Study used data from cell phone apps and watches, brain activity and lifestyle factors to generate predictions of depression; results could lead to individualized treatment plans for mental health
According to the National Alliance on Mental Illness and the World Health Organization, depression affects 16 million Americans and 322 million people worldwide. Emerging evidence suggests that the COVID-19 pandemic is further exacerbating the prevalence of depression in the general population. With this trajectory, it is evident that more effective strategies are needed for therapeutics that address this critical public health issue.
In a recent study, publishing in the June 8, 2021 online edition of Nature Translational Psychiatry, researchers at University of California San Diego School of Medicine used a combination of modalities, such as measuring brain function, cognition and lifestyle factors, to generate individualized predictions of depression.
The machine learning and personalized approach took into account several factors related to an individual’s subjective symptoms, such as sleep, exercise, diet, stress, cognitive performance and brain activity.