Does counseling help patients get active? Systematic review of the literature.
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of counseling patients to become more physically active. DATA SOURCES: PubMed was searched for articles during the past 30 years on physicians promoting physical activity. Identified studies were cross-referenced, and experts were consulted for additional articles. STUDY SELECTION: Thirteen articles described primary care counseling on exercise. Six studies were randomized controlled trials (RCTs); seven were quasi-experimental designs. Three of the four RCTs and three of the five quasi-experimental studies were short term (4 weeks to 2 months); the remaining three trials lasted longer than 6 months. Most studies used strategies to address stage of change. SYNTHESIS: Outcome measures included adoption of physical activity, stage of change, and change in physical activity level. Most studies found positive relationships between counseling and these outcomes. No reliable evaluation instruments were found, nor was the long-term effect of interventions established. CONCLUSION: Interventions that included written materials for patients, considered behaviour change strategies, and provided training and materials for physicians were effective at increasing levels of physical activity. New strategies that involve measuring and prescribing specific amounts of exercise might also improve fitness levels and hence improve outcomes of chronic disease. Shortcomings of these studies include lack of long-term data, lack of sustaining activities for family physicians, and scant cost-efficacy analysis.
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 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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.006 | 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".