Guideline-Based Digital Exercise Interventions for Reducing Body Weight and Fat and Promoting Physical Activity in Adults With Overweight and Obesity: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Digitally delivered physical exercise interventions are becoming increasingly popular in addressing the obesity epidemic. However, there remains uncertainty on their efficacy regarding the reduction of body weight (BW) and body fat, which may, at least partly, be due to variations in study designs and inconsistent adherence to international physical activity (PA) guidelines. Objective This study aimed to evaluate the effectiveness of digital exercise interventions based on PA guidelines in reducing BW and fat in adults with overweight or obesity, as well as their impact on PA-related factors. Methods This review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Comprehensive searches were performed in October 2024 across PubMed, Cochrane Library, Web of Science, and Ovid MEDLINE databases. Eligible studies included adults (aged ≥18 years) with objectively confirmed overweight or obesity who used digital interventions aligned with international PA guidelines. Risk of bias was evaluated using the Cochrane Risk of Bias (version 2) tool for randomized controlled trials and the Risk of Bias in Nonrandomized Studies of Interventions tool for nonrandomized studies. A random-effects meta-analysis with Hartung-Knapp adjustment was performed using R software. Results Out of 4948 studies identified, 188 (3.8%) were screened in full and 30 (0.6%) met the eligibility criteria. Intervention durations ranged from 8 weeks to 24 months (average 6.4, SD 5.5 months). Meta-analysis showed that guideline-based digital exercise interventions significantly reduced BW compared to controls (mean difference [MD]=−1.17 kg; P=.003; I2=0.0%), with subgroup analysis revealing greater effects in active (nondigital) controls (MD=−1.23 kg; I2=7.5%) compared to passive (waitlist) controls (MD=−0.52 kg; I2=0.0%). A significant reduction in BMI was observed (MD=−0.50 kg/m2; P=.003), although with substantial heterogeneity (I2=70.0%), and subgroup analysis showed greater effects compared to passive controls (MD=−0.70 kg/m2; I2=43.1%) rather than to active controls (MD=−0.45 kg/m2; I2=74.5%). No significant effect was observed for body fat percentage overall (MD=−0.08%; P=.84; I2=7.4%). Qualitative analysis (including findings from noncomparative studies) showed that guideline-based digital exercise interventions led to significant reductions in BW (22/25, 88% studies; range −1.3 to −8.4 kg); BMI (19/23, 83% of studies; range −0.4 to −3.4 kg/m2); waist circumference (15/16, 94% of studies; range −2.1 to −9.2 cm), body fat percentage (9/9, 100% of studies; range −0.3% to −4.1%); and fat mass (7/7, 100% of studies; range −0.4 to −6.5 kg), while findings for waist-to-hip ratio and PA outcomes were inconsistent. Conclusions Guideline-based digital PA and exercise interventions show potential in reducing excess BW in adults with overweight or obesity, with stronger effects when compared to nondigital interventions. However, their superiority over traditional methods is uncertain for BMI and body composition. Substantial variations in study designs present challenges in drawing definitive conclusions on specific characteristics of effective digital exercise tools. Trial Registration PROSPERO CRD42024620020; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024620020
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".