Mobile health assessments of geriatric elements in older patients with atrial fibrillation: The Mobile SAGE-AF Study (M-SAGE)

Background Geriatric conditions (eg, cognitive impairment, frailty) are increasingly recognized for their impact on clinical and quality-of-life outcomes in older patients with cardiovascular disease, but are not systematically assessed in the context of clinical visits owing to time constraints. Objective To examine feasibility of remote monitoring of the physical, cognitive, and psychosocial status of older adults with atrial fibrillation (AF) via a novel smartphone app over 6 months. Methods Forty participants with AF and eligible for anticoagulation therapy (CHA2DS2VASc ≥2) enrolled in an ongoing cohort study participated in a mobile health pilot study. A 6-component geriatric assessment, including validated measures of frailty, cognitive function, social support, depressive symptoms, vision, and hearing, was deployed via a smartphone app and 6-minute walk test was completed using a Fitbit. Adherence to mobile assessments was examined over 6 months. Results Participants were an average of 71 years old (range 65–86 years) and 38% were women. At 1 month, 75% (30/40) of participants completed the app-based geriatric assessment and 63% (25/40) completed the 6-minute walk test. At 6 months, 52% (15/29) completed the geriatric assessment and 28% (8/29) completed the walk test. There were no differences in demographic, clinical, or psychosocial factors between participants who completed the surveys at 6 months and those who did not. Participants, on average, required less than 10 minutes of telephone support over the 6-month period. Conclusion It is feasible, among smartphone users, to use a mobile health app and wearable activity monitor to conduct serial geriatric assessments in older patients with AF for up to 6 months

Technology, community, and equity: Considerations for collecting social determinants data

Gathering detailed information on an individual’s neighborhood environment is becoming increasingly recognized as a crucial component of understanding the impact that social determinants have on individual and public health, and this has been further highlighted by the ongoing COVID-19 pandemic. Emerging research clearly demonstrates COVID-19’s differential impact on underserved and rural communities, and it is imperative to adequately capture important neighborhood-level predictors of health outcomes to better understand the extent to which these communities have been affected, and to equitably promote their recovery and healing. mHealth tools have drastically transformed the framework of data collection within clinical and population health research and can significantly reduce accessibility barriers for research participants to allow for convenient, continuous real-time health and activity space assessments. Digital interventions leveraging remote data collection, and providing study participants with requisite devices when necessary, serves to bridge the digital divide that would otherwise preclude rural populations’ participation in key research opportunities for advancing health equity

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

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\u27 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

Does perception equal reality? Weight misperception in relation to weight-related attitudes and behaviors among overweight and obese US adults

<p>Abstract</p> <p>Background</p> <p>Weight misperception might preclude the adoption of healthful weight-related attitudes and behaviors among overweight and obese individuals, yet limited research exists in this area. We examined associations between weight misperception and several weight-related attitudes and behaviors among a nationally representative sample of overweight and obese US adults.</p> <p>Methods</p> <p>Data from the 2003-2006 National Health and Nutrition Examination Survey (NHANES) were used. Analyses included non-pregnant, overweight and obese (measured body mass index ≥ 25) adults aged 20 and older. Weight misperception was identified among those who reported themselves as "underweight" or "about the right weight". Outcome variables and sample sizes were: weight-loss attitudes/behaviors (wanting to weigh less and having tried to lose weight; n = 4,784); dietary intake (total energy intake; n = 4,894); and physical activity (meets 2008 US physical activity recommendations, insufficiently active, and sedentary; n = 5,401). Multivariable regression models were stratified by gender and race/ethnicity. Analyses were conducted in 2009-2010.</p> <p>Results</p> <p>These overweight/obese men and women who misperceived their weight were 71% (RR 0.29, 95% CI 0.25-0.34) and 65% (RR 0.35, 95% CI 0.29-0.42) less likely to report that they want to lose weight and 60% (RR 0.40, 95% CI 0.30-0.52) and 56% (RR 0.44, 95% CI 0.32-0.59) less likely to have tried to lose weight within the past year, respectively, compared to those who accurately perceived themselves as overweight. Blacks were particularly less likely to have tried to lose weight. Weight misperception was not a significant predictor of total energy intake among most subgroups, but was associated with lower total energy intake among Hispanic women (change -252.72, 95% CI -433.25, -72.18). Men who misperceived their weight were less likely (RR 0.68, 95% CI 0.52-0.89) to be insufficiently active (the strongest results were among Black men) and women who misperceived their weight were less likely (RR 0.74, 95% CI 0.54, 1.00, <it>p </it>= 0.047) to meet activity recommendations compared to being sedentary.</p> <p>Conclusion</p> <p>Overall, weight misperception among overweight and obese adults was associated with less likelihood of interest in or attempts at weight loss and less physical activity. These associations varied by gender and race/ethnicity. This study highlights the importance of focusing on inaccurate weight perceptions in targeted weight loss efforts.</p

Premature Atrial and Ventricular Contraction Detection using Photoplethysmographic Data from a Smartwatch

We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincare plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC

Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches

Detection of atrial fibrillation (AF) from a wrist watch photoplethysmogram (PPG) signal is important because the wrist watch form factor enables long term continuous monitoring of arrhythmia in an easy and non-invasive manner. We have developed a novel method not only to detect AF from a smart wrist watch PPG signal, but also to determine whether the recorded PPG signal is corrupted by motion artifacts or not. We detect motion and noise artifacts based on the accelerometer signal and variable frequency complex demodulation based time-frequency analysis of the PPG signal. After that, we use the root mean square of successive differences and sample entropy, calculated from the beat-to-beat intervals of the PPG signal, to distinguish AF from normal rhythm. We then use a premature atrial contraction detection algorithm to have more accurate AF identification and to reduce false alarms. Two separate datasets have been used in this study to test the efficacy of the proposed method, which shows a combined sensitivity, specificity and accuracy of 98.18%, 97.43% and 97.54% across the datasets

Association of religiosity and spirituality with quality of life in patients with cardiovascular disease: a systematic review

Purpose: This review systematically identified and critically appraised the available literature that has examined the association between religiosity and/or spirituality (R/S) and quality of life (QOL) in patients with cardiovascular disease (CVD). Methods: We searched several electronic online databases (PubMed, SCOPUS, PsycINFO, and CINAHL) from database inception until October 2017. Included articles were peer-reviewed, published in English, and quantitatively examined the association between R/S and QOL. We assessed the methodological quality of each included study. Results: The 15 articles included were published between 2002 and 2017. Most studies were conducted in the US and enrolled patients with heart failure. Sixteen dimensions of R/S were assessed with a variety of instruments. QOL domains examined were global, health-related, and disease-specific QOL. Ten studies reported a significant positive association between R/S and QOL, with higher spiritual well-being, intrinsic religiousness, and frequency of church attendance positively related with mental and emotional well-being. Approximately half of the included studies reported negative or null associations. Conclusions: Our findings suggest that higher levels of R/S may be related to better QOL among patients with CVD, with varying associations depending on the R/S dimension and QOL domain assessed. Future longitudinal studies in large patient samples with different CVDs and designs are needed to better understand how R/S may influence QOL. More uniformity in assessing R/S would enhance the comparability of results across studies. Understanding the influence of R/S on QOL would promote a holistic approach in managing patients with CVD

Adapting Behavioral Interventions for Social Media Delivery

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

Feasibility of atrial fibrillation detection from a novel wearable armband device

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

MI-PACE Home-Based Cardiac Telerehabilitation Program for Heart Attack Survivors: Usability Study

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