{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T09:21:35Z","timestamp":1788340895557,"version":"build-2803163510"},"reference-count":55,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Public Health Nursing"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Existing interventions for geriatric frailty have demonstrated limited overall efficacy, underscoring the need to identify more precise frailty trajectories and develop targeted therapeutic strategies. Although substantial research has examined frailty prevalence among Chinese older adults, few studies have explored longitudinal trajectories, and limited evidence suggests that patterns resemble those observed in other countries. This study aims to identify distinct frailty trajectories and their multidimensional predictors, thereby informing personalized interventions to slow frailty progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study analyzed data from 11,988 participants across four waves (2008\u20132018) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Using group\u2010based trajectory modeling (GBTM), we iteratively fitted models of varying orders to identify optimal frailty development patterns in the elderly population. Next, frailty index (FI) levels and trajectory group effects were assessed using generalized estimating equations (GEEs). Finally, multinomial logistic regression was employed to identify multidimensional predictors of trajectory group membership.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The optimal classification of frailty trajectories among Chinese older adults identified three groups: the low and stable group (53.0%), the moderate increasing group (28.4%), and the high\u2010level frailty group (18.7%). The GEE analysis revealed significant differences in frailty trajectories among participants (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.05). Multinomial logistic regression results indicated that gender, age, education, residence, residential status, economic status, dietary diversity (DD), alcohol consumption, physical activity, cognitive activity, social activity, anxiety, and loneliness were significant predictors of frailty trajectory membership (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.05). The high\u2010risk group exhibited a \u201ccumulative disadvantage\u201d profile characterized by female sex, age \u226585 years, rural residence, low economic status, experience of childhood hunger, moderate alcohol consumption, poor DD, physical inactivity, low cognitive activity, severe anxiety, pronounced loneliness, and low social participation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study identified three distinct frailty trajectories among older adults, revealing that high\u2010risk groups face a \u201ccumulative disadvantage\u201d arising from the convergence of socioeconomic deprivation, behavioral risks, and psychological vulnerability. These findings underscore the need for targeted early interventions and personalized management strategies to effectively address frailty.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Study Registration<\/jats:title>\n                    <jats:p>Not applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/phn.70134","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T12:10:41Z","timestamp":1779451841000},"page":"1233-1247","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analyzing Frailty Trajectories and Predictive Factors in Older Chinese Adults: A Group\u2010Based Trajectory Modeling"],"prefix":"10.1111","volume":"43","author":[{"given":"Cheng","family":"Shi","sequence":"first","affiliation":[{"name":"School of Health Policy &amp; Management Nanjing Medical University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"GuiMeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Health Policy &amp; Management Nanjing Medical University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ZhongHua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Health Policy &amp; Management Nanjing Medical University  Nanjing China"},{"name":"Laboratory for Digital Intelligence &amp; Health Governance Nanjing Medical University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"e_1_2_16_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcsm.12888"},{"key":"e_1_2_16_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12185803"},{"key":"e_1_2_16_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/agm2.12349"},{"key":"e_1_2_16_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanhl.2024.100651"},{"key":"e_1_2_16_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cger.2017.02.002"},{"key":"e_1_2_16_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2015.12.016"},{"key":"e_1_2_16_8_1","doi-asserted-by":"publisher","DOI":"10.13891\/j.cnki.mer.2024.09.002"},{"key":"e_1_2_16_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(22)01546\u2010X"},{"key":"e_1_2_16_10_1","doi-asserted-by":"publisher","DOI":"10.13329\/j.cnki.zyyjk.2017.0829"},{"key":"e_1_2_16_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(12)62167\u20109"},{"key":"e_1_2_16_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2017.04.018"},{"key":"e_1_2_16_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874\u2010018\u20100620\u20109"},{"key":"e_1_2_16_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43587\u2010020\u201000017\u2010z"},{"key":"e_1_2_16_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12904\u2010016\u20100124\u20105"},{"key":"e_1_2_16_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jphys.2021.11.004"},{"key":"e_1_2_16_17_1","doi-asserted-by":"publisher","DOI":"10.3760\/cma.j.cn112338\u201020220528\u201000477"},{"key":"e_1_2_16_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(19)31786\u20106"},{"key":"e_1_2_16_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/ageing\/afy081"},{"key":"e_1_2_16_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/gerona\/glz295"},{"issue":"2","key":"e_1_2_16_21_1","first-page":"386","article-title":"Association Between Socioeconomic Status, Productive Aging, and Frailty Index in Urban Elderly Population in China","volume":"54","author":"Huan Z. 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