Cancer Patients’ and Survivors’ Perceptions of the Calm App: Cross-Sectional Descriptive Study
Bibliographic record
Abstract
BACKGROUND: There is a need for tools to decrease cancer patients' and survivors' long-term symptom burden. Complementary strategies, such as meditation, can accompany pharmacologic therapy to improve symptoms. Although support programs with targeted content have wider reach, higher adherence, and greater impact, there are no consumer-based meditation apps designed specifically for cancer. OBJECTIVE: This study aimed to gather information to advise the development of a cancer-specific meditation app in a small convenience sample of cancer patients and survivors who currently use the Calm app. METHODS: Adult cancer patients and survivors who are Calm users (N=82) were recruited through the Daily Calm Facebook page. Participants completed a Web-based survey related to Calm app use and satisfaction, interest in and ideas for a cancer-specific Calm app, and demographic characteristics. Open-ended responses were inductively coded. RESULTS: Participants were aged between 18 and 72 years (mean 48.60 years, SD 15.20), mostly female (77/82, 94%), white (65/79, 82%), and non-Hispanic (70/75, 93%), and reported using Calm at least 5 times per week (49/82, 60%). Although rates of satisfaction with current Calm components were high (65/82, 79%; and 51/81, 63%), only 49% (40/82) of participants used guided meditations that they felt specifically helped with their cancer-related symptoms and survivorship, and 40% (33/82) would prefer more cancer-related content, with guided meditations for cancer-specific anxieties (eg, fear of recurrence; n=15) and coping with strong emotions (n=12) being the most common suggestions. A majority of participants (51/82, 62%) reported that they would be interested in becoming a member of a Calm cancer community (eg, in-app discussion boards: 41/46, 89%; and social media communities: 35/42, 83%). Almost half of the participants (37/82, 45%) reported that they would benefit from features that tracked symptoms in concurrence with app usage, but respondents were divided on whether this information should be shared with health care providers through the app (49/82, 60% would share). CONCLUSIONS: Responses suggest ways in which the current Calm app could be adapted to better fit cancer patients' and survivors' needs and preferences, including adding more cancer-specific content, increasing the amount of content focusing on coping with strong emotions, developing communities for Calm users who are cancer patients and survivors, and including features that track cancer-related symptoms. Given differences in opinions about which features were desirable or would be useful, there is a clear need for future cancer-specific apps to be customizable (eg, ability to turn different features on or off). Although future research should address these topics in larger, more diverse samples, these data will serve as a starting point for the development of cancer-specific meditation app and provide a framework for evaluating their effects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".