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Related Experiment Video

Updated: May 9, 2026

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder
05:19

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Published on: July 7, 2023

Comparing personalized and population-based models for predicting momentary negative affect in internalizing

Leona Hammelrath1,2, Roshan Prakash Rane3,4, Sam Gijsen3,4

  • 1Department of Clinical Psychological Intervention, Freie Universität Berlin, Berlin, Germany.

Neuroscience Applied
|May 8, 2026
PubMed
Summary
This summary is machine-generated.

Personalized machine learning models using digital phenotyping data show promise for detecting negative affect (NA) in individuals with internalizing disorders. However, current models only slightly improve upon basic predictions, highlighting the need for further research.

Keywords:
Digital phenotypingInternalizing disordersJITAIMachine learning

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Last Updated: May 9, 2026

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder
05:19

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Published on: July 7, 2023

Area of Science:

  • Psychiatry and Digital Health

Background:

  • Negative affect (NA) is a core symptom in internalizing disorders.
  • Digital phenotyping (DP) and machine learning (ML) offer potential for real-time NA detection.
  • Personalized models are crucial due to individual differences in NA expression.

Purpose of the Study:

  • To evaluate the efficacy of passive sensor data for predicting momentary NA in outpatients with internalizing disorders.
  • To compare personalized versus population-based ML models for NA prediction using DP data.

Main Methods:

  • Utilized data from 242 outpatients in the PREACT-digital project.
  • Collected passive sensor data (heart rate, steps, mobility, physical activity) and Ecological Momentary Assessments (EMA) for NA.
  • Trained personalized and population-based ML models on 19,792 data-EMA pairs.

Main Results:

  • Personalized ML models significantly outperformed population-based models in predicting momentary NA.
  • The best predictive model showed only a marginal improvement over a simple benchmark predicting the per-person mean NA.
  • Individualized approaches are essential for accurate NA detection via DP.

Conclusions:

  • While personalized ML shows potential, current DP methods have limitations in reliably predicting momentary NA.
  • Future research should explore richer data streams and advanced modeling techniques.
  • Further development is needed to enable reliable just-in-time, data-driven support for individuals with internalizing disorders.