Environmental, behavioural and multicomponent interventions to reduce adults' sitting time: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the overall effectiveness of interventions for reducing adult sedentary behaviour and to directly compare environmental, behavioural and multicomponent interventions. DESIGN: Intervention systematic review with meta-analysis. DATA SOURCES: Ovid PsycINFO, Ovid MEDLINE, EBSCOHost CINAHL, EBSCOHost SPORTDiscus and PubMed were searched from inception to 26 July 2017. ELIGIBILITY CRITERIA: Trials including randomised controlled trials, quasi-randomised, cluster-randomised, parallel group, prepost, factorial and crossover trials where the primary aim was to change the sedentary behaviour of healthy adults assessed by self-report (eg, questionnaires, logs) or objective measures (eg, accelerometry). RESULTS: Thirty-eight trials of 5983 participants published between 2003 and 2017 were included in the qualitative synthesis; 35 studies were included in the quantitative analysis (meta-analysis). The pooled effect was a significant reduction in daily sitting time of -30.37 min/day (95% CI -40.86 to -19.89) favouring the intervention group. Reductions in sitting time were similar between workplace (-29.96 min/day; 95% CI -44.05 to -15.87) and other settings (-30.47 min/day; 95% CI -44.68 to -16.26), which included community, domestic and recreational environments. Environmental interventions had the largest reduction in daily sitting time (-40.59 min/day; 95% CI -61.65 to -19.53), followed by multicomponent (-35.53 min/day; 95% CI -57.27 to -13.79) and behavioural (-23.87 min/day; 95% CI -37.24 to -10.49) interventions. CONCLUSION: Interventions targeting adult sedentary behaviour reduced daily sitting time by an average of 30 min/day, which was likely clinically meaningful.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".