{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T08:19:12Z","timestamp":1778055552408,"version":"3.51.4"},"reference-count":57,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100008947","name":"Stiftung Synapsis - Alzheimer Forschung Schweiz AFS","doi-asserted-by":"publisher","award":["2019\u2010CDA03"],"award-info":[{"award-number":["2019\u2010CDA03"]}],"id":[{"id":"10.13039\/501100008947","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Euro J of Neurology"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cognitive complaints are often considered early indicators of Alzheimer's disease (AD) and commonly lead to memory clinic consultations. Prior studies suggest stronger associations between cognitive complaints and mood than with objective cognition, but this interplay remains poorly understood. Using a machine learning\u2013supported approach, we aimed to (1) identify key predictors of cognitive complaints, and (2) compare the value of gamified versus standard neuropsychological testing in detecting subtle deficits.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this international multi\u2010center study, 98 participants (57 females; mean age 71.9, range 55\u201386) from three memory clinics completed the Cognitive Failures Questionnaire (CFQ), mood and apathy questionnaires, the tablet\u2010based gamified Adaptive Cognitive Evaluation Explorer (ACE\u2010X), and standard neuropsychological tests. Predictors of CFQ scores were examined using elastic net regression and the Boruta algorithm, followed by linear mixed\u2010effects modeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Greater mood symptoms were associated with more cognitive complaints, whereas increasing age was linked to fewer complaints. Study center accounted for additional variance. The final model explained a substantial proportion of variance (conditional R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.48, marginal R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.33). Participants had lower z\u2010scores on ACE\u2010X compared to standard testing, but neither predicted the severity of cognitive complaints.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Mood and age were main predictors of cognitive complaints in memory clinic patients. Although ACE\u2010X yielded lower normative scores than standard tests, neither cognitive measure was linked to complaints. These findings highlight the importance of systematically assessing mood, adopting personalized approaches when evaluating subjective and objective cognition, and the potential value of gamified assessments for screening populations at risk of AD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ene.70583","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:05:19Z","timestamp":1776841519000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mood and Age Predict Cognitive Complaints in Memory Clinic Patients: A Machine\u2010Learning and Linear Modeling Approach"],"prefix":"10.1111","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5345-7143","authenticated-orcid":false,"given":"Florian W.","family":"Sander","sequence":"first","affiliation":[{"name":"BrainCare@NeuroTech, Service Universitaire de Neuror\u00e9habilitation (SUN), D\u00e9partement des 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University of Bern  Bern Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esther","family":"Brill","sequence":"additional","affiliation":[{"name":"University Hospital of Old Age Psychiatry and Psychotherapy University of Bern  Bern Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Brioschi\u2010Guevara","sequence":"additional","affiliation":[{"name":"Centre Leenaards de la M\u00e9moire Centre Hospitalier Universitaire Vaudois  Lausanne Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philippe","family":"Ryvlin","sequence":"additional","affiliation":[{"name":"NeuroDigital@NeuroTech, Service de Neurologie, D\u00e9partement des Neurosciences Cliniques Centre Hospitalier Universitaire Vaudois (CHUV)  Lausanne Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7216-0674","authenticated-orcid":false,"given":"Joaquin 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