Tools to guide clinical discussions on physical activity, sedentary behaviour, and/or sleep for health promotion between health care providers and individuals who access care: A scoping review (Preprint)
Bibliographic record
Abstract
BACKGROUND Health care providers have reported low knowledge, skill, and confidence for discussing movement behaviours (i.e., physical activity, sedentary behaviour, and sleep), which may be improved with the use of tools to guide movement behaviour discussions in their practice. Past reviews have examined the psychometric properties, scoring, and behavioural outcomes of physical activity discussion tools. However, the features, perceptions, and effectiveness of discussion tools for physical activity, sedentary behaviour, and/or sleep have not yet been synthesized. OBJECTIVE The aim of this review was to report and appraise tools for movement behaviour discussions between health care providers and adults 18+ years in a primary care context within Canada or analogous countries. METHODS An integrated knowledge translation approach guided this review, whereby a working group of key stakeholders was engaged from research question formation to interpretation of findings. Three search approaches were used (i.e., peer-reviewed, grey literature, and forward searches) to identify studies reporting on perceptions and/or effectiveness of tools for physical activity, sedentary behaviour, and/or sleep. The quality of included studies was assessed using the Mixed Methods Appraisal Tool. RESULTS In total, 135 studies reporting on 61 tools (i.e., 51 on physical activity, one on sleep, and nine combining two movement behaviours) met inclusion criteria. Included tools served the purposes of assessment (n=57), counselling (n=50), prescription (n=18), and/or referral (n=12) of one or more movement behaviour. Forty-nine tools were used or intended for use by physicians; 34 tools were used or intended for use among adults without chronic conditions aged 18-64 years. The quality of the 116 studies that evaluated tool effectiveness varied. CONCLUSIONS Many tools were positively perceived and were deemed effective at enhancing knowledge of, confidence for, ability in, and frequency of movement behaviour discussions. Future tools should guide discussions of all movement behaviours in an integrated manner in line with the 24-Hour Movement Guidelines. Practically, this review offers seven evidence-based recommendations that may guide future tool development and implementation.
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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 | 0.055 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".