Meditation Mobile App Developed for Patients With and Survivors of Cancer: Feasibility Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: To address the unmet need for a commercial cancer-specific meditation app, we leveraged a long-standing partnership with a consumer-based app (ie, Calm) to develop the first commercial meditation app prototype adapted specifically for the needs of patients with cancer. Input was obtained at both the individual user and clinic levels (ie, patients with and survivors of cancer and health care providers). OBJECTIVE: This study aimed to determine the feasibility of a cancer-specific meditation app prototype. METHODS: Patients with and survivors of cancer who were recruited and enrolled in the feasibility randomized controlled trial were asked to use the prototype app daily (≥70 minutes per week) for 4 weeks. Participants completed web-based weekly questionnaires and a final poststudy questionnaire and were asked to participate in an optional web-based poststudy interview. The questionnaires and interviews covered the following feasibility categories: acceptability, demand, practicality, and adaptation. RESULTS: A total of 36 patients with and survivors of cancer completed the baseline questionnaire, 18 completed the final questionnaire, and 6 completed the optional interviews. Weekly and poststudy questionnaires indicated high overall enjoyment, ease of use, and satisfaction with the app content, aesthetics, and graphics. The objective use data indicated that the average total app use rate was 73.39 (SD 7.12) minutes per week. Interviews (N=6) revealed positive and mixed responses to the app prototype and informative differences related to preferences for narrators, emotional content, and meditation teaching but an overall appreciation for the variety of options. CONCLUSIONS: The most likely candidates for moving from cancer-specific meditation apps to dissemination are through partnering with the industry, in which name recognition and market distribution are already established (even showing a base of users from the targeted population with cancer). This study established the feasibility of a cancer-specific mobile meditation app prototype for patients with and survivors of cancer, using a commercially available app. The quantitative and qualitative data demonstrated the acceptability, demand, practicality, and adaptation of the prototype. Improvements suggested by the participants will be considered in the final app design before testing the efficacy of the app in a future study. TRIAL REGISTRATION: Clinicaltrials.gov NCT05459168; https://clinicaltrials.gov/ct2/show/record/NCT05459168.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".