Activity Pacing Patterns In Adults With Disabilities
Bibliographic record
Abstract
PURPOSE: Activity pacing is a self-management strategy targeting inefficient activity patterns to help people with disabilities to engage in an active lifestyle. This study explored how two pacing patterns, even and fluctuating, are associated with personal characteristics in people with disabilities: personal factors, perceived fatigue, self-reported attitudes towards activity pacing, and physical activity. METHODS: 58 Participants wore an Actiheart activity monitor for one week and filled in the Fatigue Severity Scale assessing perceived fatigue, the Adapted-SQUASH assessing self-reported physical activity, and a questionnaire assessing perceived attitudes towards activity pacing. Based on a previously reported method, the median split over the week of the standard deviation of accelerometer-derived activity over the day was used (measure of activity pacing). Participants were defined as even or fluctuating pacers. The differences in personal characteristics for the two groups were examined using Independent samples t-test and Mann Whitney U test. RESULTS: Even pacers (N = 27) were older (60.4 ± 16.3 yr, t(df) = 2.3(56), p = .029) and had a higher Body Mass Index (28.9 ± 4.6 kg/m2, t(df) = 3.8(56), p = <.001) than fluctuating pacers (N = 31) (49.7 ± 19.5 yr), (24.9 ± 3.5 kg/m2). Even pacers also reported lower total minutes of self-reported physical activity per week (Median = 360 mins, IQR = 165-765 mins, Mann Whitney U = 276.5, p = .027) than fluctuating pacers (Median = 652.5 mins, IQR = 270-1435 mins). Even pacers had lower total minutes of accelerometer-derived physical activities per week (Median = 106 mins, IQR = 52-237 mins, U = 60, p = <.001) compared to fluctuating pacers (Median = 695.8 mins, IQR = 420-940 mins). Perceived activity pacing and fatigue were not different between the two groups (p= > .05). CONCLUSIONS: The comparison of the groups revealed differences in personal factors and physical activity, although they experienced similar fatigue levels. Those with an even pacing pattern showed lower physical activity levels, which may imply that pacing is used to regulate fatigue through lowering physical activity peaks as a reactive response to symptom occurrence. Future research could explore how pacing, physical activity, and fatigue management advice can contribute to increased physical activity.
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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 | 0.001 | 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) | 0.001 | 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".