PERfect Day: reversible and dose-dependent control of circadian time-keeping in the mouse suprachiasmatic nucleus by translational switching of PERIOD2 protein expression.

The biological clock of the suprachiasmatic nucleus (SCN) orchestrates circadian (approximately daily) rhythms of behaviour and physiology that underpin health. SCN cell-autonomous time-keeping revolves around a transcriptional/translational feedback loop (TTFL) within which PERIOD (PER1,2) and CRYPTOCHROME (CRY1,2) proteins heterodimerise and suppress trans-activation of their encoding genes (Per1,2; Cry1,2). To explore its contribution to SCN time-keeping, we used adeno-associated virus-mediated translational switching to express PER2 (tsPER2) in organotypic SCN slices carrying bioluminescent TTFL circadian reporters. Translational switching requires provision of the non-canonical amino acid, alkyne lysine (AlkK), for protein expression. Correspondingly, AlkK, but not vehicle, induced constitutive expression of tsPER2 in SCN neurons and reversibly and dose-dependently suppressed pPer1-driven transcription in PER-deficient (Per1,2-null) SCN, illustrating the potency of PER2 in negative regulation within the TTFL. Constitutive expression of tsPER2, however, failed to initiate circadian oscillations in arrhythmic PER-deficient SCN. In rhythmic, PER-competent SCN, AlkK dose-dependently reduced the amplitude of PER2-reported oscillations as inhibition by tsPER2 progressively damped the TTFL. tsPER2 also dose-dependently lengthened the period of the SCN TTFL and neuronal calcium rhythms. Following wash-out of AlkK to remove tsPER2, the SCN regained TTFL amplitude and period. Furthermore, SCN retained their pre-washout phase: the removal of tsPER2 did not phase-shift the TTFL. Given that constitutive tsCRY1 can regulate TTFL amplitude and period, but also reset TTFL phase and initiate rhythms in CRY-deficient SCN, these results reveal overlapping and distinct properties of PER2 and CRY1 within the SCN, and emphasise the utility of translational switching to explore the functions of circadian proteins.

Atrial Fibrillation Burden on a 14-Day ECG Monitor: Findings From the GUARD-AF Trial Screening Arm.

BACKGROUND: The "burden" of atrial fibrillation (AF) detected by screening likely influences stroke risk, but the distribution of burden is not well described. OBJECTIVES: This study aims to determine the frequency of AF and the distribution of AF burden found when screening individuals ≥70 years of age with a 14-day electrocardiograph monitor. METHODS: This is a cohort study of the screening arm of a randomized AF screening trial among those ≥70 years of age without a prior AF diagnosis (between 2019 and 2021). Screening was performed with a 14-day continuous electrocardiogram patch monitor. RESULTS: Analyzable patches were returned by 5,684 (95%) of screening arm participants; the median age was 75 years (Q1-Q3: 72-78 years), 57% were female, and the median CHA2DS2-VASc score was 3 (Q1-Q3: 2-4). AF was detected in 252 participants (4.4%); 29 (0.5%) patients had continuous AF and 223 (3.9%) had paroxysmal AF. Among those with paroxysmal AF, the average indices of AF burden were of low magnitude with right-skewed distributions. The median percent time in AF was 0.46% (Q1-Q3: 0.02%-2.48%), or 75 (Q1-Q3: 3-454) minutes, and the median longest episode was 38 (Q1-Q3: 2-245) minutes. The upper quartile threshold of 2.48% time in AF corresponded to 7.6 hours. Age greater than 80 years was associated with screen-detected AF in our multivariable model (OR: 1.46; 95% CI: 1.06-2.02). CONCLUSIONS: Most AF detected in these older patients was very low burden. However, one-quarter of those with AF had multiple hours of AF, raising concern about stroke risk. These findings have implications for targeting populations for AF screening trials and for responding to heart rhythm alerts from mobile devices (GUARD-AF [A Study to Determine if Identification of Undiagnosed Atrial Fibrillation in People at least 70 Years of Age Reduces the Risk of Stroke]; NCT04126486).

A Clinician and Electronic Health Record Wearable Device Intervention to Increase Physical Activity in Patients With Obesity: Formative Qualitative Study.

BACKGROUND: The number of individuals using digital health devices has grown in recent years. A higher rate of use in patients suggests that primary care providers (PCPs) may be able to leverage these tools to effectively guide and monitor physical activity (PA) for their patients. Despite evidence that remote patient monitoring (RPM) may enhance obesity interventions, few primary care practices have implemented programs that use commercial digital health tools to promote health or reduce complications of the disease. OBJECTIVE: This formative study aimed to assess the perceptions, needs, and challenges of implementation of an electronic health record (EHR)-integrated RPM program using wearable devices to promote patient PA at a large urban primary care practice to prepare for future intervention. METHODS: Our team identified existing workflows to upload wearable data to the EHR (Epic Systems), which included direct Fitbit (Google) integration that allowed for patient PA data to be uploaded to the EHR. We identified pictorial job aids describing the clinical workflow to PCPs. We then performed semistructured interviews with PCPs (n=10) and patients with obesity (n=8) at a large urban primary care clinic regarding their preferences and barriers to the program. We presented previously developed pictorial aids with instructions for (1) providers to complete an order set, set step-count goals, and receive feedback and (2) patients to set up their wearable devices and connect them to their patient portal account. We used rapid qualitative analysis during and after the interviews to code and develop key themes for both patients and providers that addressed our research objective. RESULTS: In total, 3 themes were identified from provider interviews: (1) providers' knowledge of PA prescription is focused on general guidelines with limited knowledge on how to tailor guidance to patients, (2) providers were open to receiving PA data but were worried about being overburdened by additional patient data, and (3) providers were concerned about patients being able to equitably access and participate in digital health interventions. In addition, 3 themes were also identified from patient interviews: (1) patients received limited or nonspecific guidance regarding PA from providers and other resources, (2) patients want to share exercise metrics with the health care team and receive tailored PA guidance at regular intervals, and (3) patients need written resources to support setting up an RPM program with access to live assistance on an as-needed basis. CONCLUSIONS: Implementation of an EHR-based RPM program and associated workflow is acceptable to PCPs and patients but will require attention to provider concerns of added burdensome patient data and patient concerns of receiving tailored PA guidance. Our ongoing work will pilot the RPM program and evaluate feasibility and acceptability within a primary care setting.

Barriers and facilitators to home blood pressure monitoring in women with pregnancies complicated by hypertensive disorders: a qualitative study.

BACKGROUND/OBJECTIVE: Hypertensive disorders of pregnancy (HDP) are a major cause of maternal morbidity and mortality in the US. Improved diagnosis and treatment of HDP may be achieved through home blood pressure monitoring (HBPM). However, there are challenges to effective HBPM during pregnancy. This qualitative study was conducted to explore patients' perspectives and experiences with HBPM. METHODS: Pregnant or recently postpartum women with HDP (≥18 years) were recruited from an academic medical center to virtual focus groups from March to September 2023. The discussions centered on experiences with HDP and barriers and facilitators to HBPM. Qualitative thematic analysis was performed. RESULTS: Among 20 participants, the mean age was 33.8 (SD 5.9) years, with 35% Hispanic and 35% Black/African-American. Facilitators to HBPM included understanding the parameters/purpose of HBPM, prior experience with healthcare/duration of hypertension, free access to HBPM equipment and decision support, creating a routine, external support/counseling (e.g., partner/healthcare/family), and technology support. Barriers to HBPM included uncertainty/lack of training about the HBPM process, accessing/using HBPM equipment, the belief that clinic monitoring was sufficient/achieving good control, and activation barriers to making HBPM a priority (e.g., fear of affirming the diagnosis, higher priorities/life stressors). CONCLUSION: Many of the barriers to HBPM in pregnancy can be overcome through patient education/counseling, technology support, clinician/family reinforcement, and better access to validated blood pressure monitors. Given the importance of HBPM in improving outcomes for HDP, it is important for healthcare providers and policy makers to work to reduce barriers and amplify facilitators to HBPM for better adoption.

Effect of Screening for Undiagnosed Atrial Fibrillation on Stroke Prevention.

BACKGROUND: Atrial fibrillation (AF) often remains undiagnosed, and it independently raises the risk of ischemic stroke, which is largely reversible by oral anticoagulation. Although randomized trials using longer term screening approaches increase identification of AF, no studies have established that AF screening lowers stroke rates. OBJECTIVES: To address this knowledge gap, the GUARD-AF (Reducing Stroke by Screening for Undiagnosed Atrial Fibrillation in Elderly Individuals) trial screened participants in primary care practices using a 14-day continuous electrocardiographic monitor to determine whether screening for AF coupled with physician/patient decision-making to use oral anticoagulation reduces stroke and provides a net clinical benefit compared with usual care. METHODS: GUARD-AF was a prospective, parallel-group, randomized controlled trial designed to test whether screening for AF in people aged ≥70 years using a 14-day single-lead continuous electrocardiographic patch monitor could identify patients with undiagnosed AF and reduce stroke. Participants were randomized 1:1 to screening or usual care. The primary efficacy and safety outcomes were hospitalization due to all-cause stroke and bleeding, respectively. Analyses used the intention-to-treat population. RESULTS: Enrollment began on December 17, 2019, and involved 149 primary care sites across the United States. The COVID-19 pandemic led to premature termination of enrollment, with 11,905 participants in the intention-to-treat population. Median follow-up was 15.3 months (Q1-Q3: 13.8-17.6 months). Median age was 75 years (Q1-Q3: 72-79 years), and 56.6% were female. The risk of stroke in the screening group was 0.7% vs 0.6% in the usual care group (HR: 1.10; 95% CI: 0.69-1.75). The risk of bleeding was 1.0% in the screening group vs 1.1% in the usual care group (HR: 0.87; 95% CI: 0.60-1.26). Diagnosis of AF was 5% in the screening group and 3.3% in the usual care group, and initiation of oral anticoagulation after randomization was 4.2% and 2.8%, respectively. CONCLUSIONS: In this trial, there was no evidence that screening for AF using a 14-day continuous electrocardiographic monitor in people ≥70 years of age seen in primary care practice reduces stroke hospitalizations. Event rates were low, however, and the trial did not enroll the planned sample size.(Reducing Stroke by Screening for Undiagnosed Atrial Fibrillation in Elderly Individuals [GUARD-AF]; NCT04126486).

Implementing virtual desktops for clinical research at an academic health center: a case report.

Objectives: To address the challenges of sharing clinical research data through the implementation of cloud-based virtual desktops, enhancing collaboration among researchers while maintaining data security. Materials and Methods: This case study details the deployment of virtual desktops at UMass Chan Medical School (UMass Chan). The process involved forming a Research Informatics Steering Executive workgroup, identifying key requirements, implementing Amazon WorkSpaces, and establishing configurable data management for research support. Results: Key lessons include the significance of collaboration, balancing user-friendliness and functionality, flexibility in data management, maximizing virtual desktop efficiency within budget constraints, and continuous user feedback. The implementation of virtual desktops supports secure collaborative research, advancing medical knowledge and improving healthcare outcomes. Discussion: The structured approach to implementing virtual desktops addresses data security, regulatory compliance, and real-time collaboration challenges. Continuous feedback and iterative improvements have enhanced the system's effectiveness. Conclusion: The successful implementation of virtual desktops at UMass Chan demonstrates the potential for such systems to support secure, collaborative research, offering insights for similar initiatives in other academic health centers.

Advancing Point of Care Testing by Application of Machine Learning Techniques and Artificial Intelligence.

The promise of artificial intelligence (AI) has generated enthusiasm among patients, healthcare professionals, and technology developers who seek to leverage its potential to enhance the diagnosis and management of an increasing number of chronic and acute conditions. Point-of-care testing (POCT) increases access to care because it enables care outside of traditional medical settings. Collaboration among developers, clinicians, and end users is an effective best practice for solving clinical problems. A common set of clearly defined terms that are easily understood by research teams is a valuable tool that fosters these collaborations. We present brief, accurate, and clear descriptions of terms and techniques used to develop new device and decision support technologies in association with their most common applications to POCT. This lexicon of terms used to describe AI and machine learning techniques is quick reference for healthcare professionals, researchers, developers, and patients. Commonly used methods and techniques are tabulated and presented with text providing the context of their common usage and required data characteristics. Finally, we summarize model effectiveness measurement and the assessment of component features contributions. Artificial intelligence (AI) refers to non-human techniques that infer meaning from sets of data. It can produce generalizations, classifications, predictions, and can identify associations using automated learning methods. This guide provides an overview of these methods and their application to point-of-care testing.

Agreement Between Apple Watch and Actical Step Counts in a Community Setting: Cross-Sectional Investigation From the Framingham Heart Study.

BACKGROUND: Step counting is comparable among many research-grade and consumer-grade accelerometers in laboratory settings. OBJECTIVE: The purpose of this study was to compare the agreement between Actical and Apple Watch step-counting in a community setting. METHODS: Among Third Generation Framingham Heart Study participants (N=3486), we examined the agreement of step-counting between those who wore a consumer-grade accelerometer (Apple Watch Series 0) and a research-grade accelerometer (Actical) on the same days. Secondarily, we examined the agreement during each hour when both devices were worn to account for differences in wear time between devices. RESULTS: We studied 523 participants (n=3223 person-days, mean age 51.7, SD 8.9 years; women: n=298, 57.0%). Between devices, we observed modest correlation (intraclass correlation [ICC] 0.56, 95% CI 0.54-0.59), poor continuous agreement (29.7%, n=957 of days having steps counts with ≤15% difference), a mean difference of 499 steps per day higher count by Actical, and wide limits of agreement, roughly ±9000 steps per day. However, devices showed stronger agreement in identifying who meets various steps per day thresholds (eg, at 8000 steps per day, kappa coefficient=0.49), for which devices were concordant for 74.8% (n=391) of participants. In secondary analyses, in the hours during which both devices were worn (n=456 participants, n=18,760 person-hours), the correlation was much stronger (ICC 0.86, 95% CI 0.85-0.86), but continuous agreement remained poor (27.3%, n=5115 of hours having step counts with ≤15% difference) between devices and was slightly worse for those with mobility limitations or obesity. CONCLUSIONS: Our investigation suggests poor overall agreement between steps counted by the Actical device and those counted by the Apple Watch device, with stronger agreement in discriminating who meets certain step thresholds. The impact of these challenges may be minimized if accelerometers are used by individuals to determine whether they are meeting physical activity guidelines or tracking step counts. It is also possible that some of the limitations of these older accelerometers may be improved in newer devices.

Relationship between acute SARS-CoV-2 viral clearance with Long COVID Symptoms: a cohort study.

Introduction: The relationship between SARS-CoV-2 viral dynamics during acute infection and the development of long COVID is largely unknown. Methods: A total of 7361 asymptomatic community-dwelling people enrolled in the Test Us at Home parent study between October 2021 and February 2022. Participants self-collected anterior nasal swabs for SARS-CoV-2 RT-PCR testing every 24-48 hours for 10-14 days, regardless of symptom or infection status. Participants who had no history of COVID-19 at enrollment and who were subsequently found to have ≥1 positive SARS-CoV-2 RT-PCR test during the parent study were recontacted in August 2023 and asked whether they had experienced long COVID, defined as the development of new symptoms lasting 3 months or longer following SARS-CoV-2 infection. Participant's cycle threshold values were converted into viral loads, and slopes of viral clearance were modeled using post-nadir viral loads. Using a log binomial model with the modeled slopes as the exposure, we calculated the relative risk of subsequently developing long COVID with 1-2 symptoms, 3-4 symptoms, or 5+ symptoms, adjusting for age, number of symptoms, and SARS-CoV-2 variant. Adjusted relative risk (aRR) of individual long COVID symptoms based on viral clearance was also calculated. Results: 172 participants were eligible for analyses, and 59 (34.3%) reported experiencing long COVID. The risk of long COVID with 3-4 symptoms and 5+ symptoms increased by 2.44 times (aRR: 2.44; 95% CI: 0.88-6.82) and 4.97 times (aRR: 4.97; 95% CI: 1.90-13.0) per viral load slope-unit increase, respectively. Participants who developed long COVID had significantly longer times from peak viral load to viral clearance during acute disease than those who never developed long COVID (8.65 [95% CI: 8.28-9.01] vs. 10.0 [95% CI: 9.25-10.8]). The slope of viral clearance was significantly positively associated with long COVID symptoms of fatigue (aRR: 2.86; 95% CI: 1.22-6.69), brain fog (aRR: 4.94; 95% CI: 2.21-11.0), shortness of breath (aRR: 5.05; 95% CI: 1.24-20.6), and gastrointestinal symptoms (aRR: 5.46; 95% CI: 1.54-19.3). Discussion: We observed that longer time from peak viral load to viral RNA clearance during acute COVID-19 was associated with an increased risk of developing long COVID. Further, slower clearance rates were associated with greater number of symptoms of long COVID. These findings suggest that early viral-host dynamics are mechanistically important in the subsequent development of long COVID.

Point-of-care testing preferences 2020-2022: Trends over the years.

Background: The use of point-of-care (POC) tests prior to the COVID-19 pandemic was relatively infrequent outside of the health care context. Little is known about how public opinions regarding POC tests have changed during the pandemic. Methods: We redeployed a validated survey to uncompensated volunteers to assess preferences for point-of-care testing (POCT) benefits and concerns between June and September 2022. We received a total of 292 completed surveys. Linear regression analysis was used to compare differences in survey average response scores (ARSs) from 2020 to 2022. Results: Respondent ARSs indicated agreement for all 16 POCT benefits in 2022. Of 14 POCT concerns, there were only 2 statements that respondents agreed with most frequently, which were that "Insurance might not cover the costs of the POC test" (ARS 0.9, ± 1.0) and "POC tests might not provide a definitive result" (ARS 0.1, ± 1.0). Additionally, when comparing survey responses from 2020 to 2022, we observed 8 significant trends for POCT harms and benefits. Conclusion: The public's opinion on POC tests has become more favorable over time. However, concerns regarding the affordability and reliability of POCT results persist. We suggest that stakeholders address these concerns by developing accurate POC tests that continue to improve care and facilitate access to health care for all.

Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs.

Background: Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts. Objectives: To develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs. Methods: An FCHD single-lead ("lead I" from 12-lead ECGs) ECG-AI model was developed using 167,662 ECGs (50,132 patients) from the University of Tennessee Health Sciences Center. Eighty percent of the data (5-fold cross-validation) was used for training and 20% as a holdout. Cox proportional hazards (CPH) models incorporating ECG-AI predictions with age, sex, and race were also developed. The models were tested on paired clinical single-lead and Apple Watch ECGs from 243 St. Jude Lifetime Cohort Study participants. The correlation and concordance of the predictions were assessed using Pearson correlation (R), Spearman correlation (ρ), and Cohen's kappa. Results: The ECG-AI and CPH models resulted in AUC = 0.76 and 0.79, respectively, on the 20% holdout and AUC = 0.85 and 0.87 on the Atrium Health Wake Forest Baptist external validation data. There was moderate-strong positive correlation between predictions (R = 0.74, ρ = 0.67, and κ = 0.58) when tested on the 243 paired ECGs. The clinical (lead I) and Apple Watch predictions led to the same low/high-risk FCHD classification for 99% of the participants. CPH prediction correlation resulted in an R = 0.81, ρ = 0.76, and κ = 0.78. Conclusion: Risk of FCHD can be predicted from single-lead ECGs obtained from wearable devices and are statistically concordant with lead I of a 12-lead ECG.

Paramedic-Assisted Community Evaluation After Discharge: The PACED Intervention.

OBJECTIVES: Early rehospitalization of frail older adults after hospital discharge is harmful to patients and challenging to hospitals. Mobile integrated health (MIH) programs may be an effective solution for delivering community-based transitional care. The objective of this study was to assess the feasibility and implementation of an MIH transitional care program. DESIGN: Pilot clinical trial of a transitional home visit conducted by MIH paramedics within 72 hours of hospital discharge. SETTING AND PARTICIPANTS: Patients aged ≥65 years discharged from an urban hospital with a system-adapted eFrailty index ≥0.24 were eligible to participate. METHODS: Participants were enrolled after hospital discharge. Demographic and clinical information were recorded at enrollment and 30 days after discharge from the electronic health record. Data from a comparison group of patients excluded from enrollment due to geographical location was also abstracted. Primary outcomes were intervention feasibility and implementation, which were reported descriptively. Exploratory clinical outcomes included emergency department (ED) visits and rehospitalization within 30 days. Categorical and continuous group comparisons were conducted using χ2 tests and Kruskal-Wallis testing. Binomial regression was used for comparative outcomes. RESULTS: One hundred of 134 eligible individuals (74.6%) were enrolled (median age 81, 64% female). Forty-seven participants were included in the control group (median age 80, 55.2% female). The complete protocol was performed in 92 (92.0%) visits. Paramedics identified acute clinical problems in 23 (23.0%) visits, requested additional services for participants during 34 (34.0%) encounters, and detected medication errors during 34 (34.0%). The risk of 30-day rehospitalization was lower in the Paramedic-Assisted Community Evaluation after Discharge (PACED) group compared with the control (RR, 0.40; CI, 0.19-0.84; P = .03); there was a trend toward decreased risk of 30-day ED visits (RR, 0.61; CI, 0.37-1.37; P = .23). CONCLUSIONS AND IMPLICATIONS: This pilot study of an MIH transition care program was feasible with high protocol fidelity. It yields preliminary evidence demonstrating a decreased risk of rehospitalization in frail older adults.

Association between deep neural network-derived electrocardiographic-age and incident stroke.

Background: Stroke continues to be a leading cause of death and disability worldwide despite improvements in prevention and treatment. Traditional stroke risk calculators are biased and imprecise. Novel stroke predictors need to be identified. Recently, deep neural networks (DNNs) have been used to determine age from ECGs, otherwise known as the electrocardiographic-age (ECG-age), which predicts clinical outcomes. However, the relationship between ECG-age and stroke has not been well studied. We hypothesized that ECG-age is associated with incident stroke. Methods: In this study, UK Biobank participants with available ECGs (from 2014 or later). ECG-age was estimated using a deep neural network (DNN) applied to raw ECG waveforms. We calculated the Δage (ECG-age minus chronological age) and classified individuals as having normal, accelerated, or decelerated aging if Δage was within, higher, or lower than the mean absolute error of the model, respectively. Multivariable Cox proportional hazards regression models adjusted for age, sex, and clinical factors were used to assess the association between Δage and incident stroke. Results: The study population included 67,757 UK Biobank participants (mean age 65 ± 8 years; 48.3% male). Every 10-year increase in Δage was associated with a 22% increase in incident stroke [HR, 1.22 (95% CI, 1.00-1.49)] in the multivariable-adjusted model. Accelerated aging was associated with a 42% increase in incident stroke [HR, 1.42 (95% CI, 1.12-1.80)] compared to normal aging. In addition, Δage was associated with prevalent stroke [OR, 1.28 (95% CI, 1.11-1.49)]. Conclusions: DNN-estimated ECG-age was associated with incident and prevalent stroke in the UK Biobank. Further investigation is required to determine if ECG-age can be used as a reliable biomarker of stroke risk.

Identifying a Heterogeneous Effect of Atrial Fibrillation Screening in Older Adults: A Secondary Analysis of the VITAL-AF Trial.

Background: One-time atrial fibrillation (AF) screening trials have produced mixed results; however, it is unclear if there is a subset for whom screening is effective. Identifying such a subgroup would support targeted screening. Methods: We conducted a secondary analysis of VITAL-AF, a randomized trial of one-time, single-lead ECG screening during primary care visits. We tested two approaches to identify a subgroup where screening is effective. First, we developed an effect-based model using a T-learner. Specifically, we separately predicted the likelihood of AF diagnosis under screening and usual care conditions; the difference in probabilities was the predicted screening effect. Second, we used a validated AF risk model to test for a heterogeneous screening effect. We used interaction testing to determine if observed AF diagnosis rates in the screening and usual care groups differed when stratified by decile of the predicted screening effect and predicted AF risk. Results: Baseline characteristics were similar between the screening (n=15187) and usual care (n=15078) groups (mean age 74 years, 59% female). In the effect-based analysis, in the highest decile of predicted screening effectiveness (n=3026), AF diagnosis rates were higher in the screening group (6.50 vs. 3.06 per 100 person-years, rate difference 3.45, 95%CI 1.62 to 5.28). In this group, the mean age was 84 years and 68% were female. The risk-based analysis did not identify a subgroup where screening was more effective. Predicted screening effectiveness and predicted baseline AF risk were poorly correlated (Spearman coefficient 0.13). Conclusions: In a secondary analysis of the VITAL-AF trial, we identified a small subgroup where one-time screening was associated with increased AF diagnoses using an effect-based approach. In this study, predicted AF risk was a poor proxy for predicted screening effectiveness. These data caution against the assumption that high AF risk is necessarily correlated with high screening effectiveness.

Performance of and Severe Acute Respiratory Syndrome Coronavirus 2 Diagnostics Based on Symptom Onset and Close Contact Exposure: An Analysis From the Test Us at Home Prospective Cohort Study.

Background: Understanding changes in diagnostic performance after symptom onset and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure within different populations is crucial to guide the use of diagnostics for SARS-CoV-2. Methods: The Test Us at Home study was a longitudinal cohort study that enrolled individuals across the United States between October 2021 and February 2022. Participants performed paired antigen-detection rapid diagnostic tests (Ag-RDTs) and reverse-transcriptase polymerase chain reaction (RT-PCR) tests at home every 48 hours for 15 days and self-reported symptoms and known coronavirus disease 2019 exposures immediately before testing. The percent positivity for Ag-RDTs and RT-PCR tests was calculated each day after symptom onset and exposure and stratified by vaccination status, variant, age category, and sex. Results: The highest percent positivity occurred 2 days after symptom onset (RT-PCR, 91.2%; Ag-RDT, 71.1%) and 6 days after exposure (RT-PCR, 91.8%; Ag-RDT, 86.2%). RT-PCR and Ag-RDT performance did not differ by vaccination status, variant, age category, or sex. The percent positivity for Ag-RDTs was lower among exposed, asymptomatic than among symptomatic individuals (37.5% (95% confidence interval [CI], 13.7%-69.4%) vs 90.3% (75.1%-96.7%). Cumulatively, Ag-RDTs detected 84.9% (95% CI, 78.2%-89.8%) of infections within 4 days of symptom onset. For exposed participants, Ag-RDTs detected 94.0% (95% CI, 86.7%-97.4%) of RT-PCR-confirmed infections within 6 days of exposure. Conclusions: The percent positivity for Ag-RDTs and RT-PCR tests was highest 2 days after symptom onset and 6 days after exposure, and performance increased with serial testing. The percent positivity of Ag-RDTs was lowest among asymptomatic individuals but did not differ by sex, variant, vaccination status, or age category.

Association of Smartwatch-Based Heart Rate and Physical Activity With Cardiorespiratory Fitness Measures in the Community: Cohort Study.

BACKGROUND: Resting heart rate (HR) and routine physical activity are associated with cardiorespiratory fitness levels. Commercial smartwatches permit remote HR monitoring and step count recording in real-world settings over long periods of time, but the relationship between smartwatch-measured HR and daily steps to cardiorespiratory fitness remains incompletely characterized in the community. OBJECTIVE: This study aimed to examine the association of nonactive HR and daily steps measured by a smartwatch with a multidimensional fitness assessment via cardiopulmonary exercise testing (CPET) among participants in the electronic Framingham Heart Study. METHODS: Electronic Framingham Heart Study participants were enrolled in a research examination (2016-2019) and provided with a study smartwatch that collected longitudinal HR and physical activity data for up to 3 years. At the same examination, the participants underwent CPET on a cycle ergometer. Multivariable linear models were used to test the association of CPET indices with nonactive HR and daily steps from the smartwatch. RESULTS: We included 662 participants (mean age 53, SD 9 years; n=391, 59% women, n=599, 91% White; mean nonactive HR 73, SD 6 beats per minute) with a median of 1836 (IQR 889-3559) HR records and a median of 128 (IQR 65-227) watch-wearing days for each individual. In multivariable-adjusted models, lower nonactive HR and higher daily steps were associated with higher peak oxygen uptake (VO2), % predicted peak VO2, and VO2 at the ventilatory anaerobic threshold, with false discovery rate (FDR)-adjusted P values <.001 for all. Reductions of 2.4 beats per minute in nonactive HR, or increases of nearly 1000 daily steps, corresponded to a 1.3 mL/kg/min higher peak VO2. In addition, ventilatory efficiency (VE/VCO2; FDR-adjusted P=.009), % predicted maximum HR (FDR-adjusted P<.001), and systolic blood pressure-to-workload slope (FDR-adjusted P=.01) were associated with nonactive HR but not associated with daily steps. CONCLUSIONS: Our findings suggest that smartwatch-based assessments are associated with a broad array of cardiorespiratory fitness responses in the community, including measures of global fitness (peak VO2), ventilatory efficiency, and blood pressure response to exercise. Metrics captured by wearable devices offer a valuable opportunity to use extensive data on health factors and behaviors to provide a window into individual cardiovascular fitness levels.

Automated interpretations of single-lead electrocardiograms predict incident atrial fibrillation: The VITAL-AF trial.

BACKGROUND: Single-lead electrocardiograms (1L ECGs) are increasingly used for atrial fibrillation (AF) detection. Automated 1L ECG interpretation may have prognostic value for future AF in cases in which screening does not result in a short-term AF diagnosis. OBJECTIVE: We sought to investigate the association between automated 1L ECG interpretation and incident AF. METHODS: VITAL-AF was a randomized controlled trial investigating the effectiveness of screening for AF by 1L ECGs. For this study, participants were divided into 4 groups based on automated classification of 1L ECGs. Patients with prevalent AF were excluded. Associations between groups and incident AF were assessed by Cox proportional hazards models adjusted for risk factors. The start of follow-up was defined as 60 days after the latest 1L ECG (as some individuals had numerous screening 1L ECGs). RESULTS: The study sample included never screened (n = 16,306), normal (n = 10,914), other (n = 2675), and possible AF (n = 561). Possible AF had the highest AF incidence (5.91 per 100 person-years; 95% confidence interval [CI], 4.24-8.23). Possible AF was associated with greater hazard of incident AF compared with normal (adjusted hazard ratio, 2.48; 95% CI, 1.66-3.71). Other was associated with greater hazard of incident AF compared with normal (1.41; 95% CI, 1.04-1.90). CONCLUSION: In patients undergoing AF screening with 1L ECGs without prevalent AF or AF within 60 days of screening, presumptive positive and indeterminate 1L ECG interpretations were associated with future AF. Abnormal 1L ECG recordings may identify individuals at higher risk for future AF.

Smartphone App Designed to Collect Health Information in Older Adults: Usability Study.

BACKGROUND: Studies evaluating the usability of mobile-phone assessments in older adults are limited. OBJECTIVE: This study aims to identify design-based barriers and facilitators to mobile app survey completion among 2 samples of older adults; those in the Framingham Heart Study and a more diverse sample from a hospital-based setting. METHODS: We used mixed methods to identify challenging and beneficial features of the mobile app in participants from the electronic Framingham Heart Study (n=15; mean age of 72 years; 6/15, 40% women; 15/15, 100% non-Hispanic and White) and among participants recruited from a hospital-based setting (n=15; mean age of 71 years; 7/15, 47% women; 3/15, 20% Hispanic; and 8/15, 53% non-White). A variety of app-based measures with different response formats were tested, including self-reported surveys, pictorial assessments (to indicate body pain sites), and cognitive testing tasks (eg, Trail Making Test and Stroop). Participants completed each measure using a think-aloud protocol, while being audio- and video-recorded with a qualitative interview conducted at the end of the session. Recordings were coded for participant usability errors by 2 pairs of coders. Participants completed the Mobile App Rating Scale to assess the app (response range 1=inadequate to 5=excellent). RESULTS: In electronic Framingham Heart Study participants, the average total Mobile App Rating Scale score was 7.6 (SD 1.1), with no significant differences in the hospital-based sample. In general, participants were pleased with the app and found it easy to use. A large minority had at least 1 navigational issue, most committed only once. Most older adults did not have difficulty completing the self-reported multiple-choice measures unless it included lengthy instructions but participants had usability issues with the Stroop and Trail Making Test. CONCLUSIONS: Our methods and results help guide app development and app-based survey construction for older adults, while also giving consideration to sociodemographic differences.

Cognitive impairment and treatment strategy for atrial fibrillation in older adults: The SAGE-AF study.

BACKGROUND: Cognitive impairment is strongly associated with atrial fibrillation (AF). Rate and rhythm control are the two treatment strategies for AF and the effect of treatment strategy on risk of cognitive decline and frailty is not well established. We sought to determine how treatment strategy affects geriatric-centered outcomes. METHODS: The Systematic Assessment of Geriatric Elements-AF (SAGE-AF) was a prospective, observational, cohort study. Older adults with AF were prospectively enrolled between 2016 and 2018 and followed longitudinally for 2 years. In a non-randomized fashion, participants were grouped by rate or rhythm control treatment strategy based on clinical treatment at enrollment. Baseline characteristics were compared. Longitudinal binary mixed models were used to compare treatment strategy with respect to change in cognitive function and frailty status. Cognitive function and frailty status were assessed with the Montreal Cognitive Assessment Battery and Fried frailty phenotype tools. RESULTS: 972 participants (mean age = 75, SD = 6.8; 49% female, 87% non-Hispanic white) completed baseline examination and 2-year follow-up. 408 (42%) were treated with rate control and 564 (58%) with rhythm control. The patient characteristics of the two groups were different at baseline. Participants in the rate control group were older, more likely to have persistent AF, prior stroke, be treated with warfarin and have baseline cognitive impairment. After adjusting for baseline differences, participants treated with rate control were 1.5 times more likely to be cognitively impaired over 2 years (adjusted OR: 1.47, 95% CI:1.12, 1.98) and had a greater decline in cognitive function (adjusted estimate: -0.59 (0.23), p < 0.01) in comparison to rhythm control. Frailty did not vary between the treatment strategies. CONCLUSIONS: Among those who had 2-year follow-up in non-randomized observational cohort, the decision to rate control AF in older adults was associated with increased odds of decline in cognitive function but not frailty.

Depressive symptoms are not associated with clinically important levels of digital home blood pressure in the electronic Framingham Heart Study.

Background: Depressive symptoms are common and share many biopsychosocial mechanisms with hypertension. Association studies between depressive symptoms and blood pressure (BP) have been inconsistent. Home BP monitoring may provide insight. Objective: To investigate the association between depressive symptoms and digital home BP. Methods: Electronic Framingham Heart Study (eFHS) participants were invited to obtain a smartphone app and digital BP cuff at research exam 3 (2016-2019). Participants with ≥3 weeks of home BP measurements within 1 year were included. Depressive symptoms were measured using the Center for Epidemiological Studies Depression Scale (CES-D). Multivariable linear mixed models were used to test the associations of continuous CES-D score and dichotomous depressive symptoms (CES-D ≥16) (independent) with home BP (dependent), adjusting for age, sex, cohort, number of weeks since baseline, lifestyle factors, diabetes, and cardiovascular disease. Results: Among 883 participants (mean age 54 years, 59% women, 91% White), the median CES-D score was 4. Depressive symptom prevalence was 7.6%. Mean systolic and diastolic BP at exam 3 were 119 and 76 mm Hg; hypertension prevalence was 48%. A 1 SD higher CES-D score was associated with 0.9 (95% CI: 0.18-1.56, P = .01) and 0.6 (95% CI: 0.06-1.07, P = .03) mm Hg higher home systolic BP and diastolic BP, respectively. Dichotomous depressive symptoms were not significantly associated with home BP (P > .2). Conclusion: Depressive symptoms were not associated with clinically substantive levels of home BP. The association between depression and cardiovascular disease risk factors warrants more data, which may be supported by mobile health measures.