{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T14:57:57Z","timestamp":1789743477730,"version":"4.0.1"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"vor","delay-in-days":89,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004737","name":"National Health Research Institutes","doi-asserted-by":"publisher","award":["NHRI-EX112-11025PI"],"award-info":[{"award-number":["NHRI-EX112-11025PI"]}],"id":[{"id":"10.13039\/501100004737","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Depression and Anxiety"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    This study aimed to empirically derive subgroups based on both actigraphy\u2010 and app\u2010measured rest\u2013activity rhythm (RAR) patterns and investigate the relationship between these profiles and health outcomes, including depression and obesity.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    We developed a mobile app, Rhythm, to record human\u2013smartphone interactions and calculate RAR patterns alongside standard actigraphy in 135 participants (mean age: 43.8\u2009\u00b1\u200912.3 years, 64% women) with and without major depressive disorder and\/or obesity. Wrist actigraphy and Rhythm app recorded activity data for at least 4 weeks, totaling 3978 person\u2010days. Person\u2010centered clustering was conducted to identify subgroups based on RAR characteristics, and their associations with clinical outcomes were evaluated using multivariable regression models.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    Three distinct groups with different RAR patterns were identified based on acrophase, interdaily stability (IS), and intradaily variability (IV), measured by actigraphy and human\u2013smartphone interactions, respectively. The \u201cearlier\u201d group exhibited earlier acrophase both by actigraphy and the app and had lower depressive symptom severity than the other two groups. The \u201clater\u201d group showed a later acrophase and a lower body mass index (BMI) compared to the \u201cearlier\u201d group. The \u201cirregular\u201d group, characterized by higher IV, lower IS, and desynchronized actigraphy\u2010 and app\u2010measured acrophase, was associated with higher levels of depressive symptom severity and BMI.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    Our study highlights the usefulness of human\u2013smartphone interaction patterns in providing a comprehensive understanding of individuals\u2019 circadian rhythms beyond standard actigraphy measurements. Identifying distinct RAR profiles based on both actigraphy and app measurements contributes to a better understanding of the associations between circadian disruptions and mental and physical health outcomes.\n                  <\/jats:p>","DOI":"10.1155\/da\/2617282","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T06:59:10Z","timestamp":1743490750000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Rest\u2013Activity Rhythm Patterns and Their Associations With Depression and Obesity: A Study Using Actigraphy and Human\u2013Smartphone 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