The effectiveness of interventions to improve the balance of movement behaviours over the 24-hour day among immigrants: a systematic review
Bibliographic record
Abstract
Introduction: Achieving a healthy balance of 24-hour movement behaviours – physical activity (PA), sedentary behaviour (SB), and sleep – represents a public health imperative to promote health among immigrants. Effective interventions that target the balance of movement behaviours over the 24-hour day must be evidence-based. Purpose: To systematically review and assess the type (i.e., content and delivery) and effectiveness of interventions targeting movement behaviours among adult immigrants in immigrant-adopting countries. Methods: The Preferred Reporting Items for Systematic Reviews (SR)s and Meta-Analyses and Cochrane processes guided this SR. Five databases were searched for movement interventions published until December 2022. Two independent reviewers used Covidence software to screen and extract data according to inclusion criteria. Study quality was assessed using the JBI’s critical appraisal tools for quality assessment. Results: The search yielded a total of 23 relevant studies. Of these, four studies reported on interventions targeting PA and SB, and the remaining studies targeted PA only. No studies targeted sleep. Most interventions were multicomponent (e.g., education and counselling, performing the targeted behaviour). All interventions were culturally tailored and were effective in improving at least one of the targeted movement behaviours. Conclusion: There is evidence to suggest that interventions targeting PA and SB among immigrants are effective, but there is no published evidence about interventions to promote sleep in this group. Further research is needed to inform interventions to promote the balance of movement behaviours over the 24-hour day among immigrants.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".