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

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Introduction Mobile health (mHealth) interventions have grown in popularity, particularly for chronic disease management. Uptake of these interventions depends on patient smartphone ownership. Purpose To examine the smartphone ownership rate among cardiac inpatients and identify the associated demographic factors. Methods Between February 2019 and March 2020, 565 patients were screened for potential enrolment in the TeleClinical Care (TCC) pilot study at two hospitals in Australia. All patients had an admission diagnosis of acute coronary syndrome or heart failure. Mobile phone ownership was documented at the time of screening. Retrospectively, each patient's electronic medical record was examined for: age, sex, primary diagnosis, suburb of residence, private health insurance subscription, smoking status and occupation. Continuous variables were analysed using a multinomial logistic regression model. Categorical variables were analysed using a generalised linear model. Results Mobile phone ownership was documented for 523 patients (92.6%). 60.6% of all patients owned smartphones, and 14.9% owned basic mobile phones. 24.5% of patients did not own any mobile phone. The average age of participants was 70.8 years. Smartphone ownership rates were high among patients in the 18–49 (96%), 50–59 (89%) and 60–69 (85%) year groups. The differences between these groups were not statistically significant. In the age group 70–79 years, however, smartphone ownership fell to 56.5% (p<0.001, figure 1). The relative risk (RR) of not owning a smartphone increased by 12% for each additional year of age. Overall, smartphone ownership was less more common in women than men [79/179 (44.1%) vs. 238/344 (69.2%), RR 0.78, 95% CI 0.67–0.91, P=0.003, age-adjusted) driven by a difference in patients aged 70 or above [36/131 (27.5%) vs. 82/168 (48.9%), RR 0.66, 95% 0.49–0.90, p<0.001]. After adjustment for age and sex, patients with a primary diagnosis of ACS were more likely to own a smartphone compared to those with HF [227/316 (71.8%) vs. 90/207 (43.5%), RR 1.22, 95% CI 1.04–1.43, P=0.015]. Patients with private health insurance were more likely to own a smartphone than those who were uninsured [68.9% (162/235) v 54.0% (154/285), RR 1.28, 95% CI 1.13–1.43, P<0.001, figure 2). Smartphone ownership was significantly higher in those who were currently working, compared to those who were retired (117/119, 98.3% vs. 56/87, 64.3%, RR 0.76, 95% CI 0.64 – 0.89, P=0.001), even after adjustment for age. Patients living in the region with lowest average household income had the lowest rate of smartphone ownership (52.4%). There was no significant difference in smartphone ownership based on type of occupation. Conclusion Smartphone ownership was common in this inpatient population. Patients who are older, female and of lower socioeconomic background are less likely to own smartphones, and future mHealth programs should be cognizant of this. FUNDunding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Prince of Wales Hospital, Department of Cardiology Figure 1. Smartphone ownership by age Figure 2. Insurance status

events (MACCE) and major bleeding. Multivariate models adjusted for age, comorbidities, left ventricular function, cardiac arrest and cardiogenic shock. Long-term mortality was obtained via linkage with the National Death Index. Results: 7,493 patients with STEMI received PCI, of which 494 (6.6%) were in-hospital STEMI. In-hospital STEMI were older (67.1 versus 62.4 years, p,0.001), more likely to be female (32% versus 19.9%, p,0.001) with more comorbidities. In-hospital STEMI had higher 30-day MACE (20.4% versus 9.8%, p,0.001), mortality (12.1% versus 6.9%, p,0.001), and major bleeding (4.9% versus 2.3%, p,0.001) than out-of-hospital STEMI. Adjusted STD time was 80.2 minutes for in-hospital versus 160.8 minutes for out-of-hospital STEMI (p,0.001). Occurrence of STEMI whilst an inpatient independently predicted higher 30-day MACE (OR 1.91, 95% CI 1.38-2.64, p,0.001) and 12-month mortality (OR 1.88, CI 1.29-2.73, p,0.001). Conclusions: Patients with in-hospital STEMI experience significantly higher MACE and mortality compared to outof-hospital STEMI, despite achieving shorter STD times. We need focused strategies to improve recognition and outcomes in this high-risk population.

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