Impact of Comorbidity on Fatigue Management Intervention Outcomes Among People with Multiple Sclerosis
Bibliographic record
Abstract
This exploratory secondary analysis examined whether the presence of six chronic health conditions moderated the effectiveness of a teleconference-delivered fatigue self-management education program for people with multiple sclerosis (MS). The longitudinal data used were from a randomized controlled trial involving 181 community-dwelling adults with MS. The primary outcome was fatigue impact, as measured by the Fatigue Impact Scale (FIS). Mixed-effects analysis of variance (ANOVA) models were used to determine the best-fitting model. Just under 65% (n = 112) of participants had at least one comorbid condition. Only diabetes and arthritis moderated all three FIS subscales over time. People with diabetes were slower to show improvement after intervention than people without diabetes. People with arthritis made much more dramatic initial gains compared with people without arthritis but had difficulty maintaining those gains over time. The results point to the need for greater attention to the impact of comorbidities on rehabilitation interventions. These exploratory findings suggest that fatigue self-management education protocols may need to be customized to people who are trying to incorporate MS fatigue self-management behaviors while simultaneously managing diabetes or arthritis.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".