Getting healthy sleep was recognized in 2022 by the American Heart Association as a key health behavior of Life’s Essential 8 based on growing evidence of its impact on cardiovascular health. American Heart Association guidelines recommend that adults aged ≥20 years get on average 7 to 9 hours of sleep per night, based on self-reported measures. However, many adults report getting inadequate sleep duration, a trend expected to worsen through 2050. For these reasons, the relationship between sleep, including its multidimensional components (eg, timing, efficiency, regularity, and architecture), and cardiovascular disease must be evaluated further. In this review, we summarize the current evidence on the association of multidimensional sleep with heart disease and stroke risk. In addition, we discuss the advantages and limitations of various sleep assessments from self-report to direct measurement via novel digital health technologies.

BACKGROUND:Digital health technologies have become integral to health care delivery, yet significant disparities continue to undermine equitable access and effectiveness. Approximately 16% of adults in the United States lack basic digital health literacy (DHL) skills, with barriers disproportionately affecting older adults, low-income individuals, and minoritized communities. Understanding the relationship between DHL and patient engagement is essential for addressing these inequities. PURPOSE:This integrative review examined the relationship between DHL and patient adherence to follow-up care, self-management behaviors, and telehealth engagement to identify factors contributing to health care disparities in digital health utilization. METHODS:A systematic search was conducted on August 26, 2024, across PubMed, Embase, and CINAHL. Studies published in English from January 2014 to September 2024 were included if they measured DHL using validated instruments or examined telehealth utilization patterns related to patient adherence to follow-up care, self-management behaviors, or telehealth engagement. Fourteen studies met the inclusion criteria and were appraised using the Johns Hopkins Evidence-Based Practice model. RESULTS:Higher DHL was consistently associated with improved patient behaviors, enhanced self-management, including better medication adherence, and improved telehealth engagement. Patients with higher DHL reported greater confidence using digital tools and satisfaction with virtual care. However, disparities remain entrenched, with studies consistently showing that older adults, rural populations, and individuals with lower incomes face greater barriers to effective telehealth engagement. CONCLUSIONS:DHL is a key determinant of equitable telehealth participation and patient outcomes. Integrating DHL assessment and training into routine health care delivery is essential to reducing disparities and advancing health equity.

OBJECTIVES:Medical emergency teams (METs) are activated in response to signs and symptoms, or triggers, of clinical deterioration in acute care settings. However, the patterns in which triggers manifest and impact outcomes are poorly understood. We identified and described the patterns in which multiple triggers cluster to activate pediatric METs and examined the associations between these clusters and outcomes. METHODS:Pediatric MET events from January 2015 to December 2019 in the Get With The Guidelines®-Resuscitation national registry focused on METs (N = 4289) were grouped into MET trigger clusters using cluster analyses based on triggers used to activate the MET. Differences in patient characteristics across MET trigger clusters were compared using Pearson χ2 and analysis of variance (ANOVA) tests. Hierarchical logistic regressions tested associations between trigger clusters and outcomes. RESULTS:A total of 4 MET trigger clusters were identified. The triggers that predominantly defined each cluster were as follows: Cluster 1, decreased oxygen saturation and mental status changes; Cluster 2, tachypnea, tachycardia, and staff concern; Cluster 3, new onset difficulty in breathing and staff concern; and Cluster 4, the reference cluster, tachypnea, new onset difficulty in breathing, and decreased oxygen saturation. Patients in Cluster 1 were more likely to experience acute respiratory compromise (need for emergent assisted ventilation), and patients in Clusters 1 and 3 were more likely to be transferred to critical care. CONCLUSIONS:A total of 4 MET trigger clusters were identified and have varying associations with outcomes. MET trigger clusters could guide bedside care and triage in clinical emergencies and help develop more accurate predictive models for detecting clinical deterioration.

BACKGROUND:African American caregivers disproportionately engage in high-intensity caregiving. Pain experiences of African Americans may interfere with caregiving and overall health, but little is known about the associations of caregiving activities and activity-limiting pain among African Americans. OBJECTIVE:We aimed to 1) examine risk factors for activity-limiting pain among African American caregivers and 2) analyze the relationships between caregiving intensity, patient care needs and activity-limiting pain. METHODS:In a cross-sectional analysis, using nationally representative data from the National Study of Caregiving and linked National Health and Aging Trends Study, we analyzed caregiver and care recipient factors associated with activity-limiting pain among African American caregivers. We examined the relationship between caregiving intensity, patient care needs and activity-limiting pain using multivariable logistic regression. Sampling weights were applied to make nationally representative estimates. RESULTS:Our sample (N = 1673) included mostly female (63.5%) African American caregivers, with a mean age of 55.8 ± 21.5 years. Nearly half experience pain and 11% report activity-limiting pain. In our fully adjusted, multivariable model, those with higher intensity caregiving (i.e., longer duration of caregiving) [aOR: 2.09, CI: 1.29-3.39] and higher patient care needs (i.e., supporting care recipients requiring assistance for more activities of daily living (ADLs)) [aOR: 1.15, CI: 1.02-1.29] had higher odds of activity-limiting pain compared to those with lower intensity caregiving and lower care needs. CONCLUSION:These findings underscore the importance of the intersection of race, caregiving, and pain. Future work should explore how African American caregivers cope with pain and how best to support them.

Background Patient portals are secure online platforms that have shown potential to facilitate shared decision‐making (SDM) in cardiovascular disease risk reduction. However, the role of health care providers (HCPs) in offering patient portals within the context of SDM remains poorly understood. This study aimed to examine the relationship between patient portal access offered by HCPs and patient engagement in SDM among adults with or at risk of cardiovascular disease in the United States (US). Methods This population‐based cross‐sectional study included a nationally representative sample of US adults from the 2022 Health Information National Trends Survey. We performed weighted multivariable logistic regression analyses to examine the association between patient portal access offered by HCPs and patient engagement in SDM. Results The study included a representative sample of 4234 adults with or at risk of cardiovascular disease. The mean age of the participants was 48.5 years (SD, 17.1), with 50.6% female and 62.8% White. Adults who were offered access to patient portals by HCPs (adjusted odds ratio, 2.11 [95% CI, 1.34–3.32]) and encouraged to use them (adjusted odds ratio, 1.68 [95% CI, 1.15–2.45]) were more likely to engage in SDM than their counterparts, adjusting for covariates. The extent of this association varied by demographics and social determinants of health. Conclusions Offering access to patient portals and encouragement to use them by HCPs was associated with high SDM among US adults with or at risk of cardiovascular disease. Future research is needed to explore the possible causal relationship between patient portal use and access and patient engagement in SDM.

BACKGROUND:Although telehealth cardiac rehabilitation (CR) may improve access, there are concerns about its long-term effectiveness and impact on equity as compared with in-person CR. Our objective was to tailor a patient-centered telehealth CR program for diverse populations. METHODS:CR patients and caregivers were recruited between January and September 2023 from 4 US academic medical centers. Participants engaged in human-centered design sessions to iteratively refine a telehealth CR program. Sessions had planned topics, but there was variation across sites to account for site-specific needs and participant feedback. Sessions were qualitatively analyzed using rapid template analysis with preselected behavioral science constructs and other emergent codes. RESULTS:The study included 21 participants (71% aged ≥60 years, 48% women, 62% non-Hispanic White individuals; 90% CR patients, 10% CR caregivers). Participants thought that telehealth CR could be helpful for personalized support at home and convenience but recognized that technology is not always easy to use. Some expressed concerns about the safety of telehealth CR, especially at the beginning, and desired monitoring through a mobile device or video observation of exercise. Safety protocols and technology training were developed, which addressed concerns about telehealth CR. Opportunities for social support with telehealth CR were also desired. From these findings, an implementation toolkit was developed, including a graphic program description, safety plan, home exercise plan for during and after CR, and scripts for technology training and individual and group telehealth visits. CONCLUSIONS:A patient-centered telehealth CR program and implementation toolkit were systematically tailored to address the needs of diverse populations.

BACKGROUND:Digital health technologies provide a scalable, efficient approach to implementing guideline-recommended risk factor modification in the care of patients with atrial fibrillation (AF). OBJECTIVES:This study aimed to evaluate the feasibility of a 12-week, multicomponent, virtual AF management program using a smartphone application, connected devices, and virtual coaching calls for risk factor modification. METHODS:Patients with AF were enrolled from outpatient clinics. Patients were randomized in a 1:1 ratio to either usual care only or the virtual program. The study objectives were to assess feasibility, with the goal of achieving at least 60% participant retention at 12 weeks, intervention engagement, and participant satisfaction. RESULTS:Among 61 patients enrolled (76% of those approached), the mean age was 65 ± 8 years, and 36% were women. A total of 89% of all participants were retained by 12-week follow-up. In the intervention group, at 12-weeks, 88% continued using the smartphone application, 73% continued participation in virtual coaching calls, and 80% reported being satisfied with the program. CONCLUSIONS:The mTECH Afib (Patient Centered mobile health TECHnology Enabled Atrial Fibrillation Management) trial demonstrates feasibility of conducting a randomized controlled trial using an innovative digital health technology-enabled intervention with broad patient engagement and acceptance of the program components. Large-scale clinical trials powered for health outcomes will be necessary to establish intervention efficacy.

Background While the positive effects of digital technology on cognitive function are established, the specific impacts of different types of technology activities on distinct cognitive domains remain underexplored. Objective This study aimed to examine the associations between transitions into and out of various technology activities and trajectories of cognitive domains among community-dwelling older adults without dementia. Method Data were drawn from 5566 community-dwelling older adults without dementia who participated in the National Health and Aging Trends Study from 2015 to 2022. Technology activities assessed included online shopping, banking, medication refills, social media use, and checking health conditions online. The cognitive domains measured were episodic memory, executive function, and orientation. Asymmetric effects models were used to analyze the associations between technology activity transitions and cognitive outcomes, adjusting for demographic, socioeconomic, and health-related covariates. Lagged models were applied for sensitivity analysis. Results In the asymmetric effects models, the onset of online shopping (β=.046, P=.02), medication refills (β=.073, P<.001), and social media use (β=.065, P=.01) was associated with improved episodic memory. The cessation of online shopping was associated with faster episodic memory decline (β=−.023, P=.047). In contrast, the cessation of online banking (β=−.078, P=.01) and social media use (β=−.066, P=.003) was associated with decreased episodic memory. The initiation of instrumental, social, and health-related technology activities was associated with slower cognitive decline in orientation. The lagged models further emphasized the effects of stopping online banking and starting online medication refills in relation to episodic memory, as well as the positive associations between online shopping and social media use and orientation. All significant effects were of small magnitude. Conclusions Combining findings from the main and sensitivity analyses, results suggest that interventions designed to support episodic memory in older adults should emphasize promoting the use of online medication refill services and sustaining engagement with online banking, particularly among those who have already established these habits. To support orientation, strategies should focus on facilitating adoption of online shopping and social media use, helping older adults become comfortable navigating these platforms. Future trials are needed to assess the clinical relevance of targeted interventions for specific cognitive domains, to promote the initiation and maintenance of digital activities to help mitigate domain-specific cognitive decline in aging populations.

Importance Black persons, including immigrants, in the US disparately experience poor cardiometabolic health (CMH). Limited research on the effect of lifestyle interventions that improve CMH among African immigrant populations is available. Objective To test the effectiveness of a culturally adapted, virtual lifestyle intervention on control of blood pressure (BP) and hemoglobin A1c (HbA1c) levels among African immigrants with CMH risk factors. Design, Setting, and Participants Afro-DPP (Diabetes Prevention Program), a pilot cluster-randomized clinical trial, evaluated the effectiveness of a multicomponent CMH intervention. The study took place in 2 churches with predominantly African immigrant congregations in the Baltimore-Washington, DC, metropolitan area from January 1, 2022, to July 31, 2023. Participants were adults aged 25 to 75 years with at least 2 CMH risk factors who self-identified as African immigrants and belonged to the participating churches. Analyses followed the intention-to-treat principle. Intervention Participants received a 6-month culturally adapted lifestyle intervention based on the National DPP curriculum, delivered via virtual group sessions by a lifestyle coach of African origin. The delayed intervention began 6 months later with a follow-up time of 6 months. The intervention also included remote BP and weight monitoring. Main Outcome and Measures Primary outcomes were changes in systolic and diastolic BP and HbA1c levels from baseline to 6 months. Secondary outcomes included reduced body weight and body mass index (BMI; calculated as weight in kilograms divided by height in meters squared). Results The analytic population included 60 participants (mean [SD] age, 50.6 [11.9] years; 40 [66.7%] women). In the first intervention group (n = 30), systolic BP decreased by 9.2 (95% CI, 2.5-15.9) mm Hg, diastolic BP by 6.1 (95% CI, 2.1-10.0) mm Hg, body weight by 4.9 (95% CI, 1.0-8.7) kg, and BMI by 1.1 (95% CI, 0.4-1.7) at 6 months. In the delayed intervention group (n = 30), systolic BP decreased by 11.4 (95% CI, 2.4-20.5) mm Hg, diastolic BP by 10.3 (95% CI, 5.4-15.2) mm Hg, and body weight by 3.3 (95% CI, 0.01-6.5) kg, while BMI increased by 0.3 (95% CI, -1.5 to 2.0). Conclusions and Relevance Trial findings indicate that interventions incorporating cultural adaptation and virtual components could help address CMH disparities in this population.

Black persons, including immigrants, in the US disparately experience poor cardiometabolic health (CMH). Limited research on the effect of lifestyle interventions that improve CMH among African immigrant populations is available. To test the effectiveness of a culturally adapted, virtual lifestyle intervention on control of blood pressure (BP) and hemoglobin A1c (HbA1c) levels among African immigrants with CMH risk factors. Afro-DPP (Diabetes Prevention Program), a pilot cluster-randomized clinical trial, evaluated the effectiveness of a multicomponent CMH intervention. The study took place in 2 churches with predominantly African immigrant congregations in the Baltimore–Washington, DC, metropolitan area from January 1, 2022, to July 31, 2023. Participants were adults aged 25 to 75 years with at least 2 CMH risk factors who self-identified as African immigrants and belonged to the participating churches. Analyses followed the intention-to-treat principle. Participants received a 6-month culturally adapted lifestyle intervention based on the National DPP curriculum, delivered via virtual group sessions by a lifestyle coach of African origin. The delayed intervention began 6 months later with a follow-up time of 6 months. The intervention also included remote BP and weight monitoring. Primary outcomes were changes in systolic and diastolic BP and HbA1c levels from baseline to 6 months. Secondary outcomes included reduced body weight and body mass index (BMI; calculated as weight in kilograms divided by height in meters squared). The analytic population included 60 participants (mean [SD] age, 50.6 [11.9] years; 40 [66.7%] women). In the first intervention group (n = 30), systolic BP decreased by 9.2 (95% CI, 2.5-15.9) mm Hg, diastolic BP by 6.1 (95% CI, 2.1-10.0) mm Hg, body weight by 4.9 (95% CI, 1.0-8.7) kg, and BMI by 1.1 (95% CI, 0.4-1.7) at 6 months. In the delayed intervention group (n = 30), systolic BP decreased by 11.4 (95% CI, 2.4-20.5) mm Hg, diastolic BP by 10.3 (95% CI, 5.4-15.2) mm Hg, and body weight by 3.3 (95% CI, 0.01-6.5) kg, while BMI increased by 0.3 (95% CI, −1.5 to 2.0). Trial findings indicate that interventions incorporating cultural adaptation and virtual components could help address CMH disparities in this population. ClincalTrials.gov Identifier NCT05144737

Background: Hybrid cardiac rehabilitation (HCR) is an emerging approach to increase participation in cardiac rehabilitation, which targets improvements in functional status and broader risk factor modification including lipid management. However, long-term lipid control trends of patients engaging in HCR remain unexplored. Methods: Using data from a quality improvement program initiated during COVID-19, we conducted a retrospective analysis of 68 adults eligible for HCR from Jan 2021 to Feb 2023 at the Johns Hopkins Health System (Baltimore, MD), utilizing the Corrie digital health platform. This multi-component platform combines expert knowledge with gamified education and virtual coaching to deliver HCR. Patients hospitalized for cardiovascular events qualifying for HCR were recruited for a pilot study of a randomized controlled trial (mTECH REHAB; NCT05238103). We modeled trends in low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), non-HDL-C, and triglycerides via mixed-effects regression. Results: Among 68 eligible adults, 41 participated in HCR, with a mean age of 60.1 years, 78.1% male, 24.3% Black, and 14.6% Asian/Mixed. HCR participation was significantly associated with being married (61% vs 33%, p=0.044) or employed (63% vs 26%, p=0.015). On average, 2.3 lipid panels were assessed per person over a median of 12 months (max 30 months) post-discharge. During this period, LDL-C levels decreased from 84.6 mg/dL (95% CI: 75.0-94.1) to 53.0 mg/dL (95% CI: 34.3-71.8) (p for trend = 0.003). Non-HDL-C decreased from 105.9 mg/dL (95% CI: 95.7-116.1) to 74.2 mg/dL (95% CI: 54.1-94.3) (p for trend = 0.005). Levels of HDL-C and triglycerides showed no significant changes (p for trend = 0.724 and 0.607). Among 34 participants with ≥2 lipid panels, the proportion of LDL-C <70 mg/dL increased from 41.2% at hospitalization to 64.7% at the most recent check, and LDL-C <55 mg/dL increased from 14.7% to 38.2%. Conclusion: Improvements in atherogenic lipid levels, in particular LDL-C and non-HDL-C, were observed in adults participating in HCR after a cardiovascular event. Our findings highlight the potential of HCR to support optimal lipid management in adults with cardiovascular disease.

BACKGROUND:Prior studies have shown that cardiovascular disease (CVD) can be effectively managed through telehealth. However, there are little national data on the use of telehealth in people with CVD or CVD risk factors. We aimed to determine the prevalence of telehealth visits and visit modality (video versus audio-only) in people with CVD and CVD risk factors. We also assessed their rationale and satisfaction with telehealth visits. METHODS AND RESULTS:A nationally representative sample of 6252 participants from the 2022 Health Information National Trends Survey 6 was used. We defined the CVD risk categories as having no self-reported CVD (coronary heart disease or heart failure) or CVD risk factors (hypertension, diabetes, obesity, or current smoking), CVD risk factors alone, and CVD. Multivariable logistic regression, adjusting for major sociodemographic factors, assessed the relationship between CVD risk and telehealth uptake. The weighted prevalence of using telehealth was 50% (95% CI, 44%-56%) for individuals with CVD and 40% (95% CI, 37%-43%) for those with CVD risk factors alone. Individuals with CVD had the highest odds of using any telehealth (audio-only or video) (adjusted odds ratio [OR], 2.02 [95% CI, 1.39-2.93]) when compared with those without CVD or CVD risk factors. Notably, 21% (95% CI, 16.3%-25.6%) of patients with CVD used audio-only visits (adjusted OR, 2.38 [95% CI, 1.55-3.64]) compared with patients without CVD or CVD risk factors. CONCLUSIONS:In a nationally representative survey, there was high prevalence of any (video or audio-only) telehealth visits in people with CVD, and audio-only visits comprised a significant proportion of telehealth visits in this population.

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.

HomeJournal of the American Heart AssociationVol. 13, No. 2Challenge of Optimizing Medical Therapy in Heart Failure: Unlocking the Potential of Digital Health and Patient Engagement Open AccessArticle CommentaryPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toOpen AccessArticle CommentaryPDF/EPUBChallenge of Optimizing Medical Therapy in Heart Failure: Unlocking the Potential of Digital Health and Patient Engagement Zahra Azizi, Jessica R. Golbus, Erin M. Spaulding, Phillip H. Hwang, Ana L. A. Ciminelli, Kathleen Lacar, Mario Funes Hernandez, Nisha A. Gilotra, Natasha Din, Luisa C. C. Brant, Rhoda Au, Andrea Beaton, Brahmajee K. Nallamothu, Chris T. Longenecker, Seth S. Martin, Michael P. Dorsch and Alexander T. Sandhu Zahra AziziZahra Azizi * Correspondence to: Zahra Azizi, MD, MSc, Center for Digital Health, Department of Cardiovascular Medicine, Stanford University, 3180 Porter Dr, Palo Alto, CA 94304. Email: E-mail Address: [email protected] https://orcid.org/0000-0002-7897-0934 , Center for Digital Health, , Stanford University, , Stanford, , CA, , Stanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Jessica R. GolbusJessica R. Golbus https://orcid.org/0000-0002-9538-3926 , Division of Cardiovascular Diseases, Department of Internal Medicine, , University of Michigan, , Ann Arbor, , MI, , Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), , University of Michigan, , Ann Arbor, , MI, , The Center for Clinical Management and Research, , Ann Arbor VA Medical Center, , Ann Arbor, , MI, , Erin M. SpauldingErin M. Spaulding https://orcid.org/0000-0001-8390-2277 , Johns Hopkins University School of Nursing, , Baltimore, , MD, , mTECH Center, Division of Cardiology, Department of Medicine, , Johns Hopkins University School of Medicine, , Baltimore, , MD, , Phillip H. HwangPhillip H. Hwang https://orcid.org/0000-0001-6780-6808 , Department of Epidemiology, , Boston University School of Public Health, , Boston, , MA, , Ana L. A. CiminelliAna L. A. Ciminelli https://orcid.org/0000-0002-3462-7595 , School of Medicine and Hospital das Clínicas Telehealth Center, , Universidade Federal de Minas Gerais, , Belo Horizonte, , Brazil, , Kathleen LacarKathleen Lacar , Center for Digital Health, , Stanford University, , Stanford, , CA, , Stanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Mario Funes HernandezMario Funes Hernandez https://orcid.org/0000-0002-6545-3110 , Center for Digital Health, , Stanford University, , Stanford, , CA, , Stanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Nisha A. GilotraNisha A. Gilotra https://orcid.org/0000-0002-4511-8008 , mTECH Center, Division of Cardiology, Department of Medicine, , Johns Hopkins University School of Medicine, , Baltimore, , MD, , Natasha DinNatasha Din https://orcid.org/0000-0001-5312-4451 , Center for Digital Health, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Healthcare System, , Palo Alto, , CA, , Luisa C. C. BrantLuisa C. C. Brant https://orcid.org/0000-0002-7317-1367 , School of Medicine and Hospital das Clínicas Telehealth Center, , Universidade Federal de Minas Gerais, , Belo Horizonte, , Brazil, , Rhoda AuRhoda Au https://orcid.org/0000-0001-7742-4491 , Department of Epidemiology, , Boston University School of Public Health, , Boston, , MA, , Department of Anatomy and Neurobiology, , Boston University School of Medicine, , Boston, , MA, , Andrea BeatonAndrea Beaton https://orcid.org/0000-0002-4963-355X , Department of Pediatrics, , University of Cincinnati School of Medicine, , Cincinnati, , OH, , Department of Pediatrics, , The Heart Institute at Cincinnati Children's Hospital, , Cincinnati, , OH, , Brahmajee K. NallamothuBrahmajee K. Nallamothu https://orcid.org/0000-0003-4331-6649 , Division of Cardiovascular Diseases, Department of Internal Medicine, , University of Michigan, , Ann Arbor, , MI, , Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), , University of Michigan, , Ann Arbor, , MI, , The Center for Clinical Management and Research, , Ann Arbor VA Medical Center, , Ann Arbor, , MI, , Chris T. LongeneckerChris T. Longenecker https://orcid.org/0000-0002-9468-0179 , Division of Cardiology and Department of Global Health, , University of Washington, , Seattle, , WA, , Seth S. MartinSeth S. Martin https://orcid.org/0000-0002-7021-7622 , mTECH Center, Division of Cardiology, Department of Medicine, , Johns Hopkins University School of Medicine, , Baltimore, , MD, , Michael P. DorschMichael P. Dorsch https://orcid.org/0000-0003-2910-1879 , College of Pharmacy, , University of Michigan, , Ann Arbor, , MI, and Alexander T. SandhuAlexander T. Sandhu https://orcid.org/0000-0003-3208-1143 , Center for Digital Health, , Stanford University, , Stanford, , CA, , Stanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Healthcare System, , Palo Alto, , CA, Originally published16 Jan 2024https://doi.org/10.1161/JAHA.123.030952Journal of the American Heart Association. 2024;13:e030952Nonstandard Abbreviations and AcronymsGDMTguideline‐directed medical therapyHFrEFheart failure with reduced ejection fractionHeart failure (HF) is responsible for substantial morbidity among the estimated 6 million affected adults in the United States.1 For HF with reduced ejection fraction (HFrEF), optimal guideline‐directed medical therapy (GDMT) is estimated to reduce mortality by >70% in addition to improving quality of life.1 However, GDMT remains underused.2, 3 Therefore, there is a critical need to identify strategies to improve implementation of GDMT for patients with HF.HF remote monitoring programs have traditionally focused on lifestyle management, monitoring for signs of decompensation and diuretic adjustment rather than optimization of GDMT. The impact of such programs on clinical outcomes has been inconsistent. Given the clinical benefit of GDMT, focusing digital health interventions on medication optimization may improve their overall impact.4 In addition, interventions that have focused on improving HF medical therapy rates have traditionally focused on clinician or health system–level interventions, such as clinical decision support to encourage medication uptitration, implementation of pharmacist‐ or nurse‐led titration protocols, and outpatient audit‐and‐feedback interventions.5, 6 Many of these interventions have demonstrated effectiveness, but the magnitude of improvement in GDMT initiation and optimization has often been modest.2For optimal HF management, patients ideally need to attend clinic visits, take and manage medications frequently, monitor vital signs and weight, adjust lifestyle, cope with HF stress, and participate in cardiac rehabilitation. However, there is little prior research on enhancing patient engagement to improve the use of GDMT. The EPIC‐HF (Electronically Delivered, Patient‐Activation Tool for Intensification of Medications for Chronic Heart Failure with Reduced Ejection Fraction) trial illustrated the potential of this paradigm.7 The trial randomized 306 outpatients with HFrEF to usual care or to receive patient activation tools, including a 3‐minute video on the importance of GDMT and a 1‐page paper checklist on GDMT goals. There was nearly a 20% absolute increase in GDMT initiation or intensification among the patient activation arm at 30 days. This illustrates that improving patient knowledge and motivation can substantially improve GDMT rates.Patient engagement is essential for virtual HF programs designed to optimize GDMT.8 Digital health has the potential to expand such programs to focus on activating patients to advocate for optimal care. On the basis of prior literature and findings from patient, caregiver, and clinician panels in the American Heart Association Health Technology and Innovation Strategically Focused Research Network, this viewpoint identifies current barriers in the outpatient management of HFrEF and explores approaches by which digital technology–enabled patient engagement can enhance quality of care and improve outcomes among patients with HFrEF.GAPS IN OUTPATIENT HF CAREEfforts to improve quality of care among patients with HFrEF have primarily focused on the inpatient setting.9 The benefits to inpatient GDMT initiation and titration include readily available safety and tolerability data (vital signs and laboratory values), multidisciplinary care team resources (social work, pharmacy, and nutrition), and patient and caregivers' heightened focus on HF. However, the pressure to reduce hospital length of stay and the complexity of uptitrating several classes of GDMT medications during an acute HF exacerbation prohibit full GDMT optimization by the time of discharge. The need for rapid outpatient uptitration is further strengthened by the safety, tolerability and efficacy of rapid optimization, helped by NT‐proBNP testinG, of heart failure therapies trial,10 in which early optimization of medical therapy during and following a HF hospitalization reduced the composite of HF rehospitalization and all‐cause death by 34% (risk ratio, 0.66 [95% CI, 0.50–0.86]). In addition, many patients are diagnosed with HF in the ambulatory setting and may avoid hospitalization with timely initiation and optimization of GDMT. Therefore, effective strategies to improve outpatient optimization of GDMT for HFrEF are critical.There is suboptimal uptitration of GDMT for HFrEF in outpatient settings. The change the management of patients with heart failure registry demonstrated that outpatients are rarely initiated on additional therapies or have the dose of existing therapies uptitrated. Only 20% of patients with HFrEF were on target β‐blocker doses, and only 10% experienced any uptitration of their medical therapies over a 12‐month period.2 Hence, in the following sections, we will discuss the barriers to improve outpatient HF care and explore how promoting patient engagement via digital health technology could help overcome these challenges.CLINICIAN AND HEALTH SYSTEM BARRIERSMultiple factors impact clinician‐level decision‐making on GDMT optimization and contribute to therapeutic inertia. First, HF management is a rapidly evolving field, and knowledge gaps contribute to suboptimal treatment. Clinicians may underestimate the risk faced by outpatients with mild symptoms despite their substantially higher risk of hospitalization or death compared with patients receiving primary or secondary prevention for atherosclerotic cardiovascular disease.1 Second, clinicians manage multiple concurrent conditions and have limited time to dedicate to any given medical problem or medication adjustment.There are also critical structural system‐level barriers that contribute to suboptimal GDMT implementation and intensification. The limited capacity of outpatient clinics renders in‐office uptitration every 1 to 2 weeks challenging. Gaps in care are further exacerbated for patients with limited access to cardiovascular specialists, because of full‐time jobs or transportation limitations, especially in rural settings.11 In the Veterans Health Administration, patients living with HF who lived further from specialty care were less likely to be on ≥50% of the target dose of β‐blockers or angiotensin‐converting enzyme inhibitors and angiotensin II receptor blockers.12 Finally, there is a substantial financial burden to GDMT optimization that is driven by both the out‐of‐pocket costs of brand‐name medications and the direct and indirect costs of outpatient care, including travel time and lost work.Digital health can help address these barriers by facilitating protocolized uptitration of therapy outside of traditional office visits.5, 13 Multiple studies have demonstrated that remote management programs led by nurses or pharmacists have successfully increased GDMT via protocolized uptitration.5, 13 Such programs can address financial and time costs in addition to leveraging the expansion and acceptance of telemedicine to provide virtual medication optimization with less frequent in‐person visits. Digital health solutions, including smartphone applications and paired monitoring devices, can (1) enable capture, transmission, and summation of data, such as weight and vital signs; and (2) be used to semiautomate medication initiation and uptitration via protocols. Furthermore, medication titration can be rapidly and electronically transmitted back to patients following care team confirmation. Such an approach can facilitate virtual management. Wong and colleagues successfully applied this approach in a pilot study of 20 patients with HFrEF using the Biofourmis platform that adjusted GDMT based on home vitals and a titration algorithm.14Digital health interventions must align with clinician workflows to enhance better clinical outcomes. For example, the OptiLink HF trial15 evaluated the impact of impedance‐based remote monitoring by implantable cardioverter‐defibrillator and cardiac resynchronization therapy with defibrillator devices among 1002 patients with advanced HF. They found only 55.5% of remote monitoring alerts led to contact by the clinical team consistent with the study protocol. Among patients in whom alerts led to protocolized contact, remote monitoring was associated with a reduction in the composite of cardiovascular death or HF hospitalization compared with usual care. Among patients in whom alerts did not lead to protocolized contact, there was no significant difference in outcomes compared with usual care. This study illustrates how successful interventions require alignment with clinician workflows.Implementing standardized best practices counters gaps in knowledge and the hesitance of uptitration when a patient is presumably stable. However, the effectiveness of such approaches remains reliant on patient self‐monitoring and patient‐clinician agreement on recommendations. For these reasons, there are often still large gaps in GDMT after the use of protocolized uptitration.5 As described in the following section, digital health tools can be used to empower patients to advocate for GDMT optimization. If the patient is aligned with the goal of HF GDMT uptitration and is equipped to conduct self‐monitoring, a virtual optimization program will likely have larger effects.PATIENT‐LEVEL BARRIERSMultiple patient‐level barriers contribute to limited GDMT optimization. First, there may be gaps in patients' understanding of the role of GDMT. HF education has traditionally emphasized salt and fluid restriction to reduce the risk of HF exacerbation, rather than the need for GDMT. Although an incident HF hospitalization is an ideal setting to introduce the importance of GDMT and the plan for continued uptitration, most patients will need outpatient reinforcement of these concepts and not all patients with HF are hospitalized at the time of diagnosis. The EPIC‐HF trial illustrated the impact of an animated previsit video to explain the importance of GDMT.7 Such educational content can be delivered longitudinally online or via smartphone applications. Delivering this information in manageable modules personalized to where a patient is in his/her journey may further increase its effectiveness.Beyond understanding the importance of GDMT, patients would likely benefit from a concrete understanding of how their medication regimen compares with optimal treatment. The EPIC‐HF trial used a 1‐page checklist that compared their medication regimen with target GDMT doses to illustrate gaps in their care.7 Smartphone applications can be used to help patients longitudinally track how their medication regimen compares with optimal GDMT. Furthermore, integrating the checklist with remote vital sign monitoring can provide actionable insights into how their regimen could be improved. Such data may empower patients to discuss uptitration with their care team.A second patient‐level barrier is the challenge of conceptualizing the benefits of therapy. Patients often feel better after decongestion; this symptomatic improvement reduces their urgency to optimize medical treatment. Educational content should emphasize the benefits of HF GDMT independent of baseline symptoms. In addition, most HFrEF therapies improve patient‐reported health status.1 Therefore, mobile health tools could be used to monitor the improvement in health status that occurs over time with GDMT optimization (eg, via the Kansas City Cardiomyopathy Questionnaire). Such tracking can provide biofeedback, reinforcing patient commitment to adhere to therapy and continue uptitration.16 A systematic review found applications providing biofeedback (ie, weight monitoring, blood pressure, or medication adherence) resulted in reduced hospitalizations and improved HF knowledge.16 Combining data on weight and medication adherence with structured assessments of patient‐reported health status may accentuate the effect of biofeedback.17A third potential barrier to GDMT optimization is patient concerns about the safety of rapid, virtual uptitration.18 Systematically monitoring laboratory values, vital signs, and symptoms via wireless blood pressure monitors/scales and application‐recorded patient‐reported health status may reassure patients that their response to medication changes is being monitored by their clinical team. Knowing the data are connected to the clinical team may itself improve patient adherence to monitoring.A fourth barrier to GDMT optimization is the high prevalence of cognitive impairment found in patients with HF,19 ranging from 25% to 75%, depending on the criteria used. Cognitive impairment can significantly impair a patient's ability to perform self‐monitoring, which is critical for GDMT optimization. Mobile health technologies can be used to identify patients with cognitive impairment via novel digital biomarkers, such as patient voice20; after identifying these patients, mobile health technology can facilitate family and caregivers being more involved in their care.While delivering guideline‐recommended care, clinicians also need to align care with patient priorities. This process of shared decision‐making is a challenging balance as patients living with HF have competing comorbidities, in addition to non–health‐related priorities. An average patient with HFrEF takes 6.8 prescription medications per day21 and has >5 noncardiac comorbidities.22 Patient education can help patients and their caregivers understand the potential benefits of GDMT in terms of both survival and quality of life. This will help patients make informed decisions about GDMT optimization that align with their priorities.A critical challenge for digital health solutions is being accessible to the broad range of patients experiencing HF. In addition, using digital tools can be challenging for patients with lower technology and health literacy. Ensuring digital interventions improve, rather than worsen, health equity requires such tools to be designed and tested across a broad range of patients. Health systems must both facilitate access to such interventions and provide human support to facilitate patients with lower technologic literacy using such tools.Remote HF management has focused on capturing information from patients and sharing it with clinicians to enable remote monitoring for decompensation and, more recently, to facilitate GDMT optimization. We believe there is a critical opportunity to test how these data can be shared with patients and caregivers to help them be their own best advocates for GDMT optimization.THE PATH FORWARDLeveraging digital health technologies can enhance the quality of care by not only reducing clinician burden, but also empowering patients and caregivers as active partners. The path forward starts with developing tools based on our understanding of barriers, patient motivation, and implementation science and then rigorously evaluating them in clinical trials. Then, we must focus on adapting effective tools to fit the unique needs of different populations and settings to improve not only overall quality of care but also health equity.23The American Heart Association Health Technology and Innovation Strategically Focused Research Network has identified multiple goals for digital health technology to substantially improve GDMT initiation and target dose achievement based on human‐centered design sessions with patients, caregivers, and clinicians. A digital intervention toolkit must effectively address the barriers discussed above by (a) facilitating timely GDMT initiation and optimization for clinicians, (b) empowering patients with education on the health outcomes' impact of GDMT and when it should be optimized, and (c) reducing barriers for patients and clinicians alike in adjusting GDMT by leveraging virtual rather than in‐person settings (Figure). Not achieving these goals may substantially undermine the impact of a digital health intervention on GDMT use and clinical outcomes.Download figureDownload PowerPointFigure . Conceptual framework for leveraging digital health to facilitate patient engagement in heart failure (HF) management.This figure highlights the barriers that exist at the patient, clinician, and health system levels in optimizing guideline‐directed medical therapy (GDMT) for patients with HF. Digital health technologies can overcome these barriers by allowing for regular GDMT adjustments, enabling virtual medication changes without the need for office visits, and emphasizing the importance of GDMT uptitration to patients. To move forward, a digital toolkit must be developed that focuses on patient education, including visualization, gamification, goal setting, and biofeedback. This toolkit can empower patients and caregivers as active partners and advocates, and its effectiveness can be rigorously evaluated in clinical trials using our understanding of barriers, patient motivation, and implementation science. By increasing access to care, improving GDMT, promoting healthy lifestyle decisions, and improving adherence, this toolkit can ultimately enhance quality of life, decrease hospitalization and cost of care, and increase survival. Created with BioRender.com.An HF digital health intervention should provide recommendations for GDMT optimization that integrate patient‐specific factors and clinician preferences. The intervention should be consistent with guideline‐based recommendations but can be flexible. There is not a single correct GDMT therapy order. Therefore, clinicians with experience in HF management may prefer an intervention that allows customization within the bounds of evidence. In this case, the focus would be helping clinicians implement their plan efficiently rather than telling them the right next step. For others with less expertise in HF management, a more prescriptive approach may be preferable. But most important, the intervention should include recommendations at frequent, regular intervals to optimize GDMT based on the prespecified protocol to counteract the inertia that is prevalent throughout chronic disease management.Several strategies can be used to promote patients and caregivers becoming their own most powerful advocates for safe, timely GDMT optimization. First, education should begin early in the treatment process, such as during an initial HF admission or after outpatient HF diagnosis, when patients are highly engaged in learning about their diagnosis. The education should focus on the benefits of timely optimization of GDMT. This should include an overview of the longitudinal treatment plan; patients may accept multiple titration steps if explained upfront rather than occurring as seemingly unexpected changes at later visits. Education should be available longitudinally to reinforce and expand prior material. When a new therapy is recommended, patients should have access to materials that explain the expected benefits and risks. Second, novel approaches to motivate patients to advocate for therapy optimization should be tested. This may include medication checklists, visualizations of expected GDMT benefits, gamification of GDMT achievement, or biofeedback. Patient empowerment is not only a valuable tool for improving GDMT, it is also consistent with the principles of shared decision‐making. Patients should have control and knowledge about their health. Patient activation through electronic reminders can motivate conversations with providers, and this approach was shown to improve use of GDMT when given before an upcoming clinic visit in the EPIC‐HF trial.7 Digital health tools that provide access to personal health data in a manner that is understandable and actionable may provide such empowerment.Finally, a digital intervention should simplify therapy optimization for care teams and patients. Integrating ambulatory measurements, including laboratory values, vital signs, and symptoms, with electronic health record data and preestablished protocols should make remote monitoring more efficient for clinicians. Facilitating uptitration with fewer office visits should reduce patient financial and time burden and create clinic availability for patients with worsening HF. Ideally, interventions should also meet remote patient monitoring reimbursement standards. All of these advantages could tackle the extra burdens of providing GDMT in rural communities and those with limited numbers of providers. Having a viable financial model in both the fee‐for‐service system and aligning with the transition to value‐based payment will be critical for health system support and uptake.Digital health technologies can be impactful if they promote care with proven clinical benefit, including GDMT in patients with HF. We believe our proposed toolkit can enhance HF outcomes through GDMT optimization. The current literature has not yet demonstrated the efficacy and utility of digital health in this regard. Until clinical benefit is proven in clinical trials, implementation will be difficult. Future research should leverage pragmatic methods that allow efficient evaluation of digital health tools embedded within clinical workflows. Such approaches will also allow researchers to better understand the clinical context in which therapies are effective. Even then, widespread implementation will need to overcome multiple important barriers, including data challenges (access, ownership, security, and EMR integration), privacy and financial concern, digital literacy, access disparities, and user retention. Overcoming these barriers will require bringing together experts in clinical care, informatics, health system design and implementation, and patient engagement. Once there is evidence for effective therapies, implementation science will be critical to guide how barriers can be overcome to scale such technologies in a broad and equitable manner.With the rapid expansion of digital health interventions, the systematic evaluation of different approaches will be critical. Systematic reviews that comprehensively catalogue different tools, evaluate their impact on outcomes, and identify their limitations will improve the future design and evaluation of digital health interventions.CONCLUSIONSDigital health technologies can change how we care for patients with chronic diseases, such as HF. The monitoring, education, and communication that were previously confined to brief visits in a clinician office can now be expanded to the time and space that works for the patient. Such expansion not only amplifies the data we can incorporate into clinical decision‐making but also allows us to transform how we engage patients and caregivers in driving their care. For HF, successfully breaking through long‐standing outpatient therapeutic inertia would have a substantial impact on morbidity and mortality for millions.Sources of FundingThis work was supported by the American Heart Association Health Technology and Innovation Strategically Focused Research Network.DisclosuresDrs Azizi, Spaulding, Gilotra, Martin, Golbus, and Dorsch were/are funded by American Heart Association Health Technology and Innovation Strategically Focused Research Network. Dr Sandhu is supported by the National Heart, Lung, and Blood Institute (1K23HL151672‐03). Dr Golbus is supported by the National, Heart, Lung, and Blood Institute (1K23HL168220‐01 and L30HL143700). Dr Nallamothu is a principal investigator or coinvestigator on research grants from the National Institutes of Health, Veterans Affairs Health Services Research and Development Service, the American Heart Association, Janssen, and Apple, Inc. He also receives compensation as Editor‐in‐Chief of Circulation: Cardiovascular Quality & Outcomes, a journal of the American Heart Association. Finally, he is a coinventor on US utility patent number US15/356,012 (US20170148158A1) entitled "Automated Analysis of Vasculature in Coronary Angiograms" that uses software technology with signal processing and machine learning to automate the reading of coronary angiograms, held by the University of Michigan. The patent is licensed to AngioInsight, Inc, in which Dr Nallamothu holds ownership shares and receives consultancy fees. Dr Martin: under a license agreement between Corrie Health and the Johns Hopkins University, the university owns equity in Corrie Health,

BackgroundTelemedicine 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. ObjectiveThis study aims to evaluate the prevalence of, inequities in, and primary reasons for telehealth visits a year after telemedicine expansion. MethodsWe 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. ResultsWe 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. ConclusionsAmong 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.