Evidence map›Paper›PMID 42015730›Full record

ArticleEuropean journal of neurology2026

Mood and Age Predict Cognitive Complaints in Memory Clinic Patients: A Machine-Learning and Linear Modeling Approach.

Florian W Sander, Marie Pittet, Valeria Manera, Christine Krebs, Esther Brill, Andrea Brioschi-Guevara, Philippe Ryvlin, Joaquin A Anguera, Adam Gazzaley, Philippe Robert and 4 more

Abstract readMulticenter Study
In one paragraph

Article in European journal of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Florian W SanderBrainCare@NeuroTech, Service Universitaire de Neuroréhabilitation (SUN), Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV) and Institution de Lavigny, Lausanne, Switzerland.ORCID https://orcid.org/0009-0003-5345-7143
Marie PittetBrainCare@NeuroTech, Service Universitaire de Neuroréhabilitation (SUN), Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV) and Institution de Lavigny, Lausanne, Switzerland.
Valeria ManeraLaboratoire CoBTeK (Cognition Behaviour Technology), Université Côte d'Azur, Nice, France.ORCID https://orcid.org/0000-0003-4490-4485
Christine KrebsUniversity Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
Esther BrillUniversity Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
Andrea Brioschi-GuevaraCentre Leenaards de la Mémoire, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Philippe RyvlinNeuroDigital@NeuroTech, Service de Neurologie, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
Joaquin A AngueraNeuroscape Center, University of California San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-7216-0674
Adam GazzaleyNeuroscape Center, University of California San Francisco, San Francisco, California, USA.
Philippe RobertLaboratoire CoBTeK (Cognition Behaviour Technology), Université Côte d'Azur, Nice, France.
Stefan KlöppelUniversity Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
Jean-François DémonetCentre Leenaards de la Mémoire, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Giulia BinarelliBrainCare@NeuroTech, Service Universitaire de Neuroréhabilitation (SUN), Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV) and Institution de Lavigny, Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-7401-0063
Arseny A SokolovBrainCare@NeuroTech, Service Universitaire de Neuroréhabilitation (SUN), Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV) and Institution de Lavigny, Lausanne, Switzerland.

Funding

Stiftung Synapsis - Alzheimer Forschung Schweiz AFS 2019-CDA03
6 · The paper itself

Abstract

introductionCognitive 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-supported 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.

methodsIn this international multi-center study, 98 participants (57 females; mean age 71.9, range 55-86) from three memory clinics completed the Cognitive Failures Questionnaire (CFQ), mood and apathy questionnaires, the tablet-based gamified Adaptive Cognitive Evaluation Explorer (ACE-X), and standard neuropsychological tests. Predictors of CFQ scores were examined using elastic net regression and the Boruta algorithm, followed by linear mixed-effects modeling.

resultsGreater 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 DISCUSSION: Mood and age were main predictors of cognitive complaints in memory clinic patients. Although ACE-X 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.

Indexed as

AffectAgingMachine LearningMemory DisordersAgedAged, 80 and overAge FactorsAlzheimer DiseaseFemaleHumansLinear ModelsMaleMiddle AgedNeuropsychological TestsPredictive Learning ModelsAlzheimer's diseasecognitive complaintsmachine learningmoodserious videogames

Identifiers

PMID42015730
PMCPMC13100495

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.