{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T07:24:56Z","timestamp":1791876296772,"version":"4.3.4"},"reference-count":45,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Bipolar disorders (BDs) represent a significant global health challenge, with frequent and severe affective episodes that impair quality of life. Accurate, early prediction of these episodes remains difficult. Recent advances in mobile sensing offer new possibilities to detect prodromal changes via smart digital phenotypes, such as geolocation data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to examine whether spatial exploratory behavior, assessed via passive GPS data, can predict depressive and manic episodes in individuals with BD. Specifically, we evaluated the predictive value of unique places visited and related mobility metrics using statistical process control (SPC) techniques to identify both early deviations indicative of prodromal states and changes occurring during ongoing affective episodes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using high-resolution GPS data from the BipoSense dataset, we applied Density-Based Spatial Clustering of Applications with Noise to extract behavioral mobility indicators: number of unique places visited, frequency of location changes, and time spent per location. We implemented exponentially weighted moving average (EWMA)\u2013based SPC to identify \u201cout-of-bounds\u201d deviations from individual baselines. We then tested the alignment of these deviations with affective episodes and the prodromal periods. Optimization of SPC parameters (\u03bb and control limit L) was performed to enhance predictive accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The analysis included 28 participants with BD and a total of 10,213 observation days, covering 26 depressive and 20 (hypo)manic episodes. Examining whether control limits distinguish affective episodes from euthymic days via multilevel models revealed that median time spent at clusters indicated both depressive and (hypo)manic episodes the best, whereas the number of unique clusters showed no significant association with phase transitions. While EWMA-SPC detected behavioral deviations during affective episodes, no single variable consistently met predefined thresholds for both sensitivity and specificity. Optimized SPC settings improved performance, but the number of unique places alone did not robustly predict prodromal or acute episodes. No statistically significant predictive accuracy (eg, sensitivity &gt;70% and specificity &gt;70%) was achieved for any individual indicator (\n                      <jats:italic>P<\/jats:italic>\n                      &gt;.05). However, some SPC charts suggested within-person temporal deviations preceding episodes, indicating limited yet potentially informative patterns.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Although unique places visited alone may not suffice as a predictive marker, the application of EWMA-based SPC to GPS data holds promise for the development of smart digital phenotypes. Although our analysis to predict upcoming episodes did not yield robust predictive accuracy in its current form, it provides a promising conceptual framework for individualized, low-burden behavioral monitoring. Further research is needed to refine existing digital biomarkers, develop new ones, and validate their clinical utility in reducing the frequency and severity of illness phases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/77272","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T00:30:08Z","timestamp":1776731408000},"page":"e77272-e77272","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Affective Episodes in Bipolar Disorder Using Statistical Process Control Analysis of GPS-Based Mobility Patterns: Quantitative Study"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5814-1338","authenticated-orcid":false,"given":"Marvin","family":"Guth","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6203-1851","authenticated-orcid":false,"given":"Carl","family":"Bittendorf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6568-7016","authenticated-orcid":false,"given":"Clemens","family":"Krug","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8475-8972","authenticated-orcid":false,"given":"Vera Miriam","family":"Ludwig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7550-8552","authenticated-orcid":false,"given":"Esther","family":"Muehlbauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8449-4017","authenticated-orcid":false,"given":"Lisa-Marie","family":"Hartnagel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9135-1679","authenticated-orcid":false,"given":"Emanuel","family":"Severus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2666-859X","authenticated-orcid":false,"given":"Michael","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4286-5830","authenticated-orcid":false,"given":"Philipp","family":"Ritter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2769-5944","authenticated-orcid":false,"given":"Ulrich W","family":"Ebner-Priemer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"issue":"2","key":"R1","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/S2215-0366(21)00395-3","article-title":"Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\u20132019: a systematic analysis for the Global Burden of Disease Study 2019","volume":"9","author":"GBD 2019 Mental Disorders Collaborators","journal-title":"Lancet Psychiatry"},{"issue":"10027","key":"R2","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1016\/S0140-6736(15)00241-X","article-title":"Bipolar disorder","volume":"387","author":"Grande","journal-title":"Lancet"},{"issue":"2","key":"R3","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1111\/bdi.12609","article-title":"Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) 2018 guidelines for the management of patients with bipolar disorder","volume":"20","author":"Yatham","journal-title":"Bipolar Disord"},{"issue":"4","key":"R4","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/S2215-0366(19)30380-3","article-title":"Digital phenotyping: hype or hope?","volume":"7","author":"Ebner-Priemer","journal-title":"Lancet Psychiatry"},{"issue":"1","key":"R5","doi-asserted-by":"publisher","DOI":"10.1186\/s12888-018-1929-y","article-title":"Effectiveness of smartphone-based ambulatory assessment (SBAA-BD) including a predicting system for upcoming episodes in the long-term treatment of patients with bipolar disorders: study protocol for a randomized controlled single-blind trial","volume":"18","author":"M\u00fchlbauer","journal-title":"BMC Psychiatry"},{"issue":"6","key":"R6","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1111\/bdi.13243","article-title":"Smartphone\u2010based interventions in bipolar disorder: systematic review and meta\u2010analyses of efficacy. 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