Development of a behavioural intervention for cognitive fatigability in multiple sclerosis: Protocol for a pilot and feasibility study
Bibliographic record
Abstract
Background Up to 90% of people with multiple sclerosis (PwMS) subjectively report fatigue as one of their worst symptoms. Fatigability is an objectively measured component of fatigue. Cognitive fatigability (CF) is a breakdown in task performance following sustained cognitive effort. There is a paucity of interventions targeting CF in MS. The prior success of behavioural interventions at improving subjective fatigue suggests that their adaptation may yield similar results for CF. Given the relationship between CF, sleep quality, and mood, a behavioural intervention targeting these factors, such as cognitive behavioural therapy (CBT), is warranted. Given the multidimensional nature of fatigue, a multifaceted approach targeting lifestyle factors and coping (e.g., fatigue management education supplemented by CBT for insomnia and exercise) might prove efficacious. Aim We describe a protocol for a pilot feasibility study to design and implement a multi-dimensional behavioural intervention to improve CF in PwMS. Methods Stage 1: development of a multi-dimensional group-based videoconference-delivered behavioural intervention based on a previously successful fatigue management program for PwMS. A facilitator manual will be drafted. Course material will focus on four themes: body (sleep and physical activity), mood (impact of depression and anxiety), mind (cognitive contributions), and context (pacing and communication). Stage 2: a needs assessment survey will be completed by 100 PwMS for input on what factors are important contributors to their CF. Modifications will be made to the course material and manual. Stage 3: the facilitator-delivered intervention will include 20 PwMS. After baseline assessment, participants will attend weekly 70-min videoconference group sessions for 8 weeks, including homework assignments. Follow-up assessment will re-evaluate outcomes. Stage 4: analysis and dissemination of results. The primary outcome is improvement in CF. Additional feasibility outcomes will determine if a randomized control trial (RCT) is pursued. Stage 5: refine the intervention based on outcomes and feedback from participants. Determining which aspects participants felt were most effective will help inform RCT design. Conclusion The long-term goal is to ensure that PwMS have access to effective interventions in real-world settings to improve quality of life and enhance their ability to participate in cognitively demanding activities that they enjoy.
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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 | 0.034 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.012 |
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".