Structured multi-modular curriculum-based eating disorder intervention method and system

Abstract

The application relates to an eating disorder intervention method and system based on a structured multi-module course, the method comprising: a user terminal sending a course access request to a server; the server obtaining a user course progress according to the course access request, determining a target course unit matched with the user course progress according to a course arrangement rule, searching for course unit data of the target course unit from a structured course database, and sending the course unit data to the user terminal; the user terminal presenting the course unit data of the target course unit to the user, guiding the user to perform an intervention task through an interactive interface, collecting corresponding structured user state data, and sending the collected user state data to the server; and the server judging whether a specified task of the target course unit is completed according to the user state data, updating the user course progress if the specified task is completed, and not updating the user course progress if the specified task is not completed. The application realizes forced guidance, standardized implementation and automatic management of a structured treatment process.

Description

Technical Field

[0001] This application relates to the field of computer and digital health technology, and in particular to an eating disorder intervention method and system based on a structured multi-module curriculum. Background Technology

[0002] Eating disorders, such as anorexia nervosa and bulimia, are mental illnesses that seriously endanger physical and mental health. Structured psychotherapies such as DBT (Dialectical Behavior Therapy) have been proven effective in intervening in these disorders. In recent years, in order to improve the accessibility of treatment and reduce dropout rates, digital intervention methods based on mobile terminals (such as smartphone applications and WeChat mini-programs) have become an important research and development direction.

[0003] However, successfully migrating highly structured, sequential treatment protocols such as DBT to digital platforms faces significant technical challenges. Current digital intervention tools have at least the following shortcomings: First, regarding the systematic execution of the treatment process, most applications merely provide treatment content as a static "article library" or "video collection" to users, lacking a mandatory course arrangement and guidance mechanism. Users can freely choose what to watch, easily deviating from the necessary logical sequence and progressive steps of treatment, resulting in a significant reduction in the intervention effect.

[0004] Secondly, regarding the specificity of intervention implementation and the validity of data, existing tools suffer from severe homogenization in their interaction methods, often employing generic diaries, ratings, or question-and-answer forms. They fail to integrate specialized assessment and training tools targeting core symptoms of eating disorders (such as emotional eating and behavioral dysregulation), such as standard "emotion regulation models" or "behavioral chain analysis" interfaces. This results in user data with limited dimensions and low structure, making in-depth quantitative analysis and efficacy evaluation difficult.

[0005] Finally, regarding the objectivity and automation of progress management, existing solutions often rely on users marking completion themselves or using only the passage of time as the basis for unlocking new content, failing to truly determine whether users have completed the core intervention tasks required by the current course unit. This mechanism cannot guarantee the depth of user engagement, nor can it provide therapists with objective progress data. Summary of the Invention

[0006] To address the aforementioned shortcomings or deficiencies, this application provides an intervention method and system for eating disorders based on a structured multi-module curriculum.

[0007] This application provides an intervention method for eating disorders based on a structured multi-module curriculum, according to a first aspect, the method comprising: The user terminal sends a course access request to the server; The server obtains the user's course progress based on the course access request, determines the target course unit matching the user's course progress according to the preset course arrangement rules, searches for the course unit data of the target course unit in the structured course database, and sends it to the user terminal; the structured course database includes course unit data of multiple course units arranged in a preset time sequence. The user terminal presents the course unit data of the target course unit to the user, guides the user to perform intervention tasks through the interactive interface and collects the corresponding structured user status data, and sends the collected user status data to the server; the interactive interface includes the emotion regulation model interface, the behavior chain analysis interface and / or the multidimensional diary card interface. The server determines whether the specified task of the target course unit has been completed based on the user's status data. If it has been completed, the user's course progress is updated; otherwise, the user's course progress is not updated.

[0008] In some embodiments, the method further includes: The user terminal sends a request for visualized data to the server; In response to the visualization data request, the server generates a visualization trend chart based on the quantitative information in the user's historical user status data and sends the visualization trend chart to the user terminal. The user terminal presents users with visual trend charts.

[0009] In some embodiments, the operation of the server generating a visual trend chart based on quantitative information in the user's historical user status data includes: The server extracts quantitative scores of the user's binge eating impulses submitted by the user through the multidimensional diary card interface over multiple consecutive days. Based on the extracted quantitative scores, a line chart is generated with the date as the horizontal axis and the score as the vertical axis, and the line chart is used as a visual trend chart.

[0010] In some embodiments, when a user terminal collects user status data through a multidimensional diary card interface, it receives a quantitative score of the user's eating-related impulses or behaviors input through the multidimensional diary card interface, and sends the user status data containing the quantitative score to the server.

[0011] In some embodiments, the method further includes: When the user terminal presents course unit data to the user, if it detects that the user has triggered a specific skill keyword in the course content, it sends a skill details request to the server. The server searches for matching skill details based on the skill details request and sends the found skill details to the user terminal; The user terminal displays skill details to the user.

[0012] In some embodiments, the operation of the server determining the target course unit matching the user's course progress according to preset course arrangement rules includes: The server will determine the course unit corresponding to the completion date of the user's course progress as the target course unit.

[0013] In some embodiments, the method further includes: In response to the user's access to the skill card pack function, the user terminal sends a skill list request to the server; The server returns a list of skill cards to the user terminal based on the skill list request; The user terminal presents a list of skill cards to the user and sends a skill content retrieval request to the server when it detects that the user has viewed a specific skill card. The server determines the target skill card based on the skill content retrieval request and sends the skill content of the target skill card to the user terminal. The user terminal presents the skill content of the target skill card to the user.

[0014] In some embodiments, the method further includes: Based on the user's historical user status data, the server analyzes the high-risk time periods when the user exhibits eating-related impulses or problem behaviors. Before the time point corresponding to the high-risk time period mode, the server sends a proactive intervention prompt notification to the user terminal. In response to the proactive intervention notification, the user terminal presents a proactive intervention message to the user, which includes suggestions for coping skills and / or reminders for mindfulness practice during high-risk periods.

[0015] In some embodiments, the method further includes: The server records users' historical access data and practice feedback data for skill details or target skill cards. Based on the historical access data and practice feedback data, it builds a personalized skill recommendation model for users. When preset trigger conditions are met, personalized skill recommendation information is pushed to the user's terminal according to the skill recommendation model. Trigger conditions include: detecting abnormal user status data, or the user entering a preset high-risk situation, or the user actively requesting a recommendation.

[0016] According to the second aspect, this application provides an eating disorder intervention system based on a structured multi-module curriculum, the system including a user terminal and a server; The user terminal is used to send course access requests to the server; The server is used to obtain the user's course progress based on the course access request, determine the target course unit that matches the user's course progress according to the preset course arrangement rules, search for the course unit data of the target course unit from the structured course database, and send it to the user terminal; the structured course database includes course unit data of multiple course units arranged in a preset time sequence. The user terminal is also used to present the course unit data of the target course unit to the user, guide the user to perform intervention tasks through the interactive interface and collect the corresponding structured user status data, and send the collected user status data to the server; the interactive interface includes the emotion regulation model interface, the behavior chain analysis interface and / or the multidimensional diary card interface. The server is also used to determine whether the specified task of the target course unit has been completed based on the user status data. If it has been completed, the user's course progress is updated; if it has not been completed, the user's course progress is not updated.

[0017] The technical solution described in this application centrally controls the course learning process via a server, forcing users to execute courses sequentially. Each time a user requests to learn, the server uniquely determines and assigns the required course unit based on the user's current progress and preset timing rules, rather than allowing the user to choose. During the learning process, the server guides users through standardized psychological intervention exercises via interactive interfaces such as emotion regulation models and behavioral chain analysis, automatically collecting structured behavioral and emotional data. Finally, by analyzing the structured data submitted by the user, the server automatically determines whether the task is completed and decides whether to update the progress and unlock subsequent units. This achieves mandatory guidance, standardized implementation, and automated management of a structured treatment process in a digital environment, ensuring high fidelity of treatment and high user compliance. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an eating disorder intervention method based on a structured multi-module curriculum, as described in one or more embodiments of this application. Figure 2 This is an overall flowchart of the eating disorder intervention method in one or more embodiments of this application; Figure 3 This is a functional diagram of an electronic prescription app based on DBT treatment in one or more embodiments of this application; Figure 4 This is a flowchart of an eating disorder intervention in one or more embodiments of this application; Figure 5 This is a schematic diagram of the interactive interface of the 28-day course map in one or more embodiments of this application; Figure 6 This is a schematic diagram of the data input interface of the DBT emotion regulation model in one or more embodiments of this application; Figure 7 This is a schematic diagram of the diary card interaction interface in one or more embodiments of this application; Figure 8 This is a schematic diagram of the interactive interface of the skill card pack in one or more embodiments of this application; Figure 9 This is a schematic diagram of the architecture of an eating disorder intervention system based on a structured multi-module curriculum, one or more embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the described embodiments are merely some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0020] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0022] To address the shortcomings or defects of related technologies, this application provides an eating disorder intervention method based on a structured multi-module curriculum. This method can force users to execute courses in a preset sequence, guide users to complete standardized intervention tasks and collect structured data through a dedicated interactive interface during the course, and automatically manage user progress based on task completion data. This digital intervention method enables high-fidelity and high-compliance implementation of structured psychotherapy in a digital environment.

[0023] In some exemplary embodiments of this application, such as Figure 1As shown, the method includes steps S110 to S140, and each step is described in detail below.

[0024] S110: The user terminal sends a course access request to the server.

[0025] A server refers to a backend service system deployed in the cloud or locally, responsible for business logic processing, data storage, and interaction scheduling. A user terminal refers to a mobile smart device (or simply mobile terminal) used by a user that has a specific application (such as a WeChat mini-program or mobile app) installed or can access a specific webpage, such as a smartphone or tablet. The user terminal and server work together to implement eating disorder intervention methods. An exemplary overall flowchart can be found here. Figure 2 As shown, from Figure 2 As can be seen, the server's execution logic can be divided into multiple modules based on their functions, including a course management module, a user interaction module, a data processing and feedback module, and a skills integration module. User terminals may have intervention programs installed (i.e., programs used to intervene in a user's eating disorder, such as applications or applets). In some examples, the relevant functions of the intervention program can be found in [reference needed]. Figure 3 As shown, in this example, the DBT-based electronic prescription app serves as the intervention program, encompassing various functions such as basic courses, feedback mechanisms, personalization, support systems, and physiological monitoring. The intervention process for eating disorders in this example can be found in [link to relevant documentation]. Figure 4 As shown.

[0026] A course access request is a network request message generated by a user terminal. It carries at least a user identifier (such as User ID or OpenID) to uniquely identify the user. This request can initiate a learning session with the server.

[0027] Specifically, a user can open the intervention application (which can be an application or a mini-program) on their terminal (i.e., the user terminal) and then click on the "Start Learning," "Today's Lesson," or similar control on the application's main interface. The application (user terminal) will capture this click event and call an internal function to encapsulate the currently logged-in user's user_id (user identifier) ​​into an HTTP or HTTPS request (i.e., a course access request) that conforms to a predefined interface specification. The user terminal will then send this request to the specified server interface over the network.

[0028] S120: The server obtains the user's course progress based on the course access request, determines the target course unit matching the user's course progress according to the preset course arrangement rules, searches for the course unit data of the target course unit in the structured course database, and sends it to the user terminal; the structured course database includes course unit data of multiple course units arranged in a preset time sequence.

[0029] User course progress is used to record a user's position in the current structured course. Specifically, it can be a state variable stored in a server database. Typically, it records the identifier of the user's latest completed or pending course unit (such as `completed_unit_id`).

[0030] The preset course scheduling rules are used to deduce the next or current course unit that the user should study based on their course progress. Specifically, these rules can be algorithms or mapping relationships preset in the server program logic. For example, the course scheduling rules could be: "Target course unit ID = User's latest completed unit ID + 1", or "If the user's progress is day N, then the target unit is the content of day N+1", etc.

[0031] A target course unit refers to a course unit uniquely assigned to the user for learning in this session, calculated according to the course arrangement rules. The structured course database is a relational or non-relational database on the server that stores course unit data for all course units included in the structured course. This data is organized and indexed in a preset, immutable chronological order (e.g., Day 1, Day 2, ..., Day 28) or logical order. Each course unit is also categorized into at least five progressive course modules arranged in a predetermined logical order. These modules include at least a general skills module, a mindfulness skills module, an emotion regulation module, a pain tolerance module, and an interpersonal effectiveness module. The modules are described below.

[0032] The General Skills Module is a universal tool used to establish a foundation for therapy, helping users understand the therapy model and learn to analyze their own behavioral patterns. In eating disorder interventions, the General Skills Module has multiple uses. For example, it can introduce DBT (Dietary Therapy) and dialectical thinking to help users understand that "total rejection" and "black and white" thinking (such as "either perfect dieting or extreme binge eating") are part of the problem, and learn to find balance in contradictions (such as "I can both lose weight and enjoy delicious food"); it can guide users to conduct short-term and long-term pros and cons analysis of behaviors such as "binge eating" or "excessive dieting," enhancing their motivation to change; it can help users conduct behavioral chain analysis, such as learning to analyze the complete chain before, during, and after a binge eating behavior (e.g., stressful event -> emotion "anxiety" -> thought "eating something will make me feel better" -> binge eating behavior -> stronger shame after a brief respite), which forms the basis for subsequent targeted interventions; it can also teach users to use standardized diary card tools to provide continuous data tracking throughout the therapy period.

[0033] The mindfulness skills module cultivates the ability to "be mindful of the present moment without judgment," helping users break free from automatic, emotional reaction patterns. In eating disorder interventions, the mindfulness skills module has multiple applications. For example, it can help users observe their thoughts and bodily sensations like a scientist when eating urges arise (e.g., "I notice my stomach is empty, and I feel very agitated"), rather than being immediately controlled by them; it can also help users reduce harsh judgments about food, body image, and themselves (e.g., "I ate junk food again, I'm such a failure"), learn to bring their attention back to what they are doing, and break the vicious cycle of immersion in regret or anxiety; it can specifically train users to slow down and engage all their senses to experience the eating process, aiming to restore a natural awareness of hunger and satiety, and combat "mechanical" or "emotional" uncontrolled eating.

[0034] The emotion regulation module helps users understand and name emotions, learn to reduce emotional vulnerability, and proactively adjust unwanted emotions. In eating disorder interventions, the emotion regulation module helps users identify the core emotions that lead to emotional eating (such as loneliness, anger, emptiness) and learn to acknowledge their existence without resistance; it helps users test facts against opposite behaviors, such as when the emotion "I feel fat and ugly" triggers the urge to diet or purge, learning to test this thought with objective facts and take actions contrary to the impulse (such as eating normally, looking in the mirror and saying neutral descriptions); it can teach users to reduce emotional vulnerability by taking care of their bodies (Treat Physical Illness, Eat balanced, Avoid mood-altering drugs, Sleep balanced, Exercise moderately), which is the physiological basis for preventing emotional eating; and it can help users accumulate positive experiences, such as encouraging users to plan small activities that bring a sense of accomplishment or pleasure to increase positive emotional reserves and combat the negative emotions that trigger binge eating.

[0035] The Pain Tolerance module helps users learn how to cope safely through emotional storms without resorting to problem behaviors (such as binge eating or self-harm) during crises or periods of extreme distress. In eating disorder interventions, the Pain Tolerance module helps users learn crisis survival strategies and STOP techniques. For example, when binge eating urges are very strong and feel uncontrollable, users can use the Stop-Take a step back-Observe-Proceed mindfully technique to interrupt automatic reactions. It also helps users learn TIP techniques, such as physiological interventions like changing body temperature (washing the face with cold water), high-intensity exercise (vigorous activity in a short period), and rhythmic breathing, to quickly reduce intense painful emotions and impulse levels. Furthermore, it helps users learn distraction and self-soothing techniques, such as guiding users to temporarily divert their attention from pain and food cravings with other activities (such as making a phone call, doing puzzles, or smelling aromatherapy) and using their five senses (such as listening to music or hugging a blanket) for self-soothing.

[0036] The interpersonal efficacy module helps users learn how to effectively express needs, set boundaries, and maintain self-esteem in relationships, thereby reducing emotional fluctuations and problem behaviors caused by interpersonal stress. In eating disorder interventions, the interpersonal efficacy module can help users learn goal efficacy, such as how to clearly express their food needs to family or friends (e.g., "Please don't comment on what I ate at gatherings"); help users learn relationship efficacy, such as how to refuse pressure to eat without damaging relationships, or how to say "no"; and help users learn self-esteem efficacy, such as how to maintain self-respect in social situations and not let the opinions of others shake their eating plans or sense of self-worth.

[0037] The five course modules mentioned above can form a complete treatment loop, namely: 1. Basic (General Skills) -> 2. Inner Awareness (Mindfulness) -> 3. Emotional Management (Emotion Regulation) -> 4. Crisis Coping (Pain Tolerance) -> 5. External Application (Interpersonal Effectiveness).

[0038] Users can better understand and apply the more complex skills in subsequent modules only after mastering the core concepts of the previous module. This structured, progressive curriculum design is key to the systematization and digitization of DBT therapy in this application, and ensures the achievement of intervention effects.

[0039] After receiving a course access request from a user terminal, the server parses the request to extract the user identifier `user_id`. The server uses this `user_id` as a key to query the user progress table in the database to obtain the user's course progress `current_progress`, for example, with a value of "Day 5 completed". Next, the server determines the target course unit matching the user's course progress according to the course scheduling rules. For example, using `current_progress` as input, the server calls the course scheduling rules module. If the module executes: "If current_progress indicates Day 5 is completed, then target_unit = Day 6", the server uses the calculated target course unit `target_unit` (e.g., "Day 6") as the query condition to access the course content table in the structured course database, retrieving all data corresponding to Day 6, including text, images, video links, and identifiers of the interactive interface types to be invoked. The server packages this course unit data into a response message in JSON or other formats and returns it to the user terminal over the network.

[0040] This step ensures that each user receives intervention strictly according to a pre-set sequence that conforms to the treatment logic (such as the progressive order of DBT skill modules), thereby solving the problem of incomplete treatment caused by fragmented content and users being able to skip around at will in related technologies.

[0041] S130: The user terminal presents the course unit data of the target course unit to the user, guides the user to perform intervention tasks through the interactive interface and collects the corresponding structured user status data, and sends the collected user status data to the server; the interactive interface includes the emotion regulation model interface, the behavior chain analysis interface and / or the multidimensional diary card interface.

[0042] An interactive interface refers to a graphical input interface dynamically rendered by a user terminal based on instructions issued by a server, used to complete specific psychological intervention tasks. Its type is determined by the identifiers carried in the course unit data. There are various types of interactive interfaces, such as the emotion regulation model interface, which is a standardized form interface designed based on DBT therapy, guiding users to analyze the relationship between emotions and behavior. Its interface content can be found in [reference needed]. Figure 6As shown; the Behavioral Chain Analysis interface is a guide for users to conduct step-by-step retrospective and prospective analysis of specific problem behaviors (such as binge eating), including structured sections such as "Preceding Events," "Chain Thoughts / Feelings," "Problem Behaviors," and "Short-Term / Long-Term Consequences"; the Multidimensional Diary Card interface is a comprehensive data recording interface that can include eating diary cards, emotion diary cards, and skill practice record cards. Eating diary cards can provide sliders (e.g., 0-6 levels) for users to quantify and record "intensity of binge eating urges" or fill in values ​​such as "number of binge eating episodes"; emotion diary cards can provide labels or text boxes for users to describe major emotional events; skill practice record cards can provide checkboxes or lists for users to select DBT skills practiced that day (such as "mindful breathing," "STOP techniques," etc.). Figure 7 The image shows a food record card that users can use to fill in their daily meals.

[0043] Structured user status data refers to the data generated by the user terminal after the user fills in and submits relevant content in the aforementioned interactive interface. It is a highly structured, quantifiable, and analyzable data object generated based on preset fields, such as: {"urge_level": 4, "binge_count": 1, "emotion": "anxiety", "skills_used": ["mindful eating"]}.

[0044] In this step, after the user terminal receives the course unit data returned by the server, the terminal application (such as an app or mini-program for eating disorder intervention) parses the course unit data, first rendering the teaching content portion of the target course unit (such as text and video). Next, based on the interaction type specified in the course unit data (such as "interaction_type": "emotion_regulation_model"), the user terminal calls the corresponding UI component library to render the "emotion regulation model" input form on the screen. Guided by the interactive interface, the user selects or fills in content in the corresponding input boxes, sliders, and drop-down menus. After filling in the content, the user can click the "Submit" button to trigger the content submission instruction. The terminal application collects all the content entered by the user in the interactive interface, organizes it into predefined key-value pair data (i.e., structured user state data), attaches the user identifier `user_id` and the course unit identifier `course_unit_id`, and finally sends it to the server via another API request.

[0045] This step deeply integrates theoretical teaching with professional practice. Specifically, through standardized digital tools (dedicated interactive interfaces), it guides users to complete exercises in a specific format necessary for treatment, ensuring the professionalism and fidelity of the intervention. Simultaneously, it transforms previously subjective and fragmented user feedback into objective, unified, and machine-readable structured data, providing high-quality data raw materials for efficacy evaluation and intelligent feedback (such as progress assessment in step S140 and the generation of subsequent charts).

[0046] S140: The server determines whether the specified task of the target course unit has been completed based on the user status data. If it has been completed, the user's course progress is updated; if it has not been completed, the user's course progress is not updated.

[0047] A designated task refers to the core interactive exercises that the user must complete in the current course unit, and the completion criteria can be preset in the server's data processing logic. Updating the user's course progress refers to modifying the user's course progress record in the database on the server side, such as pointing the course progress to the target course unit that the user has currently completed.

[0048] In this step, after receiving structured user status data submitted by the user terminal, the server determines whether the specified task of the target course unit has been completed based on the user status data. For example, the server calls preset judgment logic to detect whether the specified task of the target course unit has been completed, such as "if all four main fields of the emotion regulation model data are not empty, then it is determined to be completed"; or "if the 'impulse intensity' field in the eating diary card has been submitted with a value, then it is determined to be completed". If the judgment logic returns "complete", the server updates the user's course progress current_progress field in the user progress table in the database to the current course unit identifier course_unit_id (for example, from "Day 5" to "Day 6"). If the judgment logic returns "not completed", the server does not perform any update operation, so the user's course progress current_progress will remain unchanged. After completing the above operations, the server can return a response to the user terminal, informing them whether the task submission was successful or failed, and whether the progress has been updated. Figure 5 The image shows an example interactive interface for a 28-day course map, from which users can see which course units are included in the 28-day course and the completion status of each course unit.

[0049] This step enables automated and objective course progress management. It replaces subjective user-marked completion with data-driven, rule-based machine judgment, ensuring that the treatment process only progresses when users genuinely participate and meet minimum practice standards. This creates a powerful positive incentive and constraint mechanism; users must diligently complete current tasks to unlock subsequent content, significantly improving user adherence and treatment plan completion rates, fundamentally solving the problem of high user dropout rates in existing technologies.

[0050] In some embodiments, the method further includes: (1) The user terminal sends a visual data request to the server; A visualization data request is a network request message generated by a user terminal and sent to a server. It carries at least a user identifier and may optionally include time range parameters (such as "last 7 days" or "all") or chart type parameters for the desired chart. Through this request, the user terminal instructs the server to generate a graphical report reflecting changes in the user's historical status.

[0051] Specifically, within the user terminal's intervention program (referring to an application or app used to intervene in a user's eating disorder), a separate entry point for features such as "Progress Report," "My Trends," or similar functions is set up. When the user clicks this entry point control, the user terminal's intervention program captures the click event, encapsulates the currently logged-in user's user identifier (user_id) into a new API request (i.e., a visualization data request), and sends this request to the server.

[0052] (2) In response to the visualization data request, the server generates a visualization trend chart based on the quantitative information in the user's historical user status data and sends the visualization trend chart to the user terminal; Historical user status data refers to the collection of structured data submitted by users in all past course units through interactive interfaces (such as the multidimensional diary card interface) within the intervention program and stored in the server database. Quantitative information refers to numerical data fields extracted from historical user status data that can be used for mathematical calculations and trend analysis. Quantitative information may include: daily scores (e.g., 0-6 points) for "bulimia / overeating impulses" extracted from "eating diary cards," and information such as "number of occurrences of problematic eating behaviors" extracted from "eating diary cards." Visual trend charts are generated based on the above quantitative information, arranged and rendered in chronological order (e.g., by date), to visually demonstrate the patterns of data change over time.

[0053] Specifically, after receiving a visualization data request, the server parses the user identifier (user_id) and other possible parameters from the request. Then, using the user identifier (user_id) as an index, it queries the user status data table in the database, filtering out all data records submitted by the user within a specified time period (e.g., the last 28 days by default) of type "diary card". Next, it extracts the values ​​of specific fields such as "binge eating impulse score" and / or "binge eating frequency" and their corresponding submission dates from these records, forming a time-series dataset. Then, the server calls an integrated chart rendering engine (such as Apache ECharts, a Chart.js backend library, or a custom service) to render the time-series dataset into a visualization trend chart. Finally, the server renders the visualization trend chart as an image format (such as PNG or JPEG) or structured chart data (such as JSON configuration) and returns it as the response content to the user terminal.

[0054] (3) The user terminal presents a visual trend chart to the user.

[0055] The server-generated visual trend charts are sent to the user's terminal for display, providing the user with quantitative and longitudinal feedback on changes in their behavior.

[0056] Specifically, after the user terminal receives the visualized trend chart returned by the server, the intervention program loads and displays the chart on the dedicated "Progress Report" page. If the visualized trend chart is in image format, it is displayed directly; if it is structured chart data, the chart is re-rendered by the user terminal's front-end chart library (such as wx-charts in WeChat mini-programs or ECharts on the web). The chart can include a title (such as "My Binge Eating Impulse Trend Chart"), axis labels, and necessary legends to ensure that the user can understand it correctly.

[0057] To address the issues of "users finding it difficult to perceive progress and lacking sustained motivation" in existing technologies, this embodiment can automatically aggregate and transform users' discrete, day-to-day self-reports into continuous, intuitive visual charts. This makes subtle changes that were previously difficult to perceive (such as a slow decline in impulse levels) readily apparent, helping users change their cognition and behavior.

[0058] In some embodiments, the operation of the server generating a visual trend chart based on quantitative information in the user's historical user status data includes: The server extracts quantitative scores of the user's binge eating impulses submitted by the user through the multidimensional diary card interface over multiple consecutive days. Based on the extracted quantitative scores, a line chart is generated with the date as the horizontal axis and the score as the vertical axis, and the line chart is used as a visual trend chart.

[0059] The quantitative score of binge eating impulse refers to the result of a user's self-assessment of the "intensity of the urge to binge eat / overeat" in the "Eating Diary Card" section of the multidimensional diary card interface, through methods such as sliders, numerical input, or level selection. The quantitative score of binge eating impulse is a key and traceable therapeutic indicator, which can be obtained using a Likert scale, for example, 0 points (no impulse at all) to 6 points (extremely strong impulse, almost impossible to resist).

[0060] Specifically, when the server receives a visualization data request, it uses the user ID as the query condition to filter all records in the user status data table whose date type (data_type) field is "Eating Diary Card" within a specific time period (e.g., the last 30 days). For each record that meets the condition, the values ​​of the impulse score (urge_score) and the record date (record_date) are extracted. Then, the extracted record dates and impulse scores are sorted in ascending order by date to construct a time-series dataset. For example: [("2023-10-01", 5), ("2023-10-02", 4), ("2023-10-03", 3),...]. Finally, the server calls the chart rendering engine to render this time-series dataset into a chart. The chart rendering engine uses "date" as the horizontal axis (X-axis) and "score / frequency" as the vertical axis (Y-axis) to generate a clear line chart. Each point on the line in the chart represents the user's self-assessment status on that day.

[0061] This embodiment uses a specially extracted "bulimia urge score," an indicator that directly reflects the core pathopsychology of eating disorders, to create a chart. This makes the generated chart more clinically relevant and provides stronger guidance for intervention. The line graph encourages users to make self-comparisons over time ("How am I today compared to last week?"), rather than engaging in unhelpful horizontal comparisons with others. This helps build a healthy sense of self-efficacy based on personal progress.

[0062] Accordingly, when collecting user status data through the multidimensional diary card interface, the user terminal receives the quantitative score of the user's eating-related impulses or behaviors through the multidimensional diary card interface, and sends the user status data containing the quantitative score to the server.

[0063] Specifically, when the user terminal needs to load the "Multidimensional Diary Card Interface" based on course unit data or daily task requirements, a form containing the "Eating Diary Card" section is dynamically rendered. This section is designed with clear labels (such as "How strong was my urge to binge eat today?") and a numerical slider or point selection rating control (for example, displaying 7 selectable levels from 0 to 6). Users can drag the slider or click to select the corresponding numerical level (such as "4") based on their actual feelings that day. This interactive process is intuitive and quick, lowering the threshold for data input. When the user clicks the "Submit" button, the intervention application on the user terminal not only submits other diary card content but also encapsulates the selected value (such as {"urge_level": 4}) as a specific key-value pair in the "User Status Data" into structured data and sends it to the server for storage.

[0064] In some embodiments, the method further includes: (1) When the user terminal presents course unit data to the user, if it detects that the user has triggered a specific skill keyword in the course content, it sends a skill details request to the server. Specific skill keywords refer to words or phrases that are specially marked in the course unit text presented to users and are associated with a specific skill item in a predefined skill library. For example, in course text explaining how to deal with strong impulses, terms such as "STOP skills," "impulse surfing," or "pros and cons analysis" can be marked as keywords. These keywords are usually presented in the user interface with highlights, underlines, different colors, or links (such as clickable blue text) to indicate their interactivity to the user.

[0065] A skill details request is a network request generated by the user terminal after detecting the user's triggering operation (referring to the user's interactive action on the interactive interface to a specific skill keyword, such as clicking, long pressing, mouse hovering, etc.). It carries at least the skill identifier corresponding to the triggered keyword and the user identifier.

[0066] Specifically, the user terminal retrieves course unit data (such as HTML format or JSON data with structured markup) from the server. When rendering the text content, the user terminal parses the embedded tags in the data, identifies specific skill keywords, and renders them as interactive UI elements (e.g., rendered as a... in a WeChat mini-program). <text>The application creates a component and binds it to a bindtap click event. While reading course content, users can click on keywords they find interesting or unfamiliar (such as "TIP skills"). The user's intervention program captures this click event and retrieves the skill identifier (skill_id) associated with the clicked keyword (e.g., "skill_101") from the event parameters. The application then constructs an API request (i.e., a skill details request), which can at least carry the skill identifier (skill_id) and the current user's user identifier (user_id).

[0067] (2) The server searches for matching skill details based on the skill details request and sends the found skill details to the user terminal; Skill details refer to a detailed description of a complete intervention skill, which can be stored in a skill details table in the server database (the skill details table stores the complete details of all DBT intervention skills). Skill details include at least the skill name, core definition and principles, step-by-step operation guidelines, application scenario examples, practice points, and links to possible audio / video guidance materials, in the form of a highly structured "skill card" suitable for quick reference.

[0068] Specifically, after receiving a skill details request from a user terminal, the server parses the skill identifier (skill_id) from the request, then queries the skill details table using the skill identifier (skill_id) as the key to obtain the skill details content corresponding to the skill identifier (skill_id). Finally, the obtained skill details content is encapsulated into a response message and returned to the user terminal.

[0069] (3) The user terminal displays skill details to the user.

[0070] User terminals can use minimized context switching to display skill details, thus maintaining the user's flow state in learning. There are several ways to minimize context switching, such as pop-ups (i.e., displaying a floating layer on top of the current course page to show skill details, which the user can close to immediately return to the original course reading position), bottom drawers or side panels (i.e., sliding out a panel from the edge of the screen to display details, which can also be easily closed to return), and embedded expansion (i.e., dynamically expanding the details area directly below the keywords), etc.

[0071] When displaying skill details, the user terminal can render the skill name, detailed steps, and vivid application examples in the display area, and can also provide buttons for further operations such as "Start Practice" or "Add to My Card Pack".

[0072] This embodiment embeds dynamic, context-sensitive interactive entry points into static learning content. This transforms a user's immediate learning need ("I want to know what this skill is right now") into a clear, machine-processable instruction. Compared to the cumbersome process in related technologies where users need to exit the current reading, manually navigate to the tool library, and then search, this embodiment allows users to instantly access auxiliary information. This immediate and convenient access method allows users to review and study skills at any time, greatly increasing the likelihood of skills being practically understood and applied. This effectively solves the core problem of the disconnect between theory and practice in related technologies, improving the efficiency of skill transformation and the overall effectiveness of intervention.

[0073] In some embodiments, the operation of the server determining the target course unit matching the user's course progress according to the preset course arrangement rules includes: the server determining the course unit on the day following the completion date corresponding to the user's course progress as the target course unit.

[0074] The completion date corresponding to a user's course progress refers to the date recorded in the server database as the date the user last successfully completed and updated their progress for a course unit. This date serves as the time base for executing the "daily progression" rule. For example, if the user last completed the course on "Day 5," then that completion date is the specific calendar date corresponding to "Day 5" in the course design (e.g., 2023-10-05). The next day refers to the next calendar day immediately following the completion date. In the program logic, this can be achieved by adding one day to the completion date (e.g., target_date = completed_date + 1 day).

[0075] Specifically, after receiving a course access request, the server parses the user identifier (user_id) from the request. Then, it queries the user's learning progress table in the database using the user identifier (user_id) to obtain the two most crucial fields in the user record: the last completed course unit identifier (last_completed_unit_id) (e.g., "day_5") and the last completion date (last_completion_date) (e.g., "2023-10-05"). Next, it calls a date calculation function to add one day to last_completion_date to obtain the target date (target_date) (i.e., "2023-10-06"). The server can pre-store a date-course unit mapping table, which defines a fixed date bound to each course unit in the structured course (e.g., Day1 corresponds to the start date, Day2 corresponds to the start date + 1 day, and so on). The server uses the calculated target date (target_date) to query this mapping table to find the unique course unit identifier (e.g., "day_6") corresponding to the target date. This course unit identifier corresponds to the target course unit. Subsequently, the course unit data for the target course unit can be extracted from the database.

[0076] This embodiment achieves strict temporal enforcement and rhythm by mandating a "completion date + 1 day" rule, forcing users to learn continuously and on a daily basis. Users cannot learn future content in advance, nor can they quickly complete multiple units within the same day. This simulates and reinforces the continuous and stable intervention rhythm required for structured psychotherapy, ensuring the integrity of the therapeutic dosage. Furthermore, because the rules are simple and transparent (i.e., "complete one lesson each day, unlock a new lesson the next day"), it establishes clear behavioral expectations for users, helping to cultivate healthy digital habits of daily login and task completion, forming a "daily ritual" for therapy, and improving users' daily adherence and regularity of participation.

[0077] In some embodiments, the method further includes: (1) In response to the user's access to the skill card pack function, the user terminal sends a skill list request to the server; The Skill Pack feature refers to a separate, aggregated functional module entry point within the user terminal application (which may appear as a "Skills" icon in the bottom navigation bar, a "Toolbox" in the main menu, etc.). This feature allows users to view and practice all learned or preset intervention skills at any time, regardless of their current course progress. A Skill List Request is a network request generated by the user terminal when the user triggers the command to access the Skill Pack feature. It requests a summary list of skills available to the user from the server.

[0078] (2) The server returns the skill card list data to the user terminal based on the skill list request; The skill card list data refers to a structured data collection returned by the server, which contains summary information for multiple skill cards. Each summary includes a skill representation, skill name, skill icon, brief description, and mastery status label (such as "learned" or "to be learned").

[0079] Specifically, after receiving a skill list request, the server queries the main skill library table in the database and combines it with the user's learning record table to assemble a personalized skill list for the user. For example, it can filter out all skills and mark which skills the user has already encountered ("unlocked") and which skills have not yet been learned ("to be unlocked" or only basic skills are displayed) according to the course progress. Finally, the server returns the relevant data of the assembled skill list to the user's terminal.

[0080] (3) The user terminal presents the skill card list data to the user, and when it detects that the user has viewed a specific skill card, it sends a skill content retrieval request to the server; A specific skill card refers to the card (virtual card) selected by the user from the list of skill cards presented on the user terminal's interactive interface. A skill content retrieval request is a network request generated by the user terminal when the user clicks on a skill card; this request is used to retrieve the complete and detailed content of that skill from the server.

[0081] Specifically, after receiving the skill card list data, the user terminal renders it into a visual card grid or list interface, for example... Figure 8 The interface shown includes various skill card packs. When browsing the interface, users can click on a skill card of interest (such as the "STOP Skill" card). After the user's terminal application captures this click event, it can obtain the skill identifier (skill_id) corresponding to the clicked card from the event binding data, and then generate a skill content retrieval request using the skill identifier (skill_id) as the core parameter and send it to the server.

[0082] (4) The server determines the target skill card based on the skill content acquisition request and sends the skill content of the target skill card to the user terminal; Specifically, the server queries the database based on the skill identifier skill_id to obtain complete skill details, and then returns the skill details to the user terminal.

[0083] (5) The user terminal presents the skill content of the target skill card to the user.

[0084] After receiving the skill content, the user terminal can be redirected to a separate skill details page to show the user all the information about the skill (including text, images, audio guidance, etc.) in a panoramic view. The details page can also provide interactive buttons such as "Start Practice" so that users can start practicing immediately when needed.

[0085] In some embodiments, the method further includes: (1) The server analyzes the high-risk time periods when users exhibit eating-related impulses or problem behaviors based on the user's historical user status data; High-risk time periods refer to repetitive and regular time windows identified through data analysis where the probability of a user experiencing eating-related impulses (such as high impulse scores) or problem behaviors (such as recorded binge eating) is significantly higher than at other times. Examples include "Sunday evenings from 8:00 PM to 10:00 PM" or "after lunch breaks on weekdays." This is a core component of the personalized risk profile built by the server for each user.

[0086] In this embodiment, the risk pattern analysis module in the server backend can be started periodically (e.g., weekly). It extracts all user status data submitted by a specific user (user_id) from the database over a relatively long period (e.g., the past 4-8 weeks). The risk pattern analysis module tags the data from the "multidimensional diary card interface," for example, extracting the timestamp of each record and converting it into time features such as "day of the week" and "hour," and extracting "bulimia urge intensity quantification score" and "whether problem behavior was reported" as result labels (e.g., marking records with a score greater than or equal to 4 or those that reported bulimia behavior as "high-risk events"). The risk pattern analysis module can apply statistical analysis (e.g., aggregation statistics, association rule mining) or simple machine learning methods to find clustering patterns of "high-risk events" in terms of time features. For example, it might calculate that the frequency of high-risk events occurring on "Fridays" and "after 18:00" exceeds 70%. Finally, the identified stable patterns (such as {"day_of_week": [5], "start_hour": 18, "end_hour": 23, "confidence": 0.75}, indicating that Friday evening after 6 pm is a high-risk period with a confidence level of 75%) are stored in the user's personal profile as the user's "high-risk period pattern".

[0087] (2) The server sends an active intervention prompt notification to the user terminal before the time point corresponding to the high-risk time period mode; Proactive intervention alerts are system messages initiated by the server and pushed to the user's terminal. They are used to alert the user and provide support before a predicted risk occurs.

[0088] Specifically, a scheduled task (such as a Cron Job) can be deployed on the server side. For each user with an identified risk pattern, the server creates an independent push notification task for that user based on the "time point" defined in their pattern (e.g., 30 minutes before the start of the high-risk period). When the scheduled time arrives, the task is triggered. The server will generate a specific notification message based on the user's risk pattern and their historically used or system-recommended skills, such as: "The system has detected that you typically face challenges on Friday evenings. There are 30 minutes left until the high-risk period. We suggest you practice your 'STOP' technique or do 5 minutes of mindful breathing to prepare for any potential impulses." The server can then send the encapsulated message content along with the user's device identifier (such as a Device Token or OpenID) through an integrated push gateway (such as Tencent Cloud Push or JPush), thus completing the proactive notification delivery.

[0089] (3) The user terminal responds to the proactive intervention prompt notification and presents the proactive intervention prompt message to the user. The proactive intervention prompt message includes suggestions for coping skills and / or reminders for mindfulness practice during high-risk periods.

[0090] Specifically, after receiving a push notification from the server, the user's operating system push service or the background process of the intervention program can present the notification to the user in the form of a system notification bar banner, lock screen message, or in-app pop-up. Even if the user does not actively open the application, they can still see the reminder. Clicking the notification will take the user directly to the corresponding skill practice page or mindfulness-guided audio playback interface within the intervention program, achieving a seamless transition from "prompt" to "action".

[0091] This embodiment calculates and mines temporal patterns in users' own behavioral data, transforming users' vague subjective feelings of "I'm prone to losing control at night" into precise, quantifiable, machine-identifiable, and processable "high-risk time period patterns." Furthermore, it transforms the static patterns obtained in the previous step into dynamic, time-sensitive intervention actions. By intervening during the critical decision-making window before risks occur, it breaks the user's inherent, automated behavioral response chain, transforming unconscious tendency towards problematic behavior into a conscious, skill-driven coping process, thereby effectively improving the timeliness and preventative nature of the intervention.

[0092] In some embodiments, the method further includes: The server records users' historical access data and practice feedback data for skill details or target skill cards. Based on the historical access data and practice feedback data, it builds a personalized skill recommendation model for users. When preset trigger conditions are met, personalized skill recommendation information is pushed to the user's terminal according to the skill recommendation model. Trigger conditions include: detecting abnormal user status data, or the user entering a preset high-risk situation, or the user actively requesting a recommendation.

[0093] Historical access data refers to implicit behavioral data recorded on the server side regarding user interactions with skill content. This includes, but is not limited to, the frequency of accessing a specific skill, the duration of each access, and the specific time and context of the access (e.g., accessing immediately after completing a diary card). Practice feedback data refers to explicit evaluation data submitted by users through the system interface after actively practicing a skill. Typically, a brief feedback module is included in the skill practice interface. For example, after practicing "Pros and Cons Analysis," a pop-up question might appear asking, "Was this practice helpful to you?" and providing a star rating (e.g., 1-5 stars) or options such as "Helpful / Neutral / Not Helpful" for the user to choose from. This is direct feedback from the user regarding the subjective utility of the skill. Historical access data and practice feedback data together constitute a user skill interaction profile, a subset of data dynamically maintained by the server for each user, used to characterize the user's preferences, usage habits, and perceived effects on different skills.

[0094] It should be noted that user terminals and intervention programs need to obtain user permission before collecting user-related data (such as the frequency of access to specific skills, the duration of a single visit, and the specific time and context in which the visit occurs).

[0095] Personalized skill recommendation models refer to data processing algorithms or rule sets that generate skill recommendation sequences specifically for individual users.

[0096] Personalized skill recommendations refer to one or more recommended items organized from a list of skills recommended based on a skill recommendation model and combined with the specific context of the triggering conditions. For example, when triggered by "abnormal impulsiveness score", the message might be: "We noticed that you have been more impulsive recently. Based on your usage history, trying the 'Impulsive Surfing' skill that was effective for you might be helpful." Specifically, when a user accesses any skill details page, the server records an access event in the background log while returning the content. The access event includes the user ID (user_id), skill ID (skill_id), access time (timestamp), and access source (which can be from a course unit, such as from_course_unit_12, or from a skill package, such as from_skill_package). After the user completes an exercise and submits feedback on the skill details page, the server associates and stores the feedback rating or options with the corresponding skill ID (skill_id) and user ID (user_id). This data can be stored in a structured format in the server's user behavior log table.

[0097] The server can periodically (or in real-time) aggregate all skill interaction data for a user. For each skill identifier (skill_id), its corresponding feature vector is calculated, such as: [total number of visits, average dwell time, last visit time, average feedback rating]. Then, one or more recommendation algorithms are used to build the model. For example: a. If a user frequently accesses and provides positive feedback on skills under the "Emotion Regulation" module (such as "Fact Checking"), the model will tend to recommend other skills in the same "Emotion Regulation" module (such as "Contradictory Behavior"); b. In anonymized group data, other user groups with similar behavior patterns to the current user are identified, and skills with high ratings or frequently used by these groups can be recommended to the current user; c. Skills are directly sorted based on the user's average feedback rating for each skill, prioritizing those with higher ratings. The output of the skill recommendation model is a skill recommendation list generated for the user, including skill identifiers and their recommendation weights (or priorities). This list can be cached or stored in the user's personal configuration.

[0098] The server can monitor the user's terminal data stream (such as status data, time, and active requests). If any preset condition is met, the skill recommendation process is triggered. For example, the server calls the user's skill recommendation model to obtain a list of recommended skills; then, based on the type of trigger condition, the list is contextually filtered and sorted (e.g., if triggered by "low mood score," skills such as "emotion regulation" and "self-soothing" are recommended first; if triggered by "entering evening," skills that help with relaxation and coping with nighttime impulses are recommended). Finally, the final recommendation text is generated and sent to the user's terminal via in-app messages or push notifications.

[0099] This embodiment allows skill recommendations to be highly linked to the user's specific difficulties (represented by triggering conditions), making the skill suggestions recommended to the user targeted and timely, greatly improving the effectiveness of intervention measures. Thus, when users face difficulties, they don't need to wander aimlessly through a massive skill library; the system directly provides them with a few personalized options that are most likely to help their current situation, thereby lowering the barrier to entry for users and improving intervention efficiency. Furthermore, each access to and feedback from the user regarding a newly recommended skill is fed back into the model as new data to optimize future recommendations. This allows the system to continuously evolve and personalize along with the user's recovery process, thereby recommending more accurate skills to the user.

[0100] It should be noted that, regarding the various steps included in the structured multi-module curriculum-based eating disorder intervention method provided in any of the above embodiments, unless explicitly stated herein, there is no strict order restriction on the execution of these steps; they can be executed in other orders. Furthermore, at least some of these steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is also not necessarily sequential, but can be performed alternately or in rotation with other steps or at least a portion of the sub-steps or stages of other steps.

[0101] Based on the same inventive concept, this application also provides an eating disorder intervention system based on a structured multi-module curriculum. In some embodiments, such as Figure 9 As shown, the eating disorder intervention system based on a structured multi-module curriculum includes a user terminal 10 (which may be a laptop computer 11, a PC computer 12, a mobile terminal 13, etc.) and a server 20.

[0102] The user terminal is used to send course access requests to the server; The server is used to obtain the user's course progress based on the course access request, determine the target course unit that matches the user's course progress according to the preset course arrangement rules, search for the course unit data of the target course unit from the structured course database, and send it to the user terminal; the structured course database includes course unit data of multiple course units arranged in a preset time sequence. The user terminal is also used to present the course unit data of the target course unit to the user, guide the user to perform intervention tasks through the interactive interface and collect the corresponding structured user status data, and send the collected user status data to the server; the interactive interface includes the emotion regulation model interface, the behavior chain analysis interface and / or the multidimensional diary card interface. The server is also used to determine whether the specified task of the target course unit has been completed based on the user status data. If it has been completed, the user's course progress is updated; if it has not been completed, the user's course progress is not updated.

[0103] Specific limitations regarding the structured multi-module curriculum-based eating disorder intervention system can be found in the above section on the limitations of the structured multi-module curriculum-based eating disorder intervention method, and will not be repeated here. Each module in the aforementioned structured multi-module curriculum-based eating disorder intervention system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0104] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0105] Those skilled in the art will understand that implementing all or part of the processes in the above method embodiments can be accomplished by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchlink, DRAM (SLDRAM), memory bus, direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0106] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.< / text>

Claims

1. An intervention method for eating disorders based on a structured multi-module curriculum, characterized in that, The method includes: The user terminal sends a course access request to the server; The server obtains the user's course progress based on the course access request, determines the target course unit matching the user's course progress according to the preset course arrangement rules, searches for the course unit data of the target course unit in the structured course database, and sends it to the user terminal; the structured course database includes course unit data of multiple course units arranged in a preset time sequence. The user terminal presents the course unit data of the target course unit to the user, guides the user to perform intervention tasks and collects corresponding structured user status data through the interactive interface, and sends the collected user status data to the server; the interactive interface includes an emotion regulation model interface, a behavior chain analysis interface and / or a multidimensional diary card interface. The server determines whether the specified task of the target course unit has been completed based on the user status data. If it has been completed, the user's course progress is updated; if it has not been completed, the user's course progress is not updated.

2. The method according to claim 1, characterized in that, The method further includes: The user terminal sends a visual data request to the server; In response to the visualization data request, the server generates a visualization trend chart based on the quantitative information in the user's historical user status data, and sends the visualization trend chart to the user terminal. The user terminal presents the visualized trend chart to the user.

3. The method according to claim 2, characterized in that, The operation of the server generating a visual trend chart based on quantitative information in the user's historical user status data includes: The server extracts the quantitative scores of the user's binge eating impulses submitted by the user through the multidimensional diary card interface over multiple consecutive days, and generates a line graph with the date as the horizontal axis and the score as the vertical axis based on the extracted quantitative scores, using the line graph as a visual trend chart.

4. The method according to claim 3, characterized in that, When the user terminal collects user status data through the multidimensional diary card interface, it receives a quantitative score of the user's eating-related impulses or behaviors through the multidimensional diary card interface, and sends the user status data containing the quantitative score to the server.

5. The method according to claim 1, characterized in that, The method further includes: When the user terminal presents course unit data to the user, if it detects that the user has triggered a specific skill keyword in the course content, it sends a skill details request to the server. The server searches for matching skill details based on the skill details request and sends the found skill details to the user terminal; The user terminal displays the skill details to the user.

6. The method according to claim 1, characterized in that, The operation of the server in determining the target course unit matching the user's course progress according to preset course arrangement rules includes: The server determines the course unit corresponding to the completion date of the user's course progress as the target course unit.

7. The method according to claim 1, characterized in that, The method further includes: In response to the user's access to the skill card pack function, the user terminal sends a skill list request to the server. The server returns a list of skill cards to the user terminal based on the skill list request. The user terminal presents the skill card list data to the user, and when it detects that the user has viewed a specific skill card, it sends a skill content retrieval request to the server. The server determines the target skill card based on the skill content acquisition request and sends the skill content of the target skill card to the user terminal; The user terminal presents the skill content of the target skill card to the user.

8. The method according to claim 2 or 3, characterized in that, The method further includes: Based on the user's historical user status data, the server analyzes the high-risk time periods when the user exhibits eating-related urges or problem behaviors. The server sends an active intervention notification to the user terminal before the time point corresponding to the high-risk time period pattern. In response to the proactive intervention notification, the user terminal presents a proactive intervention message to the user, which includes suggestions for coping skills and / or reminders for mindfulness practice during the high-risk period.

9. The method according to claim 5 or 7, characterized in that, The method further includes: The server records the user's historical access data and practice feedback data for the skill details or the skill content of the target skill card. Based on the historical access data and practice feedback data, a personalized skill recommendation model is constructed for the user. When preset trigger conditions are met, personalized skill recommendation information is pushed to the user's terminal according to the skill recommendation model. The trigger conditions include: detecting abnormal user status data, or the user entering a preset high-risk situation, or the user actively requesting a recommendation.

10. An eating disorder intervention system based on a structured multi-module curriculum, characterized in that, The system includes user terminals and servers; The user terminal is used to send a course access request to the server; The server is configured to obtain the user's course progress according to the course access request, determine the target course unit matching the user's course progress according to the preset course arrangement rules, search for the course unit data of the target course unit in the structured course database, and send it to the user terminal; the structured course database includes course unit data of multiple course units arranged in a preset time sequence. The user terminal is also used to present the course unit data of the target course unit to the user, guide the user to perform intervention tasks and collect corresponding structured user status data through the interactive interface, and send the collected user status data to the server; the interactive interface includes an emotion regulation model interface, a behavior chain analysis interface and / or a multidimensional diary card interface. The server is also configured to determine whether the specified task of the target course unit has been completed based on the user status data. If it has been completed, the user's course progress is updated; if it has not been completed, the user's course progress is not updated.

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