Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study.

Jason Nan, Suzanna Purpura, Satish Jaiswal, Houtan Afshar, Vojislav Maric, James K Manchanda, Charles T Taylor, Dhakshin Ramanathan, Jyoti Mishra
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Abstract

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) pilot clinical trial (NCT05662254). Participants completed a two-week digital monitoring phase using smartphone-based ecological momentary assessments (EMAs, 4×/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). Finally, decision algorithms and a large-language-model (LLM) could match human coach-led iMAP assignment with up to 95% accuracy. The PerMA trial presents a personalized lifestyle intervention approach for depression and merits scale-up and RCT testing to establish clinical efficacy. PERMA was registered with ClinicalTrials.gov under registry number NCT05662254.

个性化机器学习引导干预优化抑郁症患者的生活方式行为:一项试点研究。
个性化数据驱动的抑郁症干预措施是非常必要的。在这里,我们利用N-of-1机器学习(ML)来优化针对抑郁症的行为生活方式干预。50名轻度至中度抑郁症患者参加了单臂、开放标签的个性化情绪增强(PerMA)试点临床试验(NCT05662254)。参与者完成了为期两周的数字监测阶段,使用基于智能手机的生态瞬间评估(EMAs,每天4次)以及智能手表跟踪情绪和生活方式因素(睡眠/运动/饮食/社交关系)。从这些数据中生成个性化ML模型,以确定最能预测个人情绪的生活方式因素,并将结果转化为个性化情绪增强计划(imap),由参与者在每周一次的健康教练指导下实施六周。干预完成者(n = 40)抑郁症状显著减轻(主要结局自评PHQ9 -3.5±3.8,Cohen’s d = -0.89, CI [-1.25 -0.53], p
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