0868 Low-Fidelity Usability Testing of a Smartphone App to Treat Insomnia in Cancer Survivors
Bibliographic record
Abstract
Abstract Introduction Cancer patients and survivors experience insomnia at a higher rate than the general population. Mobile health (mHealth) applications have considerable promise for the treatment of insomnia, but have been criticized for not following evidence-based treatment guidelines. To make insomnia treatment more accessible for survivors, iCANSleep has been developed using user-centered design principles to administer Cognitive-Behavioral Therapy for Insomnia via a smartphone app. The current research presents insights from the low-fidelity usability testing phase of development Methods Usability testing is an iterative process of testing an intervention’s user-interface and then applying the results to redesign the prototype to meet users’ needs. Eight cancer survivors from across Canada viewed a video demonstration of the app and completed semi-structured interviews about the app’s design and content. All interviews took place through video conferencing software and were recorded. Researchers transcribed interview responses and analyzed them for common themes. Results Participants (71% Female; Mage: 53) were generally very impressed with the design and layout of the app. Most (75%) participants were very interested in trying the app and reported that they would complete the app’s treatment program. Participants appreciated the integration of cancer-specific stories and material, the app’s simple, user-friendly design, the use of incentives and reinforcements, and the depth and breadth of its content. Those interviewed commented on the comprehensive sleep diary within the app and its apparent simplicity. Suggestions were made to streamline the onboarding and replace animations with real people. Conclusion User-centered development of the app is promising. The app’s patient-centeredness, accessibility, simplicity, and thoroughness were highlighted as strengths. The development team will integrate these results into the continued app development while working to make onboarding material more engaging. It is important to continue to engage patients throughout to ensure user engagement and effective care. Support (if any) Samlau Kutana is a trainee in the Cancer Research Training Program of the Beatrice Hunter Cancer Research Institute, with funds provided by the Canadian Cancer Society’s JD Irving, Limited – Excellence in Cancer Research Fund. Dr. Sheila Garland is supported by a Canadian Cancer Society Emerging Scholar Award (Survivorship) (grant #707146).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 | 0.001 |
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".