The effectiveness of eHealth interventions on physical activity and measures of obesity among working‐age women: a systematic review and meta‐analysis
Bibliographic record
Abstract
Summary Physical inactivity and obesity are modifiable risk factors for cardiovascular disease, particularly in women. eHealth interventions may increase physical activity and improve obesity‐related outcomes among women. The objective of this study was to review the evidence of the effectiveness of eHealth interventions to increase moderate‐to‐vigorous physical activity among working‐age women. The secondary objective was to examine their effectiveness on improving obesity‐related outcomes. A comprehensive search strategy was developed for eight electronic databases; through July 2016. All studies consisting of >80% women of working‐age (18–65 years) in high income countries were included. Multiple unblinded reviewers determined study eligibility and extracted data. Risk of bias was evaluated using the Cochrane Risk of Bias Tool and data quality using the Grading of Recommendations Assessment, Development and Evaluation approach. Data were pooled using a random‐effects model. Sixty studies were included in the review of which 20 were in the meta‐analysis. The meta‐analysis demonstrated eHealth interventions improved moderate‐to‐vigorous physical activity (standard mean difference = 1.13, 95% confidence interval: 0.58, 1.68, P < 0.0001); an increase of ~25 min week−1. No changes were observed in obesity‐related outcomes; waist circumference (P = 0.06), body mass (P = 0.05) and body mass index (P = 0.35). eHealth interventions are effective at increasing min week−1 of moderate‐to‐vigorous physical activity among working‐age women from high income countries.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".