Prioritizing Components of a Dyadic Physical Activity Intervention for People With Moderate to Severe Multiple Sclerosis and Their Care Partners: A Modified e-Delphi Study
Bibliographic record
Abstract
BACKGROUND: People with moderate to severe multiple sclerosis (MS) and their family care partners do not engage in sufficient physical activity (PA) for health benefits. Dyadic PA interventions need to be developed to benefit each individual and the dyad. The objective of this study was to engage expert stakeholders in prioritizing and refining key intervention content, delivery methods, and the practical/logistical aspects of a dyadic PA intervention for persons with MS and their care partners. METHODS: Thirty-two stakeholders (14 clinicians, 11 people with MS, 5 MS care partners, and 2 representatives of organizations that provide support services for people with MS and/or MS care partners) completed 2 rounds of a modified e-Delphi survey. In round 1, participants rated items across 3 domains: key intervention content (n = 8), delivery methods (n = 9), and practical/logistical aspects (n = 4). Participants contributed additional ideas about these domains, which were incorporated into round 2. Items that did not reach consensus in round 1 were forwarded to round 2 for rerating. Data were analyzed using descriptive statistics and content analysis. RESULTS: A 24-item list of recommendations was generated, including ensuring that presentation of the intervention content encouraged lifestyle activities in addition to exercise, using videoconferencing rather than teleconferencing as a delivery platform, and stressing the importance of flexibility during the support calls. CONCLUSIONS: Feedback will be used to improve the quality of the intervention. The next step in this line of research involves evaluating the refined intervention in a pilot feasibility trial.
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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 | 0.058 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".