Use of digital therapy with adaptive target setting to improve experiential negative symptoms of schizophrenia in patients

Abstract

Provided herein are methods of improving experiential negative symptoms of schizophrenia in a user in need thereof. A computing system may obtain a first indicator associated with a user prior to a plurality of time instances. The computing system may repeatedly execute in a plurality of time instances, identifying a profile selected based on an endpoint for improving the experiential negative symptom. The computing system may repeatedly present the group of content items identified by the profile to prompt the user to perform an activity intended to implement the endpoint. The computing system may obtain a second indicator associated with the user after the plurality of time instances. When the second indicator is lower than the first indicator, the user may show an improvement in an experiential negative symptom of schizophrenia. The computing system can improve the efficacy of the drug for treating schizophrenia being taken by the user.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority to U.S. Provisional 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 contents of which are incorporated herein by reference. Background Art

[0003] After adjusting for prevalence, schizophrenia is the leading cause of disability. Given the chronic nature of schizophrenia, individuals with schizophrenia often experience low educational attainment, poor quality of life, difficulty living independently, and socio-occupational impairment. This impairment is so severe that only 10% to 20% of individuals with schizophrenia work full-time or part-time, and the majority of individuals with schizophrenia require public funding, with lost productivity being a major cost driver. In addition to the burden borne by individuals with schizophrenia, caregivers of individuals with schizophrenia also bear a significant burden. This disability, and the resulting burden on individuals and caregivers, often persists or worsens over time, as evidenced by a recent meta-analysis finding that only 14% of individuals with schizophrenia achieve functional recovery.

[0004] Beyond disability, schizophrenia carries a significant personal burden. Mortality rates are 2.5 times higher than those found in the general population, and people with schizophrenia die 28.5 years earlier than the general population. This alarmingly early mortality rate is largely driven by high suicide rates. People with schizophrenia have a 20-fold higher suicide rate than the general population. As a result, people with schizophrenia account for 23% of all annual suicide deaths.

[0005] A significant factor contributing to this heavy burden is the presence of significant negative symptoms. Negative symptoms are often considered the first symptoms of schizophrenia. Consequently, 73% of people experience negative symptoms before the onset of positive symptoms, 90% of people experiencing their first episode of psychosis also experience negative symptoms, and 35% to 70% of people with schizophrenia continue to experience negative symptoms even after starting treatment. Despite their early onset, negative symptoms are not effectively treated with standard of care (SOC) or any other medications, and are a strong predictor of functional impairment as they lead to worsening functioning over time.

[0006] Current schizophrenia guidelines recommend antipsychotic medications and adjunctive psychosocial interventions. Antipsychotics are effective for treating positive symptoms, but they are associated with significant side effects, leading to high rates of medication nonadherence. Furthermore, no pharmacological interventions are available to treat negative symptoms. Therefore, adjunctive psychosocial interventions are recommended for symptoms that are not well treated with medication, such as negative symptoms.

[0007] Significant barriers to treatment access and adherence limit the efficacy of standard-of-care (SOC) treatment for negative symptoms. First, no medications, biologics, or medical devices are currently approved or licensed as adjunctive treatments for experiencing negative symptoms in schizophrenia. Second, while adjunctive psychosocial interventions are therefore recommended for treating negative symptoms, there is a shortage of psychiatrists and other adequately trained clinicians to deliver treatment, and implementation gaps in the training of community providers to adhere to evidence-based practices further exacerbate this problem. These limitations result in only 10% of people with schizophrenia receiving evidence-based adjunctive psychosocial interventions to treat their negative symptoms. Consequently, there is an unmet need for accessible treatments that target and improve negative symptoms.

[0008] An application can be deployed and installed on a computing device to provide a digital therapeutic to address a user's behavioral or psychological condition. As part of the digital therapeutic, the application itself can be configured to present a user interface containing various elements for display and perform a number of actions in response to user interactions with the user interface elements. Once deployed, the application may struggle to dynamically adapt to user responses, resulting in a reduced likelihood of user interaction with the application. As a result, the efficacy of the digital therapeutic and user adherence to the digital therapeutic may be significantly reduced.

[0009] In a network environment, an application on a client can present one or more content items received as part of a message from a server. Content items can be customized for the client's specific user and can be selected based on any number of factors, such as user history, device type, location, or time of day. Content items can also contain scripts (e.g., event listeners or handlers) to specify presentation functionality when loaded into the client application. For example, a content item might be provided as part of a digital therapeutics platform and might include text related to a user's associated condition (e.g., smoking cessation, dieting, exercise, and mental illness). Upon receiving the content item, the application on the client can display it as a user interface element within the application's graphical user interface (GUI). While the content item is displayed by the application, the user can interact with one or more of the user interface elements to trigger functionality associated with the content item.

[0010] While providing content items in this manner can perform designated functions, the range of possible functions for a user, as well as the selection of additional subsequent content items, can be significantly limited. In the context of digital therapeutics, the selection and provision of content items may not be sufficient to achieve endpoints associated with a user's condition. First, the digital therapeutic application itself, once deployed and installed on the client, can have relatively static and fixed functionality, without the need to update the application. For example, once installed, the application may provide a limited number of different configurations for the graphical user interface that presents content items. Second, a user's state and behavior can change over time, and the factors used to pre-select content items may not be sufficient to adapt to the user's changing state.

[0011] Furthermore, the content items themselves may lack logic to handle such changes and may therefore be too generic, irrelevant to the user's state at the time of presentation, resulting in poor quality human-computer interaction (HCI) between the application and the user. This can lead to an increase in computing resources required to load and render these content items on the application, as well as network bandwidth required for communication back and forth between the client and server. From an HCI perspective, the lack of adaptability in content items can lead to reduced user engagement and adherence to the digital therapeutics provided through the presentation of content items. Summary of the Invention

[0012] To address these and other challenges, this paper proposes a system and method for dynamically selecting application profiles based on an adaptive goal setting (AGS) framework. Initially based on baseline user measurements and subsequently based on user response data, profiles can be selected and personalized for the user. Providing digital therapeutics via profiles on the app in this manner can improve user engagement and adherence, ultimately increasing the efficacy of the digital therapeutic. The AGS framework can continuously and dynamically configure the app to be tailored to the user and their endpoints by capturing user interests (meeting the user's needs) and delivering personalized content, as well as adjusting goals based on baseline proficiency and metrics (matching goals to the user's current capabilities). The framework can provide users with daily activity engagement (e.g., multiple times per day) so that they can utilize the app on their personal mobile device, increasing engagement and treatment efficacy. (In contrast, traditional models only allow for interaction between the practitioner and the user when the user can attend an in-person clinic, which might be once a week.) The AGS framework can also provide emotional relief strategies to address user states and improve retention. Furthermore, the AGS framework can include personalized features such as daily mood check-ins, pre- and post-activity self-assessments, personal values exploration, and an "act now" option.

[0013] A server (or computing device) can select a configuration file and provide it to an application to present content. Each configuration file can include logic or modules for an automaton (e.g., a finite state machine (FSM)) for one or more endpoints, specifying one or more activities to be performed by the application on the client within a set time period. A configuration file can identify groups of states and transitions for a digital therapeutic application. Each state can correspond to a level of activity associated with the endpoint and can specify the activities to be performed by the application at that level. Each transition can correspond to a condition that is satisfied to transition from one state to the next. A condition can be, for example, the success or failure of a specified activity within a set time period.

[0014] The selection, provisioning, and internal logic of the profile itself can be aligned with the Adaptive Goal Setting (AGS) framework. The AGS framework can support the execution of activities for endpoints (also referred to herein as goals, objectives, or goals) to adaptively address the user's situation. The framework can include skill introduction, developing a plan to achieve the endpoint, keeping track of the user's progress, and adaptively changing the activities to be incorporated into the endpoint.

[0015] To this end, during the onboarding phase, a user profile can be constructed for the user using the user's responses to prompts (e.g., questionnaires) related to the user's condition and endpoints. The user profile can then be used to determine one or more endpoints and select a profile to implement the endpoints as part of a plan to address the user's condition. The AGS framework can be used to accommodate, for example, different categories of goals (e.g., quitting smoking, dieting for weight loss, improving exercise habits, and addressing symptoms of mental illness); different types of activities (e.g., walking, running, swimming, hiking, dancing, and stair climbing); activities of different difficulty levels (e.g., lead climbing, top rope climbing, bouldering); activity duration (e.g., 5 minutes, 10 minutes, and 1 hour); activity frequency (e.g., once a week, once a day, and three times a day); and time of day when activities are performed (e.g., morning, afternoon, and after meals).

[0016] Profiles can provide automaton logic with persistent state, allowing user progress to remain stable across usage sessions. The profile itself can be a plug-in for the application and can be modified for a specific endpoint (e.g., by a clinician) without requiring application reconfiguration. Automaton logic can be used to store groups of variables such as level, activity, endpoint, and frequency. For example, a group of variables might correspond to a user performing Level 4 Activity A for 5 minutes every other morning. Based on this information, a profile can be constructed to provide a group of content items to guide the user in performing and recording activities via the client application. As the user progresses through the automaton logic, the level, activity, endpoint, and frequency can be updated to provide a customized experience at each state. The logic can adjust content items up or down based on responses from the application to suit the user's current state. Furthermore, response data can be used to adaptively determine different endpoints over the course of a session, and various profiles can be dynamically selected based on the endpoint to be provided to the application.

[0017] By providing profiles in this manner, applications can be customized based on requests and offer users a wider user experience tailored to different endpoints, thereby improving the HCI between users and applications. Because content items can be selected based on the user's associated condition according to the profile, content items can lead to higher user engagement with digital therapeutic applications. Due to the higher likelihood of engagement, digital therapeutics provided by the application can have a higher efficacy in addressing the condition and increasing user adherence to the digital therapeutic. Profiles can also reduce the need to update the application itself to provide additional functionality. Dynamically selecting and providing profiles can reduce the consumption of computing resources (such as processor, memory, and network bandwidth) that would otherwise be required to serve and load irrelevant content items on the application. Furthermore, profiles can reduce the consumption of network bandwidth associated with the round-trip communication associated with requesting and retrieving content items.

[0018] Aspects of the present disclosure relate to systems, methods, and non-transitory computer-readable media for selecting a configuration file for an application. A computing system may maintain a plurality of configuration files readable by the application. Each of the plurality of configuration files may identify a corresponding group of content items to prompt a user to perform at least one of a plurality of activities via the application, the group of content items being intended to achieve a corresponding endpoint from a plurality of endpoints. The computing system may determine one of the plurality of endpoints to address a condition of the user. The computing system may select a configuration file from the plurality of configuration files that identifies a group of content items for one of the plurality of activities to be performed by the user via the application, the group of content items being intended to achieve the endpoint. The computing system may provide the configuration file to the application to present the group of content items to prompt the user to perform the activity via the application.

[0019] In some embodiments, a computing system may receive response data from an application program that identifies one or more interactions of a user with a group of content items presented via the application program for implementing an endpoint. The computing system may determine a second endpoint from among a plurality of endpoints based on the response data for the endpoint. The computing system may select a second profile from among a plurality of profiles to provide to the application program based on the second endpoint.

[0020] In some embodiments, the computing system may identify, from a profile of a user, a first level among a plurality of levels intended for achieving an endpoint. The computing system may determine a transition from the first level to a second level based on response data identifying one or more interactions performed by the user with a group of content items presented via an application. The computing system may select a second profile from the plurality of profiles based on the transition to the second level. In some embodiments, the computing system may determine, based on the user performing an activity intended for achieving the endpoint, whether to transition to the same first level or to a second level among the plurality of levels intended for achieving a second endpoint.

[0021] In some embodiments, each of a plurality of profiles may identify corresponding criteria that define a first metric of a user for selecting a corresponding profile, the profile identifying a group of content items to prompt the user to perform an activity. The computing system may determine a second metric based on the user's profile. The second metric may identify at least one of: (i) a likelihood that the user will perform the activity; or (ii) a predicted efficacy of the activity for the user's intended endpoint. In some embodiments, the computing system may identify the activity from the plurality of activities based on a comparison of the first metric and the second metric.

[0022] In some embodiments, the computing system may receive a response from the application identifying a user emotion in response to a prompt presented via the application at a defined time. In some embodiments, the computing system may identify an activity from a plurality of activities based on the user emotion indicated in the response. In some embodiments, the computing system may receive a response from the application identifying a plurality of personal values of the user. In some embodiments, the computing system may identify an activity from a plurality of activities based on a plurality of personal values associated with the user identified in the response. In some embodiments, the computing system may determine a progress indicator based on the execution of the activity intended to achieve an endpoint. In some embodiments, the computing system may present, via the application, a link between the progress indicator and the personal values associated with the user.

[0023] In some embodiments, the computing system may present a prompt to the user via the application indicating a first score associated with the activity before the activity is performed via the application. In some embodiments, the computing system may store a response identifying the first score associated with the activity from the application. In some embodiments, the computing system may present a prompt to the user via the application indicating a second score associated with the activity after the activity is performed via the application. In some embodiments, the computing system may present a comparison of the second score to the first score via the application. In some embodiments, the computing system may determine the endpoint from the plurality of endpoints based on at least one of: (i) a baseline assessment and (ii) an indication that the user requested an activity intended to achieve the endpoint.

[0024] In some embodiments, at least one of the plurality of profiles may define a finite state machine. The finite state machine may identify a plurality of states including at least a first state and a second state, each state in the plurality of states corresponding to an intensity level for a corresponding activity and specifying an output. The output may identify one or more content items to be presented via a user interface of the application. The finite state machine may identify a plurality of transitions, each transition in the plurality of transitions specifying an event to be detected via the user interface of the application to transition from the first state to the second state, the event corresponding to an interaction to be performed via the application for the corresponding activity.

[0025] In some embodiments, the plurality of endpoints may be associated with at least one of a plurality of categories for endpoints. In some embodiments, a first subset of the plurality of endpoints may be associated with a first category, and a second subset of the plurality of endpoints may be associated with a second category. In some embodiments, at least in part while performing an activity via the application, the user may be taking medication to address a condition. In some embodiments, the condition may include a mental illness.

[0026] Aspects of the present disclosure relate to a method for improving negative symptoms experienced by a user in need of schizophrenia. One or more processors may obtain a first metric associated with the user prior to a plurality of time instances. The one or more processors may repeatedly identify a profile for providing to an application at each of the plurality of time instances, the profile selected from the plurality of profiles based on an endpoint for improving negative symptoms experienced. The profile may identify a group of content items for implementing one of a plurality of activities intended to implement the endpoint. In response to providing the profile to the application, the one or more processors may repeatedly present the group of content items identified by the profile via the application to prompt the user to perform the activity intended to implement the endpoint. The one or more processors may repeatedly receive response data identifying one or more interactions performed by the user with the group of content items. The one or more processors may obtain a second metric associated with the user after at least one of the plurality of time instances. When the second metric is statistically different from the first metric, the user may demonstrate improvement in negative symptoms experienced by schizophrenia.

[0027] In some embodiments, the first indicator and the second indicator may include scores of at least one of the following: the Motivation and Pleasure Scale of the Clinical Assessment Interview for Negative Symptoms (CAINS), the Performance Scale of the Clinical Assessment Interview for Negative Symptoms (CAINS-EXP), the Positive and Negative Syndrome Scale (PANSS), the Personal and Social Performance Scale (PSP), the Defeatism Beliefs Subscale of the Disability Attitudes Scale (DAS), the Patient Global Impression of Improvement (PGI-I), the Patient Global Impression of Severity (PGI-S), the Clinical Global Impression of Severity (CGI-S), the WHO Disability Assessment Schedule 2.0 (WHODAS2.0), or the Schizophrenia Quality of Life Scale-Revised 4 (SQLS-R4).

[0028] In some embodiments, the experienced negative symptoms of schizophrenia may include one or more of the following: blunted affect, aphasia (decreased speech), avolition (decreased goal-directed activity due to decreased motivation), social anhedonia, and anhedonia (decreased experience of pleasure). In some embodiments, the user may be an adult or an older adolescent. In some embodiments, the user may have experienced at least moderate to severe severity of negative symptoms prior to the first activity. In some embodiments, the user may have a score of ≤30 on the Motivation and Pleasure Scale (MAPS) prior to the first activity.

[0029] 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 medication may include risperidone, quetiapine, olanzapine, ziprasidone, paliperidone, aripiprazole, or iclepertin. In some embodiments, the user may be male, female, or non-binary.

[0030] In some embodiments, at least one of the plurality of profiles may identify a metric defining a user for selection criteria for another of the plurality of profiles, the metric identifying at least one of: (i) a likelihood that the user will perform an activity; or (ii) a predicted efficacy of the activity for the user to achieve an endpoint. In some embodiments, the endpoint may be selected from the plurality of endpoints for a first time instance in the plurality of time instances based on a baseline assessment of the user's experienced negative symptoms of schizophrenia.

[0031] In some embodiments, the endpoint can be selected from a plurality of endpoints based on personal values indicated by the user. In some embodiments, the endpoint can be selected from a plurality of endpoints based on response data identifying one or more interactions by the user with a group of content items presented via the application at a previous time instance. In some embodiments, the endpoint can be selected from a plurality of endpoints in response to a transition intended to effectuate the endpoint from a first level to a second level. The transition can be determined based on the response data.

[0032] In some embodiments, an endpoint may be selected from a plurality of endpoints based on an emotion indicated by a 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, an entertainment domain, and a productivity domain. In some embodiments, a comparison of a first score performed before the performance of a corresponding second activity and a second score performed after the performance of the corresponding second activity may be presented to the user. In some embodiments, a profile may be selected based on an endpoint change determined using response data from a previous time instance. In some embodiments, one or more processors may determine whether to continue repeating a plurality of time instances based on an amount of time since a baseline metric was obtained. Repeating each of the plurality of time instances may include repeating the time instance in response to a determination to continue.

[0033] In some embodiments, repeating for each of the plurality of time instances may include, for a time instance, updating an endpoint based on response data identifying one or more interactions of a user with a group of content items presented via the application from a previous time instance. In some embodiments, repeating for each of the plurality of time instances may include converting human-readable instructions of a configuration file to generate a package including instructions in a machine-executable format. In some embodiments, obtaining the first metric and the second metric may include obtaining the first metric and the second metric from a source separate from the application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The foregoing and other objects, aspects, features and advantages of the present disclosure will become more apparent and better understood by referring to the following description taken in conjunction with the accompanying drawings, in which:

[0035] Figure 1 depicts a block diagram of a system for selecting a configuration file for an application according to an illustrative embodiment;

[0036] Figure 2A a block diagram depicting a process for maintaining profiles in a system for selecting profiles in accordance with an illustrative embodiment;

[0037] Figure 2B a block diagram depicting a process for evaluating a user profile to provide a profile in a system for selecting a profile according to an illustrative embodiment;

[0038] Figure 2C a block diagram depicting a process for processing a configuration package in a system for selecting a configuration file according to an illustrative embodiment;

[0039] Figure 2D a block diagram depicting a process for modifying a user interface in a system for selecting a configuration file in accordance with an illustrative embodiment;

[0040] Figure 2E a block diagram depicting a process for evaluating response data to provide a profile in a system for selecting a profile according to an illustrative embodiment;

[0041] Figure 3 a flowchart depicting a method of selecting a configuration file for an application according to an illustrative embodiment;

[0042] Figure 4 depicts an architectural block diagram of a system for adaptive targeting of selected profiles in accordance with an illustrative embodiment;

[0043] Figure 5A a flowchart depicting a method of performing adaptive targeting in a selection profile in accordance with an illustrative embodiment;

[0044] Figure 5B a flowchart depicting a method of performing an activity according to a configuration file according to an illustrative embodiment;

[0045] Figure 6A -C each depicts a block diagram of a configuration file for executing a routine according to an illustrative embodiment;

[0046] Figure 7A -F each depicts an example of a screenshot of a prompt for assessing a user's physical activity in accordance with an illustrative embodiment;

[0047] Figure 8A -L each depicts an example of a screenshot of a prompt for building a user profile associated with an endpoint to pursue a hobby at a first step of Level 0 in accordance with an illustrative embodiment;

[0048] Figure 9A -L each depicts an example of a screenshot of a prompt for a user to perform an activity associated with an endpoint to pursue a hobby at a second step of level 0 in accordance with an illustrative embodiment;

[0049] Figure 10A -L each depicts an example of a screenshot of a prompt for a user to perform an activity associated with an endpoint to pursue a hobby at a third step of Level 0 in accordance with an illustrative embodiment;

[0050] Figure 11A -G each depicts an example of a screenshot of a prompt for a user to perform an activity associated with an endpoint to pursue a hobby at a fourth step of level 0 in accordance with an illustrative embodiment;

[0051] Figure 12A -D each depicts an example of a screenshot of a prompt for a user to perform an activity associated with an endpoint to pursue a hobby at a first step of Level 1 in accordance with an illustrative embodiment;

[0052] Figure 13A -K each depicts an example of a screenshot of a prompt for a user to perform an activity associated with an endpoint to pursue a hobby at a second step of Level 1 in accordance with an illustrative embodiment;

[0053] Figure 14A -J each depicts an example of a screenshot of a prompt for a user to perform an activity associated with an endpoint to pursue a hobby at a third step of Level 1 in accordance with an illustrative embodiment;

[0054] Figure 15A -J each depicts an example of a screenshot of a prompt for a user to perform an activity associated with an endpoint to pursue a hobby at a third step of Level 1 in accordance with an illustrative embodiment;

[0055] Figure 16A-J each depicts an example of a screenshot of a prompt for a user to perform an activity associated with an endpoint to pursue a hobby at a fourth step of Level 1 in accordance with an illustrative embodiment;

[0056] Figure 17A -G each depicts an example of a screenshot of a prompt for a user to indicate a user emotion for selecting a next endpoint in accordance with an illustrative embodiment;

[0057] Figure 18A -C each depicts an example of a screenshot of a prompt for a user to subsequently enter personal values according to an illustrative embodiment;

[0058] Figure 19A -D each depicts an example of a screenshot for presenting prompts for pre-activity and post-activity assessments in accordance with an illustrative embodiment;

[0059] Figure 20 Depicting a method of improving experiential negative symptoms of schizophrenia in a user in need thereof according to an illustrative embodiment;

[0060] Figure 21 Depicting the timing of the study protocol for a multicenter, exploratory, single-arm study to evaluate the feasibility and acceptability of a simplified version of a digital therapeutic application for the treatment of adults diagnosed with schizophrenia;

[0061] Figure 22 A timeline diagram depicting the study protocol for combined treatment of subjects with experiential negative symptoms of schizophrenia with digital therapeutics and antipsychotic medication;

[0062] Figure 23A and 23B A timeline depicting the activities and assessments of the participants; and

[0063] Figure 24 is a block diagram of a server system and a client computer system in accordance with an illustrative embodiment. DETAILED DESCRIPTION

[0064] In order to read the description of each embodiment below, the following list of sections of the specification and their corresponding contents may be helpful:

[0065] Section A describes embodiments of systems and methods for selecting a profile for an application;

[0066] Section B describes embodiments of methods for ameliorating experiential negative symptoms of schizophrenia in a subject in need thereof; and

[0067] Section C describes a network and computing environment that can be used to practice the embodiments described herein.

[0068] A. Systems and methods for selecting a configuration file for an application

[0069] Now refer to Figure 1 , depicts a block diagram of a system 100 for selecting a configuration file for an application. Generally speaking, system 100 may include at least one application configuration service 105, one or more user devices 110A-N (collectively, user devices 110), and at least one database 115, communicatively coupled to each other via at least one network 120. Application configuration service 105 may include, among other things, at least one file indexer 125, at least one configuration 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. Database 115 may store, maintain, or otherwise include, among other things, a set of configuration files 155A-N (collectively, configuration files 155) and a set of user profiles 160A-N (collectively, user profiles 160). At least one of user devices 110 may include at least one application 165. Application 165 may include, among other things, at least one configuration file creator 170, at least one behavior manager 175, at least one layout processor 180, and at least one event bus 185. The application 165 may also provide at least one user interface 190 that includes one or more user interface elements 195A-N (hereinafter collectively referred to as UI elements 195 ).

[0070] Each component in system 100 (e.g., application configuration service 105 and its components, and each user device 110 and its components) can be executed, processed, or implemented using hardware or a combination of hardware, such as system 1700 described in detail in Section B herein. In some embodiments, application configuration service 105 can be part of user device 110 (e.g., as part of application 165). In some embodiments, at least a portion of the functionality of application configuration service 105 (including file indexer 125, configuration profile evaluator 130, configuration selector 135, configuration packager 140, content manager 145, and progress tracker 150) can be executed on user device 110. For example, the operations of configuration profile evaluator 130, configuration selector 135, and configuration packager 140 can be performed on user device 110.

[0071] More specifically, application configuration service 105 (sometimes collectively referred to herein as a service) can be any computing device comprising one or more processors coupled with memory and software and capable of performing the various processes and tasks described herein. Application configuration service 105 can communicate with one or more user devices 110 and database 115 via network 120. Application configuration service 105 can be located, positioned, or otherwise associated with at least one server group. A server group can correspond to a data center, branch office, or site where one or more servers corresponding to application configuration service 105 are located.

[0072] In the application configuration service 105, the file indexer 125 can generate and store configuration files 155 to be provided to the application 165. The profile evaluator 130 can create a user profile 160 for the user of the application 165 on the user device 110. The endpoint selector 135 can determine the user's endpoint and select the configuration file 155 to provide. The configuration packager 140 can provide the configuration file 155 to be loaded onto the application 165. The content manager 145 can identify and provide content for presentation via UI elements 195 of the user interface 190 for the application 165. The progress tracker 150 can update the user profile 160 and manage the redetermination of the endpoint for the user. The functions of the various components of the application configuration service 105 can be performed by the application 165 on the user device 110.

[0073] User device 110 (sometimes referred to herein as a client, client device, or end-user computing device) can be any computing device that includes one or more processors coupled with memory and software and capable of performing the various processes and tasks described herein. User device 110 can communicate with application configuration service 105 and database 115 via network 120. User device 110 can be a smartphone, other mobile phone, tablet computer, wearable computing device (e.g., smartwatch, glasses), or laptop computer. User device 110 can be used to access application 165. In some embodiments, application 165 can be downloaded and installed on user device 110 (e.g., via a digital distribution platform). In some embodiments, application 165 can be a web application with resources accessible via network 120.

[0074] The application 165 executing on the user device 110 may be a digital therapeutic application and may provide one or more sessions (sometimes referred to herein as therapy sessions) to address at least one condition of the user. The user's condition may include, for example, habits (e.g., smoking, dieting, or exercise), chronic pain (e.g., associated with or encompassing arthritis, migraines, fibromyalgia, back pain, Lyme disease, endometriosis, repetitive stress injury, irritable bowel syndrome, inflammatory bowel disease, and cancer pain), skin conditions (e.g., atopic dermatitis, psoriasis, pruritus, and eczema), affective disorders (e.g., depression, bipolar disorder, or dysthymic disorder), cognitive disorders (e.g., mild cognitive impairment (MCI), Alzheimer's disease, multiple sclerosis, and schizophrenia), and other conditions (e.g., narcolepsy and tumors). Affective and cognitive disorders may be psychological (or psychiatric) diseases or disorders.

[0075] The user may be taking medication, at least in part, to address the condition while the session is being provided via application 120. Application 120 can enhance the effectiveness of the medication the user is currently taking to address the condition. For example, if the medication is for pain relief, the user may be taking acetaminophen; a nonsteroidal anti-inflammatory drug; an antidepressant; an anticonvulsant; or other medications. For skin conditions, the user may be taking steroids, antihistamines, or topical antiseptics. For cognitive impairment, the user may be taking cholinesterase inhibitors such as memantine or iclepertin, or antipsychotics such as risperidone, quetiapine, olanzapine, ziprasidone, paliperidone, or aripiprazole. For neurological conditions, the user may be taking stimulants or antidepressants. For affective disorders, the user may be taking antidepressants or mood stabilizers. The user of application 120 may also be receiving other psychotherapy for these conditions.

[0076] The application 165 may include, present, or otherwise provide a user interface 190 including one or more UI elements 195 to a user of the user device 110 based on a configuration file 155 loaded onto the application 165. The UI elements 195 may correspond to visual components of the user interface 190, such as command buttons, text boxes, check boxes, radio buttons, menu items, sliders, etc. The application 165 may be a digital therapeutic application and may provide a session (sometimes referred to herein as a therapy session) via the user interface 190 intended to achieve one or more endpoints for a user (sometimes referred to herein as a patient, individual, or subject).

[0077] Now refer to Figure 2A, depicts a block diagram of a process 200 for maintaining configuration files in a system 100 for selecting configuration files. The process 200 may include or correspond to operations in the system 100 for storing and cataloging configuration files for applications. Under the process 200, a file indexer 125 executing on an application configuration service 105 may retrieve, identify, or otherwise receive at least one configuration file 155. The configuration file 155 may come from another source, such as a computing device of a developer who created the configuration file 155. For example, a clinician may write a script to generate a series of configuration files 155 for providing a digital therapeutic.

[0078] Configuration files 155 may include instructions for configuring, defining, or otherwise specifying various functionalities of a package (or plug-in) to provide to application 165. Configuration files 155 may include or correspond to one or more files that include instructions defining the layout (e.g., the presentation of UI elements 195 on user interface 190) and behavior (e.g., the functionality of UI elements 195) of application 165. The functionality specified by configuration files 155 may be separate from the built-in logic and functionality of application 165 (including the built-in logic and functionality of application configuration service 105).

[0079] In some embodiments, the instructions included in configuration file 155 may be in a human-readable format, such as Another Markup Language (YAML), Extensible Markup Language (XML), and JavaScript Object Notation (JSON). The instruction format of configuration file 155 may be a human-readable data serialization language that defines structured data. This format may differ from a binary format that can be read and executed by one or more processors running application 165. In this way, the developer's workload for writing human-readable instructions in configuration file 155 may be less than the workload for writing instructions in other formats (e.g., high-level programming languages such as Java, C++, or Python). Furthermore, because configuration file 155 specifies customized functionality for application 165 without modifying the underlying code of application 165, configuration files 155 can be loaded and exchanged between applications 165, thereby expanding the functionality of application 165.

[0080] 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 based on an automaton, such as a finite state machine (FSM), a decision tree, or a push-down automaton. The routine logic 202 may identify a group of content items that prompt the user to perform a group of activities aimed at achieving one or more endpoints (sometimes referred to herein as goals, objectives, or goals) to address a condition of the user of the application 165. For example, conditions may include smoking, obesity, psychological disorders (e.g., schizophrenia), cognitive impairment, and depression on the user's part. The activities defined by the routine logic 202 may include activities for treating or managing the condition. The group of activities may form steps along the user's progress toward reaching a target endpoint for a given condition, such as quitting smoking.

[0081] The routine logic 202 of the configuration file 155 may identify or include, among other things, groups of states 204A-N (hereinafter collectively referred to as states 204), groups of transitions 206A-N (hereinafter collectively referred to as transitions 206), and groups of levels 208A-N (hereinafter collectively referred to as levels 208). The groups of states 204, transitions 206, and levels 208 may be used in combination to define or specify a group of activities to be performed by a user of the application 165 to achieve one or more endpoints. Each state 204 may define, identify, or otherwise specify at least one output generated by the routine logic 202 when invoked. The output may be specific to a particular activity of the group of activities and may include one or more operations performed by the application 165. In some embodiments, the output may identify a user interface element 195 presented via the user interface 190 of the application 165. For example, the output may include or specify a set of content items to be generated or retrieved (e.g., using one or more identifiers (e.g., uniform resource locators (URLs)) for presentation as user interface elements 195 in the user interface 190. In some embodiments, the output may identify another configuration file 155 to be loaded. For example, the output may include an identifier (e.g., a file name) of the next configuration file 155 to be loaded onto the application 165. The at least one state 204 may correspond to the state 204 from which the routine logic 202 is to begin (e.g., state 204A as shown).

[0082] Between a pair of states 204, routine logic 202 may define, specify, or otherwise include at least one transition 206. Each transition 206 may define, identify, or otherwise specify an event to be detected via application 165 to transition or update routine logic 202 from one state 204 to another state 204. The event may correspond to a user interaction received via application 165 on user device 110. For example, transition 206 may specify that to move from one state 204A to the next state 204B, the user is to record the completion of Exercise A via the user interface 1685 of application 165. In some embodiments, routine logic 202 may include at least two transitions 206 from a given state 204. For example, routine logic 202 may identify one transition 206 for a successful completion of an activity associated with state 204. Conversely, routine logic 202 may identify another transition 206 for an unsuccessful completion of the activity associated with state 204.

[0083] Within the groups of states 204 and transitions 206, the routine logic 202 may define, identify, or otherwise include levels 208. In some embodiments, the levels 208 may be defined, identified, or otherwise specified within the states 204 or transitions 206 themselves. Depending on the levels 208, the group of activities specified by the corresponding group of states 204 may be defined based on the duration, frequency, and time of day of the activities. In some embodiments, the levels 208 may correspond to the same endpoint or different endpoints. For example, a first level 208A may correspond to a lower level for a given endpoint, while a second level 208B may correspond to a higher level for the same endpoint or another endpoint. Generally, as the activity is successfully completed from the previous state 204, the higher the level 208, the more difficult or intense the activity associated with the given state 204 may be in terms of duration, frequency, or both. For example, for a group of activities, one state 204A at a lower level 208A may specify that activity B is to be performed once a week in the evening, while another state 204B-1 at a higher level 208B may specify that activity C is to be performed five times a week in the morning. Conversely, the higher the level 208, the less difficult or intense the activities associated with a given state 204 may be, in terms of duration or frequency, or both, as the activity failed to complete from the previous state 204. The activities associated with a given state 204 may be different from the activities associated with the previous state 204. For example, the activities may be part of a mitigation measure for the unsuccessful completion of a previous activity.

[0084] By defining states 204, transitions 206, and levels 208, the routine logic 202 of the profile 155 can titrate the type, frequency, and duration of activities that a specified user performs via the application 165. In addition, the routine logic 202 can adaptively generate output in response to the actions of the user of the application 165. Examples of routine logic 202 (including states 204, transitions 206, and levels 208) are described herein in conjunction with Figure 6A -C details. Routine logic 202 across the group of profiles 155 can be used to implement an adaptive goal setting (AGS) framework. For example, Figure 6A As shown in Figure 2-C, the user can take a baseline assessment and select a desired outcome. A one-day (1D) goal (or endpoint) can then be presented to the user to guide them. The user can then proceed to complete a three-day (3D) goal at the same or lower level, depending on their success. The user can gradually complete the 3D goal until reaching the lowest or highest level. The user can then select a new goal to work towards (if sufficient time remains).

[0085] Each profile 155 can correspond to one or more goals, selected based on a baseline assessment of the user's functional abilities to inform the goals and associated activity levels. An assessment of the user's interests or aspirations in a domain can be used to identify the content of the goals and associated activities for the goals and profile 155. After introduction, the user can receive up to any number of goals as part of their profile 155. For example, if there are four phases, the user might receive four goals, then four more, then four more, and then eight more. Each goal can be associated with, for example, four daily activities. Users who complete all activities associated with a goal can move to the next level of goals within the same interest or aspiration. When a user completes a goal, the application 165 can provide positive affirmation and offer the opportunity to enjoy the positive emotions associated with completing their goal. If the minimum or maximum goal level for an interest or aspiration has been reached, a new goal can be presented by the application 165 at a level determined from the initial baseline assessment. If a certain number of goals within the same interest or aspiration have been reached, the user can be asked to reselect the interest or aspiration.

[0086] When a profile 155 is provided to an application 165, it can provide one or more activities related to the endpoint or goal for the user of the application 165 to perform and complete. The profile 155 can provide the user with the option (e.g., via a user interface element 195 on the user interface 190 of the application 165) to perform the specified activities when prompted or at a later time. Activities can be configured to increase the likelihood of reflecting on and practicing the therapeutic skills learned through the activities. Profiles 155 and activities can be selected based on personal values associated with the endpoint classification.

[0087] By optionally providing profiles 155, application 165 can provide a continuous daily activity within the endpoint. When a user completes an activity, application 165 can provide positive affirmation. If the user indicates during their daily check-in that they did not complete an activity from the previous day, application 165 can prompt the user to perform the same daily activity to continue achieving their endpoint or goal. If the user has been inactive or has not completed the previous activity, the new activity can be locked. For example, after a certain period of inactivity or incomplete activities (e.g., 1-2 days), application 165 can prompt the user to continue performing the same daily activity. After another period of inactivity or incomplete activities (e.g., 3 or more days), application 165 can provide the user with the option to repeat the previously presented activity or obtain the activity from a new endpoint.

[0088] Continuing, at some stage, if the user selects to obtain activities from a new endpoint, activities may be presented via application 165 in descending order within the same expectation. If the user has already reached the minimum level for an endpoint, the user may be presented with a repeat of the previously presented activities. For other profiles 155, if the user selects activities from a new endpoint, the user may be presented with the option to reselect the expectation or area. After presenting the user with daily activities, application 165 may prompt the user to start the activity immediately or set a time later that day to complete the activity. If the user chooses to set a reminder, application 165 may send a notification at the selected time. The user may have the option to start a scheduled activity before or after the scheduled time via the main screen of application 165.

[0089] Furthermore, the profile 155 may identify, define, or specify at least one selection criterion 210 for selecting the corresponding profile 155. In some embodiments, the selection criterion 210 may be separate from and associated with the profile 155. For example, the association between the selection criterion 210 and the profile 155 may be stored in the database 115 using one or more data structures (such as a linked list, tree, table, matrix, map, heap, or hash table). The selection criterion 210 may specify one or more parameters identified by the user profile 160 for which the selection profile 155 is provided to the application 165 on the user device 110. When the parameters specified in the selection criterion 210 match the parameters identified in the user profile 160, the profile 155 may be selected for provision to the application 165. In some embodiments, the selection criterion 210 may define one or more baseline metrics derived from the user profile 160 for which the profile 155 is selected. When the metrics derived from the parameters of the user profile 160 meet (e.g., are greater than or equal to) the baseline metrics specified by the selection criterion 210, the profile 155 may be selected for provision to the application 165. Additional details regarding the parameters or baseline metrics for selection criteria 210 are described below.

[0090] After receiving configuration file 155, file indexer 125 may store and maintain configuration file 155 on database 115. Database 115 may be part of application configuration service 105 or otherwise accessible to application configuration service 105. In some embodiments, file indexer 125 may store an association between configuration file 155 and the source from which configuration file 155 was received. In some embodiments, file indexer 125 may store an association between configuration file 155 and a version identifier of configuration file 155. Configuration file 155 may be maintained on database 115 for provisioning to instances of application 165 across various clients 110. Because configuration file 155 is separate from application 165, configuration file 155 may be easily updated and interchangeable.

[0091] Now refer to Figure 2B, depicts a block diagram of a process 220 for evaluating a user profile to provide a profile in a system 100 for selecting a profile. The process 220 may include or correspond to operations in the system 100 for evaluating parameters in a user profile to select a profile 155 to generate a package for providing to an application 165. Under the process 220, a profile creator 170 of an application 165 executing on a user device 110 may generate, output, or otherwise create a user profile 160 for a user 222 of the application 165. The user profile 160 may be generated based on one or more responses from the user 222. The responses may identify, define, or otherwise be associated with an endpoint and a condition, as indicated by the user 222.

[0092] In generating a user profile 160 , profile creator 170 may display, render, or otherwise present at least one prompt 224 for receiving a response from user 222 . Prompt 224 may be presented to user 222 using one or more user interface elements 195 on user interface 190 . In some embodiments, prompt 224 may be presented when application 165 is installed on user device 110 . In some embodiments, prompt 224 may be presented in response to an interaction on user interface 190 with application 165 . The interaction may correspond to a request by user 222 to another endpoint or a new group of activities performed via application 165 . In some embodiments, prompt 224 may be presented based on a defined time period, such as once every 2-6 hours, once every morning, or once a week. Prompt 224 may identify or include a group of questions for user 222 . The questions themselves may correspond to text, audio, or visual content on user interface elements 195 of user interface 190 . User 222 may type or enter responses to the questions via other user interface elements 195 , such as radio buttons, command buttons, text boxes, sliders, or checkboxes.

[0093] Profile creator 170 may include one or more event listeners to detect, retrieve, or otherwise receive responses to prompts 224 presented via user interface 190. The set of questions presented in prompt 224 may be written, set, or otherwise configured by application configuration service 105 or an administrator of application 165. The questions may ask user 222 to indicate, among other things, at least one desired endpoint (sometimes referred to herein as a goal, objective, or purpose) to be achieved and at least one condition (e.g., behavioral, psychological, or physiological) to be addressed. The questions may also ask user 222 to identify at least one state of user 222 (e.g., an emotional, affective, behavioral, or physiological state). The questions may also inquire about user 222's preferences for performing activities or routines, such as routine type, frequency, duration, day of the week, time of day, and so on. Responses to the questions may be recorded, entered, or typed via user interface element 195 on user interface 190.

[0094] In some embodiments, the group of questions presented by the questionnaire prompt 224 may be based on a questionnaire strategy. The questionnaire strategy may identify rules for selecting questions based on responses to previous questions in the group. The responses to the questions may be used to generate a user profile 160 for the user 222. For example, when the user 222 has responded to a preference for hobby A (Hobby A) as the type of activity to be performed and recorded via the application 165, the questionnaire may specify the presentation of a subset of questions for the frequency, duration, and level of Hobby A's hobby. In some embodiments, the questionnaire may be a validated clinical assessment related to a physical or psychological (or mental) condition, such as the Structured Clinical Interview (SCID) for the Diagnostic and Statistical Manual of Mental Disorders (DSM) for assessing diagnoses from the DSM; the Mini-International Neuropsychiatric Interview (MINI); the Clinical Assessment Interview for Negative Symptoms (CAINS) scale, which assesses the motivational and hedonic domains of negative symptoms; and the Hamilton Depression Rating Scale (HDRS), among others. Examples of prompts 224 and the groups of questions they present are Figure 7A -F (related to the user's physical activity). In the example shown, the questionnaire strategy can be used when the answer to the previous question about whether physical activity is performed is yes ( Figure 7A ), specifying prompt 224 to present a question about how much time the user has spent performing physical activity ( Figure 7B ).

[0095] Using the responses to the questions presented via prompt 224, profile creator 170 may determine or generate one or more parameters 226A-N (hereinafter collectively referred to as parameters 226). Parameters 226 may identify or include any number of factors used to select one or more profiles 155 to be provided to application 165. Parameters 226 may identify the endpoint to be implemented and the condition of user 222 to be addressed. Parameters 226 may identify the state of user 222 (e.g., mood, emotion, behavior, physiological state). Parameters 226 may identify, among other things, the activity type, frequency, duration, and time (e.g., day of the week or time of day) of the routine, as indicated by user 222 via prompt 224. In some embodiments, parameters 226 may identify, among other things, an identifier for user device 110, a device type for user device 110, an identifier for application 165, and the location of user 222. Upon generation, profile creator 170 may include one or more parameters 226 in user profile 160. User profile 160 may be introduced based on a template. The template may include fields for containing or inserting values for parameters 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 to the application configuration service 105 as part of a message.

[0096] The profile evaluator 130 executing on the application configuration service 105 can retrieve, receive, or otherwise identify a user profile 160 from the user device 110. In some embodiments, the profile evaluator 130 can execute on the user device 110 as part of the application 165, for example, to evaluate the user profile 160 generated by the profile creator 170 on the user device 110. After receiving the user profile 160 from the user device 110, the profile evaluator 130 can store and maintain the user profile 160 on the database 115. In some embodiments, the profile evaluator 130 can obtain, retrieve, or otherwise identify the user profile 160 from the database 115. The user profile 160 associated with the user 222 of the user device 110 can be stored and maintained on the database 115. The user profile 160 maintained on the database 115 can be updated using the instance of the user profile 160 received from the user device 110. Upon identification, the profile evaluator 130 can parse the user profile 160 to extract or identify one or more parameters 226 in the user profile 160.

[0097] Using the parameters 226, the profile evaluator 130 may calculate, determine, or generate one or more metrics for the user 222 associated with the user profile 160. The metrics derived from the parameters 226 of the user profile 160 may be used to select the profile 155 to be provided to the application 165. The generation of the metrics may be based on a function of the parameters 226. For example, the parameters 226 may include the user's Figure 7A -D, and may be related to physical activity, such as whether the user 222 has performed physical activity and how much time the user 222 has spent performing physical activity. In this example, the profile evaluator 130 may generate a metric related to the user 222's physical activity, such as a physical activity level.

[0098] In some embodiments, profile evaluator 130 may calculate, determine, or otherwise generate at least one metric for user profile 160, which may be based on the user's responses to a clinical assessment questionnaire, such as the SCID, MINI, CAINS, and HDRS. The metric may be generated as a function of the clinical assessment. The metric may identify characteristics of a condition, such as the severity of the condition. When the metric is determined before performing any activity, it may be determined as a baseline assessment metric for user 222. When the metric is determined after one or more activities, it may be determined as an assessment metric for user 222.

[0099] In some embodiments, profile evaluator 130 may generate a metric to identify the extent of a characteristic of user 222's condition. The characteristic of user 222 may include, for example, the severity of a behavioral condition, a physical condition, or a psychological (or mental) condition. In some embodiments, the metric may identify the likelihood that user 222 will perform a certain type of activity when prompted via application 165. For example, a metric may indicate the likelihood that user 222 will perform a hobby when prompted via application 165's user interface 190, while another metric may indicate the likelihood that user 222 will exercise when prompted via application 165's user interface 190. In some embodiments, the metric may identify the predicted effectiveness of the activity type in achieving one or more endpoints. For example, a metric may identify how effective running would be in addressing a user's condition related to improving diet, which the user has identified as a desired endpoint.

[0100] Additionally, the profile evaluator 130 may store and maintain the user profile 160, including the parameters 226, in the database 115. In some embodiments, the profile evaluator 130 may store and maintain metrics derived from the parameters 226 in the database 115. In some embodiments, the profile evaluator 130 may include these metrics in the user profile 160. In some embodiments, the profile evaluator 130 may generate associations between the metrics and the user profile 160 using one or more data structures. Upon generating, the profile evaluator 130 may store and maintain the associations on the database 115. In some embodiments, the profile evaluator 130 may perform the functions described herein in conjunction with the profile creator 170, or vice versa.

[0101] Configuration selector 135 executing on application configuration service 105 may select, identify, or otherwise determine one or more endpoints to address user 222's condition. Generally, an endpoint may correspond to an outcome or metric associated with addressing user 222's condition. An endpoint may correspond to a completion mark for a defined set of activities intended to address user 222's condition. For example, an endpoint may be associated with a set number of activities within a defined time window (e.g., four activities per day), and user 222's completion of the activities may correspond to achievement of the endpoint. An endpoint may also correspond to a measured improvement in user 222's condition. For example, an endpoint may be associated with a perceived decrease in pain (or other indicator) associated with user 222's condition.

[0102] In some embodiments, endpoints can be divided into one or more categories (sometimes referred to herein as domains). For example, a group of endpoints can be associated with a first category, while another group of endpoints can be associated with a different second category. Each endpoint can be associated with a set number of activities to be completed, such as four activities. Each domain can be associated with a set number of endpoints, such as four endpoints. Each domain can be associated with a set number of endpoints, such as four endpoints. Each domain can be associated with a set number of parameters indicated by user 222, such as three expectations. Endpoints can include, for example, social domains, leisure domains, or productivity domains. These domains can be associated with solving specific situations. A subset of endpoints associated with the social domain can be used to improve the user's sociability. Another subset of endpoints associated with the leisure domain can identify activities for performing leisure exercises or tasks for the user. Another subset of endpoints associated with the productivity domain can include activities for improving the user's concentration or work habits.

[0103] Configuration selector 135 may determine endpoints for user 222 based on the condition that user 222 needs to address. For example, if the condition indicated by user 222 is dieting, configuration selector 135 may select one or more endpoints related to dieting, such as eating a snack, taking a walk, or jogging. In some embodiments, configuration selector 135 may determine endpoints based on at least one activity (or activity type) indicated by user 222 that is intended to achieve the endpoint. In some embodiments, configuration selector 135 may determine endpoints based on parameters 226 of user profile 160. For example, configuration selector 135 may determine endpoints using activity type, frequency, duration, and time, as identified by user 222. In some embodiments, configuration selector 135 may determine endpoints based on metrics from a clinical assessment (e.g., baseline assessment indicators from the clinical assessment, likelihood of the user performing the activity, or predicted efficacy).

[0104] In some embodiments, the configuration selector 135 can select or identify one or more activities associated with the endpoint to be performed by the user 222 via the application 165. The identification of the activity can be based on the parameters 226 as indicated by the user 222. For example, when the user 222 indicates the desired activity type, the configuration selector 135 can identify an activity, such as Activity D, Activity E, or Activity F. In some embodiments, the configuration selector 135 can be executed on the user device 110 as part of the application 165, for example, to identify the endpoint for the user 222 of the user device 110. The identification of the endpoint can be based on the parameters 226 of the user profile 160 and the selection criteria 210 for each endpoint. As discussed, the selection criteria 210 can specify the parameters 226 for which the profile 155 associated with the endpoint is selected.

[0105] The configuration selector 135 may identify or select one or more configuration files 155 from the entire set of configuration files 155 to provide to the application 165 on the user device 110. Based on the endpoint (or activity intended to achieve the endpoint), the configuration selector 135 may select the associated configuration files 155 to provide to the application 165. In some embodiments, the configuration selector 135 may select multiple configuration files 155 for at least one endpoint. For example, when the determined endpoint is for addressing smoking cessation, the configuration selector 135 may identify the set of configuration files 155 marked as addressing smoking cessation in the selection criteria 210. Conversely, for each routine identified as not included, the configuration selector 135 may refrain from selecting the associated configuration file 155 for presentation to the application 165.

[0106] To make a selection, configuration selector 135 may identify selection criteria 210 from database 115 for each configuration file 155. In some embodiments, configuration selector 135 may parse each configuration file 155 to extract or identify selection criteria 210 for the corresponding endpoint. In some embodiments, configuration selector 135 may identify selection criteria 210 for one or more specified activities. Upon identification, configuration selector 135 may compare parameters 226 with selection criteria 210 for the endpoint. When parameters 226 match the parameters specified by selection criteria 210, configuration selector 135 may select configuration file 155. Otherwise, when parameters 226 do not match the parameters specified by selection criteria 210, configuration selector 135 may refrain from selecting configuration file 155.

[0107] In some embodiments, the routine selector 315 can compare the metrics derived from the parameters 226 with the selection criteria 210 for the routine to determine whether to select a given profile 155. When the determined metrics meet (e.g., are greater than or equal to, or within) the metrics specified by the selection criteria 210, the routine selector 315 can select the profile 155 for provision. Otherwise, when the parameters 226 do not meet (e.g., are less than or exceed) the parameters specified by the selection criteria 210, the routine selector 315 can refrain from selecting the corresponding profile 155 for provision.

[0108] Upon selection, the configuration selector 135 may relay, communicate, or otherwise provide the identification of the selected profile 155 (or, by extension, routine) to the profile evaluator 130 for setting or updating the user profile 160. The user profile 160 is stored in the database 115 and can be used to track the routines and profiles 155 provided to the user 222, as well as the user's 222's progress within each routine, using the states 204 and levels 208 of the routine logic 202. When the user profile 160 is stored as part of the initial selection of the profile 155, the profile evaluator 130 may identify the state 204 and level 208, respectively, for each selected routine as the initial state (e.g., state 204A) and initial level (e.g., level 208A). The profile evaluator 130 may store and maintain the initial state 204 and initial level 208 on the database 115 for each selected profile 155 in the user profile 160.

[0109] The configuration packager 140 executing on the application configuration service 105 can generate, output, or otherwise create at least one package 228 using the selected configuration file 155. In some embodiments, the configuration packager 140 can be executed on the user device 110, for example, to generate the package 228 to be loaded by the application 165. The package 228 can include instructions for configuring, defining, or otherwise specifying various functions to be performed on the application 165. Generally, the instructions in the package 228 can be machine-readable code. The instructions of the package 228 can also define the routine logic 202, including the group of states, transitions 206, and levels 208. In addition, the instructions of the package 228 can also define the user interface elements 195 for the output of the state 204 in the routine logic 202. Because the package 228 is generated independently of the application 165, the package 228 to be included by the application 165 can be easily interchangeable based on the user situation to be addressed.

[0110] When generated, the configuration packager 140 can create a separate file in which the specifications for the package 228 are stored. The configuration packager 140 can parse one or more configuration files 155 to read or recognize the instructions in their original format. In some embodiments, when generating the package 228, the configuration packager 140 can insert, append, or otherwise include information from the user profile 160 into the package 228. For example, the configuration package 140 can populate the status 204, level 208, and user name 222 into fields of the package 228 when generated. When the package 228 is loaded onto the application 165, the included information can be presented via the user interface element 195.

[0111] After identifying the configuration file 155, the configuration packager 140 may generate or determine equivalent instructions in an executable format for inclusion into the application 165. In some embodiments, the configuration packager 140 may compile the configuration file 155 to generate instructions in a low-level language. When compiled, the instructions in the package 228 may be in a low-level language, such as binary, bytecode, assembly, object code, or machine code, to be read by the processor executing the application 165. The low-level language instructions in the package 228 may differ from the human-readable instructions in the configuration file 155 because the instructions 228 are readable and processable by the processor running the application 165. In some embodiments, the configuration packager 140 may translate the configuration file 155 to generate instructions in an intermediate format to be added to the application 165. For example, the instructions may be in a language of a similar level as the original version of the configuration file 155, such as JavaScript or TypeScript. Once generated, the configuration packager 140 may write the equivalent instructions into a file in the package 228 and repeat the execution until the configuration file 155 is complete.

[0112] Upon generation, configuration packager 140 may provide instructions for adding or including package 228 to application 165. In some embodiments, configuration packager 140 may insert or inject package 228 into application 165 before providing it to user device 110. For example, configuration packager 140 may inject package 228 into application 165 that already includes other components, such as behavior manager 175, layout processor 180, and event bus 185. Configuration packager 140 may provide application 165 containing injected package 228 to user device 110 via a digital distribution platform (e.g., an application marketplace or store). User device 110 may request to download or retrieve application 165 from application configuration service 105 (or the digital distribution platform) for installation. Upon receipt, user device 110 may decompress and install application 165 including package 228.

[0113] Configuration packager 140 may send, transmit, or otherwise provide instructions for adding or including package 228 to an application 165 installed on user device 110. For example, user device 110 may have previously installed application 165 received from application configuration service 105 (e.g., via a digital distribution platform). In some embodiments, user device 110 may subsequently request an update to the configuration of application 165. In some embodiments, configuration packager 140 may identify or determine the update to be provided to application 165 via configuration file 155. For example, a system administrator of application configuration service 1605 may instruct an instance of application 165 to be updated. Having identified the update, configuration packager 140 may then provide instructions for package 228 without requiring other components of application 165 to be provided. Upon receipt, user device 110 (or application 165 itself) may update the installed application 165 to include package 228. In some embodiments, the received instructions for package 228 may be in an intermediate format, and application 165 may further compile the instructions to generate a low-level format that runs on user device 110.

[0114] Now refer to Figure 2C, depicts a block diagram of a process 240 for processing a configuration package in the system 100 for selecting a configuration file. The process 240 may include or correspond to operations performed by the system 100 when loading and executing routine logic 202, as defined in the package 228. Under the process 240, the application 165 (or an application service for the application 165) may perform initialization operations, such as initiating execution of the behavior manager 175, the layout processor 180, the event bus 185, and the user interface 190. The application 165 may execute various logic and operations defined for the application 165 outside of the package 228 received from the application configuration service 105. The application 165 may retrieve, identify, or otherwise receive the package 228 from the application configuration service 105.

[0115] The activity manager 175 of the application 165 executing on the user device 110 can parse the package 228 to read, load, and run the routine logic 202. As described above, the routine logic 202 can correspond to a set of activities performed, recorded, and logged by the user 222 via the application 165 on the user device 110. In some embodiments, the activity manager 175 can parse multiple packages 228 to run multiple corresponding routine logics 202. For each routine logic 202, the activity manager 175 can track the current state 204 of the routine logic 202. In some embodiments, the activity manager 175 can use or maintain an identifier for the current state 204 of each routine logic 202 for tracking. In addition, the activity manager 175 can track the current level 208 of the user 222 as defined in the routine logic 202. In some embodiments, the activity manager 175 can identify the current level 208 based on the current state 204 of the routine logic 202. Upon initialization, the current state 204 of the routine logic 202 can correspond to the starting state 204 (e.g., state 204A in the illustrated example). The current level 208 may correspond to the first level 208 (eg, level 208A in the illustrated example).

[0116] At the same time, the behavior manager 175 can monitor or listen for at least one event 242 on one or more user interface elements 195 in the user interface 190 via the event bus 185. The event 242 can correspond to an activity performed by the user 222 of the application 165. For example, the user interface 190 can present a prompt to the user 222 to perform Exercise B, and the user 222 can indicate completion of the exercise by interacting with one of the user interface elements 195 on the user interface 190. Thus, the event 242 can correspond to the completion of the activity as specified by the state 204 in the routine logic 202.

[0117] In some embodiments, behavior manager 175 may monitor or listen for events 242 from application 165 or another process of user device 110. In this case, event 242 may correspond to the occurrence of an action performed by application 165 or a process of user device 110 that was not triggered by an interaction from user 222. For example, behavior manager 175 may receive, via a system timer on user device 110, the time that has elapsed since a prompt to perform an activity was presented. Behavior manager 175 may compare the elapsed time with the time span for completing the activity specified by state 204. Behavior manager 175 may identify the exceeding of the specified time as event 242.

[0118] In some embodiments, behavior manager 175 may prompt user 222 to select or identify whether to perform the activity presented in the prompt. For example, after receiving a prompt for their activity, user 222 may select "Perform Now" or "Perform Later" to increase the likelihood of completion through multiple avenues. The former option may allow user 222 to indicate that they have been activated by the activity. This is primarily due to the ability to capture the user when they are motivated, compared to a different, more fixed setup that only allows the latter option. The latter option may reinforce the behavior of planning an activity, selecting a time, receiving a reminder, and then performing the activity using application 165 at a specified later time. When it is determined that the activity is to be performed at a later time, behavior manager 175 may store the instruction and re-present the prompt for the activity at the specified time. Otherwise, when instructed to perform the activity, behavior manager 175 may continue further processing to load routine logic 202 of package 228.

[0119] Based on the detection of event 242, behavior manager 175 may determine or select at least one routine logic 202 in the corresponding package 228 to call. In some embodiments, behavior manager 175 may transmit or pass the detected event 242 to package 228 via event bus 185. Event bus 185 may correspond to an interface between package 228 and various components of application 165, such as behavior manager 175 and layout processor 180. Through the pass, behavior manager 175 may check the detected event 242 against the event specified by transition 206 against the current state 204. As discussed above, behavior manager 175 may track the current state 204 and current level 208 of each routine logic 202 in the corresponding package 228. In some embodiments, behavior manager 175 may detect or receive the results of the check of the detected event 242 via event bus 185.

[0120] The behavior manager 175 can identify a designated event from the routine logic 202 for each transition 206 associated with the current state 204 to check against the detected event 242. When the detected event 242 does not correspond to the specifications in any transition 206 of 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 can also avoid calling the routine logic 202. Maintaining the routine logic 202 in the current state 204 can correspond to the user 222 not having completed the routine activity specified for the routine logic 202 for any transition 206 associated with the current state 204. The behavior manager 175 can continue to check the detected event 242 against the specifications of the routine logic 202 in other packages 228.

[0121] Conversely, when the detected event 242 corresponds to a specification in one of the transitions 206 of the current state 204, the behavior manager 175 may select the routine logic 202 to invoke. Upon invocation, the behavior manager 175 may update the current state 204 and current level 208 of the routine logic 202 to the next state 204 according to the transition 206. The updating of the routine logic 202 from the current state 204 to the next state 204 may correspond to the user 222 completing (successfully or unsuccessfully) a routine activity specified by the routine logic 202, as identified by at least one of the transitions 206 associated with the current state 204. For example, upon successful completion of an activity specified by the corresponding transition 206, the current state 204 may be updated from the initial state 204A to the state 204B-1. Conversely, upon unsuccessful completion of an activity specified by the corresponding transition 206, the current state 204 may transition from the initial state 204A to the state 204B-2.

[0122] Furthermore, by invoking the routine logic 202 of the package 280, the behavior manager 175 can retrieve or identify the output 244 identified by the next state 204 of the routine logic 202. The output 244 can be produced or generated by the state 204 as specified in the routine logic 202 when called. As discussed above, the output 244 can identify a user interface element 195 to be presented via the user interface 190 of the application 165. In some embodiments, the output 244 can specify a modification to be applied to the user interface element 195 of the user interface 190. The behavior manager 175 can transmit or pass the output 244 to the layout processor 180 via the event bus 185.

[0123] Now refer to Figure 2D, depicts a block diagram of a process 260 for modifying a user interface in a system 100 for selecting a configuration file. Process 260 may include or correspond to the operations of the application configuration service 105 and the application 165 when one of the routine logic 202 defined in the package 228 is invoked. Under process 260, the layout processor 180 of the application 165 executing on the user device 110 may update, change, or otherwise modify the user interface elements 195 of the user interface 190 based on the output 244. By configuring the user interface 190, the layout processor 180 may associate or bind the state 204 (and level 208) in the routine logic 202 of the package 228 to the user interface elements 195 of the user interface 190. In some embodiments, the layout processor 180, in conjunction with the behavior manager 175, may maintain the association or binding of the state 204 (and level 208) of the routine logic 202 with the user interface elements 195 of the user interface 190. For example, the layout processor 180 may track the relationship between the state 204 (and level 208 ) of the most recently invoked routine logic 202 and the user interface element 195 rendered or presented via the user interface 190 .

[0124] During modification, layout processor 180 may determine whether to send content request 262 to application configuration service 105 or another remote service (e.g., a service associated with the developer of configuration file 155). In some embodiments, layout processor 180 may execute on user device 110, for example, to identify content to be presented via user interface 190. Output 244 may rely on at least one content item 264A-N (hereinafter collectively referred to as content item 264). Content item 264 may be stored and maintained on a database (e.g., database 115, as shown) and may include images, videos, and other objects provided during the execution of application 165. If output 244 does not specify the retrieval of content item 264, layout processor 180 may refrain from transmitting content request 262 to application configuration service 105. Layout processor 180 may also proceed to modify user interface element 195 of user interface 190 based on output 244. On the other hand, if output 244 specifies the retrieval of content, layout processor 180 may determine to send content request 262 to application configuration service 105. The layout processor 180 can generate a content request 262 to include at least one identifier referencing a content item 264 to be retrieved from the application configuration service 105. The identifier can be specified by the output 244 from the present state 204 of the routine logic 202.

[0125] The content manager 145 executing on the application configuration service 105 may 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 may parse the request 262 to identify a content item 264 to be provided to the user device 110 for presentation on the user interface 190. In some embodiments, the content manager 145 may use an identifier in the request 262 to access the database 115 to retrieve, obtain, or identify the content item 264 referenced by the identifier. The content item 264 may be information in visual or audio media and may include images, video, audio, or any other object to be presented on the user interface 190. For example, for the endpoint associated with the invoked routine logic 202, the content item 264 may include audio to play along with Exercise C. Upon identification, the content manager 145 may send, return, or otherwise provide the content item 264 to the user device 110.

[0126] The layout processor 180 may then retrieve, identify, or receive content items 264 from the application configuration service 105 (or a remote service). Upon receiving the content items 264, the layout processor 180 may insert, append, or otherwise include the content items 264 in the user interface 190. The layout processor 180 may include the content items 264 in one or more user interface elements 195 for presentation as specified in the output 244 from the routine logic 202. Furthermore, the layout processor 180 may modify the user interface elements 195 of the user interface 190 based on the output 244. For example, the layout processor 180 may instantiate the user interface elements 195, set the color and other visual characteristics of each user interface element 195 itself, set the font and size of the text in each user interface element 195, and specify the placement of the user interface elements 195 on the display of the user device 110.

[0127] In some embodiments, layout processor 180 may generate or determine rendering instructions using output 244 specified by routine logic 202. Output 244 may identify a group of instructions (e.g., in raw or low-level format) corresponding to respective user interface elements 195 to be included in user interface 190. Rendering instructions may take the form of a display list or a render tree. Upon identification, layout processor 180 may parse output 244 for instructions corresponding to the group of user interface elements 195. For each instruction, layout processor 180 may generate an equivalent entry (e.g., a render tree node) to include in the rendering instructions. By generating, layout processor 180 may render user interface element 195 for user interface 190 according to the rendering instructions.

[0128] exist Figure 8A Examples of content items 264 to be presented as user interface elements 195 on user interface 190 are depicted in Figures 9A-L (Level 1 for trying a new hobby, choosing an old hobby), 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 forming a habit), 13A-K (Level 2 for forming a habit), 14A-J (Level 2 for forming a habit), 15A-J (Level 3 for trying a new habit), and 16A-J (Level 4 for forming a new habit). Similar to questionnaire prompts 224, content items 264 may also include prompts for questions for user 222 to answer. Each question depicted in the examples may correspond to at least one content item 264 and may be associated with a transition 206 from one state 204 to another state 204. Upon interacting with a button or triggering a transition 206 from one state 204 to another state 204, the depicted prompts may be presented to user 222.

[0129] Now refer to Figure 2E , depicts a block diagram of a process for evaluating response data to provide a profile and selecting a profile in the system 100 for selecting a profile. Process 280 may include or correspond to operations in the system 100 for evaluating responses and providing a new package. Under process 280, the behavior manager 175 may send, transmit, or provide at least one response data 282 (sometimes referred to as a log entry) to the application configuration service 105. Upon detecting one or more events 242, the behavior manager 175 may write or generate response data 282. Response data 282 may identify or include various events 242 based on the group of activities defined in the package 228 provided to the user device 110. For example, information included in response data 282 may include or identify the current state 204 and current level 208 of each routine logic 202; updates to the state 204 or level 208 in the routine logic 202; indications of the completion or failure of routines associated with the routine logic 202; detected events 242, timestamps for the detection of each event 242, an identifier for the user 222, and an identifier for the user device 110, among other things. By generating, the behavior manager 175 can send the response data 282 to the application configuration service 105.

[0130] In some embodiments, behavior manager 175 may call or invoke profile creator 170 to aggregate, collect, or otherwise receive additional responses from user 222 via questionnaire prompt 224. Prompt 224 may be presented at defined times, such as at the beginning of the day, at the end of the day, every 4-6 hours, weekly, or monthly. For example, user 222 may interact with user interface element 195 to present prompt 224, thereby presenting a set of questions. As previously described, the questions may require user 222 to indicate a desired endpoint to be achieved, a condition to be addressed, or preferences for performing an activity or routine (such as routine type, frequency, duration, day of the week, and time of day). The questions may also be part of a clinical assessment interview as described above. Behavior manager 175 may receive user 222's responses to the set of questions via prompt 224 in a manner similar to that discussed above. Upon receipt, behavior manager 175 may include the responses from user 222 in response data 282.

[0131] In some embodiments, the behavior manager 175 may determine or identify at least one state (eg, mood, emotion, behavior, or physiological state) of the user 222 based on responses made by the user 222 via the questionnaire prompts 224 . Figure 17A An example of an emotion-specific questionnaire prompt 224 is depicted in Figure 1-G. Emotional check-ins, as shown in the example, can help user 222 increase the likelihood of performing an activity (e.g., at the time of prompting or later). Check-ins can also help user 222 build confidence in performing activities through application 165, adapting to user 222's emotional state and striving to achieve an endpoint. Prompt 224 in the example shown can allow user 222 to indicate their emotion (sometimes referred to herein as an emotional state), such as happiness, sadness, anger, fear, disgust, surprise, or excitement. The response can be used to select a profile 155 to provide targeted mitigation measures. Other states can include behavioral states (e.g., resting, eating, working, studying, relaxing, interacting, playing, or exploring) or physiological states (e.g., resting, active, stressed, or tense). Behavior manager 175 can invoke profile creator 170 to present questionnaire prompt 224 via user interface element 195 of user interface 190, prompting user 222 to indicate their state. The questionnaire prompt 224 may be presented to the user 222 at a defined time (e.g., once every 4-6 hours, once every evening, or once a week). Through the questionnaire prompt 224, the behavior manager 175 may receive a response indicating the status of the user 222. Upon receiving the response, the behavior manager 175 may include the response from the user 222 in the response data 282.

[0132] In some embodiments, behavior manager 175 may determine or identify one or more personal values of user 222 from the response of user 222 to prompt 224 . Figure 18A An example of a questionnaire prompt 224 for personal values is depicted in -C. In the example shown, Figure 18A The interface in can provide the user 222 with possible scenarios to consider, Figure 18B The interface in may provide the user 222 with an opportunity to explore the value or deviation of a goal or endpoint in mind, and Figure 18C The prompt in the questionnaire 224 may allow user 222 to enter one or more personal values to be recurring throughout the session. Personal values may identify characteristics of an activity that user 222 desires to perform, or endpoints that user 222 identifies as goals when performing an activity. Questionnaire prompt 224 may be presented to user 222 at defined times (e.g., once every 4-6 hours, once every evening, or once a week). Behavior manager 175 may receive a response indicating one or more personal values via questionnaire prompt 224. Upon receipt, behavior manager 175 may include the response from user 222 in response data 282.

[0133] In some embodiments, when executing the routine logic 202 of the configuration file 155, the behavior manager 175 may determine, obtain, or otherwise identify a rating associated with the activity before the activity is performed. To do so, the behavior manager 175 may present a prompt 224 indicating the rating associated with the activity before the activity is performed. Furthermore, the behavior manager 175 may determine, obtain, or otherwise identify a rating associated with the activity after the activity is performed, via the application 165. To do so, the behavior manager 175 may present a prompt 224 indicating the rating associated with the activity after the activity is performed. The rating may be obtained from the user 222's response to one or more prompts 224 indicating the rating. The pre-performance rating may indicate the user's 222 self-assessed value of their expectation of resolving the situation or achieving the desired endpoint by performing the activity identified in the prompt 224. The post-performance rating may indicate the user's 222 self-assessed value of their experience of resolving the situation or achieving the desired endpoint after performing the activity identified in the prompt 224.

[0134] Regarding the scoring, the application 165, via the profile 155, can provide course content that explains the connection between thoughts, emotions, and behaviors to inform the user 222 of the premise of cognitive restructuring and how the activity addresses the situation. The application 165, via the profile 155, can provide one or more interactive activities to help the user 222 understand the thought patterns that lead to defeatist beliefs associated with negative symptoms.

[0135] For example, in the introductory phase, after the concept of pre- and post-activity surveys is introduced to user 222, behavior manager 175 administers the survey to the user a given number of times (e.g., 4 times). Before user 222 begins an activity, behavior manager 175 may prompt user 222 with anticipation questions about their activity to select on a scale of 1 to 10. After user 222 completes an activity, behavior manager 175 may prompt user 222 with reflection questions about their activity to select on a scale of 1 to 10. In the active phase, behavior manager 175 may prompt user 222 for a survey before and after each activity. Before user 222 begins an activity, behavior manager 175 may prompt user 222 with anticipation questions about their activity to select on a scale of 1 to 10. After user 222 completes an activity, behavior manager 175 may prompt user 222 with reflection questions about their activity to select on a scale of 1 to 10. Repeat

[0136] exist Figure 19A Figure 2-D depicts an example of a questionnaire prompt 224 for self-assessment ratings before and after an activity. In the example shown, the prompt may encourage the user to exercise focus on their expectations for each activity. The questions in prompt 224 may be pre- and post-activity questions, and the response data may be used to reflect the user's willingness to combat defeatist beliefs and demonstrate growth. These self-assessments by user 222 can be used to reflect perceived change. Demonstrating progress can be a powerful driver of trust, leading to a change in user perception that can motivate user 222 to more closely adhere to the digital therapeutic provided through profile 155. After performing an activity, user 222 may be prompted to reflect on the experience. User 222 may be shown pre- and post-activity responses to encourage adherence and continued performance of the activity presented via application 165. Through questionnaire prompt 224, behavior manager 175 may receive responses indicating ratings. Upon receiving the responses, behavior manager 175 may include responses from user 222 in response data 282.

[0137] The progress tracker 150 executing on the application configuration service 105 may use the response data 282 to change, modify, or otherwise update the user profile 160 maintained on the database 115. As previously discussed, the user profile 160 maintained on the database 115 can be used to track the progress of each routine provided by the user 222 via the package 228. The progress tracker 150 may retrieve, identify, or otherwise receive the response data 282 from the user device 110. Upon receiving the response data, the progress tracker 150 may parse the response data 282 to extract or identify the information included therein. In some embodiments, the progress tracker 150 may store and maintain the response data 282 (e.g., including an indication of the user 222's status, one or more personal values, or ratings) on the database 115. The progress tracker 150 may store the response data 282 on the database 115 using a log record associated with the user profile 160 or the user 222. The log record may be a data structure associated with the user profile 160.

[0138] Based on the information parsed from the response data 282, the progress tracker 150 can set, update, or otherwise modify the user profile 160. Using the identifier of the user 222 from the response data 282, the progress tracker 150 can identify the user profile 160 associated with the user 222. The progress tracker 150 can identify the current recorded level 208 of the user 222 from the user profile 160. The level 208 can correspond to a stage or progress toward achieving a given endpoint, group of activities, or resolution status, etc. For each profile 155 selected for the user 222, the user profile 160 can identify the current state 204 and the current level 208 in the routine logic 202.

[0139] Upon identification, the progress tracker 150 can determine whether a transition from the current level 208 to the next level 208 has occurred based on the response data 282. The progress tracker 150 can extract or identify the level 208 of the user 222 from the response data 282. In some embodiments, the progress tracker 150 can determine whether the level 208 has transitioned for the user 222 to achieve an endpoint. When the user 222 has completed the endpoint or activity, the level 208 indicated in the response data 282 can be higher than the level 208 currently identified in the user profile 160. When the user 222 has not yet completed the endpoint or activity, the level 208 indicated in the response data 282 can be the same as or lower than the level 208 currently identified in the user profile 160. In some embodiments, the progress tracker 150 can use one or more interactions identified in the response data 282 to determine the new state 204 and level 208 based on the routine logic 202 of the profile 155 selected for the user 222. The new level 208 may be closer to achieving a different endpoint than the previous level 208 indicated in the user profile 160 .

[0140] 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 a transition has occurred. If the levels are not different, the progress tracker 150 can determine that the user 222 has not transitioned from the current level 208. If the levels are different, the progress tracker 150 can determine that a transition has occurred between the current level 208 and 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, respectively, as identified in the response data 282. 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. When the current level 208 is lower than the level 208 identified from the response data 282, the progress tracker 150 can determine a transition to a higher level. Conversely, when the current level 208 is higher than the indicated level 208, the progress tracker 150 can determine a transition to a lower level.

[0141] Furthermore, the progress tracker 150 may use information from the response data 282 to set, update, or otherwise modify parameters 226 in the user profile 160. As previously discussed, the parameters 226 may identify the endpoint to be achieved, the condition of the user 222 to be addressed, the status of the user 222, the type of routine, the frequency, duration, and time (e.g., day of the week or time of day) of the routine, etc. In some embodiments, the progress tracker 150 may adjust, set, or change the frequency and duration based on an indication of success or failure of the routine selected for the user 222, or an update in the status 204 or level 208. For example, when the response data 282 indicates that the routine was successfully completed, the progress tracker 150 may increase the frequency or duration of the routine. Conversely, when the response data 282 indicates that the routine could not be completed, the progress tracker 150 may decrease the frequency or duration of the routine.

[0142] In some embodiments, progress tracker 150 may identify or determine at least one progress indicator regarding a personal value based on response data 282. Response data 282 may identify the performance of activities intended to achieve an endpoint, such as one or more interactions with content items presented via user interface 190 of application 165. The progress indicator may identify or correspond to a measure of improvement or degradation in satisfying the personal value by performing the activities specified in profile 155. For example, for the personal value of excitement, progress tracker 150 may determine a relatively high progress indicator when response data 282 indicates that the user is satisfied with the performed activities. Upon determining this, progress tracker 150 may transmit, send, or otherwise provide a link between the progress indicator and the personal value for presentation via user interface 190 of application 165. For example, the link may be presented as part of prompt 224 or on user interface 190 after prompt 224 is presented. In some embodiments, progress tracker 150 may provide the progress indicator as part of a subsequent package provided to application 165. The progress indicator may be presented to user 222 to encourage reflection and promote a sense of progress.

[0143] In some embodiments, progress tracker 150 may compare the scores obtained before and after the performance of an activity, as identified in response data 282. Based on the comparison, progress tracker 150 may calculate, generate, or otherwise determine an indicator of the difference identified between the two scores. Upon determination, progress tracker 150 may transmit, send, or otherwise provide the comparison between the scores (or the difference indicator, or both) for presentation via application 165. For example, the comparison may be presented as part of prompt 224 or on user interface 190 after prompt 224 is presented. Presenting the comparison can reinforce the concept of experimentation in performing an activity, regardless of the user's 222 expected outcome. Presenting pre- and post-activity evaluations can also challenge user 222's preconceived notions and defeatist perspectives with evidence of the user's own experience of progressing differently than expected. In some embodiments, progress tracker 150 may provide the comparison or difference indicator as part of a subsequent package provided to application 165.

[0144] As user profile 160 is updated, progress tracker 150 may modify, set, or change the endpoint to be achieved. In some embodiments, progress tracker 150 may modify, set, or change the status of user 222 based on information parsed from response data 282. This information may include responses received via prompts 224. Progress tracker 150 may replace, change, or otherwise set the endpoint or status of user 222 indicated in user profile 160 with the endpoint or status, respectively, as indicated in response data 282. Changes to endpoints and status may result in changes to parameters 226 in user profile 160. In some embodiments, progress tracker 150 may calculate, determine, or generate new metrics based on the updated parameters 226. Generating metrics using parameters 226 may be performed in a similar manner as discussed above. For example, progress tracker 150 may use functions to calculate characteristic values of user 222 and the likelihood that user 222 will perform a given routine, among other things. Once generated, progress tracker 150 may store and maintain the new metrics with user profile 160 in database 115.

[0145] Configuration selector 135 may select, identify, or otherwise determine one or more new endpoints for user 222. Identification of endpoints (and activities associated with them) may be similar to that discussed above and may be based on parameters 226 of user profile 160 and selection criteria 210. For example, a change to user profile 160 may include an update to status 204 or level 208, an indication of successful completion, or an increase in the duration or frequency of a routine. In this case, configuration selector 135 may select the next endpoint with both an increase in duration and frequency of the activity. In contrast, a change to user profile 160 may include an update to status 204 or level 208, an indication of incompleteness, or a decrease in the duration or frequency of a routine. In this scenario, configuration selector 135 may select a routine with a decreased duration and frequency. Continuing, changes to user profile 160 may include modifications to user 222's endpoint or status. Based on these changes, configuration selector 135 may select activities for the new endpoint or status.

[0146] In some embodiments, configuration selector 135 may use information obtained from response data 282 received via prompt 224 to select a new endpoint (or activity) for user 222. This information may include information generated by progress tracker 150 from response data 282. In some embodiments, configuration selector 135 may identify or select a new endpoint based on the state (e.g., mood, emotion, behavior, or physiological state) of user 222 as indicated in response data 282. For example, when user 222's state indicates a state of sadness, configuration selector 135 may select an endpoint designed to comfort user 222 while performing an activity to address user 222's condition. In some embodiments, configuration selector 135 may identify or select a new endpoint based on personal values identified by user 222. For example, configuration selector 135 may select an endpoint designed to provide activities related to adventure, self-care, or the arts, as indicated in response data 282.

[0147] In some embodiments, configuration selector 135 may identify or determine a new endpoint for user 222 to address a condition from at least one of the social domain, entertainment domain, or productivity domain. Based on information derived from response data 282, configuration selector 135 may modify, update, or otherwise change the domain to the new domain for user 222. For example, upon completion of the last level 208 in a given domain (e.g., the social domain), indicating progress along that domain, configuration selector 135 may select a different endpoint (e.g., the entertainment domain or the productivity domain). In this manner, configuration selector 135 may adaptively and dynamically select endpoints for user 222 across different domains to address the user's condition based on response data 282 from user 222. This may allow user 222 to perform activities specified by the endpoints and build skills along the identified domain.

[0148] By determining the new endpoint, configuration selector 135 can select one or more configuration files 155 from the entire set of configuration files 155 to provide to application 165. Using the newly selected endpoint or activity, configuration file 155 selection can proceed in a similar manner as previously discussed. In some embodiments, configuration selector 135 can select one or more configuration files 155 based on the transition at level 208. Using the selected configuration files 155, configuration packager 140 can generate at least one new package 228'. Package 228' can be generated in a similar manner as described above and can include instructions for configuring functionality to be executed on application 165 according to routine logic 202, as defined by configuration file 155. Once generated, configuration packager 140 can provide package 228' with the newly selected configuration files 155 to application 165. Application 165, in turn, can receive and load package 228'. Using package 228', application 165 can repeat the operations described above.

[0149] By selecting and providing a configuration file 155 in this manner, the application configuration service 105 can configure the functionality of the application 165 tailored to the responses indicated by the user 222. The configuration file 155 can provide the user 222 with a broader experience and a range of content, via content items 264 adapted to the user's 222 status and interactions, in accordance with the routine logic 202. Thus, the configuration file 155 can improve the quality of human-computer interaction (HCI) between the user 222 and the application 165. In the context of digital therapeutics, the configuration file 155 and the content items 264 identified therein can lead to higher user engagement with the application 165. The higher the likelihood of interaction, the more effective the digital therapeutic provided by the application 165 can be in resolving the condition and improving the user's 222 adherence to the digital therapeutic. The configuration file 155 can also reduce the consumption of computing resources (e.g., computing resources on both the user device 110 and the application configuration service 105) that would otherwise be used to provide and load irrelevant content on the application 165. Furthermore, the configuration file 155 can reduce the need to update the application 165 itself to provide additional functionality, further conserving computing resources. The configuration files 155 (and by extension the packages 228 ) may reduce network bandwidth consumption for round-trip communications associated with requesting and retrieving content.

[0150] Now refer to Figure 3 , depicts a flow chart of a method 300 for selecting a configuration file for an application. The functionality of the method 300 may be implemented by using or in conjunction with Figure 1-2E (such as application configuration service 105 and user device 110) or Figure 24Any of the components discussed above may be implemented by executing the commands (such as computing system 2400). In general, a server may identify a user profile (305). The server may determine an endpoint for the user (310). The server may identify a configuration file (315). The server may provide a package (320). The server may receive response data (325). The server may determine a metric (330). The server may determine whether to update the configuration (335). If the server determines to update, the server may select a new endpoint and repeat the functions in (310). Otherwise, if the server determines not to update, the server may wait for additional response data and repeat the functions from (325).

[0151] Now refer to Figure 4 , depicts an architectural block diagram of a system for adaptive targeting with profile selection. The architecture can be implemented using components of system 100 (such as application configuration service 105 and application 165 on user device 110). As shown, the architecture can divide adaptive targeting (AGS) into three parts. First, a discovery component can select skills (e.g., endpoints) to offer to a user. This selection can be based in part on the user's history. Second, skills can be organized into modules A, B, C, ..., N corresponding to the user (e.g., in the form of profile 155). All skill logic can be included in the modules, which can be independent so that each module's behavior is new and unique. Third, a notification component can be used to remind and encourage users to perform skills organized in modules. Subsequently, the discovery component can check with 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 the architecture's functionality can repeat.

[0152] Now refer to Figure 5A , depicts a flow chart of a method for performing adaptive target setting when selecting a profile. The method can be performed or implemented using components of system 100, such as application configuration service 105 and user device 110. For example, at least one profile 155 can be used to define and perform at least a portion of the illustrated method. As shown, the system can present a profile to the user. Based on the profile, the system can receive the user's selection of an activity. The system can set a level for the user's activity. The user can confirm the level and activity selection through interaction. The system can monitor user interaction to determine whether the user is idle. If idle, the system can check in with the user and prompt the user to perform an activity. Upon completion, the system can update the user's level, notify the user of the level change, and re-evaluate the user. The system can also present help tips to guide the user on how to perform the activity. The system can further re-evaluate based on the results. The system can also retrieve statistics about the user's activity.

[0153] Now refer to Figure 5B , depicts a flow chart of a method for executing an activity based on a configuration file. The method can be performed or implemented using components of system 100, such as application configuration service 105 and user device 110. For example, at least one configuration file 155 can be used to define and execute at least a portion of the illustrated method. As shown, the system can determine whether the user is performing an activity now (e.g., within a time window of the current time) or later (e.g., outside the time window of the current time). If the activity is to be performed now, the system can determine whether there is a problem with performing the activity now. If so, the system can identify the cause of the blockage or impediment and present tools (e.g., a user interface) to resolve the issue. On the other hand, if the activity is to be performed later, the system can determine whether to provide a reminder or change the activity. If a reminder is determined to be required, the system can present a reminder. If a change is determined to be required, the system can identify a new activity.

[0154] B. Methods for ameliorating experiential negative symptoms of schizophrenia in patients in need thereof

[0155] Individuals experiencing negative symptoms of schizophrenia may experience a decline or impairment in certain functions and abilities, which impacts their quality of life and daily activities. The severity or impact of negative symptoms of schizophrenia on an individual can be measured using various scales, such as the Clinical Assessment Interview-Motivation and Pleasure of Negative Symptoms (CAINS-MAP). The CAINS-MAP measures individuals in various areas related to experiencing negative symptoms of schizophrenia, such as recreational, social, or productive activities.

[0156] Such individuals can be provided with a digital therapeutic application configured with the Adaptive Goal Setting (AGS) framework to improve experiential negative symptoms. Under the AGS framework, the application can adaptively determine endpoints based on the user's responses and feedback. Each endpoint can define a group of one or more activities performed by the user through the application to address experiential negative symptoms of schizophrenia. Endpoints can be determined based on groups of domains related to components of a schizophrenia-specific measurement scale (e.g., social interaction, recreation, and productivity), and each of these domains can specify activities to be performed according to the domain.

[0157] Using the identified endpoints, the application can load a selected configuration file to present content items that prompt the user to perform a specified activity. For example, the application can identify endpoints for a specified activity in the social domain (such as a face-to-face conversation with a clinician) and then provide a corresponding configuration file. The configuration file can be converted from a human-readable instruction format to a format that the application can read and execute. Once loaded, the application can present content items identified by the configuration file through the user interface, prompting the user to perform the social interaction activity. The application can then monitor the user's interactions with user interface elements to indicate completion of the specified activity.

[0158] Using additional user response and feedback data, the application can update the user's endpoint and can provide the application with other profiles selected based on the new endpoint. Continuing with the previous example, upon determining that an endpoint in the social domain associated with a social interaction activity has been completed, the application can determine a new endpoint in the productivity domain that specifies that the user is to complete a physical activity. After loading the corresponding profile, the application can present the content items specified by the file, prompting the user to perform the activity. This can be repeated multiple times over a period of time to select and provide profiles with different activities for the user to perform. By using the response data to adaptively and dynamically determine the user's endpoints across different domains to address the experiential negative symptoms of schizophrenia, the application can provide a selected profile with content items designed to improve the user's activities along a specific domain. By repeatedly using the application over time, users of the application can experience improvements in the experiential negative symptoms of schizophrenia as measured using various scales as described herein.

[0159] Now refer to Figure 20, depicts a flow chart of a method 2000 for improving negative symptoms experienced by a user in need of schizophrenia. Method 2000 can be performed by any component or actor described herein, such as application configuration service 105, user device 110, or user 222. Method 2000 can be used in conjunction with any functionality or action described in Examples 1 and 2 in Sections A or B herein. In short, method 2000 can include obtaining baseline metrics (2005). Method 2000 can include determining an endpoint (2010). Method 2000 can include identifying a profile (2015). Method 2000 can include presenting a group of content items (2020). Method 2000 can include obtaining session metrics (2025). Method 2000 can include determining whether to continue (2030). Method 2000 can include identifying or determining whether the session metrics have improved compared to the baseline metrics (2035). In some embodiments (e.g., as shown), method 2000 can include determining that the user has shown improvement when the session metrics have improved compared to the baseline metrics (2040). Method 2000 may include, when the session metric has not improved over the baseline metric, determining that the user is not showing improvement ( 2045 ).

[0160] More specifically, method 2000 may include retrieving, identifying, or otherwise obtaining baseline metrics (2005). Prior to performing any activities via a digital therapeutic application (e.g., application 165 or a research application described herein), the baseline metrics may be associated with a user (e.g., user 222) diagnosed with schizophrenia with negative symptoms. The user's experienced negative symptoms of schizophrenia may include, for example, one or more of the following: blunted affect, aphasia (decreased speech), avolition (decreased goal-directed activity due to decreased motivation), ansociality, and anhedonia (decreased experience of pleasure).

[0161] Users can have any demographic or characteristic, such as age (e.g., adult (18+), older adolescent (18-24)) or gender (e.g., male, female, or non-binary). Users can also be on a stable dose of antipsychotic medication for at least a period of time (e.g., 12 weeks) prior to engaging in the first activity through the digital therapeutic application. Antipsychotic medications can include risperidone, quetiapine, olanzapine, ziprasidone, paliperidone, or aripiprazole, among others. Other medications, such as iclepertin GlyT1 inhibitors, may also be used.

[0162] Baseline metrics can be obtained (e.g., by a clinician or application 165) before a user performs an activity through a digital therapeutic application (e.g., application 165 or a research application described herein). In some embodiments, baseline metrics can be obtained from a source independent of the application. For example, a clinician examining the user can determine baseline metrics and provide them for storage. In some embodiments, a computing system (e.g., application 165 or application configuration service 105) can determine baseline metrics through user interaction with prompts (e.g., a metrics questionnaire) presented through a user interface. In some embodiments, baseline metrics can be obtained a period of time (e.g., 1-20 weeks) prior to the first activity. Baseline indicators may include, for example, scores on one or more of the following: the Motivation and Pleasure Scale of the Clinical Assessment Interview for Negative Symptoms (CAINS), the Performance Scale of the Clinical Assessment Interview for Negative Symptoms (CAINS-EXP), the Positive and Negative Syndrome Scale (PANSS), the Personal and Social Performance Scale (PSP), the Defeatism Beliefs Subscale of the Disability Attitudes Scale (DAS), the Patient Global Impression of Improvement Scale (PGI-I), the Patient Global Impression of Severity Scale (PGI-S), the Clinical Global Impression of Severity Scale (CGI-S), the WHO Disability Assessment Schedule 2.0 (WHODAS2.0), or the Schizophrenia Quality of Life Scale-Revised 4 (SQLS-R4), etc.

[0163] A user who is about to perform an activity through a digital therapeutic application may have a baseline metric below a certain threshold. The threshold may indicate that the user is experiencing or has moderate to severe symptoms associated with negative symptoms of schizophrenia. The user may have been experiencing at least moderate to severe severity of negative symptoms prior to the first activity (e.g., as measured by baseline metrics). For example, the user may have a score of ≤30 on the Motivation and Pleasure Scale (MAPS) prior to the first activity.

[0164] Method 2000 may include identifying or determining at least one endpoint for improving experienced negative symptoms (2010). A computer system (e.g., application configuration service 105 or user device 110) may identify or determine an endpoint from a group of endpoints to improve experienced negative symptoms. The endpoint may correspond to a completion mark for a defined group of activities intended to improve experienced negative symptoms. In some embodiments, the endpoint may be selected from the group of endpoints based on a baseline assessment of experienced negative symptoms of schizophrenia by the user (e.g., before the user performs any activities). In some embodiments, the endpoint may be selected from the group of endpoints based on response data identifying one or more interactions of the user with a group of content items presented via the application at a previous time instance (e.g., after the user performs at least one activity when prompted).

[0165] In some embodiments, groups of endpoints can be associated with one or more categories or domains, such as a social domain, an entertainment domain, or a productivity domain. The domains of the groups of endpoints can be associated with the types of indicators used to evaluate users. For example, the domains of social, entertainment, and productivity can be associated with the CAINS-MAP scale, which can be used to measure various aspects or domains of a user's experiential negative symptoms of schizophrenia. Endpoints associated with the social domain can identify activities for improving the user's sociability, endpoints associated with the entertainment domain can identify activities for performing leisure exercises or tasks for the user, and endpoints associated with the productivity domain can include activities for improving the user's attention or work habits. Each domain can be associated with multiple expectations. For example, an entertainment module can have expectations for hobbies, creativity, and exercise. A productivity module can have an example of washing dishes as an activity. Endpoints can also be associated with multiple levels.

[0166] The computing system may select an endpoint based on any number of factors (e.g., as described in Section A). In some embodiments, the selection of an endpoint may be based on a baseline assessment of the first activity. The selection may be based on an assessment of a subsequent activity obtained subsequently. In some embodiments, the selection of an endpoint for a subsequent activity may be based on response data identifying an interaction with a previously presented content item. In some embodiments, the selection of an endpoint (for either the first activity or a subsequent activity) may be based on one or more personal values indicated by the user. Personal values may identify characteristics of an activity that the user wishes to perform, or endpoints that the user identifies as objectives when performing an activity.

[0167] In some embodiments, the selection of an endpoint (for either the first activity or a subsequent activity) can be based on a state indicated by the user. The state can be associated with the user's emotional, behavioral, or physiological state. In some embodiments, the computing system can identify or select an endpoint based on a transition from one level to another, as determined using response data after performing at least one activity. In some embodiments, the computing system can determine an endpoint for a change from one domain to another. For example, when evaluating a user on the CAINS-MAP scale, the computing system can first identify an endpoint associated with the user's productivity domain. Using additional response data, the computing system can update the endpoint to an endpoint associated with the social or entertainment domain. For example, the computing system can change the user's endpoint when the response data indicates a statistically significant improvement in the productivity domain (e.g., as indicated by a metric) or when a determination is made to transition to another level.

[0168] Method 2000 may include identifying or selecting a profile from a plurality of profiles based on the endpoint (2015). Based on the endpoint, the computing system may identify or select a profile corresponding to the endpoint (e.g., profile 155). The profile may identify a group of content items prompting a user to perform one or more activities aimed at achieving a determined endpoint for improving experiential negative symptoms. In some embodiments, at least one of the profiles may identify criteria defining a metric for selecting another profile from the plurality of profiles. The metric may identify, for example, a likelihood that the user will perform the activity or a predicted efficacy of the user's activity aimed at achieving the endpoint. In some embodiments, the profile may be selected based on a change in the endpoint determined using response data from a previous time instance.

[0169] In some embodiments, the computing system can identify activities intended to achieve an endpoint based on a user profile (e.g., user profile 160) or other indicators (e.g., personal values, status, etc.). In some embodiments, the computing system can identify activities intended to achieve an endpoint based on response data after performing at least a first activity. Based on the identification of the activity, the computing system can select a profile with a group of content items to present.

[0170] Method 2000 may include displaying, rendering, or otherwise presenting a group of content items (2020). The computing system may identify one or more content items from a configuration file for prompting a user to perform an activity. The computing system may present the group of content items identified from the configuration file. The group of content items may identify a first activity (when presented to the user at a first time instance) or a corresponding second activity (when presented to the user at a time instance subsequent to the first time instance). The computing system may monitor one or more interactions of the user with the content items while performing the prompted activity. The presentation and functionality of the content items may be as described above in Section A.

[0171] In some embodiments, the computing system may prompt the user to provide an evaluation score in conjunction with the performance of an activity identified in a group of content items. The computing system may receive the score before the activity is performed and another score after the activity is performed. The computing system may determine a comparison between the scores before and after the activity is performed. Upon receiving the comparison, the computing system may present the comparison of the scores before and after the activity is performed to the user.

[0172] Method 2000 may include retrieving, identifying, or otherwise obtaining session metrics (2025). Session metrics may be obtained (e.g., by a clinician or application 165) after a user performs one or more activities through the digital therapeutic application. In some embodiments, session metrics may be obtained from a source independent of the application. For example, a clinician examining the user may determine session metrics and provide the session metrics for storage. In some embodiments, a computing system (e.g., application 165 or application configuration service 105) may determine session metrics via user interaction with prompts (e.g., a metrics questionnaire) presented through a user interface. Session indicators can include, for example, scores on one or more of the following: the Motivation and Pleasure Scale of the Clinical Assessment Interview for Negative Symptoms (CAINS), the Performance Scale of the Clinical Assessment Interview for Negative Symptoms (CAINS-EXP), the Positive and Negative Syndrome Scale (PANSS), the Personal and Social Performance Scale (PSP), the Defeatism Beliefs Subscale of the Disability Attitudes Scale (DAS), the Patient Global Impression of Improvement (PGI-I), the Patient Global Impression of Severity (PGI-S), the Clinical Global Impression of Severity (CGI-S), the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), or the Schizophrenia Quality of Life Scale-Revised 4 (SQLS-R4). Session indicators can be the same type of baseline indicators obtained before any activity is performed.

[0173] In some embodiments, the computing system may identify, obtain, or otherwise receive response data (e.g., response data 282). The response data may identify one or more interactions of the user with the group of content items. For example, at the end of the onboarding phase, the digital therapeutic application may present a questionnaire to the user. The questionnaire may ask the user several questions corresponding to different categories or domains of endpoints related to entertainment, social interaction, and productivity. The computing system may use the response data identifying the answers to the questionnaire to recommend upcoming domains. The application may offer the user the option to select a different domain independent of the application's recommendation. Using these inputs (the assessment and the user's selection of the domain of interest), the application may select an endpoint personalized for the user to begin their experience. Based on the success or failure of the user's goals, the application may continue to personalize the experience to adapt to the user at multiple points throughout their journey.

[0174] The method may include determining whether to continue (2030). This determination may be based on a set length of the trial (e.g., between 5 days and 25 weeks) or a set number of endpoints, activities, or sessions provided to the user (e.g., corresponding to respective time instances). A determination to continue and repeat from (2010) may be made when the amount of time since obtaining the baseline metric or the first activity has not exceeded the set length. In some embodiments, the computing system may determine to continue and repeat from (2010) when the number of endpoints, activities, or sessions has exceeded a set number. The computing system may repeat the determination of endpoints (2010), the identification of profiles (2015), and the presentation of the group of content items (2020), etc., within a group of time instances (sometimes referred to herein as sessions). The presentation of content items identifying the selected activity may be repeated until a determination to stop is made. Otherwise, the determination may stop when the amount of time since obtaining the baseline metric or the first activity has exceeded the set length. In some embodiments, the computing system may determine to stop when the number of endpoints, activities, or sessions has not exceeded a set number.

[0175] Method 2000 may include, upon determining to stop, determining whether the session metric is less than the baseline metric (2030). To determine this, the computing system may compare the baseline metric prior to the first activity with a session metric obtained after performing one or more subsequent activities (e.g., at or near the end of a set time period). In some embodiments, the compared session metric may be from the last time instance in which the content item was repeatedly presented (e.g., after the determination to stop). In some embodiments, the compared session metric may be from at least one time instance in which the content item was repeatedly presented (e.g., regardless of the determination to continue or stop).

[0176] Method 2000 may include identifying or determining whether a session metric has improved compared to a baseline metric (2035). The improvement may correspond to an improvement in the degree of negative symptoms experienced with schizophrenia. The improvement may correspond to when the session metric is statistically different from the baseline metric (e.g., by a statistically significant margin). An improvement may be indicated when the session metric has increased by a first predetermined margin compared to the baseline metric, or when the session metric has decreased by a second predetermined margin compared to the baseline metric. The margin may identify or define a difference between the baseline metric and the session metric at which it is determined that the user has shown an improvement in the degree of negative symptoms experienced with schizophrenia. Whether the improvement indicates an increase or a decrease may depend on the type of metric used to measure the user's degree of negative symptoms experienced with schizophrenia. The margin may also depend on the type of metric used and may generally correspond to a difference that indicates a clear difference in the degree of negative symptoms experienced with schizophrenia to the clinician or user, or a statistically significant result in the difference between the baseline and session metric.

[0177] Method 2000 may include determining that improvement is shown when the session metric is determined to be an improvement over the baseline metric (2040). In some embodiments, improvement may be determined to have occurred (e.g., by a computing 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, improvement may be determined to have occurred when the session CAINS-EXP metric decreases by a second predetermined margin from the baseline CAINS-EXP metric. In some embodiments, improvement may be determined to have occurred when the session PANSS metric decreases by a second predetermined margin from the baseline PANSS metric.

[0178] In some embodiments, an improvement may be determined to have occurred when the session PSP metric increases by a first predetermined margin from the baseline PSP metric. In some embodiments, an improvement may be determined to have occurred when the session DAS metric decreases by a second predetermined margin from the baseline DAS metric. In some embodiments, an improvement may be determined to have occurred when the session CGI-S metric decreases by a second predetermined margin from the baseline CGI-S metric. In some embodiments, an improvement may be determined to have occurred when the session PGI-I metric decreases by a second predetermined margin from the baseline PGI-I metric. In some embodiments, an improvement may be determined to have occurred when the session PGI-S metric decreases by a second predetermined margin from the baseline PGI-S metric.

[0179] In some embodiments, improvement can be determined when the session EQ-5D-5L index increases by a first predetermined margin from the baseline EQ-5D-5L index. In some embodiments, improvement can be determined when the session SDS index increases by a first predetermined margin from the baseline SDS index. In some embodiments, improvement can be determined when the session WHODAS index decreases by a second predetermined margin from the baseline WHODAS index. In some embodiments, improvement can be determined when the session SQLS-R4 index decreases by a second predetermined margin from the baseline SQLS-R4 index.

[0180] Method 2000 may include determining that no improvement is shown when the session metric is determined not to have improved over the baseline metric (2045). In some embodiments, no improvement may be determined (e.g., by a computing system or a clinician examining the user) when the session CAINS-MAP metric has not decreased by a second predetermined margin from the baseline CAINS-MAP metric. In some embodiments, no improvement may be determined when the session CAINS-EXP metric has not decreased by a second predetermined margin from the baseline CAINS-EXP metric. In some embodiments, no improvement may be determined when the session PANSS metric has not decreased by a second predetermined margin from the baseline PANSS metric.

[0181] In some embodiments, when the session PSP metric does not increase by a first predetermined margin from the baseline PSP metric, it may be determined that no improvement has occurred. In some embodiments, when the session DAS metric does not decrease by a second predetermined margin from the baseline DAS metric, it may be determined that no improvement has occurred. In some embodiments, when the session CGI-S metric does not decrease by a second predetermined margin from the baseline CGI-S metric, it may be determined that no improvement has occurred. In some embodiments, when the session PGI-I metric does not decrease by a second predetermined margin from the baseline PGI-I metric, it may be determined that no improvement has occurred. In some embodiments, when the session PGI-S metric does not decrease by a second predetermined margin from the baseline PGI-S metric, it may be determined that no improvement has occurred.

[0182] In some embodiments, a determination that no improvement has occurred may be made when the session EQ-5D-5L index has not increased by a first predetermined margin from the baseline EQ-5D-5L index. In some embodiments, a determination that no improvement has occurred may be made when the session SDS index has not increased by a first predetermined margin from the baseline SDS index. In some embodiments, a determination that no improvement has occurred may be made when the session WHODAS index has not decreased by a second predetermined margin from the baseline WHODAS index. In some embodiments, a determination that no improvement has occurred may be made when the session SQLS-R4 index has not decreased by a second predetermined margin from the baseline SQLS-R4 index.

[0183] In some embodiments, method 2000 may include determining that the user demonstrates improvement in experiencing negative symptoms of schizophrenia when the session metric is below the baseline metric (e.g., when the user is provided with the trial according to Example 1). Improvement in experiencing negative symptoms of schizophrenia may correspond to a decrease in CAINS-MAP scores or DAS scores, etc. Otherwise, method 2000 may include determining that the user does not demonstrate improvement in experiencing negative symptoms of schizophrenia when the session metric is above the baseline metric. In some embodiments, method 2000 may include determining that the user demonstrates improvement when the session metric is above the baseline metric. Improvement in experiencing negative symptoms of schizophrenia may correspond to an increase in PSP scores, etc. Conversely, method 2000 may include determining that the user does not demonstrate improvement when the session metric is equal to or less than the baseline metric.

[0184] Example 1: A multicenter, exploratory, single-arm study to evaluate the feasibility and acceptability of a simplified version of a digital therapeutic application for the treatment of adults diagnosed with schizophrenia

[0185] summary

[0186] Indications: Adults with negative symptoms of schizophrenia

[0187] Introduction: A digital therapeutic application (e.g., Application 165, or also referred to herein as CT-155 or the study application) is an investigational prescription digital therapeutic (DTx) that delivers a software-based, interactive intervention for the negative symptoms of schizophrenia. During the DTx development lifecycle, iterations of the DTx can be scientifically evaluated in a user population that is clinically representative of the target patient population. The data generated via this evaluation can be used to drive modifications and optimization of specific therapeutic components included in a given DTx. The purpose of the proposed study was to evaluate the feasibility and acceptability of using a simplified version of the digital therapeutic application (the study application) in adults with schizophrenia who were experiencing at least moderate experiential negative symptoms.

[0188] purpose

[0189] The main aim was to explore the feasibility and acceptability of simplified treatment using a digital therapeutic application.

[0190] The exploration aims are as follows:

[0191] • Explore changes in experienced negative symptoms from baseline to end of study

[0192] • Explore changes in social functioning from baseline to end of study

[0193] • Explore changes in defeatist beliefs from baseline to end of study

[0194] • Explore compliance in everyday research applications

[0195] • Explore correlations between experiential negative symptoms and changes in baseline motivation and enjoyment, baseline personal and social performance, baseline digital literacy, baseline cognitive functioning, change in digital work alliance strength, and change in defeatist beliefs from baseline to week 7

[0196] • Explore participants' expectations of treatment benefits throughout the 7 weeks of study app use

[0197] Study endpoints

[0198] •Primary endpoints include:

[0199] o Study the feasibility and acceptability of the application, defined as:

[0200] o Participants' ratings of the quality and satisfaction with the study app at Week 7 as measured by the Mobile Application Rating Scale (MARS)

[0201] o Participant feedback obtained during follow-up qualitative participant interviews and the Human Factors Questionnaire (HFQ)

[0202] • Exploratory endpoints include:

[0203] o Change from baseline to week 7 in experienced negative symptoms as assessed by the Clinical Assessment Interview for Negative Symptoms (CAINS) Motivation and Pleasure Scale

[0204] o Change in social functioning from baseline to week 7 as assessed by the Personal and Social Performance Scale (PSP)

[0205] o Change in defeatist beliefs between baseline and week 7 as assessed by the defeatist beliefs subscale of the Dysfunctional Attitudes Scale (DAS)

[0206] o Participation in the study with daily study app compliance from baseline to week 7

[0207] o Correlations between experienced negative symptoms and changes in baseline motivation and enjoyment, baseline personal and social performance, baseline digital literacy, baseline cognitive functioning, change in digital working alliance strength, and defeatist beliefs from baseline to week 7

[0208] o Expected benefit of the treatment of the investigational application as assessed by the Expected Benefit Questionnaire (EBQ)

[0209] o Participant engagement with the study app as measured by participant app usage data captured within the app

[0210] Study Design: This was a multicenter, exploratory, single-arm study to evaluate the feasibility and acceptability of a simplified digital therapeutic application in adults diagnosed with schizophrenia. Eligible participants had to have a diagnosis of schizophrenia according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and experience at least moderate to severe negative symptoms, as demonstrated by a score of ≤30 on the Motivation and Pleasure Scale-Self-Report (MAP-SR). Participants had to have been taking a stable dose of antipsychotic medication for at least 12 weeks (3 months) prior to enrollment (Day 1). Participants who met the eligibility criteria could be enrolled in the study on Day 1.

[0211] Now refer to Figure 21 Figure 2 depicts the study screening protocol used to conduct a multicenter, exploratory, single-arm study to evaluate the feasibility and acceptability of treatment with a simplified version of a digital therapeutic application. As shown, the study consisted of a screening period of up to 7 days, a 49-day participation period, and a follow-up period of up to 7 days.

[0212] Screening Period (Day -7 to Day 1): All participants who have provided informed consent will enter a 7-day screening period to determine eligibility. During this period, assessments will be performed during in-person clinic visits. Site staff will direct eligible participants to the study app by helping them download and install it on their primary iPhone or Android smartphone.

[0213] Participation Period (Day 1 to Day 49): Eligible participants will be enrolled during an in-person clinic visit on Day 1. During this period, assessments and activities will be performed during in-person clinic visits according to the Schedule of Activities and Assessments (SoA). Participants will be instructed to visit and perform tasks daily as directed by the study application.

[0214] Follow-up Period (Day 50-56): Participants will enter a 7-day follow-up period during which they may attend an in-person clinic visit to complete follow-up assessments based on the SoA. Participants may not perform any activity within the app.

[0215] Planned number of participants includes: Up to 48 participants may be enrolled in this study.

[0216] Study Entry Criteria

[0217] Inclusion criteria included:

[0218] Participants were considered eligible for the study if all of the following criteria were met:

[0219] 1. Willing and able to provide written informed consent to participate in the study, attend study visits, and comply with study-related requirements and assessments.

[0220] 2. Aged between 18 and 64 years at the time of informed consent.

[0221] 3. Fluent in English reading and writing, and confirmed to be able to read and understand the informed consent form.

[0222] 4. A primary diagnosis of schizophrenia using the diagnostic criteria for schizophrenia as defined in the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) within at least 1 year prior to screening.

[0223] 5. In a stable stage of disease as assessed by the investigator after review of medical records or written discussion with the treating physician.

[0224] 6. The patient is receiving outpatient treatment at the time of screening and has not received hospitalization for schizophrenia within 12 weeks before screening.

[0225] 7. Taking a stable dose of antipsychotic medication for at least 12 weeks before enrollment (Day 1) as determined by the investigator.

[0226] 8. Have achieved a score of 30 or less on the MAP-SR as assessed at the Screening Visit.

[0227] 9. Participant self-reported being the sole user of an iPhone with iOS 13 or later or a smartphone with Android OS 10 or later, and willing to download and use the study application as required by the protocol.

[0228] 10. Be the owner of the email address and have regular access to it.

[0229] 11. Have regular access to the internet via a cellular data plan and / or Wi-Fi.

[0230] 12. Have stable housing and have lived in the same residence for at least 12 weeks prior to screening and are not expected to change housing during the duration of the study.

[0231] 13. Understand how the research application will be used during the screening visit, as assessed by the investigator during the clinical research application installation and activation activities.

[0232] Exclusion criteria:

[0233] Participants were determined to be ineligible for the study if any of the following criteria were met:

[0234] 1. Currently being treated with two or more antipsychotic drugs (including two or more dosage forms).

[0235] 2. Currently being treated with clozapine or haloperidol.

[0236] 3. The researcher believes that the patient has significant positive symptoms that will hinder effective participation in treatment of negative symptoms.

[0237] 4. Currently receiving psychotherapy or have received psychotherapy within 12 weeks prior to screening.

[0238] 5. Uninvestigated diagnosis that meets the criteria of the International Classification of Diseases, Tenth Revision (ICD-10) or DSM-5, including schizophrenia-like disorder, schizoaffective disorder, or psychosis nonspecific disorder.

[0239] 6. Patients with post-traumatic stress disorder (PTSD), bipolar disorder, major depression, developmental disorders, or any serious disorder that would interfere with compliance with the protocol, as determined by the investigator.

[0240] 7. Have a substance or alcohol use disorder (excluding caffeine and nicotine) that, in the investigator's judgment, will interfere with compliance with the protocol.

[0241] 8. As determined by the investigator, concomitant medications and / or treatments that currently require or may require abstinence during the study.

[0242] 9. Currently participating in another clinical study (interventional or observational) involving an investigational drug or device.

[0243] 10. Previous participation in clinical research.

[0244] 11. Suicidal ideation or behavior as assessed by the Columbia-Suicide Severity Rating Scale (C-SSRS):

[0245] a. Participants with a “yes” response to item 4 or 5 of the C-SSRS suicidal ideation item within 12 weeks prior to screening or at the baseline visit.

[0246] b. Participants with a “yes” response to the C-SSRS suicidal behavior item within 26 weeks prior to screening or at the baseline visit.

[0247] c. Participants who are considered by the researcher to be at serious risk for suicide.

[0248] 12. Any evidence of clinically significant concomitant illness or any other clinical condition that, in the investigator's judgment, may compromise the safety of the participant while participating in the clinical study.

[0249] Test Product and Administration Mode: During the screening visit, site staff assisted eligible participants in downloading and installing the study app on their smartphone devices. Participants activated the participation module of the app at the baseline visit (Day 1).

[0250] Study Duration: The duration of participation is approximately 9 weeks, as follows:

[0251] • Screening period: up to 7 days

[0252] • Participation period: 49 days

[0253] • Follow-up period: up to 7 days

[0254] • Sample size: Up to 48 participants can be enrolled in the study

[0255] Statistical Analysis: A statistical analysis plan (SAP) detailing the study objectives and endpoint analyses should include:

[0256] •The extent of participant engagement in the research app can be assessed, as measured by predefined research app engagement metrics.

[0257] • Conduct qualitative analysis of participant feedback captured in follow-up qualitative participant interviews and the Human Factors Questionnaire (HFQ)

[0258] • Change in the strength of the digital working alliance from baseline to week 7, as assessed by the Mobile Agnew Relationship Measure (mARM)

[0259] • The extent of global negative schizophrenia symptoms as assessed by CAINS will be assessed.

[0260] • The extent of negative schizophrenia symptoms as assessed by CAINS will be assessed at Week 7.

[0261] • Improvement in negative schizophrenia symptoms from baseline to week 7 will be assessed as mean change by CAINS.

[0262] • The expected benefit of treatment in people with more severe and moderate negative symptoms as assessed by the EBQ will be assessed.

[0263] • Change from baseline to week 7 in defeatist beliefs as assessed by the DAS will be assessed as mean change.

[0264] • The association between research app use and negative schizophrenia symptom intensity as assessed by predefined research app engagement indicators and CAINS will be evaluated.

[0265] •The degree of association between baseline levels of experienced negative symptoms as assessed by CAINS, baseline social functioning as assessed by PSP, baseline digital literacy as assessed by MDPQ, baseline cognitive functioning as assessed by BACS and AltoidAApp, strength of digital working alliance as assessed by mARM, and expected degree of treatment benefit will be assessed using correlation coefficients.

[0266] • The correlation between participation in research applications and benefit expectations as assessed by predefined research application participation indicators and the EBQ will be evaluated.

[0267] The Digital Therapeutic App is a novel prescription digital therapeutic (DTx) that provides an interactive, software-based intervention to treat experiential negative symptoms in patients with schizophrenia who are stable on standard-of-care antipsychotic medications (SOC). The Digital Therapeutic App's therapeutic techniques were selected based on clinical evidence, and the individual components were designed based on the fundamentals of face-to-face therapy to provide maximum support for adults with schizophrenia. The Digital Therapeutic App's therapeutic techniques have been shown to work synergistically to produce an effect on experiential negative symptoms.

[0268] During the DTx development lifecycle, iterations of the DTx can be scientifically evaluated in user populations that are clinically representative of the target patient population. The data generated via this evaluation can be used to drive modifications and optimizations of specific therapeutic components included in a given DTx.

[0269] The aim of the proposed study was to evaluate the feasibility and acceptability of a simplified version of a digital therapeutic application (the study application) for adult participants with schizophrenia who were experiencing at least moderate experiential negative symptoms.

[0270] Digital therapeutic apps

[0271] Schizophrenia treatment guidelines recommend antipsychotic medications and adjunctive psychosocial interventions. As previously mentioned, medications are effective in treating positive symptoms, but no pharmacological interventions are currently available for the treatment of negative symptoms. Therefore, adjunctive psychosocial interventions are recommended for symptoms that cannot be treated with medication, such as negative symptoms. Apps, such as App 165, offer therapeutic techniques that translate the fundamental principles of face-to-face psychosocial treatments into digital therapies, and do so in a manner that adheres to the latest evidence-based recommendations.

[0272] The digital therapeutic app is designed as an adjunctive digital treatment for experiencing negative symptoms in people with schizophrenia who are stable on the SOC. The digital therapeutic app will be available by prescription only and is intended for use only under clinician supervision. Given the age of onset of schizophrenia, it is clinically important that the product be evaluated and labeled for use in adults (18 to 64 years old).

[0273] Digital therapeutic apps are designed to address the need for accessible treatments that can fill existing gaps in SOC. Studies have shown that smartphone penetration is nearing saturation, with over 80% of people with schizophrenia reporting owning a smartphone. Despite the lack of available validated mobile treatments, most people with schizophrenia already use technology to help manage their illness, from coping with auditory hallucinations to setting medication reminders. Digital therapeutic apps deliver validated digital therapeutics to people with schizophrenia, enhancing their ongoing care.

[0274] The study app was designed to deliver a simplified version of a full treatment cycle using a digital therapeutic app. This design allowed for inferences about the usability and acceptability of the digital therapeutic app but did not require full treatment with the digital therapeutic app.

[0275] Objectives: To evaluate the interaction of treatment sessions with skills training and adaptive goal setting (AGS) activities for older adolescents and adults with schizophrenia and experiential negative symptoms (ENS).

[0276] Investigational devices under evaluation

[0277] Digital therapeutics apps (research apps)

[0278] The digital therapeutic app (also referred to herein as the "study app") was developed as a prescription-assisted digital therapeutic designed to target experiential negative symptoms in adults and older adolescents with schizophrenia who are stable on SOC. Treatment delivery is guided by a validated mechanistic model to address important clinical needs identified by people with schizophrenia. The therapeutic techniques within the app were selected based on clinical evidence, and the individual components were designed based on the fundamentals of face-to-face therapy to provide maximum support to people with schizophrenia. Additionally, messages supporting treatment delivery, engagement, and adherence are sent throughout an individual's engagement with the app. The app's therapeutic techniques work synergistically to produce an effect on experiential negative symptoms.

[0279] Application processing and storage

[0280] Research App Download

[0281] During the screening visit, site staff assisted participants in downloading and installing the app. Installation instructions are provided in the study app site guide. Site staff confirmed that the study app had been downloaded to the eCRF.

[0282] Research Application Activation

[0283] During the baseline visit, site staff assisted participants in activating the app. Activation instructions are provided in the Study App Site Guide. Site staff confirmed that the study app was activated in the eCRF. Only participants who were confirmed eligible and enrolled in the study were allowed to activate the study app.

[0284] Research app deactivation and uninstallation

[0285] After the Week 7 visit (Day 49), the study app was automatically deactivated and was not available to participants. Site staff instructed participants who completed the study or terminated the study early to uninstall the study app. Uninstallation instructions were provided in the site instructions for the study app. Site staff confirmed that this instruction was provided to participants in the eCRF.

[0286] Research application compliance

[0287] Participants were instructed to use the study app as directed by the study app. Participants were presented with daily tasks, activities, and / or assignments. This study did not define compliance with this protocol. However, engagement with the study app was measured.

[0288] Continued access to study apps after study ends

[0289] After the participation period (Day 49) is complete, the study app becomes inactive. After Day 49, participants will no longer be able to access the content provided by the study app between Days 1 and 49.

[0290] Concomitant therapy

[0291] Participants were required to have been taking a stable dose of a SOC antipsychotic medication for at least 12 weeks prior to enrollment (Day 1). Dose adjustments were permitted during the study as described in the corresponding package inserts for their current medications.

[0292] Participants were not allowed to be treated with more than two (2) antipsychotic medications (including more than two dosage forms). Treatment with clozapine or haloperidol was prohibited. No additional psychotherapy was allowed during the study.

[0293] Lifestyle considerations

[0294] Participants should be able to use their smartphones regularly for the duration of the trial. They should also be able to attend clinic visits during the trial. Participants should refrain from using alcohol or recreational drugs while accessing the study app.

[0295] Overall study design

[0296] This is a multicenter, exploratory, single-arm study evaluating the overall effectiveness of a simplified version of a digital therapeutic application in adults 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 experience at least moderate to severe negative symptoms, as evidenced by a score of ≤30 on the Motivation and Pleasure Scale-Self-Report (MAP-SR). Participants must 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 could be enrolled in the study on Day 1.

[0297] like Figure 21 As shown in the , participation duration was approximately 9 weeks, consisting of a screening period of up to 7 days, a participation period of 49 days, and a follow-up period of up to 7 days.

[0298] Activities and assessments during the 7-day screening period, 49-day participation period, and 7-day follow-up period were completed according to the Schedule of Activities and Assessments (SoA). Site staff implemented procedures during in-person clinical visits. Participants were assessed based on a validated standard clinician-rated and participant-rated Schizophrenia Outcome Scale at screening and baseline. Participants were assessed based on both validated and qualitative participant-rated outcome scales to assess study endpoints during the participation and follow-up periods. Participants' engagement with the application (sometimes referred to herein as the "study application") was assessed based on data captured in the application. Participants were also assessed for AEs throughout the duration of the trial. Sites may conduct unscheduled in-person visits or remote visits via telephone at any time if requested by the investigator or study participant, or to assess safety concerns.

[0299] Screening period (Day -7 to Day 1)

[0300] During the in-person screening visit, participants sign the Informed Consent Form (ICF) and complete all activities and assessments listed in the SoA. Following this initial screening visit, participants enter a 7-day screening period during which they continue to be assessed for eligibility and interest in the trial. However, the screening visit can occur on the same day as the baseline visit (Day 1).

[0301] Site staff directed eligible participants to the study app by assisting them in downloading and installing the study app onto the participant's personal iPhone or Android smartphone.

[0302] Participation period (Day 1 to Day 49)

[0303] Participant eligibility was confirmed during the in-person baseline visit on Day 1. Participants were considered eligible to receive a study application if they continued to meet all inclusion criteria and did not meet exclusion criteria.

[0304] Up to 48 eligible participants were enrolled at approximately 15 study centers across the United States. During this time, activities and assessments were performed clinically by site staff and participants. All assessments were completed according to the SoA.

[0305] After enrollment, the study app was activated using a unique activation code, and site staff could check that the study app was functioning properly. During the 49-day participation period, participants were instructed to access and perform tasks daily as directed by the study app.

[0306] Follow-up period (day 50 to day 56)

[0307] Participants will enter a 7-day follow-up period during which they will not be able to use the study app. Activities and assessments during this period will be completed according to the SoA. Participants will join the product development team in an individual capacity for a 60-minute qualitative interview via remote teleconferencing software to discuss their experience using the study app. The Week 8 follow-up visit can occur up to 6 days prior to Day 56.

[0308] End of study definition

[0309] The end of the study was defined as the date of last contact, or the date of last attempted contact, of the last participant who completed or withdrew from the trial.

[0310] Participants who were assessed at their last scheduled visit (Day 49, Week 7) were defined as trial completers.

[0311] Research Assessment and Procedures

[0312] Study assessments and procedures, including their timing, are outlined in the SoA. Compliance with study design requirements, including those specified in the SoA, is crucial and essential to the conduct of the study. Every effort should be made to ensure that protocol-mandated assessments and procedures are completed as described. All clinician-administered scales should be administered by appropriately trained individuals. Study procedures are described herein. Study assessments are described below.

[0313] Learning Assessments and Scales

[0314] The following assessment scale was used in this study at the time provided in the SoA.

[0315] Research Application Participation

[0316] Participant engagement with a research application can be automatically captured by the research application. The following metrics are captured:

[0317] • The number of available courses completed out of the total number of courses allocated in the research application

[0318] • The number of days the study app was open out of the total available treatment days

[0319] • Number of times the research app was opened via notification clicks

[0320] • Number of times the study app was opened via the SMS link

[0321] • Daily check-in completion quantity out of the total assigned amount

[0322] • Overall percentage of assigned tasks completed

[0323] • Duration of each application usage session

[0324] • The number of times each skill is practiced

[0325] • Treatment focus selected by participant

[0326] • Percentage of goals successfully achieved

[0327] • The percentage of goal-focused tasks completed by users

[0328] Motivation and Pleasure Scale-Self-Report (MAP-SR)

[0329] The MAP-SR is a validated self-report instrument derived from the CAINS that assesses the motivation and enjoyment domains of negative symptoms in patients with psychiatric disorders. The scale consists of 15 items that capture motivation, effort, interest, and enjoyment in different areas of life. All items are rated on a 5-point Likert scale, with lower scores reflecting greater severity.

[0330] Clinical Assessment Interview for Negative Symptoms (CAINS)

[0331] The CAINS is a validated, 13-item, interview-based assessment administered by clinicians that consists of two subscales measuring two factors of negative symptoms: motivation and pleasure (MAP) and performance (EXP). CAINS items are scored on a 4-point scale, with lower scores indicating lower severity of negative symptoms.

[0332] The MAP scale consists of nine items that measure interest and participation in motivated behaviors, as well as the experience of enjoyment in social, occupational, and recreational areas. Each item is scored based on the patient's reported behavior and experience. The EXP scale consists of four items that measure speech intonation (prosody), nonverbal expressions (gestures, posture), facial expressions, and speech output (aphasia). Items are scored based on observation during the interview.

[0333] Personal and Social Performance Scale (PSP)

[0334] The PSP is a validated, clinician-rated scale that measures personal and social functioning in four areas: socially rewarding activities (e.g., work and school), personal and social relationships, self-care, and disruptive and aggressive behavior. Each area is scored on a 0-100 scale with anchor points at 10-point intervals.

[0335] Mobile Device Proficiency Questionnaire (MDPQ)

[0336] The MDPQ is a validated, self-administered questionnaire that measures older adults' mobile device proficiency. It consists of 46 items rated on a 5-point Likert scale. The MDPQ assesses five areas of digital literacy: information and data literacy; communication and collaboration; digital content creation; safety; and problem solving.

[0337] Altoida Computerized Cognitive Assessment

[0338] The Altoida app is a validated computerized cognitive assessment that provides digital biomarker data for cognitive and functional abilities, including 13 neurocognitive domains (covering daily function and cognition) that map to major neurocognitive networks, such as complex attention and cognitive processing speed. Nearly eight hundred (800) independent features are collected during augmented reality and motor tasks, such as reaction time, speed, attention-based and memory-based assessments, as well as input (or lack thereof) from each of the device's sensors: accelerometer, gyroscope, magnetometer, camera, microphone, and touchscreen.

[0339] The assessment was completed clinically by the participant on a study iPad and took 10 minutes.

[0340] Brief Assessment of Cognition in Schizophrenia (BACS) subscales

[0341] The Brief Assessment of Cognition in Schizophrenia (BACS) is a validated paper-and-pencil cognitive assessment that includes several subtests: a symbolic encoding task to assess working memory; a verbal memory task; and a digit sequencing task to assess attention and information processing speed. These three subtests can be combined into the Short Form Global Cognitive Screening Tool (BACS-SF).

[0342] •Symbol Coding Task: Participants have 90 seconds to write the numbers 1-9 that match the symbols on the answer sheet. The total task duration is 3 minutes.

[0343] • Verbal Memory Task: Participants were presented with 15 words and then asked to recall as many as possible. The procedure was repeated 5 times. The total task duration was 7 minutes.

[0344] • Number sorting: Participants were presented with a series of numbers of increasing length and were required to tell the experimenter the order in which they should be sorted, from lowest to highest. The total task duration was 5 minutes.

[0345] Defeatist Performance Beliefs Subscale of the Dysfunctional Attitudes Scale (DAS)

[0346] The Defeatist Performance Beliefs subscale is a 15-item subset of the validated Dysfunctional Attitudes Inventory. Participants rate the items on a scale of 1-7, with higher scores indicating more severe defeatist thinking.

[0347] Benefit Expectancy Questionnaire (EBQ)

[0348] The EBQ is a non-validated participant-completed measure designed to assess participants' expectations of receiving treatment benefits from using the study app. Participants were asked to rate three of five items on a scale ranging from "strongly disagree" to "strongly agree."

[0349] Mobile Agnew Relationship Scale Questionnaire (mARM)

[0350] The mARM is a validated patient-rated scale that assesses mobile health interventions for mental health conditions. It is adapted from the well-validated Agnew Relationship Rating Scale (ARM) and consists of 25 items rated on a 7-item scale ranging from "strongly disagree" to "strongly agree."

[0351] Human Factors Questionnaire (HFQ)

[0352] The HFQ is a non-validated participant-completed measure consisting of 5 questions designed to assess participants' experience using the study application.

[0353] Mobile Application Rating Scale (MARS)

[0354] MARS is a validated 23-item scale that categorizes and evaluates the quality of mobile health apps. It assesses multiple aspects of an app: engagement, functionality, aesthetics, information quality, and subjective quality. A mean score is calculated for each section of the scale, and the total MARS mean score describes the overall quality of the app.

[0355] Qualitative interviews

[0356] Participants may individually complete unvalidated qualitative interviews with product development team members during which they may be asked for their feedback on the research app.

[0357] result

[0358] Individual and mean CAINS-MAP scores were obtained at baseline and at the end of study (EOS) (i.e., after 7 weeks of treatment) using the digital therapeutic app. After 7 weeks of using the digital therapeutic app, the mean (95% confidence interval) CAINS-MAP score decreased significantly by 3.4 (1.2, 5.7), or approximately 17% (p=0.004), indicating improved experiential negative symptoms (ENS) in patients. There was no correlation between baseline ENS and the number of completed sessions. Patients with schizophrenia demonstrated a reduction in experiential negative symptoms after 7 weeks of participation in the CT-155 beta app, as measured by baseline CAINS-MAP and EOS scores. Patients with more severe experiential negative symptoms experienced a more sustained benefit from the app compared to those with less severe symptoms.

[0359] Example 2: Combination treatment of digital therapeutics and antipsychotic medication in subjects experiencing negative symptoms of schizophrenia

[0360] summary

[0361] Indication: The digital therapeutic app (e.g., App-165 or also referred to herein as CT-155 or the investigational app) is an investigational prescription digital therapeutic (PDT) intended to treat adults and older adolescents experiencing negative symptoms of schizophrenia (adjunct to standard antipsychotic treatment; SOC).

[0362] Introduction: The digital therapeutic application provides a software-based interactive intervention for experiential negative symptoms of schizophrenia. The purpose of the proposed study is to evaluate the efficacy of CT-155 as an adjunctive treatment to SOC compared to a comparator digital control in participants 18 years of age or older diagnosed with experiential negative symptoms of schizophrenia.

[0363] Objective: To evaluate the efficacy of CT-155 in reducing experienced negative symptoms compared with a digital control in adults and older adolescent participants diagnosed with schizophrenia.

[0364] Evaluation Criteria

[0365] • Primary efficacy endpoint

[0366] Change from baseline to week 16 in experienced negative symptoms as assessed by the Clinical Assessment Interview Scale for Negative Symptoms, Motivation, and Pleasure (CAINS-MAP) compared to a digital control

[0367] • Secondary efficacy endpoints

[0368] Change from baseline in motivational and hedonic symptoms at Week 8 as assessed by the CAINS-MAP compared to digital control

[0369] Change from baseline in expressed negative symptoms at Weeks 8 and 16 as assessed by the Clinical Assessment Interview-Expression of Negative Symptoms (CAINS-EXP) compared to digital control

[0370] o Change from baseline in positive symptoms at Weeks 8 and 16 as assessed by the Positive and Negative Syndrome Scale (PANSS) compared to digital control

[0371] o Change from baseline in social functioning at weeks 8 and 16, as assessed by the Personal and Social Performance Scale (PSP), compared to digital controls

[0372] o 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), compared to a digital control

[0373] o Patient Global Impression of Improvement (PGI-I) at Weeks 8 and 16 compared to digital control

[0374] • Exploratory endpoints

[0375] oKey Engagement Metrics

[0376] o Change from baseline in disease severity at Weeks 8 and 16 as assessed by the Clinical Global Severity Scale (CGI-S) compared to numeric control

[0377] o Change from baseline in disease severity at weeks 8 and 16, as assessed by the World Health Organization Disability Assessment Scale 2.0 (WHODAS 2.0), compared to digital controls

[0378] o Change from baseline in illness severity at Weeks 8 and 16 as assessed by the Schizophrenia Quality of Life Scale-Revised 4 (SQLS-R4) compared to digital control

[0379] Study Design: This will be a multicenter, randomized, double-blind, controlled trial to evaluate the efficacy and safety of CT-155 in adults and older 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 must experience at least moderate to severe severity of experienced negative symptoms, as demonstrated by a score of ≥2 (moderate to severe) in at least two of the three CAINS-MAP domains (social, work, or recreation). Participants must have been taking a stable dose of antipsychotic medication for 12 weeks prior to randomization (Day 1). Participants who meet the eligibility criteria will be randomized on Day 1 to receive either CT-155 or a comparator digital control (study app).

[0380] Referring to Figure 25, a study design for a combination of digital therapeutics and antipsychotic medication for subjects experiencing negative symptoms of schizophrenia is depicted. The study consists of a screening period of up to 14 days, a double-blind intervention period of 16 weeks, and a follow-up period of 4 weeks.

[0381] Study Design

[0382] Screening Period (Day -14 to Day -1): All eligible participants (per investigator) will enter a screening period of up to 14 days (minimum 7 days) to determine eligibility. Participants who meet all applicable inclusion criteria and no exclusion criteria at the screening visit will be directed to the study app by downloading and installing the app on their personal iPhone or Android smartphone.

[0383] Double-blind intervention period (Day 1 to Week 16): Approximately 432 eligible participants will be randomized in a 1:1 ratio (digital control) to approximately 40 study centers in the U.S. during an in-person clinic visit on Day 1. Assessments and activities during this period will be performed during an in-person clinic visit or remotely by telephone, depending on the SoA.

[0384] Follow-up Period (Weeks 16 to 20): Participants will enter a 4-week follow-up period during which they will not receive the randomized intervention. Assessments and activities during this period will be completed via remote telephone interviews based on the SoA.

[0385] Planned Number of Participants: Approximately 432 participants will be randomized in this study.

[0386] Study Entry Criteria

[0387] Inclusion Criteria: Participants will be eligible for entry into the study if all of the following criteria are met:

[0388] 1. Willing and able to provide written informed consent to participate in the study, attend study visits, and comply with study-related requirements and assessments.

[0389] 2. Adults or older adolescents must be 18 years of age or older at the time of informed consent.

[0390] 3. Fluent in English reading and writing, and confirmed to be able to read and understand the informed consent form.

[0391] 4. A primary diagnosis of schizophrenia using the diagnostic criteria for schizophrenia as defined in DSM-5 within at least 6 months prior to the screening visit.

[0392] 5. In a stable stage of disease as assessed by the investigator after review of medical records or written discussion with the treating physician.

[0393] 6. The patient is receiving outpatient treatment at the time of screening and has not received hospitalization for schizophrenia within 12 weeks before screening.

[0394] 7. Been taking a stable dose of antipsychotic medication for at least 12 weeks before randomization (Day 1), with dose adjustments allowed during the study (and 12 weeks before randomization) as described in the corresponding package insert of their current medication as determined by the investigator.

[0395] 8. A mean score of ≥ 2 (moderate to severe) in at least two of the three CAINS-MAP domains (social, work, or recreation) at the screening visit and baseline (Day 1).

[0396] 9. Be the sole user of an iPhone with iPhone operating system (iOS) 14 or later or a smartphone with Android operating system (OS) 10 or later, and be willing to download and use the designated research application as required by the agreement.

[0397] 10. Willing and able to receive SMS text messages and push messages on a smartphone.

[0398] 11. Be the owner of the email address and have regular access to it.

[0399] 12. Have regular access to the internet via a cellular data plan and / or Wi-Fi.

[0400] 13. Have stable housing and have lived in the same residence for at least 12 weeks prior to screening and are not expected to change housing during the duration of the study.

[0401] 14. Be aware of and interested in using the study app during the Screening Period and Baseline Visit (Day 1).

[0402] Exclusion Criteria: Participants will be ineligible for entry into the study if any of the following criteria are met:

[0403] 1. Currently being treated with two or more antipsychotic drugs (including two or more dosage forms).

[0404] 2. Currently being treated with clozapine or haloperidol, or having been treated with clozapine within 5 years prior to the screening visit.

[0405] 3. At the screening or baseline visit of the PANSS, a positive symptom item score of >4 (moderate) has been obtained for P1-delusion, P2-confusion, P3-hallucination, P6-suspiciousness, or any item score of >5 (moderate to severe), thus indicating significant positive symptoms.

[0406] 4. Based on the investigator's assessment, currently receiving or having received psychological treatment defined as individual or group-based structured treatment (such as cognitive behavioral therapy, social skills training, or occupational / vocational therapy) within 6 months (26 weeks) before screening.

[0407] 5. Compliant with DSM-5 for diagnoses not investigated that would affect their compliance with the protocol, including schizophrenia-like, schizoaffective, or psychosis nonspecific disorders (post-traumatic stress disorder [PTSD], bipolar disorder, major depressive disorder, or developmental disorder).

[0408] 6. Meet DSM-5 criteria for a current depressive, manic, or hypomanic episode.

[0409] 7. Have a current diagnosis of a substance or alcohol use disorder (excluding caffeine and nicotine) as defined in DSM-5 within 6 months (26 weeks) of the screening visit.

[0410] 8. Positive urine drug screen for amphetamines (including MDMA / ecstasy), phencyclidine (PCP), cocaine, opioids, benzodiazepines, or barbiturates at screening or baseline visit before randomization. Participants with positive urine drug test and / or recreational use of tetrahydrocannabinol (THC) may be enrolled at the investigator's discretion.

[0411] 9. Participated in clinical research related to digital therapeutic applications; or participated in user research on digital therapeutic applications.

[0412] 10. Participated in another clinical study (interventional or observational) within the past 6 months (26 weeks).

[0413] 11. As determined by the investigator, it is or may be necessary to withhold concomitant medications and / or treatments during the trial.

[0414] 12. Suicidal ideation or behavior as assessed by the C-SSRS:

[0415] a. Participants who responded “yes” to item 4 or 5 of the C-SSRS suicidal ideation item within 3 months (12 weeks) prior to screening or at the baseline visit.

[0416] b. Participants with a “yes” response to the C-SSRS suicidal behavior item within the last 6 months (26 weeks) before screening or at the baseline visit.

[0417] 13. Participants who are considered by researchers to be at serious risk for suicide.

[0418] Test Product and Administration Mode: Eligible participants will download and install the study app on their smartphones at the screening visit. At the baseline visit (Day 1), participants will be randomized to the digital therapeutic app or a digital control.

[0419] Study Duration: Participation duration will be approximately 22 weeks as follows:

[0420] • Screening period: up to 14 days

[0421] •Intervention period: 16 weeks

[0422] • Follow-up period: 4 weeks

[0423] Sample size: Approximately 432 participants will be randomized to the study.

[0424] Statistical Analysis: The primary endpoint was the change from baseline to week 16 in experienced negative symptoms, as assessed by the CAINS-MAP scale. The null hypothesis was that there was no difference in the means between the treatment groups, and the alternative hypothesis was that the means were different. To demonstrate this, assuming a standardized effect (Cohen's d) of 0.35 between treatment groups, 173 participants per group were required to achieve 90% power using a 5% type I error rate (two-sided). Assuming a 20% early termination rate, 432 participants needed to be randomized into the study in a 1:1 ratio (approximately 216 per group). The sample size was calculated using a two-independent t-test and assuming equal variances.

[0425] With the response variable in which the change from baseline at each visit of the CAINS-MAP is assessed, the primary endpoint of change from baseline to week 16 in experienced negative symptoms will be analyzed using mixed model repeated measures (MMRM). The model will include an intercept and the following covariates: experienced negative symptoms at baseline, visit (as a categorical variable), treatment group, treatment by visit interaction term, and baseline by visit interaction term.

[0426] The preferred method for intra-patient correlations is an unstructured covariance matrix. Mitigation measures in the event of non-convergence will be discussed in the Statistical Analysis Plan (SAP). Only data from planned visits will be used for this analysis. The 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 a greater decrease in CAINS-MAP is observed in the digital therapeutic application group compared with the digital control group. Analyses of secondary endpoints will be similarly analyzed using the MMRM. Exploratory endpoints will be summarized descriptively by treatment group.

[0427] Figure 23A and 23BA timeline of participant activities and assessments is depicted. Abbreviations: app = application; BACS = Brief Assessment of Cognitions in Schizophrenia; C-SSRS = Columbia-Suicidality Severity Rating Scale; CAINS = Clinical Assessment Interview for Negative Symptoms; CGI-S = Clinician Global Impression of Severity; DAS = Defeatist Beliefs subscale of the Disability Attitudes Scale; DSM-5 = Diagnostic and Statistical Manual of Mental Disorders, fifth edition; EQ-5D-5L = EuroQol 5-Dimension 5-Level Scale; ET = premature termination; MINI = Mini-International Neuropsychiatric Interview; PANSS = Positive and Negative Symptom Scale; PGI-I = Patient Global Impression of Improvement; PGI-S = Patient Global Impression of Severity; PSP = Personal and Social Performance Scale; SDS = Sheehan Disability Scale; SQLS-R4 = Schizophrenia Quality of Life Scale-Revised 4; WHODAS = World Health Organization Disability Assessment Schedule 2.0.

[0428] a. All remote access will be conducted via telephone.

[0429] b. Urine drug screen will be conducted on site.

[0430] c. Urine pregnancy test will be conducted on-site using the test strip method.

[0431] d. CAINS was performed by a centralized blinded review team.

[0432] The screening period was at least seven (7) days prior to the baseline visit.

[0433] Digital therapeutic apps under research

[0434] Current schizophrenia treatment guidelines recommend antipsychotic medications and adjunctive psychosocial interventions. As mentioned earlier, medications are effective in treating positive symptoms, but there are no pharmacological interventions available for the treatment of negative symptoms. Therefore, for symptoms that cannot be treated with pharmacological interventions, such as negative symptoms, adjunctive psychosocial interventions are recommended. Digital therapy apps provide therapeutic technology that translates the basic principles of face-to-face psychosocial treatments into digital treatments, and do so in a manner that adheres to the latest evidence-based recommendations. Digital therapy apps provide an adaptive framework for supporting participants to build the skills they need to make progress toward their personalized goals in their daily lives.

[0435] The digital therapeutic app is designed as an adjunctive digital treatment for experiencing negative symptoms in adults and older adolescents with schizophrenia who are stable on the SOC. The digital therapeutic app will be available by prescription only and is intended for use only under clinician supervision. Given the age of onset of schizophrenia, it is clinically important that the product be evaluated and labeled for use in both older adolescents (18 to 21 years) and adults (22 to 64 years).

[0436] Digital therapeutic apps are designed to address the need for accessible treatments that fill existing gaps in SOC. Recent studies have shown that smartphone penetration is nearing saturation, with over 80% of people with schizophrenia reporting owning a smartphone. Despite the lack of available validated mobile treatments, most people with schizophrenia already use technology to help manage their illness, from coping with auditory hallucinations to setting medication reminders. Digital therapeutic apps will provide adults and older adolescents with schizophrenia with validated digital therapeutics, enhancing their ongoing care.

[0437] Benefit / risk assessment

[0438] risk assessment

[0439] Due to the software-only nature of both interventions, no adverse events (AEs) are expected to be specific to the digital therapeutic app or the comparator digital control. The primary potential risk of AEs for participants is exacerbation of negative symptoms of schizophrenia. As indicated, there is also a risk of injury when performing exercise activities outside of the study app. These and other potential risks are likely to be minimal and no greater than those associated with the SOC behavioral therapy for the treatment of schizophrenia.

[0440] Participants may experience some AEs due to their underlying conditions or use of adjunctive therapy. The risk profiles of SOCs used in clinical practice are well understood and detailed in their corresponding package inserts.

[0441] Benefit Assessment

[0442] Trial participants can directly benefit from an interactive, software-based intervention featuring cognitive training and information delivery. The digital therapeutic application is an adaptation of cognitive behavioral therapy (CBT), which has been well-validated as a treatment option for negative symptoms associated with schizophrenia. It integrates multiple neurobehavioral therapy techniques that work synergistically to reverse negative symptoms associated with schizophrenia, as described in the psychological model of negative symptomatology.

[0443] Overall Benefit:Risk Conclusion

[0444] Digital therapeutic apps may have efficacy without significant risks. Digital therapeutic apps can improve experiential negative symptoms.

[0445] Study Design

[0446] Overall design

[0447] This is a multicenter, randomized, double-blind, controlled trial evaluating the efficacy and safety of a digital therapeutic application in adults and older 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 experience at least moderate to severe negative symptom severity, as demonstrated by a score of ≥2 (moderate to severe) in at least two of the three CAINS-MAP domains (social, work, or recreation). Participants must have been taking a stable dose of antipsychotic medication for 12 weeks prior to randomization (Day 1). Participants who meet the eligibility criteria will be randomized on Day 1 to receive either the digital therapeutic application or a comparator digital control (study app). Trial participants will be blinded to the hypothesis and treatment assignment and will be informed that they will receive one of the two digital interventions being studied. The duration of participation is approximately 22 weeks, consisting of a 2-week screening period, a 16-week double-blind intervention period, and a 4-week follow-up period.

[0448] To mitigate participants' expectations, participants in this trial will be blinded to the efficacy hypothesis and their treatment assignment. Eligible participants will be informed by trial site staff that: a) they will participate in the trial for up to 22 weeks (including a follow-up period) and will be randomly assigned to receive one of two digital therapeutics; and b) the trial aims to compare the efficacy of these two digital therapeutics when used in combination with SOC. Both treatments will be presented as potentially beneficial for improving schizophrenia. Participants should not be told about the digital therapeutic app or the digital control; both will be referred to solely as the study app.

[0449] Activities and assessments during the 2-week screening period, the 16-week double-blind intervention period, and the 4-week follow-up period will be completed according to the Schedule of Activities and Assessments (SoA). Site staff will administer procedures during in-person clinical visits or remote visits via telephone. During the screening, intervention, and follow-up periods, participants will be assessed using validated standard clinical ratings and the participant-rated Schizophrenia Outcome Scale. Safety assessments will also be conducted throughout the trial. Sites may conduct unscheduled in-person or remote visits at any time if necessary to assess safety issues or concerns.

[0450] Screening period (Day -14 to Day -1)

[0451] During the in-person screening visit, participants will sign an informed consent form and will complete all assessments and activities listed in the SoA.

[0452] Eligible participants will be introduced to the study app by unblinded site staff (except the investigator / assessor) by downloading and installing the study app software onto their personal iPhone or Android smartphone. Participants will complete various tasks within the study app's onboarding module for at least seven days to confirm their understanding of and interest in the trial and use of the study app.

[0453] Double-blind intervention period (Day 1 to Week 16)

[0454] Participant eligibility will be confirmed during the face-to-face visit on Day 1. Participants will be considered for randomization based on the following criteria:

[0455] Based on the investigator's assessment, all inclusion criteria continue to be met and no exclusion criteria are met.

[0456] Approximately 432 eligible participants will be randomized in a 1:1 ratio to approximately 40 study centers in the United States (CT-155: digital control). Assessments and activities during this period will be performed during in-person clinic visits or remotely via medical technology. All assessments will be completed according to the SoA.

[0457] After randomization, during the baseline visit, unblinded trial site staff will assist participants in activating the assigned software module in the study app (digital therapeutic app (study app) or digital control) and confirm its proper functioning.

[0458] During the 16-week intervention period, participants will be instructed on the study app to access and perform the tasks at approximately the same time each day.

[0459] The efficacy assessment scale has been described in detail. The efficacy assessment of CAINS will be performed by a central blinded review team.

[0460] To reduce the risk of unblinding, trial site staff will instruct participants not to discuss what they did and saw in the study app.

[0461] Follow-up period (week 16 to week 20)

[0462] After Week 16, the study app will no longer actively prompt participants. Participants will enter a 4-week follow-up period, during which they will not receive the randomized intervention. Assessments and activities during this period will be completed via remote telephone visits according to the study plan (SoA). After Week 20, the app will cease operation.

[0463] After participants leave the trial and complete all final visit procedures, site staff will inform participants of the trial hypothesis (e.g., the hypothesis that one digital therapeutic is more beneficial in improving negative symptoms of schizophrenia), but that a trial is needed to confirm it. Site staff will be provided with a debriefing guide to assist with this discussion with participants.

[0464] End of study definition

[0465] The end of the study was defined as the date of last contact, or the date of last attempted contact, of the last participant who completed or withdrew from the trial.

[0466] For the purposes of this trial, participants who completed the trial assessments on Day 112 (Week 16) will be defined as trial completers.

[0467] Study population

[0468] Eligible participants who complete informed consent will be assigned a unique participant identification number at screening. No pre-approval protocol deviations (also known as protocol waivers or exemptions) from recruitment and enrollment criteria will be permitted.

[0469] Inclusion criteria

[0470] Participants will be eligible for entry into the study if all of the following inclusion criteria are met:

[0471] 1. Willing and able to provide written informed consent to participate in the study, attend study visits, and comply with study-related requirements and assessments.

[0472] 2. Adults or older adolescents must be 18 years of age or older at the time of informed consent.

[0473] 3. Fluent in English reading and writing, and confirmed to be able to read and understand the informed consent form.

[0474] 4. A primary diagnosis of schizophrenia using the diagnostic criteria for schizophrenia as defined in DSM-5 within at least 6 months prior to the screening visit.

[0475] 5. In a stable stage of disease as assessed by the investigator after review of medical records or written discussion with the treating physician.

[0476] 6. The patient is receiving outpatient treatment at the time of screening and has not received hospitalization for schizophrenia within 12 weeks before screening.

[0477] 7. Been taking a stable dose of antipsychotic medication for at least 12 weeks before randomization (Day 1), with dose adjustments allowed during the study (and 12 weeks before randomization) as described in the corresponding package insert of their current medication as determined by the investigator.

[0478] 8. A mean score of ≥ 2 (moderate to severe) in at least two of the three CAINS-MAP domains (social, work, or recreation) at the screening visit and baseline (Day 1).

[0479] 9. Be the sole user of an iPhone with iPhone operating system (iOS) 14 or later or a smartphone with Android operating system (OS) 10 or later, and be willing to download and use the designated research application as required by the agreement.

[0480] 10. Willing and able to receive SMS text messages and push messages on a smartphone.

[0481] 11. Be the owner of the email address and have regular access to it.

[0482] 12. Have regular access to the internet via a cellular data plan and / or Wi-Fi.

[0483] 13. Have stable housing and have lived in the same residence for at least 12 weeks prior to screening and are not expected to change housing during the duration of the study.

[0484] 14. Be aware of and interested in using the study app during the Screening Period and Baseline Visit (Day 1).

[0485] Exclusion Criteria: Participants will not be eligible for entry into the study if any of the following exclusion criteria are met:

[0486] 1. Currently being treated with two or more antipsychotic drugs (including two or more dosage forms).

[0487] 2. Currently being treated with clozapine or haloperidol, or having been treated with clozapine within 5 years prior to the screening visit.

[0488] 3. At the screening or baseline visit of the PANSS, a positive symptom item score of >4 (moderate) has been obtained for P1-delusion, P2-confusion, P3-hallucination, P6-suspiciousness, or any item score of >5 (moderate to severe), thus indicating significant positive symptoms.

[0489] 4. Based on the investigator's assessment, currently receiving or having received psychological treatment defined as individual or group-based structured treatment (such as cognitive behavioral therapy, social skills training, or occupational / vocational therapy) within 6 months (26 weeks) before screening.

[0490] 5. Compliant with DSM-5 for diagnoses not investigated that would affect their compliance with the protocol, including schizophrenia-like, schizoaffective, or psychosis nonspecific disorders (post-traumatic stress disorder [PTSD], bipolar disorder, major depressive disorder, or developmental disorder).

[0491] 6. Meet DSM-5 criteria for a current depressive, manic, or hypomanic episode.

[0492] 7. Have a current diagnosis of a substance or alcohol use disorder (excluding caffeine and nicotine) as defined in DSM-5 within 6 months (26 weeks) of the screening visit.

[0493] 8. Positive urine drug screen for amphetamines (including MDMA / ecstasy), phencyclidine (PCP), cocaine, opioids, benzodiazepines, or barbiturates at screening or baseline visit prior to randomization. Participants with positive urine drug tests and / or recreational THC use may be enrolled at the investigator's discretion.

[0494] 9. Participation in previous clinical research on digital therapeutic applications; or participation in user research on digital therapeutic applications.

[0495] 10. Participated in another clinical study (interventional or observational) within the past 6 months (26 weeks).

[0496] 11. As determined by the investigator, it is or may be necessary to withhold concomitant medications and / or treatments during the trial.

[0497] 12. Suicidal ideation or behavior as assessed by the C-SSRS

[0498] 13. Participants who responded “yes” to item 4 or 5 of the C-SSRS suicidal ideation item within 3 months (12 weeks) prior to screening or at the baseline visit.

[0499] 14. Participants with a “yes” response to the C-SSRS suicidal behavior item within the last 6 months (26 weeks) prior to screening or at the baseline visit.

[0500] 15. Participants who are considered by researchers to be at serious risk for suicide.

[0501] Lifestyle considerations

[0502] Participants should have regular access to their smartphones for the duration of the trial. Additionally, they should be able to attend clinical and telehealth visits during the trial. Participants should refrain from using alcohol or recreational drugs while accessing the study app. Occasional recreational use of THC is permitted.

[0503] Study interventions and concomitant treatments

[0504] Study intervention administration

[0505] The Digital Therapeutic App R-001 study mobile application (Study App) will administer one of two study interventions to participants. The study interventions are the digital therapeutic app PDT and the comparator, a digital control (Table 2).

[0506] Table 2: Study interventions

[0507]

[0508] The digital therapeutic application is being developed as an adjunctive prescription digital therapeutic designed to target experiential negative symptoms in adults and older adolescents with schizophrenia who are stable on SOC. Treatment delivery is guided by a validated mechanistic model to address significant clinical needs identified by individuals with schizophrenia. The therapeutic technologies in CT-155 were selected based on clinical evidence, and each component was designed based on the fundamental principles of face-to-face therapy to provide maximal support to individuals with schizophrenia.

[0509] As described in further detail below, digital therapeutic applications achieve their primary purpose of improving experiential negative symptoms by integrating multiple neurobehavioral therapy techniques with established evidence. These therapy techniques work synergistically to help people set goals (adaptive goal setting) that promote real-world engagement (behavioral activation), while removing barriers (cognitive restructuring), and providing skills that facilitate goal achievement (social skills training, positive affect training).

[0510] Digital comparison

[0511] In this trial, a digital control app will serve as the comparator group. The apps will be compared for common elements of digital apps (receiving notifications, on-demand access) and for daily engagement with the novel app.

[0512] Preparation / Processing / Accountability / Disposal

[0513] Generally speaking:

[0514] •A designated unblinded individual must confirm download and activation of the study app.

[0515] •Only participants enrolled in the study will receive the study intervention.

[0516] • The study app will automatically stop running at the end of Week 20. Designated unblinded trial site staff will instruct participants to uninstall the study app during their last study visit or upon discontinuation from the study.

[0517] Measures to reduce bias: randomization and blinding

[0518] Study Use of IVRS / IWRS: All participants will be centrally assigned to the randomized study intervention using an interactive voice / web response system (IVRS / IWRS). Before the start of the study, each site will be provided with a telephone number and access instructions for the IVRS and / or login information and instructions for the IWRS.

[0519] The study intervention (study application) will be downloaded, activated, and deleted during the study visits summarized in the SoA.

[0520] Unblinding (IVRS / IWRS) The IVRS / IWRS will be programmed with unblinding instructions. In emergency situations, the investigator has the sole responsibility to determine whether unblinding a participant's intervention assignment is necessary. The safety of the participant must always be the primary consideration when making such decisions. If the investigator determines that unblinding is necessary, the investigator should make every effort to contact the sponsor before unblinding the participant's intervention assignment, unless doing so would delay the participant's emergency treatment. Once a participant's intervention assignment is unblinded, the sponsor must be notified within 24 hours of unblinding. The date and reason for unblinding must be recorded in the source documentation and case report form, if applicable.

[0521] This is a blinded study with an unblinded investigator role. Participants will be randomized in a 1:1 ratio to receive either the digital therapeutic app or the digital control app as the study intervention via the study app. Throughout the study, the investigator, designated site personnel, and assessors will remain blinded to each participant's assigned study intervention. To maintain this blinding, designated unblinded site personnel will assist study participants in downloading and verifying installation of the study app. Designated unblinded site personnel will also be responsible for conducting compliance checks.

[0522] Description of the methods used for blinded assessment: To further prevent bias, a centralized blinded assessment team will perform the CAINS assessment.

[0523] Study hypothesis blinding: Study participants will be blinded to the study's efficacy hypothesis. Both treatment groups will be presented to participants as possible treatments for schizophrenia. Participants will not be informed of the digital therapeutic app or the digital control. This approach limits the risk of unblinding participants to treatment assignment and the expected efficacy.

[0524] The assigned safety officer may unblind the intervention assignment for any participant with an SAE. If an SAE requires an expedited regulatory report to one or more regulatory agencies, a copy of the report identifying the participant's intervention assignment may be sent to the investigator, in accordance with local regulations and / or sponsor policy.

[0525] Study intervention compliance

[0526] During the treatment period, all randomized participants were instructed to use the study app as directed in the study app. Participants in both the digital therapeutic app and the digital control group were considered adherent on a given treatment day if they completed at least one of the available daily activities. Participants were considered adherent on the study if they adhered on at least 67 of the 112 total treatment days (approximately 60%). Completion of assigned activities was measured and recorded using in-app participation metrics and defined in the Statistical Analysis Plan (SAP).

[0527] Compliance monitoring

[0528] To check whether participants are complying with the treatment assigned by the study application, site staff will conduct compliance checks at weeks 4, 8, and 12. During these visits, designated non-blinded site staff will obtain participant compliance data via the compliance code read from the study application. This code will indicate how participants should comply with treatment before accessing the code. The researcher will use this information to remind participants that they should use the study application every day and identify any technical issues they may have that may interfere with treatment compliance. The guidance and language for this conversation will be specified in the study application investigator guide.

[0529] Concomitant therapy

[0530] Participants must have been taking a stable dose of antipsychotic medication for at least 12 weeks before randomization (Day 1). Dose adjustments will be allowed during the study as described in the appropriate package insert for their current medication.

[0531] Participants were not allowed to be treated with more than two (2) antipsychotic medications (including more than two dosage forms). Treatment with clozapine or haloperidol was prohibited.

[0532] Any additional psychological treatment, including cognitive behavioral therapy, social skills training, and motivational enhancement therapy, was not allowed during the study period.

[0533] Occasional use of recreational 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 medications was not grounds for exclusion from the trial.

[0534] Participant discontinuation / withdrawal from the study

[0535] Site staff should make every effort to encourage participants to remain in the study and receive the study intervention when medically safe to do so. Participants who prematurely discontinue the study intervention must complete the early discontinuation procedures as described in the SoA. Ideally, participants who prematurely discontinue the study intervention should be observed until the end of the trial, as if they were still receiving blinded study treatment. For all participants, the reason for withdrawal from the study intervention (e.g., AE) must be recorded in the CRF. These data will be included in the trial database and reported.

[0536] Participants who are not actively using the study intervention may be less motivated to adhere to the study visit schedule. Investigators and site staff should strive to detect early signs of waning interest and readily present the following options to such participants to encourage continued participation:

[0537] • Early D / C Option 1: Continue with scheduled visits

[0538] • Early D / C Option 2: Perform all remaining study visits. At the scheduled clinical visit, only the following assessments are required:

[0539] oPANSS

[0540] oCAINS and CAINS-MAP

[0541] oCGI-S

[0542] oSSRS

[0543] oAdverse events

[0544] oConcomitant treatment

[0545] • Early D / C Option 3: Discontinue participation in the remainder of the study but allow collection of information on the occurrence of psychiatric disorders / relapse and vital status through the participant or a surrogate designated by the participant (e.g., family member, spouse, partner, legal representative, or natural person) approximately 16 weeks after randomization.

[0546] • Early D / C Option 4: Same as Option 3 above, but with the possibility of collecting the occurrence of psychiatric disorders / relapse and vital status approximately 16 weeks after randomization by reviewing participant medical information from alternative sources (e.g., physician notes, hospital records, etc.).

[0547] Participants will be asked to select the most stringent form of follow-up they are willing to comply with. Participants who refuse all four of the above will be considered to have completely withdrawn their consent to participate in the study.

[0548] The study application will be disabled on the discontinuing participant's smartphone device.

[0549] Research Assessment and Procedures

[0550] Study assessments and procedures, including their timing, are outlined in the SoA. Compliance with study design requirements, including those specified in the SoA, is critical and essential to the conduct of the study. No waivers or exemptions from the protocol will be permitted. Every effort should be made to ensure that assessments and procedures required by the protocol are completed as described. All clinician-administered scales should be administered by appropriately trained individuals.

[0551] Efficacy evaluation

[0552] This trial will use the following efficacy assessment scale within the timeframe provided in the SoA. A description of the scale and the corresponding scoring algorithm for all endpoints will be provided in the Statistical Analysis Plan (SAP).

[0553] Clinical Assessment Interview for Negative Symptoms (CAINS)

[0554] CAINS is a 13-item interview-based assessment consisting of two subscales measuring two factors of negative symptoms: motivation and pleasure (MAP) and performance (EXP). CAINS items are scored on a 5-point scale, with lower scores indicating lower severity of negative symptoms. Please note that CAINS is administered by a centralized, blinded scoring team, not by site clinicians.

[0555] The MAP scale consists of nine items that measure interest and participation in motivated behaviors, as well as the experience of enjoyment in social, occupational, and recreational areas. Each item is scored based on the patient's reported behavior and experience. The EXP scale consists of four items that measure speech intonation (prosody), nonverbal expressions (gestures, posture), facial expressions, and speech output (aphasia). Items are scored based on observation during the interview.

[0556] Positive and Negative Syndrome Scale (PANSS)

[0557] The PANSS consists of three subscales containing a total of 30 symptom components. For each symptom component, the severity is rated on a 7-point scale, where 1 indicates no symptoms and 7 indicates extremely severe symptoms. The symptom components for each subscale are as follows:

[0558] Positive subscale (composed of 7 positive symptoms: delusions, conceptual confusion, hallucinatory behavior, excitement, grandiose, suspicious / persecution, and hostility).

[0559] Negative subscale (7 negative symptom constructs: blunted affect, emotional withdrawal, poor rapport, passive / apathetic social withdrawal, difficulty with abstract thinking, lack of spontaneity and conversational fluency, and stereotyped thinking).

[0560] The general psychopathology subscale (16 symptoms): somatic worry, anxiety, guilt, tension, behavior and posture, depression, bradykinesia, uncooperativeness, abnormal thought content, disorientation, inattention, lack of judgment and insight, volitional disturbance, poor impulse control, preoccupation, and active social avoidance)

[0561] Personal and Social Performance Scale (PSP)

[0562] The PSP is a validated, clinician-rated scale that measures personal and social functioning in four areas: socially rewarding activities (e.g., work and school), personal and social relationships, self-care, and disruptive and aggressive behavior. Each area is scored on a 0-100 scale with anchor points at 10-point intervals.

[0563] Defeatist Beliefs Subscale of the Dysfunctional Attitudes Scale (DAS)

[0564] The Defeatist Performance Beliefs subscale is a 15-item subset of the Dysfunctional Attitudes scale. Items are rated on a scale of 1-7, with higher scores indicating more severe defeatist thinking.

[0565] Mini-International Neuropsychiatric Interview (MINI)

[0566] At the screening visit, the MINI (version 7.0.2 for DSM-5) will be used as a structured interview tool for eligibility assessment. The MINI is a widely used structured diagnostic interview tool developed for DSM-5 mental disorders. Qualified assessors will conduct the interviews.

[0567] The results of the MINI will be compared with the inclusion / exclusion criteria for the assessment of comorbid diagnoses.

[0568] Clinical Global Impression-Severity (CGI-S)

[0569] The CGI-S is a standardized, clinician-rated, global rating scale that uses a 7-point Likert scale to measure the severity of negative symptoms experienced during the past seven days. Higher scores on the CGI-S indicate greater illness severity. Response options include: 0 = Not assessed; 1 = Normal, not at all ill; 2 = Borderline psychosis; 3 = Mild illness; 4 = Moderate illness; 5 = Marked illness; 6 = Severe illness; and 7 = Extreme illness.

[0570] Patient Global Impression of Improvement (PGI-I)

[0571] The PGI-I is a patient-reported outcome that measures participatory subjective improvement in the severity of experienced negative symptoms on a 7-point scale. Higher scores on the PGI-I indicate subjective reports of disease worsening during treatment.

[0572] Patient Global Impression of Severity (PGI-S)

[0573] The PGI-S is a patient-reported outcome that measures the subjective severity of experienced negative symptoms on a 5-point scale. Higher scores on the PGI-S indicate a higher subjective report of illness severity.

[0574] EQ-5D-5L

[0575] The EQ-5D-5L is a standardized, concise self-report instrument for measuring health status. It consists of two components: the EQ-5D descriptive system and the EQ visual analogue scale. The descriptive system includes five dimensions: mobility, self-care, usual activities, pain / discomfort, and anxiety / depression. Participants are asked to indicate their health status by selecting the box next to the most appropriate description in each of the five dimensions. The EQ visual analogue scale records the participant's overall self-rated health on a visual analogue scale ranging from "the best health you can imagine" to "the worst health you can imagine."

[0576] Sheehan Disability Scale (SDS)

[0577] The SDS is a brief, 5-item self-report instrument that assesses functional impairment in three domains: work / school, social life, and family life.

[0578] WHO Disability Assessment Schedule (WHODAS2.0)

[0579] The WHODAS 2.0 is a 36-item self-assessment scale that measures participants' functioning and disability in six life domains: cognition (understanding and communicating), mobility (moving and getting around), self-care (hygiene, dressing, eating, and being alone), getting along (interacting with others), activities of life (family responsibilities, leisure, work, and school), and participation (community and society).

[0580] Schizophrenia Quality of Life Scale-Revised 4 (SQLS-R4)

[0581] The SQLS-R4 is a 33-item self-assessment scale that uses a 5-point scale to assess quality of life across the psychosocial feelings domain and the vitality / cognition domain.

[0582] Brief Assessment of Cognition in Schizophrenia (BACS) subscales

[0583] The Brief Assessment of Cognition in Schizophrenia (BACS) is a validated paper-and-pencil cognitive assessment that includes several subtests: a symbolic encoding task to assess working memory; a verbal memory task; and a digit sequencing task to assess attention and information processing speed. These three subtests can be combined into the Short Form Global Cognitive Screening Tool (BACS-SF).

[0584] Symbol Coding Task: Participants had 90 seconds to write the numbers 1-9 that matched the symbols on the answer sheet. The total task duration was 3 minutes.

[0585] Verbal Memory Task: Participants were presented with 15 words and then asked to recall as many as possible. This procedure was repeated five times. The total task duration was 7 minutes.

[0586] Number sorting: Participants were presented with a series of numbers of increasing length and were required to tell the experimenter the order from lowest to highest. The total task duration was 5 minutes.

[0587] Research Application Participation Assessment

[0588] Participant engagement with a research app can be automatically captured by the research app. The following metrics are captured.

[0589] •Number of times users open the app

[0590] • The number of days the user has opened the app

[0591] •Number of courses completed by each user

[0592] •Number of times each user exercises a skill

[0593] • Number of completed modules

[0594] •The number of emotional check-ins completed by each user during the daily check-in period

[0595] • Number of daily adaptive goal setting (AGS) activities completed

[0596] • Number of goals completed by each user

[0597] Statistical considerations

[0598] Participants' health economic data will be assessed as exploratory endpoints.

[0599] Statistical assumptions

[0600] The null hypothesis is that there is no difference between the means of the treatment groups, and the alternative hypothesis is that the means are different.

[0601] If the two-sided p-value is less than 0.05 and the results favor the digital therapeutic app group, the null hypothesis will be rejected in favor of the alternative hypothesis.

[0602] Sample size determination

[0603] The primary endpoint was the change from baseline to week 16 in experienced negative symptoms, as assessed by the CAINS-MAP scale. The null hypothesis was that there was no difference in the means between the treatment groups, and the alternative hypothesis was that the means were different. To demonstrate this, and assuming a standardized effect (Cohen's d) of 0.35 between the treatment groups, 173 participants per group were required to achieve 90% power using a 5% type I error rate (two-sided). Assuming a 20% early termination rate, 432 participants needed to be randomized to the study in a 1:1 ratio (approximately 216 per group). The sample size was calculated using a two-independent-sample t-test and assuming equal variances.

[0604] Analysis Group

[0605] For the purpose of analysis, the following analysis groups were defined:

[0606] Analysis Group: Description

[0607] Participants: All participants who signed the ICF.

[0608] Intent-to-treat (ITT): All randomized participants who received the intervention assigned during randomization and recorded in the database, regardless of successful activation or use of the study app. Participants who received treatment without being randomized will not be considered randomized and will not be included in any efficacy or safety analyses.

[0609] This analysis group will serve as a supporting analysis for the primary endpoint.

[0610] Modified intention-to-treat (mITT): All randomized participants who were assigned the intervention based on randomization and whose application was activated by successfully entering the access code into the study application, who used the application at least once, and who had at least one evaluable baseline measurement. This will be the primary analysis group for all efficacy analyses.

[0611] Per-Protocol (PP): All randomized participants who completed 16 weeks of treatment with an adherence level of at least 60% during the 16-week treatment period and had no major protocol deviations that affected the calculation of the primary endpoint. This analysis group will be used as a supportive analysis for the primary endpoint.

[0612] • Participants with the following deviations will be excluded from the PP group:

[0613] • Participants who received an intervention different from the randomized intervention

[0614] • Participants who do not meet the inclusion / exclusion criteria

[0615] • Participants who use banned drugs

[0616] • Additional criteria for exclusion from PP groups can be defined in SAP.

[0617] General considerations

[0618] For continuous variables, summary tables will provide the number of observations [n], mean, standard deviation, median, minimum, and maximum values. For categorical variables, summary tables will provide the number of observations [n] and the frequency of each category (including missing data). Tables will be presented by treatment and overall population, where appropriate. Modeling and testing for each endpoint will also be described.

[0619] Disposition of participants

[0620] The number of participants screened and enrolled in the study and the reasons for nonrandomization will be presented. The number of participants who discontinued the study will be presented by reason for discontinuation. The number of participants in the ITT, mITT, PP, and safety analysis groups will be presented.

[0621] Demographic and baseline characteristics

[0622] To assess the comparability of the two groups at baseline, demographic characteristics, and baseline characteristics, data will be summarized by treatment group. These summaries will be presented by mITT group. If the total number of participants between the mITT and safety, ITT, and PP analysis groups differs by more than 5, summaries will be presented by these groups.

[0623] Primary endpoint

[0624] The primary analysis will be performed on the mITT group. The mITT group will be supported by analyses of the ITT group to assess similarities.

[0625] The primary endpoint was the change from baseline to week 16 in experienced negative symptoms, as assessed by the CAINS-MAP scale. The null hypothesis was that there was no difference in the means between the treatment groups, and the alternative hypothesis was that the means were different. To demonstrate this, and assuming a standardized effect (Cohen's d) of 0.35 between the treatment groups, 173 participants per group were required to achieve 90% power using a 5% type I error rate (two-sided). Assuming a 20% early termination rate, 432 participants needed to be randomized to the study in a 1:1 ratio (approximately 216 per group). The sample size was calculated using a two-independent-sample t-test and assuming equal variances.

[0626] With the response variable in which the change from baseline at each visit of the CAINS-MAP is assessed, the primary endpoint of change from baseline to week 16 in experienced negative symptoms will be analyzed using mixed model repeated measures (MMRM). The model will include an intercept and the following covariates: experienced negative symptoms at baseline, visit (as a categorical variable), treatment group, treatment by visit interaction term, and baseline by visit interaction term.

[0627] The preferred method for intra-patient correlations is an unstructured covariance matrix. Mitigation measures in the event of non-convergence will be discussed in the SAP. Only data from planned visits will be used for this analysis. The study will be considered successful if the two-sided p-value for the difference in baseline change between the two treatment groups at Week 16 is less than 0.05, and a greater decrease in CAINS-MAP is observed in the digital therapeutic app group compared to the digital control group.

[0628] Missing data will not be imputed for the primary analyses. All measures available for each participant will be used.

[0629] The planned number of sites is approximately 40. SAP will consider merging sites (e.g., by region or center type) and assess the impact of such merging in an exploratory manner. The analysis will be replicated on the PP group.

[0630] Sensitivity analysis

[0631] Missing data will be handled using multiple imputation (MI) with missing at random (MAR), where imputation will be performed within randomized groups and the primary model will be repeated for each complete data group. Detailed information will be provided in the statistical analysis plan.

[0632] To assess the robustness of the results to missing data under the MAR assumption, a tipping point analysis will be conducted. In this analysis, all missing data in the sham group will be imputed based on observations from that group (implying the MAR assumption). Missing data in the digital therapeutic app group will be imputed under different sets of assumptions, starting with the MAR assumption and gradually decreasing the effect until a tipping point is reached. Details will be provided in the SAP.

[0633] Subgroup

[0634] The main analysis will be repeated with the following subgroups:

[0635] • Length of time since diagnosis of schizophrenia (0-5 years, more than 5 years)

[0636] • Baseline CAINS-MAP negative symptom severity (moderate, moderately severe, severe)

[0637] • Severity of cognitive impairment (to be defined in SAP)

[0638] • Gender (male / female)

[0639] •Age group (18-21, 22-64, 65+)

[0640] Secondary endpoints

[0641] Analyses of secondary endpoints will be conducted using the mITT analysis set. Secondary endpoints will be analyzed in a similar manner to the primary endpoint. Analyses will use the MMRM to assess differences between treatment groups. Additional details will be provided in the SAP.

[0642] Multiple controls

[0643] Secondary endpoints, sensitivity analyses, and subgroup adjustments will not be performed for multiplicity.

[0644] It is expected that users presented with a digital therapeutic application (e.g., application 165) according to the methods described herein will show improvement in the negative symptoms of schizophrenia. The user may be taking an antipsychotic medication (e.g., Risperdal, Seroquel, Zyprexa, Zeldox, Invega, or Abilify) in conjunction with the digital therapeutic application. Improvement will be measured using any number of indicators, such as the Clinical Assessment Interview for Negative Symptoms, the Motivation and Pleasure Scale (CAINS-MAP), the Defeatism Beliefs subscale of the Dysfunction Attitudes Scale (DAS) (relative to a control digital application); or the Patient Global Impression of Improvement (PGI-I). As prompted by the digital therapeutic application, the indicator can be measured after performing one or more activities.

[0645] Exemplary embodiments may include a method for improving experiential negative symptoms of schizophrenia in a subject receiving antipsychotic medication, comprising exposing the subject to a prescription digital therapeutic (DTx) disclosed herein. Antipsychotic medications include iperidone (Risperdal), quetiapine (Seroquel), olanzapine (Zyprexa), ziprasidone (Zeldox), paliperidone (Invega), and aripiprazole (Abilify). In some embodiments, the method may include assessing the severity of experiential negative symptoms of schizophrenia in the subject after exposure to the prescribed DTx. In some embodiments, the severity of experiential negative symptoms of schizophrenia may be determined using the Clinical Assessment Interview for Negative Symptoms, Motivation, and Pleasure Scale (CAINS-MAP).

[0646] In some embodiments, the method may include assessing changes from baseline in motivation and hedonic symptoms at week 8 as assessed by the CAINS-MAP relative to a comparator digital application; or assessing changes from baseline in manifest negative symptoms at weeks 8 and 16 as assessed by the Clinical Assessment Interview for Negative Symptoms, Manifestation Scale (CAINS-EXP) relative to a comparator digital application; or assessing changes from baseline in positive symptoms at weeks 8 and 16 as assessed by the Positive and Negative Syndrome Scale (PANSS) relative to a comparator digital application; or assessing changes from baseline in social functioning at weeks 8 and 16 as assessed by the Personal and Social Performance Scale (PSP) relative to a comparator digital application; or assessing 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) relative to a comparator digital application; or assessing the Patient Global Impression of Improvement (PGI-I) at weeks 8 and 16 relative to a comparator digital application.

[0647] In some embodiments, the experiential negative symptoms of schizophrenia may include blunted affect, aphasia (decreased speech), avolition (decreased goal-directed activity due to decreased motivation), social impairment, and anhedonia (decreased experience of pleasure). In some embodiments, the subject may have been taking a stable dose of an antipsychotic medication for at least 12 weeks prior to receiving the prescribed DTx treatment.

[0648] Exemplary embodiments may include a method for ameliorating experiential negative symptoms of schizophrenia in a subject in need thereof, comprising exposing the subject to a prescription digital therapeutic (DTx) disclosed herein. In some embodiments, the method may include assessing the severity of the subject's experiential negative symptoms of schizophrenia following exposure to the prescribed DTx. In some embodiments, the severity of experiential negative symptoms of schizophrenia may be determined using the Motivation and Pleasure Scale - Self-Report (MAP-SR). In some embodiments, the severity of experiential negative symptoms of schizophrenia may be determined using the Clinical Assessment Interview for Negative Symptoms (CAINS). In some embodiments, experiential negative symptoms of schizophrenia may include blunted affect, aphasia (decreased speech), avolition (reduced goal-directed activity due to decreased motivation), ansociality, and anhedonia (reduced experience of pleasure).

[0649] Exemplary embodiments may include a method for improving experiential negative symptoms of schizophrenia in a subject receiving an antipsychotic medication, comprising exposing the subject to a prescription digital therapeutic (DTx) disclosed herein. In some embodiments, the antipsychotic medication may be iperidone (Risperdal), quetiapine (Seroquel), olanzapine (Zyprexa), ziprasidone (Zeldox), paliperidone (Invega), aripiprazole (Abilify), or iclepertin, among others. The method may include assessing the severity of experiential negative symptoms of schizophrenia in the subject after exposure to the prescribed DTx. In some embodiments, the severity of experiential negative symptoms of schizophrenia is determined using the Clinical Assessment Interview-Mapped Scale for Negative Symptoms, Motivation, and Pleasure (CAINS-MAP).

[0650] In some embodiments, the method may include assessing changes from baseline in motivation and hedonic symptoms at week 8 as assessed by the CAINS-MAP relative to a comparator digital application; or assessing changes from baseline in manifest negative symptoms at weeks 8 and 16 as assessed by the Clinical Assessment Interview for Negative Symptoms, Manifestation Scale (CAINS-EXP) relative to a comparator digital application; or assessing changes from baseline in positive symptoms at weeks 8 and 16 as assessed by the Positive and Negative Syndrome Scale (PANSS) relative to a comparator digital application; or assessing changes from baseline in social functioning at weeks 8 and 16 as assessed by the Personal and Social Performance Scale (PSP) relative to a comparator digital application; or assessing 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) relative to a comparator digital application; or assessing the Patient Global Impression of Improvement (PGI-I) at weeks 8 and 16 relative to a comparator digital application.

[0651] In some embodiments, the experiential negative symptoms of schizophrenia may include blunted affect, aphasia (decreased speech), avolition (decreased goal-directed activity due to decreased motivation), social impairment, and anhedonia (decreased experience of pleasure). In some embodiments, the subject has been taking a stable dose of an antipsychotic medication for at least 12 weeks prior to receiving the prescribed DTx treatment.

[0652] C. Network and computing environment

[0653] Various operations described herein can be implemented on a computer system. Figure 24A simplified block diagram of a representative server system 2400, client computing system 2414, and network 2426 that can be used to implement certain embodiments of the present disclosure is shown. In various embodiments, server system 2400 or a similar system can implement the services or servers described herein, or portions thereof. Client computing system 2414 or a similar system can implement the clients described herein. System 100 described herein can be similar to server system 2400. Server system 2400 can have a modular design incorporating multiple modules 2402 (e.g., blades in a blade server embodiment); while two modules 2402 are shown, any number of modules can be provided. Each module 2402 can include a processing unit 2404 and a local storage device 2406.

[0654] Processing unit 2404 may include a single processor, which may have one or more cores, or multiple processors. In some embodiments, processing unit 2404 may include a general-purpose main processor and one or more specialized coprocessors, such as a graphics processor or a digital signal processor. In some embodiments, part or all of processing unit 2404 may be implemented using custom circuitry, such as an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA). In some embodiments, such an integrated circuit executes instructions stored on the circuit itself. In other embodiments, processing unit 2404 may execute instructions stored in local storage device 2406. Processing unit 2404 may include any combination of any type of processor.

[0655] Local storage device 2406 may include volatile storage media (e.g., DRAM, SRAM, SDRAM, etc.) and / or non-volatile storage media (e.g., magnetic or optical disks, flash memory, etc.). The storage media contained in local storage device 2406 may be fixed, removable, or upgradeable, as needed. Local storage device 2406 may be physically or logically divided into various subunits, such as system memory, read-only memory (ROM), and permanent storage. System memory may be a read-write memory device or a volatile read-write memory device, such as dynamic random access memory. System memory may store some or all of the instructions and data required by processing unit 2404 during operation. ROM may store static data and instructions required by processing unit 2404. Persistent storage may be a non-volatile read-write memory device that can store instructions and data even when module 2402 is powered off. As used herein, the term "storage medium" includes any medium that can store data indefinitely (susceptible to being overwritten, electrical interference, power outages, etc.), but does not include carrier waves and transient electronic signals propagated over wireless or wired connections.

[0656] In some embodiments, the local storage device 2406 may store one or more software programs executed by the processing unit 2404, such as an operating system and / or programs that implement various server functions (such as the functions of the system 100 or any other system described herein, or the functions of any other server associated with the system 100 or any other system described herein).

[0657] "Software" generally refers to sequences of instructions that, when executed by processing unit 2404, cause server system 2400 (or portions thereof) to perform various operations, thereby defining one or more specific machine embodiments for executing and implementing the software program operations. Instructions may be stored as firmware residing in read-only memory and / or as program code stored in non-volatile storage media, which may be read into volatile working memory for execution by processing unit 2404. Software may be implemented as a single program or as a collection of separate programs or program modules that interact as needed. Processing unit 2404 may retrieve program instructions to execute and data to process from local storage device 2406 (or non-local storage, described below) to perform the various operations described above.

[0658] In some server systems 2400, multiple modules 2402 may be interconnected via a bus or other interconnect 2408, thereby forming a local area network that supports communication between the modules 2402 and other components of the server system 2400. The interconnect 2408 may be implemented using various technologies, including server racks, hubs, routers, etc.

[0659] Wide area network (WAN) interface 2410 can provide data communication capabilities between a local area network (e.g., via interconnect 2408) and a network 2426 (such as the Internet). Other technologies can be used to communicatively couple server system 2400 to network 2426, including wired (e.g., Ethernet, IEEE 802.3 standard) and / or wireless technologies (e.g., Wi-Fi, IEEE 802.11 standard).

[0660] In some embodiments, local storage device 2406 is intended to provide working memory for processing unit 2404, thereby providing fast access to programs and / or data to be processed while reducing traffic on interconnect 2408. Storage of larger amounts of data can be provided on a local area network by one or more mass storage subsystems 2412 that can be connected to interconnect 2408. 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 data storage or other data collection generated, used, or maintained by a service or server as described herein can be stored in mass storage subsystem 2412. In some embodiments, additional data storage resources can be accessed via WAN interface 2410 (possibly with increased latency).

[0661] The server system 2400 can operate in response to requests received via the WAN interface 2410. For example, one of the modules 2402 can implement monitoring functions in response to received requests and assign discrete tasks to other modules 2402. Work distribution techniques can be used. After processing the request, the results can be returned to the requester via the WAN interface 2410. Such operations can generally be automated. In addition, in some embodiments, the WAN interface 2410 can connect multiple server systems 2400 to each other, thereby providing a scalable system capable of managing a large number of activities. Other techniques for managing server systems and server clusters (collections of collaborating server systems) can be used, including dynamic resource allocation and reallocation.

[0662] The server system 2400 can interact with various user-owned or user-operated devices via a wide area network, such as the Internet. Figure 24 24 is an example of a user-operated device as a client computing system 2414. The client computing system 2414 can be implemented as, for example, a consumer device such as a smartphone, other mobile phone, tablet computer, wearable computing device (e.g., smart watch, glasses), desktop computer, laptop computer, etc.

[0663] For example, client computing system 2414 may communicate via WAN interface 2410. Client computing system 2414 may include computer components such as processing unit 2416, storage device 2418, network interface 2420, user input device 2422, and user output device 2424. Client computing system 2414 may be a computing device implemented in various form factors, such as a desktop computer, laptop computer, tablet computer, smartphone, other mobile computing device, wearable computing device, etc.

[0664] The processing unit 2416 and storage device 2418 can be similar to the processing unit 2404 and local storage device 2406 described above. Appropriate devices can be selected based on the requirements to be deployed 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 can be equipped with program code that can be executed by the processing unit 2416 to implement various interactions with the server system 2400.

[0665] The network interface 2420 can provide a connection 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 can include a wired interface (e.g., Ethernet) and / or a wireless interface implementing various radio frequency data communication standards, such as Wi-Fi, Bluetooth, or a cellular data network standard (e.g., 3G, 4G, 5G, 24G, LTE, etc.).

[0666] User input device 2422 may include any device (or devices) via which a user can provide signals to client computing system 2414; client computing system 2414 may interpret the signals as indicating a particular user request or information. In various embodiments, user input device 2422 may include any or all of a keyboard, touchpad, touch screen, mouse or other pointing device, scroll wheel, click wheel, dial, buttons, switches, keypad, microphone, and the like.

[0667] User output devices 2424 may include any device through which client computing system 2414 can provide information to a user. For example, user output devices 2424 may include displays generated by or transmitted to client computing system 2414. Displays may incorporate various image generation technologies, such as liquid crystal displays (LCDs), light-emitting diodes (LEDs) (including organic light-emitting diodes (OLEDs)), projection systems, cathode ray tubes (CRTs), and supporting electronics (e.g., digital-to-analog converters or analog-to-digital converters, signal processors, etc.). Some embodiments may include devices such as touch screens that function as both input and output devices. In some embodiments, other user output devices 2424 may be provided in addition to or in place of a display. Examples include indicator lights, speakers, tactile "display" devices, printers, and the like.

[0668] Some embodiments include electronic components, such as microprocessors, storage devices, and memories, that store computer program instructions in computer-readable storage media. Many of the features described herein can be implemented as processes, which are specified as groups of program instructions encoded on computer-readable storage media. When these program instructions are executed by one or more processing units, they cause the processing units to perform the various operations indicated in the program instructions. Examples of program instructions or computer code include machine code (such as produced by a compiler) and files containing higher-level code that is executed by a computer, electronic component, or microprocessor using an interpreter. With appropriate programming, processing units 2404 and 2416 can provide various functions for server system 2400 and client computing system 2414, including any of the functions described herein as being performed by a server or client, or other functions.

[0669] It should be understood that the server system 2400 and the client computing system 2414 are illustrative and can be varied and modified. The computer systems used in conjunction with the embodiments of the present disclosure may have other functions not specifically described herein. In addition, although the server system 2400 and the client computing system 2414 are described with reference to specific blocks, it should be understood that these blocks are defined for ease of description and are not intended to imply a specific physical arrangement of component parts. For example, different blocks can, but need not, be located in the same facility, the same server rack, or the same motherboard. In addition, blocks do not necessarily correspond to physically different components. Blocks can be configured to perform various operations, such as by programming a processor or providing appropriate control circuitry, and various blocks may or may not be reconfigurable, depending on how the initial configuration is obtained. The embodiments of the present disclosure can be implemented in various devices, including electronic devices implemented using any combination of circuits and software.

[0670] Although the present disclosure has been described in conjunction with specific embodiments, it will be understood by those skilled in the art that various modifications are possible. Embodiments of the present disclosure may be implemented using various computer systems and communication technologies, including but not limited to the specific examples described herein. Embodiments of the present disclosure may be implemented using any combination of dedicated components and / or programmable processors and / or other programmable devices. The various processes described herein may be implemented in any combination on the same processor or on different processors. When a component is described as being configured to perform certain operations, such configuration may be achieved by, for example, designing an electronic circuit for performing the operation, programming a programmable electronic circuit (such as a microprocessor) for performing the operation, or any combination of the above. In addition, although the above embodiments may refer to specific hardware and software components, it will be understood by those skilled in the art that different combinations of hardware and / or software components may also be used, and that specific operations described as being implemented in hardware may also be implemented in software, and vice versa.

[0671] Computer programs incorporating various features of the present disclosure may be encoded and stored on various computer-readable storage media; suitable media include magnetic disks or tapes, optical storage media (e.g., compact disks (CDs) or digital versatile disks (DVDs), flash memory, and other non-transitory media). Computer-readable media encoded with the program code may be packaged with a compatible electronic device, or the program code may be provided separately from the electronic device (e.g., via Internet download or as a separately packaged computer-readable storage medium).

[0672] Therefore, although the disclosure has been described with respect to particular embodiments, it should be understood that the disclosure is intended to cover all modifications and equivalents that come within the scope of the following claims.

Claims

1. A method for improving experiential negative symptoms of schizophrenia in a user in need thereof, comprising: obtaining, by one or more processors, a first metric associated with the user a plurality of time instances ago; Repeating, by the one or more processors for each time instance in the plurality of time instances: identifying a profile for providing to an application, the profile selected from a plurality of profiles based on an endpoint for improving the experienced negative symptom, the profile identifying a set of content items for one of a plurality of activities intended to achieve the endpoint, In response to providing the configuration file to the application, presenting, via the application, the set of content items identified by the configuration file to prompt the user to perform the activity directed to achieving the endpoint, and receiving response data identifying one or more interactions of the user with the group of content items; as well as obtaining, by the one or more processors, a second metric associated with the user after at least one time instance in the plurality of time instances, Wherein, when the second indicator is statistically different from the first indicator, the user shows improvement in experienced negative symptoms of schizophrenia.

2. The method according to claim 1, wherein The first and second indicators include scores of at least one of the following: the Motivation and Pleasure Scale of the Clinical Assessment Interview for Negative Symptoms (CAINS), the Performance Scale of the Clinical Assessment Interview for Negative Symptoms (CAINS-EXP), the Positive and Negative Symptom Scale (PANSS), the Personal and Social Performance Scale (PSP), the Defeatism Beliefs Subscale of the Disability Attitudes Scale (DAS), the Patient Global Impression of Improvement Scale (PGI-I), the Patient Global Impression of Severity Scale (PGI-S), the Clinical Global Impression of Severity Scale (CGI-S), the WHO Disability Assessment Scale 2.0 (WHODAS2.0) or the Schizophrenia Quality of Life Scale-Revised 4 (SQLS-R4).

3. The method according to claim 1, wherein The experiential negative symptoms of schizophrenia include one or more of the following: affective blunting, aphasia, loss of will, social impairment, and anhedonia.

4. The method according to claim 1, wherein The user is an adult or an older teenager.

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

6. The method according to claim 1, wherein The user has a score of ≤30 on the Motivation and Pleasure Scale (MAPS) before 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: Such drugs include risperidone, quetiapine, olanzapine, ziprasidone, paliperidone, aripiprazole, or ecliptin.

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 profiles identifies criteria that define a metric for selecting another of the plurality of profiles, the metric identifying at least one of: (i) a likelihood that the user will perform the activity; or (ii) a predicted efficacy of the activity for the user to aim to achieve the endpoint.

11. The method according to claim 1, wherein The endpoint is selected from a plurality of endpoints for a first time instance of the plurality of time instances based on a baseline assessment of the user's experienced negative symptoms of schizophrenia.

12. The method according to claim 1, wherein The endpoint is selected from a plurality of endpoints based on 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, wherein 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 emotion 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, an entertainment domain, and a productivity domain.

16. The method according to claim 1, wherein A comparison of a first rating performed before performance of a corresponding second activity and a second rating performed after performance of the corresponding second activity is presented to the user.

17. The method of claim 1, further comprising determining, by the one or more processors, whether to continue repeating the plurality of time instances based on an amount of time since obtaining the baseline metric, and wherein Repeating for each of the plurality of time instances further includes repeating the time instance in response to determining to continue.

18. The method according to claim 1, wherein Repeating for each of the plurality of time instances further includes, for a time instance, updating the endpoint based on the response data identifying the one or more interactions of the user with the group of content items presented via the application from a previous time instance.

19. The method according to claim 1, wherein Repeating for each of the plurality of time instances further includes converting the human-readable instructions of the configuration file to generate a package including instructions in a machine-executable format.

20. The method according to claim 1, wherein Obtaining the first metric and the second metric further includes obtaining the first metric and the second metric from a source separate from the application.

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