Exploring participant perceptions of a virtually supported home exercise program for people with multiple myeloma using a novel eHealth application: A qualitative study
Bibliographic record
Abstract
Introduction: Supervision, tailoring, and flexibility have been proposed as key program elements for delivering successful exercise programs for people with Multiple Myeloma (MM). However, no studies to date have evaluated the acceptability of an intervention employing these components. The aim of this study was to determine the acceptability of a virtually-supported exercise program and eHealth application for people with MM. Methods: A qualitative description method was used. One-on-one interviews were conducted with participants who completed the exercise program. Content analysis was used to analyze verbatim transcripts from interviews. Results: 20 participants were interviewed (64.9±6.7 years of age, n=12 females). Participants had positive perceptions of the exercise program. Three themes emerged related to strengths/limitations: One Size Does Not Fit All, App Usability, and Sustainability. Supportive and Responsive Programming was a main strength of the program, characterized as programming that was tailored, involved active support, and delivered by appropriate personnel. The inclusion of Diverse Exercise Opportunities was also regarded as a strength, as it accommodated the preferences of all participants. Participants felt the app was simple and user friendly but had a few less intuitive components. Finally, participants wanted the study to transition into a sustainable, ongoing program. Conclusion: The virtually-supported exercise program and eHealth application were acceptable for people with MM. Programs should employ tailoring, active support, and appropriate personnel to bolster acceptability and include both supervised and flexible exercise formats. eHealth apps should be simple to use so technology proficiency is not a barrier to participation.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".