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Atrial fibrillation (AF) is a major cause of morbidity and mortality globally, and much of this is driven by challenges in its timely diagnosis and treatment. Existing and emerging mobile technologies have been used to successfully identify AF in a variety of clinical and community settings, and while these technologies offer great promise for revolutionizing AF detection and screening, several major barriers may impede their effectiveness. The unclear clinical significance of device-detected AF, potential challenges in integrating patient-generated data into existing healthcare systems and clinical workflows, harm resulting from potential false positives, and identifying the appropriate scope of population-based screening efforts are all potential concerns that warrant further investigation. It is crucial for stakeholders such as healthcare providers, researchers, funding agencies, insurers, and engineers to actively work together in fulfilling the tremendous potential of mobile technologies to improve AF identification and management on a population level.
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Fibrilación Atrial/diagnóstico , Electrocardiografía/métodos , Determinación de la Frecuencia Cardíaca/métodos , Computadoras de Mano/normas , Electrocardiografía/instrumentación , Determinación de la Frecuencia Cardíaca/instrumentación , Humanos , Dispositivos Electrónicos Vestibles/normasRESUMEN
Hexokinase-II (HK-II) catalyzes the first step of glycolysis and also functions as a protective molecule; however, its role in protective autophagy has not been determined. Results showed that inhibition of HK-II diminished, while overexpression of HK-II potentiated, autophagy induced by glucose deprivation in cardiomyocyte and noncardiomyocyte cells. Immunoprecipitation studies revealed that HK-II binds to and inhibits the autophagy suppressor, mTOR complex 1 (TORC1), and that this binding was increased by glucose deprivation. The TOS motif, a scaffold sequence responsible for binding TORC1 substrates, is present in HK-II, and mutating it blocked its ability to bind to TORC1 and regulate protective autophagy. The transition from glycolysis to autophagy appears to be regulated by a decrease in glucose-6 phosphate. We suggest that HK-II binds TORC1 as a decoy substrate and provides a previously unrecognized mechanism for switching cells from a metabolic economy, based on plentiful energy, to one of conservation, under starvation.
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Autofagia , Regulación Enzimológica de la Expresión Génica , Glucosa/metabolismo , Hexoquinasa/metabolismo , Complejos Multiproteicos/metabolismo , Miocitos Cardíacos/enzimología , Serina-Treonina Quinasas TOR/metabolismo , Secuencias de Aminoácidos , Animales , Células Cultivadas , Privación de Alimentos , Glucosa-6-Fosfato/metabolismo , Inmunoprecipitación , Diana Mecanicista del Complejo 1 de la Rapamicina , Mutación , Estrés Oxidativo , Fosforilación , ARN Interferente Pequeño/metabolismo , RatasRESUMEN
BACKGROUND: Health care satisfaction is a key component of patient-centered care. Prior research on transgender populations has been based on convenience samples, and/or grouped all gender minorities into a single category. OBJECTIVE: The objective of this study was to quantify differences in health care satisfaction among transgender men, transgender women, gender nonconforming, and cisgender adults in a diverse multistate sample. RESEARCH DESIGN: Cross-sectional analysis of 2014-2018 Behavioral Risk Factor Surveillance System data from 20 states, using multivariable logistic models. SUBJECTS: We identified 167,468 transgender men, transgender women, gender-nonconforming people, cisgender women, and cisgender men and compared past year health care satisfaction across these groups. RESULTS: Transgender men and women had the highest prevalence of being "not at all satisfied" with the health care they received (14.6% and 8.6%, respectively), and gender-nonconforming people had the lowest prevalence of being "very satisfied" with their health care (55.7%). After adjustment for sociodemographic characteristics, transgender men were more likely to report being "not at all satisfied" with health care than cisgender men (odds ratio: 4.45, 95% confidence interval: 1.72-11.5) and cisgender women (odds ratio: 3.40, 95% confidence interval: 1.31-8.80). CONCLUSIONS: Findings indicate that transgender and gender-nonconforming adults report considerably less health care satisfaction relative to their cisgender peers. Interventions to address factors driving these differences are needed.
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Satisfacción del Paciente/estadística & datos numéricos , Minorías Sexuales y de Género/estadística & datos numéricos , Personas Transgénero/estadística & datos numéricos , Adolescente , Adulto , Factores de Edad , Anciano , Sistema de Vigilancia de Factor de Riesgo Conductual , Estudios Transversales , Femenino , Identidad de Género , Estado de Salud , Humanos , Masculino , Salud Mental , Persona de Mediana Edad , Factores de Riesgo , Conducta Sexual , Minorías Sexuales y de Género/psicología , Factores Socioeconómicos , Personas Transgénero/psicología , Adulto JovenRESUMEN
BACKGROUND: Atrial fibrillation (AF) portends poor prognoses in intensive care unit patients with sepsis. However, AF research is challenging: Previous studies demonstrate that International Classification of Disease (ICD) codes may underestimate the incidence of AF, but chart review is expensive and often not feasible. We aim to examine the accuracy of nurse-charted AF and its temporal precision in critical care patients with sepsis. METHODS: Patients with sepsis with continuous electrocardiogram (ECG) waveforms were identified from the Medical Information Mart for Intensive Care (MIMIC-III) database, a de-identified, single-center intensive care unit electronic health record (EHR) source. We selected a random sample of ECGs of 6 to 50 hours' duration for manual review. Nurse-charted AF occurrence and onset time and ICD-9-coded AF were compared to gold-standard ECG adjudication by a board-certified cardiac electrophysiologist blinded to AF status. Descriptive statistics were calculated for all variables in patients diagnosed with AF by nurse charting, ICD-9 code, or both. RESULTS: From 142 ECG waveforms (58 AF and 84 sinus rhythm), nurse charting identified AF events with 93% sensitivity (95% confidence interval [CI]: 87%-100%) and 87% specificity (95% CI: 80%-94%) compared to the gold standard manual ECG review. Furthermore, nurse-charted AF onset time was within 1 hour of expert reader onset time for 85% of the reviewed tracings. The ICD-9 codes were 97% sensitive (95% CI: 88-100%) and 82% specific (95% CI: 74-90%) for incident AF during admission but unable to identify AF time of onset. CONCLUSION: Nurse documentation of AF in EHR is accurate and has high precision for determining AF onset to within 1 hour. Our study suggests that nurse-charted AF in the EHR represents a potentially novel method for AF case identification, timing, and burden estimation.
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Fibrilación Atrial/diagnóstico , Electrocardiografía , Registros Electrónicos de Salud/estadística & datos numéricos , Unidades de Cuidados Intensivos , Sepsis/fisiopatología , Adulto , Anciano , Fibrilación Atrial/fisiopatología , Cuidados Críticos , Femenino , Humanos , Clasificación Internacional de Enfermedades , Masculino , Persona de Mediana Edad , Pronóstico , Reproducibilidad de los Resultados , Sepsis/complicaciones , Sepsis/diagnósticoRESUMEN
Patients are increasingly using online social networks (ie, social media) to connect with other patients and health care professionals--a trend called peer-to-peer health care. Because online social networks provide a means for health care professionals to communicate with patients, and for patients to communicate with each other, an opportunity exists to use social media as a modality to deliver behavioral interventions. Social media-delivered behavioral interventions have the potential to reduce the expense of behavioral interventions by eliminating visits, as well as increase our access to patients by becoming embedded in their social media feeds. Trials of online social network-delivered behavioral interventions have shown promise, but much is unknown about intervention development and methodology. In this paper, we discuss the process by which investigators can translate behavioral interventions for social media delivery. We present a model that describes the steps and decision points in this process, including the necessary training and reporting requirements. We also discuss issues pertinent to social media-delivered interventions, including cost, scalability, and privacy. Finally, we identify areas of research that are needed to optimize this emerging behavioral intervention modality.
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Promoción de la Salud/métodos , Medios de Comunicación Sociales , Apoyo Social , Conductas Relacionadas con la Salud , Humanos , Conducta en la Búsqueda de InformaciónRESUMEN
Activation of RhoA, a low molecular-weight G-protein, plays an important role in protecting the heart against ischemic stress. Studies using non-cardiac cells demonstrate that the expression and subsequent secretion of the matricellular protein CCN1 is induced by GPCR agonists that activate RhoA. In this study we determined whether and how CCN1 is induced by GPCR agonists in cardiomyocytes and examined the role of CCN1 in ischemic cardioprotection in cardiomyocytes and the isolated perfused heart. In neonatal rat ventricular myocytes (NRVMs), sphingosine 1-phosphate (S1P), lysophosphatidic acid (LPA) and endothelin-1 induced robust increases in CCN1 expression while phenylephrine, isoproterenol and carbachol had little or no effect. The ability of agonists to activate the small G-protein RhoA correlated with their ability to induce CCN1. CCN1 induction by S1P was blocked when RhoA function was inhibited with C3 exoenzyme or a pharmacological RhoA inhibitor. Conversely overexpression of RhoA was sufficient to induce CCN1 expression. To delineate the signals downstream of RhoA we tested the role of MRTF-A (MKL1), a co-activator of SRF, in S1P-mediated CCN1 expression. S1P increased the nuclear accumulation of MRTF-A and this was inhibited by the functional inactivation of RhoA. In addition, pharmacological inhibitors of MRTF-A or knockdown of MRTF-A significantly diminished S1P-mediated CCN1 expression, indicating a requirement for RhoA/MRTF-A signaling. We also present data indicating that CCN1 is secreted following agonist treatment and RhoA activation, and binds to cells where it can serve an autocrine function. To determine the functional significance of CCN1 expression and signaling, simulated ischemia/reperfusion (sI/R)-induced apoptosis was assessed in NRVMs. The ability of S1P to protect against sI/R was significantly reduced by the inhibition of RhoA, ROCK or MRTF-A or by CCN1 knockdown. We also demonstrate that ischemia/reperfusion induces CCN1 expression in the isolated perfused heart and that this functions as a cardioprotective mechanism, evidenced by the significant increase in infarct development in response to I/R in the cardiac specific CCN1 KO relative to control mice. Our findings implicate CCN1 as a mediator of cardioprotection induced by GPCR agonists that activate RhoA/MRTF-A signaling.
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Cardiotónicos/metabolismo , Proteína 61 Rica en Cisteína/metabolismo , Isquemia Miocárdica/metabolismo , Factores de Transcripción/metabolismo , Proteína de Unión al GTP rhoA/metabolismo , Animales , Animales Recién Nacidos , Membrana Celular/efectos de los fármacos , Membrana Celular/metabolismo , Núcleo Celular/efectos de los fármacos , Núcleo Celular/metabolismo , Ventrículos Cardíacos/citología , Técnicas In Vitro , Lisofosfolípidos/farmacología , Ratones Noqueados , Modelos Biológicos , Isquemia Miocárdica/patología , Miocitos Cardíacos/efectos de los fármacos , Miocitos Cardíacos/metabolismo , Unión Proteica/efectos de los fármacos , ARN Interferente Pequeño/metabolismo , Ratas , Ratas Sprague-Dawley , Receptores Acoplados a Proteínas G/agonistas , Receptores Acoplados a Proteínas G/metabolismo , Daño por Reperfusión/metabolismo , Daño por Reperfusión/patología , Esfingosina/análogos & derivados , Esfingosina/farmacologíaRESUMEN
Accumulating evidence links structural sexism to gendered health inequities, yet methodological challenges have precluded comprehensive examinations into life-course and/or intersectional effects. To help address this gap, we introduce an analytic framework that uses sequential conditional mean models (SCMMs) to jointly account for longitudinal exposure trajectories and moderation by multiple dimensions of social identity/position, which we then apply to study how early life-course exposure to U.S. state-level structural sexism shapes mental health outcomes within and between gender groups. Data came from the Growing Up Today Study, a cohort of 16,875 children aged 9-14 years in 1996 who we followed through 2016. Using a composite index of relevant public policies and societal conditions (e.g., abortion bans, wage gaps), we assigned each U.S. state a year-specific structural sexism score and calculated participants' cumulative exposure by averaging the scores associated with states they had lived in during the study period, weighted according to duration of time spent in each. We then fit a series of SCMMs to estimate overall and group-specific associations between cumulative exposure from baseline through a given study wave and subsequent depressive symptomology; we also fit models using simplified (i.e., non-cumulative) exposure variables for comparison purposes. Analyses revealed that cumulative exposure to structural sexism: (1) was associated with significantly increased odds of experiencing depressive symptoms by the subsequent wave; (2) disproportionately impacted multiply marginalized groups (e.g., sexual minority girls/women); and (3) was more strongly associated with depressive symptomology compared to static or point-in-time exposure operationalizations (e.g., exposure in a single year). Substantively, these findings suggest that long-term exposure to structural sexism may contribute to the inequitable social patterning of mental distress among young people living in the U.S. More broadly, the proposed analytic framework represents a promising approach to examining the complex links between structural sexism and health across the life course and for diverse social groups.
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Sexismo , Humanos , Femenino , Niño , Adolescente , Masculino , Sexismo/psicología , Estados Unidos , Salud Poblacional/estadística & datos numéricos , Estudios Longitudinales , Disparidades en el Estado de SaludRESUMEN
OBJECTIVE: We propose an efficient approach based on a convolutional denoising autoencoder (CDA) network to reduce motion and noise artifacts (MNA) from corrupted atrial fibrillation (AF) and non-AF photoplethysmography (PPG) data segments so that an accurate PPG-signal-derived heart rate can be obtained. Our method's main innovation is the optimization of the CDA performance for both rhythms using more AF than non-AF data for training the AF-specific CDA model and vice versa for the non-AF CDA network. METHODS: To evaluate this unconventional training scheme, our proposed network was trained and tested on 25-sec PPG data segments from 48 subjects from two different databases-the Pulsewatch dataset and Stanford University's publicly available PPG dataset. In total, our dataset contains 10,773 data segments: 7,001 segments for training and 3,772 independent segments from out-of-sample subjects for testing. RESULTS: Using real-life corrupted PPG segments, our approach significantly reduced the average heart rate root mean square error (RMSE) of the reconstructed PPG segments by 45.74% and 23% compared to the corrupted non-AF and AF data, respectively. Further, our approach exhibited lower RMSE, and higher sensitivity and PPV for detected peaks compared to the reconstructed data produced by the alternative methods. CONCLUSION: These results show the promise of our approach as a reliable denoising method, which should be used prior to AF detection algorithms for an accurate cardiac health monitoring involving wearable devices. SIGNIFICANCE: PPG signals collected from wearables are vulnerable to MNA, which limits their use as a reliable measurement, particularly in uncontrolled real-life environments.
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Fibrilación Atrial , Fotopletismografía , Humanos , Fotopletismografía/métodos , Fibrilación Atrial/diagnóstico , Frecuencia Cardíaca/fisiología , Monitoreo Fisiológico , Movimiento (Física) , Algoritmos , Procesamiento de Señales Asistido por Computador , ArtefactosRESUMEN
BACKGROUND: The prevalence of atrial fibrillation (AF) increases with age and can lead to stroke. Therefore, older adults may benefit the most from AF screening. However, older adult populations tend to lag more than younger groups in the adoption of, and comfort with, the use of mobile health (mHealth) apps. Furthermore, although mobile apps that can detect AF are available to the public, most are designed for intermittent AF detection and for younger users. No app designed for long-term AF monitoring has released detailed system design specifications that can handle large data collections, especially in this age group. OBJECTIVE: This study aimed to design an innovative smartwatch-based AF monitoring mHealth solution in collaboration with older adult participants and clinicians. METHODS: The Pulsewatch system is designed to link smartwatches and smartphone apps, a website for data verification, and user data organization on a cloud server. The smartwatch in the Pulsewatch system is designed to continuously monitor the pulse rate with embedded AF detection algorithms, and the smartphone in the Pulsewatch system is designed to serve as the data-transferring hub to the cloud storage server. RESULTS: We implemented the Pulsewatch system based on the functionality that patients and caregivers recommended. The user interfaces of the smartwatch and smartphone apps were specifically designed for older adults at risk for AF. We improved our Pulsewatch system based on feedback from focus groups consisting of patients with stroke and clinicians. The Pulsewatch system was used by the intervention group for up to 6 weeks in the 2 phases of our randomized clinical trial. At the conclusion of phase 1, 90 trial participants who had used the Pulsewatch app and smartwatch for 14 days completed a System Usability Scale to assess the usability of the Pulsewatch system; of 88 participants, 56 (64%) endorsed that the smartwatch app is "easy to use." For phases 1 and 2 of the study, we collected 9224.4 hours of smartwatch recordings from the participants. The longest recording streak in phase 2 was 21 days of consecutive recordings out of the 30 days of data collection. CONCLUSIONS: This is one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring. In this paper, we report on the system's usability and opportunities to increase the acceptability of mHealth solutions among older patients with cognitive impairment. TRIAL REGISTRATION: ClinicalTrials.gov NCT03761394; https://www.clinicaltrials.gov/ct2/show/NCT03761394. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cvdhj.2021.07.002.
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BACKGROUND: Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers. OBJECTIVE: This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors. METHODS: Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period. RESULTS: A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days. CONCLUSIONS: Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear. TRIAL REGISTRATION: ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cvdhj.2021.07.002.
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BACKGROUND: Gendered inequities in disordered eating are well-documented, yet few studies have examined their structural drivers. To help fill this gap, we investigated whether cumulative exposure to state-level structural sexism from childhood through young adulthood potentiates differences in disordered eating risk between cisgender girls/women and boys/men. METHODS: Participants came from the Growing Up Today Study (N = 16,875), a cohort of children aged 9-14 years in 1996 who we followed through 2016. Using a composite index of relevant state policies and social inequalities from the Institute for Women's Policy Research, we categorized states as having high or low levels of structural sexism and summed the number of years participants had lived in a high structural sexism state during the study period to quantify their cumulative exposure. We fit sequential conditional mean models to estimate the effect of cumulative exposure on risk of four outcomes (chronic dieting, purging, binge eating, and overeating), controlling for individual- and state-level confounders via propensity scores. We then tested whether effects differed between girls/women and boys/men by including cumulative-exposure-by-gender-identity interaction terms and calculating the relative excess risk due to interaction (RERI). RESULTS: In the full sample, each additional year of living in a high structural sexism state was associated with a 5% increased risk of purging (95% confidence interval (CI): 3%, 7%), an 8% increased risk of binge eating (95% CI: 6%, 10%), and a 9% increased risk of overeating (95% CI: 8%, 11%). Risk increases were larger on average for girls/women than for boys/men, and girls/women who had lived in a high structural sexism state for four or more years had excess risk of chronic dieting (RERI: 0.64, 95% CI: 0.18, 1.10), purging (RERI: 2.64, 95% CI: 1.24, 4.30), and binge eating (RERI: 2.21, 95% CI: 0.93, 3.50). CONCLUSIONS: Structural sexism may contribute to inequities in disordered eating between cisgender girls/women and boys/men. Future research should include transgender and gender diverse participants, explore intersectional effects, and identify underlying mechanisms to inform policy-oriented interventions.
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Bulimia , Trastornos de Alimentación y de la Ingestión de Alimentos , Adulto , Niño , Estudios de Cohortes , Trastornos de Alimentación y de la Ingestión de Alimentos/epidemiología , Femenino , Humanos , Masculino , Estudios Prospectivos , Sexismo , Adulto JovenRESUMEN
Background: Cancer survivors face increased risk of heart disease, including atrial fibrillation (AF). Certain types of technology, such as consumer wearable devices, can be useful to monitor for AF, but little is known about wearables and AF monitoring in cancer survivor populations. Objective: The purpose of this study was to understand technology usage and preferences in cancer survivors with or at risk for AF, and to describe demographic factors associated with wearable device ownership in this population. Methods: Eligible patients completed a remote survey assessment regarding use of commercial wearable devices. The survey contained questions designed to assess commercial wearable device use, electronic health communications, and perceptions regarding the participant's cardiac health. Results: A total of 424 cancer survivors (mean age 74.2 years; 53.1% female; 98.8% white) were studied. Although most participants owned a smartphone (85.9%), only 31.8% owned a wearable device. Over half (53.5%) of cancer survivors were worried about their heart health. Overall, patients believed arrhythmias (79.7%) were the most important heart condition for a wearable to detect. Survivors reported being most willing to share blood pressure (95.6%) and heart rate (95.3%) data with their providers and were least willing to share information about their diet, weight, and physical activity using these devices. Conclusion: Understanding factors such as device ownership, usage, and heart health concerns in cancer survivors can play an important role in improving cardiovascular monitoring and its accessibility. Long-term patient outcomes may be improved by incorporating wearable devices into routine care of cancer survivors.
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Purpose: Research indicates that sexual minority populations experience mental health inequities. However, few studies have examined mental health outcomes in sexual minority populations while including intersecting dimensions of social identity. This study had two objectives: (1) to quantify the prevalence of frequent mental distress among U.S. adults across intersecting social identity categories and (2) to evaluate the contribution of intersectional interactions to observed inequities. Methods: Using data from the Behavioral Risk Factor Surveillance System 2014-2019 (N = 1,024,261), we performed an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA). Participants were nested in 45 intersectional groups defined by combining 3 sexual orientation (gay/lesbian, bisexual, and heterosexual), 5 gender identity (transgender women, transgender men, gender nonconforming, cisgender women, and cisgender men), and 3 racial/ethnic (non-Hispanic Black, Hispanic/Latinx, and non-Hispanic White) categories. We estimated the predicted probability of frequent mental distress for each stratum. We then calculated the variance partition coefficient (VPC) and proportional change in variance (PCV). Results: We found that multiply marginalized groups tended to have the highest prevalence of frequent mental distress. Groups with racial/ethnic minority individuals were equally represented among low- and high-prevalence groups. The VPC indicated that slightly over 10% of observed variance in prevalence was attributable to group-level differences, while the PCV revealed that a small but meaningful amount of observed heterogeneity in prevalence was due to intersectional interactions between the dimensions of social identity. Conclusion: I-MAIHDA is a promising method for examining the patterning of sexual orientation-based mental health inequities at the population level.
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Etnicidad , Minorías Sexuales y de Género , Adulto , Femenino , Identidad de Género , Humanos , Masculino , Salud Mental , Grupos Minoritarios , Conducta SexualRESUMEN
Background: Smartwatches can be used for atrial fibrillation (AF) detection, but little is known about how older adults at risk for AF perceive their usability. Methods: We employed a mixed-methods study design using data from the ongoing Pulsewatch study, a randomized clinical trial (NCT03761394) examining the accuracy of a smartwatch-smartphone app dyad (Samsung/Android) compared to usual care with a patch monitor (Cardea SOLO™ ECG System) for detection of AF among older stroke survivors. To be eligible to participate in Pulsewatch, participants needed to be at least 50 years of age, have had an ischemic stroke, and have no major contraindications to anticoagulation therapy should AF be detected. After 14 days of use, usability was measured by the System Usability Scale (SUS) and investigator-generated questions. Qualitative interviews were conducted, transcribed, and coded via thematic analysis. Results: Ninety participants in the Pulsewatch trial were randomized to use a smartwatch-smartphone app dyad for 14 days (average age: 65 years, 41% female, 87% White), and 46% found it to be highly usable (SUS ≥68). In quantitative surveys, participants who used an assistive device (eg, wheelchair) and those with history of anxiety or depression were more likely to report anxiety associated with watch use. In qualitative interviews, study participants reported wanting a streamlined system that was more focused on rhythm monitoring and a smartwatch with a longer battery life. In-person training and support greatly improved their experience, and participants overwhelmingly preferred use of a smartwatch over traditional cardiac monitoring owing to its comfort, appearance, and convenience. Conclusion: Older adults at high risk for AF who were randomized to use a smartwatch-app dyad for AF monitoring over 14 days found it to be usable for AF detection and preferred their use to the use of a patch monitor. However, participants reported that a simpler device interface and longer smartwatch battery life would increase the system's usability.
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Background: Little is known about online health information-seeking behavior among older adults with atrial fibrillation (AF) and its association with self-reported outcomes. Objective: To examine patient characteristics associated with online health information seeking and the association between information seeking and low AF-related quality of life and high perceived efficacy in patient-physician interaction. Methods: We used data from the SAGE-AF (Systematic Assessment of Geriatric Elements in AF) study, which includes older participants aged ≥65 years with AF and a CHA2DS2-VASc risk score ≥2. To assess online health information seeking, participants who reported using the Internet were asked at baseline if they used the Internet to search for advice or information about their health in the past 4 weeks (not at all vs at least once). Atrial Fibrillation Effect on Quality of Life and Perceived Efficacy in Patient-Physician Interactions questionnaires were used to examine AF-related quality of life (QOL) and patient-reported confidence in physicians. Logistic regression models were used to examine demographic and clinical factors associated with online health information seeking and associations between information seeking and low AF-related QOL (AFEQT <80) and high perceived efficacy for patient-physician interactions (PEPPI ≥45). Results: A total of 874 online participants (mean age 74.5 years, 51% male, 91% non-Hispanic White) were studied. Approximately 60% of participants sought health information online. Participants aged 74 years or older and those on anticoagulation were less likely, while those with a college degree were more likely, to seek online health information after adjusting for potential confounders. Participants who sought health information online, compared to those who did not, were significantly more likely to have a low AF-related QOL, but less likely to self-report confidence in patient-physician interaction (aOR = 1.56, 95% CI: 1.15-2.13; aOR = 0.68, 95% CI: 0.49-0.93, respectively). Conclusion: Clinicians should consider barriers to patient-physician interaction in older adults who seek health information online, encourage shared decision-making, and provide patients with a list of online resources for AF in addition to disease education plans to help patients manage their health.
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Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.
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Fibrilación Atrial/diagnóstico , Monitoreo Fisiológico , Algoritmos , Enfermedad Crítica , Electrocardiografía , Humanos , Aprendizaje Automático , Sepsis , Máquina de Vectores de Soporte , Análisis de OndículasRESUMEN
BACKGROUND: The Life's Simple 7 (LS7) is a guiding metric for primordial/primary prevention of cardiovascular disease. However, little is known about the prevalence and distribution of LS7 metrics in patients with an acute coronary syndrome at the time of hospitalization. METHODS: Data were obtained from patients hospitalized for an acute coronary syndrome at 6 hospitals in Central Massachusetts and Georgia (2011-2013). The LS7 assessed patient's smoking, diet, and physical activity based on self-reported measures, and patients' body mass index, blood pressure, and serum cholesterol and glucose levels were abstracted from medical records. All items were operationalized into 3 categories: poor (0), intermediate (1), or ideal (2). A total summary cardiovascular health score (0-14) was obtained and categorized into tertiles (0-5, 6-7, and 8-14). RESULTS: The average age of study participants (nâ¯=â¯1110) was 59.6 years and 35% were women. Cardiovascular health scores ranged from 0-12 (meanâ¯=â¯6.2). Patients with higher scores were older, white, had lower burden of comorbidities, had fewer symptoms of anxiety, depression, and stress, better quality of life, more social support, and greater healthcare activation. One-third of patients had only 1 ideal cardiovascular health measure, less than 1% had 5, and no participant had more than 5 ideal factors. CONCLUSIONS: Our results indicate that patients with acute coronary syndrome have poor cardiovascular health. Sociodemographic, clinical, and psychosocial characteristics differed across cardiovascular health groups. These findings highlight potential areas for educational and therapeutic interventions to reduce the risk of cardiovascular disease and promote cardiovascular health in adult men and women.
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Síndrome Coronario Agudo/epidemiología , Presión Sanguínea , Índice de Masa Corporal , Dieta/estadística & datos numéricos , Ejercicio Físico , Fumar/epidemiología , Síndrome Coronario Agudo/metabolismo , Síndrome Coronario Agudo/psicología , Anciano , Ansiedad/psicología , Glucemia/metabolismo , Colesterol/metabolismo , Depresión/psicología , Femenino , Hospitalización , Humanos , Masculino , Persona de Mediana Edad , Calidad de Vida , Apoyo SocialRESUMEN
BACKGROUND: Cardiac rehabilitation programs, consisting of exercise training and disease management interventions, reduce morbidity and mortality after acute myocardial infarction. OBJECTIVE: In this pilot study, we aimed to developed and assess the feasibility of delivering a health watch-informed 12-week cardiac telerehabilitation program to acute myocardial infarction survivors who declined to participate in center-based cardiac rehabilitation. METHODS: We enrolled patients hospitalized after acute myocardial infarction at an academic medical center who were eligible for but declined to participate in center-based cardiac rehabilitation. Each participant underwent a baseline exercise stress test. Participants received a health watch, which monitored heart rate and physical activity, and a tablet computer with an app that displayed progress toward accomplishing weekly walking and exercise goals. Results were transmitted to a cardiac rehabilitation nurse via a secure connection. For 12 weeks, participants exercised at home and also participated in weekly phone counseling sessions with the nurse, who provided personalized cardiac rehabilitation solutions and standard cardiac rehabilitation education. We assessed usability of the system, adherence to weekly exercise and walking goals, counseling session attendance, and disease-specific quality of life. RESULTS: Of 18 participants (age: mean 59 years, SD 7) who completed the 12-week telerehabilitation program, 6 (33%) were women, and 6 (33%) had ST-elevation myocardial infarction. Participants wore the health watch for a median of 12.7 hours (IQR 11.1, 13.8) per day and completed a median of 86% of exercise goals. Participants, on average, walked 121 minutes per week (SD 175) and spent 189 minutes per week (SD 210) in their target exercise heart rate zone. Overall, participants found the system to be highly usable (System Usability Scale score: median 83, IQR 65, 100). CONCLUSIONS: This pilot study established the feasibility of delivering cardiac telerehabilitation at home to acute myocardial infarction survivors via a health watch-based program and telephone counseling sessions. Usability and adherence to health watch use, exercise recommendations, and counseling sessions were high. Further studies are warranted to compare patient outcomes and health care resource utilization between center-based rehabilitation and telerehabilitation.
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Background: The electronic Framingham Heart Study (eFHS) is an ongoing nested study, which includes FHS study participants, examining associations between health data from mobile devices with cardiovascular risk factors and disease. Objective: To describe application (app) design, report user characteristics, and describe usability and survey response rates. Methods: Eligible FHS participants were consented and offered a smartwatch (Apple Watch), a digital blood pressure (BP) cuff, and the eFHS smartphone app for administering surveys remotely. We assessed usability of the new app using 2 domains (functionality, aesthetics) of the Mobile App Rating Scale (MARS) and assessed survey completion rates at baseline and 3 months. Results: A total of 196 participants were recruited using the enhanced eFHS app. Of these, 97 (49.5%) completed the MARS instrument. Average age of participants was 53 ± 9 years, 51.5% were women, and 93.8% were white. Eighty-six percent of participants completed at least 1 measure on the baseline survey, and 50% completed the 3-month assessment. Overall subjective score of the app was 4.2 ± 0.7 on a scale from 1 to 5 stars. Of those who shared their health data with others, 46% shared their BP and 7.7% shared their physical activity with a health care provider. Conclusion: Participants rated the new, enhanced eFHS app positively overall. Mobile app survey completion rates were high, consistent with positive in-app ratings from participants. These mobile data collection modalities offer clinicians new opportunities to engage in conversations about health behaviors.
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Background: Atrial fibrillation (AF) is the world's most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic. Objective: To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device. Methods: In our 2-step algorithm, we first calculate the R-R interval variability-based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects. Results: When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy. Conclusion: Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy.