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The promise of digital phenotyping for the early detection of risk for depression in adolescents – Nature Mental Health

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Eugenia Giampetruzzi
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Advances in consumer wearables and machine learning enable the continuous detection of depression risk through behavioral and physiological data. In this Comment, we emphasize the crucial need to apply these technologies to adolescents, highlighting the potential for early detection and prevention of depression during this pivotal developmental stage.

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Fig. 1: Clinical staging model for depression.

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Funding

Preparation of this Comment was facilitated by National Institute of Mental Health grant R37MH101495 to I.H.G. and a National Science Foundation Graduate Research Fellowship to E.G.

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Authors and Affiliations

  1. Department of Psychology, Stanford University, Stanford, CA, USA

    Eugenia Giampetruzzi, Chase Antonacci & Ian H. Gotlib

  2. Neurosciences Interdepartmental Program, Stanford University, Stanford, CA, USA

    Chase Antonacci

Authors

  1. Eugenia Giampetruzzi
  2. Chase Antonacci
  3. Ian H. Gotlib

Corresponding author

Correspondence to Eugenia Giampetruzzi.

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Competing interests

The authors declare no competing interests.

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Giampetruzzi, E., Antonacci, C. & Gotlib, I.H. The promise of digital phenotyping for the early detection of risk for depression in adolescents. Nat. Mental Health (2026). https://doi.org/10.1038/s44220-026-00679-5

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