Evaluation of Huawei smart wearables for detection of atrial fibrillation in patients following ischemic stroke: The Liverpool-Huawei stroke study

Harrison, Stephanie L; Buckley, Benjamin J R; Zheng, Yalin; Hill, Andrew; Hlaing, Thant; Davies, Ruth; Guo, Yutao; Lane, Deirdre A.; Lip, Gregory Y.H.; Choi, Sylvia E; Elsheikh, Sandra; Sagris, Dimitrios; Joddrell, Martha; Alobaida, Muath; McCarthy, E; Eustace, I; Hoad, K; Hoad, S J Katie; Logantha, Sunil Jit R. J.; McDowell, Garry; Shantsila, Alena; Gue, Ying X; Isanejad, Masoud; Jones, I. S.

doi:10.1016/j.ahj.2022.12.004

Cited by 9 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The developments that occur are in the form of wearable devices (Khatun et al, 2022), performance analysis applications (Hummel, Fehr, & Ferger, 2013), sports training applications (Ni & Mao, 2021), and others. Smartwatches are one of the devices that are widely used for exercising by connecting via an application on Android (Harrison et al, 2023). The measurements that can be taken by this device are very complex, starting from footsteps (Schmidt, Wille, Rheinländer, Wehn, & Jaitner, 2018), calories burned (Xie et al, 2018), oxygen levels (Putra, Suarmin, & Ekariani, 2022), heart rate (Ichwana, Ikhlas, & Ekariani, 2018), to estimating exercise intensity and volume (Adamakis, 2020).…”

Section: Discussionmentioning

confidence: 99%

Analysis of Android-Based Applications in Physical Education and Sports: Systematic Review

Al Ardha,

Nurhasan,

Nur

et al. 2024

Retos

View full text Add to dashboard Cite

Android-based applications have always experienced rapid progress and development over time. This development has an impact on various fields or scopes that exist around it. This study aims to comprehensively review the implementation of android applications in physical education and sports. This study used a systematic review method by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 331 articles from the SCOPUS database were screened to determine 10 relevant articles. These articles were then subjected to a systematic review. The results of the systematic review presented the research that has been conducted covering several fields, such as learning media, training, health, and sport biomechanics. The presentation of research data from the review results contributed to the development of research in the field of Android-based applications in physical education and sports. On the other hand, research related to Android-based applications needs to be done intensely in physical education. Keywords: technological development, physical activity, literature review, performance analysis.

show abstract

Section: Discussionmentioning

confidence: 99%

Analysis of Android-Based Applications in Physical Education and Sports: Systematic Review

Al Ardha,

Nurhasan,

Nur

et al. 2024

Retos

View full text Add to dashboard Cite

show abstract

“…Fortunately, there are ongoing efforts to focus on this understudied but high-risk population. The Liverpool-Huawei stroke study is an ongoing prospective study that plans to enroll 1000 stroke patients and monitor them for 4 weeks using a Huawei wearable device [ 23 ]. Eligibility criteria are quite similar compared to Pulsewatch, and it will be quite interesting to compare the incidence of AF between the studies.…”

Section: Discussionmentioning

confidence: 99%

Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled Trial

Ding,

Tran,

Lessard

et al. 2023

JMIR Cardio

View full text Add to dashboard Cite

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

show abstract

“…We utilized Huawei Band 6 devices with photoplethysmography (PPG) sensors (100 Hz) to record the physiological signals and calculate the continuous inter-beat interval and heart rates. Huawei smart wearable devices had previously been employed in previous clinical studies for cardiovascular disease [ 29 – 31 ]. We then used inter-beat interval records to calculate Heart rate variability (HRV) parameters, such as Standard Deviation of Normal-to-Normal Intervals (SDNN), Root Mean Square of Successive Differences (RMSSD) and so on.…”

Section: Methodsmentioning

confidence: 99%

Prediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study

Lin,

Zheng,

Wang

et al. 2024

BMC Psychiatry

View full text Add to dashboard Cite

Background Depression and anxiety are common and disabling mental health problems in children and young adults. Group cognitive behavioral therapy (GCBT) is considered that an efficient and effective treatment for these significant public health concerns, but not all participants respond equally well. The aim of this study was to examine the predictive ability of heart rate variability (HRV), based on sensor data from consumer-grade wearable devices to detect GCBT effectiveness in early intervention. Methods In a study of 33 college students with depression and anxiety, participants were randomly assigned to either GCBT group or a wait-list control (WLC) group. They wore smart wearable devices to measure their physiological activities and signals in daily life. The HRV parameters were calculated and compared between the groups. The study also assessed correlations between participants’ symptoms, HRV, and GCBT outcomes. Results The study showed that participants in GCBT had significant improvement in depression and anxiety symptoms after four weeks. Higher HRV was associated with greater improvement in depressive and anxious symptoms following GCBT. Additionally, HRV played a noteworthy role in determining how effective GCBT was in improve anxiety(P = 0.002) and depression(P = 0.020), and its predictive power remained significant even when considering other factors. Conclusion HRV may be a useful predictor of GCBT treatment efficacy. Identifying predictors of treatment response can help personalize treatment and improve outcomes for individuals with depression and anxiety. Trial registration The trial has been retrospectively registered on [22/06/2023] with the registration number [NCT05913349] in the ClinicalTrials.gov. Condensed abstract Variations in heart rate variability (HRV) have been associated with depression and anxiety, but the relationship of baseline HRV to treatment outcome in depression and anxiety is unclear. This study predicted GCBT effectiveness using HRV measured by wearable devices. 33 students with depression and anxiety participated in a trial comparing GCBT and wait-list control. HRV parameters from wearables correlated with symptoms (PHQ, PSS) and GCBT effectiveness. Baseline HRV levels are strongly associated with GCBT treatment outcomes. HRV may serve as a useful predictor of efficacy of GCBT treatment,facilitating personalized treatment approaches for individuals with depression and anxiety.

show abstract

Evaluation of Huawei smart wearables for detection of atrial fibrillation in patients following ischemic stroke: The Liverpool-Huawei stroke study

Cited by 9 publications

References 31 publications

Analysis of Android-Based Applications in Physical Education and Sports: Systematic Review

Analysis of Android-Based Applications in Physical Education and Sports: Systematic Review

Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled Trial

Prediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study

Contact Info

Product

Resources

About