Stress Management

Stress management research focuses on developing and validating effective methods for stress detection and reduction, aiming to improve mental and physical well-being. Current research emphasizes personalized interventions, leveraging machine learning algorithms (like Support Vector Machines and neural networks) to analyze physiological data (e.g., heart rate variability, EEG) and textual data (e.g., social media posts) for accurate stress detection and prediction. These advancements, particularly the use of mobile health (mHealth) and virtual reality (VR) technologies, offer promising avenues for timely and tailored stress management strategies. The ultimate goal is to translate these findings into accessible and effective tools for preventing and mitigating the negative impacts of stress.

Papers

July 16, 2024

Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Study
Chaya Ben Yehuda, Ran Gilad-Bachrach, Yarin Udi
Health Intervention User Engagement Field Study Digital Intervention Ecological Momentary Assessment Stress Management

May 6, 2024

Stressor Type Matters! -- Exploring Factors Influencing Cross-Dataset Generalizability of Physiological Stress Detection
Pooja Prajod, Bhargavi Mahesh, Elisabeth André
Stronger Generalizability Key Factor Stress Detection Heart Rate Variability Stress Datasets Stress Detection Model Stress Management

February 7, 2024

CapsF: Capsule Fusion for Extracting psychiatric stressors for suicide from twitter
Mohammad Ali Dadgostarnia, Ramin Mousa, Saba Hesaraki
Twitter Resource Suicidal Ideation 3D Capsule Anxiety Detection Stress Management Persian Tweet

December 10, 2023

Stress Management Using Virtual Reality-Based Attention Training
Rojaina Mahmoud, Mona Mamdouh, Omneya Attallah, Ahmad Al-Kabbany
Virtual Co Presence Human Stress Stress Management Stress Level Reality Based Attention Training

August 8, 2023

Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review
Moein Razavi, Samira Ziyadidegan, Reza Jahromi, Saber Kazeminasab, Vahid Janfaza, Ahmadreza Mahmoudzadeh, Elaheh Baharlouei, Farzan Sasangohar
Deep Learning Machine Learning Continuous Chronic Stress Data Preprocessing Anxiety Detection Stress Detection Model Stress Management

July 7, 2023

Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data
Tanvir Islam, Peter Washington
Self Supervised Learning Time Series Personalized Prediction Wearable Stress Stress Detection Model Stress Management

November 15, 2022

Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics
Carlos Aguirre, Mark Dredze, Philip Resnik
Language Model Topic Model Depression Symptom Continuous Chronic Stress Socio Demographic Stress Management

October 28, 2022

Review on Classification Techniques used in Biophysiological Stress Monitoring
Talha Iqbal, Adnan Elahi, Atif Shahzad, William Wijns
Narrative Review Classification Algorithm Stress Detection Physical Activity Continuous Chronic Stress Wearable Stress Stress Management

Stress Management

Papers

Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Study

Stressor Type Matters! -- Exploring Factors Influencing Cross-Dataset Generalizability of Physiological Stress Detection

CapsF: Capsule Fusion for Extracting psychiatric stressors for suicide from twitter

Stress Management Using Virtual Reality-Based Attention Training

Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review

Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data

Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics

Review on Classification Techniques used in Biophysiological Stress Monitoring