Mobile Health App for Prostate Cancer Patients on Androgen Deprivation Therapy: Qualitative Usability Study
Bibliographic record
Abstract
BACKGROUND: Androgen deprivation therapy (ADT) increases the risk of metabolic adverse effects among patients with prostate cancer. The transformative impact of mobile health (mHealth) apps may benefit men managing activity and nutrition at home. OBJECTIVE: This study aimed to evaluate the usability and patient experience of a newly developed mHealth app among prostate cancer patients on ADT and physicians' beliefs about the potential benefits of using this app. METHODS: This study took place over 2 months, beginning in March 2019. A sample of 5 patients (age 45-75 years) initiating ADT participated in a semistructured focus group discussion with a facilitator. The study participants also included 5 specialist physicians who provided in-depth interviews. An institutional review board-approved script was used to guide both the focus group and physician interviews. Usability was tested through specific scenarios presented to the patients, including downloading the mHealth app, entering information on physical activity and meals, and navigating the app. The focus group and interviews were audio recorded and transcribed. Content analysis was used to analyze the transcripts iteratively and exhaustively. Thematic discrepancies between reviewers were resolved through consensus. RESULTS: The mean age of the patients was 62 years. This group included 4 White and 1 Latin American patients. The physician specialists included 2 urologists, 2 medical oncologists, and 1 radiation oncologist. Analyses revealed that the patients appreciated the holistic care enabled by the app. Difficulties were observed with registration of the app among 60% (3/5) of the patients; however, all the patients were able to input information about their physical activity and navigate the options within the app. Most patients (4/5, 80%) were able to input data on their recent meal. Among the health care physicians, the dominant themes reflected in the interviews included undermining of patients ability to use technology, patients' fear of technology, and concern for the ability of older patients to access technology. CONCLUSIONS: The patients reported an overall positive experience of using an mHealth app to record and track diet and exercise. Usability was observed to be an important factor for adoption and was determined by ease of registration and use, intuitive appearance of the app, and focus on holistic cancer care. The physicians believed that the app was easy to use but raised concerns about usability among older men who may not typically use smartphone apps.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".