Engagement With a Relaxation and Mindfulness Mobile App Among People With Cancer: Exploratory Analysis of Use Data and Self-Reports From a Randomized Controlled Trial
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Abstract
Background: Mobile health (mHealth) apps offer unique opportunities to support self-care and behavior change, but poor user engagement limits their effectiveness. This is particularly true for fully automated mHealth apps without any human support. Human support in mHealth apps is associated with better engagement but at the cost of reduced scalability. Objective: This work aimed to (1) describe the theory-informed development of a fully automated relaxation and mindfulness app to reduce distress in people with cancer (CanRelax app 2.0), (2) describe engagement with the app on multiple levels within a fully automated randomized controlled trial over 10 weeks, and (3) examine whether engagement was related to user characteristics. Methods: The CanRelax app 2.0 was developed in iterative processes involving input from people with cancer and relevant experts. The app includes evidence-based relaxation exercises, personalized weekly coaching sessions with a rule-based conversational agent, 39 self-enactable behavior change techniques, a self-monitoring dashboard with gamification elements, highly tailored reminder notifications, an educational video clip, and personalized in-app letters. For the larger study, German-speaking adults diagnosed with cancer within the last 5 years were recruited via the web in Switzerland, Austria, and Germany. Engagement was analyzed in a sample of 100 study participants with multiple measures on a micro level (completed coaching sessions, relaxation exercises practiced with the app, and feedback on the app) and a macro level (relaxation exercises practiced without the app and self-efficacy toward self-set weekly relaxation goals). Results: In week 10, a total of 62% (62/100) of the participants were actively using the CanRelax app 2.0. No associations were identified between engagement and level of distress at baseline, sex assigned at birth, educational attainment, or age. At the micro level, 71.88% (3520/4897) of all relaxation exercises and 714 coaching sessions were completed in the app, and all participants who provided feedback (52/100, 52%) expressed positive app experiences. At the macro level, 28.12% (1377/4897) of relaxation exercises were completed without the app, and participants’ self-efficacy remained stable at a high level. At the same time, participants raised their weekly relaxation goals, which indicates a potential relative increase in self-efficacy. Conclusions: The CanRelax app 2.0 achieved promising engagement even though it provided no human support. Fully automated social components might have compensated for the lack of human involvement and should be investigated further. More than one-quarter (1377/4897, 28.12%) of all relaxation exercises were practiced without the app, highlighting the importance of assessing engagement on multiple levels.
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Relational agents have been shown to foster therapeutic rapport with users, support long-term engagement, and have been deployed across various clinical and nonclinical populations, including cognitively healthy older adults.
“Relational agents have been shown to foster therapeutic rapport with users [32-34], support long-term engagement [35], and have been deployed across various clinical [35-37] and nonclinical populations [38-40], including (cognitively healthy) older adults [41-43].”
From Toward a Smartphone-Based and Conversational Agent–Delivered Just-in-Time Adaptive Holistic Lifestyle Intervention for Older Adults Affected by Cognitive Decline: Two-Week Proof-of-Concept Study (2025) · cites this paper as [35]
The Methods (Abstract; page not provided) describe a conversational agent used with people with cancer, supporting its deployment in a clinical population. The Discussion (Comparison With Prior Work; page not provided) reports 62% app engagement in week 10 but says only that the agent’s coaching sessions *possibly* helped sustain engagement. It does not establish that the agent itself supported long-term engagement. The rapport, nonclinical-population, and older-adult clauses cite other sources. (LLM, conf=0.86)
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Nißen, M., Harperink, S., Joshi, P., Bischof, A. Y., Schachner, T., Lukic, Y. X., Schneider, F., Santhanam, P., von Wangenheim, F., Ollier, J., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/67174 · Cited by 7 · see citing works
Self-citationBrill, E., Vinay, R., Nißen, M., Joshi, P., Klöppel, S., & Kowatsch, T. (2025) · JMIR Formative Research · doi:10.2196/66885 · Cited by 5 · see citing works
Self-citationBischof, A. Y., Budig, T., Schläpfer, S., Lukic, Y. X., Schneider, F., Santhanam, P., Barth, J., Witt, C. M., & Kowatsch, T. (2025) · JMIR Serious Games · doi:10.2196/70297 · Cited by 5 · see citing works
Self-citationGlaser, S. L. C., Fraterman, I., van Brummelen, N., Tibollo, V., Del Campo, L. M., Mallo, H., Wilgenhof, S., Wilk, S., Gisko, V., Khadakou, V., et al. (2025) · JMIR Formative Research · doi:10.2196/57659 · Cited by 5 · see citing works
Barth, J., Schläpfer, S., Schneider, F., Santhanam, P., Kowatsch, T., Heinz, P., Held, U., Eicher, M., & Witt, C. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01688-x · Cited by 4 · see citing works
Self-citationSchläpfer, S., Astakhov, G., Pawel, S., Eicher, M., Kowatsch, T., Held, L., Witt, C. M., & Barth, J. (2024) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2024.111864 · Cited by 4 · see citing works
Self-citationvon der Warth, R., Nicolay, N. H., Binder, H., Boerries, M., Zöller, D., Grosu, A., & Farin‐Glattacker, E. (2025) · JMIR Cancer · doi:10.2196/68411 · Cited by 2 · see citing works
Schläpfer, S., Barth, J., Heinz, P., Held, U., Kowatsch, T., & Witt, C. M. (2026) · Internet Interventions · doi:10.1016/j.invent.2026.100929 · Cited by 2 · see citing works
Self-citationShen, Z., Shi, S., Mao, L., Li, C., & Xie, J. (2026) · Journal of Medical Internet Research · doi:10.2196/105599
Gasparini, G., Panatto, D., Kermarek, I. D., Valchi, L., Massaro, E., Lai, P. L., Burlando, M., Icardi, G., & Cozzani, E. (2025) · JMIR Formative Research · doi:10.2196/80113
Luo, J., Qin, N., Li, Y., 秦东仁, Zhu, J., Zhang, H., Zhou, Y., Cheng, Q., Duan, Y., Xie, J., et al. (2026) · doi:10.2196/preprints.100723
Kreuzberg, M. S., Mellaert, S. V., Kramer, L. L., Tabak, M., & Kelders, S. M. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001604
- Authors
- Schläpfer, S., Schneider, F., Santhanam, P., Eicher, M., Kowatsch, T., Witt, C. M., & Barth, J.
- Affiliations
- University of Zurich (CH)
- ETH Zurich (CH)
- Centre Hospitalier Universitaire Vaudois (CH)
- Corresponding author
- Schläpfer, Sonja
- ORCID
- Venue
- JMIR Cancer
- Publisher
- JMIR Publications Inc.
- Published
- 2024-05-31
- Volume
- 10
- Pages
- e52386
- ISSN
- 2369-1999 (ISSN-L)
- PMC
- PMC11179041
- OpenAlex
- W4399214941
- Cited by
- 14
- References
- 93
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 3
- FWCI
- 5.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Cancer (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- SERVAL (Université de Lausanne) (submittedVersion, cc-by)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- IRIS (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Universität Zürich
- Universitätsspital Zürich
- Swiss Cancer Research Foundation (KFS 4556-08-2018)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Oncology › Cancer survivorship and care
- Keywords
- mobile health apps, app engagement, relaxation techniques, mindfulness, cancer patients, distress, chatbots, self-efficacy, behaviour change techniques, gamification, self-monitoring, personalized coaching, user feedback, user-centered design, digital mental health interventions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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