Feasibility of a tailored and virtually supported home exercise program for people with multiple myeloma using a novel eHealth application
Bibliographic record
Abstract
Introduction: eHealth exercise interventions have the unique ability to leverage the benefits of in-person programming (tailoring and supervision) with the benefits of home programming (flexibility). There may be a role for eHealth-delivered exercise for people with multiple myeloma (MM), as exercise tailoring and supervision are critical for successful outcomes due to the significant impacts/risks of myeloma-related side effects. The purpose of this study was to determine the safety, feasibility, and preliminary efficacy of a 12-week virtually supported eHealth exercise program. Methods: Participants with MM completed a 12-week virtually supported home exercise program involving virtually supervised group workouts, independent workouts, and aerobic exercise. Tailoring was facilitated by the functionality of HEAL-Me, a novel eHealth app. Participants completed virtual fitness assessments and questionnaires at baseline and week 12. Results: Twenty-nine participants consented, 26 completed all follow-up testing (90%). Exercise adherence was 90% (group), 83% (independent), and 90% (aerobic). No serious adverse events (grade ≥3) occurred. Significant improvements were found for quality of life and physical fitness. There was a high level of program/app satisfaction: 96% of participants agreed or strongly agreed that the exercise program was beneficial, 93% found it enjoyable, 89% were satisfied or very satisfied with delivery through the HEAL-Me app, and 48% felt that the eHealth program helped them manage cancer-related symptoms and side-effects. Conclusion: An eHealth intervention that is individually tailored and includes virtual supervision and active support from the healthcare team is feasible and acceptable to people with MM. The findings from this study warrant investigation using a large-scale randomized controlled trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".