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With the ubiquity of mobile devices, the availability of mobile health (mHealth) applications for cardiovascular disease (CVD) has markedly increased in recent years. Older adults represent a population with a high CVD burden and therefore have the potential to benefit considerably from interventions that utilize mHealth. Traditional facility‐based cardiac rehabilitation represents one intervention that is currently underutilized for CVD patients and, because of the unique barriers that older adults face, represents an attractive target for mHealth interventions. Despite potential barriers to mHealth adoption in older populations, there is also evidence that older patients may be willing to adopt these technologies. In this review, we highlight the potential for mHealth uptake for older adults with CVD, with a particular focus on mHealth cardiac rehabilitation (mHealth‐CR) and evidence being generated in this field.

Wearable accelerometers, which continuously record physical activity metrics, are commonly used in mobile health-enabled cardiac rehabilitation (mHealth-CR). The association between adherence to accelerometer use during mHealth-CR and improvement in clinical outcomes, such as functional capacity, is understudied. The emergence of artificial intelligence (AI) technology provides novel opportunities to investigate accelerometry use patterns in relation to mHealth-CR outcomes. In this study, we sought to use an AI clustering framework to identify distinct behavioral phenotypes of adherence to accelerometer use. We then aimed to quantify the association of these adherence phenotypes with functional capacity improvements in older adults undergoing mHealth-CR. We analyzed data from the RESILIENT (Rehabilitation at Home Using Mobile Health in Older Adults After Hospitalization for Ischemic Heart Disease) trial, the largest randomized clinical study to date comparing mHealth-CR versus usual care in older adults (aged ≥65 years). Intervention arm participants were instructed to wear a Fitbit accelerometer for the 3-month study duration. Adherence to accelerometer use was quantified as overall adherence (percentage of days worn) via k-means clustering AI-derived measures and compared with changes in 6-minute walk distance (6-MWD), adjusted for demographic and clinical covariates. Among 271 participants with a mean age of 71 years (SD 8), of whom 198 (73%) were male, accelerometers were worn for an average of 76 days (95% confidence limits 73,78) over 3 months. Adjusted analyses showed a weak association between days of wear and improvement in 6-MWD, with every 30 additional days associated with an 11-meter improvement (P=.08). Our k-means clustering framework identified adherence phenotypes at two resolutions: low resolution (k=2 clusters) and high resolution (k=8 clusters). The consistently high adherence cluster trended toward a 24.6-meter improvement in 6-MWD compared to the low and declining adherence clusters (n=39; 95% CI 0.7-49.9; P=.06). The 8-cluster phenotyping revealed a richer set of adherence patterns, with the consistently high adherence cluster in this analysis having a 38.5-meter (95% CI 2.2-74.7; P=.04) improvement in 6-MWD than the low adherence cluster, as well as greater average daily steps over the 3-month intervention (mean 7518, SD 3415 vs mean 4800, SD 2920 steps; P=.008). A time-series AI clustering framework identified a range of behavioral phenotypes representing different degrees of adherence to accelerometer use. Regression analysis identified a weak association between the higher adherence phenotype and functional capacity improvement in older adults undergoing mHealth-CR. Our AI-derived accelerometry adherence phenotypes may offer a new approach to tailor mHealth-CR regimens to individual patients, potentially leading to better outcomes in this high-risk population. ClinicalTrials.gov NCT03978130; https://clinicaltrials.gov/study/NCT03978130. RR2-10.2196/32163.

Background Results from national surveys indicate that many older adults reported delayed medical care during the acute phase of the COVID-19 pandemic, yet few studies have used objective data to characterize healthcare utilization among vulnerable older adults in that period. In this study, we characterized healthcare utilization during the acute pandemic phase (March 7–October 6, 2020) and examined risk factors for total disruption of care among older adults with multiple chronic conditions (MCC) in New York City. Methods This retrospective cohort study used electronic health record data from NYC patients aged ≥ 50 years with a diagnosis of either hypertension or diabetes and at least one other chronic condition seen within six months prior to pandemic onset and after the acute pandemic period at one of several major academic medical centers contributing to the NYC INSIGHT clinical research network ( n =276,383). We characterized patients by baseline (pre-pandemic) health status using cutoffs of systolic blood pressure (SBP) < 140mmHg and hemoglobin A1C (HbA1c) < 8.0% as: controlled (below both cutoffs), moderately uncontrolled (below one), or poorly controlled (above both, SBP > 160, HbA1C > 9.0%). Patients were then assessed for total disruption versus some care during shutdown using recommended care schedules per baseline health status. We identified independent predictors for total disruption using logistic regression, including age, sex, race/ethnicity, baseline health status, neighborhood poverty, COVID infection, number of chronic conditions, and quartile of prior healthcare visits. Results Among patients, 52.9% were categorized as controlled at baseline, 31.4% moderately uncontrolled, and 15.7% poorly controlled. Patients with poor baseline control were more likely to be older, female, non-white and from higher poverty neighborhoods than controlled patients ( P  < 0.001). Having fewer pre-pandemic healthcare visits was associated with total disruption during the acute pandemic period (adjusted odds ratio [aOR], 8.61, 95% Confidence Interval [CI], 8.30-8.93, comparing lowest to highest quartile). Other predictors of total disruption included self-reported Asian race, and older age. Conclusions This study identified patient groups at elevated risk for care disruption. Targeted outreach strategies during crises using prior healthcare utilization patterns and disease management measures from disease registries may improve care continuity.

Background Some of the most promising strategies to reduce hospital readmissions in heart failure (HF) is through the timely receipt of home health care (HHC), delivered by Medicare-certified home health agencies (HHAs), and outpatient medical follow-up after hospital discharge. Yet national data show that only 12% of Medicare beneficiaries receive these evidence-based practices, representing an implementation gap. To advance the science and improve outcomes in HF, we will test the effectiveness and implementation of an intervention called Improving TRansitions ANd OutcomeS for Heart FailurE Patients in Home Health CaRe (I-TRANSFER-HF), comprised of early and intensive HHC nurse visits combined with an early outpatient medical visit post-discharge, among HF patients receiving HHC. Methods This study will use a Hybrid Type 1, stepped wedge randomized trial design, to test the effectiveness and implementation of I-TRANSFER-HF in partnership with four geographically diverse dyads of hospitals and HHAs (“hospital-HHA” dyads) across the US. Aim 1 will test the effectiveness of I-TRANSFER-HF to reduce 30-day readmissions (primary outcome) and ED visits (secondary outcome), and increase days at home (secondary outcome) among HF patients who receive timely follow-up compared to usual care. Hospital-HHA dyads will be randomized to cross over from a baseline period of no intervention to the intervention in a randomized sequential order. Medicare claims data from each dyad and from comparison dyads selected within the national dataset will be used to ascertain outcomes. Hypotheses will be tested with generalized mixed models. Aim 2 will assess the determinants of I-TRANSFER-HF’s implementation using a mixed-methods approach and is guided by the Consolidated Framework for Implementation Research 2.0 (CFIR 2.0). Qualitative interviews will be conducted with key stakeholders across the hospital-HHA dyads to assess acceptability, barriers, and facilitators of implementation; feasibility and process measures will be assessed with Medicare claims data. Discussion As the first pragmatic trial of promoting timely HHC and outpatient follow-up in HF, this study has the potential to dramatically improve care and outcomes for HF patients and produce novel insights for the implementation of HHC nationally. Trial registration This trial has been registered on ClinicalTrials.Gov (#NCT06118983). Registered on 10/31/2023, https://clinicaltrials.gov/study/NCT06118983?id=NCT06118983&rank=1 .

Background We sought to examine whether people with a diagnosis of cardiovascular disease (CVD) experienced a greater incidence of subsequent cognitive impairment (CI) compared to people without CVD, as suggested by prior studies, using a large longitudinal cohort. Methods We employed Health and Retirement Study (HRS) data collected biennially from 1998 to 2014 in 1305 U.S. adults age ≥ 65 newly diagnosed with CVD vs. 2610 age- and gender-matched controls. Diagnosis of CVD was adjudicated with an established HRS methodology and included self-reported coronary heart disease, angina, heart failure, myocardial infarction, or other heart conditions. CI was defined as a score  <  11 on the 27-point modified Telephone Interview for Cognitive Status. We examined incidence of CI over an 8-year period using a cumulative incidence function accounting for the competing risk of death. Results Mean age at study entry was 73 years, 55% were female, and 13% were non-white. Cognitive impairment developed in 1029 participants over 8 years. The probability of death over the study period was greater in the CVD group (19.8% vs. 13.8%, absolute difference 6.0, 95% confidence interval 2.2 to 9.7%). The cumulative incidence analysis, which adjusted for the competing risk of death, showed no significant difference in likelihood of cognitive impairment between the CVD and control groups (29.7% vs. 30.6%, absolute difference − 0.9, 95% confidence interval − 5.6 to 3.7%). This finding did not change after adjusting for relevant demographic and clinical characteristics using a proportional subdistribution hazard regression model. Conclusions Overall, we found no increased risk of subsequent CI among participants with CVD (compared with no CVD), despite previous studies indicating that incident CVD accelerates cognitive decline.

Older patients who undergo mitral valve surgery (MVS) have high 1-year survival rates, but little is known about the experience of survivors. Our objective was to determine trends in 1-year hospital readmission rates and length of stay (LOS) in these individuals. We included 100% of Medicare Fee-for-Service patients ≥65 years of age who underwent MVS between 1999-2010 and survived to 1 year (N = 146,877). We used proportional hazards regression to analyze the post-MVS 1-year readmission rate in each year, mean hospital LOS (after index admission), and readmission rates by subgroups (age, sex, race). The 1-year survival rate among patients undergoing MVS was 81.3%. Among survivors, 49.1% experienced a hospital readmission within 1 year. The post-MVS 1-year readmission rate declined from 1999-2010 (49.5% to 46.9%, P<0.01), and mean hospital LOS decreased from 6.2 to 5.3 (P<0.01). Readmission rates were highest in oldest patients, but declined in all age subgroups (65-74: 47.4% to 44.4%; 75-84: 51.4% to 49.2%, ≥85: 56.4% to 50.0%, all P<0.01). There were declines in women and men (women: 51.7% to 50.8%, P<0.01; men: 46.9% to 43.0%, P<0.01), and in whites and patients of other race, but not in blacks (whites: 49.0% to 46.2%, P<0.01; other: 55.0% to 48.9%, P<0.01; blacks: 58.1% to 59.0%, P = 0.18). Among older adults surviving MVS to 1 year, slightly fewer than half experience a hospital readmission. There has been a modest decline in both the readmission rate and LOS over time, with worse outcomes in women and blacks.

The value of primary preventative therapies for cardiovascular disease (CVD) in older adults (age ≥75 years) is less certain than in younger patients. There is a lack of quality evidence in older adults due to underenrolment in pivotal trials. While aspirin is no longer recommended for routine use in primary prevention of CVD in older adults, statins may be efficacious. However, it is unclear which patient subgroups may benefit most, and guidelines differ between expert panels. Three relevant geriatric conditions (cognitive impairment, functional impairment and polypharmacy) may influence therapeutic decision making; for example, baseline frailty may affect statin efficacy, and some have advocated for deprescription in this scenario. Evidence regarding statins and incident functional decline are mixed, and vigilance for adverse effects is important, especially in the setting of polypharmacy. However, aspirin has not been shown to affect incident cognitive or functional decline, and its lack of efficacy extends to patients with baseline cognitive impairment or frailty. Ultimately, the utility of primary preventative therapies for CVD in older adults depends on potential lifetime benefit. Rather than basing treatment decisions on absolute risk alone, consideration of comorbidities, polypharmacy and life expectancy should play a significant role in decision making. Coronary calcium score and new tools for risk stratification validated in older adults that account for the competing risk of death may aid in evaluating potential benefits. Given the complexity of therapeutic decisions in this context, shared decision making provides an important framework.

Ensuring reliability of Large Language Models (LLMs) in clinical tasks is crucial. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss’ Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation. For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT’s errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.

Acute myocardial infarction (AMI) may contribute to health status declines including “independence loss” and “physical function decline.” Despite the importance of these outcomes for prognosis and quality of life, their incidence and predictors have not been well described. We studied 2,002 patients with AMI enrolled across 24 sites in the TRIUMPH registry who completed assessments of independence and physical function at the time of AMI and 1 year later. Independence was evaluated by the EuroQol-5D (mobility, self-care, and usual activities), and physical function was assessed with the Short Form-12 physical component score. Declines in ≥1 level on EuroQol-5D and >5 points in PCS were considered clinically significant changes. Hierarchical, multivariable, modified Poisson regression models accounting for within-site variability were used to identify predictors of independence loss and physical function decline. One-year post AMI, 43.0% of patients experienced health status declines: 12.8% independence loss alone, 15.2% physical function decline alone, and 15.0% both. After adjustment, variables that predicted independence loss included female sex, nonwhite race, unmarried status, uninsured status, end-stage renal disease, and depression. Variables that predicted physical function decline were uninsured status, lack of cardiac rehabilitation referral, and absence of pre-AMI angina. Age was not predictive of either outcome after adjustment. >40% of patients experience independence loss or physical function decline 1 year after AMI. These changes are distinct but can occur simultaneously. Although some risk factors are not modifiable, others suggest potential targets for strategies to preserve patients' health status.

Randomized trials of anti-hypertensive treatment demonstrating reduced risk of cardiovascular events in older adults included participants with less comorbidity than clinical populations. Whether these results generalize to all older adults, most of whom have multiple chronic conditions, is uncertain. To determine the association between anti-hypertensive medications and CV events and mortality in a nationally representative population of older adults. Competing risk analysis with propensity score adjustment and matching in the Medicare Current Beneficiary Survey cohort over three-year follow-up through 2010. 4,961 community-living participants with hypertension. Anti-hypertensive medication intensity, based on standardized daily dose for each anti-hypertensive medication class participants used. Cardiovascular events (myocardial infarction, unstable angina, cardiac revascularization, stroke, and hospitalizations for heart failure) and mortality. Of 4,961 participants, 14.1% received no anti-hypertensives; 54.6% received moderate, and 31.3% received high, anti-hypertensive intensity. During follow-up, 1,247 participants (25.1%) experienced cardiovascular events; 837 participants (16.9%) died. Of deaths, 430 (51.4%) occurred in participants who experienced cardiovascular events during follow-up. In the propensity score adjusted cohort, after adjusting for propensity score and other covariates, neither moderate (adjusted hazard ratio, 1.08 [95% CI, 0.89-1.32]) nor high (1.16 [0.94-1.43]) anti-hypertensive intensity was associated with experiencing cardiovascular events. The hazard ratio for death among all participants was 0.79 [0.65-0.97] in the moderate, and 0.72 [0.58-0.91] in the high intensity groups compared with those receiving no anti-hypertensives. Among participants who experienced cardiovascular events, the hazard ratio for death was 0.65 [0.48-0.87] and 0.58 [0.42-0.80] in the moderate and high intensity groups, respectively. Results were similar in the propensity score-matched subcohort. In this nationally representative cohort of older adults, anti-hypertensive treatment was associated with reduced mortality but not cardiovascular events. Whether RCT results generalize to older adults with multiple chronic conditions remains uncertain.