A DIGITAL SYSTEM AND A METHOD FOR MENTAL HEALTH INTERVENTION AND THERAPY

Abstract

ABSTRACT A DIGITAL SYSTEM AND A METHOD FOR MENTAL HEALTH INTERVENTION AND THERAPY The present disclosure discloses a digital system and a method for mental health intervention and therapy. The system(100) comprises a learning management application(102) installed on a server(104), accessible via a user’s computing device(126); a pre-intervention assessment module(106) to receive user input data through a pre-intervention structured assessment form and register the user; a data pre-processing module(108) to compute mental health dimension score; a cloud-based storage module(110) to securely store the computer mental health dimension score; a stream-intervention module(112) to deliver video-based mental health interventions over a structured four-week program; a post-intervention assessment module(114) to provide the user with assignments or reflections related to the interventions after each video session; an analyzer module(116) to compare and analyze the pre-intervention and post-intervention mental health data and generates an analyzer report; a threshold alert module(118) to flag significant fluctuations in mental health data; a notification module(120) configured to transmit alerts to registered professionals.

Specification

Description:FIELD
The present disclosure generally relates to the field of medical systems. More particularly, the present disclosure relates to a digital system and a method for mental health intervention and therapy.
BACKGROUND
The background information herein below relates to the present disclosure but is not necessarily prior art.
Existing digital systems for mental health intervention have expanded access to resources through online platforms, self-assessment tools, and teletherapy, making mental health support more flexible and accessible. These systems often offer modules for cognitive behavioural therapy (CBT), mindfulness, and virtual counselling, allowing users to engage in mental health care privately and at their convenience. However, significant technical limitations limit their effectiveness in fully addressing users' diverse needs.
One major challenge is ensuring robust data security and privacy for sensitive mental health information, as many systems struggle to comply with strict data protection regulations. Without proper encryption and secure storage, users face risks that can undermine trust and adoption, especially among those in need of consistent support. Additionally, most current platforms lack personalization and adaptive content delivery, meaning users receive generic interventions that don't adjust to their specific progress or mental health states, which may reduce engagement and therapeutic impact.
Further limitations include the scarcity of real-time data analysis and crisis detection, with few systems able to actively monitor user progress or send dynamic alerts to professionals when necessary. This can delay intervention in critical situations where timely support is essential. Scalability also poses a challenge, as many systems lack cloud-based infrastructure, limiting their ability to serve large, geographically diverse populations without performance issues. Lastly, the limited use of machine learning restricts the potential for in-depth insights and precise, personalized recommendations based on user data trends.
There is, therefore felt a need for a digital system and a method for mental health intervention and therapy that alleviates the aforementioned drawbacks.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
An object of the present disclosure is to provide a digital system and a method for mental health intervention and therapy.
Another object of the present disclosure is to provide a digital system for mental health care in a single platform.
Still, another object of the present disclosure is to provide a digital system with structured pre- and post-intervention assessment.
Yet another object of the present disclosure is to provide a digital system that automates mental health data analysis, enabling real-time tracking of users' mental health trends and providing data-driven insights for both users and mental health professionals.
Still another object of the present disclosure is to provide a digital system that ensures user data privacy and compliance with data security standards.
Yet another object of the present disclosure is to provide a digital system with a responsive and dynamic intervention process across varied geographical locations.
Still another object of the present disclosure is to provide a digital system with real-time notifications to mental health professionals.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages a digital system and a method for mental health intervention and therapy. The system comprises a learning management application and a server.
The learning management application is installed on a server, and accessible via a user's computing device.
The learning management application includes a pre-intervention assessment module, a data pre-processing module, a cloud-based storage module, a stream-intervention module, a post-intervention assessment module, an analyzer module, a threshold alert module, and a notification module.
The pre-intervention assessment module is configured to receive user input data through a pre-intervention structured assessment form and register the user with a unique identification number.
The data pre-processing module is configured to compute mental health dimension score based on the user input data, the mental health dimensions including anxiety, depression, behavioral control, emotional affect, life satisfaction, emotional ties, psychological distress, and psychological well-being.
The cloud-based storage module is configured to securely store the computer's mental health dimension score.
The stream-intervention module is configured to deliver video-based mental health interventions over a structured four-week program, with only one video available per week.
The post-intervention assessment module is configured to provide the user with assignments or reflections related to the interventions after each video session.
The analyzer module is configured to compare and analyze the pre-intervention and the post-intervention mental health data by means of a set of analyzer rules, and is further configured to generate an analyzer report indicating changes in the mental health dimensions of each user.
The threshold alert module is configured to flag significant fluctuations in mental health data where the comparison results exceed pre-set thresholds.
The notification module is configured to transmit alerts to registered mental health professionals if immediate intervention is required, based on the flagged mental health data.
In an embodiment, the server comprises:
• a data storage is configured to securely store assessment and intervention data; and
• a computation module is configured to process and analyze the collected mental health data.
In an embodiment, the server further comprises:
• a communication protocol configured to perform secure data transmission using HTTPS to ensure privacy between the user interface, server, and application modules; and
• a user interface module configured to interact with the system, fill assessments, and access video interventions.
In an embodiment, the analyzer module is configured to automatically generate a detailed report stored as an Excel file, indexed by the user's unique identification number.
In an embodiment, the threshold alert module is configured with customizable thresholds based on the severity of mental health dimension fluctuations and is capable of adjusting alerts based on participant demographics or individual risk factors.
In an embodiment, activities and assignments include pre-recorded interventional videos for mental health improvement and small follow-up activities/assignments/reflections.
In an embodiment, the set of analyzer rules is a set of instructions used to implement one or more machine learning models in combination to improve accuracy in analyzing mental health data over time.
In an embodiment, the notification module is integrated with mobile push notifications and email alerts to notify the health professionals of emergencies or significant mental health risks in real-time.
In an embodiment, the computation module supports cloud-based processing to enable scalability for multiple concurrent users across different geographical locations.
The present disclosure also envisages a method for mental health intervention and therapy. The method comprises the following steps:
• installing a learning management application on a server, accessible via a user's computing device;
• receiving, by a pre-intervention assessment module, user input data through a pre-intervention structured assessment form and registering the user with a unique identification number;
• computing, by a data pre-processing module, mental health dimension score based on the user input data, the mental health dimensions including anxiety, depression, behavioral control, emotional affect, life satisfaction, emotional ties, psychological distress, and psychological well-being;
• securely storing, by a cloud-based storage module, the computer mental health dimension score;
• delivering, by a stream-intervention module the video-based mental health interventions over a structured four-week program, with only one video available per week;
• providing, by a post-intervention assessment module, the user with assignments or reflections related to the interventions after each video session;
• comparing and analyzing, by an analyzer module, the pre-intervention and the post-intervention mental health data by means of a set of analyzer rules, and generating an analyzer report indicating changes in the mental health dimensions of each user;
• flagging, by a threshold alert module significant fluctuation in mental health data where the comparison result exceeds pre-set thresholds; and
• transmitting, by a notification module, alerts to registered mental health professionals if immediate intervention is required, based on the flagged mental health data.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
A digital system and a method for mental health intervention and therapy of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a block diagram of a digital system for mental health intervention and therapy in accordance with an embodiment of the present disclosure;
Figures 2A-2B illustrate a flowchart for a method for mental health intervention and therapy in accordance with an embodiment of the present disclosure; and
Figure 3 illustrates an execution flow of the mental health intervention and therapy in accordance with an embodiment of the present disclosure.
LIST OF REFERENCE NUMERALS
100 - System
102 - Learning Management Application
104 - Server
104a - Data Storage
104b - Computation Module
106 - Pre-Intervention Assessment Module
108 - Data Pre-Processing Module
110 - Cloud-Based Storage Module
112 - Stream-Intervention Module
114 - Post-Intervention Assessment Module
116 - Analyzer Module
118 - Threshold Alert Module
120 - Notification Module
122 - Communication Protocol
124 - User Interface Module
126 - Computing Device
DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details, are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a," "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "including," and "having," are open ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.
When an element is referred to as being "engaged to," "connected to," or "coupled to" another element, it may be directly engaged, connected, or coupled to the other element. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed elements.
Digital mental health systems face significant technical limitations, primarily in data security and privacy, where inadequate encryption and compliance with standards like HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation) threaten user trust. Additionally, lack of personalization results in generic interventions that fail to adapt to individual needs, potentially reducing user engagement and effectiveness. Insufficient real-time data analysis and crisis detection further hamper these platforms, as they lack automated alerts for timely intervention in critical situations. Scalability is also an issue, with many systems lacking cloud infrastructure, leading to performance issues as user demand grows. Lastly, the absence of machine learning integration limits the precision of assessments and the ability to provide tailored insights, restricting the depth of support available. Addressing these technical limitations is essential for more secure, personalized, and responsive mental health care platforms.
To address the issues of the existing systems and methods, the present disclosure envisages a digital system (hereinafter referred to as "system 100") for mental health intervention and therapy and a method (hereinafter referred to as "method 200") for mental health intervention and therapy. The system 100 will now be described with reference to Figure 1 and the method 200 will be described with reference to Figures 2A-2B.
Referring to Figure 1, the system 100 comprises a learning management application 102 and a server 110.
The learning management application 102 is installed on a server 104, accessible via a user's computing device 126.
In an embodiment, the server 104 comprises:
• a data storage 104a is configured to securely store assessment and intervention data; and
• a computation module 104b is configured to process and analyze the collected mental health data.
In an embodiment, the computation module 104b supports cloud-based processing to enable scalability for multiple concurrent users across different geographical locations.
The learning management application 102 includes a pre-intervention assessment module 106, a data pre-processing module 108, a cloud-based storage module 110, a stream-intervention module 112, a post-intervention assessment module 114, an analyzer module 116, a threshold alert module 118, and a notification module 120.
The pre-intervention assessment module 106 is configured to receive user input data through a pre-intervention structured assessment form and register the user with a unique identification number.
In an embodiment, the user scans a digital code to access the pre-intervention structured assessment form to provide the inputs, wherein the digital code includes a QR code, barcode, scan code, or any code capable of accessing the pre-intervention structured assessment form.
In an embodiment, the pre-intervention assessment module 106 is configured to perform mental health risk screening and flag high-risk users for immediate health professionals' intervention.
The data pre-processing module 108 is configured to compute mental health dimension scores based on the user input data, the mental health dimensions including anxiety, depression, behavioural control, emotional affect, life satisfaction, emotional ties, psychological distress, and psychological well-being.
The cloud-based storage module 110 is configured to securely store the computer's mental health dimension score.
The stream-intervention module 112 is configured to deliver video-based mental health interventions over a structured four-week program, with only one video available per week.
In an embodiment, the stream-intervention module 112 dynamically adjusts content based on user engagement and performance throughout the four-week program.
The post-intervention assessment module 114 is configured to provide the user with assignments or reflections related to the interventions after each video session.
The analyzer module 116 is configured to compare and analyze the pre-intervention and the post-intervention mental health data by means of a set of analyzer rules, and is further configured to generate an analyzer report indicating changes in the mental health dimensions of each user.
In an embodiment, the set of analyzer rules is a set of instructions used to implement one or more machine learning models in combination to improve accuracy in analyzing mental health data over time.
In an embodiment, the analyzer module 116 is configured to automatically generate a detailed report stored as an Excel file, indexed by the user's unique identification number.
The threshold alert module 118 is configured to flag significant fluctuations in mental health data where the comparison result exceeds pre-set thresholds.
In an embodiment, the threshold alert module 118 is configured with customizable thresholds based on the severity of mental health dimension fluctuations and is capable of adjusting alerts based on participant demographics or individual risk factors.
The notification module 120 is configured to transmit alerts to registered mental health professionals if immediate intervention is required, based on the flagged mental health data.
In an embodiment, the notification module 120 is integrated with mobile push notifications and email alerts to notify health professionals of emergencies or significant mental health risks in real-time.
In an embodiment, the system 100 comprises:
• a communication protocol 122 is configured to perform secure data transmission using HTTPS to ensure privacy between the user interface, server, and application modules; and
• a user interface module 124 configured to interact with the system, fill assessments, and access video interventions.
In an embodiment, the activities and assignments include pre-recorded interventional videos for mental health improvement and small follow-up activities/assignments/reflections.
In an embodiment, the system introduces a dynamic content delivery system that personalizes the intervention experience based on user engagement and performance. As the user progresses through the structured four-week program, the stream-intervention module 112 adjusts video content in response to the user's engagement level and their performance on post-session activities. High-engagement users may receive additional resources, while those requiring a slower pace may be offered simplified content and additional support. Additionally, the post-intervention adaptive assessments 114 adapt future activities based on user responses, creating a custom-tailored intervention experience. The analyzer module 116 employs machine learning algorithms to continuously enhance data analysis accuracy, using trends in user behavior to adjust analyzer rules over time. This adaptability fosters a more engaging, effective intervention experience tailored to the individual's mental health needs.
In an embodiment the system's automated capabilities for crisis detection and real-time intervention alerts. The threshold alert module 118 is equipped with customizable thresholds tailored to user-specific risk factors. For example, users with high baseline levels of psychological distress may have lower thresholds for alert activation, ensuring a sensitive and tailored approach to crisis detection. When data fluctuations surpass these thresholds, the notification module 120 automatically triggers real-time alerts to registered mental health professionals via push notifications or email, allowing them to intervene promptly. This automated detection and notification system enables mental health professionals to respond to potential crises in real time, prioritizing at-risk users and enhancing overall system responsiveness to user needs.
In an embodiment, the system's scalable design, enables it to support a large, geographically dispersed user base. The cloud-based computation module 104b provides scalable data processing, ensuring that the system can handle simultaneous access by multiple users without compromising performance. This scalability is further supported by the system's geographically distributed access, which allows users from various locations to participate in interventions seamlessly. The cloud-based architecture also facilitates dynamic content distribution, allowing for efficient delivery of video interventions and easy scalability across large user groups. The system can also support content localization, ensuring accessibility and relevance to users from different regions. This embodiment thus ensures a robust, accessible platform capable of reaching a wide audience.
In this embodiment, the system leverages real-time, data-driven analysis to provide comprehensive insights into users' mental health. The analyzer module 116 uses rule-based or machine learning algorithms to analyze pre- and post-intervention data, identifying significant mental health changes. Machine learning models within this module evolve as more data is gathered, refining their accuracy and enabling a deeper understanding of trends in user mental health. The system also generates a report in Excel format, indexed by the user's unique ID, making it accessible to mental health professionals. This report allows professionals to track changes in mental health dimensions over time, providing a data-driven foundation for clinical decision-making. Through this embodiment, the system provides detailed, real-time insights, enabling professionals to make proactive, informed care recommendations.
In an embodiment, the system includes rigorous data security protocols. All data transmission between the user's device, the server, and system modules is secured using HTTPS 122, ensuring that user information remains private and protected. The user interface module 124 provides an intuitive, secure platform where users can complete assessments, access video interventions, and view their progress, creating a cohesive and user-friendly experience.
In this embodiment, the four-week program, the computation module 104b supports real-time data processing and analysis across a cloud-based infrastructure, enabling the system to handle concurrent users and adapt to increased demand without compromising performance. This scalability also ensures that users from various locations can access the system reliably, as the cloud infrastructure supports geographically distributed access and content delivery.
Upon program completion, the system provides users with a final report summarizing their progress across mental health dimensions, using both the baseline and post-intervention data. This report helps users and mental health professionals evaluate the overall effectiveness of the intervention. For users requiring additional support, the system can recommend further steps or professional resources, helping them transition from digital intervention to ongoing mental health support if necessary.
Figures 2A-2B illustrate a flowchart for a method for mental health intervention and therapy in accordance with an embodiment of the present disclosure. The order in which method 200 is described is not intended to be construed as a limitation, and any number of the described method steps may be combined in any order to implement method 200, or an alternative method. Furthermore, method 200 may be implemented by processing resource or computing device(s) through any suitable hardware, non-transitory machine-readable medium/instructions, or a combination thereof. The method 200 comprises the following steps:
At step 202, the method 200 includes installing a learning management application 102 on a server 104, accessible via a user's computing device 126.
At step 204, the method 200 includes receiving, by a pre-intervention assessment module 106, user input data through a pre-intervention structured assessment form and registering the user with a unique identification number.
At step 206, the method 200 includes computing, by a data pre-processing module 108, mental health dimension score based on the user input data, the mental health dimensions including anxiety, depression, behavioral control, emotional affect, life satisfaction, emotional ties, psychological distress, and psychological well-being.
At step 208, the method 200 includes securely storing, by a cloud-based storage module 110, the computer mental health dimension score.
At step 210, the method 200 includes delivering, by a stream-intervention module 112, the video-based mental health interventions over a structured four-week program, with only one video available per week.
At step 212, the method 200 includes providing, by a post-intervention assessment module 114, the user with assignments or reflections related to the interventions after each video session.
At step 214, the method 200 includes comparing and analyzing, by an analyzer module 116, the pre-intervention and the post-intervention mental health data by means of a set of analyzers rules, and generating an analyzer report indicating changes in the mental health dimensions of each user.
At step 216, the method 200 includes flagging, by a threshold alert module 118 significant fluctuations in mental health data where the comparison result exceeds pre-set thresholds.
At step 218, the method 200 includes transmitting, by a notification module 120, alerts to registered mental health professionals if immediate intervention is required, based on the flagged mental health data.
Figure 3 illustrates an execution flow of the mental health intervention and therapy in accordance with an embodiment of the present disclosure. Figure 3 illustrates the flow and execution of a digital system for mental health intervention and therapy. The system consists of several components designed to provide a structured intervention program and analyze mental health changes. Here's a step-by-step description:
Pre-Intervention Mental Health Assessment: The system begins with a pre-intervention mental health assessment where users fill out a form or other digital forms to provide their baseline mental health data. The form collects data on various mental health dimensions such as anxiety, depression, emotional well-being, etc.
The collected data is securely stored in cloud-based storage, ensuring that the user's data is accessible for further analysis.
Video-Based Intervention Program: After completing the pre-assessment, users proceed to a video-based intervention in the Learning Management System (LMS). The intervention spans four weeks, and the user receives one intervention video per week.
Users are expected to complete follow-up activities or assignments after each session, which helps reinforce the therapeutic content of the video sessions. This phase corresponds to the stream-intervention module (112), which manages the delivery of structured interventions.
After each weekly session, users are redirected to the post-intervention assessment form.
Post-Intervention Mental Health Assessment: At the end of the intervention program, users complete a post-intervention mental health assessment, similar to the pre-intervention assessment, to evaluate changes in their mental health condition.
The post-intervention data is also stored securely in cloud-based storage, ready for comparison and analysis.
Data Computation: Both the pre-intervention data and post-intervention data are processed using computational software, which computes the changes in the user's mental health scores. The data is then sent back to the respective storage locations for further analysis.
This step relates to the data pre-processing module 108, which computes the mental health scores, and the analyzer module 116, which processes the data for comparison.
Comparator and Output: The computed data is sent to a comparator, where the system compares the pre- and post-intervention mental health data. This comparison is done according to predefined rules.
The result of this comparison is the output, indicating any significant changes in the user's mental health dimensions over the course of the intervention program. If significant fluctuations are detected, the system may flag the data, which leads to notifying mental health professionals.
Emergency Counselling Session: If the comparison indicates a critical fluctuation in the user's mental health status, the system triggers an alert and informs a counsellor, arranging a counseling session on an emergency basis. This aligns with the threshold alert module 118 and notification module 120 described, which handle real-time notifications to health professionals.
In an exemplary, the system performs the following steps:
Step 1: Form Filling: Pre-Intervention Assessment
The participants (Youth) will access a form link to fill out a questionnaire on mental health after agreeing with informed consent. Informed consent also includes a statement saying that if your mental health is assessed using this system and calls for any situation that needs immediate attention, you may be contacted by one of psychologists/psychiatrists who is a part of this process. So, the participant's contact number and email are collected to reach out only in case of immediate support requirements based on the assessment result.
The questionnaire consists of Mental Health Inventory-38, which assesses their mental health. Every student will be assigned a unique ID to be identified with their email or name search. Every student's mental health data collected using the form will be processed and automatically computed (using software) for the scores of mental health dimensions, i.e., Anxiety, Depression, Loss of Behavioural/Emotional Control, General Positive Affect, Life Satisfaction, Emotional Ties, Psychological Distress, Psychological Well-Being, and Global Mental Health Index. The computed data is saved in a cloud-based database.
Step 2: Redirected from Forms to Learning Management System (LMS)
Once students complete the form, they will be redirected to enroll in an LMS learning management system. The LMS contains fully loaded pre-recorded interventional videos for mental health improvement, spanning four weeks; only one video opens per week, and after watching the video, they will have to complete a short assignment and submit the assignment. Like this, the video-based interventional learning and small follow-up activity/assignment/reflection will go on for four weeks.
Step 3: Post Intervention Assessment:
A new form link will be made available to them as a pop-up message on the seventh day after the completion of the video learning, follow-up activity/assignment/reflection.
The form consists of the questionnaire they answered earlier, without changing. The student must fill out the form again and submit it. This data will be again automatically computed (using software) for the scores of mental health dimensions, i.e., Anxiety, Depression, Loss of Behavioural/Emotional Control, General Positive Affect, Life Satisfaction, Emotional Ties, Psychological Distress, Psychological Well-Being, and Global Mental Health Index.
This computed data is saved in the same cloud-based database where the pre-interventional assessment data was stored.
The pre-interventional and post-interventional assessment data of mental health will be compared using a comparator software for every participant based on the participant ID without any mismatch.
The comparator output is saved in computational software. Any changes in mental health dimensions will be computed, and a detailed report will be generated, automatically stored in an Excel sheet with a unique ID assigned earlier.
Any abnormally high fluctuations in Mental Health Dimensions requiring immediate attention would be identified on a priority basis using a red flag, whose thresholds are pre-set in the computational software.
An automatic email alert would be sent to a qualified rehabilitation psychologist pre-registered for this process and assigned based on availability. The participant will receive a call from the psychologist and be given further assessment and therapy. If the situation worsens, the psychologist may assign this to a pre-registered psychiatrist and see that the participant gets immediate treatment/therapy, whatever is applicable.
After gathering enough data, the system analyzes the effectiveness of the video-based intervention in mitigating psychological distress and enhancing well-being using statistical inferences.
In an operative configuration, the system for mental health intervention and therapy begins with the user accessing the learning management application 102 via a computing device 126, such as a smartphone, tablet, or computer. Upon first access, the user is prompted to complete a pre-intervention assessment through the pre-intervention assessment module 106. This assessment consists of a structured form that gathers data on mental health dimensions like anxiety, depression, emotional affect, behavioural control, and life satisfaction. Each user is assigned a unique identification number to ensure secure tracking and personalized intervention.
Once the pre-intervention assessment data is submitted, the data pre-processing module 108 processes this data to compute an initial mental health dimension score for each aspect evaluated. This baseline score acts as the reference for all subsequent analyses and helps personalize the upcoming interventions. The processed scores are then securely stored in the cloud-based storage module 110, ensuring that sensitive user information is encrypted and protected against unauthorized access. This module plays a critical role in maintaining data integrity and compliance with privacy standards. The user is then introduced to the stream-intervention module 112, which delivers a structured four-week video intervention program. The intervention is designed to be gradual, with one video released each week. Each session covers core mental health topics tailored to support the user in building resilience, managing anxiety, or improving emotional well-being. After each video, the post-intervention assessment module 114 engages the user in follow-up activities, such as assignments, reflections, or questionnaires, to reinforce learning and encourage the practical application of therapeutic strategies. The user's performance and engagement with these activities are recorded, enabling further personalization of the intervention content.
Following each weekly session and post-activity, the system's analyzer module 116 compares the user's new data with their baseline scores, using a set of analyzer rules or machine learning algorithms to detect trends, improvements, or declines in mental health dimensions. This module identifies significant changes in areas like anxiety or psychological distress, generating a detailed report that tracks the user's progress and highlights any noteworthy patterns. This analysis is stored in an Excel-format report indexed by the user's unique ID, making it accessible to authorized mental health professionals if required.
If the analyzer detects substantial fluctuations exceeding preset thresholds, the threshold alert module 118 flags these changes. These thresholds are customizable, allowing the system to adjust sensitivity based on each user's initial risk factors, demographics, and specific needs. For example, a user with a history of high psychological distress may trigger an alert with smaller fluctuations compared to a low-risk user. When an alert is generated, the notification module 120 sends an immediate notification to registered mental health professionals via push notifications or email. This real-time alert mechanism ensures that at-risk users receive prompt attention from a professional when needed.
Throughout the program, the stream-intervention module 112 dynamically adapts its content based on the user's engagement levels. If a user is highly engaged, additional resources or advanced topics may be introduced, while users who struggle with the material may receive simplified content or additional support. The system tracks these engagement metrics and adjusts the content, creating a more personalized and effective intervention experience.
Advantageously, the system 100 enables real-time analysis of mental health data, empowering both users and professionals to make timely and informed decisions regarding intervention needs. With automated assessments, real-time data processing, and AI-based analysis, health professionals can focus on high-priority cases, reducing the time spent on manual data analysis. The system's ability to dynamically adjust content based on user engagement and performance throughout the program improves user engagement and the likelihood of positive mental health outcomes. The cloud-based design and communication protocols ensure scalability, enabling the system to support multiple concurrent users across different locations without compromising performance or data security. Secure storage and transmission protocols protect sensitive mental health data, ensuring compliance with data protection regulations, which is critical for mental health applications. The system's use of machine learning within the analyzer module allows for more precise identification of mental health patterns, potentially leading to better-tailored interventions and insights. By delivering therapy content and monitoring digitally, the system reduces the need for in-person consultations for every user, making mental health support more accessible and affordable.


The key features:
• Automated Pre- and Post-Intervention Assessments: Using Forms for both pre- and post-intervention assessments allows for a streamlined and user-friendly data collection process. Participants fill out the same questionnaire before and after the intervention, enabling a direct comparison of mental health dimensions.
• Unique Participant Identification: Each participant is assigned a unique ID linked to their email or name, ensuring that their data can be accurately tracked and analyzed without any mismatches. This feature enhances data integrity and confidentiality.
• Cloud-Based Data Storage and Processing: The collected data is stored in a cloud-based database, which allows for easy access and management of participant information. This setup facilitates real-time data processing and analysis, making it efficient to generate reports and insights.
• Video-Based Intervention with Structured Learning: The intervention consists of pre-recorded videos delivered over four weeks, with only one video accessible weekly. This structured approach encourages gradual learning and reflection, promoting better retention and application of the material.
• Integrated Follow-Up Activities: After watching each video, participants must complete a short assignment or reflection. This integration of follow-up activities reinforces the learning experience and encourages active engagement with the content.
• Automated Comparison and Reporting: The system employs comparator software to analyze pre- and post-intervention data for each participant. This automated process generates detailed reports highlighting changes in mental health dimensions, providing valuable insights into the effectiveness of the intervention.
• Red Flag System for Immediate Attention: The system includes a unique feature that identifies any significant fluctuations in mental health dimensions that may require immediate attention. Pre-set thresholds in the computational software trigger alerts, ensuring that participants in crisis are prioritized for support.
• Automatic Email Alerts to Psychologists: If a participant's data indicates a need for immediate intervention, the system automatically sends an email alert to a qualified rehabilitation psychologist. This feature ensures timely follow-up and intervention, which is crucial in mental health care.
• Potential for Statistical Analysis:
o The system is designed to gather sufficient data over time, allowing for statistical analysis of the intervention's effectiveness.
o This capability can lead to evidence-based improvements in mental health strategies and interventions.
o These features collectively create a comprehensive and innovative approach to mental health intervention for youth, enhancing both the assessment and support processes in a way that is not commonly found in existing systems.
The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope and spirit of the disclosure and appended claims. For example, due to the nature of software, functions described above can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.
The foregoing description of the embodiments has been provided for purposes of illustration and is not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment but are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.
The following are the uses and Applications:
• Mental Health Assessment: The primary use of the invention is to conduct comprehensive mental health assessments for youth. By utilizing a structured questionnaire, it can effectively evaluate various mental health dimensions such as anxiety, depression, and overall psychological well-being.
• Digital Mental Health Interventions: The system serves as a platform for delivering video-based mental health interventions. This application allows participants to engage with educational content designed to improve their mental health over a structured four-week program.
• Real-Time Monitoring and Support: The system can be used for ongoing monitoring of participants' mental health. The automated alert feature ensures that any significant changes in mental health status are promptly addressed, facilitating timely support from mental health professionals.
• Research and Data Analysis: The system can be utilized in research settings to gather data on the effectiveness of mental health interventions. Researchers can analyze the collected data to draw insights and conclusions about the impact of digital interventions on youth mental health.
• Training and Education for Mental Health Professionals: The system can also be used as a training tool for mental health professionals, providing them with insights into the mental health status of youth and the effectiveness of various interventions.
• Community Mental Health Programs: Organizations focused on youth mental health can implement this system as part of community outreach programs, providing accessible mental health resources to a broader audience.
The following are the Benefits of the system:
• Increased Accessibility: The digital format allows youth to access mental health resources from anywhere, reducing barriers related to location, transportation, and stigma associated with seeking help.
• Enhanced Engagement: The combination of video content and interactive assignments fosters greater engagement among participants, making the learning process more enjoyable and effective.
• Timely Interventions: The automated alert system ensures that participants who exhibit concerning changes in their mental health receive immediate attention, potentially preventing crises and improving outcomes.
• Data-Driven Insights: The ability to collect and analyze data systematically allows for evidence-based decision-making, helping to refine and improve mental health interventions over time.
• Holistic Approach: By integrating assessment, intervention, and follow-up support, the invention provides a comprehensive approach to mental health care, addressing multiple aspects of well-being.
• Scalability and Flexibility: The cloud-based infrastructure allows the system to scale easily, accommodating a growing number of participants without compromising service quality. It can also be adapted for different age groups or mental health issues.
The following are the Major Applications:
• School-Based Mental Health Programs: Schools can implement this system to provide mental health support to students, helping to identify and address issues early on.
• Community Health Initiatives: Community organizations can use the system to reach out to youth in various settings, providing essential mental health resources and support.
• Telehealth Services: The system can be integrated into telehealth platforms, allowing mental health professionals to conduct assessments and provide interventions remotely.
• Research Institutions: Academic and research institutions can utilize the system to study the effectiveness of digital mental health interventions and contribute to the body of knowledge in this field.
TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a digital system and a method for mental health intervention and therapy that:
• enables real-time analysis of mental health data;
• provides automated assessment;
• dynamically adjusts content based on user engagement and performance;
• ensures scalability;
• precises identification of mental health patterns, potentially leading to better-tailored interventions and insights; and
• reduces the need for in-person consultations for every user, making mental health support more accessible and affordable.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveals the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
The use of the expression "at least" or "at least one" suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation. , Claims:WE CLAIM:
1. A digital system (100) for mental health intervention and therapy, said system (100) comprising:
• a learning management application (102) installed on a server (104), accessible via a user's computing device (126), said learning management application (102) comprising:
o a pre-intervention assessment module (106) configured to receive user input data through a pre-intervention structured assessment form and register the user with a unique identification number;
o a data pre-processing module (108) configured to compute mental health dimension scores based on the user input data, said mental health dimensions including anxiety, depression, behavioral control, emotional affect, life satisfaction, emotional ties, psychological distress, and psychological well-being;
o a cloud-based storage module (110) configured to securely store the computer's mental health dimension score;
o a stream-intervention module (112) configured to deliver video-based mental health interventions over a structured four-week program, with only one video available per week;
o a post-intervention assessment module (114) configured to provide the user with assignments or reflections related to the interventions after each video session;
o an analyzer module (116) configured to compare and analyze said pre-intervention and said post-intervention mental health data by means of a set of analyzer rules, and further configured to generate an analyzer report indicating changes in the mental health dimensions of each user;
o a threshold alert module (118) configured to flag significant fluctuations in mental health data where the comparison results exceed pre-set thresholds; and
o a notification module (120) configured to transmit alerts to registered mental health professionals if immediate intervention is required, based on the flagged mental health data.
2. The system (100) as claimed in claim 1, wherein said server (104) comprises:
• a data storage (104a) configured to securely store assessment and intervention data; and
• a computation module (104b) configured to process and analyze said collected mental health data.
3. The system (100) as claimed in claim 1, wherein the analyzer module (116) is configured to automatically generate a detailed report stored as an Excel file, indexed by the user's unique identification number.
4. The system (100) as claimed in claim 1, wherein the threshold alert module (118) is configured with customizable thresholds based on the severity of mental health dimension fluctuations and is capable of adjusting alerts based on participant demographics or individual risk factors.
5. The system (100) as claimed in claim 1, wherein said system (100) comprises:
• a communication protocol (122) configured to perform secure data transmission using HTTPS to ensure privacy between the user interface, server, and application modules; and
• a user interface module (124) configured to interact with the system, fill assessments, and access video interventions.
6. The system as claimed in claim 1, wherein said pre-intervention assessment module (106) is configured to perform mental health risk screening and flag high-risk users for immediate health professionals' intervention.
7. The system as claimed in claim 1, wherein said set of analyzer rules is a set of instructions used to implement one or more machine learning models in combination to improve accuracy in analyzing mental health data over time.
8. The system as claimed in claim 1, wherein said notification module (120) is integrated with mobile push notifications and email alerts to notify the health professionals of emergencies or significant mental health risks in real-time.
9. The system as claimed in claim 2, wherein said computation module (104b) supports cloud-based processing to enable scalability for multiple concurrent users across different geographical locations.
10. A method (200) for digital mental health intervention and therapy, said method (200) comprises the following steps:
• installing a learning management application (102) on a server (104), accessible via a user's computing device (126);
• receiving, by a pre-intervention assessment module (106), user input data through a pre-intervention structured assessment form and registering the user with a unique identification number;
• computing, by a data pre-processing module (108), mental health dimension score based on the user input data, said mental health dimensions including anxiety, depression, behavioral control, emotional affect, life satisfaction, emotional ties, psychological distress, and psychological well-being;
• securely storing, by a cloud-based storage module (110), the computer mental health dimension score;
• delivering, by a stream-intervention module (112), the video-based mental health interventions over a structured four-week program, with only one video available per week;
• providing, by a post-intervention assessment module (114), the user with assignments or reflections related to the interventions after each video session;
• comparing and analyzing, by an analyzer module (116), said pre-intervention and said post-intervention mental health data by means of a set of analyzer rules, and generating an analyzer report indicating changes in the mental health dimensions of each user;
• flagging, by a threshold alert module (118) significant fluctuations in mental health data where the comparison result exceeds pre-set thresholds; and
• transmitting, by a notification module (120), alerts registered mental health professionals if immediate intervention is required, based on the flagged mental health data.
Dated this 06th Day of November, 2024

_______________________________
MOHAN RAJKUMAR DEWAN, IN/PA - 25
OF R. K. DEWAN & CO.
AUTHORIZED AGENT OF APPLICANT

TO,
THE CONTROLLER OF PATENTS
THE PATENT OFFICE, AT CHENNAI\

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