Association between Patient‐Reported Health Status and Physical Activity Six Months after Upper and Lower Limb Fractures in Working‐Aged Adults
Bibliographic record
Abstract
INTRODUCTION: Physical activity limitations are common following upper and lower limb fractures in adults of working age. There is a lack of research investigating the factors associated with these limitations, such as pain, mental health problems, and mobility impairments. OBJECTIVES: To report health status (EQ-5D) 6 months after upper and lower limb fracture in adults of working age (ages 18-69 years), and to determine the association between sedentary behavior (sitting time) and physical activity (steps, moderate-intensity physical activity [MPA]) 2 weeks and 6 months post-fracture with health status 6 months post-fracture. DESIGN: Prospective cohort study. SETTING: Major (level I) trauma center. PARTICIPANTS: Sixty-three adults 18-69 years of age with upper or lower limb fractures who were recruited consecutively. MAIN OUTCOME MEASURES: Participants wore ActiGraph and activPAL accelerometers for 10 days, 2 weeks and 6 months post-fracture. At 6 months, participants completed the EQ-5D. We used linear mixed-effects multivariable regression analyses to explore associations between EQ-5D domains and sitting time, steps, or physical activity. RESULTS: Participants with mobility problems (compared to participants without) were highly sedentary at 2 weeks (β = 0.86, P = .04), took fewer steps/d (Ratio of Geometric Means [RGM] = 0.62, P = .02) and engaged in less MPA (RGM = 0.32, P = .01). In addition, they engaged in less MPA at 6 months (RGM = 0.52, P = .02). Participants with self-care problems (compared with participants without) took fewer steps per day at 6 months (RGM = 0.78, P = .04), and engaged in less MPA at 2 weeks (RGM = 0.31, P = .01) and 6 months (RGM = 0.48, P = .02). CONCLUSIONS: Adults with mobility and self-care problems 6 months post-fracture engaged in high levels of sedentary behavior and low levels of physical activity. These findings can guide clinicians on health problems to target in order to maximize recovery of physical activity following fracture.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".