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青年期メンタルヘルスにおけるデジタル表現型:アクティブおよびパッシブスマートフォンデータからメンタルヘルスリスクを予測するための機械学習を用いた実現可能性調査

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.

Journal of medical Internet research
|February 4, 2026
PubMed
まとめ

スマートフォンデジタル表現型は、アクティブおよびパッシブデータを統合することにより、青年期のメンタルヘルスリスクを効果的に予測します。このスケーラブルなアプローチは、非臨床的若年層における早期検出を支援し、地域社会の予防における重要なギャップを埋めます。

キーワード:
EMA人工知能デジタルヘルス早期介入生態学的瞬間評価mHealthモバイルアプリケーションモバイルヘルススマートフォンセンシング若年層のメンタルヘルス

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科学分野:

  • デジタルヘルス。
  • メンタルヘルスにおける機械学習。
  • 青年心理学。

背景:

  • 青年は精神障害に対して非常に脆弱であり、通常25歳までに発症します。
  • 精神症状のある青年のほとんどは専門的な支援を求めていません。
  • デジタル表現型は、若年層における早期リスク検出のための低負担な方法を提供します。

主な方法:

  • 103人の青年が14日間Mindcraftアプリを使用し、毎日の自己報告とパッシブセンサーデータを収集しました。
  • メンタルヘルスアウトカムの二項分類のために、対照事前学習を用いた深層学習モデルが開発されました。
  • パフォーマンスは、Leave-one-subject-out交差検証を使用して評価され、他の機械学習モデルと比較されました。

結論:

  • スマートフォンベースのデジタル表現型は、非臨床的な青年におけるメンタルヘルスリスクの予測において実現可能であり、有用です。
  • この統合データアプローチは、早期検出とスケーラブルな介入のために有望です。
  • これらの発見は、青年期のメンタルヘルスに対する地域ベースの予防努力を支持します。