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September 23, 2025Open Access

Predicting changes in mental health with temporal Ecological Momentary Assessment features: a longitudinal study on the predictive accuracy and optimal time windows of (measuring) daily affective states (Preprint)

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Authors

LBLea BerkemeierWKW. KamphuisHOHilbrand Oldenhuis

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Overview

Longitudinal study shows how time windows influence predictive accuracy in mental health outcomes, suggesting EMA's potential.

Key Points

  • Temporal ecological momentary assessment features can predict changes in mental health outcomes, albeit at a limited scale.
  • Highest predictive accuracy for distress was observed with a 3-day window, achieving R2pred=0.44, indicating significant insights.
  • The study utilized daily EMA and monthly questionnaires over nine months, exploring the impact of time windows on predictions.
  • Findings suggest shorter windows between EMA and retrospective assessments provide a more detailed portrayal of mental health changes.

Cite This Study

Berkemeier et al. (2025) studied this question.

synapsesocial.com/papers/68d4739d31b076d99fa6bcd1https://doi.org/10.2196/preprints.84554
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  4. 4Measuring Affective State: Subject-Dependent and -Independent Prediction Based on Longitudinal Multimodal Sensing2024 · 9 citations