Does sleep impact mobility in adults? A scoping review
Bibliographic record
Abstract
ABSTRACT Objective To review the literature on if sleep impacts mobility in adults. Review method used This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA – ScR) guidelines and the Joanna Briggs Institute’s (JBI) updated methodology for scoping reviews. Data sources Ovid Medline, Web of Science, Embase.com, and CINAHL databases were searched. Google Scholar and hand-searching were further reviewed for grey literature. Review methods Seven of the authors participated in the data screening and extraction process. Citations were randomly divided so that each was screened separately by two team members. Similarly, data were extracted from included articles independently by two reviewers with a third dedicated to resolving discrepancies. Results The search strategy generated 8,772 references, 697 articles underwent full-text screening, and 108 eligible articles were included in the scoping review. Most studies (n = 69; 64%) included only self-reported sleep, and “walking” was the most common mobility category (n = 99). Most studies (n = 60; 56%) reported a positive association between sleep and mobility, indicating better sleep was associated with better mobility or worse sleep was associated with worse mobility. Most studies including people with cardiovascular, kidney, metabolic, mental health, neurological, and pulmonary conditions reported a positive association between sleep and mobility. Conclusion Most studies reported a positive association between sleep and mobility. However, due to the variety of sleep and mobility outcomes used, it was challenging to compare studies and synthesize results. Further, due to a relatively small sample size and variety of health conditions, conclusions cannot be drawn, and further research is needed.
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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 | 0.028 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".