2026 ECNP Congress

PS02-2095

Actigraphy-derived physical activity profiles in bipolar disorder: results from the bipcom project

Pietro Carmellini — A. Cuomo, A. Dourmas, G. Sanasi, M. Pinzi, M.B. Rescalli, D. Koukouna, E. Caselani, D. Martella, M. Magno, G. De Girolamo, A. Fagiolini · University Of Siena, Department of Molecular Medicine - psychiatry unit, Siena, Italy

2026 ECNP Congress

Bipolar disorder is associated with a markedly increased risk of physical comorbidities, including metabolic dysregulation and cardiovascular disease, contributing to premature mortality [1]. Reduced physical activity and disturbances in circadian rhythms have been proposed as key behavioural mechanisms underlying this excess risk, yet objective evidence remains limited [2-4], particularly in studies including healthy comparison groups. The aim of the present study was to characterise daily physical activity profiles in individuals with bipolar disorder type I and type II compared with healthy controls, using actigraphy-derived measures, and to examine differences in temporal activity patterns across the day. Data were derived from the clinical cohort of the bipcom project, a multicentre European study [5]. The sample included 51 healthy controls, 57 individuals with bipolar disorder type I, and 59 with bipolar disorder type II. Physical activity was continuously recorded using wrist-worn actigraphy devices. Analyses focused on daytime activity between 6 am and 10 pm. Activity levels were modelled using linear mixed-effects models to account for repeated measurements within individuals. Fixed effects included diagnostic group, age, sex, and day of the week, while random intercepts and slopes were specified to capture inter-individual variability in baseline activity and temporal patterns. To study group differences, an interaction term between time of day and diagnostic group was included as an additional fixed effect. Time of day was modelled as a non-linear function using restricted cubic splines with three knots. This modelling approach allowed for a flexible and data-driven representation of temporal activity trends across groups. Predicted activity profiles showed a consistent diurnal pattern across all groups, characterised by a gradual increase in activity from morning to early afternoon, followed by a progressive decline during the evening. A significant overall effect of diagnostic group was observed (p<0.001), indicating robust between-group differences in activity profiles. Healthy controls exhibited the highest activity levels throughout the day, followed by individuals with bipolar disorder type II and, to a lesser extent, those with bipolar disorder type I. Differences between groups were evident across the daytime period and were more pronounced during peak activity hours, although the magnitude of separation between clinical groups appeared modest. These findings provide objective evidence of reduced physical activity in bipolar disorder, with a gradient across diagnostic subtypes. The use of actigraphy allows for a detailed and ecologically valid characterisation of daily activity patterns, capturing subtle but clinically relevant behavioural alterations that may not be detected through self-report measures. Given the established links between physical inactivity and metabolic risk, these results support the potential role of actigraphy-based digital phenotyping in identifying modifiable behavioural targets in bipolar disorder and in detecting individuals at increased physical health risk. Further longitudinal analyses are warranted to determine whether these activity patterns are associated with future metabolic and clinical outcomes.

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