European heart journal. Digital health最新文献

{"title":"Mobile phone calls, genetic susceptibility, and new-onset hypertension: results from 212 046 UK Biobank participants.","authors":"Ziliang Ye,&nbsp;Yanjun Zhang,&nbsp;Yuanyuan Zhang,&nbsp;Sisi Yang,&nbsp;Mengyi Liu,&nbsp;Qimeng Wu,&nbsp;Chun Zhou,&nbsp;Panpan He,&nbsp;Xiaoqin Gan,&nbsp;Xianhui Qin","doi":"10.1093/ehjdh/ztad024","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad024","url":null,"abstract":"<p><strong>Aims: </strong>The relationship between mobile phone use for making or receiving calls and hypertension risk remains uncertain. We aimed to examine the associations of mobile phone use for making or receiving calls and the use frequency with new-onset hypertension in the general population, using data from the UK Biobank.</p><p><strong>Methods and results: </strong>A total of 212 046 participants without prior hypertension in the UK Biobank were included. Participants who have been using a mobile phone at least once per week to make or receive calls were defined as mobile phone users. The primary outcome was new-onset hypertension. During a median follow-up of 12.0 years, 13 984 participants developed new-onset hypertension. Compared with mobile phone non-users, a significantly higher risk of new-onset hypertension was found in mobile phone users [hazards ratio (HR), 1.07; 95% confidence interval (CI): 1.01-1.12]. Among mobile phone users, compared with those with a weekly usage time of mobile phones for making or receiving calls <5 mins, significantly higher risks of new-onset hypertension were found in participants with a weekly usage time of 30-59 mins (HR, 1.08; 95%CI: 1.01-1.16), 1-3 h (HR, 1.13; 95%CI: 1.06-1.22), 4-6 h (HR, 1.16; 95%CI: 1.04-1.29), and >6 h (HR, 1.25; 95%CI: 1.13-1.39) (<i>P</i> for trend <0.001). Moreover, participants with both high genetic risks of hypertension and longer weekly usage time of mobile phones making or receiving calls had the highest risk of new-onset hypertension.</p><p><strong>Conclusions: </strong>Mobile phone use for making or receiving calls was significantly associated with a higher risk of new-onset hypertension, especially among high-frequency users.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/0c/7c/ztad024.PMC10232238.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9566517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can machine learning unravel unsuspected, clinically important factors predictive of long-term mortality in complex coronary artery disease? A call for 'big data'.","authors":"Kai Ninomiya,&nbsp;Shigetaka Kageyama,&nbsp;Scot Garg,&nbsp;Shinichiro Masuda,&nbsp;Nozomi Kotoku,&nbsp;Pruthvi C Revaiah,&nbsp;Neil O'leary,&nbsp;Yoshinobu Onuma,&nbsp;Patrick W Serruys","doi":"10.1093/ehjdh/ztad014","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad014","url":null,"abstract":"<p><strong>Aims: </strong>Risk stratification and individual risk prediction play a key role in making treatment decisions in patients with complex coronary artery disease (CAD). The aim of this study was to assess whether machine learning (ML) algorithms can improve discriminative ability and identify unsuspected, but potentially important, factors in the prediction of long-term mortality following percutaneous coronary intervention or coronary artery bypass grafting in patients with complex CAD.</p><p><strong>Methods and results: </strong>To predict long-term mortality, the ML algorisms were applied to the SYNTAXES database with 75 pre-procedural variables including demographic and clinical factors, blood sampling, imaging, and patient-reported outcomes. The discriminative ability and feature importance of the ML model was assessed in the derivation cohort of the SYNTAXES trial using a 10-fold cross-validation approach. The ML model showed an acceptable discrimination (area under the curve = 0.76) in cross-validation. C-reactive protein, patient-reported pre-procedural mental status, gamma-glutamyl transferase, and HbA1c were identified as important variables predicting 10-year mortality.</p><p><strong>Conclusion: </strong>The ML algorithms disclosed unsuspected, but potentially important prognostic factors of very long-term mortality among patients with CAD. A 'mega-analysis' based on large randomized or non-randomized data, the so-called 'big data', may be warranted to confirm these findings.</p><p><strong>Clinical trial registration: </strong>SYNTAXES ClinicalTrials.gov reference: NCT03417050, SYNTAX ClinicalTrials.gov reference: NCT00114972.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ac/da/ztad014.PMC10232230.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9566521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meet key digital health thought leaders: David Albert.","authors":"Nico Bruining","doi":"10.1093/ehjdh/ztad020","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad020","url":null,"abstract":"CardioPulse Digital talks to the Founder and Chief Medical Officer of AliveCor: Dr. David Albert David E.","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a8/c8/ztad020.PMC10232255.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9566523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChatGPT's ability to classify virtual reality studies in cardiology.","authors":"Yuichiro Nakaya,&nbsp;Akinori Higaki,&nbsp;Osamu Yamaguchi","doi":"10.1093/ehjdh/ztad026","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad026","url":null,"abstract":"We recently published a novel categorization of studies related to virtual reality (VR) in your journal, European Heart Journal—Digital Health . 1 Our categorization is based on the usage of VR devices, where type A studies refer to those in which healthcare providers use VR devices and type B studies refer to those in which patients use them. Using this sim-ple definition, we clarified the study trends and characteristics of the two research directions. In this study, we used a classical natural language processing (NLP) methodology, specifically ‘term frequency– inverse document frequency’ to develop an automatic abstract categorizer, which is available as a web application at https://ahigaki-vr-categorizer-str-app-gb1m6v.streamlit.app","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3f/a5/ztad026.PMC10232268.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9621354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based risk stratification for mortality in patients with severe aortic regurgitation.","authors":"Vidhu Anand,&nbsp;Hanwen Hu,&nbsp;Alexander D Weston,&nbsp;Christopher G Scott,&nbsp;Hector I Michelena,&nbsp;Sorin V Pislaru,&nbsp;Rickey E Carter,&nbsp;Patricia A Pellikka","doi":"10.1093/ehjdh/ztad006","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad006","url":null,"abstract":"<p><strong>Aims: </strong>The current guidelines recommend aortic valve intervention in patients with severe aortic regurgitation (AR) with the onset of symptoms, left ventricular enlargement, or systolic dysfunction. Recent studies have suggested that we might be missing the window of early intervention in a significant number of patients by following the guidelines.</p><p><strong>Methods and results: </strong>The overarching goal was to determine if machine learning (ML)-based algorithms could be trained to identify patients at risk for death from AR independent of aortic valve replacement (AVR). Models were trained with five-fold cross-validation on a dataset of 1035 patients, and performance was reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a conditional random survival forest model. A subset of 19/41 variables was selected for inclusion in the final model. Variable selection was performed with 10-fold cross-validation using random survival forest model. The top variables included were age, body surface area, body mass index, diastolic blood pressure, New York Heart Association class, AVR, comorbidities, ejection fraction, end-diastolic volume, and end-systolic dimension, and the relative variable importance averaged across five splits of cross-validation in each repeat were evaluated. The concordance index for predicting survival of the best-performing model was 0.84 at 1 year, 0.86 at 2 years, and 0.87 overall, respectively.</p><p><strong>Conclusion: </strong>Using common echocardiographic parameters and patient characteristics, we successfully trained multiple ML models to predict survival in patients with severe AR. This technique could be applied to identify high-risk patients who would benefit from early intervention, thereby improving patient outcomes.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/41/f5/ztad006.PMC10232267.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9571913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid virtual fractional flow reserve using 3D computational fluid dynamics.","authors":"Thomas Newman, Raunak Borker, Louise Aubiniere-Robb, Justin Hendrickson, Dipankar Choudhury, Ian Halliday, John Fenner, Andrew Narracott, D Rodney Hose, Rebecca Gosling, Julian P Gunn, Paul D Morris","doi":"10.1093/ehjdh/ztad028","DOIUrl":"10.1093/ehjdh/ztad028","url":null,"abstract":"<p><strong>Aims: </strong>Over the last ten years, virtual Fractional Flow Reserve (vFFR) has improved the utility of Fractional Flow Reserve (FFR), a globally recommended assessment to guide coronary interventions. Although the speed of vFFR computation has accelerated, techniques utilising full 3D computational fluid dynamics (CFD) solutions rather than simplified analytical solutions still require significant time to compute.</p><p><strong>Methods and results: </strong>This study investigated the speed, accuracy and cost of a novel 3D-CFD software method based upon a graphic processing unit (GPU) computation, compared with the existing fastest central processing unit (CPU)-based 3D-CFD technique, on 40 angiographic cases. The novel GPU simulation was significantly faster than the CPU method (median 31.7 s (Interquartile Range (IQR) 24.0-44.4s) vs. 607.5 s (490-964 s), <i>P</i> < 0.0001). The novel GPU technique was 99.6% (IQR 99.3-99.9) accurate relative to the CPU method. The initial cost of the GPU hardware was greater than the CPU (£4080 vs. £2876), but the median energy consumption per case was significantly less using the GPU method (8.44 (6.80-13.39) Wh vs. 2.60 (2.16-3.12) Wh, <i>P</i> < 0.0001).</p><p><strong>Conclusion: </strong>This study demonstrates that vFFR can be computed using 3D-CFD with up to 28-fold acceleration than previous techniques with no clinically significant sacrifice in accuracy.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9938895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study","authors":"A. Javed, D. Kim, S. Hershman, A. Shcherbina, Anderson Johnson, Alex Tolas, J. O’Sullivan, Michael V. McConnell, L. Lazzeroni, A. King, J. Christle, M. Oppezzo, C. Mattsson, Robert A. Harrington, M. Wheeler, Euan A Ashley","doi":"10.1101/2023.04.09.23287650","DOIUrl":"https://doi.org/10.1101/2023.04.09.23287650","url":null,"abstract":"Background: Physical activity is strongly protective against the development of chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity. Our randomized crossover trial has continued to digitally enroll participants, allowing increasing statistical power for greater precision in subsequent analyses. Methods: We offered enrollment to adults aged >=18 years with access to an iPhone and the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomly allocated to four 7-day interventions. Interventions consisted of: 1) daily personalized e-coaching based on the individuals baseline activity patterns, 2) daily prompts to complete 10,000 steps, 3) hourly prompts to stand following inactivity, and 4) daily instructions to read guidelines from the American Heart Association website. The trial was completed in a free-living setting, where neither the participants or investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis. This trial is registered with ClinicalTrials.gov, NCT03090321. Findings: Between January 1, 2017 and April 1, 2022, 4500 participants consented to enroll in the trial, of whom 2458 completed 7-days of baseline monitoring (mean daily steps 4232+/-73) and at least one day of one of the four interventions. The greater statistical power afforded by continued passive enrollment revealed that e-coaching prompts, tailored to an individual, increased step count significantly more than other interventions (402+/-71 steps, P=7.1x10-8). Interpretation: Digital studies can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we show that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. Funding: Stanford Data Science Initiative and Catalyst Program, Apple, Google","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62358665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of machine learning algorithms to predict life-threatening ventricular arrhythmia in sepsis.","authors":"Le Li, Zhuxin Zhang, Likun Zhou, Zhenhao Zhang, Yulong Xiong, Zhao Hu, Yan Yao","doi":"10.1093/ehjdh/ztad025","DOIUrl":"10.1093/ehjdh/ztad025","url":null,"abstract":"<p><strong>Aims: </strong>Life-threatening ventricular arrhythmias (LTVAs) are common manifestations of sepsis. The majority of sepsis patients with LTVA are unresponsive to initial standard treatment and thus have a poor prognosis. There are very limited studies focusing on the early identification of patients at high risk of LTVA in sepsis to perform optimal preventive treatment interventions. We aimed to develop a prediction model to predict LTVA in sepsis using machine learning (ML) approaches.</p><p><strong>Methods and results: </strong>Six ML algorithms including CatBoost, LightGBM, and XGBoost were employed to perform the model fitting. The least absolute shrinkage and selection operator (LASSO) regression was used to identify key features. Methods of model evaluation involved in this study included area under the receiver operating characteristic curve (AUROC), for model discrimination, calibration curve, and Brier score, for model calibration. Finally, we validated the prediction model both internally and externally. A total of 27 139 patients with sepsis were identified in this study, 1136 (4.2%) suffered from LTVA during hospitalization. We screened out 10 key features from the initial 54 variables via LASSO regression to improve the practicability of the model. CatBoost showed the best prediction performance among the six ML algorithms, with excellent discrimination (AUROC = 0.874) and calibration (Brier score = 0.157). The remarkable performance of the model was presented in the external validation cohort (<i>n</i> = 9492), with an AUROC of 0.836, suggesting certain generalizability of the model. Finally, a nomogram with risk classification of LTVA was shown in this study.</p><p><strong>Conclusion: </strong>We established and validated a machine leaning-based prediction model, which was conducive to early identification of high-risk LTVA patients in sepsis, thus appropriate methods could be conducted to improve outcomes.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b8/c8/ztad025.PMC10232270.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9568846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.","authors":"Joseph Keunhong Yi, Tyler Hyungtaek Rim, Sungha Park, Sung Soo Kim, Hyeon Chang Kim, Chan Joo Lee, Hyeonmin Kim, Geunyoung Lee, James Soo Ghim Lim, Yong Yu Tan, Marco Yu, Yih-Chung Tham, Ameet Bakhai, Eduard Shantsila, Paul Leeson, Gregory Y H Lip, Calvin W L Chin, Ching-Yu Cheng","doi":"10.1093/ehjdh/ztad023","DOIUrl":"10.1093/ehjdh/ztad023","url":null,"abstract":"<p><strong>Aims: </strong>This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD.</p><p><strong>Methods and results: </strong>We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively.</p><p><strong>Conclusion: </strong>The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/98/11/ztad023.PMC10232236.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9571920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smartwatch-derived heart rate variability: a head-to-head comparison with the gold standard in cardiovascular disease.","authors":"Fabian Theurl, Michael Schreinlechner, Nikolay Sappler, Michael Toifl, Theresa Dolejsi, Florian Hofer, Celine Massmann, Christian Steinbring, Silvia Komarek, Kurt Mölgg, Benjamin Dejakum, Christian Böhme, Rudolf Kirchmair, Sebastian Reinstadler, Axel Bauer","doi":"10.1093/ehjdh/ztad022","DOIUrl":"10.1093/ehjdh/ztad022","url":null,"abstract":"<p><strong>Aims: </strong>We aimed to investigate the concordance between heart rate variability (HRV) derived from the photoplethysmographic (PPG) signal of a commercially available smartwatch compared with the gold-standard high-resolution electrocardiogram (ECG)-derived HRV in patients with cardiovascular disease.</p><p><strong>Methods and results: </strong>We prospectively enrolled 104 survivors of acute ST-elevation myocardial infarction, 129 patients after an ischaemic stroke, and 30 controls. All subjects underwent simultaneous recording of a smartwatch (Garmin vivoactive 4; Garmin Ltd, Olathe, KS, USA)-derived PPG signal and a high-resolution (1000 Hz) ECG for 30 min under standardized conditions. HRV measures in time and frequency domain, non-linear measures, as well as deceleration capacity (DC) were calculated according to previously published technologies from both signals. Lin's concordance correlation coefficient (<i>ρ</i>_c) between smartwatch-derived and ECG-based HRV markers was used as a measure of diagnostic accuracy. A very high concordance within the whole study cohort was observed for the mean heart rate (<i>ρ</i>_c = 0.9998), standard deviation of the averages of normal-to-normal (NN) intervals in all 5min segments (SDANN; <i>ρ</i>_c = 0.9617), and very low frequency power (VLF power; <i>ρ</i>_c = 0.9613). In contrast, detrended fluctuation analysis (DF-α1; <i>ρ</i>_c = 0.5919) and the square mean root of the sum of squares of adjacent NN-interval differences (rMSSD; <i>ρ</i>_c = 0.6617) showed only moderate concordance.</p><p><strong>Conclusion: </strong>Smartwatch-derived HRV provides a practical alternative with excellent accuracy compared with ECG-based HRV for global markers and those characterizing lower frequency components. However, caution is warranted with HRV markers that predominantly assess short-term variability.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c0/d9/ztad022.PMC10232241.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9568842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}