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<p>The integration of artificial intelligence (AI) and pervasive computing offers new opportunities to sense mental health symptoms and deliver just-in-time adaptive interventions via mobile devices. This pilot study tested personalized versus generalized machine learning models for detecting individual and family mental health symptoms as a foundational step toward JITAI development, using data collected through the Colliga app on smart devices. Over a 60-day period, data from 35 families resulted in approximately 14 million data points across 52 data streams. Findings showed that personalized models consistently outperformed generalized models. Model performance varied significantly based on individual factors and symptom profiles, underscoring the need for tailored approaches. These preliminary findings suggest that successful implementation of passive sensing technologies for mental health will require accounting for users’ unique characteristics. Further research with larger samples is needed to refine the models, address data heterogeneity, and develop scalable systems for personalized mental health interventions.</p>

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Developing personalized algorithms for sensing mental health symptoms in daily life

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月6日

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  • Adela C. Timmons,
  • Abdullah Aman Tutul,
  • Kleanthis Avramidis,
  • Jacqueline B. Duong,
  • Kayla E. Carta,
  • Sierra N. Walters,
  • Grace A. Jumonville,
  • Alyssa S. Carrasco,
  • Gabrielle F. Freitag,
  • Daniela N. Romero,
  • Matthew W. Ahle,
  • Jonathan S. Comer,
  • Shrikanth S. Narayanan,
  • Ishita P. Khurd,
  • Theodora Chaspari

摘要

The integration of artificial intelligence (AI) and pervasive computing offers new opportunities to sense mental health symptoms and deliver just-in-time adaptive interventions via mobile devices. This pilot study tested personalized versus generalized machine learning models for detecting individual and family mental health symptoms as a foundational step toward JITAI development, using data collected through the Colliga app on smart devices. Over a 60-day period, data from 35 families resulted in approximately 14 million data points across 52 data streams. Findings showed that personalized models consistently outperformed generalized models. Model performance varied significantly based on individual factors and symptom profiles, underscoring the need for tailored approaches. These preliminary findings suggest that successful implementation of passive sensing technologies for mental health will require accounting for users’ unique characteristics. Further research with larger samples is needed to refine the models, address data heterogeneity, and develop scalable systems for personalized mental health interventions.