Self-administered Meditation Application Intervention for Cancer Patients With Psychosocial Distress: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: We explored the use of a novel smart phone-based application (APP) for delivery and monitoring of meditation to treat mood symptoms experienced by cancer patients. METHODS: We assessed the feasibility of using a meditation delivery and tracking APP over 2-weeks and its impact on cancer patients' self-reported anxiety and depression. Outpatients reporting depression and/or anxiety were recruited and randomized to the APP or waitlist control group. Assessments included an expectancy scale, exit survey, mood rating before and after each meditation, and the Edmonton Symptom Assessment Scale (ESAS-FS), Hospital Anxiety and Depression Scale (HADS), and Pittsburgh Sleep Quality Index (PSQI) at baseline and after 2-weeks. The primary aim was to assess feasibility; secondary aims included satisfaction with the APP, association between meditation frequency and length with self-reported symptoms, and change in symptom measures (symptoms, anxiety, depression, and sleep). RESULTS: Our study included 35 participants (17 meditation group; 18 controls) who were primarily female (94%) with breast cancer (60%). The 61% enrollment rate and 71% adherence rate met pre-specified feasibility criteria. Most meditation group participants described the APP as "Useful" to "Very Useful" and would "Probably" or "Definitely" recommend its use. Mixed model analysis revealed a statistically significant association between meditation length (5, 10, or 15 minutes) and change in anxiety, with 15-minute sessions associated with greater reductions in anxiety. In the exit survey, more meditation group vs. control group participants reported improved focus, mood, and sleep. Study groups differed significantly by ESAS fatigue score change; the meditation group decreased a median of 1.5 pts (IQR 2.5) and the control group increased a median of 0.5 points (IQR 2). The meditation group, but not the control group, experienced statistically significant improvement in ESAS fatigue, depression, anxiety, appetite, and physical, psychological, and global distress. Change in PSQI and HADS anxiety and depression scores did not reveal any statistically significant between-group differences. CONCLUSIONS: This pilot study demonstrated the feasibility and acceptability of a meditation APP for cancer patients. Meditation APP users reported improvement in several measures of symptom distress. Future studies should explore ways to enhance the APP's usability and clinical benefit.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".