Correction: Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation.

[This corrects the article DOI: 10.2196/14124.].

Cost-effectiveness of a Digital Health Intervention for Acute Myocardial Infarction Recovery.

BACKGROUND: Acute myocardial infarction (AMI) is a common cause of hospital admissions, readmissions, and mortality worldwide. Digital health interventions (DHIs) that promote self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction may improve health outcomes in this population. The "Corrie" DHI consists of a smartphone application, smartwatch, and wireless blood pressure monitor to support medication tracking, education, vital signs monitoring, and care coordination. We aimed to assess the cost-effectiveness of this DHI plus standard of care in reducing 30-day readmissions among AMI patients in comparison to standard of care alone. METHODS: A Markov model was used to explore cost-effectiveness from the hospital perspective. The time horizon of the analysis was 1 year, with 30-day cycles, using inflation-adjusted cost data with no discount rate. Currencies were quantified in US dollars, and effectiveness was measured in quality-adjusted life-years (QALYs). The results were interpreted as an incremental cost-effectiveness ratio at a threshold of $100,000 per QALY. Univariate sensitivity and multivariate probabilistic sensitivity analyses tested model uncertainty. RESULTS: The DHI reduced costs and increased QALYs on average, dominating standard of care in 99.7% of simulations in the probabilistic analysis. Based on the assumption that the DHI costs $2750 per patient, use of the DHI leads to a cost-savings of $7274 per patient compared with standard of care alone. CONCLUSIONS: Our results demonstrate that this DHI is cost-saving through the reduction of risk for all-cause readmission following AMI. DHIs that promote improved adherence with guideline-based health care can reduce hospital readmissions and associated costs.

Digital Health Intervention in Acute Myocardial Infarction.

BACKGROUND: Thirty-day readmissions among patients with acute myocardial infarction (AMI) contribute to the US health care burden of preventable complications and costs. Digital health interventions (DHIs) may improve patient health care self-management and outcomes. We aimed to determine if patients with AMI using a DHI have lower 30-day unplanned all-cause readmissions than a historical control. METHODS: This nonrandomized controlled trial with a historical control, conducted at 4 US hospitals from 2015 to 2019, included 1064 patients with AMI (DHI n=200, control n=864). The DHI integrated a smartphone application, smartwatch, and blood pressure monitor to support guideline-directed care during hospitalization and through 30-days post-discharge via (1) medication reminders, (2) vital sign and activity tracking, (3) education, and (4) outpatient care coordination. The Patient Activation Measure assessed patient knowledge, skills, and confidence for health care self-management. All-cause 30-day readmissions were measured through administrative databases. Propensity score-adjusted Cox proportional hazard models estimated hazard ratios of readmission for the DHI group relative to the control group. RESULTS: Following propensity score adjustment, baseline characteristics were well-balanced between the DHI versus control patients (standardized differences <0.07), including a mean age of 59.3 versus 60.1 years, 30% versus 29% Women, 70% versus 70% White, 54% versus 54% with private insurance, 61% versus 60% patients with a non ST-elevation myocardial infarction, and 15% versus 15% with high comorbidity burden. DHI patients were predominantly in the highest levels of patient activation for health care self-management (mean score 71.7±16.6 at 30 days). The DHI group had fewer all-cause 30-day readmissions than the control group (6.5% versus 16.8%, respectively). Adjusting for hospital site and a propensity score inclusive of age, sex, race, AMI type, comorbidities, and 6 additional confounding factors, the DHI group had a 52% lower risk for all-cause 30-day readmissions (hazard ratio, 0.48 [95% CI, 0.26-0.88]). Similar results were obtained in a sensitivity analysis employing propensity matching. CONCLUSIONS: Our results suggest that in patients with AMI, the DHI may be associated with high patient activation for health care self-management and lower risk of all-cause unplanned 30-day readmissions. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03760796.

Sociodemographic Characteristics Predicting Digital Health Intervention Use After Acute Myocardial Infarction.

Increasing evidence suggests that digital health interventions (DHIs) are an effective tool to reduce hospital readmissions by improving adherence to guideline-directed therapy. We investigated whether sociodemographic characteristics influence use of a DHI targeting 30-day readmission reduction after acute myocardial infarction (AMI). Covariates included age, sex, race, native versus loaner iPhone, access to a Bluetooth-enabled blood pressure monitor, and disease severity as marked by treatment with CABG. Age, sex, and race were not significantly associated with DHI use before or after covariate adjustment (fully adjusted OR 0.98 (95%CI: 0.95-1.01), 0.6 (95%CI: 0.29-1.25), and 1.22 (95% CI: 0.60-2.48), respectively). Being married was associated with high DHI use (OR 2.12; 95% CI 1.02-4.39). Our findings suggest that DHIs may have a role in achieving equity in cardiovascular health given similar use by age, sex, and race. The presence of a spouse, perhaps a proxy for enhanced caregiver support, may encourage DHI use.

Heart rate trajectories in patients recovering from acute myocardial infarction: A longitudinal analysis of Apple Watch heart rate recordings.

Background: Using mobile health, vital signs such as heart rate (HR) can be used to assess a patient's recovery process from acute events including acute myocardial infarction (AMI). Objective: We aimed to characterize clinical correlates associated with HR change in the subacute period among patients recovering from AMI. Methods: HR measurements were collected from 91 patients (4447 HR recordings) enrolled in the MiCORE study using the Apple Watch and Corrie smartphone application. Mixed regression models were used to estimate the associations of patient-level characteristics during hospital admission with HR changes over 30 days postdischarge. Results: The mean daily HR at admission was 78.0 beats per minute (bpm) (95% confidence interval 76.1 to 79.8), declining 0.2 bpm/day (-0.3 to -0.1) under a linear model of HR change. History of coronary artery bypass graft, history of depression, or being discharged on anticoagulants was associated with a higher admission HR. Having a history of hypertension, type 2 diabetes mellitus (T2DM), or hyperlipidemia was associated with a slower decrease in HR over time, but not with HR during admission. Conclusion: While a declining HR was observed in AMI patients over 30 days postdischarge, patients with hypertension, T2DM, or hyperlipidemia showed a slower decrease in HR relative to their counterparts. This study demonstrates the feasibility of using wearables to model the recovery process of patients with AMI and represents a first step in helping pinpoint patients vulnerable to decompensation.

Early blood pressure assessment after acute myocardial infarction: Insights using digital health technology.

OBJECTIVE: There is rising interest in digital health in preventive cardiology, particularly for blood pressure (BP) management. In a digital health study of early BP assessment following acute myocardial infarction (AMI), we sought to examine feasibility and the (1) proportion of post-AMI patients with controlled BP and hypotension, and (2) association between prior cardiovascular disease (CVD) and BP post-AMI. METHODS: In this substudy of the parent Myocardial infarction, COmbined-device, Recovery Enhancement (MiCORE) study, type 1 AMI patients were enrolled between October 2017 and April 2019. Participants self-monitored their BP through 30 days after hospital discharge using an FDA-approved wireless BP monitor connected with a smartphone application. Linear mixed-effects models assessed the association between prior CVD and BP trajectory post-discharge, adjusting for antihypertensive medications and a propensity score inclusive of CVD risk factors. RESULTS: Sixty-eight AMI patients (mean age 58 â€‹± â€‹10 years, 75% male, 68% white race, 68% history of hypertension, 24% prior CVD) provided 2638 measurements over 30 days. The percentage of BP control <130/80 â€‹mmHg was 59.6% (95% CI: 54.3-64.9%) and <140/90 â€‹mmHg was 83.7% (95% CI: 80.3-87.2%). The percentage of systolic BP â€‹<90 â€‹mmHg was 1.1% (95% CI: 0.17-2.0%) and the percentage of diastolic BP â€‹<60 â€‹mmHg was 3.9% (95% CI: 2.6-5.2%). Prior CVD was associated with 12.2 â€‹mmHg higher mean daily systolic BP during admission (95% CI: 3.5-20.9 â€‹mmHg), which persisted over follow-up. There was no association between prior CVD and diastolic BP. CONCLUSION: The digital health program was feasible and ~40% of post-AMI patients who engaged in it had uncontrolled BP according to recent guideline cutpoints, while hypotension occurred rarely. The gap in BP control was especially large in patients in whom AMI represented recurrent CVD. These data suggest an opportunity for more aggressive secondary prevention early after MI as care models integrate digital health.

Corrie Health Digital Platform for Self-Management in Secondary Prevention After Acute Myocardial Infarction.

BACKGROUND: Unplanned readmissions after hospitalization for acute myocardial infarction are among the leading causes of preventable morbidity, mortality, and healthcare costs. Digital health interventions could be an effective tool in promoting self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction. A digital health intervention developed at Johns Hopkins-the Corrie Health Digital Platform (Corrie)-includes the first cardiology Apple CareKit smartphone application, which is paired with an Apple Watch and iHealth Bluetooth-enabled blood pressure cuff. Corrie targets: (1) self-management of cardiac medications, (2) self-tracking of vital signs, (3) education about cardiovascular disease through articles and animated videos, and (4) care coordination that includes outpatient follow-up appointments. METHODS AND RESULTS: The 3 phases of the MiCORE study (Myocardial infarction, Combined-device, Recovery Enhancement) include (1) the development of Corrie, (2) a pilot study to assess the usability and feasibility of Corrie, and (3) a prospective research study to primarily compare time to first readmission within 30 days postdischarge among patients with Corrie to patients in the historical standard of care comparison group. In Phase 2, the feasibility of deploying Corrie in an acute care setting was established among a sample of 60 patients with acute myocardial infarction. Phase 3 is ongoing and patients from 4 hospitals are being enrolled as early as possible during their hospital stay if they are 18 years or older, admitted with acute myocardial infarction (ST-segment-elevation myocardial infarction or type I non-ST-segment-elevation myocardial infarction), and own a smartphone. Patients are either being enrolled with their own personal devices or they are provided an iPhone and/or Apple Watch for the duration of the study. Phase 3 started in October 2017 and we aim to recruit 140 participants. CONCLUSIONS: This article will provide an in-depth understanding of the feasibility associated with implementing a digital health intervention in an acute care setting and the potential of Corrie as a self-management tool for acute myocardial infarction recovery.

Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation.

The explosion of mobile health (mHealth) interventions has prompted significant investment and exploration that has extended past industry into academia. Although research in this space is emerging, it focuses on the clinical and population level impact across different populations. To realize the full potential of mHealth, an intimate understanding of how mHealth is being used by patients and potential differences in usage between various demographic groups must also be prioritized. In this viewpoint, we use our experiences in building an mHealth intervention that incorporates an iOS app, Bluetooth-enabled blood pressure cuff, and Apple Watch to share knowledge on (1) how user interaction data can be tracked in the context of health care privacy laws, (2) what is required for effective, nuanced communication between clinicians and engineers to design mHealth interventions that are patient-centered and have high clinical impact, and (3) how to handle and set up a process to handle user interaction data efficiently.