Managing Fatigue with Technology for Individuals with Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate whether the use of a mobile health application (mHealth app) in conjunction with energy conservation management techniques will result in a decrease in fatigue for adults with multiple sclerosis. METHOD: Using a quantitative, exploratory, pre-posttest design, we examined outcomes associated with the use of the mHealth app, Pace My Day, by seven participants during one chosen task over two weeks. The app reinforced the use of energy conservation management techniques during the chosen task. Outcome measures included Modified Fatigue Impact Scale (MFIS) and Canadian Occupational Performance Measure (COPM). RESULTS: There was a significant decrease in the MFIS scores indicating a decrease in fatigue over the two-week period t (6) =5.75, p=0.001. Additionally, there was a significant increase in satisfaction with performance of the chosen task as measured by the COPM over the two-week period t (6) =-3.359, p=0.015. CONCLUSION: The use of a mHealth app to support energy conservation management education was found to significantly reduce levels of fatigue and increase self-perceived performance and satisfaction with task execution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".