An overweight and obesity population weight management system, method and device based on standardized operation process and storage medium

Abstract

This invention discloses a weight management system, method, device, and storage medium for overweight and obese individuals based on standardized operating procedures, belonging to the field of health management technology. It includes an information collection and standardized input module; a health risk analysis and assessment module; a personalized intervention plan configuration module; a dynamic intervention execution and tracking management module; and an effect evaluation and optimization feedback module. The system of this invention achieves standardized collection of multi-dimensional basic data of users, health risk assessment and classification, standardized health risk assessment and classification, and effect evaluation and optimization feedback by constructing a collaborative operating system of the information collection and standardized input module, the health risk analysis and assessment module, the personalized intervention plan configuration module, the dynamic intervention execution and tracking management module, and the effect evaluation and optimization feedback module. Simultaneously, it identifies abnormal behaviors and generates early warning information through early warning rules, and uses the effect evaluation and optimization feedback module to comprehensively evaluate and dynamically optimize the intervention plan execution process.

Description

Technical Field

[0001] This invention relates to the field of health management technology, specifically to a weight management system, method, device, and storage medium for overweight and obese individuals based on standardized operating procedures. Background Technology

[0002] With the continued increase in the number of obese and overweight individuals, weight management has gradually become an important research direction in the field of public health. Current weight management models typically combine dietary control, exercise intervention, and behavioral management, achieving weight regulation through health assessments and lifestyle interventions. In practical applications, some health management institutions or digital health platforms have begun to introduce information management tools to record and analyze users' weight, diet, exercise, and psychological state information to assist in developing weight management plans. Simultaneously, with the development of health management concepts, multi-dimensional health data collection, risk assessment, and personalized intervention program development are gradually becoming important trends in weight management, attempting to achieve dynamic optimization management through continuous tracking and effect evaluation.

[0003] However, existing weight management solutions still have significant shortcomings. Current weight management models often rely on single-dimensional data or experience-based guidance, lacking a structured information collection system and making it difficult to comprehensively assess a user's health status. Risk assessment methods are fragmented, lacking standardized risk staging, grading, and referral mechanisms, making it difficult to prioritize interventions. Existing intervention programs lack personalization and dynamic adjustment capabilities, making it difficult to adapt to changes in user behavior during implementation. Current technologies generally lack a unified effect evaluation and feedback mechanism, making it difficult to quantitatively assess and continuously optimize intervention effects. Furthermore, the intervention of psychological and behavioral factors in weight management is insufficient, resulting in poor user compliance and a tendency for rebound or temporary abandonment. Therefore, how to construct a standardized weight management system and method with multi-dimensional data collection, risk assessment, personalized intervention, dynamic implementation tracking, and effect evaluation feedback mechanisms has become a pressing technical problem to be solved in this field. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing weight management methods suffer from incomplete collection of multi-dimensional health information, lack of standardized grading mechanisms for health risk assessment, lack of personalized and dynamic adjustment capabilities for intervention programs, lack of continuous tracking and early warning mechanisms during intervention execution, and lack of systematic evaluation and optimization feedback for intervention effects. The invention also addresses how to construct a weight management system and method for overweight and obese individuals based on standardized operating procedures, capable of collaboratively managing multi-dimensional basic data collection, risk assessment, personalized intervention program generation, dynamic intervention execution and tracking, and effect evaluation and optimization feedback.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a weight management system for overweight and obese individuals based on standardized operating procedures, comprising: an information collection and standardized input module for collecting multi-dimensional basic data and performing integrity verification; a health risk analysis and assessment module for converting multi-dimensional basic data into risk assessment results and identifying risk levels and intervention priorities; a personalized intervention plan configuration module for converting the risk assessment results into personalized intervention plans; a dynamic intervention execution and tracking management module for executing corresponding management tasks according to the personalized intervention plan and continuously recording and tracking behavioral data during the execution process; and an effect evaluation and optimization feedback module for evaluating the intervention plan and dynamically adjusting the intervention plan based on the evaluation results. The effect evaluation and optimization feedback module includes comprehensively evaluating the multi-dimensional basic data during the execution of the personalized intervention plan and adjusting the personalized intervention plan based on the evaluation results. When the multi-dimensional basic data indicators meet different preset conditions, it executes reinforcement intervention, positive feedback, and personalized intervention plan reconstruction respectively, and performs correlation analysis on the multi-dimensional basic data to identify abnormal behaviors and generate collaborative judgment prompts, and generates nutritional support suggestions when adjusting exercise prescriptions.

[0007] As a preferred embodiment of the weight management system for overweight and obese individuals based on standardized operating procedures described in this invention, the step of converting multi-dimensional basic data into risk assessment results includes: performing overweight determination based on the multi-dimensional basic data, performing a comprehensive health risk assessment, generating a health risk assessment report, identifying the user's risk level and intervention priority, and generating a referral suggestion and triggering the referral process when the risk level meets preset referral conditions.

[0008] As a preferred embodiment of the weight management system for overweight and obese individuals based on standardized operating procedures described in this invention, the step of converting the risk assessment results into a personalized intervention plan includes generating a personalized plan based on the risk assessment results, and forming a personalized intervention plan based on the personalized plan, which serves as the basis for subsequent intervention implementation and follow-up management.

[0009] As a preferred embodiment of the weight management system for overweight and obese individuals based on standardized operating procedures described in this invention, the continuous recording and tracking management of behavioral data during the execution process includes establishing early warning rules based on the dynamic intervention execution and tracking management module.

[0010] The warning rules include warning rules for abnormal weight fluctuations, warning rules for excessive dietary fat intake, and warning rules for decreased exercise adherence.

[0011] As a preferred embodiment of the weight management system for overweight and obese individuals based on standardized operating procedures described in this invention, the abnormal weight fluctuation early warning rule includes: acquiring user weight record data, comparing and analyzing the weight data within a continuous monitoring period with the baseline weight, and generating abnormal weight fluctuation early warning information when the weight change meets preset abnormal fluctuation conditions.

[0012] The dietary fat over-intake warning rule includes obtaining the user's daily oil and fat intake records, comparing the actual intake with the target oil intake set in the personalized intervention plan, and generating dietary fat over-intake warning information when the actual intake meets the preset over-intake conditions.

[0013] The exercise compliance decline warning rule includes obtaining the user's exercise completion record and comparing the actual exercise completed with the exercise prescription set in the personalized intervention plan. When the exercise completion meets the preset compliance decline conditions, an exercise compliance decline warning message is generated.

[0014] As a preferred embodiment of the weight management system for overweight and obese individuals based on standardized operating procedures described in this invention, the evaluation of the intervention plan includes a comprehensive evaluation of multi-dimensional basic data during the implementation of the personalized intervention plan.

[0015] Multi-dimensional basic data includes user medical background, weight, diet, exercise, and psychological data.

[0016] When the body weight reaches the preset weight target, but the dietary structure indicators do not meet the preset dietary conditions, the assessment is that the body has enhanced dietary knowledge.

[0017] When the weight indicator fails to reach the preset weight target, but the exercise indicator reaches the preset exercise volume and the dietary indicator meets the preset dietary conditions, it is assessed as a positive deviation state of behavior.

[0018] When the number of preset indicators among weight, diet, exercise, and psychological indicators does not meet the preset comprehensive indicator conditions, the assessment is considered to be in a multi-dimensional lagging state.

[0019] When changes in psychological state meet preset psychological conditions, and abnormal changes occur in dietary and exercise behaviors, the condition is assessed as an abnormal emotional and behavioral state.

[0020] As a preferred embodiment of the weight management system for overweight and obese individuals based on standardized operating procedures described in this invention, wherein: when the assessment indicates a state of enhanced dietary knowledge, enhanced intervention targeting dietary behavior and nutritional knowledge is initiated, and intervention items in the personalized intervention plan are invoked for precise intervention.

[0021] When the assessment indicates a positive behavioral deviation, a positive feedback strategy is implemented and the current intervention plan is maintained.

[0022] When the assessment shows a multi-dimensional lag, a comprehensive assessment process is triggered and a personalized intervention plan is restructured.

[0023] When an emotional and behavioral abnormality is assessed, a synergistic assessment of emotional eating and physical apathy is generated, and a combination of psychological interventions is implemented.

[0024] When an exercise prescription is adjusted, the energy expenditure corresponding to the exercise prescription is calculated and analyzed in conjunction with the nutritional intake in the dietary records. When the nutritional intake meets the preset nutritional conditions, nutritional support recommendations are generated.

[0025] Another objective of this invention is to provide a weight management method for overweight and obese individuals based on standardized operating procedures. This method can form a closed-loop process for weight management by establishing a collaborative operating mechanism that includes information collection and standardized entry, health risk analysis and assessment, personalized intervention program configuration, dynamic intervention execution and tracking management, and effect evaluation and optimization feedback. This solves the problems of insufficient utilization of multi-dimensional health data, lack of personalized and dynamic adjustment capabilities of intervention programs, and lack of continuous evaluation and optimization mechanisms for intervention effects in current weight management technologies.

[0026] As a preferred embodiment of the weight management system for overweight and obese individuals based on standardized operating procedures described in this invention, the system includes: an information collection and standardized input module that collects and standardizes multi-dimensional basic data from users; a health risk analysis and assessment module that analyzes the multi-dimensional basic data, generates risk assessment results, and identifies risk levels and intervention priorities; a personalized intervention plan configuration module that generates personalized intervention plans based on the risk assessment results; a dynamic intervention execution and tracking management module that executes intervention tasks according to the personalized intervention plans, continuously records and tracks behavioral data during the execution process, establishes early warning rules, and generates early warning information; and an effect evaluation and optimization feedback module that comprehensively evaluates the execution process of the personalized intervention plans and dynamically adjusts the intervention plans based on the evaluation results, forming a continuously iterative closed loop for weight management.

[0027] Another object of the present invention is to provide a weight management device for overweight and obese individuals based on standardized operating procedures, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a weight management method for overweight and obese individuals based on standardized operating procedures.

[0028] Another object of the present invention is to provide a storage medium for weight management of overweight and obese individuals based on standardized operating procedures, wherein a computer program is stored thereon, and when the computer program is executed by a processor, it implements the steps of a weight management method for overweight and obese individuals based on standardized operating procedures.

[0029] The beneficial effects of this invention are as follows: The weight management system for overweight and obese individuals based on standardized operating procedures provided by this invention constructs a collaborative operating system comprising an information collection and standardized input module, a health risk analysis and assessment module, a personalized intervention plan configuration module, a dynamic intervention execution and tracking management module, and an effect evaluation and optimization feedback module. This system enables standardized collection of multi-dimensional basic data of users, health risk assessment and classification, and generation of personalized intervention plans based on the assessment results. During the intervention execution process, behavioral data is continuously recorded and tracked. At the same time, abnormal behaviors are identified and warning information is generated through early warning rules. The effect evaluation and optimization feedback module is used to comprehensively evaluate and dynamically optimize the intervention plan execution process, thereby forming a continuously iterative closed-loop process for weight management. This improves the standardization of the weight management process, the personalization and dynamic adjustment capabilities of the intervention plan, and the assessability and continuous optimization capabilities of the management effect, thereby enhancing the stability, replicability, and long-term intervention effect of the weight management service. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a topology diagram of a weight management system for overweight and obese individuals based on standardized operating procedures, provided in Embodiment 1 of the present invention.

[0032] Figure 2 This is a schematic diagram of a comprehensive assessment and intervention for weight management in an overweight and obese population based on a standardized operating procedure, as provided in Embodiment 1 of the present invention.

[0033] Figure 3This is a time-series interaction diagram of a weight management method for overweight and obese individuals based on a standardized operating procedure, provided in Embodiment 2 of the present invention. Detailed Implementation

[0034] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0035] Example 1, referring to Figures 1-2 As one embodiment of the present invention, a weight management system for overweight and obese individuals based on standardized operating procedures is provided, comprising: The information collection and standardized entry module 100 is used to collect multi-dimensional basic data and perform integrity verification.

[0036] The collection of multi-dimensional basic data includes user medical background, weight, diet, exercise, and psychological data.

[0037] Furthermore, the information collection and standardized entry module 100 includes tools for collecting demographic characteristics and medical background, assessment tools for health literacy and knowledge level, assessment tools for dietary habits, 24-hour dietary intake recording tools, physical activity safety screening tools, daily physical activity level assessment tools, GAD-7 anxiety symptom self-assessment tools, and PHQ-9 depression symptom self-assessment tools.

[0038] The data collected covers multiple dimensions, including medical background, weight, diet, exercise, and psychology.

[0039] It should be noted that multi-dimensional basic data is used to build a unified data foundation covering basic status identification, behavior execution monitoring, anomaly warning triggering, and solution optimization feedback.

[0040] Specifically, medical background data is primarily used to characterize the service recipients' basic health status, past medical history, demographic characteristics, and starting point information for health management. Its role is to provide background constraints for overweight / obesity assessment and staging, and to serve as the basis for project selection, safety boundary setting, and intervention priority determination in subsequent personalized intervention programs. Weight data is primarily used to characterize the service recipients' body shape changes and weight loss progress during the management period. On one hand, it serves as data for overweight determination, risk assessment, and milestone judgments; on the other hand, it is a core input in the early warning rules for abnormal weight fluctuations, used to compare and analyze weight data from the continuous monitoring period with baseline weight. Dietary data is primarily used to reflect the service recipients' daily nutritional intake structure, fat intake, and dietary behavior patterns. It is used for dietary habit assessment, early warning of excessive dietary fat intake, judgment of dietary structure improvement, and generation of nutritional support recommendations when exercise prescriptions are adjusted. Exercise data is primarily used to characterize the service recipients' physical activity level, exercise completion status, and exercise prescription adherence. It is used to form early warning rules for declining exercise adherence, assess the effectiveness of intervention implementation, and determine whether to maintain the current plan or trigger plan restructuring during the effectiveness evaluation phase. Psychological data is primarily used to characterize changes in psychological state during anxiety, depression, and behavioral performance, to identify behavioral abnormalities such as emotional eating and exercise apathy, and to conduct correlation analysis with dietary and exercise records to support the triggering of combined psychological interventions.

[0041] Furthermore, medical background, weight, diet, exercise, and psychology correspond to the basic physiological state dimension, outcome representation dimension, intake behavior dimension, consumption behavior dimension, and behavior-driven dimension in weight management for overweight and obese individuals, respectively. This comprehensively supports the entire process required by this invention, including risk assessment, plan generation, execution monitoring, early warning triggering, and effect feedback. Medical background data addresses the questions of who the target population is and what the management boundaries are; weight data addresses whether the results have changed; dietary data addresses whether intake has deviated from the target; exercise data addresses whether the prescription requirements have been met; and psychological data addresses the driving factors behind behavioral abnormalities.

[0042] The Health Risk Analysis and Assessment Module 200 is used to transform multi-dimensional basic data into risk assessment results and identify risk levels and intervention priorities.

[0043] Transforming multi-dimensional basic data into risk assessment results includes: determining overweight based on multi-dimensional basic data, performing a comprehensive health risk assessment, generating a health risk assessment report, identifying the user's risk level and intervention priority, and generating referral suggestions and triggering the referral process when the risk level meets the preset referral conditions.

[0044] Furthermore, the health risk analysis and assessment module 200 includes an overweight / obesity determination and staging assessment tool (based on BMI and waist circumference standards) and a multi-dimensional comprehensive health risk assessment tool (integrating dietary, exercise, psychological, and metabolic risks).

[0045] Based on multi-dimensional basic data, we perform standardized determination and staging of overweight and obesity (based on BMI and waist circumference) and comprehensive assessment of dietary, exercise, psychological and metabolic risks, and generate risk assessment results.

[0046] The personalized intervention program configuration module 300 is used to transform risk assessment results into personalized intervention programs.

[0047] It should be noted that the staging results output by the overweight / obesity determination and staging assessment tool are used to determine whether a user meets the preset management conditions.

[0048] A preferred option for pre-defined management conditions is that the household is not obese stage 2 or above and does not have related metabolic diseases, and has no serious psychological disorders or other contraindications.

[0049] When a user's weight management results meet preset referral criteria, such as obesity stage 2 or higher accompanied by metabolic diseases like hypertension or diabetes, or a GAD-7 score of 15 or higher and a PHQ-9 score reaching the severe depression threshold, a referral suggestion is automatically generated, triggering the referral process. The referral suggestion information is then output to the management terminal, including the recommended department, a reason for the referral, and an urgency level indicator. This referral control node ensures that weight management services are conducted within safe boundaries, preventing health risks to users with medical contraindications or serious psychological problems due to inappropriate intervention, thus establishing a safe access and risk control mechanism for weight management.

[0050] Transforming risk assessment results into personalized intervention plans involves generating a personalized plan based on the risk assessment results, and then formulating a personalized intervention plan based on the personalized plan, which serves as the basis for subsequent intervention implementation and follow-up management.

[0051] Furthermore, the personalized intervention program configuration module 300 includes a personalized intervention plan (clearly defining the goals, projects, self-monitoring content and implementation process), a service informed consent form (informing the recipient of the content, risks and benefits, and obtaining written confirmation), and an initial management activity record (serving as formal proof of service initiation).

[0052] Based on the risk assessment results, we work with clients to develop a written health intervention plan and create a personalized intervention program that clearly defines the goals, projects, self-monitoring content, and procedures.

[0053] The Dynamic Intervention Execution and Tracking Management Module 400 is used to execute corresponding management tasks based on personalized intervention plans and to continuously record and track behavioral data during the execution process.

[0054] Continuous recording and tracking management of behavioral data during the execution process includes establishing early warning rules based on the dynamic intervention execution and tracking management module 400.

[0055] Furthermore, in the development of personalized intervention plans, not only is a final weight loss goal set, but it is also mandatory to establish phased milestone goals.

[0056] Behavioral development milestones: For example, setting a goal of developing a daily breakfast habit within 4 weeks, with the achievement condition being recording breakfast for at least 25 out of 28 consecutive days. Upon achievement, a "Dietary Expert" electronic badge is awarded as a standardized incentive.

[0057] Skill mastery milestones: For example, setting a goal of mastering the calculation method for food exchange portions, the achievement condition being the completion and accurate calculation of a complete meal log for two consecutive weeks. Upon achievement, public praise in the group is given as a standardized incentive.

[0058] Phased outcome-based milestones: For example, set a goal of losing 2 kg in 4 weeks. The condition for achieving this is that the weight loss from the baseline is more than 2 kg in the 4th week. Once achieved, advanced course content is unlocked as an incentive.

[0059] Meanwhile, when users enter a plateau, the system's guidance managers utilize the pre-set fine-tuning techniques in the Standard Operating Procedures (SOP). For dietary adaptation plateaus, the method involves adjusting the order of eating, i.e., eating soup, vegetables, meat, and staple foods in that order. This works by slowing gastric emptying and increasing satiety. For exercise adaptation plateaus, the method involves trying new sports, such as replacing brisk walking with swimming. This works by breaking muscle adaptation and increasing energy expenditure. For psychological burnout plateaus, mindful eating practices are used. This works by rebuilding a healthy relationship with food and reducing emotional eating. By providing new stimuli, user interest and engagement are maintained, preventing a decline in motivation due to habituation.

[0060] The warning rules include warning rules for abnormal weight fluctuations, warning rules for excessive dietary fat intake, and warning rules for decreased exercise adherence.

[0061] The rules for early warning of abnormal weight fluctuations include acquiring user weight record data, comparing and analyzing the weight data within a continuous monitoring period with the baseline weight, and generating early warning information for abnormal weight fluctuations when the weight change meets the preset abnormal fluctuation conditions.

[0062] The dietary fat over-intake warning rule includes obtaining the user's daily oil and fat intake records and comparing the actual intake with the target oil intake set in the personalized intervention plan. When the actual intake meets the preset over-intake conditions, a dietary fat over-intake warning message is generated.

[0063] The exercise compliance decline warning rule includes obtaining the user's exercise completion record and comparing the actual exercise completed with the exercise prescription set in the personalized intervention plan. When the exercise completion meets the preset compliance decline conditions, an exercise compliance decline warning message is generated.

[0064] It should be noted that the exercise prescription includes screening the user for exercise risks using the Physical Activity Safety Screening Tool (PAR-Q) to confirm that there are no contraindications to exercise. Then, the aerobic capacity is assessed using the 3-minute step test and the muscle strength level is assessed using the grip strength test. Finally, an aerobic exercise prescription is developed based on the FITT principle and an resistance exercise prescription is developed based on the FIRST principle, forming a complete exercise intervention plan.

[0065] Furthermore, the dynamic intervention implementation and tracking management module 400 includes a continuous dietary intake recording tool (for weekly monitoring of implementation), a daily self-monitoring calendar (for recording daily food intake, exercise, water intake, weight, waist circumference, etc.), a weekly progress summary tool (for managers to summarize the completion of core indicators each week), and a monthly summary recording tool (for offline assessment at the end of each month to record changes and guidance content for the month).

[0066] Furthermore, using daily self-monitoring calendars and weekly progress summary tools can break down long-term, vague weight loss goals into daily and weekly visible and achievable micro-tasks.

[0067] Daily Water Intake Target: Drink at least 2000 ml of water daily and check the box in the calendar tool to record it. Daily Meal Plate Target Target: Meal that conforms to the 211 meal plate structure (half vegetables, one-quarter protein, and one-quarter staple food) daily and check the box in the calendar tool to record it. Weekly Exercise Target Target: Achieve an exercise completion rate of 80% or higher for the week; the weekly progress summary tool will automatically calculate this.

[0068] Complete the check-in and receive an instant record. Psychological research shows that completing micro-tasks can activate the brain's reward circuit and increase the willingness to continue participating.

[0069] The intervention was implemented according to a personalized plan and followed a standardized process of initial health management → 3-month intensive period → 3-9 month follow-up period, with daily tracking using a daily self-monitoring calendar.

[0070] The abnormal weight fluctuation warning rule is based on the weekly progress summary tool, and calculates the daily weight fluctuation rate relative to the baseline as follows: , in, For the first The percentage change in daily body weight relative to baseline body weight For the first My weight every morning on an empty stomach. The baseline weight of the service recipients upon enrollment.

[0071] A preferred scheme for setting abnormal fluctuation conditions is as follows: if the relative change in weight from baseline weight is greater than or equal to 2% for 5 or more days within any consecutive 14 days, a yellow alert is triggered. The basis for setting abnormal fluctuation conditions is that under normal physiological conditions, the daily weight fluctuation of adults is usually within the range of ±0.5% to ±1.0%, mainly affected by factors such as water, sodium intake, and intestinal contents. Fluctuations exceeding ±2% (e.g., fluctuations exceeding 1.2kg for a 60kg person) suggest the possible existence of non-physiological factors, such as fluid retention, rapid fat breakdown, or drastic changes in eating behavior. Multiple fluctuations of this magnitude within two consecutive weeks indicate that weight control has deviated from a stable track, and early intervention can prevent the trend from worsening.

[0072] The data for the excessive dietary fat intake warning rule is based on a continuous dietary intake record tool. An optimal scheme for pre-setting excessive intake conditions is expressed as follows: , in, This refers to the actual number of fat exchange portions consumed daily. The number of daily oil and fat food exchange portions set according to the individualized intervention plan.

[0073] When the condition is met for three consecutive days When this occurs, a yellow alert will be triggered.

[0074] The pre-set excessive intake criteria are based on the fact that fats, including cooking oil, nuts, and fatty meats, provide approximately 90 kcal of energy and 10 grams of fat per serving. Exceeding the target by 50% means an additional 5 grams (per serving) or more of fat intake that day, significantly impacting total calorie balance. For example, with a typical daily fat target of 2.5 servings (approximately 25 grams of fat), exceeding the target by 50% results in 3.75 servings (approximately 37.5 grams of fat), increasing the proportion of energy derived from fat by approximately 5 to 8 percentage points. Exceeding the target for three consecutive days suggests the user may be experiencing emotional eating, frequent dining out, or misconceptions about nutrition, requiring immediate behavioral correction.

[0075] The data for the warning rule on declining exercise adherence is based on the weekly progress summary tool, and the weekly exercise completion rate is calculated as follows: , in, This week's exercise completion rate, This refers to the actual number of exercise sessions completed this week. Plan your exercise frequency for each week.

[0076] A preferred approach to pre-setting conditions for decreased compliance is: if the exercise completion rate is less than 80% this week and this occurs for two consecutive weeks, a yellow alert is triggered.

[0077] The basis for setting the pre-set condition for declining adherence is that exercise adherence is a key predictor of long-term weight management success. Numerous behavioral studies have confirmed that when the adherence rate falls below 80%, users enter a high-risk period of behavioral decline, significantly increasing the probability of complete abandonment. For example, if a user plans to exercise 5 times a week and completes 4 times (80%), but only completes 3 times (60%), a calorie deficit accumulates, and the habit is difficult to solidify. Two consecutive weeks with adherence rates below 80% indicate systemic adherence obstacles, such as time conflicts, decreased motivation, or physical discomfort, requiring proactive intervention from a fitness instructor to identify the causes and adjust the plan.

[0078] The effectiveness evaluation and optimization feedback module 500 is used to evaluate the intervention program and dynamically adjust the intervention program based on the evaluation results.

[0079] The 500-module effect evaluation and optimization feedback module includes a health knowledge retest tool (to assess changes in knowledge level), a dietary habit retest tool (to assess improvements in dietary behavior), a physical activity level retest tool (to assess changes in exercise behavior), a psychological state retest tool (to assess improvements in anxiety and depression), a physiological indicator summary tool (to extract data such as final weight and waist circumference), an effect comprehensive evaluation tool (to integrate retest data and compare it with the baseline), and a service satisfaction survey tool (to collect user feedback).

[0080] A comprehensive evaluation of multi-dimensional basic data during the implementation of personalized intervention programs is conducted, and the personalized intervention programs are adjusted based on the evaluation results. When the multi-dimensional basic data indicators meet different preset conditions, reinforcement intervention, positive feedback, and personalized intervention program reconstruction are implemented respectively. Correlation analysis is performed on the multi-dimensional basic data to identify abnormal behaviors and generate collaborative judgment prompts. Nutritional support recommendations are generated when adjusting exercise prescriptions.

[0081] The evaluation of the intervention program includes a comprehensive assessment of multi-dimensional basic data during the implementation of the personalized intervention program.

[0082] Multi-dimensional basic data includes user medical background, weight, diet, exercise, and psychological data.

[0083] When the weight reaches the preset weight target, but the dietary structure index does not meet the preset dietary conditions, it is assessed as a state of enhanced dietary knowledge. Preferably, when the weight reaches the preset weight target, but the change in the dietary structure score does not meet the preset dietary conditions (e.g., an increase of less than 20%), it is assessed as a state of enhanced dietary knowledge.

[0084] When the weight index fails to reach the preset weight target, the exercise index reaches the preset exercise volume, and the dietary index meets the preset dietary conditions, it is assessed as a positive deviation state of behavior. Preferably, when the weight index fails to reach the preset weight target, but the exercise completion rate is consistently higher than 80% and the dietary structure score improves by more than 30%.

[0085] When a preset number of indicators among weight, diet, exercise, and psychological indicators fail to meet the preset comprehensive indicator conditions, the assessment is considered to be in a multi-dimensional lag state. Preferably, when three or more of the four indicators (weight, diet, exercise, and psychological) fail to meet the preset comprehensive indicator conditions, the assessment is considered to be in a multi-dimensional lag state.

[0086] When changes in psychological state meet preset psychological conditions, and abnormal changes occur in dietary and exercise behaviors, the condition is assessed as an abnormal emotional and behavioral state.

[0087] The intervention plan is dynamically adjusted based on the assessment results. When the assessment indicates an enhanced state of dietary knowledge, an enhanced intervention targeting dietary behavior and nutritional knowledge is initiated, and intervention items in the personalized intervention plan are invoked for precise intervention.

[0088] When the assessment indicates a positive behavioral deviation, a positive feedback strategy is implemented and the current intervention plan is maintained.

[0089] When the assessment shows a multi-dimensional lag, a comprehensive assessment process is triggered and a personalized intervention plan is restructured.

[0090] When assessed as an abnormal emotional and behavioral state, a synergistic assessment of emotional eating and physical apathy is generated, and a combination of psychological interventions is implemented, such as... Figure 2 As shown.

[0091] When an exercise prescription is adjusted, the energy expenditure corresponding to the exercise prescription is calculated and analyzed in conjunction with the nutritional intake in the dietary records. When the nutritional intake meets the preset nutritional conditions, nutritional support recommendations are generated.

[0092] Furthermore, based on the GAD-7 anxiety symptom self-assessment tool, the PHQ-9 depression symptom self-assessment tool, the continuous dietary intake record tool, and the weekly progress summary tool, we established association analysis rules for psychology, diet, and exercise.

[0093] A preferred approach to pre-setting psychological conditions is to: set Baseline GAD-7 or PHQ-9 score, Rate the current period. If If the intake of sugary foods increases significantly in a short period of time, the system will automatically retrieve the user's dietary and exercise records for the past 7 days to determine whether the intake of sugary foods has increased, i.e., whether the exchange rate of fruits or grains and tubers in the past 7 days has increased by more than 30% compared with the previous average; at the same time, it will determine whether exercise has been interrupted, i.e. whether the number of times exercise has been completed in the past 7 days has not exceeded 1 time.

[0094] If any of the above conditions are met, the system generates a collaborative assessment prompt indicating suspected emotional eating or exercise apathy, guiding the administrator to adopt a personalized intervention plan, including a combination of mindfulness eating or emotional catharsis, rather than treating the psychological problem in isolation.

[0095] Epidemiological studies have shown that for every 5-point increase in anxiety and depression scores, the risk of emotional eating increases approximately twofold. This can be further investigated by setting... The pre-set psychological conditions can effectively identify high-risk groups who require psychological intervention.

[0096] When an exercise prescription is adjusted, the energy expenditure corresponding to the exercise prescription is calculated and analyzed in conjunction with the nutritional intake in the dietary records. When the nutritional intake meets the preset nutritional conditions, nutritional support recommendations are generated.

[0097] Based on the exercise prescription included in the personalized intervention plan and the continuous dietary intake recording tool, a principle for linking exercise and nutrition for adjustment is established: When the exercise prescription is adjusted, such as increasing the intensity of resistance training, the system will automatically perform the following quantitative calculations according to the FIRST principle: estimate the additional exercise energy expenditure ΔE, calculated by multiplying the number of new sets by the number of repetitions per set by the estimated energy expenditure per exercise, and simultaneously query the average protein intake over the past 7 days. This data comes from dietary records. Then... With target protein intake To make a comparison, if If so, then protein intake is considered to be at the lower limit.

[0098] At this time, the system will simultaneously generate nutritional support recommendations to increase the intake of high-quality protein by one serving per day. Each serving is equivalent to about 7 to 10 grams of protein, such as one egg or a palm-sized amount of lean meat, to ensure that exercise and nutritional intervention are optimized in sync.

[0099] Sports nutrition studies have shown that insufficient protein intake during resistance training will affect muscle synthesis and basal metabolic maintenance. When intake is less than 90% of the target value, supplementing with one serving of high-quality protein can effectively support exercise results.

[0100] Example 2, refer to Figure 3As an embodiment of the present invention, a weight management method for overweight and obese individuals based on a standardized operating procedure is provided, including an information collection and standardized entry module 100 that collects and standardizes the user's multi-dimensional basic data.

[0101] The Health Risk Analysis and Assessment Module 200 analyzes multi-dimensional basic data, generates risk assessment results, and identifies risk levels and intervention priorities.

[0102] The personalized intervention plan configuration module 300 generates personalized intervention plans based on the risk assessment results;

[0103] The dynamic intervention execution and tracking management module 400 executes intervention tasks according to the personalized intervention plan, continuously records and tracks behavioral data during the execution process, establishes early warning rules, and generates early warning information.

[0104] The effectiveness evaluation and optimization feedback module 500 comprehensively evaluates the implementation process of the personalized intervention plan and dynamically adjusts the intervention plan based on the evaluation results, forming a continuously iterative closed loop for weight management.

[0105] like Figure 3 As shown, the information collection and standardized entry module 100 collects multi-dimensional basic data and performs data integrity verification. The multi-dimensional basic data includes medical background, weight, diet, exercise, and psychological data. Based on this, the health risk analysis and assessment module 200 analyzes and processes the multi-dimensional basic data, completes risk identification, risk level determination, and intervention priority determination, and transmits the risk assessment results 201 to the personalized intervention plan configuration module 300. The personalized intervention plan configuration module 300 generates a personalized intervention plan 301 based on the risk assessment results and sends it to the intervention execution and tracking management module 400 to enter the execution stage. During the intervention execution process, user behavior data is continuously recorded, and early warning rules are established based on weight fluctuations, dietary fat intake, and exercise compliance. When the early warning conditions are met, an early warning message is triggered and sent to the effect evaluation and optimization feedback module 500. The module performs a comprehensive evaluation of the dimensions such as weight, diet, exercise, and psychology, and further executes enhanced intervention, positive feedback, plan reconstruction, abnormal behavior correlation analysis, and nutritional support suggestions. The optimized intervention results are fed back to the personalized intervention plan configuration module 300 for personalized intervention plan adjustments.

[0106] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a weight management method for overweight and obese individuals based on a standardized operating procedure as proposed in the above embodiment.

[0107] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements a weight management method for overweight and obese individuals based on a standardized operating procedure as proposed in the above embodiment.

[0108] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0110] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0111] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0112] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A weight management system for overweight and obese individuals based on standardized operating procedures, characterized in that, include: The information collection and standardized entry module (100) is used to collect multi-dimensional basic data and perform integrity verification. The health risk analysis and assessment module (200) is used to transform multi-dimensional basic data into risk assessment results (201) and identify risk levels and intervention priorities; A personalized intervention program configuration module (300) is used to convert the risk assessment results into a personalized intervention program (301). The dynamic intervention execution and tracking management module (400) is used to execute corresponding management tasks according to the personalized intervention plan and to continuously record and track behavioral data during the execution process; The effect evaluation and optimization feedback module (500) is used to evaluate the intervention program and dynamically adjust the intervention program based on the evaluation results; The effect evaluation and optimization feedback module (500) includes a comprehensive evaluation of multi-dimensional basic data during the implementation of the personalized intervention plan, and adjustments to the personalized intervention plan based on the evaluation results. When the multi-dimensional basic data indicators meet different preset conditions, reinforcement intervention, positive feedback and personalized intervention plan reconstruction are performed respectively. The multi-dimensional basic data are also analyzed for correlation, abnormal behaviors are identified and collaborative judgment prompts are generated, and nutritional support suggestions are generated when the exercise prescription is adjusted.

2. The weight management system for overweight and obese individuals based on standardized operating procedures as described in claim 1, characterized in that: The process of transforming multi-dimensional basic data into risk assessment results includes: performing overweight determination based on the multi-dimensional basic data, performing a comprehensive health risk assessment, generating a health risk assessment report, identifying the user's risk level and intervention priority, and generating a referral suggestion and triggering the referral process when the risk level meets the preset referral conditions.

3. The weight management system for overweight and obese individuals based on standardized operating procedures as described in claim 1 or 2, characterized in that: The step of transforming the risk assessment results into a personalized intervention plan includes generating a personalized plan based on the risk assessment results, and forming a personalized intervention plan based on the personalized plan, which serves as the basis for subsequent intervention implementation and follow-up management.

4. The weight management system for overweight and obese individuals based on standardized operating procedures as described in claim 3, characterized in that: The continuous recording and tracking management of behavioral data during the execution process includes establishing early warning rules based on the dynamic intervention execution and tracking management module (400); The warning rules include warning rules for abnormal weight fluctuations, warning rules for excessive dietary fat intake, and warning rules for decreased exercise adherence.

5. The weight management system for overweight and obese individuals based on standardized operating procedures as described in any one of claims 1, 2, and 4, characterized in that: The abnormal weight fluctuation early warning rule includes: acquiring user weight record data, comparing and analyzing the weight data within a continuous monitoring period with the baseline weight, and generating abnormal weight fluctuation early warning information when the weight change meets the preset abnormal fluctuation conditions. The dietary fat intake over-ear warning rule includes obtaining the user's daily oil and fat food intake record, comparing the actual intake with the target oil and fat intake set in the personalized intervention plan, and generating dietary fat intake over-ear warning information when the actual intake meets the preset over-ear condition. The exercise compliance decline warning rule includes obtaining the user's exercise completion record and comparing the actual exercise completed with the exercise prescription set in the personalized intervention plan. When the exercise completion meets the preset compliance decline conditions, an exercise compliance decline warning message is generated.

6. The weight management system for overweight and obese individuals based on standardized operating procedures as described in claim 5, characterized in that: The evaluation of the intervention plan includes a comprehensive evaluation of multi-dimensional basic data during the implementation of the personalized intervention plan. Multi-dimensional basic data includes user medical background, weight, diet, exercise, and psychological data; When the weight reaches the preset weight target, but the dietary structure indicators do not meet the preset dietary conditions, the assessment is that the dietary knowledge is enhanced. When the weight indicator fails to reach the preset weight target, the exercise indicator reaches the preset exercise volume, and the dietary indicator meets the preset dietary conditions, it is assessed as a positive deviation state of behavior. When the number of preset indicators among weight, diet, exercise, and psychological indicators does not meet the preset comprehensive indicator conditions, the assessment is in a multi-dimensional lagging state. When changes in psychological state meet preset psychological conditions, and abnormal changes occur in dietary and exercise behaviors, the condition is assessed as an abnormal emotional and behavioral state.

7. The weight management system for overweight and obese individuals based on standardized operating procedures as described in any one of claims 1, 2, 4, and 6, characterized in that: The dynamic adjustment of the intervention plan based on the assessment results includes, when the assessment indicates an enhanced state of dietary knowledge, initiating enhanced interventions targeting dietary behavior and nutritional knowledge, and calling upon intervention items in the personalized intervention plan for precise intervention. When the assessment indicates a positive behavioral deviation, implement a positive feedback strategy and maintain the current intervention plan; When the assessment indicates a multi-dimensional lag, a comprehensive assessment process is triggered and a personalized intervention plan is restructured. When assessed as an abnormal emotional and behavioral state, a collaborative assessment of emotional eating and physical apathy is generated, and a combination of psychological interventions is implemented. When an exercise prescription is adjusted, the energy expenditure corresponding to the exercise prescription is calculated and analyzed in conjunction with the nutritional intake in the dietary records. When the nutritional intake meets the preset nutritional conditions, nutritional support recommendations are generated.

8. A method for weight management of overweight and obese individuals based on standardized operating procedures, employing the weight management system for overweight and obese individuals based on standardized operating procedures as described in any one of claims 1 to 7, characterized in that: This includes an information collection and standardized entry module (100) that collects multi-dimensional basic data from users and performs standardized entry; The health risk analysis and assessment module (200) analyzes the multi-dimensional basic data, generates risk assessment results, and identifies risk levels and intervention priorities; The personalized intervention plan configuration module (300) generates a personalized intervention plan based on the risk assessment results; The dynamic intervention execution and tracking management module (400) executes intervention tasks according to the personalized intervention plan, continuously records and tracks behavioral data during the execution process, establishes early warning rules, and generates early warning information. The effect evaluation and optimization feedback module (500) comprehensively evaluates the implementation process of the personalized intervention program and dynamically adjusts the intervention program based on the evaluation results, forming a continuously iterative closed loop of weight management.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the weight management method for overweight and obese individuals based on standardized operating procedures as described in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the weight management method for overweight and obese individuals based on standardized operating procedures as described in claim 8.

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