Background: Obesity and related disorders are rising globally, especially in China, where they are linked to chronic diseases like diabetes and cardiovascular issues. As intermittent fasting (IF) gains popularity for weight management, the use of IF apps has increased, yet their quality varies significantly. A systematic evaluation of these apps is essential to assess their effectiveness and reliability.
Objective: This study aimed to conduct a comprehensive evaluation of IF apps available in the Chinese mobile app market. We concentrated on evaluating their features, quality, and overall user experience to help users avoid low-quality options and direct app developers to enhance their offers.
Methods: A systematic search was performed across 5 major app stores in China, including the Apple App Store, Huawei AppGallery, Oppo Software Store, Vivo App Store, and Xiaomi Market. "Fasting", "Intermittent Fasting", "Time-Restricted Feeding", "Time-Restricted Fasting", "Time-Restricted Eating" and "Meal Skipping" were used as keywords to identify relevant apps, which were then screened based on inclusion and exclusion criteria. The evaluation was conducted using the user version of the Mobile Application Rating Scale (uMARS). The uMARS assessment examined 4 key subscales: engagement, functionality, aesthetics, and information. Each app was independently evaluated by 2 raters who underwent uniform training to ensure consistency in scoring.
Results: A total of 35 apps were assessed for the study. These apps mostly contain features such as fasting timer (100.0%), recording weight (97.14%), fasting reminder (85.71%), and recording water intake (85.71%). All of the apps have an obvious privacy protection. Most of the apps (79%) have tools for quantifying users' health status. The results showed that the overall average uMARS score across the apps was 4.35 (SD 0.51). The subscale scores were as follows: engagement 4.42 (SD 0.47), functionality 4.65 (SD 0.31), aesthetics 4.19 (SD 0.64), and information 4.15 (SD 0.58). The functionality subscale had the highest mean score, while the aesthetic subscale showed the greatest range of scores, from 2.17 to 5.00. The overall uMARS score was significantly positively correlated with the subscale scores (r=0.786-0.953, P<.001). The user ratings in the app stores did not significantly correlate with the uMARS overall scores (r=-0.290, P=.091). Strong inter-rater reliability was confirmed by intraclass correlation coefficients (ICC=0.809-0.909 across subscales).
Conclusions: All the apps reveal high overall quality but gaps in professional engagement and social features. Limited clinical input may undermine the evidence-based accuracy and long-term applicability of some apps. Developers are encouraged to collaborate with health care professionals to enhance content reliability and incorporate social features to b
Background: Total knee arthroplasty (TKA) is commonly performed to manage end-stage knee osteoarthritis, yet postsurgical recovery varies significantly among patients. Lifestyle modification and rehabilitation interventions play a critical role in optimizing outcomes. While telerehabilitation has shown promise in enhancing accessibility and compliance, its role in supporting lifestyle behavior change alongside supervised sensorimotor training remains underexplored.
Objective: This study aimed to evaluate the effects of a home-based lifestyle modification program delivered through web-based telerehabilitation monitoring in addition to supervised sensorimotor training, in improving physical function, pain, balance, quality of life (QOL), and adherence in patients undergoing TKA.
Methods: A single-blinded randomized controlled trial was conducted among 52 participants undergoing primary TKA, who were randomly assigned to either the intervention group (IG) (supervised sensorimotor training plus a telerehabilitation-supported lifestyle modification program) or the control group (CG) (supervised sensorimotor training alone and a traditional home exercise plan). The intervention lasted 22 weeks, and participants were assessed at baseline (presurgery), 14 weeks, and 22 weeks postsurgery. Outcome measures included joint position sense (JPS), musculoskeletal ultrasound of the rectus femoris muscle, Berg Balance Scale, knee function using the Knee Injury and Osteoarthritis Outcome Score, and QOL via EuroQol 5-dimension 5-level questionnaire.
Results: Significant improvements were observed in the IG across all outcomes compared with the CG. Notably, the IG showed greater improvements in musculoskeletal ultrasound thickness. JPS showed superior accuracy in the experimental group (baseline [3.2 degrees] to 22 wk postsurgery [0.05 degrees]) compared with the CG (baseline [3.1 degrees] to 22 wk postsurgery [1.8 degrees]), with significant improvements noted (P=.001, Cohen d=3.1 vs 0.7), Knee Injury and Osteoarthritis Outcome Score subscales (pain, symptoms, activities of daily living, sport, and QOL), and JPS (mean absolute error 0.05 vs 1.8 degrees). Berg Balance Scale demonstrated significant gains in balance for the experimental group (baseline [34] to 22 wk postsurgery [53]) relative to the CG (baseline [37] to 22 wk postsurgery [48]), with substantial differences observed (P=.001, Cohen d=1.8 vs 0.4). The EuroQol 5-dimension 5-level questionnaire health-related QOL scores were markedly higher for the experimental group (baseline [45.4] to 22 wk postsurgery [88.1]) compared with the CG (baseline [42.8] to 22 wk postsurgery [70.9]), indicating substantial gains in overall health status (P=.001, Cohen d=2.4 vs 1.3). The IG also reported higher compliance, with 81.8% (18/22) achieving over 90% adherence compared with 68.18% (15/22) in the CG.
Conclusions:
Background: Engagement with digital mental health interventions is often measured as a summary-level variable and remains underresearched despite its importance for meaningful symptom change. This study deepens understanding of engagement in a digital eating disorder intervention, recovery record, by measuring engagement with unique components of the app, on 2 different devices (phone and watch), and at a summary level.
Objective: This study described and modeled how individuals engaged with the app across a variety of measures of engagement and identified baseline predictors of engagement.
Methods: Participants with current binge-eating behavior were recruited as part of the Binge Eating Genetics Initiative study to use a digital eating disorder intervention for 4 weeks. Demographic and severity of illness variables were captured in the baseline survey at enrollment, and engagement data were captured through both an iPhone and Apple Watch version of the intervention. Engagement was characterized by log type (urge, behavior, mood, or meal), device type (logs on phone or watch), and overall usage (total logs) and averaged each week for 4 weeks. Descriptives were tabulated for demographic and engagement variables, and multilevel growth models were conducted for each measure of engagement with baseline characteristics and time as predictors.
Results: Participants (N=893) self-reported as primarily White (743/871, 85%), non-Hispanic (801/893, 90%), females (772/893, 87%) with a mean age of 29.6 (SD 7.4) years and mean current BMI of 32.5 (SD 9.8) kg/m2 and used the app for a mean of 24 days. Most logs were captured on phones (217,143/225,927; 96%), and mood logs were the most used app component (174,818/282,136; 62% of logs). All measures of engagement declined over time, as illustrated by the visualizations, but each measure of engagement illustrated unique participant trajectories over time. Time was a significant negative predictor in every multilevel model. Sex and ethnicity were also significant predictors across several measures of engagement, with female and Hispanic participants demonstrating greater engagement than male and non-Hispanic counterparts. Other baseline characteristics (age, current BMI, and binge episodes in the past 28 days) were significant predictors of 1 measure of engagement each.
Conclusions: This study highlighted that engagement is far more complex and nuanced than is typically described in research, and that specific components and mode of delivery may have unique engagement profiles and predictors. Future work would benefit from developing early engagement models informed by baseline characteristics to predict intervention outcomes, thereby tailoring digital eating disorder interventions at the individual level.

