Cardiorespiratory Fitness in Internal Medicine Residents: Are Future Physicians Becoming Deconditioned?
Bibliographic record
Abstract
ABSTRACT Background Previous studies have shown a falloff in physicians' physical activity from medical school to residency. Poor fitness may result in stress, increase resident burnout, and contribute to mortality from cardiovascular disease and other causes. Physicians with poor exercise habits are also less likely to counsel patients about exercise. Prior studies have reported resident physical activity but not cardiorespiratory fitness age. Objective The study was conducted in 2 residency programs (3 hospitals) to assess internal medicine residents' exercise habits as well as their cardiorespiratory fitness age. Methods Data regarding physical fitness levels and exercise habits were collected in an anonymous cross-sectional survey. Cardiopulmonary fitness age was determined using fitness calculator based on the Nord-Trøndelag Health Study (HUNT). Results Of 199 eligible physicians, 125 (63%) responded to the survey. Of respondents, 11 (9%) reported never having exercised prior to residency and 45 (36%) reported not exercising during residency (P < .001). In addition, 42 (34%) reported exercising every day prior to residency, while only 5 (4%) reported exercising daily during residency (P < .001), with 99 (79%) participants indicating residency obligations as their main barrier to exercise. We found residents' calculated mean fitness age to be 5.6 years higher than their mean chronological age (P < .001). Conclusions Internal medicine residents reported significant decreases in physical activity and fitness. Residents attributed time constraints due to training as a key barrier to physical activity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".