{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T19:02:40Z","timestamp":1790362960713,"version":"4.1.0"},"reference-count":89,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Ment Health"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Perinatal mental health disorders affect approximately 20% of pregnant and postpartum individuals, and are associated with substantial maternal and infant morbidity. Traditional assessment relies on infrequent, subjective self-reports. Mobile devices, including smartphones and wearables, offer opportunities for continuous and objective measurement, but evidence on their assessment utility in perinatal populations remains fragmented.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This review aimed to examine the application of wearable devices and smartphones for detecting and predicting perinatal mental health outcomes, with emphasis on predictive performance, informative features, and methodological rigor.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (PROSPERO CRD420251249218). Six databases (PubMed, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ACM Digital Library) were searched initially in January 2026 and supplemented by an amended search in June 2026, with no publication date restrictions. Evidence was synthesized narratively, and the risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool With AI extension).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The initial and supplementary searches yielded 1952 unique records after deduplication, of which 10 studies met the inclusion criteria. The included studies covered postpartum depression, prenatal stress, discrete emotions during pregnancy (eg, happiness, anxiety, and sadness), and maternal loneliness. High discrimination metrics were reported for postpartum depression in individual studies, including a multiclass area under the curve of 0.85, a binary area under the curve of 0.871, and an\n                      <jats:italic>F<\/jats:italic>\n                      <jats:sub>1<\/jats:sub>\n                      -score of 0.9872. Heart rate variability, GPS-derived mobility, physical activity, and sleep features were most frequently reported as useful, and their interpretation requires perinatal-specific contextualization. Methodological quality was limited, with 80% (12\/15) of PROBAST+AI assessment units rated as having high overall quality concern or risk of bias, mainly due to small samples, limited validation, inadequate handling of missing data, and potential overfitting in the analysis domain.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Mobile sensing shows preliminary potential for perinatal mental health assessment, but current evidence does not yet support clinical screening or decision-making, and independent external validation in perinatal populations is currently lacking. Progress toward clinical utility requires broader mental health outcome coverage, larger longitudinal cohorts, standardized analytical and reporting practices, adoption of modeling approaches better suited to perinatal trajectories, independent external validation, and human-centered monitoring designs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/101677","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T14:39:49Z","timestamp":1787668789000},"page":"e101677-e101677","source":"Crossref","is-referenced-by-count":0,"title":["Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1897-0308","authenticated-orcid":false,"given":"Yifan","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3086-6166","authenticated-orcid":false,"given":"Kemeng","family":"Che","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7715-7573","authenticated-orcid":false,"given":"Tella","family":"Lantta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2743-3589","authenticated-orcid":false,"given":"Anna","family":"Axelin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5003-299X","authenticated-orcid":false,"given":"Iman","family":"Azimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9392-3589","authenticated-orcid":false,"given":"Pasi","family":"Liljeberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1002\/wps.20769","article-title":"Perinatal mental health: a review of progress and challenges","volume":"19","author":"Howard","journal-title":"World Psychiatry"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanepe.2025.101312","article-title":"Prevalence and incidence of moderate and severe mental illness in the second postpartum year in England (1995-2020): a national retrospective cohort study using primary care data","volume":"53","author":"Jones","journal-title":"Lancet Reg Health Eur"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2020.05.112","article-title":"Emerging issues and questions on peripartum depression prevention, diagnosis and treatment: a consensus report from the COST Action Riseup-PPD","volume":"274","author":"Fonseca","journal-title":"J Affect Disord"},{"issue":"3","key":"R4","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/S2215-0366(16)00003-1","article-title":"Suicide in perinatal and non-perinatal women in contact with psychiatric services: 15 year findings from a UK national inquiry","volume":"3","author":"Khalifeh","journal-title":"Lancet Psychiatry"},{"issue":"9956","key":"R5","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.1016\/S0140-6736(14)61277-0","article-title":"Effects of perinatal mental disorders on the fetus and child","volume":"384","author":"Stein","journal-title":"The Lancet"},{"key":"R6","unstructured":"Perinatal mental health. 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