Physicians’ and Medical Students’ Perceptions of Physical Activity Monitors in Patient Care
Bibliographic record
Abstract
ABSTRACT Introduction Wearable activity monitors may serve as valuable tools for promoting healthy lifestyle behaviors in health care. We assessed the perceptions of physicians and medical students regarding the barriers and facilitators influencing the integration of activity monitors into clinical care. Methods A cross-sectional survey was completed by 55 physicians (41.3 ± 7.9 yr old; 89% male) and 31 medical students (23.6 ± 2.3 yr old; 52% male). The survey included Likert-scale items assessing perceived barriers and facilitators and the willingness of participants to use activity monitors with patients. Logistic regression was used to examine variables associated with interest in implementing wearables, adjusting for age, sex, patient volume, and attitudes toward technology. Results Compared to physicians, medical students were more likely to view monitors as improving practice efficiency and personalizing care and expressed greater enthusiasm to learn about them (all P < 0.03). Physicians more frequently cited barriers such as increased workload, electronic medical record integration challenges, and cost (all P < 0.03). Medical students were more concerned about impersonal care ( P = 0.04). Among physicians, older age predicted lower interest in monitor implementation (odds ratio = 0.93, 95% confidence interval = 0.86–0.99, P = 0.04). Stronger beliefs that wearables improve efficiency, usefulness, communication, and patient empowerment, along with availability of technical support were associated with greater interest in their adoption (all odds ratios >2.20, P < 0.001). Conclusion Medical students and younger physicians showed strong interest in incorporating wearable activity monitors into patient care. Supporting integration through targeted training and system-level supports may facilitate broader clinical adoption to help more patients lead physically active lifestyles.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".