Digital Technologies in Cardiac Rehabilitation: A Science Advisory From the American Heart Association.

Cardiac rehabilitation has strong evidence of benefit across many cardiovascular conditions but is underused. Even for those patients who participate in cardiac rehabilitation, there is the potential to better support them in improving behaviors known to promote optimal cardiovascular health and in sustaining those behaviors over time. Digital technology has the potential to address many of the challenges of traditional center-based cardiac rehabilitation and to augment care delivery. This American Heart Association science advisory was assembled to guide the development and implementation of digital cardiac rehabilitation interventions that can be translated effectively into clinical care, improve health outcomes, and promote health equity. This advisory thus describes the individual digital components that can be delivered in isolation or as part of a larger cardiac rehabilitation telehealth program and highlights challenges and future directions for digital technology generally and when used in cardiac rehabilitation specifically. It is also intended to provide guidance to researchers reporting digital interventions and clinicians implementing these interventions in practice and to advance a framework for equity-centered digital health in cardiac rehabilitation.

Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data.

Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes and saving costs. However, the decision-making process is complex, nuanced, and time-consuming, requiring cardiologists with specialized expertise and training in heart failure and transplantation. In this study, we propose two logistic tensor regression-based models to predict patients with heart failure warranting evaluation for advanced heart failure therapies using irregularly spaced sequential electronic health records at the population and individual levels. The clinical features were collected at the previous visit and the predictions were made at the very beginning of the subsequent visit. Patient-wise ten-fold cross-validation experiments were performed. Standard LTR achieved an average F1 score of 0.708, AUC of 0.903, and AUPRC of 0.836. Personalized LTR obtained an F1 score of 0.670, an AUC of 0.869 and an AUPRC of 0.839. The two models not only outperformed all other machine learning models to which they were compared but also improved the performance and robustness of the other models via weight transfer. The AUPRC scores of support vector machine, random forest, and Naive Bayes are improved by 8.87%, 7.24%, and 11.38%, respectively. The two models can evaluate the importance of clinical features associated with advanced therapy referral. The five most important medical codes, including chronic kidney disease, hypotension, pulmonary heart disease, mitral regurgitation, and atherosclerotic heart disease, were reviewed and validated with literature and by heart failure cardiologists. Our proposed models effectively utilize EHRs for potential advanced therapies necessity in heart failure patients while explaining the importance of comorbidities and other clinical events. The information learned from trained model training could offer further insight into risk factors contributing to the progression of heart failure at both the population and individual levels.

Recruitment Strategies of a Decentralized Randomized Placebo Controlled Clinical Trial: The Canagliflozin Impact on Health Status, Quality of Life and Functional Status in Heart Failure (CHIEF-HF) Trial.

BACKGROUND: There has been growing Interest in patient-centered clinical trials using mobile technologies to reduce the need for in-person visits. The CHIEF-HF (Canagliflozin Impact on Health Status, Quality of Life and Functional Status in Heart Failure) trial was designed as a double-blind, randomized, fully decentralized clinical trial (DCT) that identified, consented, treated, and followed participants without any in-person visits. Patient-reported questionnaires were the primary outcome, which were collected by a mobile application. To inform future DCTs, we sought to describe the strategies used in successful trial recruitment. METHODS: This article describes the operational structure and novel strategies employed in a completely DCT by summarizing the recruitment, enrollment, engagement, retention, and follow-up processes used in the execution of the trial at 18 centers. RESULTS: A total of 18 sites contacted 130,832 potential participants, of which 2572 (2.0%) opened a hyperlink to the study website, completed a brief survey, and agreed to be contacted for potential inclusion. Of these, 1333 were eligible, and 658 consented; there were 182 screen failures, due primarily to baseline Kansas City Cardiomyopathy Questionnaire scores' not meeting inclusion criteria, resulting in 476 participants' being enrolled (18.5%). There was significant site-level variation in the number of patients invited (median = 2976; range 73-46,920) and in those agreeing to be contacted (median = 2.4%; range 0.05%-16.4%). At the site with the highest enrollment, patients contacted by electronic medical record portal messaging were more likely to opt into the study successfully than those contacted by e-mail alone (7.8% vs 4.4%). CONCLUSIONS: CHIEF-HF used a novel design and operational structure to test the efficacy of a therapeutic treatment, but marked variability across sites and strategies for recruiting participants was observed. This approach may be advantageous for clinical research across a broader range of therapeutic areas, but further optimization of recruitment efforts is warranted. REGISTRATION: NCT04252287 https://clinicaltrials.gov/ct2/show/NCT04252287.

Longitudinal Change and Predictors of Myocardial Flow Reserve by Positron Emission Tomography for the Evaluation of Cardiac Allograft Vasculopathy Following Heart Transplantation.

BACKGROUND: Positron emission tomography (PET) myocardial flow reserve (MFR) is a noninvasive method of detecting cardiac allograft vasculopathy in recipients of heart transplants (HTs). There are limited data on longitudinal change and predictors of MFR following HT. METHODS: We conducted a retrospective analysis of HT recipients undergoing PET myocardial perfusion imaging at an academic center. Multivariable linear and Cox regression models were constructed to identify longitudinal trends, predictors and the prognostic value of MFR after HT. RESULTS: Of HT recipients, 183 underwent 658 PET studies. The average MFR was 2.34 ± 0.70. MFR initially increased during the first 3 years following HT (+ 0.12 per year; P = 0.01) before beginning to decline at an annual rate of -0.06 per year (P < 0.001). MFR declines preceding acute rejection and improves after treatment. Treatment with mammalian target of rapamycin (mTOR) inhibitors (37.2%) slowed the rate of annual MFR decline (P = 0.03). Higher-intensity statin therapy was associated with improved MFR. Longer time post-transplant (P < 0.001), hypertension (P < 0.001), chronic kidney disease (P < 0.001), diabetes mellitus (P = 0.038), antibody-mediated rejection (P = 0.040), and cytomegalovirus infection (P = 0.034) were associated with reduced MFR. Reduced MFR (HR: 7.6, 95% CI: 4.4-13.4; P < 0.001) and PET-defined ischemia (HR: 2.3, 95% CI: 1.4-3.9; P < 0.001) were associated with a higher risk of the composite outcome of mortality, retransplantation, heart failure hospitalization, acute coronary syndrome, or revascularization. CONCLUSION: MFR declines after the third post-transplant year and is prognostic for cardiovascular events. Cardiometabolic risk-factor modification and treatment with higher-intensity statin therapy and mechanistic target of rapamycin inhibitors are associated with a higher MFR.

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

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

A Community Participatory Approach to Creating Contextually Tailored mHealth Notifications: myBPmyLife Project.

Just-in-time adaptive interventions (JITAIs) are a novel approach to mobile health (mHealth) interventions, sending contextually tailored behavior change notifications to participants when they are more likely to engage, determined by data from wearable devices. We describe a community participatory approach to JITAI notification development for the myBPmyLife Project, a JITAI focused on decreasing sodium consumption and increasing physical activity to reduce blood pressure. Eighty-six participants were interviewed, 50 at a federally qualified health center (FQHC) and 36 at a university clinic. Participants were asked to provide encouraging physical activity and low-sodium diet notifications and provided feedback on researcher-generated notifications to inform revisions. Participant notifications were thematically analyzed using an inductive approach. Participants noted challenging vocabulary, phrasing, and culturally incongruent suggestions in some of the researcher-generated notifications. Community-generated notifications were more direct, used colloquial language, and contained themes of grace. The FQHC participants' notifications expressed more compassion, religiosity, and addressed health-related social needs. University clinic participants' notifications frequently focused on office environments. In summary, our participatory approach to notification development embedded a distinctive community voice within our notifications. Our approach may be generalizable to other communities and serve as a model to create tailored mHealth notifications to their focus population.

Virtual AppLication-supported Environment To INcrease Exercise (VALENTINE) during cardiac rehabilitation study: Rationale and design.

BACKGROUND: In-person, exercise-based cardiac rehabilitation improves physical activity and reduces morbidity and mortality for patients with cardiovascular disease. However, activity levels may not be optimized and decline over time after patients graduate from cardiac rehabilitation. Scalable interventions through mobile health (mHealth) technologies have the potential to augment activity levels and extend the benefits of cardiac rehabilitation. METHODS: The VALENTINE Study is a prospective, randomized-controlled, remotely-administered trial designed to evaluate an mHealth intervention to supplement cardiac rehabilitation for low- and moderate-risk patients (ClinicalTrials.gov NCT04587882). Participants are randomized to the control or intervention arms of the study. Both groups receive a compatible smartwatch (Fitbit Versa 2 or Apple Watch 4) and usual care. Participants in the intervention arm of the study additionally receive a just-in-time adaptive intervention (JITAI) delivered as contextually tailored notifications promoting low-level physical activity and exercise throughout the day. In addition, they have access to activity tracking and goal setting through the mobile study application and receive weekly activity summaries via email. The primary outcome is change in 6-minute walk distance at 6-months and, secondarily, change in average daily step count. Exploratory analyses will examine the impact of notifications on immediate short-term smartwatch-measured step counts and exercise minutes. CONCLUSIONS: The VALENTINE study leverages innovative techniques in behavioral and cardiovascular disease research and will make a significant contribution to our understanding of how to support patients using mHealth technologies to promote and sustain physical activity.

Detection of Low Cardiac Index using a Polyvinylidene Fluoride-Based Wearable Ring and Convolutional Neural Networks.

This study investigated the use of a wearable ring made of polyvinylidene fluoride film to identify a low cardiac index (≤2 L/min). The waveform generated by the ring contains patterns that may be indicative of low blood pressure and/or high vascular resistance, both of which are markers of a low cardiac index. In particular, the waveform contains reflection waves whose timing and amplitude are correlated with pulse travel time and vascular resistance, respectively. Hence, the pattern of the waveform is expected to vary in response to changes in blood pressure and vascular resistance. By analyzing the morphology of the waveform, our aim was to create a tool to identify patients with low cardiac index. This was done using a convolutional neural network which was trained on data from animal models. The model was then tested on waveforms that were collected from patients undergoing pulmonary artery catheterization. The results indicate high accuracy in classifying patients with a low cardiac index, achieving an area under the receiver operating characteristics and precision-recall curves of 0.88 and 0.71, respectively.

Lower Extremity Function Is Independently Associated With Hospitalization Burden in Heart Failure With Preserved Ejection Fraction.

BACKGROUND: Frailty reflects decreased resilience to physiological stressors; its prevalence and prognosis are not fully defined in heart failure with preserved ejection fraction (HFpEF). METHODS: The Short Physical Performance Battery (SPPB) was prospectively obtained in 114 outpatients with HFpEF. The SPPB tests gait speed, tandem balance, and timed chair rises, each scored from 0 to 4 points. Severe and mild frailty were respectively defined as an SPPB score ≤6 and 7-9 points. We used risk-adjusted logistic, Poisson, and negative binominal regression, respectively, to assess the relationship between SPPB score and risk of death or all-cause hospitalization, number of hospitalizations, and days hospitalized or dead longer than 6 months. RESULTS: Patients were similar to other HFpEF cohorts (age 68 ± 13 years, 58% female, body mass index 36 ± 8 kg/m2, multiple comorbidities). Mean SPPB score was 6.9 ± 3.2, and 80% of patients were at least mildly frail. Over a 6-month period, the SPPB score independently predicted death or all-cause hospitalization (odds ratio 0.81 per point, 95% confidence interval [CI] 0.69-0.94, P = .006), number of hospitalizations (incidence rate ratio 0.92 per point, 95% CI 0.86-0.97, P = .006), and days hospitalized or dead (incidence rate ratio 0.85 per point, 95% CI 0.73-0.99, P = .04). CONCLUSIONS: Lower extremity function, as measured by the SPPB, independently predicts hospitalization burden in outpatients with HFpEF. Additional studies are warranted to explore shared mechanisms and treatment implications of frailty in HFpEF.

Supercomputing for the parallelization of whole genome analysis.

MOTIVATION: The declining cost of generating DNA sequence is promoting an increase in whole genome sequencing, especially as applied to the human genome. Whole genome analysis requires the alignment and comparison of raw sequence data, and results in a computational bottleneck because of limited ability to analyze multiple genomes simultaneously. RESULTS: We now adapted a Cray XE6 supercomputer to achieve the parallelization required for concurrent multiple genome analysis. This approach not only markedly speeds computational time but also results in increased usable sequence per genome. Relying on publically available software, the Cray XE6 has the capacity to align and call variants on 240 whole genomes in ∼50 h. Multisample variant calling is also accelerated. AVAILABILITY AND IMPLEMENTATION: The MegaSeq workflow is designed to harness the size and memory of the Cray XE6, housed at Argonne National Laboratory, for whole genome analysis in a platform designed to better match current and emerging sequencing volume.

Cytomegalovirus infection is associated with impaired myocardial flow reserve after heart transplantation.

BACKGROUND: Cardiac allograft vasculopathy (CAV) limits long-term survival after heart transplantation (HT). This study evaluates the relationship between clinically significant cytomegalovirus infection (CS-CMVi) and CAV using cardiac positron emission tomography (PET). METHODS: We retrospectively evaluated HT patients from 2005 to 2019 who underwent cardiac PET for CAV evaluation. Multivariable linear and logistic regression models were used to evaluate the association between CS-CMVi and myocardial flow reserve (MFR). Kaplan-Meier and Cox regression analyses were used to assess the relationship between CS-CMV, MFR, and clinical outcomes. RESULTS: Thirty-two (31.1%) of 103 HT patients developed CS-CMVi at a median 9 months after HT. Patients with CS-CMVi had a significantly lower MFR at year 1 and 3, driven by reduction in stress myocardial blood flow. Patients with CS-CMVi had a faster rate of decline in MFR compared to those without infection (-0.10 vs -0.06 per year, p < 0.001). CS-CMVi was an independent predictor of abnormal MFR (<2.0) (odds ratio: 3.8, 95% confidence intervals (CI): 1.4-10.7, p = 0.001) and a lower MFR (ß = -0.39, 95% CI: -0.63 to -0.16, p = 0.001) at year 3. In adjusted survival analyses, both abnormal MFR (log-rank p < 0.001; hazard ratio [HR]: 5.7, 95% CI: 4.2-7.2) and CS-CMVi (log-rank p = 0.028; HR: 3.3, 95% CI: 1.8-4.8) were significant predictors of the primary outcome of all-cause mortality, retransplantation, heart failure hospitalization, and acute coronary syndrome. CONCLUSIONS: CS-CMVi is an independent predictor of reduced MFR following HT. These findings suggest that CMV infection is an important risk factor in the development and progression of CAV.

Acceptability of a Text Message-Based Mobile Health Intervention to Promote Physical Activity in Cardiac Rehabilitation Enrollees: A Qualitative Substudy of Participant Perspectives.

BACKGROUND: Mobile health (mHealth) interventions have the potential to deliver longitudinal support to users outside of episodic clinical encounters. We performed a qualitative substudy to assess the acceptability of a text message-based mHealth intervention designed to increase and sustain physical activity in cardiac rehabilitation enrollees. METHODS AND RESULTS: Semistructured interviews were conducted with intervention arm participants of a randomized controlled trial delivered to low- and moderate-risk cardiac rehabilitation enrollees. Interviews explored participants' interaction with the mobile application, reflections on tailored text messages, integration with cardiac rehabilitation, and opportunities for improvement. Transcripts were thematically analyzed using an iteratively developed codebook. Sample size consisted of 17 participants with mean age of 65.7 (SD 8.2) years; 29% were women, 29% had low functional capacity, and 12% were non-White. Four themes emerged from interviews: engagement, health impact, personalization, and future directions. Participants engaged meaningfully with the mHealth intervention, finding it beneficial in promoting increased physical activity. However, participants desired greater personalization to their individual health goals, fitness levels, and real-time environment. Generally, those with lower functional capacity and less experience with exercise were more likely to view the intervention positively. Finally, participants identified future directions for the intervention including better incorporation of exercise physiologists and social support systems. CONCLUSIONS: Cardiac rehabilitation enrollees viewed a text message-based mHealth intervention favorably, suggesting the potentially high usefulness of mHealth technologies in this population. Addressing participant-identified needs on increased user customization and inclusion of clinical and social support is crucial to enhancing the effectiveness of future mHealth interventions. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04587882.

Text Messages to Promote Physical Activity in Patients With Cardiovascular Disease: A Micro-Randomized Trial of a Just-In-Time Adaptive Intervention.

BACKGROUND: Text messages may enhance physical activity levels in patients with cardiovascular disease, including those enrolled in cardiac rehabilitation. However, the independent and long-term effects of text messages remain uncertain. METHODS: The VALENTINE study (Virtual Application-supported Environment to Increase Exercise) was a micro-randomized trial that delivered text messages through a smartwatch (Apple Watch or Fitbit Versa) to participants initiating cardiac rehabilitation. Participants were randomized 4× per day over 6-months to receive no text message or a message encouraging low-level physical activity. Text messages were tailored on contextual factors (eg, weather). Our primary outcome was step count 60 minutes following a text message, and we used a centered and weighted least squares mean method to estimate causal effects. Given potential measurement differences between devices determined a priori, data were assessed separately for Apple Watch and Fitbit Versa users over 3 time periods corresponding to the initiation (0-30 days), maintenance (31-120 days), and completion (121-182 days) of cardiac rehabilitation. RESULTS: One hundred eight participants were included with 70 552 randomizations over 6 months; mean age was 59.5 (SD, 10.7) years with 36 (32.4%) female and 68 (63.0%) Apple Watch participants. For Apple Watch participants, text messages led to a trend in increased step count by 10% in the 60-minutes following a message during days 1 to 30 (95% CI, -1% to +20%), with no effect from days 31 to 120 (+1% [95% CI, -4% to +5%]), and a significant 6% increase during days 121 to 182 (95% CI, +0% to +11%). For Fitbit users, text messages significantly increased step count by 17% (95% CI, +7% to +28%) in the 60-minutes following a message in the first 30 days of the study with no effect subsequently. CONCLUSIONS: In patients undergoing cardiac rehabilitation, contextually tailored text messages may increase physical activity, but this effect varies over time and by device. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04587882.

A Physical Activity and Diet Just-in-Time Adaptive Intervention to Reduce Blood Pressure: The myBPmyLife Study Rationale and Design.

BACKGROUND: Smartphone applications and wearable devices are promising mobile health interventions for hypertension self-management. However, most mobile health interventions fail to use contextual data, potentially diminishing their impact. The myBPmyLife Study is a just-in-time adaptive intervention designed to promote personalized self-management for patients with hypertension. METHODS AND RESULTS: The study is a 6-month prospective, randomized-controlled, remotely administered trial. Participants were recruited from the University of Michigan Health in Ann Arbor, Michigan or the Hamilton Community Health Network, a federally qualified health center network in Flint, Michigan. Participants were randomized to a mobile application with a just-in-time adaptive intervention promoting physical activity and lower-sodium food choices as well as weekly goal setting or usual care. The mobile study application encourages goal attainment through a central visualization displaying participants' progress toward their goals for physical activity and lower-sodium food choices. Participants in both groups are followed for up for 6 months with a primary end point of change in systolic blood pressure. Exploratory analyses will examine the impact of notifications on step count and self-reported lower-sodium food choices. The study launched on December 9, 2021, with 484 participants enrolled as of March 31, 2023. Enrollment of participants was completed on July 3, 2023. After 6 months of follow-up, it is expected that results will be available in the spring of 2024. CONCLUSIONS: The myBPmyLife study is an innovative mobile health trial designed to evaluate the effects of a just-in-time adaptive intervention focused on improving physical activity and dietary sodium intake on blood pressure in diverse patients with hypertension. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT05154929.

A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies.

A model's interpretability is essential to many practical applications such as clinical decision support systems. In this article, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible for advanced therapies as proof of principle. From our results on this particular application, the proposed network achieves the highest F1 score. The network is capable of learning rules that can be interpreted and used by clinical providers. In addition, existing fuzzy domain knowledge can be easily transferred into the network and facilitate model training. In our application, with the existing knowledge, the F1 score was improved by over 5%. The characteristics of the proposed network make it promising in applications requiring model reliability and justification.

Predicting need for heart failure advanced therapies using an interpretable tropical geometry-based fuzzy neural network.

BACKGROUND: Timely referral for advanced therapies (i.e., heart transplantation, left ventricular assist device) is critical for ensuring optimal outcomes for heart failure patients. Using electronic health records, our goal was to use data from a single hospitalization to develop an interpretable clinical decision-making system for predicting the need for advanced therapies at the subsequent hospitalization. METHODS: Michigan Medicine heart failure patients from 2013-2021 with a left ventricular ejection fraction ≤ 35% and at least two heart failure hospitalizations within one year were used to train an interpretable machine learning model constructed using fuzzy logic and tropical geometry. Clinical knowledge was used to initialize the model. The performance and robustness of the model were evaluated with the mean and standard deviation of the area under the receiver operating curve (AUC), the area under the precision-recall curve (AUPRC), and the F1 score of the ensemble. We inferred membership functions from the model for continuous clinical variables, extracted decision rules, and then evaluated their relative importance. RESULTS: The model was trained and validated using data from 557 heart failure hospitalizations from 300 patients, of whom 193 received advanced therapies. The mean (standard deviation) of AUC, AUPRC, and F1 scores of the proposed model initialized with clinical knowledge was 0.747 (0.080), 0.642 (0.080), and 0.569 (0.067), respectively, showing superior predictive performance or increased interpretability over other machine learning methods. The model learned critical risk factors predicting the need for advanced therapies in the subsequent hospitalization. Furthermore, our model displayed transparent rule sets composed of these critical concepts to justify the prediction. CONCLUSION: These results demonstrate the ability to successfully predict the need for advanced heart failure therapies by generating transparent and accessible clinical rules although further research is needed to prospectively validate the risk factors identified by the model.

Evaluation of atrial fibrillation using wearable device signals and home blood pressure data in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) Study: A Subgroup Analysis (MIPACT-AFib).

Background: The rising adoption of wearable technology increases the potential to identify arrhythmias. However, specificity of these notifications is poorly defined and may cause anxiety and unnecessary resource utilization. Herein, we report results of a follow-up screening protocol for incident atrial fibrillation/flutter (AF) within a large observational digital health study. Methods: The MIPACT Study enrolled 6,765 adult patients who were provided an Apple Watch and blood pressure (BP) monitors. From March to July 2019, participants were asked to contact the study team for any irregular heart rate (HR) notification. They were assessed using structured questionnaires and asked to provide 6 Apple Watch EKGs. Those with arrhythmias or non-diagnostic EKGs were sent 7-day monitors. The EHR was reviewed after 3 years to determine if participants developed arrhythmias. Results: 86 participants received notifications and met inclusion criteria. Mean age was 50.5 (SD 16.9) years, and 46 (53.3%) were female. Of 76 participants assessed by the study team, 32 (42.1%) reported anxiety surrounding notifications. Of 59 participants who sent at least 1 EKG, 52 (88.1%) were in sinus rhythm, 3 (5.1%) AF, 2 (3.4%) indeterminate, and 2 (3.4%) sinus bradycardia. Cardiac monitor demonstrated AF in 2 of 3 participants with AF on Apple Watch EKGs. 2 contacted their PCPs and were diagnosed with AF. In total, 5 cases of AF were diagnosed with 1 additional case identified during EHR review. Conclusion: Wearable devices produce alarms that can frequently be anxiety provoking. Research is needed to determine the implications of these alarms and appropriate follow-up.