{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T07:22:47Z","timestamp":1772090567028,"version":"3.50.1"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003996","name":"Kunming Medical University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003996","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Clarifying heterogeneous diabetes distress (DD) trajectories and their predictors from a dynamic perspective is crucial. We aimed to develop a trajectory prediction model for dynamic DD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This prospective longitudinal study included 443 adults with type 2 diabetes mellitus who completed the demographics and diabetes characteristics questionnaire, scales measuring lifestyles and psychological factors (at baseline), and the Chinese version of the Diabetes Distress Scale (at baseline and at 3-, 6-, 9-, and 12-month follow-ups). After identifying the factors associated with DD, growth mixture modeling was used to determine latent longitudinal DD trajectory classes and develop a trajectory prediction model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Five DD trajectories were identified: persistently low DD (65.01%), persistently moderate DD (25.28%), persistently high DD (3.61%), decreasing DD (3.16%), and increasing DD (2.94%). Using the persistently low DD group as the reference, people with no religious belief (\n                      <jats:italic>B<\/jats:italic>\n                      \u202f=\u202f\u221224.932,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001), longer diabetes duration (\n                      <jats:italic>B<\/jats:italic>\n                      \u202f=\u202f0.042,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.037), worse self-management behaviors (\n                      <jats:italic>B<\/jats:italic>\n                      \u202f=\u202f\u22120.032,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.009), and lower self-efficacy (\n                      <jats:italic>B<\/jats:italic>\n                      \u202f=\u202f\u22120.287,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.007) tended to have a persistently moderate DD trajectory. Insomnia severity (\n                      <jats:italic>B<\/jats:italic>\n                      \u202f=\u202f0.232,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.008) and type D personality (\n                      <jats:italic>B<\/jats:italic>\n                      \u202f=\u202f2.783,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.002) were significant positive predictors of persistently high DD trajectory. Those with higher HbA1c levels (\n                      <jats:italic>B<\/jats:italic>\n                      \u202f=\u202f0.728,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.003) and lower self-efficacy (\n                      <jats:italic>B<\/jats:italic>\n                      \u202f=\u202f\u22120.858,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.044) were more likely to belong to the decreasing DD trajectory class. Self-management behaviors (\n                      <jats:italic>B<\/jats:italic>\n                      \u202f=\u202f\u22120.127,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.012) were negatively associated with belonging to the increasing DD trajectory class.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Demographics, diabetes characteristics, lifestyles, and psychosocial factors can predict dynamic heterogeneous trajectories of DD. The trajectory prediction model will enable healthcare professionals to anticipate DD trajectories and conduct targeted interventions [Trial registration: ChiCTR2100047071].<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1510444","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T06:55:00Z","timestamp":1772088900000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Trajectory prediction model of diabetes distress in adults with type 2 diabetes mellitus: a 12-month prospective longitudinal study"],"prefix":"10.3389","volume":"17","author":[{"given":"Yu-Yun","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College","place":["Beijing, China"]},{"name":"School of Nursing, Kunming Medical University","place":["Kunming, Yunnan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Peking Union Medical College Hospital","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing-Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Nursing, Xuzhou Medical University","place":["Xuzhou, Jiangsu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qun","family":"Wang","sequence":"additional","affiliation":[{"name":"Endocrinology and Metabolism Department, Peking University Third Hospital","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1186\/s12888-023-04546-w","article-title":"Cognitive behavior therapy for diabetes distress, depression, health anxiety, quality of life and treatment adherence among patients with type-II diabetes mellitus: a randomized control trial","volume":"23","author":"Abbas","year":"2023","journal-title":"BMC Psychiatry"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s00125-021-05585-2","article-title":"Interpreting global trends in type 2 diabetes complications and mortality","volume":"65","author":"Ali","year":"2013","journal-title":"Diabetologia"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s11096-021-01322-2","article-title":"The relationship between diabetes distress, medication taking, glycaemic control and self-management","volume":"44","author":"Alzubaidi","year":"2022","journal-title":"Int. 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