The Effectiveness of Physical Activity-Promoting Web- and Mobile-Based Distance Weight Loss Interventions on Body Composition in Rehabilitation Settings: Systematic Review, Meta-analysis, and Meta-Regression Analysis

Interactive Effects of Weight Recording Frequency and the Volume of Chat Communication With Health Care Professionals on Weight Loss in mHealth Interventions for Noncommunicable Diseases: Retrospective Observational Study

Background

Mobile health (mHealth) apps are increasingly used for health promotion, particularly for managing noncommunicable diseases (NCDs) through behavior modification. Understanding the factors associated with successful weight loss in such interventions can improve program effectiveness.

Objective

This study examined factors influencing weight change and the relationship between weight recording frequency and chat volume with health care professionals on weight loss in individuals with obesity and NCDs.

Methods

The participants had obesity (BMI ≥25 kg/m²) and were diagnosed with NCDs (eg, hypertension, diabetes, dyslipidemia). The program included 12 telephone consultations with health care professionals. Only participants who completed the full 6-month program, including all 12 telephone consultations, and provided an end-of-study weight were included in the analysis. The primary outcome was the rate of weight change, defined as the percentage change in weight from the initial period (first 14 days) to the final period (2 weeks before the last consultation), relative to the initial weight. The key independent variables were proportion of days with weight recording and chat communication volume (total messages exchanged). An interaction term between these variables was included to assess moderation effects in the regression analysis. The volume of communication was measured as the total number of messages exchanged, with each message, regardless of who sent it, being counted as 1 interaction. Health care staffs were instructed to send a single scheduled chat message per week following each biweekly phone consultation. These scheduled messages primarily included personalized feedback, reminders, and motivational support. In addition, providers responded to participant-initiated messages at any time during the program. Furthermore, 1 professional responded to each participant. Hierarchical multiple regression and simple slope analyses were conducted to identify relationships and interactions among these variables.

Results

The final analysis of this study included 2423 participants. Significant negative associations were found between the rate of weight change and baseline BMI (β=-.10; P<.001), proportion of days with weight recording (β=-.017; P<.001), and communication volume (β=-.193; P<.001). The interaction between proportion of days with weight recording and chat frequency also showed a significantly negative effect on weight change (β=-.01; P<.001). Simple slope analysis showed that when the proportion of days with weight recording was +1 SD above the mean, frequent chats were associated with greater weight reduction (slope=-0.60; P<.001), whereas no significant effect was observed at -1 SD (slope=-0.01; P=.94).

Conclusions

The findings suggest that both the proportion of days with weight recording and communication volume independently and interactively influence weight change in individuals with obesity and NCDs.

An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial

Background

The increasing prevalence of sedentary lifestyles has prompted the development of innovative public health interventions, such as smartphone apps that deliver personalized exercise programs. The widespread availability of mobile technologies (eg, smartphone apps and wearable activity trackers) provides a cost-effective, scalable way to remotely deliver personalized exercise programs to users. Using machine learning (ML), specifically reinforcement learning (RL), may enhance user engagement and effectiveness of these programs by tailoring them to individual preferences and needs.

Objective

The primary aim was to investigate the impact of the Samsung-developed i80 BPM app, implementing ML for exercise prescription, on user satisfaction and exercise intensity among the general population. The secondary objective was to assess the effectiveness of ML-generated exercise programs for remote prescription of exercise to members of the public.

Methods

Participants were randomized to complete 3 exercise sessions per week for 12 weeks using the i80 BPM mobile app, crossing over weekly between intervention and control conditions. The intervention condition involved individualizing exercise sessions using RL, based on user preferences such as exercise difficulty, selection, and intensity, whereas under the control condition, exercise sessions were not individualized. Exercise intensity (measured by the 10-item Borg scale) and user satisfaction (measured by the 8-item version of the Physical Activity Enjoyment Scale) were recorded after the session.

Results

In total, 62 participants (27 male and 42 female participants; mean age 43, SD 13 years) completed 559 exercise sessions over 12 weeks (9 sessions per participant). Generalized estimating equations showed that participants were more likely to exercise at a higher intensity (intervention: mean intensity 5.82, 95% CI 5.59-6.05 and control: mean intensity 5.19, 95% CI 4.97-5.41) and report higher satisfaction (RL: mean satisfaction 4, 95% CI 3.9-4.1 and baseline: mean satisfaction 3.73, 95% CI 3.6-3.8) in the RL model condition.

Conclusions

The findings suggest that RL can effectively increase both the intensity with which people exercise and their enjoyment of the sessions, highlighting the potential of ML to enhance remote exercise interventions. This study underscores the benefits of personalized exercise prescriptions in increasing adherence and satisfaction, which are crucial for the long-term effectiveness of fitness programs. Further research is warranted to explore the long-term impacts and potential scalability of RL-enhanced exercise apps in diverse populations. This study contributes to the understanding of digital health interventions in exercise science, suggesting that personalized, app-based exercise prescriptions may be more effective than traditional, nonpersonalized methods. The integration of RL into exercise apps could significantly impact public health, particularly in enhancing engagement and reducing the global burden of physical inactivity.

Effects of a digital intervention on physical activity in adults: A randomized controlled trial in a large-scale sample

Background

Physical inactivity is associated with health risks, contributing to various diseases and all-cause mortality. Despite recommendations for regular physical activity (PA), many adults remain inactive, influenced by socioeconomic and environmental factors. Digital interventions, particularly web-based PA programs, offer promising possibilities to promote PA across populations. These programs vary in their effectiveness, reflecting differences in design, user engagement, and behavior change techniques employed.

Objective

This study evaluates the effectiveness of the 12-week multimodal web-based TKFitnessCoach. The PA online program is part of the TK-HealthCoach. This study investigates the program's impact on self-reported PA levels, goal attainment, healthrelated quality of life, body weight, and eating behavior, comparing an interactive personalized web-based intervention and non-interactive web-based health information.

Methods

In a randomized controlled trial (RCT), participants were allocated to either the intervention group (IG), receiving access to the interactive TK-FitnessCoach, or the control group (CG) that was provided a static website with evidence-based information on PA. The study targeted a German-speaking adult population interested in improving health behavior. Data was assessed at T0 (beginning of the study), T1 (postintervention), T2, 6 months, and T3, 12 months follow-ups, focusing on self-reported PA at T3 and on various secondary outcomes.

Results

We achieved equally distributed sociodemographics in both the IG and the CG with a mean age of 42.8 (IG), resp. 43.1 years (CG), and female participants of 76.1 % (IG), resp. 74.7 % (CG). PA at baseline was 277.9 min/week in the IG and 273.3 min/week in the CG. Both, the IG (n = 1153 in the Intention-to-treat (ITT) dataset) and CG (n = 1177 in the ITT dataset) exhibited significant increases in PA over time (IG(T3-T0) = 72.92 min/week; CG(T3-T0) = 74.12 min/week).However, the study did not find significant differences in the effectiveness of the interactive TK-FitnessCoach compared to the non-interactive control in terms of improving PA and related health outcomes. The intensity of using the TK-FitnessCoach was not associated with PA.

Conclusions

Both programs were effective in promoting PA among adults, with no significant differences observed between the two RCT groups. This highlights the potential of digital interventions in addressing physical inactivity, suggesting that the effectiveness of such programs may not solely depend on their interactivity but also on the quality and relevance of the information provided. Further research is needed to explore optimization strategies for such interventions, especially for persons with low PA, including user engagement, behavior change techniques, and the integration of objective PA tracking methods.

Trial registration

German Clinical Trials Register DRKS00020249; https://drks.de/search/en/trial/DRKS00020249.

The Effectiveness of eHealth Interventions for Weight Loss and Weight Loss Maintenance in Adults with Overweight or Obesity: A Systematic Review of Systematic Reviews

PURPOSE OF REVIEW: The purpose of this study is to evaluate the effectiveness of eHealth interventions for weight loss and weight loss maintenance among adults with overweight or obesity through a systematic review of systematic reviews. RECENT FINDINGS: This study included 26 systematic reviews, covering a total of 338 original studies, published between 2018 and 2023. The review indicates that eHealth interventions are more effective than control interventions or no care and comparable to face-to-face interventions. The effect sizes remain relatively small when comparing eHealth interventions to any control conditions, with mean differences of weight loss results from - 0.12 kg (95% CI - 0.64 to 0.41 kg) in a review comparing eHealth interventions to face-to-face care to - 4.32 kg (- 5.08 kg to - 3.57 kg) in a review comparing eHealth interventions to no care. The methodological quality of the included studies varies considerably. However, it can be concluded that interventions with human contact work better than those that are fully automated. In conclusion, this systematic review of systematic reviews provides an updated understanding of the development of digital interventions in recent years and their effectiveness for weight loss and weight loss maintenance among adults with overweight or obesity. The findings suggest that eHealth interventions can be a valuable tool for delivering obesity care to more patients economically. Further research is needed to determine which specific types of eHealth interventions are most effective and how to best integrate them into clinical practice.

Physical Activity Level, Barriers, and Facilitators for Exercise Engagement for Chronic Community-Dwelling Stroke Survivors in Low-Income Settings: A Cross-Sectional Study in Benin

After a stroke incident, physical inactivity is common. People with stroke may perceive several barriers to performing physical activity (PA). This study aimed to document the PA level and understand the barriers and facilitators to engaging in PA for community-dwelling stroke survivors in Benin, a lower middle-income country. A cross-sectional study was conducted in three hospitals in Benin. Levels of PA were recorded by means of the Benin version of the International Physical Activity Questionnaire long form (IPAQ-LF-Benin), which is validated for stroke survivors in Benin. The perceived exercise facilitators and barriers were assessed by the Stroke Exercise Preference Inventory-13 (SEPI-13). A descriptive analysis and associations were performed with a Confidence Interval of 95% and <0.05 level of significance. A total of 87 participants (52 men, mean age of 53 ± 10 years, mean time after a stroke of 11 (IQR: 15) months and an average of 264.5 ± 178.9 m as distance on the 6 min walking test (6MWT) were included. Overall, stroke survivors in Benin reached a total PA of 985.5 (IQR: 2520) metabolic equivalent (METs)-minutes per week and were least active at work, domestic, and leisure domains with 0 MET-minutes per week. The overview of PA level showed that 52.9% of participants performed low PA intensity. However, 41.4% performed moderate PA or walking per day for at least five days per week. Important perceived barriers were lack of information (45.3%), hard-to-start exercise (39.5%), and travelling to places to exercise (29.9%). The preference for exercise was with family or friends, outdoors, for relaxation or enjoyment (90.2%), and receiving feedback (78.3%). Several socio-demographic, clinical, and community factors were significantly associated with moderate or intense PA (p < 0.05) in stroke survivors in this study. Our findings show that the PA level among chronic stroke survivors in Benin is overall too low relative to their walking capacity. Cultural factors in terms of the overprotection of the patients by their entourage and/or the low health literacy of populations to understand the effect of PA on their health may play a role. There is a need for new approaches that consider the individual barriers and facilitators to exercise.

The added value of remote technology in cardiac rehabilitation on physical function, anthropometrics, and quality of life: a cluster randomized controlled trial (Preprint)

BACKGROUND

Cardiovascular diseases (CVDs) cause most deaths globally and can reduce quality of life (QoL) of rehabilitees with cardiac disease. The risk factors of CVDs are physical inactivity and increased BMI. With physical activity, it is possible to prevent CVDs, improve QoL, and help maintain a healthy body mass. Current literature shows the possibilities of digitalization and advanced technology in supporting independent self-rehabilitation. However, the interpretation of the results is complicated owing to the studies' high heterogeneity. In addition, the added value of this technology has not been studied well, especially in cardiac rehabilitation.

OBJECTIVE

We aimed to examine the effectiveness of added remote technology in cardiac rehabilitation on physical function, anthropometrics, and QoL in rehabilitees with CVD compared with conventional rehabilitation.

METHODS

Rehabilitees were cluster randomized into 3 remote technology intervention groups (n=29) and 3 reference groups (n=30). The reference group received conventional cardiac rehabilitation, and the remote technology intervention group received conventional cardiac rehabilitation with added remote technology, namely, the Movendos mCoach app and Fitbit charge accelerometer. The 12 months of rehabilitation consisted of three 5-day in-rehabilitation periods in the rehabilitation center. Between these periods were two 6-month self-rehabilitation periods. Outcome measurements included the 6-minute walk test, body mass, BMI, waist circumference, and World Health Organization QoL-BREF questionnaire at baseline and at 6 and 12 months. Between-group differences were assessed using 2-tailed t tests and Mann-Whitney U test. Within-group differences were analyzed using a paired samples t test or Wilcoxon signed-rank test.

RESULTS

Overall, 59 rehabilitees aged 41 to 66 years (mean age 60, SD 6 years; n=48, 81% men) were included in the study. Decrement in waist circumference (6 months: 1.6 cm; P=.04; 12 months: 3 cm; P<.001) and increment in self-assessed QoL were greater (environmental factors: 0.5; P=.02) in the remote technology intervention group than the reference group. Both groups achieved statistically significant improvements in the 6-minute walk test in both time frames (P=.01-.03). Additionally, the remote technology intervention group achieved statistically significant changes in the environmental domain at 0-6 months (P=.03) and waist circumference at both time frames (P=.01), and reference group achieve statistically significant changes in waist circumference at 0-6 months (P=.02).

CONCLUSIONS

Remote cardiac rehabilitation added value to conventional cardiac rehabilitation in terms of waist circumference and QoL. The results were clinically small, but the findings suggest that adding remote technology to cardiac rehabilitation may increase beneficial health outcomes. There was some level of systematic error during rehabilitation intervention, and the sample size was relatively small. Therefore, care must be taken when generalizing the study results beyond the target population. To confirm assumptions of the added value of remote technology in rehabilitation interventions, more studies involving different rehabilitees with cardiac disease are required.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN61225589; https://www.isrctn.com/ISRCTN61225589.