{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T14:04:23Z","timestamp":1793196263422,"version":"4.5.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>Stress is an increasingly prevalent mental health condition that can have serious effects on human health. The development of stress prediction tools would greatly benefit public health by allowing policy initiatives and early stress-reducing interventions. The advent of mobile health technologies including smartphones and smartwatches has made it possible to collect objective, real-time, and continuous health data. We sought to pilot the collection of heart rate variability data from the Apple Watch electrocardiograph (ECG) sensor and apply machine learning techniques to develop a stress prediction tool. Random Forest (RF) and Support Vector Machines (SVM) were used to model stress based on ECG measurements and stress questionnaire data collected from 33 study participants. Data were stratified into socio-demographic classes to further explore our prediction model. Overall, the RF model performed slightly better than SVM, with results having an accuracy within the low end of state-of-the-art. Our models showed specificity in their capacity to assess \u201cno stress\u201d states but were less successful at capturing \u201cstress\u201d states. Overall, the results presented here suggest that, with further development and refinement, Apple Watch ECG sensor data could be used to develop a stress prediction tool. A wearable device capable of continuous, real-time stress monitoring would enable individuals to respond early to changes in their mental health. Furthermore, large-scale data collection from such devices would inform public health initiatives and policies.<\/jats:p>","DOI":"10.3389\/fdgth.2022.1058826","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T08:11:16Z","timestamp":1670573476000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["Using apple watch ECG data for heart rate variability monitoring and stress prediction: A pilot study"],"prefix":"10.3389","volume":"4","author":[{"given":"Pedro Elkind","family":"Velmovitsky","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paulo","family":"Alencar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott T.","family":"Leatherdale","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donald","family":"Cowan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Plinio Pelegrini","family":"Morita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"B1","year":""},{"key":"B2","year":""},{"key":"B3","first-page":"816","volume-title":"Handbook of health survey methods","author":"Johnson","year":"2015"},{"key":"B4","volume-title":"Epidemiology","author":"Gordis","year":"2014"},{"key":"B5","doi-asserted-by":"publisher","first-page":"2015","DOI":"10.1371\/journal.pone.0193997","article-title":"Factors associated with patient and health system delays in diagnosis and treatment of tuberculosis in Montenegro, 2015\u20132016","volume":"13","author":"Bojovic","year":"2018","journal-title":"PLoS One"},{"key":"B6","first-page":"18","author":"Velmovitsky","year":"2022"},{"key":"B7","first-page":"6049","author":"Velmovitsky","year":"2022"},{"key":"B8","year":""},{"key":"B9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/B978-0-12-816427-3.00005-1","article-title":"Design of mobile health technology","volume-title":"Design for health","author":"Morita","year":"2020"},{"key":"B10","year":""},{"key":"B11","author":"Lovett","year":""},{"key":"B12","doi-asserted-by":"publisher","first-page":"103139","DOI":"10.1016\/j.jbi.2019.103139","article-title":"Stress detection in daily life scenarios using smart phones and wearable sensors: a survey","volume":"92","author":"Can","year":"2019","journal-title":"J Biomed Inform"},{"key":"B13","first-page":"13","article-title":"Ecological momentary assessment: what it is and why it is a method of the future in clinical psychopharmacology","volume":"31","author":"Moskowitz","year":"2006","journal-title":"J Psychiatry Neurosci"},{"key":"B14","year":""},{"key":"B15","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1002\/eat.22799","article-title":"Stress is associated with exercise differently among individuals with higher and lower eating disorder symptoms: an ecological momentary assessment study","volume":"50","author":"Sala","year":"2017","journal-title":"Int J Eat Disord"},{"key":"B16","first-page":"3","author":"Wang","year":"2014"},{"key":"B17","year":""},{"key":"B18","year":""},{"key":"B19","year":""},{"key":"B20","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1007\/s11517-006-0119-0","article-title":"Heart rate variability: a review","volume":"44","author":"Acharya","year":"2006","journal-title":"Med Biol Eng Comput"},{"key":"B21","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1093\/oxfordjournals.eurheartj.a014868","article-title":"Heart rate variability\u2014standards of measurement, physiological interpretation, and clinical use","volume":"17","year":"1996","journal-title":"Eur Heart J"},{"key":"B22","year":""},{"key":"B23","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1002\/(SICI)1234-981X(199610)4:4%3C313::AID-EURO139%3E3.0.CO;2-Z","article-title":"Stress and gender","volume":"4","author":"Frankenhaeuser","year":"1996","journal-title":"Eur Rev"},{"key":"B24","year":""},{"key":"B25","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1089\/tmj.2014.0104","article-title":"Reliability of ultra-short-term analysis as a surrogate of standard 5-min analysis of heart rate variability","volume":"21","author":"Baek","year":"2015","journal-title":"Telemed e-Health"},{"key":"B26","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1097\/JGP.0b013e31826573cf","article-title":"Stress, inflammation, and aging","volume":"20","author":"Lavretsky","year":"2012","journal-title":"Am J Geriatr Psychiatry"},{"key":"B27","year":""},{"key":"B28","doi-asserted-by":"publisher","first-page":"1674","DOI":"10.1016\/S0140-6736(06)68735-7","article-title":"Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study","volume":"367","author":"Bauer","year":"2006","journal-title":"Lancet"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2022.1058826\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T08:11:19Z","timestamp":1670573479000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2022.1058826\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,9]]},"references-count":28,"alternative-id":["10.3389\/fdgth.2022.1058826"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2022.1058826","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,9]]},"article-number":"1058826"}}