Relationship between Physical Activity and Fatigue in Adults with Cystic Fibrosis
Bibliographic record
Abstract
Purpose: We examined the relationship between the amount of physical activity and level of fatigue in adults with cystic fibrosis (CF). Method: Participants were recruited from the Toronto Adult Cystic Fibrosis Centre at St. Michael's Hospital. Participants completed the Habitual Activity Estimation Scale, the Multidimensional Fatigue Inventory, and the Depression subscale of the Hospital Anxiety and Depression Scale, in that order. Descriptive statistics and linear and multiple regressions were computed. Results: Over a 6-month period, 51 individuals were approached, and 22 (10 men, 12 women) participated in this study. The participants' median age was 33, and forced expiratory volume in 1 second (FEV1) was 64% predicted. When holding both FEV1 and depression constant, a significant negative correlation was found between total active hours per weekday and general fatigue (β=–0.735, p=0.03); there was a negative trend between total active hours per weekday and physical fatigue (β=–0.579, p=0.09). Conclusions: This study is the first to demonstrate that among adults with CF, a higher level of physical activity is associated with a lower level of general and physical fatigue when controlling for lung function and level of depression. Physical activity may be used as a means of mitigating the levels of general and physical fatigue in people with CF.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".