior manager 175 can parse multiple packages 228 and execute multiple corresponding routine logic 202. For each routine logic 202, the behavior manager 175 can track the current state 204 of that routine logic 202. In some embodiments, the behavior manager 175 can use or maintain an identifier for the state to track the current state 204 of each routine logic 202. Furthermore, the behavior manager 175 can track the current level 208 of user 222 as defined in each routine logic 202. In some embodiments, the behavior manager 175 can determine the current level 208 from the current state 204 of each routine logic 202. Upon initialization, the current state 204 of the routine logic 202 can correspond to the starting state 204 (for example, state 204A in the illustrated example). The current level 208 can correspond to the initial level 208 (for example, level 208A in the illustrated example).

[0085] Simultaneously, the behavior manager 175 can monitor or detect the occurrence of at least one event 242 on one or more user interface elements 195 within the user interface 190 via the event bus 185. Event 242 can correspond to an activity performed by user 222 of application 165. For example, user interface 190 may present a prompt to user 222 to perform exercise B, and user 222 may indicate completion of this exercise through interaction with one of the user interface elements 195 on user interface 190. Event 242 can correspond to the completion of an activity specified in state 204 within routine logic 202.

[0086] In some embodiments, the behavior manager 175 can monitor or detect the occurrence of event 242 from application 165 or other processes of the user device 110. In this case, event 242 may correspond to an action performed by application 165 or a process of the user device 110 that was not triggered by interaction from user 222. For example, the behavior manager 175 can obtain the elapsed time from the presentation of a prompt to the performance of an activity via the system timer of the user device 110. The behavior manager 175 can compare the elapsed time with the time frame for activity completion specified in state 204. The behavior manager 175 can identify the exceedance of the specified time as event 242.

[0087] In some embodiments, the behavior manager 175 can prompt the user 222 to choose or specify whether to perform the activity presented in the prompt. For example, after receiving an activity prompt, the user 222 can increase the likelihood of completion through multiple paths by choosing "Do Now" or "Do Later." Choosing the former option indicates that the user 222 is behaviorally activated. This can be a significant advantage compared to a different, more fixed setting where only the latter option is allowed, as it captures the user's most purposeful moments. The latter option reinforces the behavior of planning the activity, specifying a time, receiving a reminder, and performing the activity at the specified time using application 165. If it is determined that the activity will be performed later, the behavior manager 175 can save the instructions and re-present the activity prompt at the specified time. On the other hand, if it is indicated that the activity will be performed, the behavior manager 175 can perform additional processing to load the routine logic 202 of package 228.

[0088] Based on the detection of event 242, the behavior manager 175 can identify or select at least one routine logic 202 to be invoked from within the corresponding package 228. In some embodiments, the behavior manager 175 can propagate or pass the detected event 242 to the package 228 via the event bus 185. The event bus 185 corresponds to an interface between the package 228 and various components of the application 165 (such as the behavior manager 175 and the layout handler 180). By propagating, the behavior manager 175 can match the detected event 242 with an event specified by a transition 206 corresponding to the current state 204. As described above, the behavior manager 175 can track the current state 204 and current level 208 of each routine logic 202 within each package 228. In some embodiments, the behavior manager 175 can detect or receive the results of checking the detected event 242 via the event bus 185.

[0089] From the routine logic 202, the behavior manager 175 can identify the events specified for each transition 206 associated with the current state 204 and match them with the detected events 242. If the detected events 242 do not correspond to any of the transition 206 specifications for the current state 204, the behavior manager 175 can maintain the routine logic 202 in the current state 204 and the current level 208. In some embodiments, the behavior manager 175 may not call the routine logic 202. Maintaining the routine logic 202 in the current state 204 may correspond to the user 222 having incompleted the routine activity set in the routine logic 202 with respect to any of the transition 206 associated with the current state 204. The behavior manager 175 can continue matching the detected events 242 with the specifications of the routine logic 202 in other packages 228.

[0090] Conversely, if the detected event 242 matches the specification of one of the transitions 206 of the current state 204, the behavior manager 175 can select and invoke the routine logic 202. By invoking, the behavior manager 175 can update the current state 204 and current level 208 of the routine logic 202 to the next state 204 according to the transition 206. Updating the routine logic 202 from the current state 204 to the next state 204 may correspond to the user 222 completing (successfully or unsuccessfully) the routine activity set in the routine logic 202 (specified for at least one transition 206 associated with the current state 204). For example, if the current state 204 is successfully completed as specified in the corresponding transition 206, it can be updated from the initial state 204A to state 204B-1. In contrast, if the current state 204 fails to complete as specified in the corresponding transition 206, it can be updated from the initial state 204A to state 204B-2.

[0091] Furthermore, by calling the routine logic 202 of package 280, the behavior manager 175 can obtain or identify an output 244, which is identified by the next state 204 of the routine logic 202. The output 244 may be generated or produced by the state 204 specified to the routine logic 202 when it is called. As described above, the output 244 may identify a user interface element 195 presented through the user interface 190 of application 165. In some embodiments, the output 244 may specify a modification to be applied to the user interface element 195 of the user interface 190. The behavior manager 175 can propagate or pass the output 244 to the layout handler 180 via the event bus 185.

[0092] Referring to Figure 2D, a block diagram of process 260 for modifying the user interface in system 100 for selecting a configuration file is shown. Process 260 may include or correspond to the actions of application configuration service 105 and application 165 when one of the routine logics 202 defined in package 228 is called. In process 260, the layout handler 180 of application 165 running on user device 110 may update, change, or otherwise modify user interface elements 195 of user interface 190 according to output 244. By configuring user interface 190, the layout handler 180 may associate or link states 204 (and levels 208) in routine logic 202 of package 228 with user interface elements 195 of user interface 190. In some embodiments, the layout handler 180 may work with behavior manager 175 to maintain the association or link between states 204 (and levels 208) of routine logic 202 and user interface elements 195 of user interface 190. For example, the layout handler 180 can track the relationship between the state 204 (and level 208) of the recently called routine logic 202 and the user interface elements 195 rendered or presented via the user interface 190.

[0093] In making modifications, the layout handler 180 can decide whether to send a request 262 for content to the application configuration service 105 or another remote service (for example, a service associated with the developer of the configuration file 155). In some embodiments, the layout handler 180 runs on the user device 110 and can identify content to be presented, for example, through the user interface 190. Output 244 may depend on at least one content item 264A-N (hereinafter collectively referred to as content item 264). Content item 264 may include images, videos, and other objects that are stored and maintained in a database (for example, the illustrated database 115) and provided during the execution of the application 165. If output 244 does not specify the retrieval of content item 264, the layout handler 180 may refrain from sending a content request 262 to the application configuration service 105. The layout handler 180 may also continue modifying the user interface elements 195 of the user interface 190 according to output 244. On the other hand, if output 244 specifies content retrieval, the layout handler 180 may decide to send a request 262 for content to the application configuration service 105. The layout handler 180 may generate a request 262 for content that includes at least one identifier referring to the content item 264 to be retrieved from the application configuration service 105. These identifiers may be specified by output 244 from the current state 204 of the routine logic 202.

[0094] A content manager 145, running on the application configuration service 105, can retrieve, identify, or otherwise receive a content request 262 from the user device 110. In some embodiments, the content manager 145 may reside on a remote service separate from the application configuration service 105. The content manager 145 can parse the request 262 to identify a content item 264 that will be provided to the user device 110 and displayed on the user interface 190. In some embodiments, the content manager 145 can use an identifier in the request 262 to access the database 115 and retrieve, fetch, or identify the content item 264 referenced by that identifier. The content item 264 can be information in visual or audio media and may include images, videos, audio, or other objects presented on the user interface 190. For example, the content item 264 may include audio that is played in conjunction with exercise C with respect to an endpoint associated with the called routine logic 202. This identification allows the content manager 145 to send, return, or otherwise provide the content item 264 to the user device 110.

[0095] The layout handler 180 can retrieve, identify, or receive content items 264 from the application configuration service 105 (or remote service). After receiving, the layout handler 180 can insert, add, or otherwise include content items 264 in the user interface 190. The layout handler 180 can display content items 264 in one or more user interface elements 195 as specified in output 244 from routine logic 202. At the same time, the layout handler 180 can modify user interface elements 195 in the user interface 190 according to output 244. For example, the layout handler 180 can instantiate user interface elements 195, set the color and other visual characteristics of individual user interface elements 195, set the font and size of the text of individual user interface elements 195, and assign the placement of user interface elements 195 within the display of the user device 110.

[0096] In some embodiments, the layout handler 180 can generate or identify rendering instructions using an output 244 specified by routine logic 202. The output 244 can identify a set of instructions (e.g., original or low-level form) corresponding to each user interface element 195 to be included in the user interface 190. The rendering instructions can be in the form of a display list or a rendering tree. Once identified, the layout handler 180 can parse the instructions in the output 244 corresponding to the user interface elements 195. For each instruction, the layout handler 180 can generate an equivalent entry (e.g., a rendering tree node) to be included in the rendering instructions. This generation allows the layout handler 180 to display the user interface elements 195 of the user interface 190 according to the rendering instructions.

[0097] Examples of content items 264 displayed as user interface elements 195 on the user interface 190 are shown in Figures 8A-L (Level 1 for trying a new hobby, including selection of past hobbies), 9A-L (Level 2 for trying a new hobby), 10A-J (Level 3 for trying a new hobby), 11A-G (Level 4 for trying a new hobby), 12A-D (Level 1 for building a habit), 13A-K (Level 2 for building a habit), 14A-J (Level 2 for building a habit), 15A-J (Level 3 for trying a new habit), and 16A-J (Level 4 for building a new habit). Similar to the survey prompts 224, content items 264 may include prompts for questions that the user 222 answers. Each question shown in the examples corresponds to at least one content item 264 and may be associated with a transition 206 from one state 204 to another state 204. When the user 222 interacts with a button or triggers a transition 206 from one state 204 to another state 204, the user 222 can be presented with the illustrated prompt.

[0098] Referring here to Figure 2E, a block diagram is shown of a process 220 in system 100 for selecting a configuration file, which evaluates response data to provide a configuration file. Process 280 in system 100 may include or correspond to actions to evaluate the response and provide a new package. In process 280, the action manager 175 may send, transmit, or provide at least one response data 282 (also called a record entry) to the application configuration service 105. When one or more events 242 are detected, the action manager 175 may write or generate response data 282. The response data 282 may identify or include various events 242 according to a set of activities defined in a package 228 provided to the user device 110. For example, the information contained in the response data 282 may include the current state 204 and current level 208 of each routine logic 202; updates to the state 204 or level 208 of the routine logic 202; instructions for completion or failure of the routine associated with the routine logic 202; detected events 242, timestamps of the time each event 242 was detected, the identifier of user 222, and the identifier of user device 110. This generation allows the behavior manager 175 to send the response data 282 to the application configuration service 105.

[0099] In some embodiments, the behavior manager 175 can call or run the profiler 170 to collect, aggregate, or otherwise receive additional responses from the user 222 via the questionnaire prompt 224. The prompt 224 can be presented at defined times, such as at the start of the day, at the end of the day, every 4-6 hours, once a week, once a month, etc. For example, when the user 222 interacts with the user interface element 195, the prompt 224 is displayed and a set of questions is presented. As previously mentioned, the questions may ask the user 222 about the endpoint they wish to achieve, the medical condition they wish to address, and their preferences regarding the performance of activities and routines (such as the type, frequency, duration, days of the week, and time of day of the day of the week). These questions may also be part of a clinical assessment interview, as described above. The behavior manager 175 can receive the user 222's responses to the set of questions via the prompt 224 in the same manner as described above. Upon receiving responses from the user 222, the behavior manager 175 can include the responses in response data 282.

[0100] In some embodiments, the behavior manager 175 can identify or distinguish at least one state of the user 222 (e.g., mood, emotion, behavior, or physiological state) from the user 222's response via a questionnaire prompt 224. Examples of mood questionnaire prompts 224 are shown in Figures 17A–G. The mood state confirmation shown in this example can help increase the likelihood that the user 222 will perform the activity (e.g., when or after the prompt is displayed). This state confirmation also allows the application 165 to align with the user's emotional state and work towards achieving the endpoint by building trust as the user 222 performs the activity through the application 165. The prompt 224 in the illustrated example allows the user 222 to indicate their mood (also referred to herein as an emotional state), such as happiness, sadness, anger, fear, disgust, surprise, or excitement. This response is used to select a configuration file 155 to provide targeted mitigation measures. Other states include behavioral states (e.g., resting, eating, working, studying, leisure, socializing, playing, exploring) and physiological states (e.g., resting, active, stressed, focused). The behavior manager 175 can call the profiler 170 to display a survey prompt 224 via the user interface element 195 of the user interface 190, prompting the user 222 to indicate their state. The survey prompt 224 is displayed to the user 222 at predetermined times (e.g., once every 4-6 hours, once a day in the evening, or once a week). Through the survey prompt 224, the behavior manager 175 receives a response indicating the user 222's state. By receiving this response, the behavior manager 175 can include the response from the user 222 in the response data 282.

[0101] In some embodiments, the behavior manager 175 can identify or distinguish one or more personal values ​​of user 222 from the user 222's response to the prompt 224. Examples of a personal values ​​survey prompt 224 are shown in Figures 18A–C. In the illustrated examples, the interface in Figure 18A provides a scenario for user 222 to consider, the interface in Figure 18B provides user 222 with an opportunity to consider values ​​and tendencies with goals and endpoints in mind, and the prompt in Figure 18C allows user 222 to input one or more personal values ​​that surface throughout the session. Personal values ​​can identify the characteristics of activities that user 222 wants to perform, or the endpoints that user 222 considers to be goals when performing these activities. The survey prompt 224 can be presented to user 222 at a predetermined time (e.g., once every 4–6 hours, once a day in the evening, or once a week for status checks). Through the survey prompt 224, the behavior manager 175 receives responses indicating one or more personal values. By receiving this information, the behavior manager 175 can include the response from the user 222 in the response data 282.

[0102] In some embodiments, while executing routine logic 202 of configuration file 155, the behavior manager 175 may determine, obtain, or otherwise identify an evaluation associated with an activity before the activity is performed. To identify it, the behavior manager 175 may present a prompt 224 indicating the evaluation associated with the activity before the activity is performed. Furthermore, the behavior manager 175 may determine, obtain, or otherwise identify an evaluation associated with an activity after the activity is performed via application 165. To identify it, the behavior manager 175 may present a prompt 224 indicating the evaluation associated with the activity after the activity is performed. The evaluation can be obtained from the user 222's response to one or more prompts 224 indicating the evaluation. The pre-execution evaluation may indicate a self-assessed value of the user 222's expectations when performing the activity identified in the prompt 224 in dealing with a condition or aiming to achieve an endpoint. The post-execution evaluation may indicate a self-assessed value of the user 222's experience in dealing with a condition or aiming to achieve an endpoint after performing the activity identified in the prompt 224.

[0103] Regarding assessment, application 165, through configuration file 155, can provide lesson content that explains the relationships between thoughts, feelings, and behaviors, and can explain to user 222 the premises of cognitive restructuring and how activities cope with the pathology. Using configuration file 155, application 165 can provide one or more interactive activities that help user 222 understand the thought patterns that contribute to defeatist beliefs associated with negative symptoms.

[0104] For example, during the orientation phase, after introducing the concepts of pre- and post-activity surveys to user 222, the behavior manager 175 conducts user attitude surveys a certain number of times (e.g., 4 times). Before user 222 begins an activity, the behavior manager 175 presents user 222 with pre-activity questions and allows them to select from a scale of 1 to 10. After user 222 completes an activity, the behavior manager 175 presents user 222 with reflective questions and allows them to select from a scale of 1 to 10. During the activity phase, the behavior manager 175 can conduct attitude surveys with user 222 before and after each activity. Before user 222 begins an activity, the behavior manager 175 presents user 222 with pre-activity questions and allows them to select from a scale of 1 to 10. After user 222 completes an activity, the behavior manager 175 presents user 222 with reflective questions and allows them to select from a scale of 1 to 10.

[0105] Examples of questionnaire prompts 224 for evaluation through pre- and post-activity self-assessment are shown in Figures 19A-D. In the illustrated examples, the prompts encourage the user to practice becoming aware of their own expectations in each activity. The questions in prompt 224 can be pre- and post-activity questions, and the response data can be used to reflect on the user's own will to fight defeatist beliefs and demonstrate growth. These self-assessments by user 222 can be used to reflect changes in perception. If improvement is shown, it will strongly boost confidence and may encourage changes in user 222's perception and motivate user 222 to adhere more strictly to the digital therapy provided via configuration file 155. After performing an activity, user 222 is prompted to reflect on their experience of performing the activity. User 222 may be shown pre- and post-activity responses to encourage adherence to and continued performance of the activities presented via application 165. Through questionnaire prompts 224, the behavior manager 175 can receive responses indicating evaluations. By receiving this information, the behavior manager 175 can include the response from the user 222 in the response data 282.

[0106] The progress tracker 150, running on the application configuration service 105, can modify, correct, or otherwise update the user profile 160 maintained in the database 115 using response data 282. As previously stated, the user profile 160 maintained in the database 115 can be used to track the progress of user 222 in each routine provided via package 228. The progress tracker 150 can acquire, identify, or otherwise receive the response data 282 from the user device 110. After receipt, the progress tracker 150 can parse the response data 282 to extract or identify the information contained therein. In some embodiments, the progress tracker 150 can store and maintain the response data 282 (e.g., including an indication of user 222's state, one or more personal values, or ratings) in the database 115. The progress tracker 150 can store the response data 282 in the database 115 using the user profile 160 or a log associated with user 222. This log is a data structure associated with the user profile 160.

[0107] Based on the information parsed from the response data 282, the progress tracker 150 can configure, update, or otherwise modify the user profile 160. Using the user identifier obtained from the response data 282, the progress tracker 150 can identify the user profile 160 associated with user 222. From the user profile 160, the progress tracker 150 can identify the currently recorded level 208 of user 222. Level 208 can correspond to a stage or progress toward achieving a specific endpoint, a set of activities, or a response to a medical condition. For each configuration file 155 selected for user 222, the user profile 160 can identify the current state 204 and current level 208 in the routine logic 202.

[0108] This identification allows the progress tracker 150 to determine, based on the response data 282, whether there is a transition from the current level 208 to the next level 208. From the response data 282, the progress tracker 150 can extract or identify user 222's level 208. In some embodiments, the progress tracker 150 can determine whether user 222's level 208 is transitioning toward achieving an endpoint. If user 222 has achieved an endpoint or activity, the level 208 shown in the response data 282 may be higher than the level 208 currently identified in user profile 160. If user 222 has not achieved an endpoint or activity, the level 208 shown in the response data 282 may be the same as or lower than the level 208 currently identified in user profile 160. In some embodiments, the progress tracker 150 can determine a new state 204 and level 208 using one or more interactions shown in the response data 282, in accordance with the routine logic 202 of a configuration file 155 selected for user 222. The new Level 208 may be approaching a different endpoint than the previous Level 208, as shown in user profile 160.

[0109] The progress tracker 150 can compare the level 208 from the user profile 160 with the level 208 identified in the response data 282 to determine if there is a transition. If the levels are the same, the progress tracker 150 can determine that there is no transition from the user 222's current level 208. If the levels are different, the progress tracker 150 can determine that there is a transition from the current level 208 to the next level 208. The progress tracker 150 can set the state 204 and level 208 in the user profile 160 to the state 204 and level 208 identified in the response data 282, respectively. In some embodiments, the progress tracker 150 can determine or identify a transition to a higher or lower level based on the identified level 208. If the current level 208 is lower than the level 208 identified in the response data 282, the progress tracker 150 can determine that the transition is to a higher level. Conversely, if the current level 208 is higher than the specified level 208, progress tracker 150 can decide to transition to a lower level.

[0110] Furthermore, the progress tracker 150 can use information from response data 282 to set, update, or otherwise modify parameters 226 in the user profile 160. As previously mentioned, parameters 226 can identify endpoints to be achieved, conditions that user 222 must address, user 222's status, routine type, frequency, duration, and time (e.g., day of the week, time of day). In some embodiments, the progress tracker 150 can adjust, set, or modify the frequency and duration based on indicators of success or failure of the routine selected for user 222, or updates to status 204 or level 208. For example, if response data 282 indicates successful completion of the routine, the progress tracker 150 can increase the routine's frequency or duration. Conversely, if response data 282 indicates failure to complete the routine, the progress tracker 150 can decrease the routine's frequency or duration.

[0111] In some embodiments, the progress tracker 150 can identify or determine at least one progress metric related to an individual's values ​​based on response data 282. Response data 282 can identify the performance of an activity toward achieving an endpoint, such as one or more interactions with content items presented via the user interface 190 of the application 165. The progress metric can identify or correspond to the degree of improvement or deterioration in the satisfaction of an individual's values ​​as a result of performing the activity specified in the configuration file 155. For example, taking "excitement" as an example of a personal value, if response data 228 indicates that the user is satisfied with the activity performed, the progress tracker 150 can calculate a relatively high progress metric. Once calculated, the progress tracker 150 can transmit, send, or otherwise provide the correlation between the progress metric and the personal value so that it can be displayed via the user interface 190 of the application 165. For example, this correlation can be displayed as part of a prompt 224 or on the user interface 190 after the prompt 224 is displayed. In some embodiments, the progress tracker 150 can provide the progress metric as part of a subsequent package provided to the application 165. Progress metrics can be displayed to users to encourage reflection and facilitate awareness of progress.

[0112] In some embodiments, the progress tracker 150 can compare evaluations obtained before and after the execution of an activity based on what is identified in the response data 282. Based on this comparison, the progress tracker 150 can calculate, generate, or otherwise identify a metric that shows the difference between these two evaluations. This decision allows the progress tracker 150 to transmit, send, or otherwise provide these comparisons (or difference metrics, or both) of evaluations for display via the application 165. For example, this comparison can be displayed as part of a prompt 224 or on the user interface 190 after the prompt 224 is displayed. Displaying the comparison can reinforce the concept of experimentation in the execution of the activity, regardless of the expected outcome for the user 222. Displaying pre- and post-activity assessments can overturn the user 222's preconceptions or defeatist views by presenting evidence that the user's own experience unfolded differently than expected. In some embodiments, the progress tracker 150 can provide the comparison or difference metrics as part of a subsequent package provided to the application 165.

[0113] As the user profile 160 is updated, the progress tracker 150 can change, set, or modify the endpoints to be achieved. In some embodiments, the progress tracker 150 can change, set, or modify the user's condition based on information parsed from response data 282. This information may include responses received via prompt 224. The progress tracker 150 can replace, change, or set the user's endpoints or conditions shown in the user profile 160 with the endpoints or conditions shown in response data 282, respectively. Changes to endpoints and conditions can trigger changes to parameters 226 within the user profile 160. In some embodiments, the progress tracker 150 can calculate, compute, or generate new measurements based on the updated parameters 226. The generation of measurements using parameter 226 can be performed in a manner similar to that described above. For example, the progress tracker 150 can use a function to calculate values ​​that indicate the characteristics of user 222 and the likelihood that user 222 will perform a particular routine. After generation, the progress tracker 150 can store and maintain the new measurements in the database 115 along with the user profile 160.

[0114] The configuration selector 135 can select, identify, or otherwise determine one or more new endpoints for user 222. Identification of endpoints (and activities associated with endpoints) can be done in the same manner as described above, and based on the parameters 226 and selection criteria 210 of the user profile 160. For example, a change in user profile 160 may include an update to status 204, an update to level 208, an indication of successful completion of a routine, or an increase in the duration or frequency of a routine. In this case, the configuration selector 135 can select the next endpoint with the increased duration and frequency of the activity. For example, a change in user profile 160 may include an update to status 204, an update to level 208, an indication of a routine completion failure, or a decrease in the duration or frequency of a routine. In this scenario, the configuration selector 135 can select the routine with the decreased duration and frequency. Furthermore, a change in user profile 160 may include a modification of an endpoint or user 222's condition. Based on this change, the configuration selector 135 can select an activity corresponding to the new endpoint or condition.

[0115] In some embodiments, the configuration selector 135 may select a new endpoint (or activity) for user 222 using information obtained from response data 282 received via prompt 224. This information may include information generated by the progress tracker 150 from the response data 282. In some embodiments, the configuration selector 135 may select or identify a new endpoint based on user 222's state (e.g., mood, emotion, behavior, or physiological state) as indicated in the response data 282. For example, if user 222's state indicates a state of sadness, the configuration selector 135 may select an endpoint to comfort user 222 while performing an activity to address user 222's state. In some embodiments, the configuration selector 135 may identify or select a new endpoint based on personal values ​​identified by user 222. For example, the configuration selector 135 may select an endpoint aimed at providing an activity related to adventure, self-care, or art as indicated in the response data 282.

[0116] In some embodiments, the configuration selector 135 can identify or determine new endpoints for the user 222 from at least one of the social, leisure, or productivity domains to address the user's condition. Based on information derived from response data 282, the configuration selector 135 can modify, update, or otherwise change the user's domains to new domains. For example, if the final level 208 of a particular domain (e.g., the social domain) is completed and the user indicates improvement in that domain, the configuration selector 135 can select a different endpoint (e.g., the leisure or productivity domain). Thus, based on response data 282 from the user 222, the configuration selector 135 can adaptively and dynamically select endpoints for the user 222 across diverse domains to address the user's condition. This enables the user 222 to perform the activities specified by the endpoints and build skills in the identified domains.

[0117] Upon determining a new endpoint, the configuration selector 135 can select one or more configuration files 155 from a set of configuration files 155 and provide them to the application 165. The configuration files 155 can be selected using the newly selected endpoint or activity in a manner similar to that described above. In some embodiments, the configuration selector 135 can select one or more configuration files 155 based on transitions at multiple levels 208. Using the selected configuration files 155, the configuration packager 140 can generate at least one new package 228'. The package 228' is generated in a manner similar to that described in detail above and may contain instructions for configuring a function to be executed in the application 165 according to the routine logic 202 defined in the configuration file 155. After generation, the configuration packager 140 can provide the package 228' along with the newly selected configuration files 155 to the application 165. The application 165 can then receive and load the package 228'. Using the package 228', the application 165 can repeatedly perform the actions described above.

[0118] By selecting and providing the configuration file 155 in this manner, the application configuration service 105 can customize and configure the functionality of application 165 in response to the user 222's responses. The configuration file 155 can provide user 222 with a diverse range of experiences and content through content items 264 adapted to user 222's state and interactions, following routine logic 202. This allows the configuration file 155 to improve the quality of human-computer interaction (HCI) between user 222 and application 165. In the context of digital therapy, the configuration file 155 and the content items 264 identified therein can improve user engagement with application 165. Increased interactivity may lead to a greater improvement in the effectiveness of digital therapy provided by application 165 in improving the patient's condition and potentially improve user 222's adherence to digital therapy. The configuration file 155 can also reduce the consumption of computing resources (e.g., both user device 110 and application configuration service 105) that might otherwise be used to provide and load content unrelated to application 165. Furthermore, configuration file 155 can save even more computing resources by reducing the need to update application 165 itself to provide additional functionality. Configuration file 155 (and therefore package 228 as well) can reduce the consumption of network bandwidth associated with round-trip communication related to requesting and retrieving content.

[0119] Referring here to Figure 3, a flowchart of Method 300 for selecting an application configuration file is shown. The functionality of Method 300 can be implemented or performed using any of the components described herein in relation to Figure 1-2E (e.g., application configuration service 105 and user device 110) or Figure 24 (e.g., computing system 2400). Briefly, the server can identify the user profile (305). The server can determine the endpoint for the user (310). The server can identify the configuration file (315). The server can provide the package (320). The server can receive response data (325). The server can determine the metric (330). The server can decide whether to update the configuration (335). If the decision is to update, the server selects the new endpoint and repeats the functionality from (310). Conversely, if the decision is not to update, the server waits for additional response data and can repeat the functionality from (325).

[0120] Referring to Figure 4, a block diagram of the architecture of an adaptive goal-setting system for selecting configuration files is shown. This architecture can be implemented using components of system 100 (e.g., application configuration service 105 and application 165 on user device 110). As illustrated, this architecture allows the adaptive goal-setting (AGS) to be divided into three parts. First, the discovery component can select skills (e.g., endpoints) to be offered to the user. This selection can be made in part based on the user's history. Second, a skill can be configured with corresponding modules A, B, C, ... N (e.g., in the form of configuration file 155) for the user. All the logic of the skill is contained within the module, and since the module is self-contained, its behavior can be new and unique each time the module is executed. Third, the notification component is used to remind and prompt the user to execute the skills configured in the module. The discovery component can then check the user to determine whether the user has completed the skill module. Based on this determination, the discovery component can select a new skill module and repeat the functionality of the architecture.

[0121] Referring to Figure 5A, a flowchart is shown illustrating how adaptive goal setting is performed in the selection of a configuration file. This method can be performed or implemented using components of system 100 (e.g., application configuration service 105 or user device 110). For example, at least a portion of the illustrated method can be defined and performed using at least one configuration file 155. As illustrated, the system can present introductory information to the user. From the introductory information, the system can receive the user's activity selection. The system can set the user's activity level. The user can confirm the level and activity selection through interaction. The system can monitor the user interaction and determine whether the user is inactive. If inactive, the system can perform a status check to prompt the user to perform an activity. After completion, the system can update the user's level, notify the user of the level change update, and perform a user re-evaluation. The system can also present a help prompt to instruct the user on how to perform the activity. Depending on the results, the system can perform a further re-evaluation. Meanwhile, the system can also obtain user statistics about the activity.

[0122] Referring to Figure 5B, a flowchart illustrating how to perform an activity according to a configuration file is shown. This method can be performed or implemented using components of system 100 (e.g., application configuration service 105 or user device 110). For example, at least one configuration file 155 can be used to define and perform at least part of the illustrated method. As illustrated, the system can determine whether the user should perform the activity now (e.g., within a certain time frame from the current time) or later (e.g., outside a time frame from the current time). If the activity is to be performed now, the system can determine whether there are any problems with performing it now. If there are problems, the system can identify the cause of the failure or obstruction and display tools (e.g., a user interface) to address the problem. On the other hand, if it is determined that the activity will be performed at a later time, the system can decide whether to provide a reminder or modify the activity. If it decides to provide a reminder, the system can present one. If it decides to modify the activity, the system can identify a new activity.

[0123] B. Methods that bring about the necessary improvement in the subject's empirical negative symptoms of schizophrenia Individuals experiencing empirical negative symptoms of schizophrenia may suffer from a decline or reduction in certain functions or abilities that affect their quality of life and daily routines. The severity and impact of negative symptoms of schizophrenia on an individual can be measured using various scales, including the Clinical Assessment Interview for Negative Symptoms Motivation and Pleasure (CAINS-MAP) scale. The CAINS-MAP scale can measure individuals across various areas related to empirical negative symptoms of schizophrenia, such as leisure, socialization, and productive activities.

[0124] By providing such individuals with digital therapy applications structured within an Adaptive Goal Setting (AGS) framework, improvements in empirical negative symptoms can be achieved. The AGS framework allows applications to adaptively determine endpoints based on user responses and feedback. Each endpoint can define a set of activities that the user performs through the application to treat the empirical negative symptoms of schizophrenia. These endpoints are determined from a set of domains (e.g., social, leisure, productivity) related to components of schizophrenia-specific measurement scales, and each domain can specify activities performed according to that domain.

[0125] Using the determined endpoints, the application can provide a selected configuration file to be loaded for displaying content items that prompt the user to perform a specified activity. For example, if the application specifies an activity such as a face-to-face conversation with a clinician as an endpoint in the social domain, the application can be provided with a corresponding configuration file to deliver. The configuration file can be converted from a human-readable instruction format to a format that the application can read and execute. After loading and execution, the application can present the content items identified in the configuration file through the user interface and prompt the user to engage in a social interaction activity. The application can monitor whether the user has interacted with user interface elements to indicate that the specified activity has been completed.

[0126] Using additional user responses and feedback data, when an application updates a user's endpoint, it can provide the application with other configuration files selected according to the new endpoint. Continuing the previous example, if an endpoint in the social domain associated with a social interaction activity is determined to be completed, the application can determine a new endpoint in the productivity domain specifying that the user complete a physical activity. Once the corresponding configuration file is loaded, the application can present the content items specified in this file and prompt the user to perform the activity. This can be repeated at multiple time instances over a period of time, and configuration files containing different activities performed by the user can be selected and provided. By adaptively and dynamically determining endpoints across different domains for the user to address the empirical negative symptoms of schizophrenia using response data, the application can provide selected configuration files with content items containing activities aimed at improving the user in a particular domain. Through repeated use of the application over time, users of the application can experience improvement in the empirical negative symptoms of schizophrenia, as measured by various scales as described herein.

[0127] Referring to Figure 20, a flowchart of Method 2000 is shown, which brings about the necessary improvement in the user's empirical negative symptoms of schizophrenia. Method 2000 can be performed by any of the components or actors described herein, such as the application configuration service 105, the user device 110, or the user 222. Method 2000 can be used in combination with any of the functions or operations described in Examples 1 and 2 of Sections A and B of this specification. In summary, Method 2000 may include a step of obtaining baseline metrics (2005). Method 2000 may include a step of determining the endpoint (2010). Method 2000 may include a step of identifying a configuration file (2015). Method 2000 may include a step of presenting a set of content items (2020). Method 2000 may include a step of obtaining session metrics (2025). This method may include a step of determining whether to continue (2030). Method 2000 may include identifying or determining whether a session metric is an improvement over a baseline metric (2035). In some embodiments (for example, as illustrated), Method 2000 may include a step of determining that the user is showing improvement when the session metric is an improvement over a baseline metric (2040). Method 2000 may include a step of determining that the user is not showing improvement when the session metric is not an improvement over a baseline metric (2045).

[0128] More specifically, Method 2000 may include a step of extracting, identifying, or otherwise obtaining a baseline metric (2005). The baseline metric may be associated with a user (e.g., user 222) who has been diagnosed with schizophrenia with negative symptoms before performing activities through a digital therapeutic application (e.g., Application 165 or the research app described herein). A user's empirical negative symptoms of schizophrenia may include one or more, for example, affective blunting, alogia (reduced speech volume), decreased motivation (reduced goal-oriented activity due to decreased motivation), antisocial behavior, and anhedonism (reduced pleasure experience).

[0129] Users may have any demographic or characteristic, such as age (e.g., adult (over 18), late adolescence (between 18 and 24)) or sex (e.g., male, female, or non-binary). Users may also have been taking a stable dose of antipsychotic medication for at least a certain period (e.g., 12 weeks) prior to their first activity through the digital therapeutic application. Antipsychotic medications may include, for example, risperidone, quetiapine, olanzapine, ziprasidone, paliperidone, and aripiprazole. Other medications that may be used include, for example, iclepertin (a GlyT1 inhibitor).

[0130] Baseline metrics can be obtained (by a clinician or Application 165) before the user performs an activity through a digital therapeutic application (e.g., Application 165 or the research app described herein). In some embodiments, baseline metrics can also be obtained from a source other than the application. For example, a clinician examining the user may determine and provide the baseline metrics for storage. In some embodiments, a calculation system (e.g., Application 165 or Application Configuration Service 105) can determine the baseline metrics through user interaction with prompts presented through the user interface (e.g., a questionnaire about metrics). In some embodiments, baseline metrics can be obtained prior to a certain period (e.g., 1–20 weeks) before the first activity. The baseline metric may include one or more scores from, for example, the Clinical Assessment Interview for Negative Symptoms (CAINS) Motivation and Pleasure Scale, the Clinical Assessment Interview for Negative Symptoms - Expressiveness Scale (CAINS-EXP), the Positive and Negative Symptom Rating Scale (PANSS), the Personal and Social Functioning Performance Scale (PSP), the Defeatist Beliefs Subscale of the Dysfunction Attitudes Scale (DAS), the Patient's Comprehensive Impression of Improvement Scale (PGI-I), the Patient's Comprehensive Impression of Severity Scale (PGI-S), the Clinical Comprehensive Impression of Severity Scale (CGI-S), the World Health Organization Disability Assessment Scale, 2nd Edition (WHODAS 2.0), or the Quality of Life Scale for Schizophrenia, 4th Edition (SQLS-R4).

[0131] Users engaging in activities via digital therapeutic applications may have baseline metrics below a certain threshold. This threshold indicates that the user may be experiencing or suffering from moderate to severe symptoms associated with negative symptoms of schizophrenia. The user may also have experienced moderate to severe negative symptom severity (e.g., as measured by the baseline metrics) prior to the first activity. For example, the user may have a Motivation and Pleasure Scale (MAPS) score of 30 or less prior to the first activity.

[0132] Method 2000 may include a step of identifying or determining at least one endpoint for improving empirical negative symptoms (2010). A computer system (e.g., application configuration service 105 or user device 110) may identify or determine this endpoint from a set of endpoints for improving empirical negative symptoms. The endpoint may correspond to an indicator of completion of a set of defined activities for improving empirical negative symptoms. In some embodiments, the endpoint (e.g., before the user initiates an activity) may be selected from a set of endpoints based on the user's baseline assessment of empirical negative symptoms of schizophrenia. In some embodiments, the endpoint (e.g., after the user has been prompted to perform at least one activity) may be selected from a set of endpoints based on response data that identifies one or more interactions by the user with a set of content items presented from a previous point in time via the application.

[0133] In some embodiments, a set of endpoints can be associated with one or more categories or domains, such as the social domain, the leisure domain, or the productivity domain. The domain of the set of endpoints is associated with the type of metric the user is assessed on. For example, the social, leisure, and productivity domains can be associated with the CAINS-MAP scale, which can be used to measure various aspects or domains of a user's empirical negative symptoms of schizophrenia. Endpoints associated with the social domain can identify activities that improve the user's social skills, endpoints associated with the leisure domain can identify activities for the user to engage in leisure exercise or tasks, and endpoints associated with the productivity domain can include activities that improve the user's attention or work habits. Each domain may be associated with multiple aspirations. For example, the leisure module may include aspirations such as hobbies, creativity, and exercise. The productivity module may include activities such as washing dishes. Endpoints can also be associated with multiple levels.

[0134] The calculation system can select endpoints based on any number of factors (e.g., those described in Section A). In some embodiments, endpoint selection can be based on a baseline evaluation for the initial activity. Selection can be based on evaluations obtained later for subsequent activities. In some embodiments, endpoint selection for subsequent activities can be based on response data that identifies interactions with previously presented content items. In some embodiments, endpoint selection (for the initial activity or subsequent activities) can be based on one or more personal values ​​indicated by the user. Personal values ​​can identify the characteristics of activities the user wants to perform, or the endpoints the user considers as their goal when performing these activities.

[0135] In some embodiments, endpoint selection (for the first or subsequent activity) can be based on the state exhibited by the user. This state can be associated with the user's mood, behavior, or physiological state. In some embodiments, the calculation system can identify or select endpoints based on a transition from one level to another, determined using response data, after at least one activity has been performed. In some embodiments, the calculation system can determine endpoints for transitioning from one domain to another. For example, when evaluating a user on the CAINS-MAP scale, the calculation system can first identify endpoints in the productivity domain associated with the user. Using additional response data, the calculation system can update these endpoints to endpoints associated with the social or leisure domain. For example, if the response data shows a statistically significant improvement in the productivity domain (e.g., as indicated by a metric) or a transition to another level is determined, the calculation system can change the user's endpoints.

[0136] Method 2000 may include a step of identifying or selecting one configuration file from a plurality of configuration files based on an endpoint (2015). Based on the endpoint, the computing system may identify or select a configuration file corresponding to the endpoint (e.g., configuration file 155). The configuration file may identify a set of content items that prompt the user to perform one or more activities in order to achieve a determined endpoint for improving empirical negative symptoms. In some embodiments, at least one of the plurality of configuration files identifies a decision criterion that defines a measure for selecting the other configuration files. This measure may identify, for example, the likelihood that the user will perform the activity or the expected effect of this activity on the user toward achieving the endpoint. In some embodiments, the configuration file may be selected based on a change in the endpoint determined using response data from a previous point in time.

[0137] In some embodiments, the computing system can identify activities toward achieving an endpoint based on a user profile (e.g., user profile 160) or other indicators (e.g., personal values, status). In some embodiments, the computing system can identify activities toward achieving an endpoint based on response data after performing at least a first activity. Once activities are identified, the computing system can select a configuration file having a set of content items to present.

[0138] Method 2000 may include a step of displaying, rendering, or otherwise presenting a set of content items (2020). From a configuration file, the computed system may identify one or more content items to prompt the user to perform an activity. The computed system may present the set of content items identified from the configuration file. The set of content items may identify a first activity (if presented to the user for the first time) and each may identify a corresponding second activity (if presented to the user after the first presentation). The computed system may monitor for the occurrence of one or more interactions between the user and the content items when the prompted activity is performed. The presentation and functionality of the content items may be as described in Section A above.

[0139] In some embodiments, the calculation system can prompt the user to provide an assessed evaluation in conjunction with the performance of activities identified in a set of content items. The calculation system may receive an evaluation before the activity is performed and another evaluation after the activity is performed. The calculation system can obtain a comparison of the evaluations before and after the activity is performed. After receiving the evaluations, the calculation system can present the comparison of the evaluations before and after the activity to the user.

[0140] Method 2000 may include a step of extracting, identifying, or otherwise obtaining session metrics (2025). Session metrics can be obtained (e.g., by a clinician or application 165) after performing one or more activities through a digital therapeutic application. In some embodiments, session metrics can be obtained from a source other than the application. For example, a clinician examining a user may determine and provide those session metrics for storage. In some embodiments, a computing system (e.g., application 165 or application configuration service 105) may determine session metrics through user interaction with prompts presented through a user interface (e.g., a questionnaire about metrics). Session metrics may include one or more scores from, for example, the Clinical Assessment Interview for Negative Symptoms (CAINS) Motivation and Pleasure Scale, the Clinical Assessment Interview for Negative Symptoms - Expressiveness Scale (CAINS-EXP), the Positive and Negative Symptom Rating Scale (PANSS), the Personal and Social Functioning Performance Scale (PSP), the Defeatist Beliefs Subscale of the Dysfunctional Attitudes Scale (DAS), the Patient's Comprehensive Impression of Improvement Scale (PGI-I), the Patient's Comprehensive Impression of Severity Scale (PGI-S), the Clinical Comprehensive Impression of Severity Scale (CGI-S), the World Health Organization Disability Assessment Scale, 2nd Edition (WHODAS 2.0), or the Quality of Life Scale for Schizophrenia, 4th Edition (SQLS-R4). Session metrics may be of the same type as baseline metrics obtained before performing any activity.

[0141] In some embodiments, the computation system can identify, acquire, or otherwise receive response data (e.g., response data 282). The response data can identify one or more interactions by the user with a set of content items. For example, at the end of the orientation phase, the digital therapy application may present the user with a questionnaire. This questionnaire asks the user several questions about leisure, socialization, and productivity, corresponding to different categories or areas of endpoints. The computation system can use the response data that identifies the answers to the questionnaire to recommend the next area. The application can provide the user with the option to select different areas, apart from the application's recommendations. Based on these inputs (evaluations and the user's selection of focus areas), the application can select a personalized endpoint for the user and initiate the user experience. Based on the user's success or failure in achieving their goals, the application can continuously personalize the experience at multiple points throughout the user's journey.

[0142] The method may include a step to determine whether to continue (2030). This decision can be based on a set length of the trial (e.g., 5 days to 25 weeks) or a set number of endpoints, activities, or sessions provided to the user (e.g., corresponding to individual time points). If the time since baseline metric acquisition or initial activity has not exceeded a set period, the decision may be to continue processing and repeat from (2010). In some embodiments, if the number of endpoints, activities, or sessions exceeds a set number, the calculation system may decide to continue and repeat from (2010). The calculation system may repeatedly perform endpoint determination (2010), configuration file identification (2015), and presentation of a set of content items (2020), etc., over a set of time points (which may also be referred to as sessions herein). Content items identifying selected activities may be repeatedly presented until a stop decision is made. Alternatively, if the amount of time since baseline metric acquisition or initial activity acquisition exceeds a set length, the decision may be to stop. In some embodiments, the computing system may decide to shut down if the number of endpoints, activities, or sessions does not exceed a set number.

[0143] Method 2000 may include a step to determine if the session metric is below the baseline metric when a decision to stop is made (2030). For this determination, the calculation system may compare the baseline metric before the first activity with the session metric obtained after performing one or more subsequent activities (e.g., at or near the end of a set time length). In some embodiments, the session metric being compared may be obtained from the last point in time of the repeated presentation of the content item (e.g., after the decision to stop). In some embodiments, the session metric being compared may be obtained from at least one point in time of the repeated presentation of the content item (e.g., after the decision to stop).

[0144] Method 2000 may include identifying or determining whether a session metric is an improvement over a baseline metric (2035). This improvement may correspond to an improvement in the empirical negative symptoms of schizophrenia. This improvement may correspond to a statistically significant difference (e.g., a statistically significant difference) between the session metric and the baseline metric. This improvement may be considered to have occurred when the session metric increases by a first predetermined margin compared to the baseline metric, or when the session metric decreases by a second predetermined margin compared to the baseline metric. This margin may identify or define the difference in values ​​between the baseline metric and the session metric for determining whether the user has shown improvement in the degree of empirical negative symptoms of schizophrenia. Whether the improvement is indicated by an increase or a decrease may depend on the type of metric used to measure the user with respect to the empirical negative symptoms of schizophrenia. The margin may also depend on the type of metric used and may generally correspond to a difference in values ​​that indicates a significant difference between the clinician or user regarding the degree of empirical negative symptoms of schizophrenia, or a difference in values ​​that indicates a statistically significant result in the difference between the baseline metric and the session metric.

[0145] Method 2000 may include a step of determining that an improvement has been demonstrated if the session metric is determined to be an improvement over the baseline metric (2040). In some embodiments, an improvement can be determined (e.g., by a calculation system or a clinician examining the user) when the session CAINS-MAP metric decreases by a second predetermined margin from the baseline CAINS-MAP metric. In some embodiments, an improvement can be determined when the session CAINS-EXP metric decreases by a second predetermined margin from the baseline CAINS-EXP metric. In some embodiments, an improvement can be determined when the session PANSS metric decreases by a second predetermined margin from the baseline PANSS metric.

[0146] In some embodiments, improvement can be determined when the session PSP metric increases by a first predetermined margin from the baseline PSP metric. In some embodiments, improvement can be determined when the session DAS metric decreases by a second predetermined margin from the baseline DAS metric. In some embodiments, improvement can be determined when the session CGI-S metric decreases by a second predetermined margin from the baseline CGI-S metric. In some embodiments, improvement can be determined when the session PGI-I metric decreases by a second predetermined margin from the baseline PGI-I metric. In some embodiments, improvement can be determined when the session PGI-S metric decreases by a second predetermined margin from the baseline PGI-S metric.

[0147] In some embodiments, improvement can be determined when the session EQ-5D-5L metric increases by a first predetermined margin from the baseline EQ-5D-5L metric. In some embodiments, improvement can be determined when the session SDS metric increases by a first predetermined margin from the baseline SDS metric. In some embodiments, improvement can be determined when the session WHODAS metric decreases by a second predetermined margin from the baseline WHODAS metric. In some embodiments, improvement can be determined when the session SQLS-R4 metric decreases by a second predetermined margin from the baseline SQLS-R4 metric.

[0148] Method 2000 may include a step of determining that no improvement has been demonstrated if the session metric is determined not to be an improvement over the baseline metric (2045). In some embodiments, no improvement can be determined (e.g., by a calculation system or a clinician examining the user) if the session CAINS-MAP metric has not decreased by a second predetermined margin from the baseline CAINS-MAP metric. In some embodiments, no improvement can be determined if the session CAINS-EXP metric has not decreased by a second predetermined margin from the baseline CAINS-EXP metric. In some embodiments, no improvement can be determined if the session PANSS metric has not decreased by a second predetermined margin from the baseline PANSS metric.

[0149] In some embodiments, it can be determined that no improvement has been made if the session PSP metric has not increased by a second predetermined margin from the baseline PSP metric. In some embodiments, it can be determined that no improvement has been made if the session DAS metric has not decreased by a second predetermined margin from the baseline DAS metric. In some embodiments, it can be determined that no improvement has been made if the session CGI-S metric has not decreased by a second predetermined margin from the baseline CGI-S metric. In some embodiments, it can be determined that no improvement has been made if the session PGI-I metric has not decreased by a second predetermined margin from the baseline PGI-I metric. In some embodiments, it can be determined that no improvement has been made if the session PGI-S metric has not decreased by a second predetermined margin from the baseline PGI-S metric.

[0150] In some embodiments, if the session EQ-5D-5L metric does not increase by a first predetermined margin from the baseline EQ-5D-5L metric, it can be determined that no improvement has been made. In some embodiments, if the session SDS metric does not increase by a first predetermined margin from the baseline SDS metric, it can be determined that an improvement has been made. In some embodiments, if the session WHODAS metric does not decrease by a second predetermined margin from the baseline WHODAS metric, it can be determined that no improvement has been made. In some embodiments, if the session SQLS-R4 metric does not decrease by a second predetermined margin from the baseline SQLS-R4 metric, it can be determined that no improvement has been made.

[0151] In some embodiments, Method 2000 may include a step in which the user is determined to be showing improvement in empirical negative symptoms of schizophrenia (for example, when the user is taking the test according to Example 1) when the session metric is less than the baseline metric. Improvement in empirical negative symptoms of schizophrenia may correspond to a decrease in the CAINS-MAP score or DAS score, etc. Otherwise, Method 2000 may include a step in which the user is determined not to be showing improvement in empirical negative symptoms of schizophrenia when the session metric is greater than the baseline metric. In some embodiments, Method 2000 may include a step in which the user is determined to be showing improvement when the session metric is greater than the baseline metric. Improvement in empirical negative symptoms of schizophrenia may correspond to an increase in the PSP score, etc. In contrast, Method 2000 may include a step in which the user is determined not to be showing improvement when the session metric is less than or equal to the baseline metric.

[0152] Example 1: A multicenter, exploratory, single-arm study evaluating the feasibility and acceptability of a shortened digital therapeutic application in adults diagnosed with schizophrenia. overview

[0153] Indications: Adults with negative symptoms of schizophrenia

[0154] Introduction: Digital therapeutic applications (e.g., Application 165 or referred to herein as CT-155 or Research App) were trial prescription digital therapeutics (DTx) that provided interactive, software-based interventions for the negative symptoms of schizophrenia. Throughout the DTx development lifecycle, each iterative refinement of the DTx can be scientifically evaluated in a user population clinically representative of the target patient population. Data generated through this evaluation can be used to facilitate the modification and optimization of specific therapeutic components in a given DTx. The objective of this study was to evaluate the feasibility and acceptability of using a shortened digital therapeutic application (Research App) in adults with schizophrenia exhibiting at least moderate to severe empirical negative symptoms.

[0155] the purpose

[0156] The primary objective was to explore the feasibility and acceptability of shortened treatments using this digital therapeutic application.

[0157] The exploratory objectives were as follows: ● To explore the changes in empirical negative symptoms from baseline to the end of the study. ● To explore changes in social functioning from baseline to the end of the test. ● To explore the changes in defeatist beliefs from the baseline to the end of the study. ● To investigate compliance with daily engagement requirements for the research application. ● Explore the correlation between changes in empirical negative symptoms and changes in baseline motivation and pleasure, baseline personal and social functioning, baseline digital literacy, baseline cognitive functioning, strength of the digital work alliance, and defeatist beliefs from baseline to week 7 of the study. ● To explore the expected therapeutic effects of participants over a 7-week period using a research application.

[0158] Research endpoints ● Primary endpoints: The feasibility and acceptability of research applications are defined as follows: ○ Participants' quality and satisfaction with the research app, as measured at week 7 using the Mobile App Rating Scale (MARS). ○ Feedback from participants obtained through qualitative participant follow-up interviews and the Human Factors Questionnaire (HFQ). ● Exploratory endpoints included: ○ Clinical assessment of negative symptoms: Changes in empirical negative symptoms from baseline to week 7, assessed using the Motivation and Pleasure Scale in the Clinical Assessment Interview (CAINS). ○ Changes in social functioning from baseline to week 7, as assessed by the Personal and Social Performance Scale (PSP) ○ Changes in defeatist beliefs from baseline to week 7, as assessed by the Defeatist Beliefs subscale of the Dysfunctional Attitudes Scale (DAS). ○ Adherence to daily engagement with the research app from baseline to week 7 ○ Correlations between changes in empirical negative symptoms and changes in baseline motivation and pleasure, baseline personal and social functioning, baseline digital literacy, baseline cognitive functioning, strength of the digital work alliance, and defeatist beliefs from baseline to week 7 of the study. ○ Expected therapeutic effects of research apps as assessed by the Expected Benefits Questionnaire (EBQ) ○ The degree of participants' engagement with the research application, as measured by participant app usage data obtained within the app.

[0159] Study Design: This was a multicenter, exploratory, single-arm study evaluating the feasibility and acceptability of treatment with a shortened digital therapeutic application in adults diagnosed with schizophrenia. Eligible participants were required to have been diagnosed with schizophrenia according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and to be experiencing at least moderate to severe negative symptom severity, as indicated by a score of 30 or less on the Motivation and Pleasure Scale-Self-Report (MAP-SR). Participants were required to have been taking a stable dose of antipsychotic medication for at least 12 weeks (3 months) prior to enrollment (Day 1). Participants meeting the eligibility criteria could be enrolled in the study on Day 1.

[0160] Referring here to Figure 21, a diagram of the study screen configuration used to conduct a multicenter, exploratory, single-arm study to evaluate the feasibility and acceptability of treatment using a shortened digital therapeutic application is shown. As illustrated, the study included a screening period of up to 7 days, an engagement period of 49 days, and a follow-up period of up to 7 days.

[0161] Screening Period (Day 7 to Day 1): All participants who gave informed consent entered a screening period of up to 7 days to determine their eligibility. Assessments during this period were conducted during in-person clinical visits. Facility staff assisted eligible participants in downloading and installing the research app on their primary iPhone® or Android smartphone, thereby enabling them to begin using the research app.

[0162] Participation Period (Days 1 to 49): Eligible participants were registered during an in-person clinic visit on Day 1. Assessments and activities during this period were conducted during in-person clinic visits according to the Activity and Assessment Schedule (SoA). Participants were instructed to access and complete daily tasks following instructions provided through the research app.

[0163] Follow-up period (days 50 to 56): Participants entered a follow-up period of up to 7 days, during which they participated in in-person clinic visits to complete the follow-up evaluation according to the State of Assessment (SoA). Participants did not engage in any activities within the application.

[0164] Planned number of participants: A maximum of 48 participants can be enrolled in this study.

[0165] Research participation criteria

[0166] Included selection criteria:

[0167] Participants were deemed eligible to participate in this study if they met all of the following criteria: 1. I am willing and able to provide written informed consent to participate in this study, attend research visits, and comply with research-related requirements and evaluations. 2. The person is between 18 and 64 years old at the time of informed consent. 3. Proficient in both written and spoken English, as demonstrated by the ability to read and understand informed consent forms. 4. The patient has had a primary diagnosis of schizophrenia, as defined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), for at least one year prior to screening. 5. Based on a review of medical records or documented discussions with the treating physician, the researcher determined that the patient was in a stable phase of the illness. 6. The patient is receiving outpatient treatment at the time of screening and has not been hospitalized for schizophrenia within the 12 weeks prior to screening. 7. Researchers have confirmed that participants have been taking a stable dose of antipsychotic medication for at least 12 weeks prior to registration (Day 1). 8. The patient achieved a score of 30 or less on the MAP-SR during their screening visit. 9. According to the participant's own report, they are the sole user of an iPhone® operating system (iOS) version 13 or later, or an Android operating system version 10 or later smartphone, and are willing to download and use the research application in accordance with the protocol requirements. 10. I am the owner of the email address and I access that address regularly. 11. You have regular access to the internet via your mobile phone data plan and / or Wi-Fi. 12. You have stable housing, have been staying in the same housing for at least 12 weeks prior to screening, and have no plans to change housing during the study period. 13. As assessed by researchers during the installation and activation of research applications within the facility, participants understood how to use the research application upon their screening visit.

[0168] Exclusion criteria:

[0169] Participants were deemed ineligible to participate in the study if they met any of the following criteria. 1. I am currently receiving treatment with more than two types of antipsychotic medications (including more than two different dosage forms). 2. I am currently receiving treatment with clozapine or haloperidol. 3. In the researchers' judgment, there are currently significant positive symptoms that interfere with the effective treatment of negative symptoms. 4. Are you currently receiving psychotherapy, or have you received it within the 12 weeks prior to the screening? 5. Diagnoses excluded from the survey include any of the diagnostic criteria from the International Classification of Diseases, 10th Revision (ICD-10) or DSM-5, such as schizotypal disorder, schizoaffective disorder, or nonspecific psychotic disorder. 6. Having post-traumatic stress disorder (PTSD), bipolar disorder, major depressive disorder, developmental disorder, or a significant impairment that, in the researcher's judgment, could interfere with adherence to the protocol. 7. The researcher determines that the participant has a substance or alcohol use disorder (excluding caffeine and nicotine) that could interfere with adherence to the protocol. 8. In the researchers' judgment, during the study period, participants currently require or are likely to require any of the prohibited concomitant medications and / or therapies. 9. I am currently participating in another clinical study (intervention study or observational study) involving an investigational drug or test device. 10. I have previously participated in this clinical study. 11. Having suicidal ideation or suicidal behavior as assessed by the Columbia Suicide Severity Rating Scale (C-SSRS): a. Participants who answered "yes" to either item 4 or 5 of the C-SSRS suicidal ideation section within 12 weeks prior to screening or at their baseline visit. b. Participants who answered "yes" to the suicidal behavior item on the C-SSRS within 26 weeks prior to screening or at their baseline visit. c. Participants who, in the researchers' opinion, are at high risk of suicide. 12. If, in the researcher's judgment, there is evidence of a clinically significant concomitant disease or other clinical condition that could threaten the safety of the participant during their participation in the clinical study.

[0170] Test product and application method: Upon screening visit, facility staff assisted eligible participants in downloading and installing the research application on their smartphone devices. Participants activated the application's engagement module upon baseline visit (Day 1).

[0171] Study period: The participation period was approximately 9 weeks, as follows: ● Screening period: up to 7 days ● Duration of involvement: 49 days ● Follow-up period: up to 7 days ● Sample size: Up to 48 people can participate in this study.

[0172] Statistical Analysis: The statistical analysis plan (SAP), which details the analysis of research objectives and endpoints, includes the following: ● The degree of participants' engagement with the research application, as measured by a predefined research application engagement metric. ● Qualitative analysis of participant feedback obtained through qualitative participant interviews and the Human Factors Questionnaire (HFQ) in follow-up surveys. ● Changes in the strength of the digital working alliance from baseline to week 7, as assessed by the mobile version of the AgNew Relationship Rating Scale (mARM). ● The overall severity of negative schizophrenia symptoms as assessed by the CAINS scale. ● The overall severity of negative schizophrenia symptoms, as assessed by CAINS, is evaluated at week 7. ● The degree of improvement in negative schizophrenia symptoms from baseline to week 7 is evaluated as the mean change using the CAINS scale. ● Expected therapeutic effects in individuals with more severe and moderate negative symptoms, as assessed by EBQ. ● The change in defeatist beliefs from baseline to week 7, as assessed by DAS, is evaluated as the mean change. ● The correlation between research app use and the intensity of negative symptoms of schizophrenia will be assessed using predefined research app involvement metrics and CAINS. ● The degree of association between baseline levels of empirical negative symptoms assessed by CAINS and baseline social functioning assessed by PSP, baseline digital literacy assessed by MDPQ, baseline cognitive function assessed by BACS and the Altoida app, the degree of digital working alliance strength assessed by mARM, and the degree of expectation for treatment effects is evaluated using correlation coefficients. ● The correlation between participation in the research application and the expected therapeutic effect, as assessed by predefined research pre-participation metrics and evidence-based questions (EBQs), will be evaluated.

[0173] This digital therapy application is a novel prescription digital therapy (DTx) that provides an interactive, software-based intervention aimed at treating empirical negative symptoms in patients with schizophrenia who are stable on standard antipsychotic medications (SOCs). The therapeutic techniques of the digital therapy application are selected based on clinical evidence, and each component is designed according to the fundamental principles of face-to-face therapy to provide the best possible support to adults with schizophrenia. The therapeutic techniques of the digital therapy application have been proven to have a synergistic effect on empirical negative symptoms.

[0174] In the development lifecycle of a DTx, each iterative refinement can be scientifically evaluated in a user population that clinically represents the target patient population. The data generated through this evaluation can be used to facilitate the modification and optimization of specific therapeutic components in a given DTx.

[0175] The objective of this study was to evaluate the feasibility and acceptability of treatment using a shortened digital therapeutic application (research app) in adult patients with schizophrenia exhibiting at least moderate to severe empirical negative symptoms.

[0176] Digital therapeutic applications

[0177] Treatment guidelines for schizophrenia recommend antipsychotic medications and adjunctive psychosocial interventions. As mentioned above, while medications are effective in treating positive symptoms, there are no pharmacological interventions available for treating negative symptoms. Therefore, adjunctive psychosocial interventions are recommended for symptoms such as negative symptoms that are not adequately improved by pharmacological interventions. This application (e.g., Application 165) provides a treatment approach that applies the fundamental principles of face-to-face psychosocial therapy to digital therapy, doing so in a manner that adheres to the latest evidence-based recommendations.

[0178] This digital therapeutic application is an adjunctive digital therapy designed to target empirical negative symptoms in patients with schizophrenia who are stable on standard of care (SOC). This digital therapeutic application is available by prescription only and is intended for use only under the supervision of a clinician. Given the age of onset of schizophrenia, it is clinically important that this be evaluated in adults (18 to 64 years of age) and that its use is indicated accordingly.

[0179] Digital therapeutic applications are designed to address the need for available therapies to fill existing gaps in SOC (State of Care). Studies have reported that smartphone ownership is nearly ubiquitous, with over 80% of schizophrenia patients owning smartphones. While no validated mobile therapy exists, the vast majority of schizophrenia patients already utilize technology to manage their illness, such as for responding to auditory hallucinations or setting medication reminders. This digital therapeutic application has provided people with schizophrenia with access to validated digital therapies that complement ongoing treatment.

[0180] The research app was designed to provide a shortened version of the complete treatment cycle using the digital therapeutic application. This design allowed us to infer the usability and acceptability of the digital therapeutic application, but did not require a complete treatment using the digital therapeutic application.

[0181] Included objectives: To assess how late adolescents and adults with schizophrenia and empirical negative symptoms (ENS) interact with therapeutic lessons, skills practice, and adaptive goal-setting (AGS) activities.

[0182] Test devices under evaluation

[0183] Digital therapeutic applications (research applications)

[0184] The digital therapeutic application (also referred to herein as the research app) was developed as an adjunctive digital therapy designed to target empirical negative symptoms in adults and late adolescents with schizophrenia who are stable on SOC. To address the critical clinical needs demonstrated by individuals with schizophrenia, the delivery of the therapy is guided by a validated mechanism of action model. The therapeutic techniques in this application are selected based on clinical evidence, and each component is designed according to the fundamental principles of face-to-face therapy to provide the best possible support to individuals with schizophrenia. Furthermore, messages supporting the delivery, engagement, and adherence to the therapy were transmitted throughout the entire period of individual use of the application. The therapeutic techniques included in the application were effective against empirical negative symptoms through their synergistic effects.

[0185] Handling and saving applications

[0186] Download research app

[0187] During the screening visit, facility staff assisted participants in downloading and installing the app. The installation procedure was described in the "Research App Facility Manual." Facility staff confirmed that the research app had been downloaded via eCRF.

[0188] Activate research app

[0189] Upon baseline visit, facility staff assisted participants in activating the app. The activation procedure was described in the "Study App Facility Manual." Facility staff confirmed that the study app had been activated in the eCRF. Only participants whose eligibility to participate in the study had been verified and who were registered were permitted to activate the study app.

[0190] Disabling and uninstalling research applications

[0191] After the completion of visits in week 7 (day 49), the research app was automatically disabled and became unusable by participants. Facility staff instructed participants who completed or terminated the study early to uninstall the research app. Uninstallation instructions were included in the research app site manual. Facility staff confirmed that these instructions were provided to participants via eCRF.

[0192] Adherence to research application

[0193] Participants were instructed to use the research app according to the instructions. They were presented with daily tasks, activities, and / or to-do lists. Adherence to this therapy was not defined in this study; however, the degree of engagement with the research app was measured.

[0194] Continued access to the research application after the completion of the research project.

[0195] After the end of the involvement period (day 49), the research app became inoperable. From day 50 onward, participants were no longer able to access the content provided by the research app from day 1 to day 49.

[0196] Combination therapy

[0197] Participants were required to have been taking a stable dose of SOC antipsychotic medication for at least 12 weeks prior to registration (Day 1). Dose adjustments during the study period were permitted in accordance with the instructions in the package insert of the medication being taken.

[0198] Participants were not permitted to receive treatment with more than two antipsychotic medications (including more than two dosage forms). Treatment with clozapine or haloperidol was prohibited. Additional psychotherapy was not permitted during the study period.

[0199] Lifestyle considerations

[0200] Each participant was allowed to use their own smartphone on a daily basis during the study period. Furthermore, participants were required to be able to visit the clinic during the study period. Participants were asked to refrain from using alcohol or leisure medications during the times they accessed the research app.

[0201] Overall research design

[0202] This is a multicenter, exploratory, single-arm study to evaluate the overall effects of using a shortened digital therapeutic application in adults diagnosed with schizophrenia. Eligible participants were required to have a diagnosis of schizophrenia according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and to be experiencing moderate to severe negative symptom severity, indicated by a Motivation and Pleasure Scale Self-Report (MAP-SR) score of ≤30. Participants were also required to have been taking a stable dose of antipsychotic medication for 12 weeks (3 months) prior to enrollment (Day 1). Participants who met the eligibility criteria were enrolled in the study on Day 1.

[0203] The participation period was approximately 9 weeks, as shown in Figure 21, including a screening period of up to 7 days, an engagement period of 49 days, and a follow-up period of up to 7 days.

[0204] Activities and assessments during the screening period of up to 7 days, the engagement period of 49 days, and the follow-up period of up to 7 days were conducted according to the Schedule of Activities and Assessments (SoA). Site staff performed procedures during in-person clinic visits. Participants were assessed at screening and baseline using reliable, standardized clinician-rated and participant-rated outcome scales for schizophrenia. Participants were assessed during the engagement and follow-up periods using both reliable, qualitative, and participant-rated outcome scales to assess the study endpoints. Participant engagement with the application (sometimes referred to herein as the “Study App”) was assessed based on data obtained within the application. Participants were also assessed for adverse events throughout the entire study. Sites conducted scheduled in-person or telephone remote outpatient visits at any time upon request from researchers or study participants, or to assess safety concerns.

[0205] Screening period (from day 7 to day 1)

[0206] During the in-person screening visit, participants will sign the Informed Consent Form (ICF) and complete all activities and assessments listed in the Statement of Action (SoA). Following this initial screening visit, participants will enter a screening period of up to seven days, during which their eligibility and interest in the study may be further assessed. However, the screening visit may take place on the same day as the baseline (Day 1).

[0207] Facility staff assisted eligible participants in downloading and installing the research application on their iPhone® or Android smartphone, enabling them to begin using the application.

[0208] Period of involvement (Day 1 - Day 49)

[0209] Participant eligibility was confirmed during the in-person baseline visit on day 1. Participants were considered eligible to receive the research app if they met all inclusion criteria and consistently did not meet any exclusion criteria.

[0210] Up to 48 eligible participants were enrolled at approximately 15 research institutions in the United States. Activities and evaluations during this period were conducted within the institutions, between institution staff and participants. All evaluations were conducted in accordance with the System of Analysis (SoA).

[0211] Upon registration, the research app was activated using a unique activation code, allowing facility staff to verify that the research app was functioning correctly. During the 49-day engagement period, participants were instructed to follow the instructions in the research app and access and complete assignments daily.

[0212] Follow-up period (days 50 to 56)

[0213] Participants entered a follow-up period of up to 7 days, during which they were not permitted to use the research app. Activities and evaluations during this period were conducted in accordance with the State of Analysis (SoA). Participants individually participated in a 60-minute qualitative interview with the product development team via remote conferencing software to discuss their experience using the research app. The 8-week follow-up visit could be conducted for up to 6 days prior to day 56.

[0214] Definition of the end of research

[0215] The end of this study was defined as the last contact date or last contact trial date of the last participant who completed or withdrew from the study.

[0216] Participants who were evaluated on their scheduled final visit (day 49, week 7) were defined as study completers.

[0217] Research evaluation and procedures

[0218] The study evaluation and procedures, including their timing, are summarized in the Statement of Analysis (SoA). Adherence to the study design requirements, including those specified in the SoA, was an essential and necessary condition for conducting the study. Every effort should be made to complete the evaluations and procedures required by the protocol as described. All measures administered by clinicians should be administered by appropriately trained individuals. The study procedures are described below. The study evaluation is described below.

[0219] Research evaluation and scales

[0220] In this study, the following evaluation scales were used at the time specified in the State of Analysis (SoA).

[0221] Involvement in research applications

[0222] Participant engagement with the research app can be automatically captured by the app. The following metrics were collected: ● Number of completed available lessons relative to the total number of lessons allocated within the research app ● Number of days the research app was opened out of all available treatment days ● Number of times the research app was opened via a notification click ● Number of times the research app was opened via SMS link ● The number of completed daily status checks relative to the total allocated number. ● Overall completion rate of assigned tasks ● Duration of each app usage session ● Number of times each skill was practiced ● Treatment focus selected by participants ● Achievement rate of targets ● Percentage of goal-centered tasks completed by users

[0223] Motivation and Pleasure-Self-Report (MAP-SR)

[0224] The MAP-SR is a validated self-report tool derived from the CAINS scale, used to assess motivational and pleasurable aspects of negative symptoms in patients with psychotic disorders. The scale consists of 15 items that record motivation, effort, interest, and pleasure across various areas of life. All items are rated on a 5-point Likert scale, with lower scores indicating greater severity.

[0225] Clinical Assessment Interview for Negative Symptoms (CAINS)

[0226] The CAINS is a clinician-administered, validated, 13-item interview-based assessment consisting of two subscales that measure two factors of negative symptoms: motivation and pleasure (MAP) and expressiveness (EXP). Each item in the CAINS is scored on a four-point scale, with lower scores indicating less severe negative symptoms.

[0227] The MAP scale included nine items measuring interest in and engagement with motivated behaviors, as well as experiences of pleasure in the social, occupational, and leisure domains. Each item was scored based on the behaviors and experiences reported by the patient. The EXP scale included four items measuring verbal intonation (prosody), nonverbal expressiveness (gestures, posture), facial expressions, and verbal output (aphasia). The items were assessed based on observations during the interview.

[0228] Personal and Social Functioning Scale (PSP)

[0229] The PSP is a clinician-assessed, validated reliability scale that measures personal and social functioning in four areas: socially useful activities (e.g., work and study), personal and social relationships, self-care, and disruptive and aggressive behavior. Each area is assessed on a scale of 0-100, with anchors set at 10-point intervals.

[0230] Mobile Device Proficiency Questionnaire (MDPQ)

[0231] The MDPQ is a validated, self-administered scale for measuring mobile device proficiency among older adults. The questionnaire consists of 46 items, assessed on a 5-point Likert scale. The MDPQ assesses five aspects of digital literacy: information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving.

[0232] Altoida Digital Cognitive Assessment

[0233] The Altoida app is a validated digital cognitive assessment tool that provides digital biomarker data on cognitive function and functional ability, including 13 neurocognitive domains (covering everyday functions and cognition) that correspond to major neural networks such as complex attention and cognitive processing speed. In addition to assessments based on reaction time, speed, attention, and memory, nearly 800 individual features are acquired during augmented reality and motor tasks, including device sensor inputs (or absence of such inputs) from accelerometers, gyroscopes, magnetic sensors, cameras, microphones, and touchscreens.

[0234] These assessments are conducted by participants using a research iPad (registered trademark) within the clinic and take 10 minutes.

[0235] Subscales of the Brief Assessment Scale for Cognitive Function in Schizophrenia (BACS)

[0236] The Brief Assessment Scale for Cognitive Functioning in Schizophrenia (BACS) is a validated, paper-and-pencil cognitive function assessment tool consisting of several subtests, including a symbol coding task, working memory assessment, verbal memory task, and a number ordering task, attention assessment, and information processing speed assessment. These three subtests can be combined into the Brief Assessment Scale for Cognitive Functioning in Schizophrenia Short Version (BACS-SF). ● Symbol Coding Task: Participants have 90 seconds to write down the numbers 1 through 9 corresponding to the symbols displayed on the answer sheet. The total task time is 3 minutes. ● Verbal Memory Task: Participants are presented with 15 words and asked to recall as many as possible. This procedure is repeated 5 times. The total task time is 7 minutes. ● Number Ordering: Participants are presented with a group of numbers whose length gradually increases, and are asked to tell the experimenter the numbers in order from the smallest to the largest. The total task time is 5 minutes.

[0237] Defeatist Performance Beliefs Subscale of the Dysfunctional Attitudes Scale (DAS)

[0238] The Defeatist Performance Beliefs subscale is a 15-item, validated, and reliable dysfunctional attitude scale. Participants rate items on scales 1-7, with higher scores indicating more severe defeatist thinking.

[0239] Expected Earnings Questionnaire (EBQ)

[0240] The EBQ is an unverified measurement tool completed by participants, designed to assess participants' expectations that they will receive therapeutic benefits from using the research app. Participants rate three items on a 5-point scale ranging from "strongly disagree" to "strongly agree."

[0241] Mobile version of the AgNew Relationship Assessment Scale questionnaire (mARM)

[0242] mARM is a validated patient rating scale for evaluating mobile health interventions on mental health status. This scale is adapted from the well-validated Agnew Relational Rating Scale (ARM) and consists of 25 items rated on a 7-point scale from "strongly disagree" to "strongly agree."

[0243] Human Factors Questionnaire (HFQ)

[0244] The HFQ is an unverified measurement tool consisting of five questions that participants fill out themselves, designed to evaluate participants' experience using a research application.

[0245] Mobile App Rating Scale (MARS)

[0246] MARS is a 23-item, validated reliability scale for classifying and evaluating the quality of mobile health apps. This scale assesses many aspects of an app, including engagement, functionality, aesthetics, information quality, and subjective quality. An average score is calculated for each section, and the overall MARS average score indicates the app's overall quality.

[0247] qualitative interview

[0248] Participants will have the opportunity to have individual, non-verified qualitative interviews with members of the product development team. During these interviews, participants may be asked to provide feedback on the research application.

[0249] result

[0250] Individual and mean CAINS-MAP baseline and end-of-study (EOS) values ​​(e.g., after 7 weeks of treatment) were obtained using a digital therapy application. After 7 weeks of using the digital therapy app, the mean (95% confidence interval) CAINS-MAP score decreased significantly by 3.4 points (1.2, 5.7), or approximately 17% (p=0.004), which indicated an improvement in patients' empirical negative symptoms (ENS). There was no correlation between baseline ENS and the number of sessions completed. Based on baseline and EOS scores of CAINS-MAP, schizophrenic patients showed a reduction in empirical negative symptoms after 7 weeks of involvement with the CT-155 beta app. Patients with more severe empirical negative symptoms benefited more consistently from the app than patients with milder symptoms.

[0251] Example 2: Combination therapy of digital therapy and antipsychotics in subjects with empirical negative symptoms of schizophrenia overview

[0252] Indication: The digital therapeutic application (e.g., Application 165 or those described herein as CT-155 or the investigational application) is an investigational prescription digital therapy (PDT) indicated for the treatment of adults and late adolescents with experiential negative symptoms of schizophrenia (used in addition to antipsychotic therapy in standard of care (SOC)).

[0253] Introduction: The digital therapeutic application provides an interactive, software-based intervention for experiential negative symptoms of schizophrenia. The purpose of this study is to evaluate the effectiveness of CT-155 as adjunctive therapy to SOC against a digital comparator in participants 18 years of age and older diagnosed with experiential negative symptoms of schizophrenia.

[0254] Objective: To evaluate the effectiveness of CT-155 in reducing experiential negative symptoms compared to a digital comparator in adult and late adolescent participants diagnosed with schizophrenia.

[0255] Evaluation Criteria ● Primary efficacy endpoint ○ Change from baseline to Week 16 in experiential negative symptoms compared to the digital comparator, evaluated by the Clinical Assessment Interview for Negative Symptoms - Motivation and Pleasure Scale (CAINS-MAP). ● Secondary efficacy endpoints ○ Change from baseline in motivation and pleasure symptoms at Week 8 compared to the digital comparator, evaluated by CAINS-MAP. ○ Change from baseline in expressed negative symptoms at Weeks 8 and 16 compared to the digital comparator, evaluated by the Clinical Assessment Interview for Negative Symptoms - Expressivity Scale (CAINS-EXP). ○ Change from baseline in positive symptoms at Weeks 8 and 16 compared to the digital comparator, evaluated by the Positive and Negative Syndrome Scale (PANSS). ○ Changes from baseline in social functioning at weeks 8 and 16, compared to a digital control, as assessed by the Personal and Social Performance Scale (PSP). ○ Change from baseline in self-reported defeatist beliefs at weeks 8 and 16, compared to a digital control, as assessed by the Defeatist Beliefs subscale of the Dysfunctional Attitudes Scale (DAS). ○ Comprehensive Impression Scale (PGI-I) regarding patient improvement at weeks 8 and 16 compared to a digital control. ● Exploratory endpoints ○ Key Involvement Metrics ○ Change from baseline in disease severity at weeks 8 and 16, compared to a digital control, as assessed by the Comprehensive Clinical Impression Scale (CGI-S). ○ Change from baseline in disease severity at weeks 8 and 16, compared to a digital control, as assessed by the World Health Organization Disability Assessment Scale, 2nd Edition (WHODAS 2.0). ○ Change from baseline in disease severity at weeks 8 and 16, compared to a digital control, as assessed by the Quality of Life Scale for Schizophrenia, 4th Edition (SQLS-R4).

[0256] Study Design: This is a multicenter, randomized, double-blind, controlled trial to evaluate the efficacy and safety of CT-155 in adults and late adolescents diagnosed with schizophrenia. Eligible participants must have a diagnosis of schizophrenia according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and have a moderate to severe empirical negative symptom severity, with a score of 2 or higher (moderate to severe) in at least two of the three domains (social, work, and leisure) on the CAINS-MAP. Participants must have been taking a stable dose of antipsychotic medication for 12 weeks prior to randomization (Day 1). Eligible participants will be randomly assigned on Day 1 to receive either the CT-155 intervention or a comparison digital control intervention (research app).

[0257] Referring to Figure 25, a schematic diagram of the study design for the combination therapy of digital therapy and antipsychotic medication in subjects with empirical negative symptoms of schizophrenia is shown. This study consists of a screening period of up to 14 days, a 16-week double-blind intervention period, and a 4-week follow-up period.

[0258] Research Design Overview

[0259] Screening Period (Day 14 to Day 1): All eligible participants (per researcher) will enter a screening period of up to 14 days (minimum 7 days) to determine their eligibility. Participants who meet all applicable inclusion criteria and none of the exclusion criteria upon their screening visit will begin using the application by downloading and installing the research app on their personal iPhone® or Android smartphone.

[0260] Double-blind intervention period (Day 1 to Week 16): Approximately 432 eligible participants will be randomly assigned in a 1:1 ratio (digital control group) at approximately 40 research sites in the United States during their in-person visit on Day 1. Assessment and activities during this period will be conducted via in-person clinic visits or remote telephone visits, in accordance with the System of Analysis (SoA).

[0261] Follow-up period (weeks 16 to 20): Participants will not receive the randomized intervention during the 4-week follow-up period. Assessments and activities during this period will be conducted remotely via telephone visits, in accordance with the State of Analysis (SoA).

[0262] Planned number of participants: Approximately 432 participants will be randomized in this study.

[0263] Research participation criteria

[0264] Selection Criteria: Participants will be considered eligible to participate in this study if they meet all of the following criteria. 1. I am willing and able to provide written informed consent to participate in this study, attend research visits, and comply with research-related requirements and evaluations. 2. At the time of informed consent, the person is an adult or late adolescent, i.e., 18 years of age or older. 3. Proficient in both written and spoken English, and able to read and understand informed consent forms. 4. The patient has received a primary diagnosis of schizophrenia using the diagnostic criteria for schizophrenia according to the DSM-5 definition at least six months prior to the screening visit. 5. Based on a review of medical records or documented discussions with the treating physician, the researcher determined that the patient was in a stable phase of the illness. 6. The patient is receiving outpatient treatment at the time of screening and has not been hospitalized for schizophrenia within the 12 weeks prior to screening. 7. Participants have been taking a stable dose of antipsychotic medication for at least 12 weeks prior to randomization (Day 1), but dose adjustments during the study period (and the 12 weeks prior to randomization) are permitted at the discretion of the researchers within the scope of the current medication's package insert. 8. At the time of screening and at baseline (Day 1), the mean score in at least two of the three areas of the CAINS-MAP (social, work, or leisure) must be 2 or higher (moderate to severe). 9. The user is the sole user of an iPhone® operating system (iOS) version 14 or later, or an Android operating system version 10 or later smartphone, and is willing to download and use the specified research application as required by the protocol. 10. You are willing to receive and able to receive SMS text messages and push messages on your smartphone. 11. I am the owner of the email address and I access that address regularly. 12. You have regular access to the internet via your mobile phone data plan and / or Wi-Fi. 13. Have a stable residence, have stayed in the same residence for at least 12 weeks before screening, and have no plan to change the residence during the study period. 14. Have an understanding and interest in using the research app during the screening period and at the baseline visit (day 1).

[0265] Exclusion criteria: Participants who meet any of the following criteria are not eligible to participate in the study. 1. Currently receiving treatment with more than two antipsychotic drugs (including more than two dosage forms). 2. Currently receiving treatment with clozapine or haloperidol, or having received treatment with clozapine within 5 years prior to the screening visit. 3. At the screening or baseline visit, on the PANSS scale, showing a positive symptom item score of more than 4 (moderate) for P1 delusions, P2 disorganization, P3 hallucinations, P6 suspiciousness, or a score of more than 5 (moderate to severe) for any item, indicating significant positive symptoms. 4. Currently receiving, or having received within 6 months (26 weeks) prior to screening, a psychotherapy defined as individual or group-based structured treatment (such as cognitive behavioral therapy, social skills training, or vocational / occupational therapy) according to the researcher's evaluation. 5. Meeting the DSM-5 criteria for a diagnosis that is not the subject of the investigation and that affects compliance with the protocol, including schizophrenia spectrum disorder, schizoaffective disorder, or psychotic disorder not otherwise specified (post-traumatic stress disorder [PTSD], bipolar disorder, major depressive disorder, or developmental disorder). 6. Meeting the criteria for the current episode of depression, mania, or hypomania based on DSM-5. 7. Having received a current diagnosis of a substance use disorder or alcohol use disorder (excluding caffeine and nicotine) defined by DSM-5 within the past 6 months (26 weeks) prior to the screening visit. 8. Positive results for amphetamines (including MDMA / ecstasy), phencyclidine (PCP), cocaine, opioids, benzodiazepines, or barbiturates in a urine drug screening test performed at the time of screening or prior to randomization at baseline visit. Participants with positive urine drug screening results and / or a history of recreational THC use may be included at the discretion of the researcher. 9. You have participated in a clinical study related to this digital therapeutic application, or you have participated in a user survey study related to this digital therapeutic application. 10. You have participated in any other clinical research (intervention or observational study) within the past 6 weeks (26 months). 11. In the researchers' judgment, during the study period, the patient currently requires or is likely to require any of the prohibited concomitant medications and / or therapies. 12. Having suicidal ideation or suicidal behavior as assessed by the C-SSRS: a. Participants who answered "yes" to either item 4 or 5 of the C-SSRS suicidal ideation section within 3 months (12 weeks) prior to screening or at their baseline visit. b. Participants who answered "yes" to the suicidal behavior item on the C-SSRS within 6 months (26 weeks) prior to screening or at their baseline visit. 13. Participants who, in the researchers' opinion, are at high risk of suicide.

[0266] Investigational product and method of application: Eligible participants will download and install the research app on their smartphones upon their screening visit. Participants will be randomly assigned to either the digital treatment application or the digital control at their baseline visit (Day 1).

[0267] Study period: The participation period was approximately 22 weeks, as follows: ● Screening period: up to 14 days ● Intervention period: 16 weeks ● Follow-up period: 4 weeks

[0268] Sample size: In this study, approximately 432 participants will be randomized.

[0269] Statistical Analysis: The primary endpoint is the change in empirical negative symptoms from baseline to week 16, as assessed by the CAINS-MAP scale. The null hypothesis is that there is no difference in mean values ​​between treatment groups, and the alternative hypothesis is that there is a difference in mean values. To demonstrate this, assuming a standardized effect size (Cohen's d) of 0.35 between treatment groups and a Type I error rate of 5% (two-sided), 173 subjects per group are required to achieve 90% power. Assuming a 20% early termination rate, 432 participants need to be randomly assigned in a 1:1 ratio (approximately 216 per group). Sample size was calculated using a t-test of two independent samples, assuming equal variances.

[0270] The primary endpoint, change in empirical negative symptoms from baseline to week 16, was analyzed using a mixed-model repeated measures (MMRM), with the change from baseline at each visit in which CAINS-MAP was assessed as the response variable. The model included the intercept and the following covariates: baseline empirical negative symptoms, visits (as a class variable), treatment group, treatment × visit interaction term, and baseline × visit interaction term.

[0271] The first choice for within-patient correlation is the unstructured covariance matrix. Contingency plans in case of non-convergence will be discussed in the statistical analysis plan (SAP). Only data from scheduled visits will be used for this analysis. This study will be considered successful if the two-sided p-value for the difference in change from baseline at week 16 between the two treatment groups is less than 0.05, and the reduction in CAINS-MAP is greater in the digital treatment application group than in the digital control group. Analysis of secondary endpoints will be performed in a similar manner using MMRM. Exploratory endpoints will be summarized descriptively for each treatment group.

[0272] Figures 23A and 23B show the schedule of participants' activities and evaluations. Abbreviations: app = application; BACS = Brief Cognitive Assessment Scale for Schizophrenia; C-SSRS = Columbia Suicide Severity Rating Scale; CAINS = Clinical Assessment Interview for Negative Symptoms; CGI-S = Comprehensive Impression Scale for Clinical Severity; DAS = Defeatist Beliefs Subscale of the Dysfunctional Attitudes Scale; DSM-5 = Diagnostic and Statistical Manual of Mental Disorders, 5th Edition; EQ-5D-5L = 5-Dimensional, 5-Level Health-Related Quality of Life Scale; ET = Interrupted; MINI = Brief International Neuropsychiatric Interview; PANSS = Positive and Negative Symptom Rating Scale; PGI-I = Comprehensive Impression Scale for Patient Improvement; PGI-S = Comprehensive Impression Scale for Patient Severity; PSP = Personal and Social Functioning Performance Scale; SDS = Sheehan's Disorder Scale; SQLS-R4 = Quality of Life Scale for Schizophrenia, 4th Edition; WHODAS = World Health Organization Disability Assessment Scale, 2nd Edition a. All remote consultations will be conducted by telephone. b. Urine drug testing will be conducted on-site. c. Urine pregnancy tests are performed on-site using the dipstick method. d. CAINS is conducted by a centralized, blinded evaluation team. The screening period is at least 7 days prior to the baseline visit.

[0273] Digital therapeutic applications under study

[0274] Current treatment guidelines for schizophrenia recommend antipsychotic medication and adjunctive psychosocial interventions. As mentioned above, while medications are effective in treating positive symptoms, there are no pharmacological interventions available for treating negative symptoms. Therefore, adjunctive psychosocial interventions are recommended for symptoms such as negative symptoms that are not adequately improved by pharmacological interventions. This digital therapeutic application provides a therapeutic approach that applies the fundamental principles of face-to-face psychosocial therapy to digital therapy, doing so in a manner that adheres to the latest evidence-based recommendations. The digital therapeutic application provides an adaptive framework to help participants acquire the skills necessary to achieve personalized goals in their daily lives.

[0275] The digital therapeutic application is an adjunctive digital therapy designed to target empirical negative symptoms in adults and late adolescents with stable SOC (State of Computing) schizophrenia. This digital therapeutic application is available by prescription only and is intended for use only under the supervision of a clinician. Given the age of onset of schizophrenia, it is clinically important that it be evaluated and its use is demonstrated in late adolescents (18 to 21 years) and adults (22 to 64 years).

[0276] Digital therapeutic applications are designed to address the need for available therapies to bridge existing gaps in SOC (State of Care). Recent studies have reported that smartphone ownership is nearly ubiquitous, with over 80% of schizophrenia patients owning smartphones. While no validated mobile therapy exists, the vast majority of schizophrenia patients already utilize technology to manage their illness, such as for responding to auditory hallucinations or setting medication reminders. Digital therapeutic applications provide adults and late adolescents with schizophrenia with access to validated digital therapies that complement ongoing treatment.

[0277] Profit / Risk Assessment

[0278] Risk assessment

[0279] Adverse events (AEs) specific to the digital therapy application or the digital control are not expected because both interventions are software-based. The primary potential risk to participants that could develop an AE is the exacerbation of negative symptoms of schizophrenia. There is also a risk of injury during practice activities that participants are instructed to perform outside of the study app. These and other potential risks are minimal and are considered to be equivalent to or less than those of behavioral therapy related to status of consciousness (SOC) in the treatment of schizophrenia.

[0280] Participants may experience some adverse events due to underlying medical conditions or the use of adjunctive therapies. The risk profiles of SOCs used in clinical practice are well understood and are described in detail in their respective prescribing information.

[0281] Profit evaluation

[0282] Trial participants can directly benefit from an interactive, software-based intervention characterized by cognitive training and message delivery. This digital therapeutic application is an adapted version of cognitive behavioral therapy (CBT), which has been well-established as a treatment option for negative symptoms associated with schizophrenia. It integrates multiple neurobehavioral therapeutic techniques aimed at improving negative symptoms associated with schizophrenia, as indicated in the psychological model of negative symptoms.

[0283] Overall Benefits: Final Assessment of Risks

[0284] Digital therapeutic applications may offer therapeutic benefits without significant risk. They can also improve empirically negative symptoms.

[0285] Research design

[0286] Comprehensive Design

[0287] This is a multicenter, randomized, double-blind, controlled trial to evaluate the efficacy and safety of a digital therapeutic application in adults and late adolescents diagnosed with schizophrenia. Eligible participants must have a diagnosis of schizophrenia based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and have moderate to severe negative symptom severity, as indicated by a score of 2 or higher (moderate to severe) in at least two of the three domains (social, work, and leisure) on the CAINS-MAP. Participants must have been taking a stable dose of antipsychotic medication for 12 weeks prior to randomization (Day 1). Eligible participants will be randomly assigned on Day 1 to receive either a digital therapeutic application intervention or a comparative digital control intervention (research app). Participants will be blinded to the hypothesis and treatment assignment and will be informed that they will receive one of the two digital interventions under study. The participation period is approximately 22 weeks, including a maximum of two weeks of screening, a 16-week double-blind intervention period, and a four-week follow-up period.

[0288] To control participant expectations, participants in this study will be blinded to the efficacy hypothesis and treatment assignment. Eligible participants will be notified by staff at the study site that (a) their participation in the study will last up to 22 weeks (including follow-up) and they will be randomly assigned to one of two digital therapies, and (b) the purpose of this study is to compare the efficacy of these two digital therapies used in addition to SOC. Both treatment groups will be presented as potentially helpful in improving schizophrenia. Participants should not be given any mention of the digital therapy applications or digital controls, and both should only be referred to as “research apps.”

[0289] Activities and assessments during the screening period of up to two weeks, the double-blind engagement period of sixteen weeks, and the follow-up period of four weeks will be conducted according to the Schedule of Activities and Assessments (SoA). Site staff will provide assistance during in-person or telephone visits to the clinic. Participants will be assessed at screening, intervention, and follow-up using standard, validated clinician- and participant-rated outcome scales for schizophrenia. Participants will be assessed for safety throughout the entire duration of the study. Sites may conduct scheduled in-person or telephone-based remote outpatient visits at any time if necessary to assess safety issues / concerns.

[0290] Screening period (from day -14 to day -1)

[0291] During the in-person screening visit, participants will sign an informed consent form, and all assessments and activities described in the State of Agreement (SoA) will be performed.

[0292] Open-label facility staff (other than researchers / evaluators) will download and install the research app software on eligible participants' personally owned iPhones® or Android smartphones and enable participants to begin using the research app. Participants will complete various tasks in the research app's onboarding module over a period of at least seven days to confirm their understanding of and interest in the study, as well as their use of the research app.

[0293] Double-blind intervention period (Day 1 - Week 16)

[0294] Participant eligibility will be confirmed during the in-person visit on the first day. Participants will be considered eligible for randomization based on the following criteria:

[0295] Based on the researchers' evaluation, all selection criteria must be met, and no exclusion criteria must be met.

[0296] Approximately 432 participants will be randomly assigned in a 1:1 ratio (CT-155: digital control) to approximately 40 research sites in the United States. Evaluations and activities during this period will be conducted via in-person clinic visits or telemedicine visits. All evaluations will be conducted according to the State of Analysis (SoA).

[0297] After randomization, at baseline visits, an unblinded study site staff member will assist participants in activating their assigned software module (digital therapeutic app (study app) or digital control) within the study app and verify that it is functioning correctly.

[0298] During the 16-week intervention period, the research app instructed participants to access it at approximately the same time each day and perform tasks.

[0299] The effectiveness assessment scales are described in detail. The CAINS effectiveness assessment is conducted by a centralized, blinded evaluation team.

[0300] To mitigate the risks of open-label learning, laboratory staff will instruct participants not to discuss with others what they are doing or what is displayed in the research app.

[0301] Follow-up period (weeks 16 to 20)

[0302] After the 16th week, the research app will cease proactively sending instructional messages to participants. Participants will enter a 4-week follow-up period during which they will not receive any randomized interventions. Assessments and activities during this period will be conducted via remote telephone visits in accordance with the State of Analysis (SoA). The app will be deactivated from the 20th week onward.

[0303] After participants have completed their trial and all final visit procedures are finalized, trial site staff will explain the trial's hypothesis (i.e., the hypothesis that one digital therapy is effective in improving negative symptoms of schizophrenia) to the participants, and inform them that the trial was necessary to confirm its effectiveness. Trial site staff will be provided with debriefing guidelines to assist in discussions with participants.

[0304] Definition of research completion

[0305] The end of this study is defined as the final contact date or final contact trial date of the last participant who completed or withdrew from this study.

[0306] In this study, participants who complete the study evaluation on day 112 (week 16) are defined as study completers.

[0307] research group

[0308] Eligible participants who complete the informed consent form will be assigned a unique participant identification number at the time of screening. No prior approval will be given for any protocol deviations (also known as protocol exemptions or exceptions) from the recruitment and registration criteria.

[0309] Selection Criteria

[0310] Participants are eligible to participate in this study if they meet all of the following selection criteria. 1. I am willing and able to provide written informed consent to participate in this study, attend research visits, and comply with research-related requirements and evaluations. 2. At the time of informed consent, the person is an adult or late adolescent, i.e., 18 years of age or older. 3. Proficient in both written and spoken English, and able to read and understand informed consent forms. 4. The patient has received a primary diagnosis of schizophrenia using the diagnostic criteria for schizophrenia according to the DSM-5 definition at least six months prior to the screening visit. 5. Based on a review of medical records or documented discussions with the treating physician, the researcher determined that the patient was in a stable phase of the illness. 6. The patient is receiving outpatient treatment at the time of screening and has not been hospitalized for schizophrenia within the 12 weeks prior to screening. 7. Participants have been taking a stable dose of antipsychotic medication for at least 12 weeks prior to randomization (Day 1), but dose adjustments during the study period (and the 12 weeks prior to randomization) are permitted at the discretion of the researchers within the scope of the current medication's package insert. 8. At the time of screening and at baseline (Day 1), the mean score in at least two of the three areas of the CAINS-MAP (social, work, or leisure) must be 2 or higher (moderate to severe). 9. The user is the sole user of an iPhone® operating system (iOS) version 14 or later, or an Android operating system version 10 or later smartphone, and is willing to download and use the specified research application as required by the protocol. 10. You are willing to receive and able to receive SMS text messages and push messages on your smartphone. 11. I am the owner of the email address and I access that address regularly. 12. You have regular access to the internet via your mobile phone data plan and / or Wi-Fi. 13. You have stable housing, have been staying in the same housing for at least 12 weeks prior to screening, and have no plans to change housing during the study period. 14. You understand and are interested in using the research application during the screening period and at your baseline visit (Day 1).

[0311] Exclusion Criteria: Participants who meet any of the following exclusion criteria are not eligible to participate in the study. 1. I am currently receiving treatment with more than two types of antipsychotic medications (including more than two different dosage forms). 2. You are currently receiving treatment with clozapine or haloperidol, or you have received treatment with clozapine within the past five years of your screening visit. 3. At screening or baseline visit, the patient shows a score of 4 or higher (moderate) on the PANSS scale for P1 delusion, P2 confusion, P3 hallucinations, and P6 suspicion, or a score of 5 or higher (moderate to severe) on any of the items, indicating significant positive symptoms. 4. You are currently receiving, or have received within the 6 months (26 weeks) prior to screening, psychotherapy defined by the researcher as individual or group-based structured therapy (such as cognitive behavioral therapy, social skills training, or occupational / vocational therapy). 5. The patient meets the DSM-5 criteria for a non-survey diagnosis that could affect adherence to the protocol, including schizophrenic disorder, schizoaffective disorder, or psychotic nonspecific disorder (post-traumatic stress disorder [PTSD], bipolar disorder, major depressive disorder, or developmental disorder). 6. Meets the DSM-5 criteria for a current episode of depression, mania, or hypomania. 7. In the past six months (26 weeks) prior to the screening visit, you have received a current diagnosis of substance use disorder or alcohol use disorder (excluding caffeine and nicotine) as defined in DSM-5. 8. Positive results for amphetamines (including MDMA / ecstasy), phencyclidine (PCP), cocaine, opioids, benzodiazepines, or barbiturates in a urine drug screening test performed at the time of screening or prior to randomization at baseline visit. Participants with positive urine drug screening results and / or a history of recreational THC use may be included at the discretion of the researcher. 9. Have you previously participated in a clinical study of a digital therapeutic application, or have you participated in a user survey study of a digital therapeutic application? 10. You have participated in any other clinical research (intervention or observational study) within the past 6 weeks (26 months). 11. In the researchers' judgment, during the study period, the patient currently requires or is likely to require any of the prohibited concomitant medications and / or therapies. 12. Having suicidal ideation or suicidal behavior as assessed by the C-SSRS: 13. Participants who answered "yes" to either item 4 or 5 of the C-SSRS suicidal ideation section within 3 months (12 weeks) prior to screening or at their baseline visit. 14. Participants who answered "yes" to the suicidal behavior item on the C-SSRS within 6 months (26 weeks) prior to screening or at their baseline visit. 15. Participants who, in the researchers' opinion, are at high risk of suicide.

[0312] Lifestyle considerations

[0313] Each participant must have daily access to their smartphone during the trial period. Furthermore, participants must be able to visit the clinic in person or via telemedicine during the trial period. Participants should refrain from using alcohol or recreational drugs during the times they access the research app. Occasional recreational use of THC is permitted.

[0314] Research interventions and combination therapy

[0315] Research interventions implemented

[0316] The Digital Therapeutic Application-R-001 Research Mobile Application (research app) will administer one of two research interventions to participants. The research intervention is a digital control compared to the Digital Therapeutic Application PDT (Photodynamic Therapy). (Table 2).

[0317] [Table 2]

[0318] The digital therapeutic application is under development as an adjunctive prescription digital therapy designed to target empirical negative symptoms in adults and late adolescents with stable SOC (State of Computing) schizophrenia. To address the critical clinical needs demonstrated by individuals with schizophrenia, the therapeutic delivery is guided by a reliable and validated mechanism of action model. The CT-155 therapeutic technique is selected based on clinical evidence, and each component is designed in accordance with the fundamental principles of face-to-face therapy to provide the best possible support to individuals with schizophrenia.

[0319] The digital therapeutic application achieves its primary objective of improving empirical negative symptoms by integrating multiple evidence-based neurobehavioral therapy techniques, as detailed below. These techniques work collaboratively to help patients set goals that promote real-world engagement (adaptive goal setting), remove barriers (cognitive restructuring), and provide skills that facilitate goal achievement (social skills training, positive emotion training).

[0320] Digital comparison

[0321] In this study, a digital control application will be used as the comparison group. This application will control for common elements of digital applications (receiving notifications, on-demand access) and also control for daily engagement with new applications.

[0322] Preparation / Handling / Responsibility / Disposal

[0323] Generally: ● It is mandatory for designated, non-blinded staff members to verify the download and activation of the research application. ● Only participants registered for the study can receive the research intervention. ● The research application will automatically deactivate after the 20th week. The designated open-label trial site staff will instruct participants to uninstall the study app upon their final study visit or upon discontinuation of the study.

[0324] Measures to minimize bias: randomization and blinding.

[0325] Studies using IVRS / IWRS: All participants will be assigned to a centrally randomized trial intervention using an audio / web response system (IVRS / IWRS). Prior to the start of the study, each site will be provided with the IVRS phone number and calling instructions, and / or IWRS login information and instructions.

[0326] The research intervention (research app) will be downloaded, activated, and deleted during the research visit outlined in the Statement of Agenda (SoA).

[0327] Deblinding (IVRS / IWRS) The IVRS / IWRS includes a deblinding procedure. In emergency situations, the responsibility for deciding whether or not to deblind a participant's intervention allocation information rests solely with the researcher. In such decisions, participant safety must always be the top priority. If the researcher determines that deblinding is necessary, they shall make every effort to contact the sponsor before deblinding the participant's intervention allocation, provided that such deblinding would not delay the participant's emergency treatment. If a participant's intervention allocation is deblinded, the sponsor must be notified within 24 hours of deblinding. The date and reason for deblinding, if applicable, shall be recorded in the source document and case report form.

[0328] Role of unblinded research staff in a blinded study. Participants are randomly assigned in a 1:1 ratio to either a digital therapeutic application or a digital control application as their research intervention via a study app. Researchers, designated site staff, and evaluators remain blinded to each participant's assigned research intervention throughout the entire study. To maintain this blinding, designated unblinded site staff assist research participants in downloading and installing the study app. Designated unblinded site staff are responsible for verifying compliance.

[0329] Description of the blinded evaluation method: To further protect against bias, a central blinded team of evaluators will conduct the CAINS evaluation.

[0330] Research hypothesis blinding: Study participants are blinded to the hypothesis regarding the effectiveness of this study. Both treatment groups are presented to participants as possible treatments for schizophrenia. No mention is made of digital treatment applications or digital controls. This approach reduces the risk of participants deblinding regarding allocation and expected effectiveness.

[0331] The assigned safety officer may deblind participants in intervention assignments for whom a SAE has occurred. If the SAE requires rapid reporting to one or more regulatory authorities, a copy of the report identifying the participant's intervention assignment may be sent to the researcher in accordance with local regulations and / or sponsor policies.

[0332] Adherence to research interventions

[0333] During the treatment period, all randomized participants will be instructed to use the study app according to its instructions. Participants in both the digital treatment application group and the digital control group will be considered compliant with their treatment for the day if they complete at least one available daily activity. Participants will be considered compliant in this study if they are compliant for at least 67 days (approximately 60%) of the total 112-day treatment period. Completion of assigned activities will be measured and recorded using the engagement metric within the app and defined in the statistical analysis plan (SAP).

[0334] Compliance monitoring

[0335] To ensure participants are adhering to the treatments assigned via the research app, facility staff will conduct compliance checks at weeks 4, 8, and 12. During these hospital visits, designated, open-label facility staff will collect compliance data from participants via a compliance code read from the research app. This code will indicate the participant's treatment compliance status up to the point of access. Researchers will use this information to instruct participants to use the app daily and to identify any technical issues that may be hindering treatment compliance. The content and language used in these conversations are documented in the Research Application Researcher's Guide.

[0336] Combination therapy

[0337] Participants must have been taking a stable dose of antipsychotic medication for at least 12 weeks prior to randomization (Day 1). Dosage adjustments during the study period are permitted according to the instructions in the package insert for the medication currently being taken.

[0338] Participants are not permitted to receive treatment with more than two types of antipsychotic medications (including more than two different dosage forms). Treatment with clozapine or haloperidol is prohibited.

[0339] During the study period, additional psychotherapies, including cognitive behavioral therapy, social skills training, and motivational enhancement therapy, were not permitted.

[0340] Sporadic recreational use of drugs other than synthetic cathinones (bath salts), synthetic cannabinoids (K2, Spice), inhalants, amphetamines (including MDMA / ecstasy), phencyclidine (PCP), cocaine, opioids, benzodiazepines, barbiturates, hallucinogens, or parenteral drugs is not grounds for exclusion from the study.

[0341] Participant discontinuation / withdrawal from research

[0342] To the extent that it is medically safe, facility staff shall make every effort to ensure that participants continue to participate in the study and continue the research intervention. Participants who discontinue the research intervention early must complete the early termination procedures described in the Statement of Action (SoA). Ideally, participants who discontinue the research intervention early should be observed until the end of the study, if possible, as if they were continuing the blinded research intervention. For all participants, the reason for withdrawal from the research intervention (e.g., adverse event) must be recorded in the Clinical Report Form (CRF). This data will be incorporated into the trial database and reported.

[0343] Participants who are not actively using the research intervention may have a reduced motivation to adhere to the research visit schedule. Researchers and facility staff should strive to encourage continued participation by promptly detecting early signs of declining interest and offering participants the following options. ● Early discontinuation option 1: Continue visits according to the regular appointment schedule. ● Early termination option 2: Conduct all remaining study visits. However, the evaluations to be performed at planned visits will be limited to the following: ○ PANSS ○ CAINS and CAINS-MAP ○ CGI-S ○ C-SSRS ○ Adverse events ○ Combination therapy ● Early termination option 3: Participation in the remainder of the research activities will be terminated, but information regarding the onset / relapse of mental illness and survival status will be collected approximately 16 weeks after randomization through the participant or a person designated by the participant (e.g., family, spouse, partner, legal representative, physical therapist, etc.). ● Early termination option 4: Similar to option 3 above, but allows for the collection of information on the onset / relapse of mental illness and survival status approximately 16 weeks after randomization, through a review of participants' medical information from other sources (e.g., physician's records, hospital records, etc.).

[0344] Participants will be asked to choose the most stringent follow-up method they can adhere to. Participants who refuse all four of the above options will be deemed to have completely withdrawn their consent to participate in the study.

[0345] The research app on the smartphones of participants who withdraw from the program will be deactivated.

[0346] Research evaluation and procedures

[0347] The study evaluation and procedures, including their timing, are summarized in the Statement of Analysis (SoA). Adherence to the study design requirements, including those specified in the SoA, is essential and necessary for conducting the study. No exemptions or waivers of the protocol are permitted. Every effort should be made to complete the evaluations and procedures required in the protocol exactly as described. All scales administered by clinicians should be administered by appropriately trained personnel.

[0348] Effectiveness evaluation

[0349] In this trial, the following efficacy assessment measures will be used at the time indicated in the Statement of Analysis (SoA). Descriptions of all endpoints, including the scales and their respective scoring algorithms, are provided in the Plan for Statistical Analysis (SAP).

[0350] Clinical Assessment Interview for Negative Symptoms (CAINS)

[0351] The CAINS is a 13-item interview-based assessment consisting of two subscales that measure two factors of negative symptoms: motivation and pleasure (MAP) and expressiveness (EXP). Each item in the CAINS is scored on a 5-point scale, with lower scores indicating less severe negative symptoms. It should be noted that the CAINS is administered by a centrally managed, blinded assessment team, not by individual clinicians.

[0352] The MAP scale consists of nine items that measure interest in and engagement with motivated behaviors in social, occupational, and recreational domains, as well as experiences of pleasure. Each item is scored based on the behaviors and experiences reported by the patient. The EXP scale consists of four items that measure verbal intonation (prosody), nonverbal expressiveness (gestures, posture), facial expressions, and verbal output (aphasia). The items are assessed based on observations during the interview.

[0353] Positive / Negative Symptom Rating Scale (PANSS)

[0354] The PANSS consists of three subscales comprising a total of 30 symptom components. Each symptom component is assessed on a 7-point scale, with 1 indicating no symptoms and 7 indicating extremely severe symptoms. The symptom components of each subscale are as follows:

[0355] Positive subscales (components of 7 positive symptoms: delusion, conceptual disintegration, hallucinatory behavior, agitation, grandiosity, suspicion / persecutory feelings, hostility).

[0356] Negative subscales (components of seven negative symptoms: emotional flattening, emotional withdrawal, communication difficulties, passive / indifferent social withdrawal, difficulty with abstract thinking, lack of conversational spontaneity and fluency, and stereotyped thinking).

[0357] Comprehensive psychopathology subscales (16 symptom components: physical concern, anxiety, guilt, tension, pedantry and unnatural posture, depression, hypokinesia, noncoordination, abnormal thoughts, disorientation, decreased attention, lack of judgment and insight, motivational disorder, impulse control disorder, engrossment, and active social avoidance).

[0358] Personal and Social Functioning Scale (PSP)

[0359] The PSP is a clinician-assessed, validated reliability scale that measures personal and social functioning in four areas: socially useful activities (e.g., work and study), personal and social relationships, self-care, and disruptive and aggressive behavior. Each area is assessed on a scale of 0-100, with anchors set at 10-point intervals.

[0360] Defeatist Beliefs Subscale (DAS) of the Dysfunctional Attitudes Scale

[0361] The Defeatist Performance Beliefs subscale is a 15-item dysfunctional attitude scale. Participants rate items on a scale of 1-7, with higher scores indicating more severe defeatist thinking.

[0362] Minimally Intensive International Neuropsychiatric Interview (MINI)

[0363] MINI (DSM-5 compliant version, version 7.0.2) is used as a structured interview tool for assessing eligibility during screening visits. MINI is a widely used structured diagnostic interview tool developed for the diagnosis of mental disorders based on DSM-5. The interview is conducted by a qualified evaluator.

[0364] The results of the MINI are compared against inclusion / exclusion criteria to evaluate comorbid diagnoses.

[0365] Comprehensive Impression Scale for Clinical Severity (CGI-S)

[0366] The Clinical Geriatric Assessment (CGI-S) is a standardized, clinician-assessed global rating scale that measures the severity of empirical negative symptoms over the past seven days using a 7-point Likert scale. A higher CGI-S score indicates greater severity of the illness. Response options include: 0 = not assessed; 1 = normal, not ill at all; 2 = borderline psychosis; 3 = mild; 4 = moderate; 5 = marked illness; 6 = severe; and 7 = most severely ill participant.

[0367] Comprehensive Patient Impression Scale (PGI-I)

[0368] The PGI-I is a patient-reported outcome that measures participatory subjective improvement on a 7-point scale of empirically negative symptom severity. A higher PGI-I score indicates a subjective worsening of the disease during the treatment period.

[0369] Comprehensive patient perception of severity (PGI-S)

[0370] The PGI-S is a patient-reported outcome that measures the participatory subjective severity of empirical negative symptoms on a 5-point scale. A higher PGI-S score indicates a more subjectively reported severity of the disease.

[0371] EQ-5D-5L

[0372] The EQ-5D-5L is a standardized, easy-to-use self-report assessment scale for measuring health status. It consists of two components: the EQ-5D Descriptive System and the EQ Visual Analog Scale (VAS). The descriptive system is composed of five dimensions: mobility, self-care, activities of daily living, pain / discomfort, and anxiety / depression. Participants are asked to indicate their health status by selecting the box adjacent to the description that best reflects their condition in each of the five dimensions. On the EQ Visual Analog Scale, participants record their self-assessed overall health status on a scale ranging from "best possible health condition" to "worst possible health condition."

[0373] Sheehan's Disorder Scale (SDS)

[0374] The SDS is a simplified self-report assessment form consisting of five items that evaluate functional impairments in three areas: work / school, social life, and home life.

[0375] World Health Organization Disability Assessment Scale (WHODAS 2.0)

[0376] WHODAS 2.0 is a 36-item self-assessment scale that measures participants' functioning and impairments across six life domains: cognition (understanding and communication), mobility (getting around, going out), self-care (hygiene, dressing, eating, being alone), social interaction (interacting with others), daily activities (housework, leisure, work, school), and participation (community and society).

[0377] Quality of Life Scale for Schizophrenia, Revised 4th Edition (SQLS-R4)

[0378] SQLS-R4 is a 33-item self-assessment scale that uses a 5-point scale to evaluate quality of life across psychosocial affective and vitality / cognitive domains.

[0379] Subscales of the Brief Assessment Scale for Cognitive Function in Schizophrenia (BACS)

[0380] The Brief Assessment Scale for Cognitive Functioning in Schizophrenia (BACS) is a validated, paper-and-pencil cognitive function assessment tool consisting of several subtests, including a symbol coding task to assess working memory, a verbal memory task, and a number ordering task to assess attention and information processing speed. These three subtests can be combined into the Brief Assessment Scale for Cognitive Functioning in Schizophrenia Short Version (BACS-SF).

[0381] Symbol Coding Challenge: Participants have 90 seconds to write down the numbers 1 through 9 corresponding to the symbols displayed on the answer sheet. The total challenge time is 3 minutes.

[0382] Verbal Memory Task: Participants are presented with 15 words and asked to recall as many as possible. This procedure is repeated 5 times. The total task time is 7 minutes.

[0383] Number Sorting: Participants are presented with a group of numbers whose length gradually increases, and are asked to tell the experimenter the numbers in order from the smallest to the largest. The total task time is 5 minutes.

[0384] Evaluation of involvement in research applications

[0385] Participant engagement with the research application can be automatically captured by the application. The following metrics are collected: ● Number of times the user opened the app ● Number of days the user has opened the app ● Number of lessons completed per user ● Number of skill practice sessions per user ● Number of completed modules ● Number of times the mood status check was performed during the daily status check for each user ● Number of daily activities performed using Adaptive Goal Setting (AGS) ● Number of goals achieved per user

[0386] Statistical considerations

[0387] Participants' health economic data will be evaluated as exploratory endpoints.

[0388] Statistical Hypothesis

[0389] The ineffectiveness hypothesis states that there is no difference in the mean values ​​between the treatment groups, while the alternative hypothesis states that there is a difference in the mean values.

[0390] If the two-sided p-value is less than 0.05 and the results favor the digital therapeutic application group, the null hypothesis is rejected and the alternative hypothesis is supported.

[0391] Determining the sample size

[0392] The primary endpoint is the change in empirical negative symptoms from baseline to week 16, as assessed by the CAINS-MAP scale. The invalidity hypothesis is that there is no difference in mean values ​​between treatment groups, and the alternative hypothesis is that there is a difference in mean values. To demonstrate this, assuming a standardized effect size (Cohen's d) of 0.35 between treatment groups and a Type I error rate of 5% (two-sided), 173 subjects per group are needed to achieve 90% power. Assuming a 20% early termination rate, 432 participants need to be randomly assigned in a 1:1 ratio (approximately 216 per group). Sample size was calculated using a t-test of two independent samples, assuming equal variances.

[0393] Target population for analysis

[0394] For the purpose of the analysis, the following target populations are defined.

[0395] Analysis population: Description

[0396] Registration: All participants who have signed the ICF.

[0397] Intention to Treat (ITT) analysis includes all randomized participants who were recorded in the database based on the randomized intervention, regardless of whether the research app was properly activated or used. Participants who received treatment without being randomized are not considered randomized and are not included in efficacy or safety analyses.

[0398] This analysis population will be used as a supplementary analysis for the primary endpoint.

[0399] Modified Intention to Treat (mITT): All randomized participants recorded in the database based on the randomized intervention, who have activated the app by successfully entering an access code into the study application, have at least one evaluable baseline measure, and have used the app at least once. This will be the primary analysis population for all efficacy analyses.

[0400] Protocol Adherence Group (PP): All randomized participants who completed 16 weeks of treatment while maintaining at least 60% adherence throughout the 16-week treatment period and who did not have any significant protocol deviations that would affect the calculation of the primary endpoint. This group is used for auxiliary analyses of the primary endpoint. ● Participants exhibiting the following deviations will be excluded from the PP group: ● Participants who received a randomized intervention and those who received a different intervention. ● Participants who do not meet the participation / exclusion criteria ● Participants who used prohibited drugs ● SAP may define additional criteria for exclusion from the PP group.

[0401] General Considerations

[0402] For continuous variables, the summary table shows the number of observations [n], mean, standard deviation, median, minimum, and maximum. For categorical variables, the summary table shows the number of observations [n] and frequency (including missing data) for each category. Aggregated results for each treatment group and overall are shown as appropriate. Modeling and examinations for each endpoint are explained.

[0403] Disciplinary action against participants

[0404] The number of participants enrolled in this study after screening and the reasons for not being randomized are shown. The number of participants who discontinued the study is shown by reason for discontinuation. The number of participants in the ITT, mITT, PP, and safety analysis populations are shown.

[0405] Demographic characteristics and baseline characteristics

[0406] To assess the comparability of the two groups at baseline, demographic and baseline characteristic data are summarized for each treatment group. These summaries are presented for the mITT population. If there is a difference of six or more participants between the mITT and safety, or between the ITT and PP analysis populations, summaries are presented for these populations as well.

[0407] Primary endpoints

[0408] The primary analysis will be performed on the mITT population. The results for the mITT population will be complemented by the analysis of the ITT population to enable the assessment of similarities.

[0409] The primary endpoint is the change in empirical negative symptoms from baseline to week 16, as assessed by the CAINS-MAP scale. The invalidity hypothesis is that there is no difference in mean values ​​between treatment groups, and the alternative hypothesis is that there is a difference in mean values. To demonstrate this, assuming a standardized effect size (Cohen's d) of 0.35 between treatment groups and a Type I error rate of 5% (two-sided), 173 subjects per group are needed to achieve 90% power. Assuming a 20% early termination rate, 432 participants need to be randomly assigned in a 1:1 ratio (approximately 216 per group). Sample size was calculated using a t-test of two independent samples, assuming equal variances.

[0410] The primary endpoint, the change in empirical negative symptoms from baseline to week 16, was analyzed using a mixed-model repeated measures (MMRM), with the change from baseline at each visit in which CAINS-MAP was assessed as the response variable. The model included an intercept and the following covariates: baseline empirical negative symptoms, visits (as a class variable), treatment group, treatment × visit interaction term, and baseline × visit interaction.

[0411] The first choice for within-patient correlation is an unstructured covariance matrix. Contingency plans in case of non-convergence will be discussed in the SAP. Only data from scheduled visits will be used in this analysis. This study will be considered successful if the two-sided p-value for the difference in change from baseline at week 16 between the two treatment groups is less than 0.05, and the CAINS-MAP reduction is greater in the digital treatment application group than in the digital control group.

[0412] In the primary analysis, missing data will not be imputed. All available measurements will be used for each participant.

[0413] The planned number of facilities is approximately 40. SAP will consider facility pooling (e.g., by region or center type) and exploratoryly evaluate the impact of pooled facilities. The analysis will be repeated for the PP population.

[0414] Sensitivity analysis

[0415] Missing data will be handled using multiple imputation (MI) under the assumption that the missing data is randomized (MAR), with imputation performed within randomized groups, and the main model will be iteratively applied to each complete dataset. Further details will be provided in the statistical analysis plan.

[0416] To assess the robustness of the results to missing data under the MAR assumption, a tipping point analysis is performed. In this analysis, all missing data in the sham group are imputed based on the observed values ​​of that group (representing the MAR assumption). Missing data in the digital therapeutic application group are imputed based on various sets of assumptions, starting with the MAR assumption and gradually decreasing the effect until a tipping point is reached. Further details are provided in the SAP.

[0417] subgroup

[0418] The primary analysis will be repeated for each of the following subgroups. ● Time elapsed since diagnosis of schizophrenia (0-5 years, 5 years or more) ● Severity of negative symptoms according to CAINS-MAP at baseline (moderate, moderate-severe, severe) ● Severity of cognitive impairment (as defined by SAP) ● Gender (male / female) ● Age groups (18-21 years old, 22-64 years old, 65 years and older)

[0419] Secondary endpoint

[0420] The analysis of secondary endpoints will be performed using the mITT analysis population. Secondary endpoints will be analyzed in the same manner as the primary endpoint. The analysis will concern differences between treatment groups using MMRM. Detailed information is provided in the SAP.

[0421] Multiplicity control

[0422] No adjustments for secondary endpoints, sensitivity analyses, or subgroups are made for multiplicity.

[0423] Users presented with a digital therapy application (e.g., Application 165) according to the methods described herein are expected to show improvement in the empirical negative symptoms of schizophrenia. Users may be taking antipsychotic medications (e.g., isperidone (risperidone), quetiapine (Seroquel), olanzapine (Zyprexa), ziprasidone (Zeldox), paliperidone (Invega), aripiprazole (Abilify)) in conjunction with the digital therapy application. Improvement may be measured using any number of metrics against a comparison digital application, such as the Clinical Assessment Interview for Negative Symptoms, Motivation and Pleasure (CAINS-MAP) scale, the Defeatist Beliefs subscale of the Dysfunctional Attitudes Scale (DAS), or by assessing the Patient's Comprehensive Impression Scale (PGI-I), etc. These metrics may be measured after one or more activities performed at the prompting of the digital therapy application.

[0424] Exemplary embodiments may include a method for improving the empirical negative symptoms of schizophrenia in a subject receiving antipsychotic medication, comprising having the subject experience a prescription digital therapy (DTx) disclosed herein. Examples of antipsychotic medications include isperidone (risperidone), quetiapine (Seroquel), olanzapine (Zyprexa), ziprasidone (Zeldox), paliperidone (Invega), and aripiprazole (Abilify). In some embodiments, the method may include a step following the experience of the prescription DTx to assess the severity of the subject's empirical negative symptoms of schizophrenia. In some embodiments, the severity of the empirical negative symptoms of schizophrenia can be determined using the Clinical Assessment Interview, Motivation and Pleasure Scale (CAINS-MAP).

[0425] In some embodiments, this method involves: a step of evaluating the change from baseline in motivational and pleasurable symptoms at week 8, as assessed by CAINS-MAP, against a comparison digital application; a step of evaluating the change from baseline in expressive negative symptoms at weeks 8 and 16, as assessed by the Clinical Assessment Interview and Expressiveness Scale for Negative Symptoms (CAINS-EXP), against a comparison digital control; or a step of evaluating the change from baseline in positive symptoms at weeks 8 and 16, as assessed by the Positive and Negative Symptom Rating Scale (PANSS), against a comparison digital control. This may include stages of assessment against a digital control; or stages of assessment of the change from baseline in social functioning at weeks 8 and 16, as assessed by the Personal and Social Performance Scale (PSP), against a digital control; or stages of assessment of the change from baseline in self-reported defeatist beliefs at weeks 8 and 16, as assessed by the Defeatist Beliefs subscale of the Dysfunctional Attitudes Scale (DAS), against a digital control; or stages of assessment of the Comprehensive Impression Scale for Improvement (PGI-I) at weeks 8 and 16 against a digital control.

[0426] In some embodiments, the empirical negative symptoms of schizophrenia may include emotional blunting, alogia (reduced speech), decreased motivation (reduced goal-directed activity due to decreased motivation), antisocial behavior, and anhedonism (reduced pleasure experience). In some embodiments, subjects may have been taking a stable dose of antipsychotics for at least 12 weeks prior to experiencing prescription DTx.

[0427] Exemplary embodiments may include a method for bringing about the desired improvement in a subject's empirical negative symptoms of schizophrenia, and may include a step of having the subject experience a prescribed DTx as described herein. In some embodiments, the method may include a step of assessing the severity of the subject's empirical negative symptoms of schizophrenia following the experience of the prescribed DTx. In some embodiments, the severity of the subject's empirical negative symptoms of schizophrenia may be determined using the Motivation and Pleasure Scale-Self-Report (MAP-SR). In some embodiments, the severity of the subject's empirical negative symptoms of schizophrenia may be determined using a Clinical Assessment Interview for Negative Symptoms (CAINS) assessment. In some embodiments, empirical negative symptoms of schizophrenia may include affective blunting, alogia (reduced speech), decreased motivation (reduced goal-directed activity due to decreased motivation), antisocial behavior, and anhedonism (reduced pleasure experience).

[0428] Exemplary embodiments may include a method for improving the empirical negative symptoms of schizophrenia in a subject receiving antipsychotic medication, comprising having the subject experience a prescription digital therapy (DTx) disclosed herein. Examples of antipsychotic medications include isperidone (risperidone), quetiapine (Seroquel), olanzapine (Zyprexa), ziprasidone (Zeldox), paliperidone (Invega), aripiprazole (Abilify), or iclepertin. The method may include a step following the experience of the prescription DTx to assess the severity of the subject's empirical negative symptoms of schizophrenia. In some embodiments, the severity of the empirical negative symptoms of schizophrenia is determined using the Clinical Assessment Interview, Motivation and Pleasure Scale (CAINS-MAP).

[0429] In some embodiments, this method involves: evaluating the change from baseline in motivational and pleasurable symptoms at week 8, as assessed by CAINS-MAP, against a comparison digital application; evaluating the change from baseline in expressive negative symptoms at weeks 8 and 16, as assessed by the Clinical Assessment Interview and Expressiveness Scale for Negative Symptoms (CAINS-EXP), against a comparison digital control; or evaluating the change from baseline in positive symptoms at weeks 8 and 16, as assessed by the Positive and Negative Symptom Rating Scale (PANSS), against a comparison digital control. This may include stages of evaluation; or stages of evaluation of changes from baseline in social functioning at weeks 8 and 16, as assessed by the Personal and Social Performance Scale (PSP), against a digital control; or stages of evaluation of changes from baseline in self-reported defeatist beliefs at weeks 8 and 16, as assessed by the Defeatist Beliefs subscale of the Dysfunctional Attitudes Scale (DAS), against a digital control; or stages of evaluation of the Patient's Comprehensive Impression Scale for Improvement (PGI-I) at weeks 8 and 16 against a digital control.

[0430] In some embodiments, the empirical negative symptoms of schizophrenia may include emotional blunting, alogia (reduced speech), decreased motivation (reduced goal-directed activity due to decreased motivation), antisocial behavior, and anhedonism (reduced pleasure experience). In some embodiments, subjects have been taking a stable dose of antipsychotics for at least 12 weeks prior to experiencing prescription DTx.

[0431] C. Network and Computing Environment The various operations described herein can be performed on a computer system. Figure 24 shows a simplified block diagram of a typical server system 2400, a client computing system 2414, and a network 2426 that can be used to implement a particular embodiment of this disclosure. In various embodiments, the server system 2400 or a similar system can implement the services or servers or parts thereof described herein. The client computing system 2414 or a similar system can implement the clients described herein. System 100 described herein may be similar to the server system 2400. The server system 2400 may have a modular design incorporating many modules 2402 (e.g., blades in an embodiment of a blade server), two modules 2402 are shown, but any number can be provided. Each module 2402 may include one or more processing units 2404 and local storage devices 2406.

[0432] One or more processing units 2404 may include a single processor having one or more cores, or multiple processors. In some embodiments, one or more processing units 2404 may include a general-purpose primary processor in addition to one or more dedicated coprocessors, such as a graphics processor or a digital signal processor. In some embodiments, some or all of the processing units 2404 may be implemented using customized circuits such as application-specific integrated circuits (ASICs) or user-writable grid arrays (FPGAs). In some embodiments, such integrated circuits execute instructions stored in the circuit itself. In other embodiments, one or more processing units 2404 may execute instructions stored in local memory 2406. Any combination of any type of processor may be included in one or more processing units 2404.

[0433] The local storage device 2406 may include volatile storage media (e.g., DRAM, SRAM, SDRAM, etc.) and / or non-volatile storage media (e.g., magnetic disks or optical disks, flash memory, etc.). The storage media incorporated into the local storage device 2406 may be fixed, removable, or upgradeable, as desired. The local storage device 2406 may be physically or logically divided into various units such as system memory, read-only memory (ROM), and permanent storage. The system memory may be a read-write memory device or a volatile read-write memory such as dynamic random-access memory. The system memory may store some or all of the instructions and data required by one or more processing units 2404 at runtime. The ROM may store static data and instructions required by one or more processing units 2404. The permanent storage may be a non-volatile read-write memory device that can store instructions and data even when the module 2402 is powered off. As used herein, the term “storage medium” includes any medium capable of storing data indefinitely (without overwriting, electrical interference, power loss, etc.), but does not include carrier waves or transient electronic signals propagated wirelessly or via wired connections.

[0434] In some embodiments, the local storage device 2406 can store one or more software programs executed by one or more processing units 2404, such as an operating system, and / or programs that implement various server functions, such as functions of system 100 or any other system described herein, or any other one or more servers associated with system 100 or any other system described herein.

[0435] "Software" generally refers to a sequence of instructions, when executed by one or more processing units 2404, that cause the server system 2400 (or a part thereof) to perform various operations, and thus defines a specific implementation example of one or more particular machines that execute and perform the operations of a software program. Instructions can be stored as firmware residing in read-only memory for execution by one or more processing units 2404, and / or as program code stored in a non-volatile storage medium that can be loaded into volatile working memory. Software can be implemented as a single program, or as a collection of separate programs or program modules that interact as desired. To perform the various operations described above, one or more processing units 2406 can retrieve program instructions to execute and data to process from local storage devices 2404 (or non-local storage devices described later).

[0436] In some server systems 2400, multiple modules 2402 can be interconnected via a bus or other interconnect 2408 to form a local area network that supports communication between the modules 2402 and other components of the server system 2400. The interconnect 2408 can be implemented using various technologies, including server racks, hubs, routers, etc.

[0437] The wide area network (WAN) interface 2410 can enable data communication between the local area network (e.g., via interconnect 2408) and a network 2426 such as the Internet. The server system can be connected to network 2426 in a communicative manner using other technologies, including wired technologies (e.g., Ethernet, IEEE 802.3 standard) and / or wireless technologies (e.g., Wi-Fi, IEEE 802.11 standard).

[0438] In some embodiments, the local storage device 2406 is intended to provide working memory to one or more processing units 2404, providing fast access to programs and / or data being processed while reducing traffic on the interconnect 2408. Storage for larger amounts of data can be provided on the local area network by one or more mass storage subsystems 2408 connectable to the interconnect 2412. The mass storage subsystem 2412 can be based on magnetic, optical, semiconductor, or other data storage media. Direct-attached storage, storage area networks, network-attached storage, etc., can be used. Any stored data or other aggregates of data described herein as generated, consumed, or maintained by a service or server can be stored in the mass storage subsystem 2412. In some embodiments, additional data storage resources are accessible via the WAN interface 2410 (although this may increase latency).

[0439] The server system 2400 can operate in response to requests received via the WAN interface 2410. For example, one of several modules 2402 can implement a management function and, in response to a received request, assign individual tasks to other modules 2402. Task assignment techniques can be used. Once a request is processed, the result can be returned to the requesting party via the WAN interface 2410. Generally, such operations can be automated. Furthermore, in some embodiments, the WAN interface 2410 can connect multiple server systems 2400 to each other, providing a scalable system capable of managing a large volume of activity. Other techniques can be used to manage server systems and server farms (collections of server systems cooperating with each other), including dynamic resource allocation and reallocation.

[0440] The server system 2400 can interact with various user-owned or user-operated devices via a wide-area network such as the Internet. An example of a user-operated device is shown in Figure 24 as a client computing system 2414. The client computing system 2414 can be implemented as a consumer device such as a smartphone, other mobile phone, tablet computer, wearable computing device (e.g., smartwatch, glasses), desktop computer, or laptop computer.

[0441] For example, the client computing system 2414 can communicate via the WAN interface 2410. The client computing system 2414 may include computer components such as one or more processing units 2416, a storage device 2418, a network interface 2420, a user input device 2422, and a user output device 2424. The client computing system 2414 may be a computing device implemented in various form factors, such as a desktop computer, a laptop computer, a tablet computer, a smartphone, other mobile computing devices, or a wearable computing device.

[0442] The processing unit 2416 and the storage device 2418 can be the same as the one or more processing units 2404 and local storage device 2406 described above. The appropriate devices can be selected based on the requirements imposed on the client computing system. For example, the client computing system 2414 can be implemented as a "thin" client with limited processing power, or as a high-performance computing device. The client computing system 2414 may have program code executable by one or more processing units 2416 to enable various interactions with the server system 2400.

[0443] The network interface 2420 can provide connectivity to a network 2426, such as a wide area network (e.g., the Internet), to which the WAN interface 2410 of the server system 2400 is also connected. In various embodiments, the network interface 2420 may include a wired interface (e.g., Ethernet) and / or a wireless interface implementing various wireless data communication standards such as Wi-Fi, Bluetooth, or cellular data network standards (e.g., 3G, 4G, 5G, 24G, LTE, etc.).

[0444] The user input device 2422 may include any device (or more devices) on which the user can send signals to the client computing system 2414, which can interpret such signals as indicating a specific user request or information. In various embodiments, the user input device 2422 may include any or all of the following: a keyboard, touchpad, touchscreen, mouse or other pointing device, scroll wheel, click wheel, dial, button, switch, keypad, microphone, etc.

[0445] The user output device 2424 may include any device through which the client computing system 2414 can provide information to the user. For example, the user output device 2424 may include a display-to-display shared image generated by or transmitted to the client computing system 2414. The display may incorporate various image generation technologies, such as liquid crystal displays (LCDs), light-emitting diodes (LEDs) including organic light-emitting diodes (OLEDs), projection systems, and cathode ray tubes (CRTs), along with auxiliary electronic equipment (e.g., digital-to-analog or analog-to-digital converters, signal processors, etc.). Some embodiments may include a device such as a touchscreen that functions as both an input and an output device. In some embodiments, other user output devices 2424 may be provided in addition to, or instead of, the display. Examples include indicator lights, speakers, haptic "display" devices, and printers.

[0446] Some embodiments include electronic components such as microprocessors, storage devices, and memory that store computer program instructions on a computer-readable storage medium. Many of the features described herein can be implemented as processes designated as a set of program instructions encoded on a computer-readable storage medium. When these program instructions are executed by one or more processing units, they cause one or more processing units to perform the various operations indicated by the program instructions. Examples of program instructions or computer code include machine code generated by a compiler and files containing higher-level code that is executed by a computer, electronic components, or microprocessor using an interpreter. With appropriate programming, one or more processing units 2404 and 2416 can provide a variety of functions to the server system 2400 and client computing system 2414, including any of the functions described herein as being executed by a server or a client, or other functions.

[0447] It will be understood that the server system 2400 and client computing system 2414 are illustrative and are subject to modification and alteration. Computer systems used in connection with embodiments of this disclosure may have other functions not specifically described herein. Furthermore, while the server system 2400 and client computing system 2414 are described with reference to specific blocks, it should be understood that these blocks are defined for illustrative purposes only and are not intended to imply a specific physical arrangement of components. For example, different blocks may, but are not required to, be located in the same facility, in the same server rack, or on the same motherboard. Furthermore, these blocks do not need to correspond to physically separate components. Multiple blocks may be configured to perform different operations, for example, by programming a processor or providing appropriate control circuits, and the different blocks may be reconfigurable or non-reconfigurable depending on how the initial configuration is obtained. Embodiments of this disclosure can be realized in a variety of devices, including electronic devices implemented using any combination of circuitry and software.

[0448] While this disclosure has described specific embodiments, those skilled in the art will recognize that numerous modifications are possible. Embodiments of this disclosure can be implemented using a variety of computer systems and communication technologies, including but not limited to the specific examples described herein. Embodiments of this disclosure can be implemented using dedicated components and / or any combination of programmable processors and / or other programmable devices. The various operations described herein can be performed with the same processor or any combination of different processors. Where a component is described as being configured to perform a particular operation, such configuration can be achieved, for example, by designing an electronic circuit to perform that operation, by programming a programmable electronic circuit (such as a microprocessor) to perform that operation, or by any combination thereof. Furthermore, while the embodiments described above may refer to specific hardware and software components, those skilled in the art will understand that different combinations of hardware and / or software components may also be used, that a particular operation described as being implemented in hardware may also be implemented in software, and vice versa.

[0449] Computer programs incorporating various features of this disclosure can be encoded and stored in a variety of computer-readable storage media. Suitable media include optical storage media such as magnetic disks or tapes, compact discs (CDs) or digital multipurpose discs (DVDs), flash memory, and other non-transient media. The computer-readable media encoded with the program code may be packaged together with compatible electronic devices, or the program code may be provided separately from the electronic devices (for example, via internet download or as separately packaged computer-readable storage media).

[0450] Thus, although this disclosure has described specific embodiments, it will be understood that this disclosure is intended to cover all modifications and equivalents within the scope of the following claims.

Claims

1. A method that brings about the necessary improvement in the user's empirical negative symptoms of schizophrenia: A step of obtaining a first metric associated with the user at multiple points in time using one or more processors; One or more processors, for each of the aforementioned multiple time points: A step of providing an application with a configuration file selected from a plurality of configuration files, based on the endpoint for improving the aforementioned empirical negative symptoms, wherein the configuration file identifies a set of content items relating to one of a plurality of activities toward achieving the endpoint; In response to providing the application with the one configuration file, the application presents the set of content items identified by the one configuration file to prompt the user to perform the one activity toward achieving the endpoint; The steps include receiving response data that identifies one or more interactions between the user and the set of content items, and repeating this step; The step of obtaining a second metric associated with the user after at least one of the plurality of time points by one or more processors, A method in which the user demonstrates improvement in empirical negative symptoms of schizophrenia when the second metric is statistically different from the first metric.

2. The method according to claim 1, wherein the first metric and the second metric include at least one score from the following: Clinical Assessment Interview for Negative Symptoms (CAINS) Motivation and Pleasure Scale, Clinical Assessment Interview for Negative Symptoms - Expressiveness Scale (CAINS-EXP), Positive and Negative Symptom Rating Scale (PANSS), Personal and Social Functioning Performance Scale (PSP), Defeatist Beliefs Subscale of the Dysfunctional Attitudes Scale (DAS), Comprehensive Impression of Improvement Scale (PGI-I), Comprehensive Impression of Severity Scale (PGI-S), Comprehensive Impression of Clinical Severity Scale (CGI-S), World Health Organization Disability Assessment Scale, Second Edition (WHODAS 2.0), or Quality of Life Scale for Schizophrenia, Fourth Edition (SQLS-R4).

3. The method according to claim 1, wherein the empirical negative symptoms of schizophrenia include one or more of affective blunting, allogy, decreased motivation, antisocial behavior, and anhedonia.

4. The method according to claim 1, wherein the user is an adult or a young person in late adolescence.

5. The method according to claim 1, wherein the user has experienced at least moderate to severe negative symptoms prior to the first activity.

6. The method according to claim 1, wherein the user has a Motivation and Pleasure Scale (MAPS) score of 30 or less prior to the first activity.

7. The method according to claim 1, wherein the user has been taking a stable dose of antipsychotic medication for at least 12 weeks prior to the first activity.

8. The method according to claim 7, wherein the drug includes, for example, risperidone, quetiapine, olanzapine, ziprasidone, paliperidone, aripiprazole, or iclepertin.

9. The method according to claim 1, wherein the user is male, female, or non-binary.

10. The method according to claim 1, wherein at least one of the plurality of configuration files identifies a criterion for defining an indicator for selecting another of the plurality of configuration files, the indicator identifies (i) the likelihood that the user will perform the activity, or (ii) the expected effect of the activity on the user toward achieving the endpoint.

11. The method according to claim 1, wherein the endpoint at the first of the plurality of time points is selected from the plurality of endpoints based on the user's baseline assessment of the empirical negative symptoms of schizophrenia.

12. The method according to claim 1, wherein the endpoint is selected from a plurality of endpoints based on the personal values ​​indicated by the user.

13. The method according to claim 1, wherein the endpoint is selected from a plurality of endpoints in response to a transition from a first level to a second level, and the transition is determined based on the response data.

14. The method according to claim 1, wherein the endpoint is selected from a plurality of endpoints based on a first mood indicated by the user.

15. The method according to claim 1, wherein the endpoint is associated with at least one of a plurality of domains, the plurality of domains including a social domain, a leisure domain, and a productivity domain.

16. The method according to claim 1, wherein the user is presented with a comparison between a first evaluation performed before the execution of the corresponding second activity and a second evaluation performed after the execution of the corresponding second activity.

17. The method according to claim 1, further comprising a step in which one or more processors decide to continue the repeating step over a plurality of time points based on a certain time since the acquisition of the baseline metric, and the step repeated for each of the plurality of time points further comprises a step of repeating that time point in response to the decision to continue.

18. The method according to claim 1, wherein the step repeated for each of the plurality of time points further includes, for each time point, the step of updating the endpoint based on the response data that identifies the one or more interactions of the user with the set of content items provided through the application from a previous time point.

19. The method according to claim 1, wherein the step repeated for each of the multiple time points further includes the step of converting the human-readable instructions in the configuration file to generate a package containing machine-executable instructions.

20. The method according to claim 1, wherein the steps of obtaining the first metric and the second metric further include obtaining them from a source other than the application.

Images