{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T00:25:42Z","timestamp":1792196742377,"version":"4.4.0"},"reference-count":38,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Wearable Internet of Things (IoT) devices are powerful tools for remotely collecting intensive longitudinal data. The TDK Silmee W22, a wristband-type wearable IoT device with a built-in 3-axis acceleration sensor, provides minute-by-minute physical activity data such as estimated metabolic equivalents (METs) and step counts. These measurements can be aggregated to daily estimates; however, their accuracies have not been fully explored in adults under free-living settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to assess the agreement between the TDK Silmee W22 and the research-grade activity monitor, ActiGraph wGT3X-BT, in estimating daily steps and time spent in moderate to vigorous physical activity (MVPA \u22653 METs) in adults under free-living settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A convenience sample of young to older adults was recruited from communities across several prefectures in Japan. Participants concurrently wore a TDK Silmee W22 on their nondominant wrist and an ActiGraph wGT3X-BT on the left side of the waist during waking hours for 7 consecutive days. Data were aggregated to daily steps and time spent in MVPA (\u22651952 vertical axis counts\/minute for ActiGraph) for each participant. A valid day was defined as having \u2265 10 hours of accumulated ActiGraph wear time with \u2265100 and &lt;50,000 accumulated steps from both devices. Each valid day was classified as either an active day (\u226510,000 steps\/day or \u226521.4 minutes MVPA per day) or an inactive day. Bland-Altman plots combined with multilevel analysis and \u03ba statistics were used to assess the agreement between physical activity estimates from the devices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 129 participants (n=66, 51.2% women) aged 23\u201089 years provided the final dataset of 884 observations (5 to 7 daily observations\/participant). The TDK Silmee W22 estimated an overall mean of 6369 (SE 242) steps\/day and 40.3 (SE 1.9) minutes\/day spent in MVPA. Although Bland-Altman plots suggested no obvious proportional bias, fixed biases were observed; the TDK Silmee W22 estimated \u22121203 steps\/day (95% limits of agreement [LoA] \u22124202 to 1796) and +5 minutes\/day (LoA\u221223 to 34) spent in MVPA compared with those estimated by the ActiGraph wGT3X-BT. TDK Silmee W22 and ActiGraph wGT3X-BT, respectively, classified 14% and 23.1% as active by daily step counts (\u03ba=0.65, 95% CI 0.59\u20100.72), and 70.4% and 60.9% as active by daily time spent in MVPA (\u03ba=0.64, 95% CI 0.59\u20100.69), both indicating moderate agreement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>TDK Silmee W22 underestimated step counts and overestimated time spent in MVPA compared with the research-grade ActiGraph wGT3X-BT, which may lead to misclassification of active and inactive days. Caution is warranted when using TDK Silmee W22 data over relatively short periods, as discrepancies\u2014particularly when compared with research-grade monitors\u2014may affect feedback or goal setting.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/64602","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T21:14:08Z","timestamp":1753910048000},"page":"e64602-e64602","source":"Crossref","is-referenced-by-count":6,"title":["Agreement Between TDK Silmee W22 and ActiGraph wGT3X-BT for Estimating Daily Step Counts and Moderate to Vigorous Physical Activity in Free-Living Adults: Comparative Study"],"prefix":"10.2196","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4732-3259","authenticated-orcid":false,"given":"Hiroko","family":"Shimura","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5093-2506","authenticated-orcid":false,"given":"Shinpei","family":"Okada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3093-0386","authenticated-orcid":false,"given":"Kazushi","family":"Maruo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4887-194X","authenticated-orcid":false,"given":"Kaori","family":"Daimaru","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3712-7430","authenticated-orcid":false,"given":"Naoki","family":"Deguchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3384-1696","authenticated-orcid":false,"given":"Yoshinori","family":"Fujiwara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8120-6163","authenticated-orcid":false,"given":"Hiroyuki","family":"Sasai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychsport.2020.101703","article-title":"Assessing physical behavior through accelerometry \u2013 State of the science, best practices and future directions","volume":"49","author":"Burchartz","journal-title":"Psychol Sport Exerc"},{"issue":"8","key":"R2","doi-asserted-by":"publisher","DOI":"10.2196\/10527","article-title":"Accuracy of Fitbit devices: systematic review and narrative syntheses of quantitative data","volume":"6","author":"Feehan","journal-title":"JMIR Mhealth Uhealth"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103153","article-title":"Wearable activity trackers, accuracy, adoption, acceptance and health impact: a systematic literature review","volume":"93","author":"Shin","journal-title":"J Biomed Inform"},{"issue":"4","key":"R4","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.ypmed.2009.05.002","article-title":"Physical activity: health outcomes and importance for public health policy","volume":"49","author":"Haskell","journal-title":"Prev Med"},{"key":"R5","unstructured":"Global recommendations on physical activity for health. 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