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Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health

Balasundaram Kadirvelu1, Teresa Bellido Bel2, Aglaia Freccero2

  • 1Brain & Behaviour Lab, Department of Computing and Department of Bioengineering, Imperial College London, Royal School of Mines, London, SW72AZ, United Kingdom, 44 20 7594 6373.

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|February 4, 2026
PubMed
Summary

Smartphone digital phenotyping effectively predicts adolescent mental health risks by integrating active and passive data. This scalable approach aids early detection in nonclinical youth, bridging a critical gap in community prevention.

Keywords:
EMAartificial intelligencedigital healthearly interventionecological momentary assessmentmHealthmobile applicationsmobile healthsmartphone sensingyouth mental health

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Area of Science:

  • Digital health
  • Machine learning in mental health
  • Adolescent psychology

Background:

  • Adolescents are highly vulnerable to mental disorders, with onset typically before age 25.
  • Most adolescents with mental health symptoms do not seek professional support.
  • Digital phenotyping offers a low-burden method for early risk detection in youth.

Purpose of the Study:

  • Evaluate the feasibility of a smartphone app for predicting mental health risks in nonclinical adolescents.
  • Integrate active (self-reported) and passive (sensor) data using machine learning.
  • Identify risks for internalizing/externalizing difficulties, eating disorders, insomnia, and suicidal ideation.

Main Methods:

  • 103 adolescents used the Mindcraft app for 14 days, collecting daily self-reports and passive sensor data.
  • A deep learning model with contrastive pretraining was developed for binary classification of mental health outcomes.
  • Performance was assessed using leave-one-subject-out cross-validation and compared with other ML models.

Main Results:

  • Integrated active and passive data achieved balanced accuracies of 0.67-0.77 across mental health outcomes.
  • The contrastive learning approach enhanced model stability and predictive robustness.
  • Shapley Additive Explanations (SHAP) identified clinically relevant features, confirming data integration's value.

Conclusions:

  • Smartphone-based digital phenotyping is feasible and useful for predicting mental health risks in nonclinical adolescents.
  • This integrated data approach shows promise for early detection and scalable interventions.
  • The findings support community-based prevention efforts for adolescent mental health.