A multi-site, randomized controlled trial of MS INFoRm, a fatigue self-management website for persons with multiple sclerosis: rationale and study protocol
Bibliographic record
Abstract
BACKGROUND: Fatigue is one of the most common and debilitating symptoms of multiple sclerosis (MS). The majority of approaches for managing MS fatigue typically require participation in a structured, time-limited program with a fixed sequence of topics and activities. MS INFoRm (Multiple Sclerosis: An Interactive Fatigue Management Resource) is a self-directed MS fatigue management resource incorporating principles of self-management and adult learning. Positive results from a feasibility pilot study of a USB-delivered version of MS INFoRm led to the current trial and adaptation of MS INFoRm to a website format. The specific aims of the proposed study are to (a) to determine the effectiveness and efficacy of 3-month use of MS INFoRm on fatigue impact (primary outcome) among persons with MS, (b) to determine whether 3-month use of MS INFoRm results in improvement in secondary outcomes of self- efficacy for managing MS fatigue, self-reported cognitive function, participation and autonomy, and depression, and (c) to determine whether any improvements in primary and secondary outcomes are maintained among the MS INFoRm users after 6-months. METHODS/DESIGN: Parallel group, two arm, double-blinded superiority trial with a 1:1 allocation. Two hundred persons with MS will be randomly assigned to either an intervention (MS INFoRm) or usual care control group in which they will be given 3-month access to either the MS INFoRm website (intervention group) or a control webpage containing widely available resources on MS fatigue (control group). Baseline, immediate post-intervention (3-months), and follow-up (6-months post intervention) evaluations will take place on primary (Modified Fatigue Impact Scale) and secondary (Multiple Sclerosis Self-Efficacy Scale, Perceived Deficits Questionnaire, Center for Epidemiologic Studies Depression Scale, and Impact on Participation and Autonomy Questionnaire) measures. Hypothesis testing will involve independent samples t-tests and mixed effects ANOVAs. DISCUSSION: People with MS may benefit from easily accessible and self-directed fatigue management resources based on self-management and adult learning principles. The proposed study will provide crucial evidence about the potential of MS INFoRm as a self-management tool that can be made widely available to persons with MS as a means to effectively reduce the daily impact of MS fatigue. TRIAL REGISTRATION: ClinicalTrials.gov : NCT03362541 . Posting date December 5, 2017.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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".