Prevalence and Disparities in Telehealth Use Among US Adults Following the COVID-19 Pandemic: National Cross-Sectional Survey.

BACKGROUND: Telemedicine expanded during the COVID-19 pandemic, though use differed by age, sex, race or ethnicity, educational attainment, income, and location. It is unclear if high telehealth use or inequities persisted late into the pandemic. OBJECTIVE: This study aims to evaluate the prevalence of, inequities in, and primary reasons for telehealth visits a year after telemedicine expansion. METHODS: We used cross-sectional data from the 2022 Health Information National Trends Survey (HINTS 6), the first cycle with data on telemedicine. In total, 4830 English- and Spanish-speaking US adults (aged ≥18 years) were included in this study. The primary outcomes were telehealth visit attendance in the 12 months before March 7, 2022, to November 8, 2022, and the primary reason for the most recent telehealth visit. We evaluated sociodemographic and clinical predictors of telehealth visit attendance and the primary reason for the most recent telehealth visit through Poisson regression. Analyses were weighted according to HINTS 6 standards. RESULTS: We included 4830 participants (mean age 48.3, SD 17.5 years; 50.28% women; 65.21% White). Among US adults, 38.78% reported having a telehealth visit in the previous year. Telehealth visit attendance rates were similar across age, race or ethnicity, income, and urban versus rural location. However, individuals with a telehealth visit were less likely to live in the Midwest (adjusted prevalence ratio [aPR] 0.65, 95% CI 0.54-0.77), and more likely to be women (aPR 1.21, 95% CI 1.06-1.38), college graduates or postgraduates (aPR 1.24, 95% CI 1.05-1.46), covered by health insurance (aPR 1.56, 95% CI 1.08-2.26), and married or cohabitating (aPR 1.17, 95% CI 1.03-1.32), adjusting for sociodemographic characteristics, frequency of health care visits, and comorbidities. Among participants with a telehealth visit in the past year, the primary reasons for their most recent visit were minor or acute illness (32.15%), chronic disease management (21%), mental health or substance abuse (16.94%), and an annual exam (16.22%). Older adults were more likely to report that the primary reason for their most recent telehealth visit was for chronic disease management (aPR 2.08, 95% CI 1.33-3.23), but less likely to report that it was for a mental health or substance abuse issue (aPR 0.19, 95% CI 0.10-0.35), adjusting for sociodemographic characteristics and frequency of health care visits. CONCLUSIONS: Among US adults, telehealth visit attendance was high more than a year after telemedicine expansion and did not differ by age, race or ethnicity, income, or urban versus rural location. Telehealth could continue to be leveraged following COVID-19 to improve access to care and health equity.

Satisfaction with Telehealth Care in the United States: Cross-Sectional Survey.

Background: Telehealth use remains high following the COVID-19 pandemic, but patient satisfaction with telehealth care is unclear. Methods: We used cross-sectional data from the Health Information National Trends Survey (HINTS 6). 2,058 English and Spanish-speaking U.S. adults (≥18 years) with a telehealth visit in the 12 months before March-November 2022 were included in this study. The primary outcomes were telehealth visit modality and satisfaction in the 12 months before HINTS 6. We evaluated sociodemographic predictors of telehealth visit modality and satisfaction via Poisson regression. Analyses were weighted according to HINTS standards. Results: We included 2,058 participants (48.4 ± 16.8 years; 57% women; 66% White), of which 70% had an audio-video and 30% an audio-only telehealth visit. Adults with an audio-video visit were more likely to have health insurance (adjusted prevalence ratio [aPR]: 1.55, 95% confidence interval [CI]: 1.18-2.04) and have an annual household income of ≥$75,000 (aPR: 1.18, 95% CI: 1.00-1.39) and less likely to be ≥65 years (aPR: 0.79, 95% CI: 0.70-0.89), adjusting for sociodemographic characteristics. No further inequities were noted by telehealth modality. Seventy-five percent of participants felt that their telehealth visits were as good as in-person care. No significant differences in telehealth satisfaction were observed across sociodemographic characteristics, telehealth modality, or the participants' primary reason for their most recent telehealth visit in adjusted analysis. Conclusions: Among U.S. adults with a telehealth visit, the majority had an audio-video visit and were satisfied with their care. Telehealth should continue, being offered following COVID-19, as it is uniformly valued by patients.

Rationale and Design of the mTECH-Rehab Randomized Controlled Trial: Impact of a Mobile Technology Enabled Corrie Cardiac Rehabilitation Program on Functional Status and Cardiovascular Health.

BACKGROUND: Cardiac rehabilitation (CR) is an evidence-based, guideline-recommended intervention for patients recovering from a cardiac event, surgery or procedure that improves morbidity, mortality, and functional status. CR is traditionally provided in-center, which limits access and engagement, most notably among underrepresented racial and ethnic groups due to barriers including cost, scheduling, and transportation access. This study is designed to evaluate the Corrie Hybrid CR, a technology-based, multicomponent health equity-focused intervention as an alternative to traditional in-center CR among patients recovering from a cardiac event, surgery, or procedure compared with usual care alone. METHODS: The mTECH-Rehab (Impact of a Mobile Technology Enabled Corrie CR Program) trial will randomize 200 patients who either have diagnosis of myocardial infarction or who undergo coronary artery bypass grafting surgery, percutaneous coronary intervention, heart valve repair, or replacement presenting to 4 hospitals in a large academic health system in Maryland, United States, to the Corrie Hybrid CR program combined with usual care CR (intervention group) or usual care CR alone (control group) in a parallel arm, randomized controlled trial. The Corrie Hybrid CR program leverages 5 components: (1) a patient-facing mobile application that encourages behavior change, patient empowerment, and engagement with guideline-directed therapy; (2) Food and Drug Administration-approved smart devices that collect health metrics; (3) 2 upfront in-center CR sessions to facilitate personalization, self-efficacy, and evaluation for the safety of home exercise, followed by a combination of in-center and home-based sessions per participant preference; (4) a clinician dashboard to track health data; and (5) weekly virtual coaching sessions delivered over 12 weeks for education, encouragement, and risk factor modification. The primary outcome is the mean difference between the intervention versus control groups in distance walked on the 6-minute walk test (ie, functional capacity) at 12 weeks post randomization. Key secondary and exploratory outcomes include improvement in a composite cardiovascular health metric, CR engagement, quality of life, health factors (including low-density lipoprotein-cholesterol, hemoglobin A1c, weight, diet, smoking cessation, blood pressure), and psychosocial factors. Approval for the study was granted by the local institutional review board. Results of the trial will be published once data collection and analysis have been completed. CONCLUSIONS: The Corrie Hybrid CR program has the potential to improve functional status, cardiovascular health, and CR engagement and advance equity in access to cardiac rehabilitation. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT05238103.

Using rapid response system trigger clusters to characterize patterns of clinical deterioration among hospitalized adult patients.

BACKGROUND: Many rapid response system (RRS) events are activated using multiple triggers. However, the patterns in which multiple RRS triggers occur together to activate RRS events are unknown. The purpose of this study was to identify these patterns (RRS trigger clusters) and determine their association with outcomes among hospitalized adult patients. METHODS: RRS events among adult patients from January 2015 to December 2019 in the Get With The Guidelines- Resuscitation registry's MET module were examined (n = 134,406). Cluster analysis methods were performed to identify RRS trigger clusters. Pearson's chi-squared and ANOVA tests were used to examine differences in patient characteristics across RRS trigger clusters. Multilevel logistic regressions were used to examine the associations between RRS trigger clusters and outcomes. RESULTS: Six RRS trigger clusters were identified. Predominant RRS triggers for each cluster were: tachypnea, new onset difficulty in breathing, decreased oxygen saturation (Cluster 1); tachypnea, decreased oxygen saturation, staff concern (Cluster 2); respiratory depression, decreased oxygen saturation, mental status changes (Cluster 3); tachycardia, staff concern (Cluster 4); mental status changes (Cluster 5); hypotension, staff concern (Cluster 6). Significant differences in patient characteristics were observed across clusters. Patients in Clusters 3 and 6 had an increased likelihood of in-hospital cardiac arrest (p < 0.01). All clusters had an increased risk of mortality (p < 0.01). CONCLUSIONS: We discovered six novel RRS trigger clusters with differing relationships to adverse patient outcomes. RRS trigger clusters may prove crucial in clarifying the associations between RRS events and adverse outcomes and aiding in clinician decision-making during RRS events.

Use of Human-Centered Design Methodology to Develop a Digital Toolkit to Optimize Heart Failure Guideline-Directed Medical Therapy.

BACKGROUND: Guideline-directed medical therapies (GDMTs) improve quality of life and health outcomes for patients with heart failure (HF). However, GDMT utilization is suboptimal among patients with HF. OBJECTIVE: The aims of this study were to engage key stakeholders in semistructured, virtual human-centered design sessions to identify challenges in GDMT optimization posthospitalization and inform the development of a digital toolkit aimed at optimizing HF GDMTs. METHODS: For the human-centered design sessions, we recruited (a) clinicians who care for patients with HF across 3 hospital systems, (b) patients with HF with reduced ejection fraction (ejection fraction ≤ 40%) discharged from the hospital within 30 days of enrollment, and (c) caregivers. All participants were 18 years or older, English speaking, with Internet access. RESULTS: A total of 10 clinicians (median age, 37 years [interquartile range, 35-41], 12 years [interquartile range, 10-14] of experience caring for patients with HF, 80% women, 50% White, 50% nurse practitioners) and three patients and one caregiver (median age 57 years [IQR: 53-60], 75% men, 50% Black, 75% married) were included. Five themes emerged from the clinician sessions on challenges to GDMT optimization (eg, barriers to patient buy-in). Six themes on challenges (eg, managing medications), 4 themes on motivators (eg, regaining independence), and 3 themes on facilitators (eg, social support) to HF management arose from the patient and caregiver sessions. CONCLUSIONS: The clinician, patient, and caregiver insights identified through human-centered design will inform a digital toolkit aimed at optimizing HF GDMTs, including a patient-facing smartphone application and clinician dashboard. This digital toolkit will be evaluated in a multicenter, clinical trial.

A systematic appraisal of the information, engagement, aesthetic and functional quality of nutrition-related smartphone apps for children and adolescents.

OBJECTIVE: Nutrition-related smartphone applications (apps) could improve children's nutrition knowledge and skills. However, little is known about the quality of nutrition-related apps for children. This study aimed to identify and evaluate the quality of nutrition-related smartphone apps designed for children ages 4-17. DESIGN: This systematic appraisal is guided by the Protocol for App Store Systematic Reviews. SETTING: Using Google's Advanced Search, we identified 1814 apps/1184 additional apps in an updated search on iOS, of which twenty-four were eligible. The apps' objective and subjective quality were evaluated using the twenty-three-item, five-point Mobile App Rating Scale. The objective quality scale consists of four subscales: engagement, functionality, aesthetics and information. RESULTS: Most of the apps (75 %) focussed solely on promoting nutrition skills, such as making food dishes, rather than nutrition knowledge. Of the twenty-four apps, 83 % targeted children 4-8 years old. The app objective quality mean score was 3·60 ± 0·41. The subscale mean scores were 3·20 ± 0·41 for engagement, 4·24 ± 0·47 for functionality, 4·03 ± 0·51 for aesthetics and 2·94 ± 0·62 for information. The app subjective quality mean score was 2·10 ± 0·90. CONCLUSIONS: More robust approaches to app development leveraging co-design approaches, including involving a multidisciplinary team of experts to provide evidence-based nutrition information, are warranted.

Using rapid response system trigger clusters to characterize patterns of clinical deterioration among hospitalized adult patients.

Background: Many rapid response system (RRS) events are activated using multiple triggers. However, the patterns in which RRS triggers co-occur to activate the medical emergency team (MET) to respond to RRS events is unknown. The purpose of this study was to identify and describe the patterns (RRS trigger clusters) in which RRS triggers co-occur when used to activate the MET and determine the association of these clusters with outcomes using a sample of hospitalized adult patients. Methods: RRS events among adult patients from January 2015 to December 2019 in the Get With The Guidelines- Resuscitation registry's MET module were examined (n=134,406). A combination of cluster analyses methods was performed to group patients into RRS trigger clusters based on the triggers used to activate their RRS events. Pearson's chi-squared and ANOVA tests were used to examine differences in patient characteristics across RRS trigger clusters. Multilevel logistic regression was used to examine the associations between RRS trigger clusters and outcomes following RRS events. Results: Six RRS trigger clusters were identified in the study sample. The RRS triggers that predominantly identified each cluster were as follows: tachypnea, new onset difficulty in breathing, and decreased oxygen saturation (Cluster 1); tachypnea, decreased oxygen saturation, and staff concern (Cluster 2); respiratory depression, decreased oxygen saturation, and mental status changes (Cluster 3); tachycardia and staff concern (Cluster 4); mental status changes (Cluster 5); hypotension and staff concern (Cluster 6). Significant differences in patient characteristics were observed across RRS trigger clusters. Patients in Clusters 3 and 6 were associated with an increased likelihood of in-hospital cardiac arrest (IHCA [p<0.01]), while Cluster 4 was associated with a decreased likelihood of IHCA (p<0.01). All clusters were associated with an increased risk of mortality (p<0.01). Conclusions: We discovered six novel RRS trigger clusters with differing relationships to adverse patient outcomes following RRS events. RRS trigger clusters may prove crucial in clarifying the associations between RRS events and adverse outcomes and may aid in clinician decision-making during RRS events.

The Virtual Inclusive Digital Health Intervention Design to Promote Health Equity (iDesign) Framework for Atrial Fibrillation: Co-design and Development Study.

BACKGROUND: Smartphone ownership and mobile app use are steadily increasing in individuals of diverse racial and ethnic backgrounds living in the United States. Growing adoption of technology creates a perfect opportunity for digital health interventions to increase access to health care. To successfully implement digital health interventions and engage users, intervention development should be guided by user input, which is best achieved by the process of co-design. Digital health interventions co-designed with the active engagement of users have the potential to increase the uptake of guideline recommendations, which can reduce morbidity and mortality and advance health equity. OBJECTIVE: We aimed to co-design a digital health intervention for patients with atrial fibrillation, the most common cardiac arrhythmia, with patient, caregiver, and clinician feedback and to describe our approach to human-centered design for building digital health interventions. METHODS: We conducted virtual meetings with patients with atrial fibrillation (n=8), their caregivers, and clinicians (n=8). We used the following 7 steps in our co-design process: step 1, a virtual meeting focused on defining challenges and empathizing with problems that are faced in daily life by individuals with atrial fibrillation and clinicians; step 2, a virtual meeting focused on ideation and brainstorming the top challenges identified during the first meeting; step 3, individualized onboarding of patients with an existing minimally viable version of the atrial fibrillation app; step 4, virtual prototyping of the top 3 ideas generated during ideation; step 5, further ranking by the study investigators and engineers of the ideas that were generated during ideation but were not chosen as top-3 solutions to be prototyped in step 4; step 6, ongoing engineering work to incorporate top-priority features in the app; and step 7, obtaining further feedback from patients and testing the atrial fibrillation digital health intervention in a pilot clinical study. RESULTS: The top challenges identified by patients and caregivers included addressing risk factor modification, medication adherence, and guidance during atrial fibrillation episodes. Challenges identified by clinicians were complementary and included patient education, addressing modifiable atrial fibrillation risk factors, and remote atrial fibrillation episode management. Patients brainstormed more than 30 ideas to address the top challenges, and the clinicians generated more than 20 ideas. Ranking of the ideas informed several novel or modified features aligned with the Theory of Health Behavior Change, features that were geared toward risk factor modification; patient education; rhythm, symptom, and trigger correlation for remote atrial fibrillation management; and social support. CONCLUSIONS: We co-designed an atrial fibrillation digital health intervention in partnership with patients, caregivers, and clinicians by virtually engaging in collaborative creation through the design process. We summarize our experience and describe a flexible approach to human-centered design for digital health intervention development that can guide innovative clinical investigators.

Heterogeneity in Cardiovascular Disease Risk Factor Prevalence Among White, African American, African Immigrant, and Afro-Caribbean Adults: Insights From the 2010-2018 National Health Interview Survey.

Background In the United States, Black adults have higher rates of cardiovascular disease (CVD) risk factors than White adults. However, it is unclear how CVD risk factors compare between Black ethnic subgroups, including African Americans (AAs), African immigrants (AIs), and Afro-Caribbeans, and White people. Our objective was to examine trends in CVD risk factors among 3 Black ethnic subgroups and White adults between 2010 and 2018. Methods and Results A comparative analysis of the National Health Interview Survey was conducted among 452 997 participants, examining sociodemographic characteristics and trends in 4 self-reported CVD risk factors (hypertension, diabetes, overweight/obesity, and smoking). Generalized linear models with Poisson distribution were used to obtain predictive probabilities of the CVD risk factors. The sample included 82 635 Black (89% AAs, 5% AIs, and 6% Afro-Caribbeans) and 370 362 White adults. AIs were the youngest, most educated, and least insured group. AIs had the lowest age- and sex-adjusted prevalence of all 4 CVD risk factors. AAs had the highest prevalence of hypertension (2018: 41.9%) compared with the other groups. Overweight/obesity and diabetes prevalence increased in AAs and White adults from 2010 to 2018 (P values for trend <0.001). Smoking prevalence was highest among AAs and White adults, but decreased significantly in these groups between 2010 and 2018 (P values for trend <0.001), as compared with AIs and Afro-Caribbeans. Conclusions We observed significant heterogeneity in CVD risk factors among 3 Black ethnic subgroups compared with White adults. There were disparities (among AAs) and advantages (among AIs and Afro-Caribbeans) in CVD risk factors, suggesting that race alone does not account for disparities in CVD risk factors.

Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology.

Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.

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

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

Patterns of Engagement With an Application-Based Dietary Self-Monitoring Tool Within a Randomized Controlled Feasibility Trial.

Introduction: The Dietary Approaches to Stop Hypertension dietary pattern is a proven way to manage hypertension, but adherence remains low. Dietary tracking applications offer a highly disseminable way to self-monitor intake on the pathway to reaching dietary goals but require consistent engagement to support behavior change. Few studies use longitudinal dietary self-monitoring data to assess trajectories and predictors of engagement. We used dietary self-monitoring data from participants in Dietary Approaches to Stop Hypertension Cloud (N=59), a feasibility trial to improve diet quality among women with hypertension, to identify trajectories of engagement and explore associations between participant characteristics. Methods: We used latent class growth modeling to identify trajectories of engagement with a publicly available diet tracking application and used bivariate and regression analyses to assess the associations of classifications of engagement with participant characteristics. Results: We identified 2 latent classes of engagement: consistent engagers and disengagers. Consistent engagers were more likely to be older, more educated, and married or living with a partner. Although consistent engagers exhibited slightly greater changes in Dietary Approaches to Stop Hypertension score, the difference was not significant. Conclusions: This study highlights an important yet underutilized methodologic approach for uncovering dietary self-monitoring engagement patterns. Understanding how certain individuals engage with digital technologies is an important step toward designing cost-effective behavior change interventions. Trial registration: This study is registered at www.clinicaltrials.gov NCT03215472.

Oncology Nursing Telephone Triage Workshop: Impact on Nurses' Knowledge, Confidence, and Skill.

BACKGROUND: Outpatient oncology nurses are responsible for symptom assessment/management and care coordination during telephone triage. Nursing telephone triage interventions can improve patient outcomes and clinical efficiency. Therefore, the lack of education and training in telephone triage can greatly impact patient care. OBJECTIVE: Using a prospective pretest/posttest design, we sought to determine if a telephone triage educational workshop would improve oncology nurses' knowledge, confidence, and skill over 12 weeks. INTERVENTION/METHODS: The educational intervention incorporated an online didactic lecture, group case scenario, and feedback on a virtual triage simulation. Evaluation was conducted before and after the intervention through an online, 13-item survey (knowledge and confidence) and simulation utilizing a 56-item checklist (skills). RESULTS: Thirteen oncology nurses were enrolled; 54% did not have telephone triage experience before this job. A total of 12 participants completed the workshop. From pretest to posttest, there was a median 1.0 out of 5.0 (interquartile range, 2.8) improvement in confidence (P = .008) and a 26.3% (interquartile range, 15.2) improvement in skills (P = .002). There was no difference in knowledge scores from pretest to posttest (P = .11). CONCLUSIONS: This workshop was associated with an improvement in oncology nurse confidence and skill, using telephone triage models. It benefits an existing process within the outpatient center and it highlights a new educational strategy that may optimize nursing practice and improve patient care and experience. IMPLICATIONS FOR PRACTICE: This workshop contributes to existing evidence of telephone triage models and nursing education. The findings can guide future research, nursing orientation, and educational activities within the field of nursing and telehealth.

Application of the very high risk criterion and evaluation of cholesterol guideline adherence in acute myocardial infarction patients at an urban academic medical center.

Objective: The 2018 AHA/ACC cholesterol guidelines recommend considering non-statin agents among very high-risk (VHR) patients with LDL-C ≥ 70 mg/dL after maximizing statin therapy. We aimed to evaluate the prevalence of VHR status in acute myocardial infarction (AMI) patients at hospital discharge and the adherence to guideline-directed cholesterol therapy (GDCT) within one-year follow-up post-AMI. Methods: We performed a retrospective analysis of patients who suffered a type 1 AMI between October 2015 and March 2019, and then were followed at our institution for 1 year after hospital discharge. We calculated the percentage of patients at VHR and among those with follow up lipid panels, we determined the proportion able to achieve GDCT. Results: The mean age of the 331 AMI patients was 61.0 (SD 11.9) years and 33.6% were women. Overall, 268 (81.0%) patients were categorized as having VHR at discharge. Among patients at VHR, a lipid panel was rechecked in 153 individuals (57.1%) within 1 year of discharge, with the median time to lipid recheck being 22.4 weeks (interquartile range: 10.9-40.7 weeks). Among those with a lipid panel re-check, 100 (65.4%) of patients achieved GDCT. Conclusions: Approximately 4 out of 5 AMI patients were considered VHR per the 2018 AHA/ACC guidelines, only about half had follow up lipid panels in the year following AMI, and about two-thirds of those with follow up lipid panels achieved GDCT.

Clinicians for CARE: A Systematic Review and Meta-Analysis of Interventions to Support Caregivers of Patients With Heart Disease.

Background Caregivers provide critical support for patients with chronic diseases, including heart disease, but often experience caregiver stress that negatively impacts their health, quality of life, and patient outcomes. We aimed to inform health care teams on an evidence-based approach to supporting the caregivers of patients with heart disease. Methods and Results We conducted a systematic review and meta-analysis of randomized controlled trials written in English that evaluated interventions to support caregivers of patients with heart disease. We identified 15,561 articles as of April 2, 2020 from 6 databases; of which 20 unique randomized controlled trials were evaluated, representing a total of 1570 patients and 1776 caregivers. Most interventions focused on improving quality of life, and reducing burden, depression, and anxiety; 85% (17 of 20) of the randomized controlled trials provided psychoeducation for caregivers. Interventions had mixed results, with moderate non-significant effects observed for depression (Hedges' g=-0.64; 95% CI, -1.34 to 0.06) and burden (Hedges' g=-0.51; 95% CI, -2.71 to 1.70) at 2 to 4 months postintervention and small non-significant effects observed for quality of life and anxiety. These results were limited by the heterogeneity of outcome measures and intervention delivery methods. A qualitative synthesis of major themes of the interventions resulted in clinical recommendations represented with the acronym "CARE" (Caregiver-Centered, Active engagement, Reinforcement, Education). Conclusions This systematic review highlights the need for greater understanding of the challenges faced by caregivers and the development of guidelines to help clinicians address those challenges. More research is necessary to develop clinical interventions that consistently improve caregiver outcomes.

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.

Correction: Strategies for the Successful Implementation of a Novel iPhone Loaner System (iShare) in mHealth Interventions: Prospective Study.

[This corrects the article DOI: 10.2196/16391.].