“Clinical trials have shown that most current interventions are only about 30% effective on average in remitting depression. Here, we see that effectiveness nearly doubled by targeting key lifestyle predictors with data-driven, personalized coaching,” said Mishra.
Mishra believes this intervention may be more effective because it departs from common behavioral health recommendations.
“We all know we need to eat healthier, get eight hours of sleep, and exercise 150 minutes a week,” she says. “But I think personalized insights can be more empowering than these general guidelines because they’re less overwhelming. When a person is in a state of depression, it’s impossible to change everything in your life. You’re just trying to survive and function day to day.”
Although small, this study provided the first evidence that digital monitoring, insights gained from machine learning, and brief, personalized weekly coaching delivered remotely may be a promising integrated approach for addressing mild to moderate depression in large populations. Larger controlled studies of this individualized treatment approach are needed to validate the results.
Read the full study here.
Other co-authors on the study include Jason Nunn, Susanna Purpura, Satish Jaiswal, Khotan Afshar, Vojislav Maric, James K. Manchanda, and Charles T. Taylor of the University of California, San Diego. Dakshin Ramanathan of the University of California, San Diego and VA San Diego Medical Center;
This study was funded in part by a seed grant from the Hope for Depression Research Foundation.
Disclosure: Taylor is a paid consultant for Neuphoria Therapeutics (Bionomics), atai Life Sciences, and Engrail Therapeutics and receives compensation for editorial work on UpToDate. The other authors declare no competing interests.
