Effectiveness of physical activity counselling and exercise prescription education among medical students: a systematic review
Bibliographic record
Abstract
Background: Providing physical activity counselling and prescribing exercise increases patients’ activity and cardiorespiratory fitness, but healthcare providers experience challenges in promoting activity to patients. Implementing educational intervention during medical training may be an effective strategy to promote physical activity and exercise counselling/prescriptions. The purpose of this review was to evaluate the impact of educational interventions on medical students’ physical activity counselling and exercise prescription perceptions and practices. Methods: Systematic review procedures were registered in PROSPERO (ID# CRD42022331755) prior to conducting the study. Studies were included if they conducted an educational intervention to medical students or residents aimed at improving activity practices. Sources were searched in May of 2022 and included Scopus, EMBASE, MEDLINE, CINAHL, and Academic Search Premier (n = 3412 citations without duplicates). The National Institutes of Health quality assessment tools were used. Results: Fifteen interventions were included. The average quality of the included controlled trials (n = 6) and pre-post studies with no control group (n = 9) were 5.0±1.5/12 and 6.2±1.3/14, respectively. Most studies (n = 4/6) that reported the total number of medical students providing activity counselling to patients before and after receiving intervention observed improvements in exercise counselling behaviours. Eleven of twelve, and ten of eleven studies reported increases in confidence and perceptions toward various features of exercise counselling and physical activity promotion, respectively. Conclusion: Despite the heterogeneous nature of intervention designs, this review supports that relatively brief interventions using a structured lecture format incorporated into curriculum promote acute improvements in medical students’ perceptions and confidence in providing exercise counselling, albeit based on low-moderate study quality.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".