Digital biomarkers shedding light on seasonality in mood disorders

Mood disorders are the leading cause of ‘disability’ worldwide. Up to 30 percent of individuals with major depressive disorder and bipolar disorder display a seasonal pattern of symptoms. This phenomenon is now recognized in official diagnostic manuals. Yet very little is known about the influence of day length (i.e., photoperiod) and sunlight intensity (i.e., solar insolation) on seasonal patterns in major depressive disorder and bipolar disorder.

In their new study, Kovtun and Rosenthal used a quantitative approach to examine the relationship between sunlight measures and objectively measured movement activity patterns to begin to understand the environmental factors driving seasonality in major depressive disorder and bipolar disorder. They used motor-activity recordings collected via accelerometers (which measure the rate of change of the velocity of an object with respect to time) from 23 individuals with unipolar or bipolar depression and 32 individuals without depression. Participants were recruited at the University of Bergen, Norway.

The findings revealed relationships between daytime physical activity, depressed state, photoperiod and solar insolation. In particular, more depressed states were associated with lower daytime activity, whilst daytime activity increased with photoperiod and solar insolation. Additional results suggest that the impact of solar insolation on physical activity may differ between depressed individuals and those who are not. This finding could indicate that depressed individuals exhibit an altered physiological link between energy input (i.e., solar insolation) and physical activity. On the other hand, it is also possible that increased sedentary behavior results in reduced time spent outdoors and does not allow depressed people to capitalize on the benefits of sunlight exposure.

According to the authors, the study presents a generalizable strategy to understand 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 promise in informing next-generation predictive, personalized diagnostics in mental health.

Specifically, a digital biomarker, such as accelerometer-derived motor activity patterns, could form the basis of an early warning system that alerts a clinician to initiate a timely intervention. Incorporating objectively measured sunlight exposure markers (i.e., NASA-collected solar insolation data or accelerometer-measured light exposure) could further enhance the predictive power of such tools and lay the foundation for personalized models aimed at individuals susceptible to mood disturbances with seasonal patterns.

Rosenthal and Kovtun add, “Individuals with seasonal mood disorders may not yet recognize the pattern of their illness. One of the goals of our study is to motivate the development of digital tools to assist clinicians and help affected individuals with self management of their symptoms.”