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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".