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.

Controversies in the Use of Omega-3 Fatty Acids to Prevent Atherosclerosis.

PURPOSE OF REVIEW: We discuss current controversies in the clinical use of omega-3 fatty acids (FA), primarily eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), and examine discrepancies between recent trials. Furthermore, we discuss potential side effects reported in these studies and the role of mixed omega-3 FA dietary supplements and concerns about their use. RECENT FINDINGS: REDUCE-IT showed that addition of icosapent ethyl, a highly purified form of EPA, can reduce risk of cardiovascular events among statin-treated individuals with high triglycerides. Additional supportive evidence for EPA has come from other trials and meta-analyses of omega-3 FA therapy. In contrast, trials of mixed EPA/DHA products have consistently failed to improve cardiovascular outcomes. Discrepancies in results reported in RCTs could be explained by differences in omega-3 FA products, dosing, study populations, and study designs including the placebo control formulation. Evidence obtained from highly purified forms should not be extrapolated to other mixed formulations, including "over-the-counter" omega-3 supplements. Targeting TG-rich lipoproteins represents a new frontier for mitigating ASCVD risk. Clinical and basic research evidence suggests that the use of omega-3 FA, specifically EPA, appears to slow atherosclerosis by reducing triglyceride-rich lipoproteins and/or inflammation, therefore addressing residual risk of clinical ASCVD.

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 Interventions for Cardiac Rehabilitation: Systematic Literature Review.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide. Despite strong evidence supporting the benefits of cardiac rehabilitation (CR), over 80% of eligible patients do not participate in CR. Digital health technologies (ie, the delivery of care using the internet, wearable devices, and mobile apps) have the potential to address the challenges associated with traditional facility-based CR programs, but little is known about the comprehensiveness of these interventions to serve as digital approaches to CR. Overall, there is a lack of a systematic evaluation of the current literature on digital interventions for CR. OBJECTIVE: The objective of this systematic literature review is to provide an in-depth analysis of the potential of digital health technologies to address the challenges associated with traditional CR. Through this review, we aim to summarize the current literature on digital interventions for CR, identify the key components of CR that have been successfully addressed through digital interventions, and describe the gaps in research that need to be addressed for sustainable and scalable digital CR interventions. METHODS: Our strategy for identifying the primary literature pertaining to CR with digital solutions (defined as technology employed to deliver remote care beyond the use of the telephone) included a consultation with an expert in the field of digital CR and searches of the PubMed (MEDLINE), Embase, CINAHL, and Cochrane databases for original studies published from January 1990 to October 2018. RESULTS: Our search returned 31 eligible studies, of which 22 were randomized controlled trials. The reviewed CR interventions primarily targeted physical activity counseling (31/31, 100%), baseline assessment (30/31, 97%), and exercise training (27/31, 87%). The most commonly used modalities were smartphones or mobile devices (20/31, 65%), web-based portals (18/31, 58%), and email-SMS (11/31, 35%). Approximately one-third of the studies addressed the CR core components of nutrition counseling, psychological management, and weight management. In contrast, less than a third of the studies addressed other CR core components, including the management of lipids, diabetes, smoking cessation, and blood pressure. CONCLUSIONS: Digital technologies have the potential to increase access and participation in CR by mitigating the challenges associated with traditional, facility-based CR. However, previously evaluated interventions primarily focused on physical activity counseling and exercise training. Thus, further research is required with more comprehensive CR interventions and long-term follow-up to understand the clinical impact of digital interventions.

Interprofessional education and collaboration among healthcare students and professionals: a systematic review and call for action.

Interprofessional education (IPE) is a prerequisite to building a collaborative practice environment and optimizing patient care. The purpose of this systematic review was to assess the impact of IPE on outcomes related to health-care pre-licensure learners and professionals, including: changes in attitudes/perceptions; acquisition of knowledge regarding other disciplines' roles and development of collaborative skills; and change in collaborative behavior. We searched PubMed, CINAHL, Embase, and ERIC for studies published from 2007 to 2017 in English; 19 studies were eligible. The Joanna Briggs Institute appraisal tool was used to assess the quality of the studies. Thirteen studies used a quasi-experimental design. The studies varied in terms of setting, teaching methods, assessment measures, and quality. Seventeen of the 19 studies (89%) that assessed change in attitudes toward other disciplines and value placed on a team-based approach for improving patient care, found statistically significant improvements. All seven of the studies that assessed change in collaborative behavior found statistically significant improvements. Among the 12 studies that assessed the development of collaborative skills, there were mixed results. Future directions include: conducting more studies among health-care professionals, assessing the long-term impact of IPE, objectively assessing change in collaborative behavior, and assessing the impact of IPE on patient-centered outcomes.

The importance of LDL-C lowering in atherosclerotic cardiovascular disease prevention: Lower for longer is better.

Cumulative exposure to low-density lipoprotein cholesterol (LDL-C) is a key driver of atherosclerotic cardiovascular disease (ASCVD) risk. An armamentarium of therapies to achieve robust and sustained reduction in LDL-C can reduce ASCVD risk. The gold standard for LDL-C assessment is ultracentrifugation but in routine clinical practice LDL-C is usually calculated and the most accurate calculation is the Martin/Hopkins equation. For primary prevention, consideration of estimated ASCVD risk frames decision making regarding use of statins and other therapies, and tools such as risk enhancing factors and coronary artery calcium enable tailoring of risk assessment and decision making. In patients with diabetes, lipid lowering therapy is recommended in most patients to reduce ASCVD risk with an opportunity to tailor therapy based on other risk factors. Patients with primary hypercholesterolemia and familial hypercholesterolemia (FH) with baseline LDL-C greater than or equal to 190 mg/dL are at elevated risk, and LDL-C lowering with high-intensity statin therapy is often combined with non-statin therapies to prevent ASCVD. Secondary prevention of ASCVD, including in patients with prior myocardial infarction or stroke, requires intensive lipid lowering therapy and lifestyle modification approaches. There is no established LDL-C level below which benefit ceases or safety concerns arise. When further LDL-C lowering is required beyond lifestyle modifications and statin therapy, additional medications include oral ezetimibe and bempedoic acid, or injectables such as PCSK9 monoclonal antibodies or siRNA therapy. A novel agent that acts independently of hepatic LDL receptors is evinacumab, which is approved for patients with homozygous FH. Other emerging agents are targeted at Lp(a) and CETP. In light of the expanding lipid treatment landscape, this manuscript reviews the importance of early, intensive, and sustained LDL-C-lowering for primary and secondary prevention of ASCVD.

Testing practices and clinical management of lipoprotein(a) levels: A 5-year retrospective analysis from the Johns Hopkins Hospital.

Objective: Elevated lipoprotein(a) [Lp(a)] is an independent, genetically determined risk factor for atherosclerotic cardiovascular disease (ASCVD). We evaluated the frequency of testing for elevated Lp(a) and subsequent management at the Johns Hopkins Hospital, a large academic medical center, over a 5-year period. Methods: The Johns Hopkins Hospital (JHH) electronic medical record was queried to identify patients with an encounter between 2017 and 2021, either with established ASCVD or at increased risk, defined as being on any lipid lowering medication or having LDL-C ≥ 190 mg/dL. The frequency of Lp(a) testing and of elevated levels were identified for each year. Results: Among 111,350 unique adult patients, 2,785 (2.5 %) had at least one Lp(a) test. Patients with Lp(a) testing, compared to those without testing, were younger (mean age 56 years vs. 66 years), more often female (49 % vs. 44 %), Black (24.7 % vs. 24.6 %) or "other" race/ethnicity (12 % vs 10 %), and had higher LDL-C levels (median 118 vs. 91 mg/dL; p < 0.001). The number and frequency of Lp(a) testing increased from 167 (0.57 %) in 2017 to 1155 (5.67 %) in 2021. Lp(a) levels were abnormal in 43.4 % of patients (moderate [75-125 nmol/L]: 10.3 %, high [126-600 nmol/L]: 32.2 %, severe [>600 nmol/L]: 0.9 %). Among 920 patients with high or severe Lp(a) levels, 200 (22 %) had a subsequent referral to cardiology or lipid specialist, and 180 (20 %) had a new lipid-lowering medication prescribed in the subsequent 18 months. Conclusion: Based on a single-center experience, the frequency of incident Lp(a) testing among increased-risk patients was low but increased significantly over 5-years, likely due to Lipid Clinic referrals with reflex Lp(a) testing and greater awareness about this risk factor. Future work should target appropriate population based Lp(a) testing strategies and clinical decision-making regarding risk management once Lp(a) elevation is diagnosed.

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.

A clinician's guide to addressing cardiovascular health based on a revised AHA framework.

The American Heart Association recently published updates to its definition of cardiovascular health (CVH) in its Presidential Advisory called Life's Essential 8. In particular, the update from Life's Simple 7 added a new component of sleep duration and refined definitions of prior components, including measurement of diet, nicotine exposure, blood lipids, and blood glucose. Physical activity, BMI, and blood pressure were unchanged. Together, these eight components create a composite CVH score that clinicians, policy-makers, patients, communities, and businesses can utilize to communicate in a consistent way. Life's Essential 8 also emphasizes the critical role of addressing social determinants of health to improve these individual CVH components, which strongly correlate with future cardiovascular outcomes. This framework should be used across the life spectrum including during pregnancy and childhood to allow improvements in and prevention of CVH at critical time-points. Clinicians can use this framework to advocate for digital health technologies and societal policies that help address and more seamlessly measure the 8 components of CVH with the goal of increasing quality and quantity of life.

Starting a Research Career in Cardiology: Advice for Fellows in Training and Early-Career Cardiologists.

Launching an academic career in cardiology can be challenging. Mentorship has long been considered a core component in the academic career advancement of trainees across different disciplines and career stages, including cardiovascular disease. But simply having a mentor may not be sufficient to embark on a successful academic journey in cardiology. In this paper, we share advice on starting a research career in cardiology from both the mentee and mentor viewpoints. These perspectives reflect academic career guidance models developed at the Johns Hopkins Center for Mobile Technologies to Achieve Equity in Cardiovascular Health, which is funded by an American Heart Association Strategic Focused Network grant, to emphasize training. Core principles include encouraging mentees to develop a unique professional identity cultivated by a diverse, collaborative, and effective mentorship and sponsorship team.

Associations of Demographic, Socioeconomic, and Cognitive Characteristics With Mobile Health Access: MESA (Multi-Ethnic Study of Atherosclerosis).

Background Mobile health (mHealth) has an emerging role in the prevention of cardiovascular disease. This study evaluated possible inequities in mHealth access in older adults. Methods and Results mHealth access was assessed from 2019 to 2020 in MESA (Multi-Ethnic Study of Atherosclerosis) telephone surveys of 2796 participants aged 62 to 102 years. A multivariable logistic regression model adjusted for general health status assessed associations of mHealth access measures with relevant demographic, socioeconomic, and cognitive characteristics. There were lower odds of all access measures with older age (odds ratios [ORs], 0.37-0.59 per 10 years) and annual income <$50 000 (versus ≥$50 000 ORs, 0.55-0.62), and higher odds with higher Cognitive Abilities Screening Instrument Score (ORs, 1.22-1.29 per 5 points). Men (versus women) had higher odds of internet access (OR, 1.32 [95% CI,1.05-1.66]) and computing device ownership (OR, 1.31 [95% CI, 1.05-1.63]) but lower fitness tracker ownership odds (OR, 0.70 [95% CI, 0.49-0.89]). For internet access and computing device ownership, we saw lower odds for Hispanic participants (versus White participants OR, 0.61 [95% CI, 0.44-0.85]; OR, 0.69 [95% CI, 0.50-0.95]) and less than a high school education (versus bachelor's degree or higher OR, 0.27 [95% CI, 0.18-0.40]; OR, 0.32 [95% CI, 0.28-0.62]). For internet access, lower odds were seen for Black participants (versus White participants OR, 0.64 [95% CI, 0.47-0.86]) and other health insurance (versus health maintenance organization/private OR, 0.59 [95% CI, 0.47-0.74]). Chinese participants (versus White participants) had lower internet access odds (OR, 0.63 [95% CI, 0.44-0.91]) but higher computing device ownership odds (OR, 1.87 [95% CI, 1.28-2.77]). Conclusions Among older-age adults, mHealth access varied by major demographic, socioeconomic, and cognitive characteristics, suggesting a digital divide. Novel mHealth interventions should consider individual access barriers. Registration URL: https://www.clinicaltrials.gov/; Unique identifier: NCT00005487.

Quantifying the contribution of lipoprotein(a) to all apoB containing particles.

BACKGROUND: Elevated lipoprotein (a) [Lp(a)] is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). As clinical LDL cholesterol [LDL-C] incorporates cholesterol from Lp(a) [Lp(a)-C], there is interest in quantifying the contribution of Lp(a)-C to LDL-C given implications for risk assessment, diagnosis, and treatment. Estimating Lp(a)-C is subject to inaccuracies; measuring Lp(a) particle number [Lp(a)-P] is more accurate. OBJECTIVE: To capture how Lp(a) contributes to the concentration of atherogenic particles, we demonstrate a particle-based approach using readily available measures of Lp(a)-P and apolipoprotein B (apoB). METHODS: Using the Very Large Database of Lipids (VLDbL), we compared Lp(a)-P (nmol/L) with all apoB containing particles ("apoB-P"). apoB-P was calculated by converting apoB mass to molar concentration using the preserved molecular weight of apoB100 (512 kg/mol). We calculated the percentage of Lp(a)-P relative to apoB-P by Lp(a)-P deciles and stratified by triglycerides, LDL-C, and non-HDL-C. RESULTS: 158,260 patients from the VLDbL were included. The fraction Lp(a)-P/apoB-P increased with rising Lp(a)-P. Lp(a)-P comprised on average 3% of apoB containing particles among the study population and 15% at the highest Lp(a)-P decile. Lp(a)-P/apoB-P decreased at higher levels of triglycerides and LDL-C owing to larger contributions from VLDL and LDL. CONCLUSIONS: We demonstrate a particle-based approach to quantify the contribution of Lp(a) to all apoB-containing particles using validated and widely available clinical assays. This approach keeps in line with recommendations to move away from mass-based measurements of Lp(a) and prioritize more accurate particle-based measurements. Future research applying this method could define clinically meaningful thresholds and inform use in risk assessment and management.

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.

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.].

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.

User Engagement With Smartphone Apps and Cardiovascular Disease Risk Factor Outcomes: Systematic Review.

BACKGROUND: The use of mobile health (mHealth) interventions, including smartphone apps, for the prevention of cardiovascular disease (CVD) has demonstrated mixed results for obesity, hypercholesterolemia, diabetes, and hypertension management. A major factor attributing to the variation in mHealth study results may be mHealth user engagement. OBJECTIVE: This systematic review aims to determine if user engagement with smartphone apps for the prevention and management of CVD is associated with improved CVD health behavior change and risk factor outcomes. METHODS: We conducted a comprehensive search of PubMed, CINAHL, and Embase databases from 2007 to 2020. Studies were eligible if they assessed whether user engagement with a smartphone app used by an individual to manage his or her CVD risk factors was associated with the CVD health behavior change or risk factor outcomes. For eligible studies, data were extracted on study and sample characteristics, intervention description, app user engagement measures, and the relationship between app user engagement and the CVD risk factor outcomes. App user engagement was operationalized as general usage (eg, number of log-ins or usage days per week) or self-monitoring within the app (eg, total number of entries made in the app). The quality of the studies was assessed. RESULTS: Of the 24 included studies, 17 used a randomized controlled trial design, 4 used a retrospective analysis, and 3 used a single-arm pre- and posttest design. Sample sizes ranged from 55 to 324,649 adults, with 19 studies recruiting participants from a community setting. Most of the studies assessed weight loss interventions, with 6 addressing additional CVD risk factors, including diabetes, sleep, stress, and alcohol consumption. Most of the studies that assessed the relationship between user engagement and reduction in weight (9/13, 69%), BMI (3/4, 75%), body fat percentage (1/2, 50%), waist circumference (2/3, 67%), and hemoglobin A1c (3/5, 60%) found statistically significant results, indicating that greater app user engagement was associated with better outcomes. Of 5 studies, 3 (60%) found a statistically significant relationship between higher user engagement and an increase in objectively measured physical activity. The studies assessing the relationship between user engagement and dietary and diabetes self-care behaviors, blood pressure, and lipid panel components did not find statistically significant results. CONCLUSIONS: Increased app user engagement for prevention and management of CVD may be associated with improved weight and BMI; however, only a few studies assessed other outcomes, limiting the evidence beyond this. Additional studies are needed to assess user engagement with smartphone apps targeting other important CVD risk factors, including dietary behaviors, hypercholesterolemia, diabetes, and hypertension. Further research is needed to assess mHealth user engagement in both inpatient and outpatient settings to determine the effect of integrating mHealth interventions into the existing clinical workflow and on CVD outcomes.

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.

The Johns Hopkins Ciccarone Center's expanded 'ABC's approach to highlight 2020 updates in cardiovascular disease prevention.

In recent years, improvement in outcomes related to cardiovascular disease is in part due to the prioritization and progress of primary and secondary prevention efforts. The Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease expanded 'ABC's approach is used to highlight key findings in Preventive Cardiology from 2020 and further emphasize the importance of cardiovascular prevention. This simplified approach helps clinicians focus on the most relevant and up to date recommendations for optimizing cardiovascular disease risk through accurate risk assessment and appropriate implementation of lifestyle, behavioral and pharmacologic interventions. While 2020 not only provided practice changing updates by way of clinical guidelines and randomized controlled trials on topics related to antithrombotic and lipid lowering therapy, diabetes management and risk assessment, it also provided promising data on how to improve dietary and exercise adherence and manage genetic risk. By providing clinicians with a systematic approach to cardiovascular prevention and key highlights from the prior year, the goal of significantly reducing the burden of cardiovascular disease worldwide can be achieved.