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Record W4383534565 · doi:10.1186/s12875-023-02091-9

Tools to guide clinical discussions on physical activity, sedentary behaviour, and/or sleep for health promotion between primary care providers and adults accessing care: a scoping review

2023· review· en· W4383534565 on OpenAlexafffundabout
Tamara L. Morgan, Emma Faught, Amanda Ross‐White, Michelle Fortier, Mary Duggan, Rahul Jain, Kirstin N. Lane, Amanda L. Lorbergs, Kaleigh Maclaren, Taylor McFadden, Jennifer R. Tomasone

Bibliographic record

VenueBMC Primary Care · 2023
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of VictoriaUniversity of TorontoUniversity of OttawaCanadian Medical AssociationCanadian Society for Exercise PhysiologyQueen's University
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsKnowledge translationContext (archaeology)ReferralPsychologyHealth carePromotion (chess)Medical prescriptionHealth promotionInclusion (mineral)Medical educationMedicineApplied psychologyFamily medicineNursingPublic healthSocial psychologyKnowledge managementComputer science

Abstract

fetched live from OpenAlex

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. 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 experts in medicine, knowledge translation, communications, kinesiology, and health promotion 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. Most tools were used or intended for use by physicians, followed by nurses/nurse practitioners (n = 11), and adults accessing care (n = 10). Most tools were also used or intended to be used with adults without chronic conditions aged 18-64 years (n = 34), followed by adults with chronic conditions (n = 18). 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.152
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.056
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.152
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0250.021
Science and technology studies0.0020.002
Scholarly communication0.0070.006
Open science0.0030.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.283
GPT teacher head0.523
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations14
Published2023
Admission routes3
Has abstractyes

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