SYSTEM, APPARATUS AND METHOD FOR EVENT-BASED KNOWLEDGE-REASONING SYSTEM USING ACTIVE AND PASSIVE SENSORS FOR PATIENT MONITORING AND FEEDBACK - Patent application

Abstract

summary The embodiments described herein relate to methods and devices for generating and using machine learning models, including, for example, event-based knowledge inference systems that use active and passive sensors for patient monitoring and feedback. In some embodiments, the systems, devices, and methods described herein can be for estimating adverse events based on rule-based inference. For example, the method can include using supervised, unsupervised, or reinforcement learning to build an event-based model for generating an estimate of a predictive score for a subject using a training data set, receiving a set of data streams associated with the subject, using the model and based on the data streams to estimate a predictive score for the subject, and determining the likelihood of an adverse event based on the predictive score.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 185,604, filed May 7, 2021, entitled "SYSTEMS, DEVICES, AND METHODS FOR TREATMENT OF DISORDERS USING DIGITAL THERAPIES AND PATIENT MONITORING AND FEEDBACK," and to U.S. Provisional Patent Application No. 63 / 214,553, filed June 24, 2021, entitled "METHODS, SYSTEMS AND APPARATUS FOR PROVIDING HAPTIC FEEDBACK ON A USER INTERFACE," the disclosures of each of which are incorporated herein by reference.

[0002] The embodiments described herein relate to methods and apparatus for generating and using machine learning models, including, for example, event-based knowledge reasoning systems that use active and passive sensors for patient monitoring and feedback. Such event-based knowledge reasoning systems can be generated and trained using patient data and then used to treat disorders (e.g., mood disorders, substance use disorders, or post-traumatic stress disorder (PTSD)). More specifically, the embodiments described herein relate to methods and apparatus for generating and implementing logical processing that obtains specific biological domain data associated with digital therapeutics for treating disorders and applies inference techniques for patient monitoring and feedback associated with such therapeutics and / or treatments. [Background technology]

[0003] Pharmacology has been used to treat many different types of medical conditions and disorders. Pharmacology can be administered to patients to target a particular condition or disorder. Examples of suitable pharmacotherapy can include pharmaceutical medications, biological products, and the like. Treatment of certain types of mood disorders and / or substantial use disorders can also involve counseling sessions, psychotherapy, or other types of structured interactions.

[0004] Drug therapies can often take weeks or months to achieve their full effect, and in some cases may require continued use or lead to drug dependency or other complications. Psychotherapy and other types of human interaction can be useful to treat without the complications of drug therapy, but are limited by the availability of trained professionals and may vary in effectiveness depending on the skills, time availability of trained professionals and patients, and / or the specific techniques used by trained professionals. There are also benefits associated with medically assisted therapy (MAT), i.e., the use of drug therapy with behavioral therapy or counseling, but such treatments are also limited by availability and other factors. In addition, treatment professionals can be expensive, difficult to coordinate meetings, and / or require chunks of time to interact (e.g., typically more than 30 minutes per session).

[0005] As a result, a need exists for improved methods and devices for treating disorders. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 is a schematic block diagram of a system for treating a patient, according to one embodiment. [Diagram 2]FIG. 2 is a schematic block diagram of a system for treating a patient including a mobile device and a server for administering a digital therapy and / or monitoring and collecting information about a subject, according to one embodiment. [Diagram 3] FIG. 3 is a data flow diagram illustrating information exchanged between different components of a system for treating a patient, according to one embodiment. [Figure 4] FIG. 4 is a flow chart illustrating a method for onboarding a new patient to a treatment protocol, according to one embodiment. [Diagram 5] FIG. 5 is a flow chart illustrating a method of delivering a challenge to a patient, according to one embodiment. [Figure 6] FIG. 6 is a flow chart illustrating a method for analyzing data collected from a patient, according to one embodiment. [Figure 7] FIG. 7 is a flow chart illustrating a method for analyzing data collected from a patient, according to one embodiment. [Figure 8] FIG. 8 is a flow chart illustrating example content presented on a user device according to one embodiment. [Figure 9] FIG. 9 illustrates an example schematic diagram illustrating a system of information exchange between a server and a user device (eg, electronic device) according to some embodiments. [Figure 10] FIG. 10 illustrates an example schematic diagram illustrating an electronic device implemented as a mobile device including a haptic subsystem, according to some embodiments. [Figure 11] FIG. 11 illustrates a flowchart of a process for providing feedback to a user in a survey according to some embodiments. [Figure 12] 12A-12D show example haptic effect patterns according to some embodiments. [Figure 13] FIG. 13 illustrates an example user interface of a user device according to some embodiments. [Figure 14]FIG. 14 is an example answer format with multiple axes according to some embodiments. [Figure 15] FIG. 15 illustrates generally axes representing changes in one or more characteristics associated with an example haptic effect, according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] The embodiments described herein relate to methods and devices for generating and using machine learning models, including, for example, event-based knowledge inference systems that use active and passive sensors for patient monitoring and feedback. In some embodiments, the systems, devices, and methods described herein can be for estimating adverse events based on rule-based inference. For example, the method can include using supervised, unsupervised, or reinforcement learning to build an event-based model for generating an estimate of a predictive score for a subject using a training data set, receiving a set of data streams associated with the subject, using the model and based on the data streams to estimate a predictive score for the subject, and determining the likelihood of an adverse event based on the predictive score.

[0008] In some embodiments, systems, devices, and methods for treating disorders are described herein. In some embodiments, the systems, devices, and methods described herein relate to monitoring a subject undergoing treatment for a mood disorder or substance abuse disorder and / or providing a digital therapeutic as part of a treatment regimen for such a disorder.

[0009] 1. Systems and Equipment 1.1 Digital Content and Analytics FIG. 1 illustrates an example system according to embodiments described herein. System 100 may be configured to provide digital content to a patient and / or monitor and analyze information about a patient. System 100 may be implemented as a single device or across multiple devices connected to a network 102. For example, system 100 may include one or more computing devices, including a server 110, a user device 120, a therapy provider device 130, a database(s) 140, or other computing device(s) 150. The computing devices may include component(s) located remotely from the computing device, located at a facility near the computing device, and / or integrated into the computing device.

[0010] The server 110 may include a component(s) located remotely from other computing devices and / or at a facility near the computing devices. The server 110 may be a computing device (or multiple computing devices) having a processor 112 and a memory 114 operably coupled to the processor 112. In some cases, the server 110 may be any combination of hardware-based modules (e.g., field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), digital signal processors (DSPs)) and / or software-based modules (computer code stored in the memory 114 and / or executed in the processor 112) capable of performing one or more specific functions associated with the modules. In some cases, the server 110 may be a server such as, for example, a web server, an application server, a proxy server, a telnet server, a file transfer protocol (FTP) server, a mail server, a list server, a collaboration server, and / or the like. In some cases, the server 110 may include or be communicatively coupled to a personal computing device, such as a desktop computer, a laptop computer, a personal digital assistant (PDA), a standard mobile phone, a tablet personal computer (PC), and / or the like.

[0011] The memory 114 can be, for example, a random access memory (RAM) (e.g., dynamic RAM, static RAM), a flash memory, a removable memory, a hard drive, a database, and / or the like. In some implementations, the memory 114 can include (or store) databases, processes, applications, virtual machines, and / or other software code and / or modules (stored and / or executed in hardware) and / or hardware devices and / or modules configured to perform one or more processes, such as those described with reference to FIGS. 3-7. In such implementations, instructions for performing such processes can be stored in the memory 114 and executed in the processor 112. In some implementations, the memory 112 can store content (e.g., text, audio, video, or interactive activity), patient data, and / or the like.

[0012] The processor 112 may be configured, for example, to write data into and / or read data from the memory 114, and to execute instructions stored in the memory 114. The processor 112 may also be configured to perform and / or control the operation of other components of the server 110, such as, for example, network interface cards, other peripheral processing components (not shown), etc. In some implementations, based on the instructions stored in the memory 114, the processor 112 may be configured to perform one or more steps of the processes illustrated in FIGS.

[0013] In some embodiments, the server 110 can be communicatively coupled to one or more database(s) 140. The database(s) 140 can include one or more repositories, storage devices and / or memories for storing information from patients, physicians and therapists, caregivers, and / or other individuals involved in supporting and / or administering therapy and / or care to the patient. In some embodiments, the server 100 can be coupled to a first database for storing patient information and / or assignments (e.g., content, coursework, etc.) and a second database for storing chat and / or voice data received from the patient (e.g., responses to assignments, voice acoustic data, etc.). Further details of an example database(s) are described with reference to FIG. 2.

[0014] The user device 120 may be a computing device associated with a user, such as a patient or a supporter (e.g., a caregiver or other individual providing support or care to a patient). The user device may have a processor 122 and a memory 124 operably coupled to the processor 122. In some cases, the user device 120 may be a mobile phone (e.g., a smartphone), a tablet computer, a laptop computer, a desktop computer, a portable media player, a wearable digital device (e.g., digital glasses, a wristband, a watch, a brooch, an armband, a virtual reality / augmented reality headset). The user device 120 may be any combination of hardware-based devices and / or modules (e.g., a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a digital signal processor (DSP)) and / or software-based code and / or modules (computer code stored in the memory 122 and / or executed on the processor 121) capable of performing one or more specific functions associated with the modules.

[0015] The memory 124 can be, for example, a random access memory (RAM) (e.g., dynamic RAM, static RAM), a flash memory, a removable memory, a hard drive, a database, and / or the like. In some implementations, the memory 124 can include (or store) databases, processes, applications, virtual machines, and / or other software code or modules (stored and / or executed in hardware) and / or hardware devices and / or modules configured to perform one or more processes, such as those described with reference to FIGS. 3-7. In such implementations, instructions for performing such processes can be stored in the memory 124 and executed in the processor 122. In some implementations, the memory 124 can store content (e.g., text, audio, video, or interactive activity), patient data, and / or the like.

[0016] Processor 122 may be configured, for example, to write data into and / or read data from memory 124 and to execute instructions stored in memory 124. Processor 122 may also be configured to perform and / or control the operation of other components of user device 120, such as, for example, a network interface card, other peripheral processing components (not shown), etc. In some implementations, based on instructions stored in memory 124, processor 122 may be configured to perform one or more steps of a process described with respect to Figures 3-7. In some implementations, processor 122 and processor 112 may be collectively configured to perform the process described with respect to Figures 3-7.

[0017] The user device 120 may include input / output (I / O) devices 126 (e.g., a display, a speaker, a tactile output device, a keyboard, a mouse, a microphone, a touch screen, etc.) that may include a user interface, e.g., a graphical user interface, to present information (e.g., content) to a user and receive input from a user. In some embodiments, the user device 120 may implement a mobile application that presents the user interface to the user. In some embodiments, the user interface may present content to the user, e.g., including text, audio, video, and interactive activities, to, e.g., educate the user regarding a disorder, a therapy program, and / or treatment, or to obtain information about the user regarding a treatment or therapy program. In some embodiments, the content may be provided during a digital therapy session, e.g., to treat the patient's medical condition and / or prepare the patient for a treatment or therapy. In some embodiments, the content may be provided as part of a periodic (e.g., daily, weekly, or monthly) check-in, whereby the patient is asked to provide information regarding the patient's mental and / or physical state.

[0018] In some embodiments, the user device 120 may include or be coupled to one or more sensors (not shown in FIG. 1). For example, the sensor(s) may be any suitable component that enables any of the computing devices described herein to capture information about the patient, the environment, and / or objects in the environment surrounding the computing device and / or communicate information about or to the patient or user. The sensor(s) may include, for example, an image capture device (e.g., a camera), an ambient light sensor, an audio device (e.g., a microphone), a light sensor, a proprioceptive sensor, a position sensor, a tactile sensor, a force or torque sensor, a temperature sensor, a pressure sensor, a motion sensor, a voice detector, a gyroscope, an accelerometer, a blood oxygen sensor, combinations thereof, and the like. In some embodiments, the sensor(s) may include a tactile sensor, for example, a component that can communicate force, vibration, touch, and other non-visual information to the computing device. In some embodiments, the patient device 160 may be configured to measure one or more of motion data, mobile device data (e.g., digital exhaust, metadata, device usage data), wearable device data, geolocation data, sound data, camera data, therapy session data, medical record data, input data, environmental data, social application usage data, attention data, activity data, sleep data, nutritional data, menstrual cycle data, cardiac data, voice data, social function data, or facial expression data.

[0019] In some embodiments, the user device 120 may be configured to track one or more of the patient's responses to interactive questionnaires and surveys, diary entries and / or other logging, audio-acoustic data, digital biomarker data, and the like. For example, the user device 120 may present one or more questionnaires or exercises for the patient to complete. In some implementations, the user device 120 may collect data while completing the questionnaires or exercises. Results may be made available to a therapist and / or physician. In some embodiments, when a user provides input to the user device 120, the device may generate and use haptic feedback (e.g., vibration) to interact with the patient. The vibrations may be in different patterns in different situations, as described with reference to FIGS. 9-15.

[0020] In some embodiments, the user device 120 and / or the server 110 (or other computing device) coupled to the user device 120 may be configured to process and / or analyze data from the patient and evaluate information related to the patient, e.g., whether the patient has a particular disorder, whether the patient has increased brain plasticity and / or motivation to change, etc. Based on the analysis, certain information may be provided to a therapist and / or physician, e.g., via the therapy provider device 130.

[0021] The therapy provider device 130 may refer to any device configured to be operated by one or more providers, medical professionals, therapists, caregivers, etc. Similar to the user device 120, the therapy provider device 130 may include a processor 132, a memory 134, and an I / O device 136. The therapy provider device 130 may be configured to receive information from other computing devices connected to the network 102, including, for example, information regarding a patient, alerts, etc. In some embodiments, the therapy provider device 130 may receive information from a provider, e.g., via the I / O device 136, and provide that information to one or more other computing devices. For example, during a therapy session, a therapist may input information related to a patient into the therapy provider device 130 via the I / O device 136, and such information may be integrated with other information related to the patient on one or more other computing devices, e.g., the server 110, the user device 120, etc. In some embodiments, the therapy provider device 130 can be configured to control content delivered to a patient (e.g., via the user device 120), information collected from a patient (e.g., via the user device 120), and / or monitoring and / or therapy used for a patient. For example, the therapy provider device 130 may configure the server 110, the user device 120, and / or other computing devices (e.g., caregiver devices, supporter devices, other provider devices, etc.) to monitor certain information about the patient and / or provide certain content to the patient.

[0022] In some embodiments, information about the patient, e.g., collected by user device 120, therapy provider device 130, etc., may be provided to one or more other computing devices, e.g., server 110, computing device(s) 150, etc., which may be configured to process and / or analyze the information. For example, a data processing and / or machine learning device may be configured to receive raw information collected from or about the patient and process and / or analyze the information to derive other information about the patient (e.g., vocabulary, speech-acoustic data, digital biomarker data, etc.). Further details of such data processing and / or analysis are described with reference to FIG. 2 below.

[0023] The computing device(s) 150 may include one or more additional computing devices, each including one or more processors and / or memory as described herein, which may be configured to perform certain functions. For example, the computing device(s) 150 may include a data processing device, a machine learning device, a content creation or management device, etc. Further details of such devices are described with reference to FIG. 2. In some embodiments, the computing device(s) 150 may include a supporter device, e.g., a device operated by a supporter (e.g., a family member, friend, caregiver, or other individual providing support and / or care to the patient). The support device may be configured to implement an application (e.g., a mobile application) that may assist the patient's therapy. For example, the application may be configured to assist the supporter in learning more about the patient's condition, providing encouragement to support the patient (e.g., recommendations for communication and / or sharing activities), etc. In some embodiments, the application may be configured to provide out-of-band information from the supporter to the system 100, such as, for example, information observed about the patient by the supporter. In some embodiments, the application may be configured to provide content linked to the patient's experience.

[0024] The computing devices described herein can communicate with each other via a network 102. The network 102 may be implemented as a wired network and / or a wireless network and may be any type of network (e.g., a local area network (LAN), a wide area network (WAN), a virtual network, a telecommunications network) used to operatively couple the devices. As described in more detail herein, in some embodiments, for example, the system includes computers interconnected via an Internet Service Provider (ISP) and the Internet. In some embodiments, a connection may be defined via a network between any two devices. As shown in FIG. 1, for example, a connection may be defined between one or more of a server 110, a user device 120, a therapy provider device 130, a database(s) 140, and a computing device(s) 150.

[0025] In some embodiments, the computing devices may communicate with each other (e.g., sending data to and / or receiving data from each other) and with network 102 through intermediate and / or alternative networks (not shown in FIG. 1 ). Such intermediate and / or alternative networks may be of the same type as network 102 and / or of a different type. Each computing device may be any type of device configured to transmit data over network 102 to transmit data from one or more of the other computing devices and / or receive data from one or more of the other computing devices.

[0026] 2 illustrates an example system 200, according to an embodiment. The example system 200 may include computing devices and / or other components structurally and / or functionally similar to those of the system 100. Like the system 100, the system 200 may be configured to provide psychological education, psychological training tools and / or activities, psychological patient monitoring, coordination of care and psychological education with the patient's supporters (e.g., family and / or caregivers), motivation, encouragement, appointment reminders, and the like.

[0027] System 200 can include a connected infrastructure of various computing devices (e.g., servers or serverless cloud processing). The computing devices can include, for example, servers 210, mobile devices 220, content repositories 242, databases 244, raw data repositories 246, content creation tools 252, machine learning systems 254, and data processing pipelines 256. In some embodiments, system 200 can include a separate administration device (not shown), for example, implementing an administration tool (e.g., a website or desktop-based program). In some embodiments, system 200 can be managed via one or more of servers 210, mobile devices 220, content creation tools 252, etc.

[0028] The server 210 may be structurally and / or functionally similar to the server 110 described with reference to FIG. 1. For example, the server 210 may include a memory and a processor. The server 210 may be configured to perform one or more of: processing and / or analyzing data associated with the patient, evaluating the patient based on raw and / or processed data associated with the patient, generating and sending alerts to a therapy provider, physician, and / or caregiver associated with the patient, or determining content to provide to the patient before, during, and / or after receiving a treatment or therapy. In some embodiments, the server 210 may be configured to perform user authentication, process requests to retrieve or store data related to the patient's treatment, impose content on the patient and / or supporters (e.g., family, friends, and / or other caregivers), interpret survey results, generate reports (e.g., PDF reports), schedule appointments for treatment, and / or send appointment reminders to the patient and / or physician. The server 210 may be coupled to one or more databases, including, for example, a content repository 242 , a database 244 , and a raw data repository 246 .

[0029] The mobile device 220 may be structurally and / or functionally similar to the user device 120 described with reference to FIG. 1. For example, the mobile device 220 may include memory, a processor, I / O devices, sensors, etc. In some embodiments, the mobile device 220 may be configured to implement a mobile application. The mobile application may be configured to present (e.g., present as a display, audio) content that is assigned to the user and / or supporter. In some embodiments, the content may be assigned to the user throughout a predefined period (e.g., throughout a day, or throughout the course of treatment). The content may be presented for a predefined period, e.g., between about 30 seconds and about 20 minutes, including all values ​​and subranges therebetween. The content may be delivered to the user at regular intervals, e.g., daily, weekly, monthly, etc., via the mobile device 220, for example. In some embodiments, the content delivered to a particular user may be based on rules or protocols assigned to different courses and / or assignments, as defined by the content creation tool 252 (described below).

[0030] In some embodiments, the mobile device 220 (e.g., via a mobile application) can track the completion of activities, including, for example, recording response times, activity selections, and metrics of responses provided by the user. In some embodiments, the mobile device 220 can record passive data including, for example, hand tremors, facial expressions, eye movements, and pupil measurements, as well as keyboard typing speed. In some embodiments, the mobile device 220 can be configured to send reward messages to the user for completing challenges or tasks associated with the content.

[0031] In some embodiments, the content can involve interactions in group activities. For example, the mobile device 220 can present a virtual chat to a small group of patients who perform the content and activities together. In some embodiments, the group activity can enable the group to share and communicate with each other and / or with the therapist provider in real time or substantially real time. In some embodiments, the group activity can enable the group to leave messages or complete activities for each other to be received or read by other group members at a later time period. In some embodiments, the mobile device 220 (e.g., via a mobile application) can be configured to receive and / or present push notifications to remind the user, for example, of upcoming assignments, appointments, group activities, therapy sessions, treatment sessions, etc. In some embodiments, the mobile device 220 (e.g., via a mobile application) can be configured to log a history of content, for example, to enable the user to review past content consumed. In some embodiments, the mobile device 220 (e.g., via a mobile application) can provide an avatar creation feature that enables the user to choose and / or modify a virtual avatar. The virtual avatars can be used in group activities, guided journaling, dialogues, or other interactions in mobile applications.

[0032] In some embodiments, the system 200 can include biometric data from an external sensor(s) attached to the patient, such as a wristband, ring, or other attached device. In some embodiments, the external sensor can be operably coupled to a user device, such as, for example, a mobile device 220.

[0033] The content repository 242 can be configured to store content for delivery to the patient, for example, via the mobile device 220 or another user device. The content can include passive information or interactive activities. Examples of content include articles including videos, text and / or media, audio recordings, surveys or questionnaires including open-ended or closed-ended questions, guided journaling activities or open-ended questions, meditation exercises, and the like. In some embodiments, the content can include dialogue activities that allow the user to interact in a conversation or dialogue with one or more virtual participants, where the responses are pre-written choices that guide the user through different nodes in a dialogue tree. The user can start at one node in the dialogue tree and move through the nodes depending on the selection made by the user in response to the presented dialogue. In some embodiments, the content can include a series of open-ended questions that encourage or guide the user to a deeper level of understanding of the subject. In some embodiments, the content can include meditation exercises using audio and connected mental imagery to guide the user through breathing exercises and / or thinking exercises. In some embodiments, the content may include one or more questions (e.g., survey questions) that elicit one or more responses from the user, which may lead to haptic feedback. For example, as described in more detail with reference to Figures 9-15, a device (e.g., a user device) may be configured to generate haptic feedback to interact with the patient, for example, to convey certain information to the user related to the user's response.

[0034] FIG. 8 illustrates an example of a graphical user interface (GUI) 800 for delivering or presenting content to a user, for example, on a mobile device 220. The GUI 800 may include a first section 802 for presenting media, for example, image or video content. In some embodiments, the first section 802 may present a live or pre-recorded video feed of a therapy provider. The GUI 800 may also include a second section 804 for presenting a dialogue between a user and a therapy provider, for example. In some embodiments, a user or therapy provider may have an avatar or photo associated with the user or therapy provider, and the avatar or photo may be displayed along with text entered by the user or therapy provider in section 804. In some embodiments, the user and therapy provider may have an open dialogue. Alternatively or additionally, the user may be presented with questions and asked to provide responses to those questions. For example, as illustrated in FIG. 8, the therapy provider may ask the user a question and provide the user with two possible response options, i.e., “Response 1” and “Response 2,” as identified by selection buttons at the bottom of the GUI 800. In some embodiments, the user may be asked to respond by manipulating a slider bar or other user interface element. In some embodiments, the user's response may cause the device to generate haptic feedback, for example, similar to that described with reference to Figures 9-15. In some embodiments, the user may be asked to respond to questions with voice rather than text. In some embodiments, the dialogue may be used to estimate, among other things, depression indicators, comparison of indicators of concrete versus abstract thinking, or understanding of previously presented content.

[0035] Although two sections are shown in GUI 800, it can be understood that one or more additional sections can be provided in the GUI without departing from the scope of the present disclosure. For example, GUI 800 can include additional sections that provide media, questions, etc. In some embodiments, GUI 800 can present pop-ups or sections that overlay other sections, for example, to direct a user to particular content before viewing other content.

[0036] In some embodiments, content can be recursive, e.g., content can include other content inline, and in some cases, certain content can block the completion of its parent content until the content itself is completed. For example, a video can be paused and a survey can be presented on the screen, and the survey must be completed before the video continues to play. In FIG. 8, for example, dialogue can be embedded in the video. As another example, an article can be paused and no further reading (e.g., scrolling) is possible until the video has been watched. In some embodiments, the video is also recursive, e.g., contains a survey that must be completed before the video can be resumed and the article can be unlocked for further reading.

[0037] The content can be analyzed and interpreted into indicators that can be used by other rules or triggers. For example, the content can be analyzed and used to generate indicators that indicate physiological states (e.g., depression), concrete vs. abstract thinking, comprehension of previously presented content, etc.

[0038] The content repository 242 can be operatively coupled (e.g., via a network, such as network 102) to a content creation tool or application 252. The content creation tool 252 can be, for example, an application deployed on a computing device, such as a desktop or mobile application, or a web-based application (e.g., running on a server and accessed by a computing device). The content creation tool 252 can be used to create and / or edit content, organize content into courses and / or packages of information, schedule content for specific patients and / or groups of patients, set requirements and / or precedence content relationships, and / or the like.

[0039] In some embodiments, the system 200 can deliver content that can be used in conjunction with (e.g., before, during, or after) a therapeutic drug, device, or other treatment protocol (e.g., talk therapy). For example, the system 200 can be used in conjunction with a medication including, for example, salvinorin A (sal A), ketamine or alketamine, 3,4-methylenedioxymethamphetamine (MDMA), N-dimethyltryptamine (DMT), or ibogaine or noribogaine.

[0040] For example, during the pre-treatment phase, the system 200 can be configured (e.g., via the server 210 and / or the user device 220, with information from the content repository 242 and / or other components of the system 200) to provide the user with content that prepares the user for treatment and / or collects baseline patient data. In some embodiments, the system 200 can provide educational content (e.g., videos, articles, activities) for a general mindset and specific education on how a particular medication may feel and / or impact the patient. In some embodiments, the system 200 can provide an introduction to behavioral activation content. In some embodiments, the system 200 can provide motivational interviews and / or stories. In some embodiments, the system 200 can be configured to provide content that encourages and / or motivates the user to change.

[0041] In the post-treatment phase, the system 200 can be configured to process and / or integrate the patient's experience during treatment to provide content to assist the patient. In some embodiments, the system 200 can provide psycho-educational skill content through articles, videos, gap questions, dialogue trees, guided journaling, audio meditations, podcasts, etc. In some embodiments, the system 200 can provide motivational reminders and / or feedback from motivational interviews. In some embodiments, the system 200 can provide group therapy activities. In some embodiments, the system 200 can provide surveys or questionnaires.

[0042] In some embodiments, the system 200 can be configured to assist the patient in long-term management of treatment outcomes. For example, the system 200 can be configured to provide long-term monitoring via questionnaires, dialogues, digital biomarkers, etc. The system 200 can be configured to provide content to train the user in additional skills. The system 200 can be configured to provide group therapy activities with more advanced skills and / or targets. The system 200 can be configured to provide digital, for example, by basing medication and / or next treatment suggestions on content delivered to the user when needed (e.g., coursework, assignments, referrals to additional services, rechallenge with original combination medication, etc.).

[0043] The raw data repository 246 can be configured to store information about the patient collected, for example, via the mobile device 220, sensor(s), and / or devices operated by other individuals interacting with the patient. Data collected by such devices can include, for example, timing data (e.g., time from push notification to open, time to choose from available activities, survey hesitation time, reading speed, scroll distance, button down to button up time), selection data (e.g., preferred or preferred activities, interpretation of responses such as fantasy thinking in surveys and gap questions, optimism / pessimism, and the like), phone movement data (e.g., number of steps during walking meditation, phone shaking), and the like. Data collected by such devices can also include patient responses to interactive questionnaires and surveys, patient use and / or interpretation of text, voice acoustic data (e.g., voice tone, tonal range, vocal fry, inter-word pauses, diction, pronunciation), digital biomarker data (e.g., pupillometry, facial expressions, heart rate, and the like). Data collected by such devices may also include data collected from the patient during different activities, such as, for example, sleeping, walking, and during content delivery.

[0044] The database 244 may be configured to store information to support the operation of the server 210, the mobile device 220, and / or other components of the system 200. In some embodiments, the database 244 may be configured to store processed patient data and / or analyses thereof, treatment and / or therapy protocols associated with patients and / or patient groups, rules and / or indicators for evaluating patient data, historical data (e.g., patient data, therapy data, etc.), information related to the assignment of content to patients, machine learning models and / or algorithms, etc. In some embodiments, the database 244 may be coupled to a machine learning system 254, which may be configured to process and / or analyze raw patient data from the raw data repository 246 and provide such processed and / or analyzed data to the database 244 for storage.

[0045] The machine learning system 254 can be configured to apply one or more machine learning models and / or algorithms (e.g., rule-based models) to evaluate the patient data. The machine learning system 254 can be operatively coupled to the raw data repository 246 and the database 244 and can extract relevant data from the data to be analyzed. The machine learning system 254 can be implemented on one or more computing devices and can include memory and processors such as those described with reference to the computing device illustrated in FIG. 1. In some embodiments, the machine learning system 254 can be configured to apply one or more of a general linear model, a neural network, a support vector machine (SVM), clustering, combinations thereof, and the like. In some embodiments, the machine learning models and / or algorithms can be used to process data initially collected from the patient to determine a baseline associated with the patient. Data subsequently collected by the patient can be processed by the machine learning models and / or algorithms to generate a measure of the patient's current condition, and such measure can be compared to the baseline to evaluate the patient's current condition. Further details of such evaluations are described with reference to FIGS. 6 and 7.

[0046] The data processing pipeline 256 can be configured to process data received from the server 210, the mobile device 220, or other components of the system 200. The data processing pipeline 256 can be implemented on one or more computing devices and can include memory and processors such as those described with reference to the computing devices illustrated in FIG. 1. In some embodiments, the data processing pipeline 256 can be configured to transfer and / or process non-relational patient and provider data. In some embodiments, the data processing pipeline 256 can be configured to receive, process, and / or store (or send to the database 244 or raw data repository 246 for storage) patient data including, for example, voice data, hand tremor, facial expression, eye movement, and / or pupillometry, keyboard typing speed, task completion timing, estimated reading speed, vocabulary use, and the like.

[0047] 1.2 Haptic feedback As described above, digital therapeutics can be used to assess and monitor a patient's physical and mental health. For example, when a patient undergoes drug treatment, the patient can use an electronic device, such as a mobile device, to provide health information to a medical health provider to assess and monitor the patient's health before, during, and / or after treatment so that an optimized / tailored treatment can be given to the patient.

[0048] Digital surveys are known that are presented as a simple digital representation of a paper survey. Some known digital surveys add buttons or checkboxes. However, these digital surveys are a one-way data transmission from the user of the mobile device to the device.

[0049] In some embodiments, the embodiments described herein can combine haptic feedback to a digital questionnaire to achieve two-way interaction and data transmission between the patient and the mobile device (and other computing devices communicating with the mobile device). In some embodiments, a set of questionnaire questions can be given to the patient (or a user of the mobile device). When the patient provides input to the device to answer the questionnaire questions, the device (or a mobile application on the device) can interact with the patient using haptic feedback (e.g., vibration). The vibration can be of different patterns in different situations.

[0050] In some implementations, a question or questionnaire and a virtual interface element are presented to a user, for example during a psychoeducation session or during delivery of digital content. The virtual interface element includes a plurality of selectable responses to the question. Each question is associated with a different measure of the parameter. The user selects a response from the plurality of selectable responses as a first input via the virtual interface element. A first haptic feedback is generated based on the first selectable response or the first input. When the user selects a second response from the plurality of selectable responses as a second input via the virtual interface element, a second haptic feedback is generated based on the second selectable response if the second input represents a greater measure of the parameter than the first selectable response. The second haptic feedback has a greater intensity or frequency than the first haptic feedback. The first and second haptic feedback differ in waveform, intensity, or frequency.

[0051] For example, the mobile device (or mobile application) may use haptic feedback to alert the patient that their answer is off from their last answer (e.g., how different do I feel today?). For another example, the device (or mobile application) may use haptic feedback to alert the patient that they have reached an extreme (e.g., this is the worst I have ever felt). For another example, the device (or mobile application) may use haptic feedback to alert the patient how their answer differs from the average or others in their group. In some embodiments, the haptic feedback for a survey question may use a slider scale, increasing or decreasing the haptic feedback as the patient moves their finger.

[0052] In some embodiments, haptic feedback may be used to interact with a user of a mobile device or other electronic device while the user is answering survey questions, reminding the user of past or average responses to ground their current answers, which in some examples may provide a more accurate response to a medical care provider, caregiver, or other individual.

[0053] FIG. 9 illustrates an example schematic diagram illustrating a system 900 for implementing haptic feedback for a questionnaire, or a haptic questionnaire system 900, according to some embodiments. In some embodiments, the haptic questionnaire system 900 includes a first computing device, such as a server 901, and a second computing device, such as a user device 902, configured to communicate with the server 901 via a network 903. Alternatively, in some embodiments, the system 900 does not include a server 901 in communication with the user device 902, but includes one or more computing devices, such as a user device(s) 902 having components forming an input / output (I / O) subsystem 923 (e.g., a display, a keyboard, etc.), and a haptic feedback subsystem 924 (e.g., a vibration generating device, such as a mechanical transducer, a motor, a speaker, etc.). Such an implementation is further described and illustrated with respect to FIG. 10.

[0054] The server 901 can be a computing device (or multiple computing devices) having a processor 911 and a memory 912 operably coupled to the processor 911. In some cases, the server 901 can be any combination of hardware-based modules (e.g., field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), digital signal processors (DSPs)) and / or software-based modules (computer code stored in the memory 912 and / or executing on the processor 911) capable of performing one or more specific functions associated with the modules. In some cases, the server 901 can be a server such as, for example, a web server, an application server, a proxy server, a telnet server, a file transfer protocol (FTP) server, a mail server, a list server, a collaboration server, and / or the like. In some cases, the server 901 can be a personal computing device such as a desktop computer, a laptop computer, a personal digital assistant (PDA), a standard mobile phone, a tablet personal computer (PC), and / or the like. In some embodiments, the capability provided by server 901 as described herein may be, for example, the deployment of functions on a serverless computing platform (or web computing platform, or cloud computing platform) such as AWS Lambda.

[0055] The memory 912 can be, for example, a random access memory (RAM) (e.g., dynamic RAM, static RAM, etc.), a flash memory, a removable memory, a hard drive, a database, and / or the like. In some implementations, the memory 912 can include (or store) a database, a process, an application, a virtual machine, and / or other software modules (stored and / or executed in hardware) and / or hardware modules configured to perform a haptic questionnaire process, for example, as described in connection with FIG. 11. In such implementations, instructions for performing the haptic questionnaire process and / or associated methods can be stored in the memory 912 and executed in the processor 911. In some implementations, the memory 912 can store survey questions, survey responses, patient data, haptic questionnaire instructions, and / or the like. In some implementations, a database coupled to the server 901, the user device 902, and / or the haptic feedback subsystem (not shown in FIG. 9) can store survey questions, survey responses, patient data, haptic questionnaire instructions, and / or the like.

[0056] The processor 911 may be configured, for example, to write data into and read data from the memory 912, and to execute instructions stored in the memory 912. The processor 911 may also be configured to perform and / or control the operation of other components of the server 901, such as, for example, network interface cards, other peripheral processing components (not shown), etc. In some implementations, based on the instructions stored in the memory 912, the processor 911 may be configured to perform one or more steps of a haptic questionnaire process described with respect to FIG.

[0057] The user device 902 can be a computing device having a processor 921 and a memory 922 operably coupled to the processor 921. In some cases, the user device 902 can be a mobile device (e.g., a smartphone), a tablet personal computer, a personal computing device, a desktop computer, a laptop computer, and / or the like. The user device 902 can include any combination of hardware-based modules (e.g., a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a digital signal processor (DSP)), and / or software-based modules (computer code stored in the memory 922 and / or executed in the processor 921) capable of performing one or more specific functions associated with the modules.

[0058] The memory 922 can be, for example, a random access memory (RAM) (e.g., dynamic RAM, static RAM, etc.), a flash memory, a removable memory, a hard drive, a database, and / or the like. In some implementations, the memory 922 can include (or store) databases, processes, applications, virtual machines, and / or other software modules (stored and / or executed in hardware) and / or hardware modules configured to perform a haptic questionnaire process, for example, as described in connection with FIG. 11. In such implementations, instructions for performing the haptic questionnaire process and / or associated methods can be stored in the memory 922 and executed in the processor 921. In some implementations, the memory 922 can store survey questions, survey responses, patient data, haptic questionnaire instructions, and / or the like.

[0059] The processor 921 may be configured, for example, to write data into and read data from the memory 922, and to execute instructions stored in the memory 922. The processor 921 may also be configured to perform and / or control the operation of other components of the user device 902, for example, network interface cards, other peripheral processing components (not shown), etc. In some implementations, based on instructions stored in the memory 922, the processor 921 may be configured to perform one or more steps of a haptic questionnaire process described herein (e.g., with respect to FIG. 11). In some implementations, the processor 921 and the processor 911 may be collectively configured to perform a haptic questionnaire process described herein (e.g., with respect to FIG. 11).

[0060] In some embodiments, the user device 902 may be an electronic device associated with a patient. In some embodiments, the user device 902 may be a mobile device (e.g., a smartphone, a tablet, etc.) as further described with reference to Figure 10. In some embodiments, the user device may be a shared computer at a clinic, hospital, or treatment center.

[0061] In some embodiments, the user device 902 can be configured with a user interface, e.g., a graphical user interface, that presents one or more questions to the user. In some embodiments, the user device 902 can implement a mobile application that presents the user interface to the user. In some embodiments, the one or more questions can form part of an electronic questionnaire to obtain information about the user, e.g., related to a medication or therapy program. In some embodiments, the one or more questions can be provided during a digital therapy session, e.g., to treat the patient's medical condition and / or to prepare the patient for medication or therapy. In some embodiments, the one or more questions can be provided as part of a periodic (e.g., daily, weekly, or monthly check-in) questionnaire, whereby the patient is asked to provide information regarding the patient's mental and / or physical state.

[0062] In some embodiments, the user device 902 can present one or more questions to the patient and transmit one or more responses from the patient to the server 901. The one or more questions and one or more responses can have a translation specific to the language of the user layered with the questions and / or responses. For example, the user device 902 can present a question (e.g., How are you feeling today?) on a display or other user interface and can receive input (e.g., touch input, microphone input, or keyboard entry) and transmit the input to the server 901 via the network 903. In some embodiments, the input to the user device 902 can be transmitted to the server 901 in real-time or substantially in real-time (e.g., within about 1 to about 5 seconds). The server 901 can analyze the input from the user device 902 and determine whether to instruct the user device 902 to generate or generate any haptic effects (e.g., vibration effects or patterns) based on the input. For example, the server 901 may have stored haptic questionnaire instructions that instruct the server 901 as to how to analyze input and / or generate instructions for the user device 902 as to what haptic effect to generate. In response to determining that a haptic effect should be provided to the user device 902, the server 901 may send one or more instructions back to the user device 902, for example, instructing the user device to generate or generate the determined haptic effect (e.g., a vibration effect or pattern).

[0063] Alternatively or additionally, the user device 902 can present one or more questions to the patient and process or analyze one or more responses from the patient. For example, the user device 902 can present a question (e.g., how are you feeling today?) on a display or other user interface and can receive an input (e.g., touch input, microphone input, or keyboard entry, etc.) after presenting the question. The user device 902 can store one or more instructions (e.g., haptic questionnaire instructions) in a memory (e.g., memory 922) that instruct the user device 902 how to process and / or analyze the input. For example, the user device 902 via the processor 921 can be configured to process the input to provide a modified or cleaned input. The user device 902 can pass the modified or cleaned input to the server 901 and then wait to receive additional instructions from the server 901, for example, to generate a haptic effect as described above. As another example, the user device 902 via the processor 921 can be configured to analyze the input, for example, by comparing the input to a previous input provided by the user. The user device 902 can then determine whether to generate a haptic effect based on the comparison, as described further with respect to Figure 11. In some embodiments, the user device 902 can store one or more survey definition files, each of which defines one or more survey questions, translations for prompting the questions, rules for presenting the questions on the user device, rules for presenting answers on the user device (for the user to enter or select), associated inputs, and associated haptic feedback instructions. The survey definition files can also include function definitions that convert user inputs (i.e., answers to survey questions) into one or more haptic feedbacks.For example, each questionnaire definition file may define one or more haptic feedbacks, or changes to one or more haptic feedbacks (e.g., changes in amplitude or intensity, or changes in the type of haptic feedback pattern) based on one or more inputs received at the user device 902.

[0064] In some implementations, the system 900 for implementing haptic feedback for a questionnaire or the haptic questionnaire system 900 may include a single device, such as a user device 902, having a processor 921, a memory 922, an input / output (I / O) subsystem 923 (e.g., including a display and / or one or more input devices), and a haptic feedback subsystem 924 (e.g., motors or other peripheral devices) capable of providing haptic feedback. For example, the system 900 may be implemented as a mobile device (having a mobile application executed by a processor of the mobile device). In some implementations, the system 900 may include multiple devices, such as one or more user device(s) 902. The first device may include, for example, a processor 921, a memory 922, and a display (e.g., a liquid crystal display (LCD), a cathode ray tube (CRT) display, a touch screen display, etc.), and an input device (e.g., a keyboard) forming part of the I / O subsystem 923, and the second device may include a haptic feedback subsystem 924 (e.g., a speaker embedded in the seat or other environment around the user) that communicates with the first device. For example, the user may provide answers to survey questions via the first device and receive haptic feedback via the second device. In some implementations, the first device may be configured to communicate with the server 901, and the second device may be configured to communicate with the first device. In some implementations, the first device and the second device may be configured to communicate with the server 901. In some implementations, the server 901, the user device 902, or a database coupled to the haptic feedback subsystem (not shown in FIG. 9) may store survey questions, survey responses, patient data, haptic survey instructions, and / or the like.

[0065] Examples of haptic effects include vibrations with different characteristics on the user device 902. The intensity, duration, pattern, and / or other characteristics of each haptic effect can be varied. For example, a haptic effect can be associated with n characteristics, each of which can be varied. FIG. 15 illustrates an example in which a haptic effect is associated with two characteristics (e.g., intensity and frequency), and each can be varied along an axis. A haptic effect at any time can be represented by a point 1502 in a coordinate space. For example, in response to a user positioning a slider bar to a first position, a haptic effect can be represented by a point 1502. When a user moves the slider bar to a second position, the haptic effect can be changed to a frequency, e.g., point 1502′, or to both frequency and intensity, e.g., point 1502″. Other combinations of changes, e.g., a change in intensity only, an increase in intensity and / or frequency, etc., can also be implemented based on input from the user. To further extend the model described with reference to FIG. 15, it can be appreciated that a haptic effect can be associated with any number of features and that each feature can be modulated along one or more axes, thereby allowing a haptic effect to be associated with n axes. In some implementations, for example, three axes can be used to represent the intensity, frequency, and pattern of the haptic feedback. In such implementations, one or more of the intensity, frequency, and pattern of the haptic feedback can be varied in response to input by the user. The change in one or more features can be used to represent different information to the user (e.g., the length of time it takes the user to respond to a question, how the response compares to a baseline or historical responses, etc.).

[0066] In some embodiments, a haptic effect can be associated with a particular type of pattern. Figures 12A-12D show example haptic effect patterns, according to some embodiments. In some implementations, the intensity 1202 of the vibration can be varied as a function of time 1201 with a sine wave (Figure 12A), a square wave (Figure 12B), a triangle wave (Figure 12C), a sawtooth wave (Figure 12D), any combination of the above vibration patterns, and / or the like. In some implementations, the haptic effect can be a pulse of vibration with a predetermined or adjustable frequency, amplitude, etc. For example, the vibration pulse can have a pattern of vibrating at a first intensity every 5 seconds, or a graduated pulse (e.g., a first vibration intensity pulsed every 3 seconds for the first 10 seconds, then changing to a second vibration intensity pulsed every 2 seconds for 15 seconds). For example, when the user device 902 presents a question (e.g., how are you feeling today?) on a display or other user interface, the user device can receive an input from the patient representing the status of today. If the patient's answer is different from the patient's answer yesterday, the user device generates a pulse vibration as haptic feedback to notify the patient that the answer is different from yesterday. The user device 902 can increase the intensity of the vibration, increase the frequency of the vibration, change the pattern of the vibration, or change another characteristic of the vibration when the deviation between the patient's answer today and the patient's answer yesterday increases. In some embodiments, the haptic effect can have a predetermined attack pattern and / or decay pattern. For example, the haptic effect can have an attack pattern and / or decay pattern defined by a function (e.g., an easing function).

[0067] Returning to FIG. 9, in some implementations, the patient's input to the user device 902 (to answer the survey questions) can be continuous (e.g., via a sliding scale) or discrete (e.g., multiple choice questions). The user device 902 (or in some implementations, the server 901) can generate haptic effects based on the continuous and discrete inputs. When the user device 902 receives discrete input from a user, the user device 902 can generate haptic effects based on the discrete input itself and / or other users' reactions to the survey questions (e.g., the user's indecisive or hesitant state).

[0068] In some embodiments, examples of haptic effects may include sounds (e.g., a tone, a volume, or a particular audio file), visuals (e.g., a pop-up window on the user interface, a floating window), a text message, and / or the like. In some embodiments, the user device may generate a combination of different types of haptic effects (e.g., vibrations and sounds).

[0069] FIG. 10 illustrates an example schematic diagram illustrating a mobile device 1000 including a haptic subsystem, according to some embodiments. In some embodiments, the mobile device 1000 is physically and / or functionally similar to the user device 902 discussed with respect to FIG. 9. In some embodiments, the mobile device 1000 can be configured to communicate with a server 901 via a network 903 to perform the haptic questionnaire process described with respect to FIG. 11. In some embodiments, the mobile device 1000 does not need to communicate with a server, and the mobile device 1000 itself can be configured to perform the haptic questionnaire process described with respect to FIG. 11. In some embodiments, the mobile device 1000 includes one or more of a processor, memory, peripheral interfaces, an input / output (I / O) subsystem, an audio subsystem, a haptic subsystem, a wireless communication subsystem, a camera subsystem, and / or the like. The various components of the mobile device 1000 can be coupled, for example, by one or more communication buses or signal lines. Sensors, devices, and subsystems can be coupled to the peripheral interfaces to facilitate multiple functions. Communications functions may be facilitated through one or more wireless communication subsystems, which may include, for example, receivers and / or transmitters, such as radio frequency and / or optical (e.g., infrared) receivers and transmitters. The audio subsystem may be coupled to a speaker and microphone to facilitate voice-enabled functions, such as voice recognition, voice duplication, digital recording, and telephony functions. The I / O subsystem may include a touchscreen controller and / or other input controller(s). The touchscreen controller may be coupled to a touchscreen or pad. The touchscreen and touchscreen controller may detect contact and movement, for example, using any of a number of touch-sensitive technologies.

[0070] A haptic subsystem can be utilized to facilitate haptic feedback such as vibration, force, and / or motion, and can include, for example, a spinning motor (e.g., an eccentric rotating mass or ERM), a servo motor, a piezoelectric motor, a speaker, a magnetic actuator (a thumper), a taptic engine (a linear resonant actuator, or Apple's Taptic Engine), a piezoelectric actuator, and / or the like.

[0071] The memory of the mobile device 1000 can be, for example, random access memory (RAM) (e.g., dynamic RAM, static RAM), flash memory, removable memory, hard drive, database, and / or the like. In some implementations, the memory can include (or store) databases, processes, applications, virtual machines, and / or other software modules (stored and / or executed in hardware) and / or hardware modules configured to perform a haptic questionnaire process, for example, as described in connection with FIG. 11. In such implementations, instructions for performing the haptic questionnaire process and / or associated methods can be stored in the memory and executed in the processor. In some implementations, the memory can store survey questions, survey responses, patient data, haptic questionnaire instructions, haptic questionnaire function definitions, and / or the like.

[0072] The memory can include definitions of haptic questionnaire instructions or functions. The haptic instructions can be configured to cause the mobile device 1000 to perform a haptic-based action, such as to provide haptic feedback to a user of the mobile device 1000, as described with reference to FIG.

[0073] The processor of the mobile device 1000 can be configured, for example, to write data to and read data from the memory, and to execute instructions stored in the memory. The processor can also be configured, for example, to perform and / or control the operation of other components of the mobile device. In some implementations, based on instructions stored in the memory, the processor can be configured to perform the haptic survey process described with respect to FIG.

[0074] 11 illustrates a flowchart of an example haptic feedback process 300, according to some embodiments. This haptic feedback process 300 can be implemented in a processor and / or memory (e.g., a processor 911 or memory 912 in a server 901 as discussed with respect to FIG. 9, a processor 921 or memory 922 in a user device 902 as discussed with respect to FIG. 9, and / or a processor or memory in a mobile device 1000 as discussed with respect to FIG. 10).

[0075] In step 1102, the haptic survey process includes, for example, presenting a set of survey questions on a user interface of a user device (e.g., user device 902 or mobile device 1000). FIG. 13 illustrates an example user interface 1300 of a user device, according to some embodiments. In one embodiment, the survey question 1301 can be "How are you feeling today?" The processor can present a slide bar 1302 from "sad" to "happy." The user can tap and move the slide bar to display moods between these two endpoints. In some implementations, the slide bar can display a line 1304 indicating the user's answer entered yesterday and / or a line 1303 displaying the user's average answer to the questions. As the user moves the slide bar 1302 away from the line 1303 or 1304, the user device generates a haptic effect to provide feedback to the user about the difference between the user's previous answer (e.g., yesterday's answer or average answer) and the user's current answer. The feedback can help shore up the user against yesterday's answer or average answer. The effect of this example mimics a therapist asking, "Are you sure it's that much better? Is it enough?" This type of feedback can help patients with conditions such as bipolar disorder, which can cause patients to have large, rapid mood swings.

[0076] For another example, a survey question 1305 can be "How often do you exercise?" The processor can present a multiple choice (or discrete input) 1306 for the user to choose the closest answer. The haptic survey process can provide different types of answer selections, including, but not limited to, an acuity scale (e.g., slide bar 1302), discrete input (or multiple choice 1306), grid input (having two dimensions: a horizontal dimension and a vertical dimension, where each dimension is used as an input provided to the haptic function), and / or the like. In some embodiments, the haptic survey process can provide an answer format in multiple axes (or dimensions), for example, displayed as a geometric shape that the user can move their finger (or tap the screen of the user device) to represent interactions between the multiple choices. FIG. 14 is an example answer format with multiple axes, according to some embodiments. For example, a survey question can be "How would you categorize that urge?" The answers can relate to three categories including behavior, emotion, and thought. Users can tap the screen and move their finger to categorize impulses based on behavior, emotion, and thought categories.

[0077] In step 1104, the haptic survey process includes receiving user input in response to a survey question from a set of survey questions.

[0078] In step 1106, the haptic questionnaire process includes analyzing the user input. For example, the processor may analyze the user input in response to the questionnaire questions in comparison to a previous user input or baseline, e.g., by measuring or evaluating a difference between the user input and a previous user input or baseline (e.g., by determining whether the user input differs from the previous user input or baseline by a predetermined amount or percentage). The processor may then generate a comparison result based on the analysis.

[0079] In step 1108, the haptic questionnaire process includes determining whether to provide a haptic effect (e.g., a vibration effect or pattern). For example, the processor may determine to provide a haptic effect if a comparison between the user input and a previous user input or a baseline meets a certain criterion (e.g., the comparison reaches a certain threshold). As another example, the processor may be configured to provide a haptic effect that increases in intensity or frequency as the user's response to the question increases relative to a baseline or a predetermined measure (e.g., as the user moves a slider scale).

[0080] In step 1110, the haptic questionnaire process includes sending a signal to a haptic subsystem of the mobile device to activate a haptic effect. In some embodiments, the processor can be a processor of a server (e.g., processor 911 of server 901) and can be configured to analyze the user input and send instructions to a user device (e.g., user device 902, mobile device 1000) to cause the user device to send a signal to the haptic subsystem to activate a haptic effect. In some embodiments, an on-board processor of the patient device (e.g., processor of mobile device 1000) can be configured to analyze the user input and send a signal to the haptic subsystem to activate a haptic effect.

[0081] Although the embodiments and methods described herein associate one or more haptic effects with a questionnaire and / or questions contained therein, it can be understood that any one of the haptic feedback systems and / or components described herein can be used in other settings, such as to provide feedback while a user is adjusting a setting (e.g., on a mobile device or tablet, such as in a vehicle), to provide feedback in response to questions not included in a questionnaire, to provide feedback while a user is participating in a particular activity (e.g., a workout exercise, etc.), etc. Haptic effects as described herein can be varied accordingly to provide feedback in such settings.

[0082] 2. Method 2.1 Patient Data Collection and Analysis FIG. 3 is a data flow diagram illustrating information exchanged and collected between different components of a system 300 according to an embodiment described herein. The components of the system 300 may be structurally and / or functionally similar to those described above with reference to the systems 100 and 200 illustrated in FIG. 1 and FIG. 2, respectively. As illustrated in FIG. 3, a server 310 may be configured to process assignments for a patient, including various content, for example, as described above. In one embodiment, the server 310 may send a push notification for the assignment to a mobile device 320 associated with the patient. The push notification may include or prompt the patient, for example, via a mobile application on the mobile device 320, with one or more questions associated with the assignment. The patient may provide a response to the one or more questions on the mobile device 320, which may then be provided back to the server 310. The server 310 may send the response to a data processing pipeline 356, which may process the response.

[0083] Additionally or alternatively, the server 310 may also receive other information associated with the completion of the task, evaluate that information (e.g., by calculating an interpretation of the task), and transmit such information and / or the evaluation of that information onto the data processing pipeline 356. Additionally or alternatively, the mobile device 320 may transmit timing indicators (e.g., timing associated with the completion of the task and / or the answer to a specific question) to the data processing pipeline 356. After the data processing pipeline 356 processes the received data, it may transmit the information to the raw data repository 346 or some other database for storage.

[0084] 2.2 Patient Onboarding FIG. 4 illustrates a flow diagram 400 for onboarding a new patient into the system according to embodiments described herein. As shown, the patient can interact with an administrator, for example, via a user device (e.g., user device 120 or mobile device 220), and the administrator can enter patient data into a database at 402. The patient data can be used to create an account for the user at 404. For example, a server (e.g., server 110, 210) can create an account for the user using the patient data. A registration code can be generated, for example, via the server at 406. And, a registration document including the registration code can be generated, for example, via the server at 408. The registration document can be printed at 410 and provided to the administrator for provision to the patient. The patient can register for a digital therapy course at 412 using the registration code in the registration document. For example, the patient can enter the registration code into a mobile application to provide a digital therapy course as described herein. The user can then receive an assignment (e.g., content) at 414 on the user device.

[0085] In some embodiments, the systems and devices described herein can be configured to generate a unique registration code at 406 that represents a particular course and / or challenge(s) to be delivered to the patient, e.g., based on the patient data entered at 402. For example, depending on a particular treatment and / or therapy desired and / or suitable for the patient, the systems and devices described herein can be configured to generate a registration code that, when entered by the patient into the user device, can cause the user device to present a particular challenge to the patient. The challenge can be selected to provide specific educational content and / or psychological activities to the patient based on the patient data. 2.3 Digital Therapeutics

[0086] Traditional talk therapy can be scheduled between the patient and practitioner during mutually available times. Due to travel expenses, office scheduling and staffing, and other reasons, these meetings may usually be scheduled in larger chunks, such as an hour or more. Patients with many mental health indications may not have the attention span for these long meetings, and many do not have the ability to schedule meetings during typical work hours.

[0087] Imposing therapeutic content via a patient device (e.g., a mobile device) allows the patient to receive smaller, more manageable sessions of information more frequently and / or at times that are more workable for the patient's schedule. Information can be delivered according to a spaced, regular schedule, which can increase information retention.

[0088] In some embodiments, the information can be provided in a collection of tasks that are assigned based on a manifest or schedule. The manifest or schedule can be set by the therapy provider and / or can be set according to some specific pre-defined algorithm based on patient data. The assigned content can be a combination of content types as described above.

[0089] 5 is a flow chart illustrating a method 500 of delivering content to a patient according to embodiments described herein. The content can be delivered to the patient for education, data collection, team building, and / or entertainment. The method 500 can be implemented in a processor and / or memory (e.g., processor 112 or memory 114 in server 110 as discussed with respect to FIG. 1, processor 122 or memory 124 in user device 120 as described with respect to FIG. 1, processor or memory in server 210 and / or mobile device 220 as discussed with respect to FIG. 2, and / or processor or memory in server 310 and / or mobile device 320 as discussed with respect to FIG. 3).

[0090] At 502, a challenge including certain content (e.g., text, audio, video, or interactive activity) can be delivered to the patient. The challenge can be delivered, for example, via a mobile application implemented on a user device (e.g., user device 120, mobile device 220, mobile device 320). The challenge can include educational content regarding the patient's symptoms, medications the patient is receiving or may have received, and / or any co-occurring disorders that may present themselves to a therapist, physician, or system. In some embodiments, the challenge can be delivered as a push notification on the mobile application running on the user device. The challenge can be delivered periodically, for example, multiple times a day, week, month, etc.

[0091] In some embodiments, delivery of the challenges can be timed to avoid overwhelming the user by giving too many challenges within a given interval. At 504, a time period for the patient to complete the challenges can be predicted. The time period for completing the challenges can be predicted, for example, by a server (e.g., server 110, 210, 310) or a user device, for example, based on historical data associated with the patient. In some embodiments, an algorithm can be used to predict a time period for the patient to complete the challenges, where the algorithm receives as input attributes of the assigned content (e.g., length, number of interactive gap questions, vocabulary complexity, activity and / or task complexity, etc.) and the patient's past completion speed and metrics (e.g., number of challenges completed per day or other time period, calculated reading speed, calculated attention span).

[0092] At 506, the mobile device, server, or other system components described herein can determine whether the patient has completed the assignment and can optionally record the time for completion for further analysis or evaluation of the patient. In some embodiments, in response to determining that the patient has completed the assignment, the mobile device, server, or other system components described herein can select additional assignments for the patient. Because assignments from different courses of treatment may overlap or different assignments may provide substantially identical information to a therapist or other medical professional, the systems and devices described herein can be configured to select non-overlapping assignments (e.g., remove or skip assignments). The method 500 can then return to 502, where a subsequent assignment is delivered to the patient. In some embodiments, the mobile device server or other system components described herein can collect data from the patient at 510. Such components can collect patient data during the assignment or after completion of the assignment. The collected data can be provided to other components of the systems described herein, such as servers, data processing pipelines, machine learning systems, etc., for further processing and / or analysis.

[0093] 6 illustrates a flowchart of a method 600 for processing and / or analyzing patient data. The method 600 can be implemented in a processor and / or memory (e.g., processor 112 or memory 114 in server 110 as discussed with respect to FIG. 1; processor 122 or memory 124 in user device 120 as described with respect to FIG. 1; processor or memory in server 210, mobile device 220, data processing pipeline 256, machine learning system 254, and / or other computing device as discussed with respect to FIG. 2; and / or processor or memory in server 310, mobile device 320, and / or data processing pipeline 356 as discussed with respect to FIG. 3).

[0094] As illustrated in FIG. 6, the systems and devices described herein can be configured to analyze one or more of patient responses from an interactive questionnaire at 602, as well as questionnaires and / or vocabulary from the patient responses, voice acoustic data at 606 (e.g., voice tone, tonal range, vocal fry, inter-word pauses, diction and pronunciation), or digital biomarker data at 608 (e.g., decision hesitation, activity selection, pupillometry, and facial expressions), as well as any other data that can be collected from the patient via the computing device(s) and sensor(s) described herein.

[0095] In some embodiments, the systems and devices can be configured to detect or predict co-occurring disorders, such as depression, PTSD, substance use disorders, etc., based on the analysis of the patient data at 610. In some embodiments, the co-occurring disorders can be detected through explicit questions in a questionnaire (e.g., how much did you sleep last night?), passive monitoring (e.g., how much did a wearable device or other sensor detect that the user slept last night), or indirect questions in content, conversations, and / or group activities (e.g., did the user repeatedly mention fatigue). In response to detecting the co-occurring disorders, the systems and devices can be configured to generate and send an alert to a physician and / or therapist at 614 and / or recommend content or treatments at 616 based on such detection. For example, the systems and devices can be configured to recommend changes in content presented to the patient (e.g., a different set of tasks or a different type of content) or to recommend a particular treatment or therapy for the patient (e.g., a dosing strategy, timing of dosing, and / or other therapeutic activities such as talk therapy, medication, check-ups, etc.) based on the analysis of the patient data. If no co-occurring disorder is detected, the system and device may continue to provide additional challenges to the patient and / or terminate the digital therapy.

[0096] In some embodiments, the systems and devices can be configured to detect that the patient is in a suitable mindset to receive medication, therapy, etc. In some embodiments, the systems and devices can detect increased brain plasticity and / or motivation to change using explicit questions, passive monitoring, and / or indirect questions. For example, the systems and devices can detect increased brain plasticity and / or motivation to change based on an analysis of the patient data, at 612. In some implementations, the systems and methods described herein can use software model(s) to generate a predictive score representative of the subject's condition. The software model(s) can be, for example, artificial intelligence (AI) model(s), machine learning (ML) model(s), analytical model(s), rule-based model(s), or mathematical model(s). For example, the systems and methods described herein can use a machine learning model or algorithm trained to generate a score representative of the subject's condition. In some implementations, the machine learning model(s) can include a general linear model, a neural network, a support vector machine (SVM), clustering, or a combination thereof. The machine learning model(s) can be constructed and trained using a training dataset, for example, using supervised learning, unsupervised learning, or reinforcement learning. The training dataset can include a historical dataset from the subject. The historical dataset can include historical biological data of the subject, historical digital biomarker data of the subject, and historical responses to questions associated with the digital content by the subject. The historical biological data of the subject includes at least one of historical heartbeat data, historical heart rate data, historical blood pressure data, historical body temperature, historical voice acoustic data, or historical electrocardiogram data.The subject's historical digital biomarker data includes at least one of historical activity data, historical psychomotor data, historical response time data in response to questions associated with the digital content, historical facial expression data, historical pupillometry, or historical hand gesture data. The subject's historical responses to questions associated with the digital content include at least one of historical self-reported activity data, historical self-reported condition data, or historical patient responses to questionnaires and surveys.

[0097] After the machine learning model(s) are trained using the training data, the system and method described in steps 602, 604, 608, and 612 of FIG. 6 can be implemented using the trained machine learning model(s). For example, a set of psychoeducational sessions including digital content is provided to a subject. A set of data streams associated with the subject can be collected and the trained machine learning model(s) can be used to generate a predictive score representing the subject's condition. A set of data streams associated with the subject is collected while providing the set of psychoeducational sessions. The set of data streams can include at least one of the subject's biological data, the subject's digital biomarker data, or the subject's response to a question associated with the digital content. The subject's biological data includes at least one of heartbeat data, heart rate data, blood pressure data, body temperature, voice acoustic data, or electrocardiogram data. The subject's digital biomarker data includes at least one of activity data, psychomotor data, response time data of a response to a question associated with the digital content, facial expression data, pupillometry, or hand gesture data. The subject's response to the questions associated with the digital content includes at least one of self-reported activity data, self-reported status data, or patient responses to questionnaires and surveys. Based on the set of data streams, the trained machine learning model(s) can be used to generate a prediction score representing the subject's status. Depending on the percentage of difference from baseline and / or the measurement for a predetermined threshold, the systems and devices described herein can be configured to predict the subject's status based on the prediction score. The subject's status includes the subject's degree of brain plasticity or motivation to change. For example, if it is determined that there is an increase in brain plasticity or motivation to change, the subject can be provided with an additional set of psychoeducational sessions based on the subject's prediction score and the historical data associated with the subject.

[0098] In some embodiments, the systems and devices described herein can be configured to analyze patient data using a model or algorithm that can predict the current state of the patient's brain plasticity and / or motivation to change. The model or algorithm can generate a measure (e.g., an output) that represents the patient's current level of brain plasticity and / or motivation to change. The measure can be compared to the patient's brain plasticity and / or motivation measure to change at an earlier time point (e.g., baseline) to determine whether the patient exhibits increased brain plasticity and / or motivation to change. In response to detecting a predetermined degree of increased brain plasticity and / or motivation (e.g., a predetermined percentage of change or a measure above a predetermined threshold) at 618, the systems and devices can generate and send an alert to a physician and / or therapist and / or recommend timing of treatment at 620. For example, after detecting that the patient has reached a predetermined level of motivation, the systems and devices can be configured to recommend to the physician and / or therapist to proceed with drug treatment for the patient. These can include treatment methods using drugs, therapies, etc., as described further below. If no increase in brain plasticity and / or motivation is detected, the systems and devices can return to providing additional challenges to the patient and / or terminate the digital therapy.

[0099] In some embodiments, the systems and devices can be configured to predict a potential adverse event for the patient at 622. Examples of adverse events can include suicidal ideation, major mood swings, manic episodes, and the like. In some embodiments, the systems and devices described herein can predict the adverse event by determining a significant change in a measure of the patient's mood. In some embodiments, the adverse event is a change in a measure of the patient's sleep pattern (such as a change in average sleep duration, number of awakenings per night, and the like). In some embodiments, the adverse event is a change in a measure of the patient's mood as determined by a clinical rating scale (such as the Gossop Short Opiate Withdrawal Scale (SOWS-Gossop-Hamilton Depression Rating Scale, Clinical Global Impression (CGI) scale, Montgomery-Asberg Depression Rating Scale (MADRS), Beck Depression Inventory (BDI), Tsang Self-Rating Depressive Scale, Raskin Depression Rating Scale, Depressive Symptoms Scale (IDS), Brief Depressive Symptoms Scale (QIDS), Columbia Suicide Severity Rating Scale, or Suicidal Ideation Attribution Scale).

[0100] The HAM-D scale is a 17-item scale that measures the severity of depression before, during, or after treatment. Scoring is based on the 17 items and typically takes 15-20 minutes to complete the interview and score the results. Eight items are scored on a 5-point scale ranging from 0=not present to 4=severe. Nine items are scored on a 3-point scale ranging from 0=not present to 2=severe. A score of 10-13 represents mild depression, a score of 14-17 represents mild-moderate depression, and a score above 17 represents moderate-severe depression. In some embodiments, the adverse event is a change in the patient's mood as determined by an increase in the subject's HAM-D score of about 5% to about 100%, e.g., about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 95%, or about 100%.

[0101] The MADRS scale is a 10-item scale that measures core symptoms of depression. Nine items are based on patient report and one item is based on rater observation during the assessment interview. A score of 7-19 represents mild depression, a score of 20-34 represents moderate depression, and a score above 34 represents severe depression. MADRS items are rated on a continuum of 0-6, with 0=no abnormality and 6=severe abnormality. In some embodiments, the adverse event is a change in the patient's mood as determined by an increase of about 5% to about 100%, e.g., about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 95%, or about 100%, in the subject's MADRS score.

[0102] In some embodiments, an adverse event is an increase in one or more patient symptoms indicative of the patient being acutely withdrawn from a drug dependency, such as sweating, racing heart, palpitations, muscle tension, chest tightness, difficulty breathing, tremors, nausea, vomiting, diarrhea, grand mal epilepsy, heart attack, stroke, hallucinations, and delirium tremens (DT).

[0103] In some embodiments, the adverse event may be or may be associated with one or more mental health or substance abuse disorders, including, for example, substance abuse or addition, depressive disorder, or post-traumatic stress disorder. For example, the adverse event may be an episode, event, incident, measure, symptom, etc. associated with a mental health or substance abuse disorder. In some embodiments, the mental health disorder or illness may be, for example, an anxiety disorder, a panic disorder, a phobia, an obsessive-compulsive disorder (OCD), a post-traumatic stress disorder, attention deficit disorder (ADD, attention deficit hyperactivity disorder (ADHD), a depressive disorder (e.g., major depression, persistent depressive disorder, bipolar disorder, perinatal or postpartum depression, or situational depression), or a cognitive impairment (e.g., age- or disability-related).

[0104] In some implementations, the systems and methods described herein can use software model(s) to generate a score or other measure of a patient's mood to generate a periodic score for the patient over time. The software model(s) can be, for example, artificial intelligence (AI) model(s), machine learning (ML) model(s), analytical model(s), rule-based model(s), or mathematical model(s). For example, the systems and methods described herein can use machine learning models or trained algorithms to generate a score or other measure of a patient's mood to generate a periodic score for the patient over time. In some implementations, the machine learning model(s) can include a general linear model, a neural network, a support vector machine (SVM), clustering, or a combination thereof. The machine learning model(s) can be constructed and trained using a training dataset. The training dataset can include a historical dataset from multiple historical subjects. The historical dataset can include biological data of the multiple historical subjects, digital biomarker data of the multiple historical subjects, and responses to questions associated with the digital content by the multiple historical subjects. The biological data of the plurality of historical subjects includes at least one of heartbeat data, heart rate data, blood pressure data, body temperature, voice acoustic data, or electrocardiogram data. The digital biomarker data of the plurality of historical subjects includes at least one of activity data, psychomotor data, response time data in response to questions associated with the digital content, facial expression data, pupillometry, or hand gesture data. The responses of the plurality of historical subjects to questions associated with the digital content include at least one of self-reported activity data, self-reported condition data, or patient responses to questionnaires and surveys.

[0105] After the machine learning model(s) are trained using the training data, the systems and methods described in steps 602, 604, 608, and 622 of FIG. 6 can be implemented using the trained machine learning model(s). For example, a set of data streams associated with a subject can be collected and the trained machine learning model(s) can be used to generate a predictive score for the subject. Information can be extracted from the set of data streams collected during a period before, during, or after administration of a drug to the subject. The set of data streams can include at least one of the subject's biological data, the subject's digital biomarker data, or the subject's response to a question associated with the digital content. The subject's biological data includes at least one of heartbeat data, heart rate data, blood pressure data, body temperature, voice acoustic data, or electrocardiogram data. The subject's digital biomarker data includes at least one of activity data, psychomotor data, response time data in response to a question associated with the digital content, facial expression data, pupillometry, or hand gesture data. The subject's response to the questions associated with the digital content includes at least one of self-reported activity data, self-reported condition data, or patient responses to questionnaires and surveys. Based on the information extracted from the set of data streams, the trained machine learning model(s) can be used to generate a prediction score for the subject. Depending on the percentage of difference from baseline and / or the measurement for a predefined threshold, the system and device described herein can be configured to predict whether an adverse event is likely to occur. In other words, the likelihood of an adverse event can be determined based on the prediction score.

[0106] Alternatively or additionally, the systems and methods described herein can monitor for adverse events using rule-based model(s), for example, using explicit questions in a questionnaire or conversation (e.g., "Are you thinking about harming yourself?"). In response to a prediction that an adverse event is likely to occur, the systems and devices can be configured to generate and send an alert to a physician and / or therapist at 624 and / or recommend content or treatment based on such detection at 626. For example, the systems and devices can be configured to recommend a change in content presented to the patient (e.g., a different set of assignments or a different type of content) based on an analysis of the patient data, or to recommend a particular treatment or therapy for the patient (e.g., a dosing strategy, timing of administration, and / or other therapeutic activities such as talk therapy, medication, check-ups, etc.). In some implementations, medication can be determined based on the likelihood of an adverse event. For example, in response to a likelihood of an adverse event being greater than a predetermined threshold, a treatment routine for administering medication can be determined based on historical data associated with the subject and information representative of the subject's current condition extracted from a set of data streams of the subject. The drugs may include ibogaine, noribogaine, psilocybin, psilocin, 3,4-methylenedioxymethamphetamine (MDMA), N,N-dimethyltryptamine (DMT), or salvinorin A. If no adverse events are predicted, the system and device may continue to provide additional challenges to the patient and / or terminate the digital therapy.

[0107] 7 illustrates an example method 700 of analyzing patient data according to embodiments described herein. The method 700 uses a machine learning model or algorithm (e.g., implemented by the server 110, 210, 310, and / or the machine learning system 254) to generate a predictive score or other assessment for evaluating the patient. For example, a processor executing instructions stored in a memory associated with the machine learning system (e.g., the machine learning system 254) or other computing device (e.g., the server 110, 210, 310, or the user device 120, 220, 320) can be configured to track information about the patient (e.g., mood, depression, anxiety, etc.).

[0108] In one embodiment, the processor can be configured to use the training data set to build a model for generating a predictive score for the subject at 702. The processor can receive patient data associated with the patient at 704, e.g., collected during a period before, during, or after administration of a therapy treatment to the patient. The processor can extract information corresponding to various parameters of interest from the patient data at 706. The processor can use the model to generate a predictive score for the subject at 708 based on the information extracted from the patient data. Such a method 700 can be applied to analyze one or more different types of patient data, as described with reference to FIG. 6. The processor can further determine a patient's condition, e.g., based on the predictive score by comparing the predictive score to a reference (e.g., a baseline), as described above with reference to FIG. 6.

[0109] 2.4 Content Management Content as described herein can be coded into a normalized content format in a content creation application (e.g., content creation tool 252). The application can enable a content creator (e.g., a user) to create any of the content types described herein, including, for example, rich media articles, videos, audios, surveys, and questionnaires, and the like. In addition, the application can enable the content creator to specify where recursive content appears within the content and if certain content is blocked, pending completion of other content. In some embodiments, the content creator can define how a patient's responses or interactions with the content are interpreted by the systems and devices described herein.

[0110] In some implementations, the application can store and update the digital content, for example, for a set of psychoeducational sessions. The digital content file can include a set of digital features. The set of digital features can include at least one of an interactive questionnaire or set of questions, a dialogue activity, or embedded audio or visual content. When an author creates a version of the digital content, metadata associated with the creation of the version of the digital content file is generated. The metadata can include an identifier of the author of the version of the digital content file, a period or date associated with the creation, and a reason for the creation. In addition, the version of the digital content file and the metadata associated with the version of the digital content file are hashed using a hash function to generate a pointer to the version of the digital content file. The version of the digital content, including the pointer and the metadata associated with the version of the digital content file, is stored in a content repository (e.g., content repository 242). When a user requests to retrieve the version of the digital content file, the pointer is provided to the user. The version of the digital content file, including the pointer, and the metadata associated with the version of the digital content file can be retrieved using the pointer. In some embodiments, such a method can be implemented using Git hashing and associated functions.

[0111] In one embodiment, the content management system may include a system configured to code content into a clear text format. The system may be implemented via a server (e.g., server 110, 210, 310), a content repository (e.g., content repository 242), and / or a content creation tool (e.g., content creation tool 252). The system may be configured to store the content in a version control system, e.g., on the content repository. The system may be configured to track changes to the content and map the changes to an author and / or reason for the change. The system may be configured to update, roll back or revert, and / or lock the server to a known state of content. The system may be configured to code rules for interpreting responses to content (e.g., responses to questionnaires and standardized instruments) into editable content, and to associate these rules with the applicable content or version of the digital content file that contains the content to which it applies.

[0112] In some embodiments, different versions of the digital content can be created by one or more content creators. For example, a first content creator can create a first version of a digital content file, and a second content creator can modify that version of the digital content file to create a second version of the digital content file. A computing device implementing a content creation application can be configured to generate or create metadata associated with each of the first and second versions of the digital content file, and store this metadata with the first and second versions of the digital content file, respectively. A computing device implementing a content creation application can also be configured to implement a hash function, for example, to generate a pointer or hash to each version of the digital content file, as described above. In some embodiments, the computing device can be configured to send the various versions of the digital content file to a user device (e.g., a mobile device of a user, such as a patient or supporter), and then present the digital features contained in the versions of the digital content file to the user. In some embodiments, the computing device can be configured to revert to an older or previous version of the digital content file by reverting to sending the previous version of the digital content file to the user device, which causes the user device to revert to presenting the previous version of the digital content file to the user. In some embodiments, content creation may be managed by one author or multiple authors, including first, second, third, fourth, fifth, etc. authors.

[0113] 2.5 Treatment method In some embodiments, the systems and devices described herein can be configured to perform a method of treating a condition (e.g., mood disorder, substance use disorder, anxiety, depression, bipolar disorder, opioid use disorder) in a patient in need thereof. The method can include processing patient data (e.g., collected by a user device, such as user device 120 or mobile device 220, 320) to determine a patient condition, determining that the patient has a predetermined mindset (e.g., brain plasticity or motivation to change) suitable for receiving medication based on the patient condition or determining a likelihood of an adverse event, and, in response to determining that the patient has a predetermined mindset or a high likelihood of an adverse event, administering an effective amount of medication (e.g., ibogaine, noribogaine, psilocybin, psilocin, 3,4-methylenedioxymethamphetamine (MDMA), N,N-dimethyltryptamine (DMT), or salvinorin A) to the subject to treat the condition.

[0114] In some embodiments, drug treatment or therapy can be changed or modified based on the patient's mindset or the likelihood of an adverse event. For example, the dose of the drug (e.g., about 1,000 μg to about 5,000 μg of salvinorin A or a derivative thereof per day, about 0.01 to about 500 mg of ketamine per day, about 20 mg to about 1000 mg per day, or about 1 mg to about 4 mg of ibogaine per kg of body weight per day) can be changed depending on the patient's mindset or the likelihood of an adverse event. In some embodiments, a maintenance dose or a booster dose may be administered to the patient before, during, or after the administration of the first dose, for example, based on the patient's mindset. In some embodiments, the administration of the drug can be increased or decreased (e.g., tapered) over time, for example, before, during, or after the administration of the first dose, based on the patient's mindset. In some embodiments, administration of drug therapy can be on a regular basis, e.g., once a day, twice a day, three times a day, once every two days, once every three days, three times a week, twice a week, once a week, once a month, etc. In some embodiments, a patient can be treated long-term (e.g., one year or more) with a maintenance dose of the drug. In some embodiments, administration and / or timing of administration of the drug can be based on patient data, including, for example, patient biological data, patient digital biomarker data, or patient responses to questions associated with the digital content.

[0115] In some embodiments, the systems and devices described herein can be configured to perform a method of treating a condition (e.g., mood disorder, substance use disorder, anxiety, depression, bipolar disorder, opioid use disorder) in a patient in need thereof. The method can include providing a set of psychoeducational sessions to the patient during a predetermined time period prior to administration of a medication to the subject, collecting patient data prior to, during, or after the predetermined time period, processing the patient data to determine a condition of the patient, identifying and providing a set of additional psychoeducational sessions to the subject based on the determined condition, and administering an effective amount of a medication, therapy, or the like to the subject to treat the condition.

[0116] In some embodiments, the systems and devices described herein may be configured to process additional patient data after administration of a drug, therapy, etc., to detect one or more changes in the subject's condition that may be indicative of a personality or other change in the subject, a reversal of a condition, etc.

[0117] While various embodiments have been described above, it should be understood that they are presented by way of example only and not limitation. Where the methods and / or schematic diagrams described above depict particular events and / or flow patterns occurring in a particular order, the order of the particular events and / or flow patterns may be modified. While embodiments have been specifically shown and described, it should be understood that various changes in form and details may be made.

[0118] Although various embodiments have been described as having particular features and / or combinations of components, other embodiments are possible having any combination of features and / or components from any of the embodiments as discussed above.

[0119] Some embodiments described herein relate to computer storage products having a non-transitory computer-readable medium (which may also be referred to as a non-transitory processor-readable medium) having instructions or computer code for performing various computer-implemented operations. The computer-readable medium (or processor-readable medium) is non-transitory in the sense that it does not itself include a transitory propagating signal (e.g., a propagating electromagnetic wave that carries information over a transmission medium such as space or cable). The medium and computer code (which may also be referred to as code) may be designed and constructed for a specific purpose(s). Examples of non-transitory computer-readable media include, but are not limited to, magnetic storage media such as hard disks, floppy disks, and magnetic tapes, optical storage media such as compact disks / digital video disks (CD / DVDs), compact disk read-only memories (CD-ROMs), and holographic devices, magneto-optical storage media such as optical disks, carrier wave signal processing modules, and hardware devices specifically configured to store and execute program code, such as application specific integrated circuits (ASICs), programmable logic devices (PLDs), read-only memories (ROMs), and random access memory (RAM) devices. Other embodiments described herein relate to computer program products, which may include, for example, instructions and / or computer code discussed herein.

[0120] Some embodiments and / or methods described herein can be implemented by software (executed on hardware), hardware, or a combination thereof. Hardware modules may include, for example, general-purpose processors, field programmable gate arrays (FPGAs), and / or application-specific integrated circuits (ASICs). Software modules (executed on hardware) can be expressed in a variety of software languages ​​(e.g., computer code), including C, C++, Java™, Ruby, Visual Basic™, and / or other object-oriented languages, procedural languages, or other programming languages ​​and development tools. Examples of computer code include, but are not limited to, microcode or microinstructions, machine instructions such as those produced by a compiler, code used to generate web services, and files containing higher level instructions executed by a computer using an interpreter. For example, embodiments may be implemented using imperative programming languages ​​(e.g., C, Fortran, etc.), functional programming languages ​​(Haskell, Erlang, etc.), logic programming languages ​​(e.g., Prolog), object-oriented programming languages ​​(e.g., Java, C++, etc.), interpreted languages ​​(JavaScript, typescript, Perl), or other suitable programming languages ​​and / or development tools. Additional examples of computer code include, but are not limited to, control signals, encryption code, and compression code.

Claims

1. A facility comprising: Memory, a processor operably coupled to the memory, the processor comprising: using supervised, unsupervised, or reinforcement learning to build an event-based model to estimate a predictive score for a subject using a training dataset, the training dataset comprising a historical dataset from a plurality of historical subjects, the historical dataset comprising biological data of the plurality of historical subjects, digital biomarker data of the plurality of historical subjects, and responses by the plurality of historical subjects to questions associated with the digital content; receiving a set of data streams associated with the subject, the set of data streams collected during a period before, during, or after administration of a drug to the subject, the set of data streams including at least one of biological data of the subject, digital biomarker data of the subject, or responses by the subject to questions associated with the digital content; extracting information from the set of data streams associated with the subject that corresponds to the information in the training data set; using the model to estimate a predictive score for the subject based on the information extracted from the set of data streams; determining a likelihood of an adverse event based on the prediction score; A facility configured to generate a suggested appointment or treatment routine based on the likelihood of the adverse event.

2. The equipment of claim 1 , wherein the processor is further configured to send an alert to a physician or caregiver indicating the likelihood of the adverse event and the suggested appointment or treatment plan to the physician or caregiver.

3. 2. The equipment of claim 1, wherein the plurality of historical subject biological data and the subject biological data includes at least one of heartbeat data, heart rate data, blood pressure data, body temperature, voice acoustic data, or electrocardiogram data.

4. 2. The facility of claim 1, wherein the digital biomarker data of the plurality of historical subjects and the digital biomarker data of the subject comprises at least one of activity data, psychomotor data, response time data in response to questions associated with the digital content, facial expression data, pupillometry, or hand gesture data.

5. 2. The equipment of claim 1, wherein the responses by the plurality of historical subjects to the questions associated with the digital content and the responses by the subjects to the questions associated with the digital content include at least one of self-reported activity data, self-reported condition data, or patient responses to questionnaires and surveys.

6. The facility of claim 1 , wherein the model comprises a general linear model, a neural network, a support vector machine (SVM), clustering, or a combination thereof.

7. 10. The facility of claim 1, wherein the proposed appointment or treatment routine includes administration of a medication comprising at least one of ibogaine, noribogaine, psilocybin, psilocin, 3,4-methylenedioxymethamphetamine (MDMA), N,N-dimethyltryptamine (DMT), or salvinorin A.

8. The equipment of claim 1 , wherein the processor is configured to determine the likelihood of the adverse event based on the prediction score by comparing the prediction score to a predetermined score.

9. 10. The facility of claim 1, wherein the adverse event is substance abuse or addiction and the proposed appointment or treatment routine includes administration of ibogaine or noribogaine.

10. 2. The facility of claim 1, wherein the adverse event is substance abuse or addiction and the proposed appointment or treatment routine includes administration of salvinorin A.

11. 2. The facility of claim 1, wherein the adverse event is a depressive disorder and the proposed appointment or treatment routine includes administration of psilocybin or psilocin.

12. 10. The facility of claim 1, wherein the adverse event is post-traumatic stress disorder and the proposed appointment or treatment routine includes administration of 3,4-methylenedioxymethamphetamine (MDMA).

13. 2. The facility of claim 1, wherein the adverse event is a depressive disorder and the proposed appointment or treatment routine includes administration of N,N-dimethyltryptamine (DMT).

14. A facility comprising: a memory configured to store digital content for a set of psycho-educational sessions; a processor operably coupled to the memory, the processor comprising: generating a version of the digital content file that includes a set of digital features, the set of digital features including at least one of an interactive questionnaire or set of questions, a dialogue activity, or embedded audio or visual content; generating metadata associated with a creation of the version of the digital content file, the metadata including an identifier of a first creator of the version of the digital content file, a time period or date associated with the creation, and a reason for the creation; hashing the version of the digital content file and the metadata associated with the version of the digital content file using a hash function to generate a pointer to the version of the digital content file; and in response to receiving a request from a second creator to read the version of the digital content file that includes the pointer, the facility is configured to provide the version of the digital content file and the metadata associated with the version of the digital content file to the second creator.

15. The facility of claim 14 , wherein the first creator is the same as the second creator.

16. The facility of claim 14 , wherein the processor is further configured to store the version of the digital content and the metadata associated with the version of the digital content file in the memory.

17. 15. The facility of claim 14, wherein the processor is further configured to transmit the version of the digital content file to a user device, whereby in response to receiving the version of the digital content file, the user device presents the set of digital features to a user.

18. the version of the digital content file is a first version of the digital content file, and the processor: generating a second version of the digital content file that includes a modified set of digital features that differs from the set of digital features in response to changes to the first version of the digital content file made by a third creator; The facility of claim 17 , further configured to generate metadata associated with the creation of the second version of the digital content file.

19. 20. The facility of claim 18, further configured such that the processor transmits the second version of the digital content file to the user device, whereby in response to receiving the second version of the digital content file, the user device presents the modified set of digital features to the user.

20. the processor is further configured, in response to receiving a request from a fourth creator to revert from the second version of the digital content file to the first version of the digital content file, to return to transmitting the first version of the digital content file to the user device, whereby the user device returns to presenting a set of digital features to the user; 20. The facility of claim 19, wherein the request to revert from the second version of the digital content file to the first version includes the pointer to the first version of the digital content file.

21. 15. The facility of claim 14, wherein the processor is further configured to code and associate the version of the digital content file rules for interpreting one or more responses to one or more digital features of the set of digital features into editable content.

22. A facility comprising: Memory, a processor operably coupled to the memory, the processor comprising: presenting a question and a virtual interface element to the user during the psychoeducation session, the virtual interface element including a plurality of selectable responses to the question, each of the selectable responses being associated with a different measure of a parameter; receiving a first input from the user via the virtual interface element, the first input being associated with a first selectable response from the plurality of selectable responses; generating a first haptic feedback based on the first selectable response; receiving a second input from the user via the virtual interface element, the second input being associated with a second selectable response from the plurality of selectable responses and representing a measure of the parameter that is greater than the first selectable response; The apparatus is configured to generate a second haptic feedback based on the second selectable response, the second haptic feedback having a greater intensity or frequency than the first haptic feedback.

23. 23. The arrangement of claim 22, wherein the first haptic feedback and the second haptic feedback each include one or more vibrations having a predetermined waveform, a predetermined intensity, and a predetermined frequency.

24. The arrangement of claim 22 , wherein the intensity and the frequency of the second haptic feedback are greater than the intensity and the frequency of the first haptic feedback.

25. 23. The apparatus of claim 22, wherein the processor is configured to generate the second haptic feedback by increasing the intensity or the frequency of the second haptic feedback as a difference between the first input and the second input increases.

26. 23. The arrangement of claim 22, wherein a virtual interface element includes a slider scale, and the intensity or the frequency of the first haptic feedback and the second haptic feedback is based on a position of a slider on the slider scale.

27. 23. The arrangement of claim 22, wherein the first tactile feedback and the second tactile feedback represent a difference between the first input and the second input and a past response or an average response, respectively.

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