Development of an eHealth Mindfulness-Based Music Therapy Intervention for Adults Undergoing Allogeneic Hematopoietic Stem Cell Transplantation: Qualitative Study
Bibliographic record
Abstract
Background: Allogeneic hematopoietic stem cell transplantation (allo-SCT) is an effective treatment for various hematologic cancers, though it often results in severe side effects and psychological distress, which can negatively impact health outcomes. Integrative therapies like mindfulness-based stress reduction (MBSR), mindfulness meditation (MM), and music therapy (MT) yield promising results in enhancing both psychosocial outcomes (eg, reducing anxiety and depression) and physiological adaptation (eg, decreasing inflammation) in cancer patients. Objective: We developed and refined, using focus groups and environmental and field testing, an eHealth-delivered mindfulness-based music therapy (eMBMT) intervention aimed at improving health-related quality of life, symptom burden (ie, pain, fatigue, and sleep), disease activity (ie, chronic graft-versus-host disease, cytomegalovirus activation, and infections) and psychosocial (ie, depression, anxiety, and cancer-specific distress) and physiological adaptation (ie, inflammation and immune reconstitution) tailored to adults receiving allo-SCT. Methods: eMBMT intervention content is grounded in MT, MM, and MBSR, developed by a multidisciplinary team, and adapted for adults undergoing allo-SCT. eMBMT content was refined through focus groups and usability and field testing. Focus groups used a semistructured interview guide, while field testing used the "think aloud" method. Usability was evaluated using the 30-item Usefulness, Satisfaction, and Ease of Use (USE) questionnaire. Descriptive statistics analyzed the USE questionnaire and participant characteristics, while rapid qualitative analysis was applied to focus groups and field-testing sessions. Survivors eligible to participate in the focus groups and usability and field testing were adults (>18 years old) who received an allo-SCT (<36 months) for myelodysplastic syndrome, acute myeloid leukemia, or chronic myeloid leukemia, and were in remission for greater than 3 months. Results: During the focus groups, participants (n=11; mean age 43.6, SD 17.8 years) provided qualitative feedback highlighting the shock of diagnosis, challenges during hospitalization, and coping strategies posttreatment. The eMBMT platform received positive evaluations for usefulness (mean 6.47, SD 0.29), ease of use (mean 6.92, SD 0.60), and satisfaction (mean 6.16, SD 0.82). Key themes from field testing highlighted the significance of social support, hope, and maintaining an active lifestyle. Suggestions for improvement included incorporating more representative content, reducing text, enhancing guidance, offering diverse music options, and streamlining blood sample collection. Conclusions: The eMBMT intervention is a comprehensive, user-friendly eHealth tool tailored to the unique needs of allo-SCT patients. The positive feedback and identified areas for improvement underscore its potential to enhance well-being, symptom management, and overall quality of life for cancer survivors. A future pilot randomized controlled trial will further evaluate the feasibility, acceptability, and preliminary efficacy of the eMBMT intervention in improving health-related quality of life, symptom burden, disease activity, and psychosocial and physiological adaptation.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".