The Use of Photoplethysmography in the Assessment of Mental Health: Scoping Review

Overview

Journal JMIR Ment Health

Specialties Health Services
Psychiatry
Psychology

Date 2023 May 29

PMID 37247209

Authors

Lynnette Nathalie Lyzwinski

Mohamed Elgendi

Carlo Menon

Affiliations

Soon will be listed here.

Abstract

Background: With the rise in mental health problems globally, mobile health provides opportunities for timely medical care and accessibility. One emerging area of mobile health involves the use of photoplethysmography (PPG) to assess and monitor mental health.

Objective: In recent years, there has been an increase in the use of PPG-based technology for mental health. Therefore, we conducted a review to understand how PPG has been evaluated to assess a range of mental health and psychological problems, including stress, depression, and anxiety.

Methods: A scoping review was performed using PubMed and Google Scholar databases.

Results: A total of 24 papers met the inclusion criteria and were included in this review. We identified studies that assessed mental health via PPG using finger- and face-based methods as well as smartphone-based methods. There was variation in study quality. PPG holds promise as a potential complementary technology for detecting changes in mental health, including depression and anxiety. However, rigorous validation is needed in diverse clinical populations to advance PPG technology in tackling mental health problems.

Conclusions: PPG holds promise for assessing mental health problems; however, more research is required before it can be widely recommended for clinical use.

Citing Articles

Heart rate response to cognitive load as a marker of depression and increased anxiety.

Alshanskaia E, Zhozhikashvili N, Polikanova I, Martynova O Front Psychiatry. 2024; 15:1355846.

PMID: 39056018 PMC: 11269089. DOI: 10.3389/fpsyt.2024.1355846.

Clinical Implication of Maumgyeol Basic Biotypes-Electroencephalography- and Photoplethysmogram-Based Bwave State Inventory.

Kim Y, Hwang J, Lee J, Jang S, Im Y, Yoon S Psychiatry Investig. 2024; 21(5):528-538.

PMID: 38811002 PMC: 11136575. DOI: 10.30773/pi.2023.0381.

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis.

Goda M, Charlton P, Behar J Physiol Meas. 2024; 45(4).

PMID: 38478997 PMC: 11003363. DOI: 10.1088/1361-6579/ad33a2.

Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG.

Castellano Ontiveros R, Elgendi M, Missale G, Menon C Front Physiol. 2024; 14:1296277.

PMID: 38187134 PMC: 10770840. DOI: 10.3389/fphys.2023.1296277.

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