{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:49:49Z","timestamp":1791334189406,"version":"4.3.1"},"reference-count":136,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In brief: The perinatal period represents a convergence of sleep disruption, circadian misalignment, and reproductive hormonal changes, which are the same biological axes implicated in depression and anxiety disorders. Analyses of wearable data complement clinical standards, capturing individual biological trajectories and identifying patterns that confer psychiatric risk.<\/jats:p>\n                  <jats:p>Abstract: Pregnancy and the postpartum period are known to elicit changes in sleep, circadian rhythms, and hormonal dynamics. The same biological axes are implicated in anxiety and depression in the general population. Given the high burden of perinatal mental health and the dire consequences of maternal wellbeing on the mother and infant alike, research differentiating those most at risk is a priority. Why some pregnancies experience these changes without causing mood disorders when many do is unclear but suggests there must be multiple types of disruption, some associated with more risk than others. Current standards of care rely on periodic, and often self-reported, assessments collected around scheduled visits, which can limit opportunities for early detection and timely intervention. We review evidence that wearable technology can complement the gold standard by offering continuous, high-resolution, personalized ecological monitoring of sleep, circadian rhythms, and hormonal dynamics at the individual level. These rich data, coupled with appropriate data science and machine learning techniques, enable early typing of pregnancies to support earlier diagnosis and intervention, with the potential for personalized insights, tailored treatment, and faster recovery.<\/jats:p>","DOI":"10.1093\/reprod\/xaag115","type":"journal-article","created":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T13:51:54Z","timestamp":1788616314000},"source":"Crossref","is-referenced-by-count":0,"title":["Wearable sensors offer opportunities for phenotyping perinatal mood disorders: a narrative review"],"prefix":"10.1093","volume":"172","author":[{"given":"Olimpia","family":"Carrioli","sequence":"first","affiliation":[{"name":"Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego , San Diego, CA,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauryn Keeler","family":"Bruce","sequence":"additional","affiliation":[{"name":"Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego , San Diego, CA,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saara M","family":"Kriplani","sequence":"additional","affiliation":[{"name":"Hal\u0131c\u0131o\u011flu Data Science Institute, University of California San Diego , San Diego, CA,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamar","family":"Schaap","sequence":"additional","affiliation":[{"name":"Hal\u0131c\u0131o\u011flu Data Science Institute, University of California San Diego , San Diego, CA,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caden","family":"Stewart","sequence":"additional","affiliation":[{"name":"Hal\u0131c\u0131o\u011flu Data Science Institute, University of California San Diego , San Diego, CA,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4442-3956","authenticated-orcid":false,"given":"Benjamin L","family":"Smarr","sequence":"additional","affiliation":[{"name":"Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego , San Diego, CA,","place":["United States"]},{"name":"Hal\u0131c\u0131o\u011flu Data Science Institute, University of California San Diego , San Diego, CA,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"2026100619513437700_xaag115-B1","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1002\/wps.21078","article-title":"Candidate biomarkers in psychiatric disorders: State of the field","volume":"22","author":"Abi-Dargham","year":"2023","journal-title":"World 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