Seasonality in mood disorders: Probing association of accelerometer-derived physical activity with daylength and solar insolation

TL;DR

This study explores how sunlight and day length affect mood disorders like depression and bipolar disorder by analyzing physical activity data.

Contribution

The study introduces a generalizable strategy using open-source digital tools to link sunlight exposure with motor activity in mood disorders.

Findings

Accelerometer-derived daytime physical activity is significantly associated with depression (p<0.001).

Photoperiod and solar insolation show significant associations with physical activity (p<0.001).

The study provides a strategy for using digital biomarkers to detect mood disturbances in seasonally affected individuals.

Abstract

Mood disorders are the leading cause of disability worldwide. Up to 30 percent of individuals with major depressive disorder (MDD) and bipolar disorder (BD) display a seasonal pattern of onset, a phenomenon now recognized in the official diagnostic manuals (DSM-5 and ICD-11). Very little is known about the influence of day length (photoperiod) and sunlight intensity (solar insolation) on seasonal patterns in MDD and BD. Here we report a quantitative approach to examine the relationship between sunlight measures and objectively measured motor activity patterns to understand environmental factors driving seasonality in MDD and BD. Our generalized linear model (GLM) assessment of the Depresjon dataset, which includes short-term (up to two weeks) motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls recruited to the study at the University of Bergen Norway (60.4° N latitude, 5.3° E longitude), revealed significant association of accelerometer-derived daytime physical activity with participant’s depressed state (p<0.001), photoperiod (p<0.001), and solar insolation (p<0.001). Our study presents a generalizable strategy to decipher the complex interplay between sunlight, physical activity, and depressed state using open-source digital tools. The ability to identify mood disturbances, particularly in seasonally susceptible individuals, using passive digital biomarker data offers great promise in informing next-generation predictive, personalized diagnostics in mental health.

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