Old people loneliness comprehensive management system and method based on mobile phone APP

Abstract

The invention relates to a comprehensive management system and method for elderly loneliness based on a mobile phone APP, and the system employs a three-level architecture of a host client-a control client-a data server, and achieves the intelligent and precise intervention. The host client integrates a multi-mode data acquisition function, an aging-suitable interaction interface function, a video / game intervention function and an emergency response function, and supports the elderly to autonomously complete mental health monitoring intervention. The control client is responsible for data cleaning, quality control and standardized management and provides reliable input for AI analysis. And the data server generates a personalized intervention scheme by relying on an AI model in combination with manual auditing, and dynamically optimizes a strategy. According to the system, wearable monitoring, CBT (Cathode Based Therapy) and an intelligent algorithm are combined, the goals of reducing the loneliness score of the old people and improving the social interaction frequency are expected to be achieved in future trial points, and the application prospects of the system in the aspects of accurate evaluation, risk early warning and intervention are embodied. By reducing medical resource consumption and enhancing intergenerational linkage, a replicable scheme is provided for healthy aging.

Description

Technical Field

[0001] The present invention relates to the field of smart elderly care technology, and specifically to a comprehensive management system and method for elderly people's loneliness based on a mobile phone APP. Background Art

[0002] As the global aging problem intensifies, the mental health of the elderly is receiving increasing attention, and loneliness has become a major factor affecting their quality of life. Studies have shown that long-term loneliness not only leads to psychological problems such as depression and anxiety, but can also cause physical health problems such as cardiovascular disease and cognitive decline. However, traditional loneliness intervention methods have the following limitations:

[0003] 1. Single intervention method: Traditional loneliness intervention mostly relies on face-to-face psychological counseling or group activities, which has limited coverage and is difficult to meet the personalized needs of the elderly.

[0004] 2. Incomplete data collection: Existing intervention programs usually rely solely on questionnaire assessments and lack integrated analysis of physiological and behavioral data, making it difficult to fully reflect the psychological state of the elderly.

[0005] 3. Insufficient social support: Due to physical conditions or shrinking social circles, it is difficult for the elderly to establish new social connections through traditional means, which leads to increased loneliness.

[0006] 4. High technical threshold: Existing mental health management apps are mostly targeted at young people, with complex interfaces and cumbersome operations, which are difficult to meet the needs of the elderly.

[0007] In summary, the present invention provides a comprehensive management system for loneliness in the elderly that integrates data collection, personalized intervention, social support and intelligent management, which is used to address the limitations of traditional loneliness intervention methods, such as a single intervention method, incomplete data collection, insufficient social support, and high technical barriers. Summary of the Invention

[0008] In order to solve the problems of the prior art, the present invention provides a comprehensive management system and method for elderly people's loneliness based on a mobile phone APP.

[0009] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: In the first aspect, a comprehensive management system for loneliness among the elderly based on a mobile phone APP comprises:

[0010] Host client, control client and data server;

[0011] The host client includes:

[0012] The information collection module is used to collect user data, pre-intervention baseline information, intervention process information, and post-intervention information for the elderly. The intervention process information mainly includes the user's video viewing and game intervention status. The post-intervention information is the questionnaire that users need to fill out after each video or game intervention to evaluate the quality of the intervention and the impact of the video / game on the user's loneliness and psychological state. Relevant physiological data is collected through the linked wearable device.

[0013] The main intervention interface module includes a video intervention module and a game intervention module, which are used to provide cognitive restructuring, behavioral education, mindfulness training, and social interaction intervention services;

[0014] Social modules, including community modules, family modules and background consultation modules, are used to enhance social support for the elderly;

[0015] Settings module, used to adjust the interface font size, voice prompts and language selection;

[0016] Emergency help button, used to trigger emergency warning information to the backend and family members;

[0017] The control client includes:

[0018] Information entry module, used to receive and organize data uploaded by the host client;

[0019] Data management module, used to store and manage user data and intervention process information;

[0020] The data server is used for:

[0021] Storage controls the data uploaded by the client;

[0022] Analyze data and generate personalized intervention plans through AI predictive models;

[0023] Feedback intervention analysis reports to the control client to dynamically adjust intervention measures.

[0024] In a specific implementation of the first aspect, the information collection module includes:

[0025] User data unit, which collects name, age, gender, education level, economic status, frequency of social activities, chronic disease history, and types of current medications;

[0026] The baseline information unit before the intervention was used to assess loneliness, depression, anxiety, perceived stress, suicidal ideation, emotional state, cognitive bias, rumination, and social support level through questionnaires;

[0027] Intervention process information unit, which collects information about users’ video viewing and game intervention;

[0028] The post-intervention information unit uses questionnaires to assess users' loneliness, depression, anxiety, perceived stress, suicidal ideation, emotional state, cognitive bias, rumination, and social support levels after the intervention;

[0029] The physiological data monitoring unit combines with wearable devices to collect heart rate, blood pressure and sleep quality data in real time.

[0030] In the first aspect, the host client (elderly terminal)

[0031] Information collection module

[0032] User data unit: collects basic information (name, age, etc.) through form filling or voice input to establish basic data for user portraits.

[0033] Pre-intervention baseline information unit:

[0034] Standardized tools such as the UCLA Loneliness Scale and the PHQ-9 Depression Scale were used to quantify psychological status.

[0035] Combine wearable devices (such as blood pressure bracelets and sleep monitors) to collect physiological indicators in real time and build a "psychological-physiological" correlation database.

[0036] Intervention process information unit: records behavioral data such as video viewing time and game completion rate, which are used to evaluate the quality of intervention and compliance (completion).

[0037] Post-intervention information unit: After each intervention, the psychological state is quantified using a standardized scale, and physiological indicators are collected in real time using wearable devices (such as blood pressure bracelets and sleep monitors) to evaluate the intervention effect.

[0038] Main intervention interface module

[0039] Video Intervention Module:

[0040] Popularizing Loneliness: Using animations and live-action explanations, we aim to dispel the misconceptions of loneliness held by the elderly (e.g., “loneliness ≠ failure”).

[0041] Cognitive restructuring: Based on cognitive behavioral therapy (CBT), a conversational video is designed to guide the elderly to identify negative thoughts (such as "I am always forgotten").

[0042] Behavioral education: Provide social scenario simulation videos (such as how to initiate a conversation) and accompanying practice tasks.

[0043] Mindfulness Training: Short 10-15 minute sessions are offered, combining breathing audio and body scan animations.

[0044] Game Intervention Module:

[0045] Cognitive training: Design memory games with graded difficulty (such as "object matching") and dynamically adjust the challenge level.

[0046] Social interaction: Virtual pets need to be fed in cooperation with other users to promote cross-regional social interaction.

[0047] Emotion Regulation: Emotion Recognition Games train emotion perception skills through emoji feedback.

[0048] Physical activity: Combined with the phone's gyroscope to achieve somatosensory interaction (such as waving gestures to practice Tai Chi).

[0049] Social Module

[0050] Community module: establish interest groups (such as "chess lovers"), support text / voice posting, and equip volunteer moderators to manage content.

[0051] Family module: Family members can view the elderly’s intervention progress, receive “weekly progress reports”, and participate in cooperative games (such as family quiz competitions).

[0052] Backstage consultation: 7×24 hours online psychological consultation portal, supporting text-to-speech reading.

[0053] Setting up the module

[0054] It provides "large font mode" (font ≥ 20pt), voice reading function (supports dialects, adjustable speaking speed), and a simplified main interface (only retains core functions).

[0055] Emergency button:

[0056] After a long press triggers, the system automatically sends location and health data to the community service center and family members within 5 seconds, simultaneously starts the video connection and triggers the background security closed-loop process (including alarm classification, manual confirmation and transfer to the emergency center when there is no response).

[0057] 2. Control client (medical care / family terminal)

[0058] Information entry module:

[0059] Supports batch import of community elderly files and automatically associates the data uploaded by the host client.

[0060] Data management module:

[0061] Generate visual reports (such as loneliness score change curves and abnormal physiological indicator warnings) and support PDF export.

[0062] Provide hierarchical authority management (community doctors can view the data of the elderly in their jurisdiction, while family members can only view the associated accounts).

[0063] 3.Data server (cloud core)

[0064] Data storage: Distributed databases (such as HBase) are used to store high-concurrency physical data, supporting queries in seconds.

[0065] AI Analysis:

[0066] Prediction model: Based on the XGBoost algorithm, baseline data + behavioral data are input to predict the risk of worsening loneliness.

[0067] Input features:

[0068] Baseline data: demographic characteristics (age / sex / education), underlying medical history, and baseline UCLA scale score;

[0069] Intervention data: frequency / type / duration of service use (e.g., video viewing completion rate);

[0070] Dynamic data: behavioral logs (social game participation), physiological indicators (sleep quality / heart rate variability);

[0071] Output dimensions:

[0072] ① Loneliness trend in the next three months (UCLA score fluctuation ±15% warning);

[0073] ② The risk of chronic diseases among those with worsening loneliness (risk of hypertension increased by 30%, probability of failure to control diabetes increased by 25%);

[0074] ③ Prediction of psychosomatic indicators (for every 1 point increase in loneliness score, systolic blood pressure ↑1.8 mmHg, sleep efficiency ↓6%);

[0075] Training optimization:

[0076] Time series cross validation (TSCV) is used to prevent data leakage;

[0077] Innovation: Screening key features through SHAP value;

[0078] Bayesian hyperparameter optimization was introduced, and the model AUC was improved to 0.89 (validated by Framingham cohort data).

[0079] Personalized intervention: Through reinforcement learning algorithms, the video / game recommendation list is dynamically adjusted (for example, if social games are not completed three times in a row, the proportion of cognitive training will be increased).

[0080] State input: real-time user profile (current loneliness score / physiological indicators / behavioral patterns) + prediction model output;

[0081] Action output: Dynamically generate intervention strategies;

[0082] Reward Mechanism:

[0083] Short-term feedback: user interaction completion rate (weight 0.6);

[0084] Long-term feedback: improvement in loneliness score (weight 0.4);

[0085] Innovation mechanism:

[0086] Feature fusion module: uses graph neural network (GNN) to fuse multi-source heterogeneous data;

[0087] Counterfactual compensation: When the post-intervention indicator is worse than the baseline, the compensation strategy is automatically triggered.

[0088] Manual review: Psychological counselors can intervene in the AI ​​program and adjust the intensity of intervention.

[0089] In a specific implementation of the first aspect, the video intervention module includes:

[0090] Loneliness science module, providing knowledge on the causes and impacts of loneliness;

[0091] Cognitive restructuring module, used to adjust the elderly’s cognitive patterns towards loneliness;

[0092] Behavioral education module, which provides social skills learning content;

[0093] Mindfulness training module, providing breathing exercises and body scan guidance.

[0094] In a specific implementation of the first aspect, the game intervention module includes:

[0095] Cognitive training game unit, including memory matching and number logic games;

[0096] Social interactive game unit, including virtual pet training and cooperative mission games;

[0097] Emotion Regulation Game Unit, including emotion recognition and relaxation games;

[0098] The physical activity game unit includes Tai Chi and rhythm clicking games.

[0099] In a specific implementation of the first aspect, the community module of the social module supports online community and video call functions; the family module allows family members to remotely view the intervention progress and participate in interactive games; and the background consultation module provides online help and telephone support.

[0100] In a specific implementation of the first aspect, the data server analyzes user behavior data and physiological data through an AI model, combines manual background monitoring to generate a dynamic intervention plan, and optimizes intervention measures;

[0101] AI models include but are not limited to supervised learning models, reinforcement learning models, and generative AI models.

[0102] In a specific implementation of the first aspect, the setting module supports voice-driven operation and multi-language switching, and the interface icons and font sizes can be adaptively adjusted.

[0103] In a specific implementation of the first aspect, after the emergency help button is triggered, background personnel intervene in real time and synchronize the warning information to the family member's mobile phone APP.

[0104] Secondly, a comprehensive management method for loneliness among the elderly based on a mobile phone APP includes the following steps:

[0105] Collect user data, baseline information, and physiological data through the host client;

[0106] Providing personalized psychological interventions based on video and game modules;

[0107] Using social modules to enhance social connections among seniors;

[0108] Collect user intervention process information and post-intervention information through the host client;

[0109] Integrate data through the control client and upload it to the data server;

[0110] The data server uses AI models to analyze data, generate dynamic intervention reports and feed them back to the control client.

[0111] In the second aspect, data collection and analysis

[0112] Multi-dimensional data fusion:

[0113] Automatically synchronize wearable device data (such as sleep duration and heart rate variability HRV) every morning.

[0114] Generate weekly "psychological-physiological" correlation reports (e.g., finding a correlation between decreased HRV and increased negative mood questionnaire scores).

[0115] 2. Personalized intervention implementation

[0116] Dynamic intervention plan:

[0117] If the AI ​​predicts that the loneliness score will increase, it will automatically push a mindfulness training video.

[0118] For users with low activity in the social module, cooperative task games are recommended first and active users are matched to form teams.

[0119] 3. Strengthening social support

[0120] Family collaboration mechanism:

[0121] After completing the online course on "Geriatric Psychology", family members can unlock more interactive game permissions.

[0122] Community resource integration:

[0123] The system automatically matches community activities (such as calligraphy classes) and sends them to the elderly’s APP calendar.

[0124] 4. System optimization and iteration

[0125] Feedback loop:

[0126] Conduct A / B testing of intervention effectiveness every month (e.g., comparing the effectiveness of the “video + game” combination with a single video intervention).

[0127] Manually label abnormal data (such as not using the app for 7 consecutive days) and optimize the anomaly detection threshold of the AI ​​model.

[0128] Comprehensive action and effect analysis

[0129] Scientific improvement

[0130] This study, based on the fusion analysis of physiological data (such as heart rate variability (HRV)) and psychological scales, has designed a multimodal loneliness assessment method with the potential to improve assessment accuracy. By building an AI-powered predictive model, the system can theoretically provide early warning of the risk of worsening loneliness, issuing intervention signals as early as 72 hours before the risk occurs, thereby shortening response times and improving the timeliness of interventions.

[0131] Optimized aging-friendly experience

[0132] The system interface adopts an age-friendly design, providing a simplified operation path, which is expected to improve the success rate of operation for elderly users. At the same time, the voice interaction function provides an auxiliary operation method for users with visual impairments, improving the universality and inclusiveness of the system.

[0133] Innovation of social support system

[0134] This invention introduces a family interaction module, enabling relatives to remotely participate in elderly users' daily activities and interventions, potentially enhancing children's sense of companionship and engagement. Furthermore, through the design of a community module, users are encouraged to participate in online cooperative games and virtual social activities, aiming to increase their willingness and frequency to socialize.

[0135] Economic and social benefits

[0136] This invention helps improve the digitalization of community-based psychological services, potentially reducing the frequency of offline consultations and lowering service costs. With continuous use, the system is expected to have a positive impact on users' emotional state (e.g., depression risk) and physiological safety (e.g., fall risk), suggesting promising prospects for widespread adoption.

[0137] Summary of technological innovations

[0138] Multimodal data fusion: This paper proposes to combine physiological indicators such as heart rate variability (HRV) with cognitive behavioral data for the comprehensive assessment of loneliness, enhancing the assessment dimensions and the ability to identify individual differences.

[0139] Dynamic intervention mechanism: Build a dual-track intervention mechanism based on the combination of artificial intelligence and manual intervention, support the dynamic closed-loop management process of "early warning-intervention-tracking", and improve the continuity and individualization of intervention.

[0140] Age-friendly interaction design: The system adopts human-computer interaction methods such as voice drive and body recognition, aiming to lower the operation threshold of traditional APP for elderly users and enhance their convenience and accessibility.

[0141] In a specific embodiment of the second aspect, the intervention report includes:

[0142] Trends in loneliness scores;

[0143] Feedback on changes in physiological data and psychological status before and after the intervention (i.e., improvements in participants' physiological and psychological indicators after the intervention);

[0144] Recommended targeted video or game intervention plans.

[0145] The beneficial effects of the present invention are:

[0146] 1. The present invention significantly improves the scientificity and accuracy of intervention for loneliness in the elderly through multi-dimensional data collection, AI-driven personalized intervention and aging-friendly design. The system combines video and game intervention modules to alleviate loneliness from multiple levels of cognition, behavior, and emotion, and enhances social support through community and family modules. Aging-friendly design (such as voice prompts, large font interface) and convenient operation (such as somatosensory games, video playback) optimize the user experience of the elderly. At the same time, AI models and data analysis realize dynamic intervention adjustments, providing scientific decision-making support for medical staff and their families. The present invention not only reduces medical costs, but also promotes healthy aging, and has significant social benefits and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0147] Figure 1 It is a schematic diagram of the system structure of the present invention.

[0148] Figure 2 It is a functional diagram of the host client of the present invention.

[0149] Figure 3 It is a schematic diagram of the user-side APP interface of the present invention.

[0150] Figure 4 is an intervention flow chart of the present invention.

[0151] Figure 5 It is a flowchart of the AI ​​analysis model processing of the present invention.

[0152] Figure 6 It is a functional diagram of the social module of the present invention.

[0153] Figure 7 It is a flow chart of the emergency warning response mechanism of the present invention. DETAILED DESCRIPTION

[0154] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0155] like Figures 1 to 7 The figure shows a comprehensive management system and method for loneliness among the elderly based on a mobile phone APP.

[0156] 1. The system includes a host client, a control client, and a data server.

[0157] ① Host client: collect basic information and process information of the elderly; provide intervention services for the elderly; upload basic information and process information to the control client.

[0158] ② Control client: obtain, organize and manage the host client's information (cleaning data, quality control); convert the information data and upload it to the data server.

[0159] ③Data server: stores data uploaded by the control client; obtains prediction information through the prediction model; analyzes the data to obtain an intervention analysis report, forms a personalized treatment plan, and sends it to the control client.

[0160] 2. The host client includes an information collection module, a main intervention interface module, a social module, a settings module, and an emergency help button;

[0161] ①Information collection module, used to collect user data, pre-intervention baseline information, intervention process information, and post-intervention information of the elderly;

[0162] ②The main intervention interface module is used to present the intervention process and different intervention modes, including the video intervention module and the game intervention module;

[0163] ③ Social module, used for communication and social interaction with the outside world or family, including community module, family module, and background consultation module;

[0164] ④ Settings module, used to adjust the font size of interface icons and language selection;

[0165] ⑤ Emergency help button, used in emergencies, backstage staff can intervene urgently and send warning information to family members’ mobile phones.

[0166] 3. Information collection module, used to collect user data, baseline information before intervention, and process information of the elderly completing the intervention.

[0167] 3.1 User data, used to collect basic personal information (name, date of entry and exit, gender, education level, name, age, gender, education level, economic status, frequency of social activities, history of chronic diseases and types of current medications)

[0168] 3.2 Pre-intervention baseline information, used to collect user loneliness data and other subjective and objective data.

[0169] 3.2.1 Loneliness Assessment Data:

[0170] ① Chinese version of the UCLA questionnaire (third edition) (20 items, total score 20-80 points, higher scores indicate stronger loneliness);

[0171] ② Chinese version of the De Jong Gierveld Loneliness Scale (6 items, total score 0-6 points, the higher the score, the stronger the loneliness).

[0172] 3.2.2 Other baseline information assessment data:

[0173] 3.2.2.1 Questionnaire section:

[0174] ① Depression Symptom Questionnaire (PHQ-9): This measures the severity of an individual's depressive symptoms over the past two weeks. Higher scores indicate more severe depressive symptoms, potentially impacting the individual's daily life and mental health, and requiring timely intervention.

[0175] ② Anxiety Symptom Scale (GAD-7): Assess the severity of generalized anxiety symptoms. Higher scores indicate more severe anxiety symptoms, which may affect daily life and mental health.

[0176] ③Perceived Stress Scale (PSS): This assesses an individual's subjective perception of stress in life. Higher scores indicate greater perceived stress, which may negatively impact physical and mental health, such as anxiety, depression, or physical discomfort.

[0177] ④ Beck Suicidal Ideation Inventory (BSI): Assess the intensity of an individual's suicidal ideation. Higher scores indicate stronger suicidal ideation and the need for timely intervention and support.

[0178] ⑤ Positive-Negative Affect Scale (PANAS): This assesses the intensity of an individual's positive emotions (e.g., happiness, excitement) and negative emotions (e.g., sadness, anxiety). A high score on the positive affect scale indicates a positive mood and a good psychological state; a high score on the negative affect scale indicates a negative mood and possible psychological distress.

[0179] ⑥ Positive and Negative Perception Bias Questionnaire (APNIS): This assesses an individual's cognitive bias toward positive and negative information. A high score on the positive perception bias indicates strong psychological resilience and good emotional regulation; a high score on the negative perception bias suggests a tendency toward depression or anxiety and poor emotional regulation.

[0180] ⑦ Rumination Response Scale (RRS): This measures the frequency and intensity of rumination (repeated thinking about negative events or emotions). Higher scores indicate a greater tendency to dwell on negative events or emotions, which may be associated with psychological issues such as depression and anxiety.

[0181] ⑧MSPSS: This assesses an individual's perceived social support from family, friends, and other significant others. Higher scores indicate greater perceived social support, which is generally associated with better mental health and greater life satisfaction.

[0182] 3.2.2.2 Physiological Data Monitoring: Wearable devices (such as smart bracelets and smart watches) can be used to collect real-time physiological data (such as heart rate, blood pressure, and sleep quality) of the elderly. This data can reflect the elderly’s current psychological state, and indicators such as heart rate variability (HRV) can indirectly reflect stress levels and mood swings.

[0183] 3.3 Information collection during the intervention process

[0184] The user's video viewing and game intervention will be recorded and fed back to the control client.

[0185] 3.4 Post-intervention information collection

[0186] After each video or game intervention, users are required to fill out relevant questionnaires to evaluate the quality of the intervention and the impact of the video / game on the user's loneliness and psychological state, and collect relevant physiological data through linked wearable devices.

[0187] 4. The user enters the video intervention module by clicking on the video intervention module in the main intervention interface module:

[0188] The video intervention module consists of four video series: a loneliness education module, a cognitive restructuring module, a behavioral education module, and a mindfulness training module. The videos automatically resume where they left off and offer pause and replay functions, allowing users to adjust their schedules based on their needs. Users complete the learning process by watching the videos in each video series. Upon completion, users complete a questionnaire to assess the quality of the intervention and the impact of the videos on their loneliness and psychological well-being.

[0189] ① Loneliness science module, which provides relevant science information such as "What is loneliness?", "Why does loneliness occur?", "The prevalence of loneliness?", "The impact of loneliness on the body and mind?", and "Encouraging active intervention in loneliness."

[0190] ② Cognitive Restructuring Module, which provides cognitive restructuring information based on loneliness;

[0191] ③Behavioral education module, used to provide learning information such as social skills;

[0192] ④Mindfulness training module, which provides information on mindfulness training methods such as breathing training and body scanning.

[0193] 5. The user enters the game intervention module by clicking on the game intervention module in the main intervention interface module:

[0194] The module includes four major game modules: cognitive training, social interaction, emotional regulation, and physical activity. Users click on the relevant module to engage in training. Upon completion, users are required to complete a questionnaire to assess the quality of the intervention and the impact of the game on their loneliness and psychological well-being.

[0195] 5.1 Cognitive training game module:

[0196] ①Memory Matching Game: Exercise your memory and concentration by flipping cards to match patterns or words.

[0197] ② Number or word games: such as Sudoku and crossword puzzles, help maintain cognitive abilities.

[0198] 5.2 Social Interactive Game Module:

[0199] ① Virtual social games: such as virtual pet cultivation, which encourages the elderly to take care of virtual pets and interact with other users.

[0200] ②Cooperative mission games: Complete tasks through teamwork and promote social interaction.

[0201] 5.3 Emotion Regulation Game Module:

[0202] ① Emotion recognition game: Helps the elderly understand and regulate their emotions by identifying emotions in expressions or situations.

[0203] ② Relaxation games: such as virtual gardening and painting, to help you relax.

[0204] 5.4 Physical Activity Game Module:

[0205] ① Mild physical games: such as virtual Tai Chi or yoga, combined with simple physical activities to promote physical and mental health.

[0206] ② Rhythm games: Follow the rhythm by clicking the screen to exercise hand-eye coordination.

[0207] 6. The social module includes a community module, a family module, and a background consultation module.

[0208] ① Community module: used to add social functions, such as online communities or video calls, to help the elderly establish social connections.

[0209] ② Family module: allows family members or caregivers to participate, provides the function of remotely viewing progress and reports, and can participate in some game intervention modules (link the elderly’s APP account to the APP on the family’s mobile phone, and the family can remotely view the intervention status and personalized intervention plan, and participate in some games).

[0210] ③Backstage consultation module: used to provide online help or telephone support to the elderly.

[0211] 7. The settings module includes the following functions:

[0212] ①Adjust the interface icon size and font size;

[0213] ②Whether to set voice prompts; whether to voice-driven; language mode selection (Mandarin, Cantonese, etc., the video language will also be modified accordingly), and speech speed adjustment;

[0214] ③Data synchronization (whether to upload to the cloud).

[0215] 8. The control client includes an information entry module and a data management module;

[0216] ① Information entry module, used to obtain user data, pre-intervention baseline information, intervention process information and post-intervention information of the elderly; wherein the intervention process information includes information on the elderly watching videos and completing games, and the post-intervention information receipt includes questionnaire information and physical sign data after the elderly have watched each video series module or completed the game;

[0217] ②Data management module, used to manage the user's user data, the process data of the user completing various video game intervention processes, and the intervention analysis report.

[0218] 9. Control the client to deploy a multimodal data cleaning engine, execute the medical-grade ETL conversion process, and upload the standardized data set to the data server through an encrypted channel. Build a federated learning framework on the server side, extract 32-dimensional bio-behavioral feature vectors (including HRV time-frequency domain indicators, social interaction entropy, etc.) based on feature engineering, use the XGBoost-SHAP interpretable model to achieve loneliness risk grading, and dynamically optimize the weight of intervention strategies through reinforcement learning. The system establishes a dual-track decision-making mechanism: AI automatically generates the initial version of the treatment plan (including video / game type recommendations, social intensity parameters), which is revised and issued by the human-machine collaborative review platform (expert review rate ≥15%, 100% manual verification of high-risk cases). Implement a continuous optimization closed loop: evaluate the effectiveness of the plan through A / B testing every week, use the Bayesian optimization algorithm to iterate the prediction model, and deploy differential privacy to ensure data processing compliance.

[0219] Example 1: Community Elderly Care Center Application Scenario

[0220] Scenario Description

[0221] A community nursing home equipped 100 elderly people aged 65 and above who live alone with a system to reduce their loneliness through a three-month intervention.

[0222] Implementation steps

[0223] Data collection initialization

[0224] User data: The elderly person enters information such as name, age (72±5 years old), and living alone status through the host client voice.

[0225] Baseline assessment:

[0226] The UCLA Loneliness Scale (mean score 58 points, moderate to high loneliness) and PHQ-9 Depression Scale (mean score 12 points, mild depression) were completed.

[0227] Wear a smart bracelet (such as Huawei Band 7) to collect physiological data for 3 days (resting heart rate 75±8 beats / min, sleep duration 5.2±1.1 hours).

[0228] Personalized intervention plan generation

[0229] Data server AI model analysis:

[0230] It was identified that 35% of users had "social avoidance behavior" (video viewing time > 40 minutes / day, zero interaction in social modules).

[0231] Cognitive restructuring videos and emotion regulation games are prioritized for users at high risk of depression (PHQ-9 ≥ 15 points).

[0232] Intervention implementation and adjustment

[0233] Video intervention:

[0234] Aunt Wang (78 years old) failed to complete the social interaction game for three consecutive times, and the system automatically added a "social skills animation course" (such as "how to initiate a chat").

[0235] Game Intervention:

[0236] Grandpa Li (68 years old) prefers digital games, and the system recommends the advanced version of "Memory Matching", which also embeds virtual pet interactive tasks.

[0237] Social support activation

[0238] Home Module:

[0239] Children receive the "This Week's Game Rankings" through the APP and team up with their parents to complete the "Family Quiz Challenge".

[0240] Community Module:

[0241] Organized an online "Old Songs Chain" activity, and 30% of the participants developed into offline chess friend groups.

[0242] Emergency Response

[0243] After Grandma Zhang (85 years old) triggered the emergency button, the system sent the location to the community service center within 5 seconds, and a nurse came to deal with the hypoglycemia incident within 10 minutes.

[0244] Implementation Effect

[0245] 3 months later:

[0246] The average score on the UCLA Loneliness Scale dropped to 42 points (a decrease of 27.6%).

[0247] The average daily usage time of the social module increased from 8 minutes to 23 minutes.

[0248] Improved physiological indicators: sleep duration increased to 6.5 hours, and HRV (heart rate variability) increased by 35%.

[0249] Example 2: Home-based elderly care in remote areas

[0250] Scenario Description

[0251] 50 left-behind elderly people in a rural area received remote psychological support through the system.

[0252] Technical Highlights

[0253] Low bandwidth adaptation

[0254] The video module automatically compresses to standard definition format (≤300kbps) and supports breakpoint resumption.

[0255] The game module adopts H5 lightweight design (single game size <50MB).

[0256] Dialect support

[0257] After selecting "Sichuan dialect" in the settings module, the video narration will automatically switch to dialect dubbing, and the text display will be synchronously adjusted to traditional Chinese.

[0258] Offline emergency plan

[0259] The host client stores 7 days of intervention content (such as 10 mindfulness training videos) and can still be used during network outages.

[0260] After the emergency button is triggered, the system automatically sends a text message (not APP) to the family member's mobile phone.

[0261] Implementation Effect

[0262] Solving the "digital divide" problem:

[0263] The operation success rate for users over 80 years old increased from 22% to 68%.

[0264] Social feature activation:

[0265] 25% of the elderly established online social relationships through the "virtual vegetable garden" game, and 3 of them developed into telephone chat partners.

[0266] Example 3: Targeted intervention for high-risk groups

[0267] Scenario Description

[0268] The psychology department of a tertiary hospital deployed the system for 20 elderly people with suicidal tendencies (BSI scale score ≥ 8 points).

[0269] Technology enhancement measures

[0270] Real-time monitoring and early warning

[0271] Wearable devices upload heart rate data every 5 minutes, and the AI ​​model sets abnormal thresholds (such as a heart rate greater than 120 beats / minute for 10 minutes triggers an alert).

[0272] The system automatically marks users who have not completed the intervention for three consecutive days and pushes them to psychological counselors.

[0273] Manual intervention

[0274] The counselor adjusts the intervention plan by controlling the client:

[0275] The duration of the video intervention was extended from 20 minutes to 30 minutes, and the theme of "sense of meaning in life" was added.

[0276] The game module recommends the "Emotion Diary" drawing game to assist in psychological assessment.

[0277] Implementation Effect

[0278] 3 months later:

[0279] The average score of the BSI scale dropped from 11.2 to 4.8, and the suicide risk level was changed to low risk.

[0280] 90% of users actively use the backend consultation module, and the average consultation time increases from 15 minutes to 40 minutes.

[0281] Example 4: Cross-regional family collaboration scenario

[0282] Scenario Description

[0283] Overseas children can remotely care for their parents living alone in China through the system.

[0284] Technical Implementation

[0285] Multi-terminal synchronization

[0286] The intervention progress is synchronized in real time between the family side (iOS) and the elderly side (Android), and supports switching between Chinese and English interfaces.

[0287] Develop a lightweight version of the WeChat mini-program to facilitate family members to quickly view reports through their mobile phones.

[0288] Interactive function design

[0289] Launched the "Family Memory Game": children upload old photos, the system generates a matching game, and the elderly automatically receive an electronic certificate after completing the game.

[0290] Video calls support the "screen sharing" function, so children can provide remote guidance on operations.

[0291] Implementation Effect

[0292] The frequency of parent-child interaction increased from twice a month to four times a week.

[0293] The willingness of the elderly to use social modules increased by 40%, and 75% of family members reported that they "better understood their parents' mental state."

[0294] Example 5: System Iterative Optimization

[0295] Scenario Description

[0296] The system operations team optimizes intervention strategies through data analysis.

[0297] Technical means

[0298] A / B testing

[0299] Randomly assign two groups of users:

[0300] Control group: traditional video + game intervention.

[0301] Experimental group: Added a "virtual caregiver" AI chatbot (fine-tuned based on GPT-4).

[0302] Results: The loneliness scores of the experimental group decreased 23% faster than those of the control group.

[0303] Data-driven optimization

[0304] The analysis found that the acceptance of somatosensory games among users over 70 years old is only 38%, and the system adjusted the physical activity module to a "seated yoga" teaching video.

[0305] Optimization effect

[0306] Overall user retention rate improved.

[0307] The accuracy of the system's recommended intervention plans has been improved.

[0308] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive management system for loneliness among the elderly based on mobile phone APP, characterized by: include: Host client, control client and data server; The host client includes: The information collection module is used to collect user data, pre-intervention baseline information, intervention process information, and post-intervention information for the elderly. The intervention process information mainly includes the user's video viewing and game intervention status. The post-intervention information is the questionnaire that users need to fill out after each video or game intervention to evaluate the quality of the intervention and the impact of the video / game on the user's loneliness and psychological state. Relevant physiological data is collected through the linked wearable device. The main intervention interface module includes a video intervention module and a game intervention module, which are used to provide cognitive restructuring, behavioral education, mindfulness training, and social interaction intervention services; Social modules, including community modules, family modules and background consultation modules, are used to enhance social support for the elderly; Settings module, used to adjust the interface font size, voice prompts and language selection; Emergency help button, used to trigger emergency warning information to the backend and family members; The control client includes: Information entry module, used to receive and organize data uploaded by the host client; Data management module, used to store and manage user data and intervention process information; The data server is used for: Storage controls the data uploaded by the client; Analyze data and generate personalized intervention plans through AI predictive models; Feedback intervention analysis reports to the control client to dynamically adjust intervention measures.

2. The comprehensive management system for loneliness among the elderly based on a mobile phone APP according to claim 1 is characterized by: The information collection module includes: User data unit, which collects name, age, gender, education level, economic status, frequency of social activities, chronic disease history, and types of current medications; The baseline information unit before the intervention was used to assess loneliness, depression, anxiety, perceived stress, suicidal ideation, emotional state, cognitive bias, rumination, and social support level through questionnaires; Intervention process information unit, which collects information about users’ video viewing and game intervention; The post-intervention information unit uses questionnaires to assess users' loneliness, depression, anxiety, perceived stress, suicidal ideation, emotional state, cognitive bias, rumination, and social support levels after the intervention; The physiological data monitoring unit combines with wearable devices to collect heart rate, blood pressure and sleep quality data in real time.

3. The comprehensive management system for loneliness among the elderly based on a mobile phone APP according to claim 1 is characterized by: The video intervention module includes: Loneliness science module, providing knowledge on the causes and impacts of loneliness; Cognitive restructuring module, used to adjust the elderly’s cognitive patterns towards loneliness; Behavioral education module, which provides social skills learning content; Mindfulness training module, providing breathing exercises and body scan guidance.

4. The comprehensive management system for loneliness among the elderly based on a mobile phone APP according to claim 1 is characterized by: The game intervention module includes: Cognitive training game unit, including memory matching and number logic games; Social interactive game unit, including virtual pet training and cooperative mission games; Emotion Regulation Game Unit, including emotion recognition and relaxation games; The physical activity game unit includes Tai Chi and rhythm clicking games.

5. The comprehensive management system for loneliness among the elderly based on a mobile phone APP according to claim 1 is characterized by: The community module of the social module supports online community and video call functions; the family module allows family members to remotely view the progress of the intervention and participate in interactive games; The backend consultation module provides online help and telephone support.

6. The comprehensive management system and method for loneliness among the elderly based on a mobile phone APP according to claim 1 is characterized by: The data server analyzes user behavior data and physiological data through AI models, combines manual background monitoring to generate dynamic intervention plans, and optimizes intervention measures; AI models include but are not limited to supervised learning models, reinforcement learning models, and generative AI models.

7. The comprehensive management system for loneliness among the elderly based on a mobile phone APP according to claim 1 is characterized by: The settings module supports voice-driven operation and multi-language switching, and the interface icons and font sizes can be adaptively adjusted.

8. The comprehensive management system for loneliness among the elderly based on a mobile phone APP according to claim 1 is characterized by: After the emergency help button is triggered, the background staff will intervene in real time and synchronize the warning information to the family’s mobile phone APP.

9. A comprehensive management method for loneliness in the elderly based on mobile phone APP, characterized by: The following steps are involved: Collect user data, baseline information, and physiological data through the host client; Providing personalized psychological interventions based on video and game modules; Using social modules to enhance social connections among seniors; Collect user intervention process information and post-intervention information through the host client; Integrate data through the control client and upload it to the data server; The data server uses AI models to analyze data, generate dynamic intervention reports and feed them back to the control client.

10. A comprehensive management method for loneliness among the elderly based on a mobile phone APP according to claim 9, characterized in that: The intervention report includes: Trends in loneliness scores; Feedback on changes in physiological data and psychological status before and after the intervention (i.e., improvements in participants' physiological and psychological indicators after the intervention); Recommended targeted video or game intervention plans.

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