A New Perspective on Stress Detection: An Automated Approach for Detecting Eustress and Distress.

Mohamad Awada, Burcin Becerik-Geber, Gale M Lucas, Shawn C Roll, Ruying Liu

Abstract read

In one paragraph

Article in IEEE transactions on affective computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Predicting burnout in radiology nurses: an interpretable machine learning model developed and externally validated in a multi-center study.Frontiers in public health · 2026
Article
Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis.PloS one · 2024
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mohamad AwadaViterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, USA.

Burcin Becerik-GeberViterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, USA.

Gale M Lucas1) Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, USA; 2) USC Institute for Creative Technologies, Los Angeles, CA, USA.

Shawn C RollChan Division of Occupational Science and Occupational Therapy, University of Southern California (USC), Los Angeles, CA, USA.

Ruying LiuViterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, USA.

Funding

NIOSH CDC HHS T42 OH008412

6 · The paper itself

Abstract

Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do not recognize stress appraisal and presume stress is a negative mental state. Yet, stress can be classified according to its influence on individuals; the way people perceive a stressor determines whether the stress reaction is considered as eustress (positive stress) or distress (negative stress). Thus, this study aims to assess the potential of using an ML approach to determine stress appraisal and identify eustress and distress instances using physiological and behavioral features. The results indicate that distress leads to higher perceived stress arousal compared to eustress. An XGBoost model that combined physiological and behavioral features using a 30 second time window had 83.38% and 78.79% F

Indexed as

Behavioral DataMachine LearningPhysiological DataPsychological Stress

Identifiers

PMID39421725

DOI10.1109/taffc.2023.3324910

PMCPMC11485284

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.