Health behavior outcomes in stroke survivors prescribed wearables for atrial fibrillation detection stratified by age.

BACKGROUND: Smartwatches have become readily accessible tools for detecting atrial fibrillation (AF). There remains limited data on how they affect psychosocial outcomes and engagement in older adults. We examine the health behavior outcomes of stroke survivors prescribed smartwatches for AF detection stratified by age. METHODS: We analyzed data from the Pulsewatch study, a randomized controlled trial that enrolled patients (≥ 50 years) with a history of stroke or transient ischemic attack and CHA2DS2-VASc ≥ 2. Intervention participants were equipped with a cardiac patch monitor and a smartwatch-app dyad, while control participants wore the cardiac patch monitor for up to 44 days. We evaluated health behavior parameters using standardized tools, including the Consumer Health Activation Index, the Generalized Anxiety Disorder questionnaire, the 12-Item Short Form Health Survey, and wear time of participants categorized into three age groups: Group 1 (ages 50-60), Group 2 (ages 61-69), and Group 3 (ages 70-87). We performed statistical analysis using a mixed-effects repeated measures linear regression model to examine differences amongst age groups. RESULTS: Comparative analysis between Groups 1, 2 and 3 revealed no significant differences in anxiety, patient activation, perception of physical health and wear time. The use of smartwatch technology was associated with a decrease in perception of mental health for Group 2 compared to Group 1 (ß = -3.29, P = 0.046). CONCLUSION: Stroke survivors demonstrated a willingness to use smartwatches for AF monitoring. Importantly, among these study participants, the majority did not experience negative health behavior outcomes or decreased engagement as age increased.

Noise Reduction in Photoplethysmography Signals Using a Convolutional Denoising Autoencoder With Unconventional Training Scheme.

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.

Anxiety, patient activation, and quality of life among stroke survivors prescribed smartwatches for atrial fibrillation monitoring.

Background: The detection of atrial fibrillation (AF) in stroke survivors is critical to decreasing the risk of recurrent stroke. Smartwatches have emerged as a convenient and accurate means of AF diagnosis; however, the impact on critical patient-reported outcomes, including anxiety, engagement, and quality of life, remains ill defined. Objectives: To examine the association between smartwatch prescription for AF detection and the patient-reported outcomes of anxiety, patient activation, and self-reported health. Methods: We used data from the Pulsewatch trial, a 2-phase randomized controlled trial that included participants aged 50 years or older with a history of ischemic stroke. Participants were randomized to use either a proprietary smartphone-smartwatch app for 30 days of AF monitoring or no cardiac rhythm monitoring. Validated surveys were deployed before and after the 30-day study period to assess anxiety, patient activation, and self-rated physical and mental health. Logistic regression and generalized estimation equations were used to examine the association between smartwatch prescription for AF monitoring and changes in the patient-reported outcomes. Results: A total of 110 participants (mean age 64 years, 41% female, 91% non-Hispanic White) were studied. Seventy percent of intervention participants were novice smartwatch users, as opposed to 84% of controls, and there was no significant difference in baseline rates of anxiety, activation, or self-rated health between the 2 groups. The incidence of new AF among smartwatch users was 6%. Participants who were prescribed smartwatches did not have a statistically significant change in anxiety, activation, or self-reported health as compared to those who were not prescribed smartwatches. The results held even after removing participants who received an AF alert on the watch. Conclusion: The prescription of smartwatches to stroke survivors for AF monitoring does not adversely affect key patient-reported outcomes. Further research is needed to better inform the successful deployment of smartwatches in clinical practice.

False Atrial Fibrillation Alerts from Smartwatches are Associated with Decreased Perceived Physical Well-being and Confidence in Chronic Symptoms Management.

Wrist-based wearables have been FDA approved for AF detection. However, the health behavior impact of false AF alerts from wearables on older patients at high risk for AF are not known. In this work, we analyzed data from the Pulsewatch (NCT03761394) study, which randomized patients (≥50 years) with history of stroke or transient ischemic attack to wear a patch monitor and a smartwatch linked to a smartphone running the Pulsewatch application vs to only the cardiac patch monitor over 14 days. At baseline and 14 days, participants completed validated instruments to assess for anxiety, patient activation, perceived mental and physical health, chronic symptom management self-efficacy, and medicine adherence. We employed linear regression to examine associations between false AF alerts with change in patient-reported outcomes. Receipt of false AF alerts was related to a dose-dependent decline in self-perceived physical health and levels of disease self-management. We developed a novel convolutional denoising autoencoder (CDA) to remove motion and noise artifacts in photoplethysmography (PPG) segments to optimize AF detection, which substantially reduced the number of false alerts. A promising approach to avoid negative impact of false alerts is to employ artificial intelligence driven algorithms to improve accuracy.

Usability of a smartwatch for atrial fibrillation detection in older adults after stroke.

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.

Association between risk of obstructive sleep apnea and cognitive performance, frailty, and quality of life among older adults with atrial fibrillation.

STUDY OBJECTIVES: Geriatric impairments and obstructive sleep apnea (OSA) are prevalent among older patients with atrial fibrillation (AF). Little is known about the association between OSA and geriatric impairments, including frailty, cognitive performance, and AF-related quality of life. The objective of this study was to examine the associations of OSA with frailty, cognitive performance, and AF-related quality of life among older adults with AF. METHODS: Data from the Systemic Assessment of Geriatrics Elements-AF study were used, which included AF participants 65 years and older and with a CHA2DS2-VASc ≥ 2. The STOP-BANG questionnaire was used to assess the risk of OSA. Multivariable logistic regression models were used to examine the association between risk of OSA and geriatric impairments, adjusting for sociodemographic, geriatric, and clinical characteristics. RESULTS: A total of 970 participants (mean age 75 years; 51% male) were studied. Of the 680 participants without a medical history of OSA, 26% (n = 179) of participants had a low risk of OSA, 53% (n = 360) had an intermediate risk, and 21% (n = 141) had a high risk for OSA. Compared to those with low risk of OSA, participants with an intermediate or high risk of OSA were more likely to be frail (adjusted odds ratio = 1.67, 95% confidence interval: 1.08-2.56; adjusted odds ratio = 3.00, 95% confidence interval: 1.69-5.32, respectively) in the fully adjusted models. CONCLUSIONS: Our findings identify a group of patients at high risk who would benefit from early screening for OSA. Future longitudinal studies are needed to assess the effect of OSA treatment on frailty, physical functioning, and quality of life among patients with AF. CITATION: Mehawej J, Saczynski JS, Kiefe CI, et al. Association between risk of obstructive sleep apnea and cognitive performance, frailty, and quality of life among older adults with atrial fibrillation. J Clin Sleep Med. 2022;18(2):469-475.