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Psychological Responses to Stress01:20

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Psychological responses to stress encompass the various cognitive and emotional reactions individuals experience when faced with challenging or threatening situations, such as a job loss. Prolonged exposure to stressors can disturb emotional balance, increasing negative emotions (e.g., anxiety and sadness) and diminishing positive emotions (e.g., joy and satisfaction). These persistent emotional shifts are associated with an increased risk of both physical illness and mental health issues, such...
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A Co-Segmentation Algorithm to Predict Emotional Stress From Passively Sensed mHealth Data.

Younghoon Kim1,2, Sumanta Basu1, Samprit Banerjee2

  • 1Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA.

Statistics in Medicine
|May 19, 2025
PubMed
Summary
This summary is machine-generated.

A new algorithm uses smartphone data to detect emotional stress in patients with mood disorders. This data-driven approach improves stress period identification compared to traditional machine learning methods.

Keywords:
change point detectionclassificationmHealthmachine learningmental healthstress detection

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Area of Science:

  • Computational psychiatry
  • Digital health
  • Machine learning for healthcare

Background:

  • Mood disorders and chronic pain in middle-aged and older adults are complex and often influenced by emotional stress.
  • Traditional machine learning (ML) methods struggle to capture time-varying patterns in nonstationary time-series data from passive sensing and self-reported variables.
  • Identifying short-term stress fluctuations is crucial for effective therapeutic interventions.

Purpose of the Study:

  • To develop a data-driven cosegmentation algorithm for identifying emotionally stressful states using passively sensed and self-reported smartphone data.
  • To improve the accuracy of detecting stress periods by analyzing time-varying local patterns.
  • To leverage short-time window associations between different data types for predictive modeling.

Main Methods:

  • Developed a novel cosegmentation algorithm that segments passive sensing variables by detecting change points.
  • Examined segment-specific associations between passive sensing and active (self-reported) variables.
  • Utilized identified cosegmented periods to predict future emotional stress states using standard ML methods.
  • Applied the algorithm to patient data from the ALACRITY Phase I study.

Main Results:

  • The data-driven segmentation algorithm accurately identifies periods of emotional stress.
  • The proposed method demonstrates superior accuracy in detecting stress periods compared to traditional ML methods that do not incorporate segmentation.
  • The algorithm effectively captures time-varying local patterns and short-time window associations crucial for stress detection.

Conclusions:

  • Data-driven segmentation offers a more accurate approach to identifying emotional stress periods in patients with mood disorders.
  • The developed algorithm enhances the analysis of complex, nonstationary time-series data from digital health tools.
  • This method holds promise for improving monitoring and intervention strategies in digital mental healthcare.