Article

Personalized Machine Learning guided Intervention for Optimizing Lifestyle Behaviors in Depression

2025-11-06

Abstract excerpt

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. <h4>Methods:</h4> 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 digit...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: d0a6ebf3-bb03-5312-aba8-e889b7968002
DOI: 10.1101/2025.11.04.25339502

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Personalized Machine Learning guided Intervention for Optimizing Lifestyle Behaviors in DepressionDOI 10.1101/2025.11.04.25339502Read

Relationships between depression, anxiety, and motivation in the real-world: Effects of physical activity and screentimeSemantic proximity Read

Personalized Prediction of Momentary Affect with Passive Smartphone Sensing Data in a Clinically Heterogeneous Sample: An evaluation of machine learning and mixed effects approachesSemantic proximity Read

Personalized Prediction of Momentary Affect with Passive Smartphone Sensing Data in a Clinically Heterogeneous Sample: An evaluation of machine learning and mixed effects approachesSemantic proximity Read

Digital phenotyping using wearable-determined physical behaviours and machine learning to detect depression and anxiety symptoms in a general population.Semantic proximity Read

Building an early warning system for depression: rationale, objectives, and methods of the WARN-D studySemantic proximity Read

Personalized Mood Prediction from Patterns of Behavior Collected with SmartphonesSemantic proximity Read

Digital phenotyping of depression: A multi-modal passive sensing approach to identifying novel behavioral and physiological markers of treatment response.Semantic proximity Read

Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review.Semantic proximity Read

Multimorbidity in Older Adults with Depression Study (MODS) (Behavioural Activation to improve physical and mental functioning among older people with multiple long-term conditions): Protocol for a fully powered randomised controlled trialSemantic proximity Read

Predictive Machine Learning for Personalised Medicine in Major Depressive DisorderSemantic proximity Read

Predictive Modelling of Depression Treatment Response using Individual Symptoms and Latent FactorsSemantic proximity Read

Predicting depression using electronic health records data: A systematic reviewSemantic proximity Read