Wrist-based activity sensors worn by individuals with depression and those without over the course of two weeks provided evidence for the relationship between daily sunlight exposure and physical activity, according to a study published in the open-access journal PLOS Mental Health by Oleg Kovtun and Sandra Rosenthal from Vanderbilt University. Mood disorders are a leading cause of disability worldwide, with up to 30 percent of individuals with major depressive disorder and bipolar disorder displaying a seasonal pattern of symptoms. However, very little is known about the influence of day length and sunlight intensity on seasonal patterns in these disorders.
In their study, Kovtun and Rosenthal used motor-activity recordings collected via accelerometers from 23 individuals with unipolar or bipolar depression and 32 individuals without depression. The findings revealed relationships between daytime physical activity, depressed state, photoperiod, and solar insolation. More depressed states were associated with lower daytime activity, while daytime activity increased with photoperiod and solar insolation. The impact of solar insolation on physical activity may differ between depressed individuals and those who are not, suggesting an altered physiological link in depressed individuals or potentially increased sedentary behavior leading to reduced time spent outdoors.
The study presents a generalizable strategy to understand the complex interplay between sunlight, physical activity, and depressed state using open-source digital tools. The use of digital biomarkers, such as accelerometer-derived motor activity patterns, could form the basis of an early warning system that alerts clinicians to initiate timely interventions. Incorporating objectively measured sunlight exposure markers could enhance the predictive power of such tools and lay the foundation for personalized models aimed at individuals susceptible to mood disturbances with seasonal patterns.
According to the authors, individuals with seasonal mood disorders may not yet recognize the pattern of their illness, and the goal of the study is to motivate the development of digital tools to assist clinicians and help affected individuals self-manage their symptoms. By utilizing wrist-based activity sensors and accelerometer data, clinicians may be able to identify mood disturbances in seasonally susceptible individuals and intervene early to prevent worsening symptoms. Incorporating measures of sunlight exposure could further enhance the predictive power of these tools and aid in the development of personalized diagnostics in mental health.
Overall, the study highlights the importance of understanding the relationship between daily sunlight exposure, physical activity, and depressive symptoms. By utilizing wearable technology and digital biomarkers, clinicians may be able to identify patterns in mood disorders and provide targeted interventions. The development of personalized models based on objective data could revolutionize the way mental health is diagnosed and managed, particularly in individuals with seasonal mood disturbances. Further research in this area could lead to more effective and personalized treatments for those affected by depression and bipolar disorder.
