A Smartphone App to Improve Oral Anticoagulation Adherence in Patients With Atrial Fibrillation: Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Poor adherence to oral anticoagulation in elderly patients with atrial fibrillation (AF) has been shown to negatively impact health care costs, morbidity, and mortality. Although various methods such as automated reminders, counseling, telephone support, and patient education have been effective in improving medication adherence, the burden on health care providers has been considerable. Recently, an attempt has been made to improve medication adherence without burdening health care providers by using smartphone apps; however, the use of the app for elderly patients with AF is still limited. OBJECTIVE: The purpose of this study was to determine whether the newly developed smartphone app for patients with AF (the Smart AF), which integrates education, automatic reminder, and patient engagement strategies with a simple user interface, can improve medication adherence in elderly patients with AF. METHODS: Patient enrollment was carried out by obtaining informed consent from patients with AF attending Kyoto Prefectural University of Medicine hospital between May 2019 and September 2020. Follow-up was planned at 1, 3, and 6 months after enrollment, and questionnaire reminders were automatically sent to patient apps at designated follow-up time points. A questionnaire-based survey of medication adherence was performed electronically using the self-reported 8-item Morisky Medication Adherence Scale (MMAS-8) as the survey tool. RESULTS: A total of 136 patients with AF were enrolled in this study. During the follow-up period, 112 (82%) patients underwent follow-up at 1 month, 107 (79%) at 3 months, and 96 (71%) at 6 months. The mean age of the enrolled patients was 64.3 years (SD 9.6), and male participants accounted for 79.4% (108/136) of the study population. The mean CHADS2 (congestive heart failure, hypertension, age, diabetes, previous stroke, or transient ischemic attack) score was 1.2, with hypertension being the most common comorbidity. At the time of enrollment, 126 (93%) and 10 (7%) patients were taking direct oral anticoagulants and warfarin, respectively. For medication adherence as measured according to the MMAS-8, MMAS scores at 1 month, 3 months, and 6 months were significantly improved compared with baseline MMAS scores (all P values less than .01). The overall improvement in medication adherence achieved by the 6-month intervention was as follows: 77.8% (14/18) of the patients in the high adherence group (score=8) at baseline remained in the same state, 45.3% (24/53) of the patients in the medium adherence group (score=6 to <8) at baseline moved to the high adherence group, and 72% (18/25) of the patients in the low adherence group (score <6) moved to either the medium or high adherence group. CONCLUSIONS: The Smart AF app improved medication adherence among elderly patients with AF. In the realm of medication management, an approach using a mobile health technology that emphasizes education, automatic reminder, and patient engagement may be helpful.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".