{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T12:07:46Z","timestamp":1790856466595,"version":"4.1.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Rheumatol Int"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Fatigue is a burdensome symptom in inflammatory arthritis (IA), yet meaningful longitudinal change remains poorly characterized in real-world settings. To characterize fatigue trajectories, quantify clinically meaningful improvement\/worsening, and identify baseline predictors of fatigue outcomes in a digital IA cohort. We analyzed Midaia digital application data from patients with rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, or mixed IA with baseline Brief Fatigue Inventory (BFI) data. Fatigue trajectories were modelled via cluster-robust longitudinal regression, with secondary landmark analyses classifying BFI improvement\/worsening using a prespecified pragmatic 1.0-point BFI change threshold at weeks 12 (W12), 24 (W24), and 52 (W52). Predictors were examined using logistic regression, machine learning with area under the curve (AUC) and model interpretation, and inverse probability weighting (IPW) for loss to follow-up. The cohort (2,660 patients; 3,436 valid BFI observations; 485 with follow-up) was 78.3% female, mean age 45.6\u2009\u00b1\u200912.9 years, baseline BFI 5.2\u2009\u00b1\u20092.0. The unadjusted BFI slope was \u2212\u20090.017 points\/week (95% CI\u2009\u2212\u20090.025 to \u2212\u20090.008;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001; corresponding to \u2212\u20090.20 points by W12, below the MCID); the IPW-adjusted slope was attenuated to \u2212\u20090.003 points\/week, suggesting that the crude estimate was sensitive to differential follow-up. At W12, 26.8% improved and 25.6% worsened, with similar proportions at W24 and W52. Higher baseline BFI predicted W12 improvement (OR 1.54) and lower worsening (OR 0.62); higher baseline Patient Global Assessment of Disease Activity predicted worsening (OR 1.02). Machine-learning discrimination was modest (AUC 0.651\u20130.720). Diet\/nutrition engagement was associated with higher improvement (OR 1.78) and lower worsening (OR 0.45). Fatigue trajectories in IA were heterogeneous. Landmark improvement\/worsening proportions were unchanged after IPW, whereas mean improvement was attenuated. Baseline fatigue severity dominated prediction; action-plan associations are hypothesis-generating.\n                  <\/jats:p>","DOI":"10.1007\/s00296-026-06308-9","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T11:30:52Z","timestamp":1790854252000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal fatigue outcomes and prediction in inflammatory arthritis: a real-world digital cohort study"],"prefix":"10.1007","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7965-9438","authenticated-orcid":false,"given":"Dmytro","family":"Fedkov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4166-6397","authenticated-orcid":false,"given":"Danylo","family":"Yevstifeiev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2095-5819","authenticated-orcid":false,"given":"Oleg","family":"Iaremenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0855-2945","authenticated-orcid":false,"given":"Alexandra","family":"Dopfer-Jablonka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5668-4497","authenticated-orcid":false,"given":"Uta","family":"Kiltz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9814-7331","authenticated-orcid":false,"given":"Jacqueline","family":"Detert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6102-2674","authenticated-orcid":false,"given":"Felix","family":"Lang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9969-5462","authenticated-orcid":false,"given":"Abdullah","family":"Khalil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4970-2110","authenticated-orcid":false,"given":"Peter","family":"Bramlage","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5801-630X","authenticated-orcid":false,"given":"T\u00fcrker","family":"Kurt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5725-5483","authenticated-orcid":false,"given":"Stefan","family":"Vordenb\u00e4umen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"6308_CR1","doi-asserted-by":"publisher","first-page":"v3","DOI":"10.1093\/rheumatology\/kez351","volume":"58","author":"EJF Santos","year":"2019","unstructured":"Santos EJF, Duarte C, da Silva JAP, Ferreira RJO (2019) The impact of fatigue in rheumatoid arthritis and the challenges of its assessment. 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Midaia GmbH developed the mobile application used in this study, but did not finance the study. UK has received grants and research support and consultancy fees from AbbVie, Alfasigma, Amgen, Biogen, Chugai, Eli Lilly, Fresenius, GBA, Gilead, GSK, Janssen, MSD, Novartis, Pfizer, Roche, and UCB. ADJ, JD, DY, OI, TK, and SV declare no conflicts of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was conducted following the ethical principles of the Declaration of Helsinki and Good Clinical Practice guidelines and was approved by the Local Ethics Committee of Medical Center Medical Clinic Blagomed LLC (Kyiv, Ukraine), protocol #107\/5, dated 13 August 2022. Authors fulfilled the ICMJE authorship criteria.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Written informed consent was obtained digitally from all patients before enrolment and prior to study participation.","order":3,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The corresponding author, Stefan Vordenb\u00e4umen, meets the reimbursement requirements of Heinrich Heine University D\u00fcsseldorf, which has an agreement with Springer Nature that can cover article processing charges.","order":4,"name":"Ethics","label":"Open Access Publishing note","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"270"}}