Associations between fatigue impact and physical and neurobehavioural factors: An exploration in people with progressive multiple sclerosis
Bibliographic record
Abstract
Background Fatigue is common in people with multiple sclerosis (MS). Understanding the relationship between fatigue, physical and neurobehavioural factors is important to inform future research and practice. Few studies explore this explicitly in people with progressive MS (pwPMS). Objective To explore relationships between self-reported fatigue, physical and neurobehavioural measures in a large, international progressive MS sample of cognitively impaired people recruited to the CogEx trial. Methods Baseline assessments of fatigue (Modified Fatigue Impact Scale; MFIS), aerobic capacity (VO 2peak ), time in moderate-vigorous physical activity (MVPA; accelerometery over seven-days), walking performance (6-minute walk test; 6MWT), self-reported walking difficulty (MS Walking Scale; MSWS-12), anxiety and depression (Hospital Anxiety and Depression Scale; HADS and Beck Depression Inventory-II; BDI-II), and disease impact (MS Impact Scale-29, MSIS-29) were assessed. Participants were categorised as fatigued (MFIS Total >=38) or non-fatigued (MFIS Total ≤38). Statistical Analysis Differences in individuals categorised as fatigued or non-fatigued were assessed ( t -tests, chi square). Pearson's correlation and partial correlations (adjusted for EDSS score, country, sex, and depressive symptoms) determined associations with MFIS Total, MFIS Physical , MFIS Cognitive and MFIS Psychosocial, and the other measures. Multivariable logistic regression evaluated the independent association of fatigue (categorised MFIS Total ) with physical and neurobehavioural measures. Results The sample comprised 308 pwPMS (62 % female, 27 % primary progressive, 73 % secondary progressive), mean age 52.5 ± 7.2 yrs, median EDSS score 6.0 (4.5–6.5), mean MFIS Total 44.1 ± 17.1, with 67.2 % categorised as fatigued. Fatigued participants walked shorter distances (6MWT, p = 0.043), had worse MSWS-12 scores ( p < 0.001), and lower average % in MVPA ( p = 0.026). The magnitude of associations was mostly weak between MFIS Total and physical measures ( r = 0.13 to 0.18), apart from the MSWS-12 where it was strong ( r = 0.51). The magnitude of correlations were strong between the MFIS Total and neurobehavioural measures of anxiety ( r = 0.56), depression ( r = 0.59), and measures of disease impact (MSIS-physical r = 0.67; MSIS-mental r = 0.71). This pattern was broadly similar for the MSIF subscales. The multivariable model indicated a five-point increase in MSWS-12 was associated with a 14 % increase in the odds of being fatigued (OR [95 %CI]: 1.14 [1.07–1.22], p < 0.0001) Conclusion Management of fatigue should consider both physical and neurobehavioural factors, in cognitively impaired persons with progressive MS.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".