A Smartphone App to Improve Oral Anticoagulation Adherence in Patients With Atrial Fibrillation: Prospective Observational Study

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.

The Recent Advances of Mobile Healthcare in Cardiology Practice

“…The analysis showed that studies on the use of mobile applications were conducted in the USA (n=7) ( 30 - 36 ), Sweden (n=3) ( 37 - 39 ), Republic of Korea (n=1) ( 40 ), Brazil (n=1) ( 41 ), Norway (n=2) ( 42 , 43 ), Switzerland (n=1) ( 44 ), Belgium (n=1) ( 45 ), China (n=9) ( 22 , 46 - 53 ), Romania (n=1) ( 54 ), Japan (n=1) ( 55 ), Finland (n=1) ( 24 ), France (n=1) ( 56 ), New Zealand (n=1) ( 57 ), Iran (n=2) ( 58 , 59 ), Georgia (n=1) ( 60 ), Spain (n=2) ( 61 , 62 ), Palestine (n=1) ( 63 ) and Germany (n=3) ( 64 - 66 ). Two studies were conducted simultaneously in India (n=2) ( 67 , 68 ).…”

“…By category, the research was focused on the following areas: mobile applications for diagnostic purposes (for example, ECG recording ( 30 ), assessment of heart rate ( 31 , 33 , 44 , 65 ). Some studies were carried out to validate the system for urgent care alarm ( 37 ), lifestyle changes ( 43 , 45 , 53 , 62 , 65 ), adherence to treatment ( 35 , 38 , 41 , 47 , 48 , 51 , 53 , 55 , 59 , 60 , 63 , 64 , 67 , 68 ), and physical activity of the patients ( 22 , 32 , 34 , 41 , 45 , 62 ).…”

“…In terms of application, mobile applications were designed for patients with diagnoses such as heart failure ( 24 , 46 , 58 ), supraventricular tachycardia ( 30 , 33 ), cardiac arrest ( 37 ), acute coronary syndrome ( 39 , 68 ), hypertension ( 31 , 35 , 41 , 59 - 61 , 63 ), ischemic heart disease ( 36 , 48 , 69 ), coronary artery disease ( 45 , 65 ) or after coronary artery bypass grafting ( 51 ), myocardial infarction ( 38 , 49 , 50 ), atrial fibrillation ( 52 , 55 ), obstructive coronary artery disease ( 70 ), ( 47 ) and general CVD ( 32 , 40 , 53 , 54 , 62 , 64 ).…”

Smartphone Apps for Managing Antithrombotic Therapy: Scoping Literature Review

“…A prospective observational study conducted by Senoo et al [ 30 ] showed promising results in terms of adherence improvement in older adult patients with AFib. The Medisafe medication management app is a medication management platform, which includes the name of the medication and the dose.…”

Long Short-Term Memory Network for Development and Simulation of Warfarin Dosing Model Based on Time Series Anticoagulant Data

“…To assist clinicians with individualizing warfarin therapy, we simulated individualized warfarin dosing based on LSTM_INR. The trend of using mobile applications or electronic systems for anticoagulation is likely more suitable for long-term individualized therapy and anticoagulant self-management ( 27 , 28 ). Thus, we developed an application (AI-WAR) in which we embedded these were embed in.…”