Effects of Exercise Snacks on Cardiometabolic Health and Body Composition in Adults: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aims to investigate the impact of exercise snacks (ExSn), which involve incorporating short bursts of high-intensity physical activity into daily routines, on improving cardiometabolic health and body composition in adults. Six online databases [PubMed, Web of Science, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL, and Scopus] were searched from inception through 22 May 2025, and relevant randomized controlled trials (RCTs) and non-randomized controlled trials (non-RCTs) were identified. Outcomes were analyzed using standardized mean differences and mean differences with 95% confidence intervals. Subgroup analyses were conducted based on physical activity levels and duration for each bout of ExSn. The GRADE scale was used to assess evidence certainty, while the revised Cochrane risk-of-bias tool (RoB 2) was used to evaluate the quality of RCTs, and the Risk of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool was used for non-RCTs. Twelve RCTs and two non-RCTs, involving a total of 483 adults, were selected for the systematic review; 13 studies were included in the meta-analysis. Among the RCTs, 2 studies showed a high risk of bias, and 10 showed some concerns. For the non-RCTs, 1 study had a moderate risk of bias, and 1 had a serious risk of bias. The ExSn group showed significant improvements in maximal oxygen uptake (SMD = 1.43, 95% CI 0.61 to 2.25, p < 0.001) and peak power output (SMD = 0.68, 95% CI 0.00 to 1.36, p = 0.050), and reductions in total cholesterol (SMD = -0.65, 95% CI -1.18 to -0.11, p = 0.018) and LDL cholesterol (SMD = -0.65, 95% CI -1.22 to -0.09, p = 0.023). No significant differences were found for body weight, body fat, HDL cholesterol, or triglycerides. ExSn significantly enhances cardiometabolic health, especially in physically inactive adults. As a novel, time-efficient approach, ExSn can be easily integrated into daily routines, offering a practical solution for sedentary and inactive individuals or those with limited time. These findings highlight its potential as a widely applicable public health strategy, warranting further research on long-term effects and broader applications. PROSPERO Registration Number: CRD42024554446.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.041 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".