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.

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.

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).

The PerMA trial presents a robust personalized lifestyle intervention approach for depression and merits scale-up and RCT testing. Competing Interest Statement Disclosures C.T.T. declares that in the past 3 years he has been 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 Clinical Trial NCT05662254 Funding Statement This work was supported by a seed grants from the Hope for Depression Research Foundation (JM). The BrainE software is copyrighted for commercial use (Regents of the University of California Copyright #SD2018-816) and free for research and educational purposes. The machine learning pipeline deployed here is filed as an Invention Disclosure for "Personalized Machine Learning of Depressed Mood using Wearables" (Regents of the University of California Invention Disclosure #SD2021-335). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The IRB of University of California San Diego (UCSD) gave ethical approval for this work (protocol #180140) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors