A Mobile Medication Support App and Its Impact on People Living With HIV: 12-Week User Experience and Medication Compliance Pilot Study
Bibliographic record
Abstract
BACKGROUND: The continuity of care between hospital visits conducted through mobile apps creates new opportunities for people living with HIV in situations where face-to-face interventions are difficult. OBJECTIVE: This study investigated the user experience of a mobile medication support app and its impact on improving antiretroviral therapy compliance and facilitating teleconsultations between people living with HIV and medical staff. METHODS: Two clinics in Japan were invited to participate in a 12-week trial of the medication support app between July 27, 2018, and March 31, 2021. Medication compliance was assessed based on responses to scheduled medication reminders; users, including people living with HIV and medical staff, were asked to complete an in-app satisfaction survey to rate their level of satisfaction with the app and its specific features on a 5-point Likert scale. RESULTS: A total of 10 people living with HIV and 11 medical staff were included in this study. During the trial, the medication compliance rate was 90%, and the mean response rates to symptom and medication alerts were 73% and 76%, respectively. Overall, people living with HIV and medical staff were satisfied with the medication support app (agreement rate: mean 81% and 65%, respectively). Over 80% of medical staff and people living with HIV were satisfied with the ability to record medications taken (9/11 and 8/10 medical staff and people living with HIV, respectively), record symptoms of concern (10/11 and 8/10),and inquire about drug combinations (8/10, 10/10). And further, 90% of people living with HIV were satisfied with the function for communication with medical staff (9/10). CONCLUSIONS: Our preliminary results demonstrate the feasibility of the medication support app in improving medication compliance and enhancing communication between people living with HIV and medical staff.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".