{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T20:06:03Z","timestamp":1785182763055,"version":"3.55.0"},"reference-count":38,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The COVID-19 pandemic disrupted mobility globally, but its mental health implications remain difficult to characterize because most studies relied on lockdown status, population-level mobility indicators, or self-reported mobility. These approaches may miss individual differences in actual movement patterns and cannot fully examine bidirectional relationships between mobility and mental health. Individual-level smartphone geolocation data may provide a more objective and temporally aligned measure of mobility during periods of societal disruption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to use individual-level Google location history (GLH) data and population-level Google community mobility reports (GCMRs) to examine concurrent and longitudinal relationships between pandemic-era mobility patterns and mental health symptoms in Hong Kong.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study analyzed data from the CU-COVID19 cohort study, an online longitudinal survey study of the psychological impact of the pandemic in Hong Kong. Mental health symptoms over the previous\u00a014 days\u00a0were assessed\u00a0at baseline, 6 months, and 12 months using the 9-item Patient Health Questionnaire, the 7-item Generalized Anxiety Disorder scale, and the 4-item PTSD Checklist for DSM-5. Participants provided retrospective GLH data reflecting their mobility during the corresponding 14-day survey periods. The analytic sample included 145 participants with baseline GLH data, of whom 110 had 6-month follow-up data and 49 had data available at all 3 assessment waves. GLH data\u00a0were used\u00a0to derive mobility factors\u00a0representing\u00a0journey diversity, immobility, and remoteness. Population-level mobility during the same 14-day periods\u00a0was measured\u00a0using Hong Kong\u00a0GCMR\u00a0residential stay data. Concurrent mediation models examined whether individual mobility mediated associations between population-level residential stay and mental health symptoms. Longitudinal models examined bidirectional associations between changes in individual mobility and mental health across 6-month intervals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Population-level residential stay was not directly associated with mental health. In concurrent mediation models, higher population-level residential stay was associated with lower individual journey diversity (\u03b2=\u20130.36; P&lt;.001), and lower journey diversity was associated with higher depression (\u03b2=\u20130.29; P=.02) and posttraumatic stress disorder (PTSD) (\u03b2=\u20130.35; P=.002). Bootstrapped indirect effects suggested mediation through journey diversity for depressive symptoms (\u03b2=0.11, 95% CI 0.02-0.25) and PTSD symptoms (\u03b2=0.13, 95% CI 0.05-0.27), although the depression-related indirect effect became less robust after adjustment for local and individual COVID-19 infection indicators. Longitudinally, higher baseline depressive symptoms predicted\u00a0subsequent\u00a0reductions in journey diversity (\u03b2=\u20130.15; P=.02), and reductions in journey diversity predicted higher\u00a0subsequent\u00a0depressive symptoms (\u03b2=\u20130.43; P=.008).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Individual-level mobility patterns, particularly lower journey diversity, showed more consistent associations with mental health symptoms than population-level residential stay. Findings suggest bidirectional relationships between mobility and mental\u00a0health and\u00a0demonstrate the potential of smartphone geolocation data for digital phenotyping. However, the modest and self-selected sample, limited\u00a0GCMR\u00a0availability, and observational design require cautious interpretation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/79711","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T19:35:07Z","timestamp":1785180907000},"page":"e79711","source":"Crossref","is-referenced-by-count":0,"title":["Mobility Patterns and Mental Health During the COVID-19 Pandemic: Longitudinal Observational Study Using Smartphone Mobility Data"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7445-6911","authenticated-orcid":false,"given":"Owen Ngo Wang","family":"Leung","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7252-970X","authenticated-orcid":false,"given":"Linda Chiu Wa","family":"Lam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4831-2305","authenticated-orcid":false,"given":"Ronald C","family":"Kessler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5497-2743","authenticated-orcid":false,"given":"Pim","family":"Cuijpers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0934-6385","authenticated-orcid":false,"given":"Samuel Yeung Shan","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1627-6434","authenticated-orcid":false,"given":"Arthur Dun Ping","family":"Mak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2021.03.017"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trip.2020.100255"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2023.115494"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291721000015"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1017\/S2045796022000051"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1177\/00221465251368341"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-51854-6"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2020.102906"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291722000605"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2016.10.021"},{"key":"ref11","unstructured":"Google maps is turning 15! 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