Development of a Mobile App (iCANSleep) to Treat Insomnia in Cancer Survivors: User-Centered Design Study
Bibliographic record
Abstract
Background: Insomnia affects the quality of life and health outcomes of cancer survivors. Cognitive behavioral therapy for insomnia (CBT-I) is an effective treatment for insomnia among cancer survivors, but it is not readily accessible due to the limited number of trained providers and the difficulties in providing care across wide geographical areas. Mobile health (mHealth) technologies represent a promising solution; however, these technologies are not tailored to the unique needs of cancer survivors. Objective: This study aimed to understand the needs and preferences of cancer survivors and test the usability of an evidence-based CBT-I smartphone app called iCANSleep that will be tailored and accessible to cancer survivors. Methods: A user-centered design (UCD) approach was applied, and cancer survivors were actively engaged in the app's design, usability testing, and prototype refinement. In phase 1, semistructured interviews were conducted with a purposive sample of cancer survivors (n=20) to inform the design of the app and its content. In phase 2, iterative low- (n=8) and high-fidelity (n=7) usability testing was conducted with participants until no further recommendations for change were suggested. Results: Users suggested several defining characteristics, features, and desired functionalities, including a user-friendly and evidence-based design. They saw increased accessibility and simplicity as advantages of a mobile app but expressed some concerns about data security and losing the accountability that comes with in-person treatment. User testing highlighted the preference for images of real people and diverse stories over graphics and animated videos, and offered suggestions for enhanced navigation. The first iteration of the app was developed using the information gained during the needs assessment and usability testing. Feedback was integrated into the final prototype of the iCANSleep app, which will be tested for feasibility, acceptability, and efficacy. Conclusions: Cancer survivors desire an insomnia treatment app that is simple, user-friendly, evidence-based, convenient, and secure. The iCANSleep app represents the merging of mHealth principles and best practices with evidence-based insomnia care, allowing for an intervention with minimal access barriers related to cost, geography, and provider availability. Feasibility, acceptability, and efficacy of the intervention will be maximized by following a UCD framework involving the engagement of end users at every design stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".