Exploring the Specific Needs of Persons with Multiple Sclerosis for mHealth Solutions for Physical Activity: Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is one of the world's most common neurologic disorders, with symptoms such as fatigue, cognitive problems, and issues with mobility. Evidence suggests that physical activity (PA) helps people with MS reduce fatigue and improve quality of life. The use of mobile technologies for health has grown in recent years with little involvement from relevant stakeholders. User-centered design (UCD) is a design philosophy with the goal of creating solutions specific to the needs and tasks of the intended users. UCD involves stakeholders early and often in the design process. In a preliminary study, we assessed the landscape of commercially available MS mobile health (mHealth) apps; to our knowledge, no study has explored what persons with MS and their formal care providers think of mHealth solutions for PA. OBJECTIVE: The aim of this study was to (1) explore MS-specific needs for MS mHealth solutions for PA, (2) detect perceived obstacles and facilitators for mHealth solutions from persons with MS and health care professionals, and (3) understand the motivational aspects behind adoption of mHealth solutions for MS. METHODS: A mixed-methods design study was conducted in Kliniken Valens, Switzerland, a clinic specializing in neurological rehabilitation. We explored persons with MS and health care professionals who work with them separately. The study had a qualitative part comprising focus groups and interviews, and a quantitative part with standardized tools such as satisfaction with life scale and electronic health (eHealth) literacy. RESULTS: A total of 12 persons with relapsing-remitting MS and 12 health care professionals from different backgrounds participated in the study. Participants were well-educated with an even distribution between genders. Themes identified during analysis were MS-related barriers and facilitators, mHealth design considerations, and general motivational aspects. The insights generated were used to create MS personas for design purposes. Desired mHealth features were as follows: (1) activity tracking, (2) incentives for completing tasks and objectives, (3) customizable goal setting, (4) optional sociability, and (5) game-like attitude among others. Potential barriers to mHealth apps adoption were as follows: (1) rough on-boarding experiences, (2) lack of clear use benefits, and (3) disruption of the health care provider-patient relationship. Potential facilitators were identified: (1) endorsements from experts, (2) playfulness, and (3) tailored to specific persons with MS needs. A total of 4 MS personas were developed to provide designers and computer scientists means to help in the creation of future mHealth solutions for MS. CONCLUSIONS: mHealth solutions for increasing PA in persons with MS hold promise. Allowing for realistic goal setting and positive feedback, while minimizing usability burdens, seems to be critical for the adoption of such apps. Fatigue management is especially important in this population; more attention should be brought to this area.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".