Prescribing Physical Activity Through Primary Care: Does Activity Intensity Matter?
Bibliographic record
Abstract
BACKGROUND: Physical activity guidelines recommend engaging in moderate- and vigorous-intensity physical activity to elicit health benefits. Similarly, these higher intensity ranges for activity are typically targeted in healthy living interventions (ie, exercise prescription). Comparatively less attention has been focused on changing lower intensity physical activity (ie, sedentary activity) behaviors. The purpose of this study was to explore the effects of prescribing changes to physical activity of various intensities (ie, sedentary through exercise) through the primary care setting. METHODS: Sixty older adults (aged 55-75 years; mean age 63 = 5 years) volunteered to participate, and were randomly assigned to 4 groups: 3 receiving an activity prescription intervention targeting a specific intensity of physical activity (exercise, sedentary, or both), and 1 control group. During the 12-week intervention period participants followed personalized activity programs at home. Basic clinical measures (anthropometrics, blood pressure, aerobic fitness) and blood panel for assessing cardiometabolic risk (glucose, lipid profile) were conducted at baseline (week 0) and follow-up (week 12) in a primary care office. RESULTS: There were no differences between groups at baseline (P > 0.05). The intervention changed clinical (F₅,₅₀ = 20.458, P = 0.000, ηP² = 0.672) and blood panel measures (F₅,₅₀ = 4.576, P = 0.002, ηP² = 0.314) of cardiometabolic health. Post hoc analyses indicted no differences between groups (P > 0.05). CONCLUSION: Physical activity prescription of various intensities through the primary care setting improved cardiometabolic health status. To our knowledge, this is the first report of sedentary behavior prescription (alone, or combined with exercise) in primary care. The findings support the ongoing practice of fitness assessment and physical activity prescription for chronic disease management and prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".