Perceptions of Older Men Using a Mobile Health App to Monitor Lower Urinary Tract Symptoms and Tamsulosin Side Effects: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) apps may provide an efficient way for patients with lower urinary tract symptoms (LUTS) to log and communicate symptoms and medication side effects with their clinicians. OBJECTIVE: The aim of this study was to explore the perceptions of older men with LUTS after using an mHealth app to track their symptoms and tamsulosin side effects. METHODS: Structured phone interviews were conducted after a 2-week study piloting the daily use of a mobile app to track the severity of patient-selected LUTS and tamsulosin side effects. Quantitative and qualitative data were considered. RESULTS: All 19 (100%) pilot study participants completed the poststudy interviews. Most of the men (n=13, 68%) reported that the daily questionnaires were the right length, with 32% (n=6) reporting that the questionnaires were too short. Men with more severe symptoms were less likely to report changes in perception of health or changes in self-management; 47% (n=9) of the men reported improved awareness of symptoms and 5% (n=1) adjusted fluid intake based on the questionnaire. All of the men were willing to share app data with their clinicians. Thematic analysis of qualitative data yielded eight themes: (1) orientation (setting up app, format, symptom selection, and side-effect selection), (2) triggers (routine or habit and symptom timing), (3) daily questionnaire (reporting symptoms, reporting side effects, and tailoring), (4) technology literacy, (5) perceptions (awareness, causation or relevance, data quality, convenience, usefulness, and other apps), (6) self-management, (7) clinician engagement (communication and efficiency), and (8) improvement (reference materials, flexibility, language, management recommendations, and optimize clinician engagement). CONCLUSIONS: We assessed the perceptions of men using an mHealth app to monitor and improve management of LUTS and medication side effects. LUTS management may be further optimized by tailoring the mobile app experience to meet patients' individual needs, such as tracking a greater number of symptoms and integrating the app with clinicians' visits. mHealth apps are likely a scalable modality to monitor symptoms and improve care of older men with LUTS. Further study is required to determine the best ways to tailor the mobile app and to communicate data to clinicians or incorporate data into the electronical medical record meaningfully.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".