“Clinical trials show that most current interventions only show about a 30% benefit on average in terms of depression remission; here we see a near doubling of that due to targeting the top lifestyle predictive factors with data-driven personalized coaching,” said Mishra.
Mishra thinks the intervention may be more effective because it is a departure from generic recommendations for behavioral health.
“Everybody knows that we should eat healthier diets or try to sleep eight hours or exercise 150 minutes per week and so on,” she said. “But I think personalized insights can be more empowering than these general guidelines because they’re not so overwhelming. When one is in a depressed state, it’s not possible to change everything about one’s life — you’re just trying to survive and function on a day-to-day basis.”
Though the study was small, it provides the first evidence that digital monitoring, machine learning-derived insights and brief, personalized weekly coaching delivered remotely may be a promising integrated approach to address mild-to-moderate depression in large groups of people. A larger, controlled study of this personalized therapeutic approach is needed to validate the findings.
Read the full study here.
Additional co-authors on the study include: Jason Nan, Suzanna Purpura, Satish Jaiswal, Houtan Afshar, Vojislav Maric, James K. Manchanda and Charles T. Taylor at UC San Diego; and Dhakshin Ramanathan at UC San Diego and VA San Diego Medical Center.
The study was funded in part by a seed grant from the Hope for Depression Research Foundation.
Disclosures: Taylor is a paid consultant for Neuphoria Therapeutics (Bionomics), atai Life Sciences and Engrail Therapeutics, and receives payment for editorial work for UpToDate. Other authors declare no competing interests.