Identification of Factors That Motivate People With Multiple Sclerosis to Participate in Digital Data Collection in Research: Sequential Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Digital data collection has the potential to reduce participant burden in research projects that require extensive registrations from participants. To achieve this, a digital data collection tool needs to address potential barriers and motivations for participation. OBJECTIVE: This study aimed to identify factors that may affect motivation for participation and adoption of a digital data collection tool in a research project on nutrition and multiple sclerosis (MS). METHODS: The study was designed as a sequential mixed methods study with 3 phases. In phase 1, 15 semistructured interviews were conducted in a Danish population of individuals with MS. Interview guide frameworks were based on dimensions from the electronic health literacy framework and the Health Education Impact Questionnaire. Data from phase 1 were analyzed in a content analysis, and findings were used to inform the survey design in phase 2 that validates the results from the content analysis in a larger population. The survey consisted of 14 items, and it was sent to 1000 individuals with MS (response rate 42.5%). In phase 3, participants in 3 focus group interviews discussed how findings from phases 1 and 2 might affect motivation for participation and adoption of the digital tool. RESULTS: The following 3 categories related to barriers and incentives for participation were identified in the content analysis of the 15 individual interviews: (1) life with MS, (2) use of technology, and (3) participation and incentives. Phase 1 findings were tested in phase 2's survey in a larger population (n=1000). The majority of participants were comfortable using smartphone technologies and participated actively on social media platforms. MS symptoms did cause limitations in the use of Web pages and apps when the given pages had screen clutter, too many colors, or too small buttons. Life with MS meant that most participants had to ration their energy levels. Support from family and friends was important to participants, but support could also come in the form of physical aids (walking aids and similar) and digital aids (reminders, calendar functions, and medication management). Factors that could discourage participation were particularly related to the time it would take every day. The biggest motivations for participation were to contribute to research in MS, to learn more about one's own MS and what affects it, and to be able to exchange experiences with other people with MS. CONCLUSIONS: MS causes limitations that put demands on tools developed for digital data collection. A digital data collection tool can increase chances of high adoption rates, but it needs to be supplemented with a clear and simple project design and continuous communication with participants. Motivational factors should be considered in both study design and the development of a digital data collection tool for research.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".