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A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

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Multitask Learning for Mental Health: Depression, Anxiety, Stress (DAS) Using Wearables.

Berrenur Saylam¹, Özlem Durmaz İncel¹

¹Computer Engineering Department, Boğaziçi University, 34342 İstanbul, Türkiye.

Diagnostics (Basel, Switzerland)

|March 13, 2024

Summary

This summary is machine-generated.

Digital biomarkers can predict college students

Keywords:

LSTM XGBoost deep learning digital biomarker digital health ensemble learning mental health multitask learning pervasive health random forest regression wearable devices

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Area of Science:

Computational psychiatry and digital health

Background:

Mental well-being challenges, including depression, stress, and anxiety, are prevalent among college students.
Digital biomarkers offer a novel avenue for monitoring and predicting mental health states.

Purpose of the Study:

To investigate the predictive power of digital biomarkers for depression, stress, and anxiety in college students.
To assess the alignment of digital biomarker findings with established psychology literature.
To evaluate the temporal performance of prediction methods and the benefits of multitask learning.

Main Methods:

Utilized the NetHealth dataset, containing digital biomarker data from college students.
Employed machine learning models, including random forest (RF) and XGBoost, to predict mental well-being factors.
Incorporated temporal analysis and multitask learning strategies to enhance prediction accuracy.

Main Results:

Digital biomarker modality rankings correlated with findings from conventional psychology literature.
Temporal considerations significantly improved prediction performance, especially with the random forest classifier.
Multitask learning enhanced prediction for depression and stress but not anxiety.

Conclusions:

Digital biomarkers show promise in predicting mental well-being factors in college students.
Integrating temporal data and multitask learning can optimize the accuracy of mental health predictions.
Further research is warranted to refine multitask learning approaches for specific mental health conditions like anxiety.