TY - JOUR
T1 - Smart devices and wearable technologies to detect and monitor mental health conditions and stress
T2 - A systematic review
AU - Hickey, Blake Anthony
AU - Chalmers, Taryn
AU - Newton, Phillip
AU - Lin, Chin Teng
AU - Sibbritt, David
AU - McLachlan, Craig S.
AU - Clifton-Bligh, Roderick
AU - Morley, John
AU - Lal, Sara
N1 - Funding Information:
Funding: This research was funded by the NSW Defence Innovation Network and NSW State Government, grant number DINPP2019 S1-06, StressWatch Project.
Funding Information:
Acknowledgments: The authors would like to acknowledge the Neuroscience Research Unit, School of Life Sciences, the University of Technology Sydney, through which this review was conducted. We also thank the NSW Defence Innovation Network and NSW State Government for financial support of this project through grant DINPP2019 S1-06, StressWatch Project.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/5/2
Y1 - 2021/5/2
N2 - Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.
AB - Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.
KW - Anxi-ety
KW - Depression
KW - Electroencephalogram
KW - Heart rate variability
KW - Smart technology
KW - Wearable devices
UR - http://www.scopus.com/inward/record.url?scp=85105701224&partnerID=8YFLogxK
UR - https://doi.org/10.25905/21715901.v1
U2 - 10.3390/s21103461
DO - 10.3390/s21103461
M3 - Review article
AN - SCOPUS:85105701224
SN - 1424-8220
VL - 21
JO - Sensors
JF - Sensors
IS - 10
M1 - 3461
ER -
