Abstract
Objective
To determine the efficacy of an individualized exercise plan based on WeChat and wearable device monitoring among the overweight and obese population.
Methods
One hundred thirty patients who were diagnosed as obese or overweight were included and grouped based on voluntary registration between January 2022 and December 2023. The remote WeChat exercise management model with wearable device monitoring was applied to patients in the study group (n = 92) for 3 months. The control group (n = 38) received an exercise prescription at the time of enrollment and a single education session on sports-related knowledge. Then, the patients in the control group were required to undergo self-exercise and -management for 3 months.
Results
There were significant improvements in weight, body mass index (BMI), waist circumference, hip circumference, blood pressure, body fat, body fat percentage, visceral fat area, and HOMA-IR, as well as total cholesterol, low-density lipoprotein-cholesterol, fasting blood glucose, and fasting insulin levels in the study group after intervention compared to before intervention. The changes in the control group after intervention were not significantly different than before intervention. After adjustment of the baseline differences based on covariance analysis, the following indicators were shown to have statistically significant better improvement in the study group than those the control group after intervention: decreased weight, BMI, waist circumference, hip circumference, body fat, and visceral fat area; lower total cholesterol and low-density lipoprotein-cholesterol levels; and improved glycated hemoglobin concentration. Performance was superior in the study group compared to the control group).
Conclusion
An individualized exercise prescription intervention plan may be conducive to weight loss, fat reduction, lowering blood pressure and blood lipid levels, and improvement in glucose metabolism among the overweight and obese population.
Trial Registration
The clinical trial registration number: ChiCTR2100046307, registered on 12 May 2021.
Keywords: WeChat, Wearable device, Individualized exercise intervention, Overweight, Obesity, Risk control
Introduction
Overweight and obese patients are worldwide public health challenges. [1–4] The World Health Organization (WHO) recently reported that > 39% of the global adult population are overweight and 13% are classified as obese. [1] China is one of the countries with the largest number of overweight and obese people in the world. [2].
Conventional methods on weight management have shortcomings. First, adhering to a diet for a long time may cause metabolic abnormalities, recurrence of obesity, and psychological health problems. [5,6] Second, exercise alone often fails due to personal preferences and adaptability issues, such as time constraints, cost burden, physical discomfort, and the lack of personalized adjustments on exercise plans. [7–9] A previous study showed that the success rate of self-management in weight management is only 38%. [10] In the absence of intervention from physicians or health managers, many participants failed in weight control. [10].
WeChat is a free application for smart devices that provides instant messaging services, boasting > 1 billion global users. WeChat allows users to send voice messages, videos, pictures, and text across various communication platforms and operating systems. In addition, WeChat provides features, such as “Friends Circle” and “Public Platform,” along with local-based social plugins, like “Shake” and “Floating Bottle,” to facilitate social interactions among users. Therefore, WeChat offers a complete and new possibility for social support of health management based on the enormous number of users and the powerful interaction function. [11–14].
WeChat intervention not only has significant efficacy on weight control and body fat, [11] but has also helped patients control blood glucose levels. [12] In addition, WeChat provides timely psychological support through online counselling and care guidance by physicians and support by friends and family members, which helps decrease anxiety and depressive symptoms for the users. [13,14] The wearable device that is combined with WeChat via Bluetooth provides physiologic data with continuity and accuracy, such as heart rate (HR), body temperature, and activity level, enables users to self-monitor and promotes exercise. [15–17] However, wearable devices mainly focus on sports monitoring and health data feedback, lacking social support and psychological incentives. Participants can interact with health managers in real time and receive social support through introduction of the WeChat platform, as well as receive personalized feedback and guidance. [11–14] A change in lifestyle with the assistance of WeChat and wearable devices has long-term effects on weight loss and quality of life. [15–17].
Few studies have focused on integrating wearable devices and the WeChat platform for users who intend to lose weight vie individualized exercise. This study aimed to explore the effect of combining intervention of individualized exercise with WeChat and wearable health monitoring devices on the overweight and obese population.
Materials and methods
Participants
One hundred thirty patients who were defined as overweight or obese between January 2022 and December 2023 were selected based on the principle of voluntary registration and strict screening. The definition of overweight and obesity were determined based on the “Expert consensus and group standards for weight management of the populations with overweight or obesity,” [18] as follows: 25 kg/m2 ≤ body mass index (BMI) < 29.9 kg/m2 was overweight; and 30 ≤ BMI < 35 kg/m2 was obese.
Participants self-reported exercise status when first enrolled. The researchers double checked the physical activity level of the participants and other items according to the inclusion and exclusion criteria through face-to-face talking before the personalized exercise prescription intervention. The inclusion criteria were as follows: (1) males and females between 18 and 65 years of age; (2) BMI ≥ 24 kg/m2 with a stable weight in the recent 3 months; (3) self-reported and confirmed to be sedentary (sitting still for > 6 h a day was considered sedentary), less active life (< 2 exercise sessions < 15 min/session); (4) currently not exercising or no plans for exercise; (5) no history of dieting and weight loss; (6) no history of weight-related drugs; and (7) informed consent form signed and accepted by the participants and their families.
The exclusion criteria were as follows: (1) secondary obesity or drug-induced obesity; (2) BMI ≥ 40 kg/m2; (3) unable or unwilling to sign the informed consent form; (4) definite diagnosis of cardiovascular and cerebrovascular diseases, including myocardial infarction, heart failure, atrial fibrillation, atrial flutter, and stroke; (5) definite diagnosis of a malignant tumor; (6) mental illness and cognitive impairment and cannot cooperate with the intervention plan based solely on the clinical history; (7) inability to undergo exercise plan or give the health status feedback due to severe disability; (8) severe arrhythmia or Brugada syndrome; (9) pregnant and lactating women; (10) could not complete the exercises according to the trial plan due to time, life plan, and work limits; 11) failed re-examination or follow up after intervention; and 12) unable to transmit HR data during the length of exercise and the duration of the trial.
Study design
This was a non-randomized controlled trial that was approved by the Ethics Committee of Beijing Rehabilitation Hospital, Capital Medical University (2022bkky-109, approved on 22 November 2022). The clinical trial registration number was ChiCTR2100046307 and registered on 12 May 2021. This research recruited the participants from the public. All participants signed an informed consent form and voluntarily participated in the study. All collected data were anonymized and all personally identifiable information was kept strictly confidential to ensure the privacy and security of personal information. The data confidentiality procedures of this study, such as anonymization, informed consent, and ethical review, complied with the privacy standards of GDPR and HIPAA. All personally identifiable information was de-identified in data analysis and reporting to ensure that the personal information could not be traced back to a specific individual. All data collected was stored in an encrypted database that was only accessible to members of the research team and all data storage and transmission complied with privacy protection and data security regulations. The comprehensive intervention plan of this study was formulated by the multidisciplinary team of the Health Management Center of Beijing Rehabilitation Hospital. All members of this team held clinician qualifications in rehabilitation/endocrinology and nationally certified health management professional qualification certificates. The formulators of exercise prescriptions also held qualifications as exercise prescription instructors. The dietary plan was evaluated and confirmed by the clinicians/health managers in the team.
Sample size
All participants were recruited and divided into groups based on willingness to participate until the sample size was sufficient. This study involved a pre-experiment component and analyzed 10 pre-experiment participants, among which 5 were in the experimental group and 5 were in the control group. The intervention effect was evaluated based on the main indicator, waist circumference. The difference in waist circumference before and after the intervention for each sample in the pre-experiment was − 3.28 ± 6.29 cm for the experimental group and 0.30 ± 5.33 cm for the control group. While determining the sample size, a statistical power of 0.80 (i.e., 1 - β = 0.80) and a significance level of 0.05 (i.e., α = 0.05) were determined using the sample size estimation method with a two-sample t-test. Based on the calculation, at least 34 participants were required for each group to guarantee the reliability and effectiveness of the results. This study included 92 patients in the study group and 38 in the control group.
Exercise plan
The individualized exercise prescriptions were customized for the subjects in the study and control groups. The Exercise Testing and Prescription Guidelines of the American College of Sports Medicine (ACSM) and the Exercise and Health Risk Assessment System (BH-QS-100; Beijing Dongliang Health Technology Co., Ltd., Beijing, China) were used for exercise prescriptions in both groups. All of the participants in both groups received the individualized exercise prescription through the BH-QS-100 system. The BH-QS-100 system combines a series of elements, including gender, age, medical history, cardiovascular function, and cardiopulmonary endurance exercise, to set the effective rate range of the aerobic exercises. Participants who were below the lower limit of the effective HR were defined as insufficient exercise intensity and low exercise efficiency. Participants who exceeded the upper limit of effective HR were at risk of injury and were monitored by the medical staff.
The BH-QS-100 system monitors the reserve HR, ejection time fraction, left heart load index, myocardial perfusion index, myocardial viability rate, blood pressure (including systolic blood pressure, diastolic blood pressure, pulse, and central arterial pressure), growth index (which assesses arterial elasticity), and personal speed and incline settings of the running platform to provide exercise feedback. Based on these comprehensive indicators, the BH-QS-100 system personalizes the target HR range for each participant, ensuring that the exercise intensity reaches the level of moderate-intensity aerobic exercise and is maintained within a safe and effective range. [19–21] This target HR range follows ACSM guidelines and is designed to maximize health benefits while avoiding exercise risks. Exercise intensity within the target HR range was defined in the study as effective exercise intensity, which was used to assess exercise effects and ensure that exercise interventions met the intended health goals.
The intervention plan for exercise in the study group included the following: aerobic exercise 3 times per week; all of the exercising days should be intermittent, each effective exercising time should be 40 min, and resistance exercises should be performed 2 times per week (push-ups, 10 times/group in 2 groups; abdominal curls, 6 times/group in 2 groups; and squats, 18 times/group in 2 groups). Effective exercise refers to the exercise in which the HR is within the effective HR range based on the exercise prescription. [19–21] In this study the plan for intermittent exercise with moderate intensity three times per week was mainly intended for improving the sustainability and individualization of the exercising prescription to reduce the difficulty of exercising. Such a flexible arrangement enables better adaptation of personal daily living and obligations, including personal time arrangement, work burden, and family responsibilities. Furthermore, previous studies also provided support on an effective exercising plan, which consisted of aerobic exercise with moderate intensity three times per week. [22,23] Then, the participants were required to give feedback of weight data one time per week.
The participants in the control group were provided the same exercise intervention plan as the study group. The health knowledge of the exercise prescriptions formulated for these participants was only taught once at the beginning of the trial. The participants in the control group were asked to undergo self-guided exercise without remote monitoring and guidance for self-management over 3 months and give weekly feedback of the weight data .
The exercise-related knowledge of the control and study groups originated from the formulated exercise prescriptions. The medical staff from the health management center of the author’s institution explained the formulated exercise prescriptions to the participants of the control group, including the principles of choosing aerobic exercise methods, judging exercise intensity, exercise frequency, exercise duration, and exercise intervals.
Intervention of wearable devices and WeChat groups
Both groups developed the personalized exercise prescriptions in the same way and were provided with personalized exercise prescriptions. However, the study group used wearable devices and managed exercise through WeChat (Tencent Corp.; Shenzhen, China) groups, while the control group did not receive wearable devices and no one managed WeChat groups between the control group. The participants in the control group were self-managed to do the exercises. One-on-one oral explanations and physical demonstrations for the use of wearable devices and applications were provided to the participants. The participants were permitted to return home for interventions after demonstrating proper use of the wearable device three times. In addition, videos involving the operation of wearable devices were provided. The participants could communicate any operational issues encountered during use via WeChat at any time. The management of remote exercise in the WeChat group combined with a wearable device was provided to the study group for 3 months.
The MioUse Sport Heart Rate Watch of the Bluetooth Connection to Smartphone Applications (Physi Cal, USA) was used during exercise to evaluate exercise intensity, monitoring, and guiding the HR. The HR monitoring function was opened during exercise and the watch recorded the changing curve of the HR during exercise. The HR was controlled within the effective HR range based on the exercise prescription during aerobic exercise. After exercise, the exercise-related data in the watch was uploaded to the Health Management Workstation (BH-HMW-100; Beijing Dongliang Health Technology Co., Ltd., Beijing, China) through the mobile application.
The members of the medical staff accessed the exercise status of the participants and provided guidance and feedback to the participants through daily interactions in the WeChat group conversation. Furthermore, the medical staff offered monitoring and guidance to the lifestyles and modes of diet whenever necessary to the participants and provided timely feedback and resolution to the questions raised by the participants. Psychological support was also given to the participants through the WeChat group conversations to ensure the participants were encouraged, supported, and heard. The medical staff also delivered the relevant health-related information and advice to the participants for better understanding of health knowledge, which promoted changes in unhealthy behaviors.
The medical staff who provided feedback to the intervention group were from the author’s institution (The Health Management Center). These medical staff were qualified as clinical physicians and had health and sports management professional qualification. When the physicians prescribed exercise prescriptions for the subjects, individual lifestyle and dietary information was obtained. The lifestyle information included physical activity level, cigarette smoking, and alcohol consumption. The dietary information included fruits, vegetables, staple foods, meat, dairy products, oils, salt, dining out, water intake, snacks, and eating habits.
Diet plan interventions
The participants in the study and control groups were asked to record and give feedback on the daily diet in the first 3 months of the trial to ensure no significant changes in the diet during the trial. The diet was evaluated by the participants and confirmed by the physicians according to the energy exchange, which ensured that the intake of protein, carbohydrates, and fats were 15%, 60%, and 25% of the total energy intake, respectively. The participants were asked to manage the daily diet based on energy demands once a week based on the evaluated and confirmed diet. The food intake each day was recorded by the participant and reported to the physicians.
Energy exchange serving is a metering method based on the energy contained in food. Energy exchange serving can be converted into specific types of food shares according to the composition of food to have better control on the intake of energy and nutrients. The dietary goal guaranteed a balanced intake of different nutrients. In addition, the daily energy demand was calculated based on factors, including gender, age, weight, height and activity level. The formulas for calculating the energy requirements of males and females were as follows: male, estimated energy requirement (EER) = 662 - (9.53 × age in years) + {(physical activity level [PAL]) × (15.91 × weight in kg)} + (539.6 × height in m); and female, EER = 354 - (6.91 × age in years) + {(PAL) × (9.36 × weight in kg)} + (726 × height in m). PAL = 1.4–1.6.
Indicators examined
All of the related indicators were examined for each individual in the study and control groups at baseline, including height, weight, BMI, waist circumference (WC), hip circumference (HC), blood pressure (BP), and blood biochemical indicators (fasting blood glucose [FBG], glycated hemoglobin [HbA1c], fasting insulin [FI], uric acid [UA], triglycerides [TG], total cholesterol [TC], low-density lipoprotein-cholesterol [LDL-C], and high-density lipoprotein -cholesterol [HDL-C]). The homeostatic model assessment-insulin resistance index (HOMA-IR) was used to evaluate insulin resistance, as follows: HOMA-IR = fasting blood glucose (mmol/L)×fasting insulin (mU/L)/22.5. An analyzer of the human body composition (Inbody720; Biospace, South Korea) was used to detect indicators, including body fat (BF), skeletal muscle content (SMC), and body fat percentage (BFP). A visceral fat analyzer (HDS-2000; Omron, Japan) was used to detect the visceral fat area (VFA).
The indicators were re-examined in all individuals in both groups 3 months later.
Statistical analysis
Analysis of the clinical data was performed using SPSS 26.0 statistical software (IBM, NY, USA). A χ² test was used to evaluate the significant differences between genders in each group. The measurement data are expressed as the mean differences±standard deviation (x ± s) for the data distribution. The following statistical methods were adopted to evaluate the effect of the intervention measure on different observed indicators: (1) A paired sample t-test or the Wilcoxon test was used to determine the changes in the observed indicators within the same group (the study or control group) before and after intervention and to compare the differences in the measurement values before and after intervention. (2) Analysis of covariance (ANCOVA) was used to control the effect of the potential interfering factors on the results, including the baseline values. The observed indicators before intervention served as covariates and the observed indicators after intervention served as dependent variables. The net effect of the intervention measure on the observed indicators were evaluated by ANCOVA after these covariates were controlled. A P<0.05 was considered to have statistical significance.
Results
Baseline data
A total of 92 participants were recruited into the study group (34 males and 58 females); the mean age was 35.92 ± 9.20 years. A total of 38 participants were recruited into the control group (9 males and 29 females); the mean age was 34.42 ± 8.76 years. During the trial, no patients were disenrolled. The recorded data were complete without any cases disenrolled and no data were missing in either group. No significant difference in the demographic characteristics (age, gender, and weight) was found between the two groups (P > 0.05).
Within-group changes of general data before and after intervention
Table 1 shows the within-group changes in the baseline data. The indicators, including weight (P = 0.048), BMI (P = 0.016), WC (P = 0.001), HC (P < 0.001), SBP (P < 0.001), and DBP (P < 0.001), were significantly decreased after intervention compared to before intervention in the study group (Table 1). No significant changes were detected in weight, BMI, WC, HC, SBP, and DBP after intervention compared to before intervention in the control group (P > 0.05).
Table 1.
Comparison of the subjects’ general data between both groups before and after intervention
| Indicators | Group | n | Before intervention | After intervention | P |
|---|---|---|---|---|---|
| Weight (kg) | Study Group | 92 | 79.30 ± 13.89 | 76.40 ± 13.96 | 0.048* |
| Control Group | 38 | 75.23 ± 12.16 | 75.25 ± 12.69 | 0.992 | |
| Body Mass Index (kg/m2) | Study Group | 92 | 28.33 ± 4.03 | 27.29 ± 4.12 | 0.016* |
| Control Group | 38 | 27.71 ± 2.84 | 27.71 ± 2.98 | 1.000 | |
| Waist Circumference (cm) | Study Group | 92 | 89.21 ± 9.83 | 85.61 ± 11.07 | 0.001* |
| Control Group | 38 | 88.38 ± 8.95 | 88.65 ± 9.97 | 0.862 | |
| Hip Circumference (cm) | Study Group | 92 | 102.26 ± 7.65 | 98.98 ± 8.29 | < 0.001* |
| Control Group | 38 | 100.99 ± 6.77 | 100.70 ± 6.63 | 0.791 | |
| Systolic Blood Pressure (mmHg) | Study Group | 92 | 129.14 ± 15.68 | 122.05 ± 16.56 | < 0.001* |
| Control Group | 38 | 124.71 ± 14.79 | 123.24 ± 13.30 | 0.523 | |
|
Diastolic Blood Pressure (mmHg) |
Study Group | 92 | 78.02 ± 10.67 | 72.93 ± 12.82 | < 0.001* |
| Control Group | 38 | 77.03 ± 12.43 | 74.73 ± 10.45 | 0.224 |
*P < 0.05
Between-group differences of general data after intervention
As shown in Table 2, the weight (P = 0.002), BMI (P = 0.009), WC (P = 0.006), and HC (P = 0.029) in the study group were significantly lower than the control group after intervention. No statistical difference was detected with respect to changes in SBP and DBP after intervention between the two groups (P > 0.05).
Table 2.
ANCOVA of the subjects’ general information between the two groups after intervention
| Indicators | Group | n | Before intervention | After intervention | F | P |
|---|---|---|---|---|---|---|
| Weight (kg) | Study Group | 92 | 79.30 ± 13.89 | 76.40 ± 13.96 | 10.252 | 0.002* |
| Control Group | 38 | 75.23 ± 12.16 | 75.25 ± 12.69 | |||
| Body Mass Index (kg/m2) | Study Group | 92 | 28.33 ± 4.03 | 27.29 ± 4.12 | 7.144 | 0.009* |
| Control Group | 38 | 27.71 ± 2.84 | 27.71 ± 2.98 | |||
| Waist Circumference (cm) | Study Group | 92 | 89.21 ± 9.83 | 85.61 ± 11.07 | 7.845 | 0.006* |
| Control Group | 38 | 88.38 ± 8.95 | 88.65 ± 9.97 | |||
| Hip Circumference (cm) | Study Group | 92 | 102.26 ± 7.65 | 98.98 ± 8.29 | 4.895 | 0.029* |
| Control Group | 38 | 100.99 ± 6.77 | 100.70 ± 6.63 | |||
|
Systolic Blood Pressure (mmHg) |
Study Group | 92 | 129.14 ± 15.68 | 122.05 ± 16.56 | 3.127 | 0.079 |
| Control Group | 38 | 124.71 ± 14.79 | 123.24 ± 13.30 | |||
|
Diastolic Blood Pressure (mmHg) |
Study Group | 92 | 78.02 ± 10.67 | 72.93 ± 12.82 | 3.677 | 0.057 |
| Control Group | 38 | 77.03 ± 12.43 | 74.73 ± 10.45 |
*P < 0.05
Within-group changes of the body composition before and after intervention
Table 3 shows a significant decrease in the VFA (P < 0.001), BF (P < 0.001), and BFP (P < 0.001) in the study group after intervention than before intervention. No statistical significance was detected in the other indicators (P > 0.05; Table 3). No statistical significance was detected in the body composition indicators in the control group before and after intervention (P > 0.05).
Table 3.
Comparison of the subjects’ body composition between the two groups before and after intervention
| Indicators | Group | n | Before intervention | After intervention | P |
|---|---|---|---|---|---|
| Visceral Fat Area (cm2) | Study Group | 92 | 88.94 ± 37.88 | 74.88 ± 36.49 | < 0.001* |
| Control Group | 38 | 80.64 ± 33.04 | 76.10 ± 31.13 | 0.097 | |
| Body Fat weight (kg) | Study Group | 92 | 28.66 ± 9.77 | 25.09 ± 8.99 | < 0.001* |
| Control Group | 38 | 27.00 ± 5.39 | 27.35 ± 6.08 | 0.218 | |
| Skeletal Muscle (kg) | Study Group | 92 | 28.63 ± 6.00 | 28.77 ± 5.89 | 0.225 |
| Control Group | 38 | 26.89 ± 5.89 | 26.79 ± 5.92 | 0.534 | |
| Body Fat Percentage (%) | Study Group | 92 | 34.96 ± 6.91 | 32.29 ± 7.62 | < 0.001* |
| Control Group | 38 | 35.90 ± 5.26 | 36.00 ± 5.90 | 0.697 |
*P < 0.05
Between-group differences of the body composition after intervention
The study group had a superior performance in VFA (P = 0.034), BF (P < 0.001), and BFP (P < 0.001) compared to the control group (Table 4). No statistical difference was detected in the skeletal muscle changes between the two groups (P = 0.336).
Table 4.
ANCOVA analysis of the subjects’ body composition between the two groups after intervention
| Indicators | Group | n | Before intervention | After intervention | F | P |
|---|---|---|---|---|---|---|
| Visceral Fat Area (cm2) | Study Group | 92 | 88.94 ± 37.88 | 74.88 ± 36.49 | 4.582 | 0.034* |
| Control Group | 38 | 80.64 ± 33.04 | 76.10 ± 31.13 | |||
| Body Fat (kg) | Study Group | 92 | 28.66 ± 9.77 | 25.09 ± 8.99 | 19.275 | < 0.001* |
| Control Group | 38 | 27.00 ± 5.39 | 27.35 ± 6.08 | |||
| Skeletal Muscle (kg) | Study Group | 92 | 28.63 ± 6.00 | 28.77 ± 5.89 | 0.935 | 0.336 |
| Control Group | 38 | 26.89 ± 5.89 | 26.79 ± 5.92 | |||
|
Body Fat Percentage (%) |
Study Group | 92 | 34.96 ± 6.91 | 32.29 ± 7.62 | 17.859 | < 0.001* |
| Control Group | 38 | 35.90 ± 5.26 | 36.00 ± 5.90 |
*P < 0.05
Within-group changes of the metabolic indicators before and after intervention
There TC (P < 0.001), LDL-C (P = 0.018), FBG (P < 0.001), FI (P = 0.020), and HOMA-IR (P = 0.007) were significantly decreased in the study group after intervention than before intervention (Table 5). No statistical significance was detected in the differences for UA, TG, HDL-C, and HbA1c in the control group after intervention than that before intervention (P > 0.05, Table 5).
Table 5.
Comparison of the subjects’ metabolic indicators between the two groups before and after intervention
| Indicators | Group | n | Before intervention | After intervention | P |
|---|---|---|---|---|---|
| Uric Acid (umol/L) | Study Group | 92 | 356.37 ± 94.52 | 358.84 ± 96.93 | 0.672 |
| Control Group | 38 | 325.76 ± 86.40 | 314.78 ± 87.83 | 0.416 | |
| Total Cholesterol (mmol/L) | Study Group | 92 | 4.64 ± 0.81 | 4.44 ± 0.74 | < 0.001* |
| Control Group | 38 | 4.78 ± 0.83 | 4.93 ± 0.93 | 0.090 | |
| Triglycerides (mmol/L) | Study Group | 92 | 1.44 ± 0.90 | 1.33 ± 0.78 | 0.200 |
| Control Group | 38 | 1.59 ± 1.34 | 1.54 ± 1.19 | 0.650 | |
| Low-density Lipoprotein Cholesterol (mmol/L) | Study Group | 92 | 2.90 ± 0.73 | 2.79 ± 0.68 | 0.018* |
| Control Group | 38 | 2.91 ± 0.73 | 3.01 ± 0.78 | 0.123 | |
| High-density Lipoprotein Cholesterol (mmol/L) | Study Group | 92 | 1.27 ± 0.26 | 1.25 ± 0.23 | 0.153 |
| Control Group | 38 | 1.30 ± 0.21 | 1.32 ± 0.22 | 0.520 | |
| Glycated Hemoglobin (%) | Study Group | 92 | 5.61 ± 0.50 | 5.67 ± 0.44 | 0.152 |
| Control Group | 38 | 5.66 ± 0.43 | 5.57 ± 0.46 | 0.068 | |
| Fasting Blood Glucose (mmol/L) | Study Group | 92 | 5.21 ± 0.54 | 4.96 ± 0.47 | < 0.001* |
| Control Group | 38 | 5.15 ± 0.51 | 5.04 ± 0.76 | 0.324 | |
| Fasting Insulin (µu/mL) | Study Group | 92 | 46.13 ± 64.74 | 37.13 ± 52.80 | 0.020* |
| Control Group | 38 | 18.30 ± 23.36 | 21.05 ± 25.72 | 0.218 | |
| HOMA-IR | Study Group | 92 | 10.89 ± 15.32 | 8.30 ± 11.73 | 0.007* |
| Control Group | 38 | 4.35 ± 5.90 | 4.97 ± 6.79 | 0.320 |
HOMA-IR Homeostatic model assessment-insulin resistance index
*P < 0.05
Between-group differences of the metabolic indicators after intervention
The study group had a significantly decreased TC (P < 0.001) and LDL-C (P = 0.007) and a slightly increased HbA1c (P = 0.030) compared to the control group after intervention when the differences at baseline were adjusted (Table 6). No statistical difference was detected in the UA, TG, HDL-C, FBG, FI, and HOMA-IR changes between the two groups (P > 0.05).
Table 6.
ANCOVA on the subjects’ metabolic indicators between the two groups after intervention
| Indicators | Group | n | Before intervention | After intervention | F | P |
|---|---|---|---|---|---|---|
| Uric Acid (umol/L) | Study Group | 92 | 356.37 ± 94.52 | 358.84 ± 96.93 | 2.809 | 0.096 |
| Control Group | 38 | 325.76 ± 86.40 | 314.78 ± 87.83 | |||
| Total Cholesterol (mmol/L) | Study Group | 92 | 4.64 ± 0.81 | 4.44 ± 0.74 | 18.473 | < 0.001* |
| Control Group | 38 | 4.78 ± 0.83 | 4.93 ± 0.93 | |||
| Triglycerides (mmol/L) | Study Group | 92 | 1.44 ± 0.90 | 1.33 ± 0.78 | 0.881 | 0.350 |
| Control Group | 38 | 1.59 ± 1.34 | 1.54 ± 1.19 | |||
| Low-density Lipoprotein Cholesterol (mmol/L) | Study Group | 92 | 2.90 ± 0.73 | 2.79 ± 0.68 | 7.522 | 0.007* |
| Control Group | 38 | 2.91 ± 0.73 | 3.01 ± 0.78 | |||
| High-density Lipoprotein Cholesterol (mmol/L) | Study Group | 92 | 1.27 ± 0.26 | 1.25 ± 0.23 | 3.311 | 0.071 |
| Control Group | 38 | 1.30 ± 0.21 | 1.32 ± 0.22 | |||
| Glycated Hemoglobin (%) | Study Group | 92 | 5.61 ± 0.50 | 5.67 ± 0.44 | 4.792 | 0.030* |
| Control Group | 38 | 5.66 ± 0.43 | 5.57 ± 0.46 | |||
| Fasting Blood Glucose (mmol/L) | Study Group | 92 | 5.21 ± 0.54 | 4.96 ± 0.47 | 1.960 | 0.164 |
| Control Group | 38 | 5.15 ± 0.51 | 5.04 ± 0.76 | |||
| Fasting Insulin (µu/mL) | Study Group | 92 | 46.13 ± 64.74 | 37.13 ± 52.80 | 0.362 | 0.548 |
| Control Group | 38 | 18.30 ± 23.36 | 21.05 ± 25.72 | |||
| HOMA-IR | Study Group | 92 | 10.89 ± 15.32 | 8.30 ± 11.73 | 0.516 | 0.474 |
| Control Group | 38 | 4.35 ± 5.90 | 4.97 ± 6.79 |
HOMA-IR Homeostatic model assessment-insulin resistance index
*P < 0.05
Discussion
In this study a comprehensive assessment was performed on the health status of the study and control groups after intervention with a focus on several key health indicators, including body weight, BMI, WC, HC, BP, and BF, and the distribution (BFP and VFA), blood lipid levels (TC and LDL-C), and blood glucose regulation-related indicators (FBG, FI, and HOMA-IR). HbA1c and LDL-C were significantly improved in the study group, which was superior to the control group even after the differences at baseline were adjusted by ANCOVA.
The significant decrease in weight and BMI indicated the success of weight management, which yielded the same results as several studies, suggesting that the exercise intervention significantly reduced the weight and BMI and decreased the risk of cardiovascular disease. [24] The decreases in WC and HC reflected the decrease in abdominal fat, which provided further support for these findings because the decrease in abdominal fat was closely related to an improvement in metabolic health. [25] In addition, the decrease in BP was confirmed by several studies, demonstrating the positive role of exercise intervention on cardiovascular health. [26] The significant decrease in body fat mass, BFP, and VFA in the study group was not only conducive to management of the body type but also lowered the negative effect of fat accumulation on metabolic health. [16] The significant decreases in the TC and LDL-C levels indicated that the blood lipid level had been optimized, which further proved the effectiveness of exercise intervention on cardiovascular disease risk reduction. [27] Moreover, improvement in the FBG, FI, and HOMA-IR in the study group suggested an increase in glucose regulation. The exercise intervention has the ability to improve the sensitivity of insulin and effective prevention and management of type 2 diabetes. [28] The current study showed that the integration interventions of the WeChat group management and wearable devices helped the participants effectively improve all these indicators during the exercise plan.
The social interaction characteristics of the WeChat group management and wearable device monitoring function collectively made up the core mechanism of this intervention plan. Social support has been widely recognized in health behavior changes. According to the theory advanced by Berkman and Syme, [29] social support imposes an effect on the health behavior of individuals through pressure reduction, information, and advice offering and promoting behavioral change. In addition, the sense of self-efficacy is improved by social support to improve the confidence and motivation for positive healthy behavior. [30] In the current study the WeChat group management offered an interactive and supportive social platform, allowing the participants to share experience, exchange questions, and obtain support and encouragement from peers, which increased the willingness and capacity for embracing healthy behaviors. The function of real-time data monitoring in the wearable device provided opportunities for the participants to have timely feedback and self-monitoring. Based on the theory of change, self-monitoring is one of the key steps in health behavior changes which allows the individual to better understand their behavior mode and improvement to reach the anticipated goal by adjusting behavior. [31] The real-time data offers a series of indicators, including exercise, sleep, and psychology to help the participants achieve a more intuitive understanding of their own health status and drives the impetus to participate in healthy behaviors. A study showed that the real-time mechanism significantly improves compliance and effectiveness of healthy behaviors. [32] As demonstrated by previous reports, Wechat [11–14] and wearable devices [15–17] promote health to a great degree. In the current study wearable devices monitored the participant’s exercise data and provided feedback through the mobile application in real time, then the medical staff guided and instructed the exercise plan to the participants through WeChat group conversation for remote management. Our positive results validate the remarkable effect as reported in previous studies. [11–17].
The findings of this study may have a positive effect on clinical practice and guidance of public health policy. The medical institutes and public health departments can use the intervention plan in this study as a reference when designing and implementing a health management plan for the overweight and obese population. The feedback of real-time data based on a combination of WeChat group management social interactive characteristics and wearable device monitoring better promoted changes in patient health behavior and improved the effectiveness and sustainability of such an intervention. The success of this integrated intervention model also provides beneficial experience for a corollary study and practice of health management in the future.
Although remarkable results have been made in this study, some limitations should not be neglected. Specifically, (1) The relatively small size of the sample might restrict the universality. (2) The relatively short duration of the study provided no long-term intervention effect. Given that long-term management and intervention are normally required on health issues, such as overweight and obese; evaluation on the intervention effect in the long term is necessary. (3) The ability to select the treatment group is a major limitation that may lead to feedback bias in this study. (4) Because the remote management vie wearable devices and WeChat groups, the diets between both groups cannot be provided in the same food, which may lead to some differences in the weight loss in the population. (5) In this study the effect of the potential factors on the result might not be considered. For example, the factors including lifestyle, social economic status, and psychological health might have effect on the explanation and promotion of the intervention effect. [33] A full consideration of the potential factors shall be made in a future study and the reliability of the results shall be improved through control of the confounding factors. Given that long-term management and intervention are normally required for overweight and obese participants, the plan in the current study could be controlled for the population and help establish good health habits. Due to the large number of overweight and obese participants, an individualized exercise prescription based on the WeChat group management combined with wearable device may provide the convenient way to lose weight. With the development of technology, future research can explore how to combine artificial intelligence and big data analysis to provide remote management on overweight individuals with more precise and personalized health intervention plans.
Conclusion
This study suggested that a 3-month intervention plan of the individualized exercise prescription based on the WeChat group management combined with wearable device monitoring had a short-term significant beneficial effect for improving the health status of the overweight and obese population, which is helpful for the weight loss, fat reduction, lowering BP and blood lipids, and improving glucose metabolism and insulin resistance.
Acknowledgements
Not applicable.
Abbreviations
- ACSM
the American College of Sports Medicine
- ANCOVA
Analysis of covariance
- BF
Body fat
- BFP
the body fat percentage
- BMI
Body mass index
- BP
Blood pressure
- EER
Estimated energy requirement
- FBG
Fasting blood glucose
- FI
Fasting insulin
- HbA1c
Glycated hemoglobin
- HC
Hip circumference
- HDL-C
High-density lipoprotein-cholesterol
- HOMA-IR
Homeostatic model assessment-insulin resistance
- HR
Heart rate
- LDL-C
Low-density lipoprotein-cholesterol
- SMC
Skeletal muscle content
- TC
Total cholesterol
- TG
Triglycerides
- UA
Uric acid
- VFA
Visceral fat area
- WC
Waist circumference
- WHO
World Health Organization
Authors’ contributions
Conceptualization: Rui Guan, Huijuan Liu, Bin ZhangData curation: Huijuan Liu, Feng ZhuFormal analysis: Feng Zhu, Haijing LiFunding acquisition: Bin ZhangInvestigation: Rui Guan, Haijing LiMethodology: Huijuan Liu, Feng ZhuProject administration: Bin ZhangWriting - original draft: Rui Guan, Haijing Li, Bin ZhangWriting - review & editing: Rui Guan, Huijuan Liu, Feng Zhu.
Funding
This work was supported by Beijing Rehabilitation Hospital Research Fund (2022-048).
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This was a non-randomized controlled trial which has been approved by the Ethics Committee of Beijing Rehabilitation Hospital, Capital Medical University (2022bkky-109, approved on 22 November 2022). The clinical trial registration number is ChiCTR2100046307, which was registered on 12 May 2021 for the first time. All participants signed an informed consent form and voluntarily participated in the study under the premise of full knowledge.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.