Author

A. Meyer-Lindenberg

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Open access Sep 2026

Time of day affects the relation between physical activity and feeling energetic in depression: Two real-life studies.

Short bouts of physical activity enhance feelings of energy and improve well-being. Circadian rhythm strongly influences mood, but its impact on the association of physical activity and mood is unknown. Intensive longitudinal data were obtained from two independent samples (Ns = 106 and 68), each comprising two groups characterized by distinct manifestations of physical activity and energy: participants with depression (nStudyA = 53; nStudyB = 32) and healthy participants (nStudyA = 53; nStudyB = 36). Participants reported their feelings of energy repeatedly across the day; physical activity was self-rated (Study A) or measured via accelerometers (Study B). Multilevel modeling with three-way moderation analyses was used to test the association of physical activity with feelings of energy as a function of time of day and depression. Across studies, physical activity was consistently related to feelings of energy throughout the day for healthy participants. In contrast, in participants with depression, the relation between physical activity and energy decreased across the day. That participants with depression only felt more energized from physical activity in the morning could be an important starting point for considering circadian rhythm in physical activity interventions for depression. Although the missing ethnicity information from Sample B and high education across samples constrain the generalizability of our findings, the within-person associations nevertheless likely provide relevance for clinical populations and further underscore the potential of intensive longitudinal naturalistic methods to understand complex multidimensional relationships. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

M. Reichert, I. Reinhard, Urs Braun et al. · Journal of Psychopathology a... · 0 citations

Review Open access Jul 2026

Benchmarking large language models against practicing clinicians on psychopathological assessment

Psychiatry’s reliance on language makes LLMs a natural tool for psychopathological assessment, yet structured, item-level assessments from psychiatric clinical interviews remain under-researched. In this proof-of-concept study, 10 LLMs assessed transcripts of three simulated psychiatric interviews across all 100 items of the Association for Methodology and Documentation in Psychiatry (AMDP) system, benchmarked against 108 early-career clinicians rating full audiovisual recordings, using an expert consensus panel as reference. GPT-5.1 and Gemini-3-Pro-Preview achieved the highest accuracy (0.72; 64th percentile of the clinician distribution) using majority voting across three runs with AMDP definitions as context. GPT-5.1, selected for a marginal advantage, showed per-scenario accuracies of 0.81 (depression), 0.76 (mania), and 0.60 (schizophrenia) versus clinician means of 0.79, 0.68, and 0.58. Clinicians and LLMs showed distinct error profiles: clinicians tended to over-infer symptom presence, whereas LLMs more conservatively flagged items as “not assessable” — most pronounced for observation-dependent items but present even for text-assessable items (19.4% vs. 11.4%, p < 0.001). In post hoc simulated disagreement resolutions (2091 clinician pairs; 35.5% disagreements), LLM and board-certified supervision were associated with more accurate resolutions than unsupervised random clinician selection (p < 0.0002). These proof-of-concept findings require validation in real patient interviews, larger samples, and prospective studies integrating multimodal input.

E. Lenz, J. Naamanka, Wolfgang Trabert et al. · npj Digital Medicine · 0 citations