Personalized Machine Learning guided Intervention for Optimizing Lifestyle Behaviors in Depression

Objective: Personalized data-driven interventions for depression are much needed. Here, we leveraged N-of-1 machine learning (ML) to optimally target behavioral lifestyle interventions for depression. Methods: 50 individuals with mild-to-moderate depression enrolled in the single-arm, open-label Personalized Mood Augmentation (PerMA) clinical trial ( NCT05662254 ). Participants completed a two-week digital monitoring phase using smartphone-based ecological momentary assessments (EMAs, 4x/day) plus smartwatch tracking of mood and lifestyle factors (sleep/exercise/diet/social connection). Personalized ML models were generated from these data to identify lifestyle factors most predictive of individual mood, and results were translated to individualized mood augmentation plans (iMAPs) implemented by participants for six weeks with once-a-week health coach guidance. Results: Intervention completers (n=40) showed significant reduction in depression symptoms (primary outcome self-rated PHQ9 -3.5±3.8, Cohen's d=-0.89, CI [-1.25 -0.53], p<0.001; clinician-rated HDRS -7.2±6.8, d=-1.03, CI [-1.41 -0.65], p<1E-6) with benefits sustained up to 12-week follow-up. Co-morbid anxiety was also significantly reduced (GAD7: d=-0.85, CI [-1.2, -0.49], p<0.001) and quality of life improved (d=0.68, CI [0.33, 1.02], p<0.001). Additionally, objective cognitive measures impacted in depression including selective attention (d=0.51, CI [0.18, 0.84], p<0.001), interference processing (d=0.53, CI [0.2, 0.85], p<0.01) and working memory (d=0.66, CI [0.31, 0.99], p<0.001) showed significant enhancement. EMA tracking confirmed that improvement in depressed mood was specifically predicted by improvement in individually targeted lifestyles (β=0.4±0.09, p<0.0005). Conclusion: The PerMA trial presents a robust personalized lifestyle intervention approach for depression and merits scale-up and RCT testing.