Potential physiological stress biomarkers in human sweat

Gioia, Federica; Callara, Alejandro Luis; Bruderer, Tobias; Ripszam, Matyas; Francesco, Fabio Di; Scilingo, Enzo Pasquale; Greco, Alberto

doi:10.1109/memea54994.2022.9856534

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…The statistical analysis showed significant differences in those features that are commonly correlated to the physiological state changes induced by the Stroop condition [32,46]. The significant results related to the HRV features are summarized in Figure 5.…”

Section: Statistical Analysis Resultsmentioning

confidence: 92%

A Real-Time, Open-Source, IoT-like, Wearable Monitoring Platform

Baldini

Garofalo²,

Scilingo

et al. 2023

Electronics

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The spread of informatics and electronic systems capable of the real-time monitoring of multiple psychophysiological signals has continuously grown in the last few years. In this study, we propose a novel open-source wearable monitoring platform (WMP) to synchronously acquire and process multiple physiological signals in a real-time fashion. Specifically, we developed an IoT-like modular and fully open-source platform composed of two main blocks that on the one hand connect multiple devices (the sensor fusion unit) and on the other hand process and store the sensors’ data through the internet (the remote storing and processing unit). To test the proposed platform and its computational performance, 15 subjects underwent an experimental protocol, in which they were exposed to rest and stressful sessions implementing the Stroop Color and Word Test (SCWT). Statistical analysis was performed to verify whether the WMP could monitor the expected variations in the subjects’ psychophysiological state induced by the SCWT. The WMP showed very good computational performance for data streaming, remote storing, and real-time processing. Moreover, the experimental results showed that the platform was reliable when capturing physiological changes coherently with the emotional salience of the SCWT.

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Section: Statistical Analysis Resultsmentioning

confidence: 92%

A Real-Time, Open-Source, IoT-like, Wearable Monitoring Platform

Baldini

Garofalo²,

Scilingo

et al. 2023

Electronics

Self Cite

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Predicting vasovagal reactions to needles with anticipatory facial temperature profiles

Rudokaite

Ong

Ertuğrul

et al. 2023

Sci Rep

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Around one-third of adults are scared of needles, which can result in adverse emotional and physical responses such as dizziness and fainting (e.g. vasovagal reactions; VVR) and consequently, avoidance of healthcare, treatments, and immunizations. Unfortunately, most people are not aware of vasovagal reactions until they escalate, at which time it is too late to intervene. This study aims to investigate whether facial temperature profiles measured in the waiting room, prior to a blood donation, can be used to classify who will and will not experience VVR during the donation. Average temperature profiles from six facial regions were extracted from pre-donation recordings of 193 blood donors, and machine learning was used to classify whether a donor would experience low or high levels of VVR during the donation. An XGBoost classifier was able to classify vasovagal groups from an adverse reaction during a blood donation based on this early facial temperature data, with a sensitivity of 0.87, specificity of 0.84, F1 score of 0.86, and PR-AUC of 0.93. Temperature fluctuations in the area under the nose, chin and forehead have the highest predictive value. This study is the first to demonstrate that it is possible to classify vasovagal responses during a blood donation using temperature profiles.

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Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study

Castro Ribeiro,

García Pagès,

Ballester

et al. 2024

JMIR Res Protoc

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Background Mental health conditions have become a substantial cause of disability worldwide, resulting in economic burden and strain on the public health system. Incorporating cognitive and physiological biomarkers using noninvasive sensors combined with self-reported questionnaires can provide a more accurate characterization of the individual’s well-being. Biomarkers such as heart rate variability or those extracted from the electrodermal activity signal are commonly considered as indices of autonomic nervous system functioning, providing objective indicators of stress response. A model combining a set of these biomarkers can constitute a comprehensive tool to remotely assess mental well-being and distress. Objective This study aims to design and validate a remote multiparametric tool, including physiological and cognitive variables, to objectively assess mental well-being and distress. Methods This ongoing observational study pursues to enroll 60 young participants (aged 18-34 years) in 3 groups, including participants with high mental well-being, participants with mild to moderate psychological distress, and participants diagnosed with depression or anxiety disorder. The inclusion and exclusion criteria are being evaluated through a web-based questionnaire, and for those with a mental health condition, the criteria are identified by psychologists. The assessment consists of collecting mental health self-reported measures and physiological data during a baseline state, the Stroop Color and Word Test as a stress-inducing stage, and a final recovery period. Several variables related to heart rate variability, pulse arrival time, breathing, electrodermal activity, and peripheral temperature are collected using medical and wearable devices. A second assessment is carried out after 1 month. The assessment tool will be developed using self-reported questionnaires assessing well-being (short version of Warwick-Edinburgh Mental Well-being Scale), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9) as the reference. We will perform correlation and principal component analysis to reduce the number of variables, followed by the calculation of multiple regression models. Test-retest reliability, known-group validity, and predictive validity will be assessed. Results Participant recruitment is being carried out on a university campus and in mental health services. Recruitment commenced in October 2022 and is expected to be completed by June 2024. As of July 2023, we have recruited 41 participants. Most participants correspond to the group with mild to moderate psychological distress (n=20, 49%), followed by the high mental well-being group (n=13, 32%) and those diagnosed with a mental health condition (n=8, 20%). Data preprocessing is currently ongoing, and publication of the first results is expected by September 2024. Conclusions This study will establish an initial framework for a comprehensive mental health assessment tool, taking measurements from sophisticated devices, with the goal of progressing toward a remotely accessible and objectively measured approach that maintains an acceptable level of accuracy in clinical practice and epidemiological studies. Trial Registration OSF Registries N3GCH; https://doi.org/10.17605/OSF.IO/N3GCH International Registered Report Identifier (IRRID) DERR1-10.2196/51298

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Potential physiological stress biomarkers in human sweat

Cited by 4 publications

References 30 publications

A Real-Time, Open-Source, IoT-like, Wearable Monitoring Platform

A Real-Time, Open-Source, IoT-like, Wearable Monitoring Platform

Predicting vasovagal reactions to needles with anticipatory facial temperature profiles

Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study

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