{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T21:34:37Z","timestamp":1790976877961,"version":"4.1.0"},"reference-count":61,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:00:00Z","timestamp":1659657600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Geriat Psychiatry"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>Late\u2010life major depressive disorder (MDD) can be conceptualized as a complex dynamic system. However, it is not straightforward how to analyze the covarying depressive symptoms over time in case of sparse panel data. Dynamic time warping (DTW) analysis may yield symptom networks and dimensions both at the patient and group level.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In the Netherlands Study of Depression in Older People (NESDO) depressive symptoms were assessed every 6\u00a0months using the 30\u2010item Inventory of Depressive Symptomatology (IDS) with up to 13 assessments per participant. Our sample consisted of 182 persons, aged \u2265 60\u00a0years, with an IDS total score of 26 or higher at baseline. Symptom networks dimensions, and centrality metrics were analyzed using DTW and Distatis analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The mean age was 69.8\u00a0years (SD 7.1), with 69.0% females, and a mean IDS score of 38.0 (SD\u00a0=\u00a08.7). DTW enabled visualization of an idiographic symptom network in a single NESDO participant. In the group\u2010level nomothetic approach, four depressive symptom dimensions were identified: \u201ccore symptoms\u201d, \u201clethargy\/somatic\u201d, \u201csleep\u201d, and \u201cappetite\/atypical\u201d. Items of the \u201cinternalizing symptoms\u201d dimension had the highest centrality, whose symptom changes over time were most similar to those changes of other symptoms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>DTW revealed symptom networks and dimensions based on the within\u2010person symptom changes in older MDD patients. Its centrality metrics signal the most influential symptoms, which may aid personalized care.<\/jats:p><\/jats:sec>","DOI":"10.1002\/gps.5787","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T10:33:51Z","timestamp":1658399631000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Network structure of time\u2010varying depressive symptoms through dynamic time warp analysis in late\u2010life depression"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5267-8452","authenticated-orcid":false,"given":"Denise C. 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