PA and mood are dynamically and bidirectionally linked within short temporal windows throughout the day, with stronger coupling in HC than in SSD, and the substantial individual variability observed in SSD highlights the need for personalized, sensor-informed interventions.
Individuals with schizophrenia spectrum disorders showed significantly lower daily mood compared to healthy controls.
Bayesian network models identified robust within-day bidirectional associations between physical activity and mood in healthy controls.
Within-day bidirectional coupling between physical activity and mood was attenuated and more heterogeneous in individuals with schizophrenia spectrum disorders compared to healthy controls.
The study used a multimodal ecological assessment approach combining EMA and actigraphy over 7 days in a multicenter design.
Substantial individual variability in physical activity-mood dynamics within SSD suggests that aggregated analyses may obscure clinically relevant microtemporal patterns.
This research suggests that physical activity and mood are closely linked throughout the day in a two-way relationship — being more active tends to improve mood shortly afterward, and being in a better mood tends to lead to more physical activity. This pattern was found to be very strong in healthy individuals, but weaker and more variable among people with schizophrenia spectrum disorders (SSDs), a group of serious mental health conditions. The study tracked 120 people with SSDs and 113 healthy comparison participants over 7 days using smartphone mood questionnaires (8 times per day) and wrist-worn activity monitors. An important methodological finding is that standard statistical approaches (called GLMMs) missed these real-time connections between activity and mood entirely, only detecting that people with SSDs reported lower mood overall. It took more advanced Bayesian modeling techniques — which can capture short-term, back-and-forth dynamics — to reveal these within-day links. This suggests that the timing and sequence of physical activity and mood shifts throughout the day matter, and that capturing these fine-grained patterns requires both the right measurement tools (like continuous actigraphy and frequent mood sampling) and the right analytical methods. This research suggests that for people with schizophrenia spectrum disorders, physical activity interventions may not work the same way for everyone — the wide variation in how individuals' activity and mood interact means that a one-size-fits-all approach may miss important individual differences. The authors propose that wearable sensor data combined with personalized analysis could help identify which patients might benefit most from activity-based mental health interventions and how to time those interventions for maximum effect.
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Upload Your LabsCaselani E, Carnevale M, Zarbo C, Martinelli A, Martella D, Magno M, et al.. (2026). Bayesian network analysis uncovers physical activity-mood dynamics: Insights from the DiAPAson study.. Psychological medicine. https://doi.org/10.1017/S0033291726105406