Physical Activity Levels of Physiotherapists across Practice Settings: A Cross-Sectional Comparison Using Self-Report Questionnaire and Accelerometer Measures
Bibliographic record
Abstract
Purpose: This article describes the physical activity of physiotherapists in British Columbia and examines differences across practice settings using self-report questionnaire and accelerometer-derived measures. Methods: Public and private practice physiotherapists aged 18–65 years were recruited through employee email lists and word of mouth to this cross-sectional study. Participants (n=98) completed the International Physical Activity Questionnaire–Long Form (IPAQ–L) online to quantify self-reported physical activity across various domains (occupational, leisure time, domestic, and transportation). Of these, 38 agreed to wear an accelerometer for 7 days to objectively measure physical activity. Descriptive statistics were used to describe self-reported and accelerometer-measured physical activity across domains, and inferential statistics were used to compare physical activity patterns across practice sites. The correlation and agreement between self-report questionnaire and accelerometer measures were also calculated. Results: Almost all (99%) of the physiotherapists self-reported meeting physical activity guidelines, and only 58% were classified as meeting guidelines when using accelerometers. Public practice physiotherapists self-reported more total, occupational, and domestic physical activity and had higher measured occupational physical activity than private practice physiotherapists. Overall, there was poor agreement between self-report questionnaires and accelerometers. Conclusions: Physiotherapists are an active group, with those in public practice reporting and participating in more physical activity than those in private practice.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".