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However, the accuracy with which these devices measure biometric outcomes such as heart rate, sleep and physical activity remains unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To conduct a \u2018living\u2019 (i.e. ongoing) evaluation of the accuracy of consumer wearable technologies in measuring various physiological outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic search of the literature was conducted in the following scientific databases: MEDLINE via PubMed, Embase, Cinahl and SPORTDiscus via EBSCO. The inclusion criteria required systematic reviews or meta-analyses that evaluated the validation of consumer wearable devices against accepted reference standards. In addition to publication details, review protocol, device specifics and a summary of the authors\u2019 results, we extracted data on mean absolute percentage error (MAPE), pooled absolute bias, intraclass correlation coefficients (ICCs) and mean absolute differences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 904 identified studies through the initial search, 24 systematic reviews met our inclusion criteria; these systematic reviews included 249 non-duplicate validation studies of consumer wearable devices involving 430,465 participants (43% female). Of the commercially available wearable devices released to date, approximately 11% have been validated for at least one biometric outcome. However, because a typical device can measure a multitude of biometric outcomes, the number of validation studies conducted represents just 3.5% of the total needed for a comprehensive evaluation of these devices. For heart rate, wearables showed a mean bias of\u2009\u00b1\u20093%. In arrhythmia detection, wearables exhibited a pooled sensitivity and specificity of 100% and 95%, respectively. For aerobic capacity, wearables significantly overestimated VO\n                      <jats:sub>2max<\/jats:sub>\n                      by\u2009\u00b1\u200915.24% during resting tests and\u2009\u00b1\u20099.83% during exercise tests. Physical activity intensity measurements had a mean absolute error ranging from 29 to 80%, depending on the intensity of the activity being undertaken. Wearables mostly underestimated step counts (mean absolute percentage errors ranging from \u2212\u20099 to\u2009\u200912%) and energy expenditure (mean bias\u2009=\u2009\u2009\u2212\u20093\u00a0kcal per minute, or\u2009\u2212\u20093%, with error ranging from\u2009\u2212\u200921.27 to 14.76%). For blood oxygen saturation, wearables showed a mean absolute difference of up to 2.0%. Sleep measurement showed a tendency to overestimate total sleep time (mean absolute percentage error typically\u2009&gt;\u200910%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>While consumer wearables show promise in health monitoring, a conclusive assessment of their accuracy is impeded by pervasive heterogeneity in research outcomes and methodologies. There is a need for standardised validation protocols and collaborative industry partnerships to enhance the reliability and practical applicability of wearable technology assessments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Prospero ID<\/jats:title>\n                    <jats:p>CRD42023402703.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s40279-024-02077-2","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T15:09:31Z","timestamp":1722352171000},"page":"2907-2926","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":115,"title":["Keeping Pace with Wearables: A Living Umbrella Review of Systematic Reviews Evaluating the Accuracy of Consumer Wearable Technologies in Health Measurement"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5284-856X","authenticated-orcid":false,"given":"Cailbhe","family":"Doherty","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0390-3956","authenticated-orcid":false,"given":"Maximus","family":"Baldwin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5917-6308","authenticated-orcid":false,"given":"Alison","family":"Keogh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0290-9587","authenticated-orcid":false,"given":"Brian","family":"Caulfield","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3522-9409","authenticated-orcid":false,"given":"Rob","family":"Argent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,30]]},"reference":[{"key":"2077_CR1","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1111\/1467-9566.12617","volume":"39","author":"D Lupton","year":"2016","unstructured":"Lupton D. 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