A Smartphone-Based Model of Care to Support Patients With Cardiac Disease Transitioning From Hospital to the Community (TeleClinical Care): Pilot Randomized Controlled Trial

Abstract

Background Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are frequently readmitted. This is the first randomized controlled trial of a mobile health intervention that combines telemonitoring and education for inpatients with ACS or HF to prevent readmission. Objective This study aims to investigate the feasibility, efficacy, and cost-effectiveness of a smartphone app–based model of care (TeleClinical Care [TCC]) in patients discharged after ACS or HF admission. Methods In this pilot, 2-center randomized controlled trial, TCC was applied at discharge along with usual care to intervention arm participants. Control arm participants received usual care alone. Inclusion criteria were current admission with ACS or HF, ownership of a compatible smartphone, age ≥18 years, and provision of informed consent. The primary end point was the incidence of unplanned 30-day readmissions. Secondary end points included all-cause readmissions, cardiac readmissions, cardiac rehabilitation completion, medication adherence, cost-effectiveness, and user satisfaction. Intervention arm participants received the app and Bluetooth-enabled devices for measuring weight, blood pressure, and physical activity daily plus usual care. The devices automatically transmitted recordings to the patients’ smartphones and a central server. Thresholds for blood pressure, heart rate, and weight were determined by the treating cardiologists. Readings outside these thresholds were flagged to a monitoring team, who discussed salient abnormalities with the patients’ usual care providers (cardiologists, general practitioners, or HF outreach nurses), who were responsible for further management. The app also provided educational push notifications. Participants were followed up after 6 months. Results Overall, 164 inpatients were randomized (TCC: 81/164, 49.4%; control: 83/164, 50.6%; mean age 61.5, SD 12.3 years; 130/164, 79.3% men; 128/164, 78% admitted with ACS). There were 11 unplanned 30-day readmissions in both groups (P=.97). Over a mean follow-up of 193 days, the intervention was associated with a significant reduction in unplanned hospital readmissions (21 in TCC vs 41 in the control arm; P=.02), including cardiac readmissions (11 in TCC vs 25 in the control arm; P=.03), and higher rates of cardiac rehabilitation completion (20/51, 39% vs 9/49, 18%; P=.03) and medication adherence (57/76, 75% vs 37/74, 50%; P=.002). The average usability rating for the app was 4.5/5. The intervention cost Aus $6028 (US $4342.26) per cardiac readmission saved. When modeled in a mainstream clinical setting, enrollment of 237 patients was projected to have the same expenditure compared with usual care, and enrollment of 500 patients was projected to save approximately Aus $100,000 (approximately US $70,000) annually. Conclusions TCC was feasible and safe for inpatients with either ACS or HF. The incidence of 30-day readmissions was similar; however, long-term benefits were demonstrated, including fewer readmissions over 6 months, improved medication adherence, and improved cardiac rehabilitation completion. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12618001547235; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375945

Behavior Change Effectiveness Using Nutrition Apps in People With Chronic Diseases: Scoping Review

“…Furthermore, there were different diets or treatments among the studies, with variations in targeted behavioral domains and outcomes measured in the mHealth app nutrition interventions. App features for these studies included food tracking [ 8 , 39 - 41 ], physical activity tracking [ 8 , 41 , 42 ], nutrition and exercise knowledge and recommendations [ 8 , 40 - 43 ], scheduled reminders [ 39 , 40 , 42 , 43 ], clinician portals [ 41 - 43 ], connectivity to digital health devices [ 42 ], demonstration of the desired behavior [ 40 , 43 ], and challenges [ 41 ]. The aim of all studies (6/6, 100%) was to evaluate the effectiveness of mHealth in the CVD population.…”

Adherence and persistence to pharmacotherapy in patients with heart failure: a nationwide cohort study, 2014–2020

“… 36 , 37 Utilization of new technology and decision support have demonstrated potential to improve guideline compliance in clinical practice. Examples are clinical decision support systems integrated with electronic health records, 38 , 39 mobile health interventions, 40 telemedicine/telenursing, 41 and sensor implantations. 42 The barriers to implement and maintain therapy, for clinicians, patients, and healthcare systems alike, are subject to further research and identification.…”

“…Wider implementation of structured multidisciplinary care, that is, GDMT clinics, is anticipated to benefit patient adherence with structured follow-up. 36,37 38,39 mobile health interventions, 40 telemedicine/telenursing, 41 and sensor implantations. 42 The barriers to implement and maintain therapy, for clinicians, patients, and healthcare systems alike, are subject to further research and identification.…”

Digital health technology in the prevention of heart failure and coronary artery disease

“…A 2-center RCT published by Indraratna and colleagues 4 randomized 164 patients admitted with HF or an acute coronary syndrome to a smartphone app–based model of care applied at discharge (TeleClinical Care [TCC]) ( Figures 1 and 2 ), with control arm patients receiving usual care alone. Patients were enrolled during the index admission and required a compatible smartphone to be included.…”

“…However, smartphones, mobile phones, and wearable technology offer tremendous potential for monitoring health through phone calls, text messages, data recording, and activity monitoring. 4 This technology is at the heart of digital health, which is defined as the use of digital, mobile, and wireless technologies to support the achievement of health objectives. 5 mHealth is a branch of digital health that is specific to the use of mobile devices such as smartphones and tablets.…”