Adoption, Acceptability, and Effectiveness of a Mobile Health App for Personalized Prostate Cancer Survivorship Care: Protocol for a Realist Case Study of the Ned App
Bibliographic record
Abstract
BACKGROUND: By 2030, prostate cancer will be the most commonly diagnosed cancer in North America. To mitigate this impending challenge, comprehensive support mechanisms for disease- and treatment-specific changes in health and well-being must be proactively designed and thoughtfully implemented for streamlined survivorship care. mHealth apps have been lauded as a promising complement to current outpatient treatment and monitoring strategies, but have not yet been widely used to support prostate cancer survivorship needs. A realist evaluation is needed to examine the impact of such apps on the prostate cancer survivorship experience. OBJECTIVE: We seek to gain an understanding of how an mHealth app for prostate cancer survivorship care called Ned (No Evident Disease) is adopted and accepted by patients, caregivers, and clinicians. We also aim to determine the effect of Ned on health-related quality of life, satisfaction with cancer care, unmet needs, self-efficacy, and prostate cancer-related levels of anxiety. METHODS: The Ned case study is a 12-month mixed-methods embedded single-case study with a nested within-group pre-post comparison of health outcomes. We will give 400 patients, 200 caregivers, and 10 clinicians access to Ned. Participants will be asked to complete study assessments at baseline, 2 months, 6 months, and 12 months. We will conduct 30 semistructured qualitative interviews with patients (n=20) and their caregivers (n=10) poststudy to gain insight into their experience with the app. RESULTS: We recruited our first survivor in October 2017 and anticipate completing this study by May 2019. CONCLUSIONS: This will, to our knowledge, be the first realist case study to evaluate an app for prostate cancer survivorship care. Prostate cancer survivors are set to increase in number and longevity, heightening the need for integrated survivorship solutions to provide them with optimal and durable outcomes. The knowledge gained from this study will comprehensively inform how and why Ned works, for whom, and in what circumstances. Understanding the impact of digital health interventions such as Ned on how survivors care for themselves is critical to realizing patient-centered care.
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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 | 0.005 | 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 | 0.001 | 0.001 |
| 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".