Mobile Technology Use by People Experiencing Multiple Sclerosis Fatigue: Survey Methodology
Bibliographic record
Abstract
BACKGROUND: Fatigue is one of the most commonly reported symptoms of multiple sclerosis (MS). It has a profound impact on all spheres of life, for people with MS and their relatives. It is one of the key precipitants of early retirement. Individual, group, and Internet cognitive behavioral therapy-based approaches to supporting people with MS to manage their fatigue have been shown to be effective. OBJECTIVE: The aim of this project was to (1) survey the types of mobile devices and level of Internet access people with MS use or would consider using for a health intervention and (2) characterize the levels of fatigue severity and their impact experienced by the people in our sample to provide an estimate of fatigue severity of people with MS in New Zealand. The ultimate goal of this work was to support the future development of a mobile intervention for the management of fatigue for people with MS. METHODS: Survey methodology using an online questionnaire was used to assess people with MS. A total of 51 people with MS participated. The average age was 48.5 years, and the large majority of the sample (77%) was female. RESULTS: Participants reported significant levels of fatigue as measured with the summary score of the Neurological Fatigue Index (mean 31.4 [SD 5.3]). Most (84%) respondents scored on average more than 3 on the fatigue severity questions, reflecting significant fatigue. Mobile phone usage was high with 86% of respondents reporting having a mobile phone; apps were used by 75% of respondents. Most participants (92%) accessed the Internet from home. CONCLUSIONS: New Zealand respondents with MS experienced high levels of both fatigue severity and fatigue impact. The majority of participants have a mobile device and access to the Internet. These findings, along with limited access to face-to-face cognitive behavioral therapy-based interventions, create an opportunity to develop a mobile technology platform for delivering a cognitive behavioral therapy-based intervention to decrease the severity and impact of fatigue in people with MS.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".