Cited by 136Context-Aware Activity Recognition SystemsEmotion and Mood RecognitionMental Health Research Topics

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses

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Reinertsen, Erik, Clifford, Gari D·2018·Physiological Measurement

Abstract

Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient’s home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer’s disease, and Parkinson’s disease.

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  • Reviews have concluded that actigraphy captures significant differences in physical activity and sleep parameters between individuals with psychiatric disorders and healthy controls, and that these measures change with successful treatment.

    “These reviews have concluded that, across studies, actigraphy is able to capture significant differences in physical activity and sleep parameters in individuals with psychiatric disorders compared to healthy controls, and these measures change with successful treatment [35–37].”
  • Close interrelationships between everyday behavior, physiology, and mental well-being make digital phenotyping with wearables attractive for detection of mental disorders.

    “Close interrelationships between everyday behavior, physiology, and mental well-being makes digital phenotyping with wearables especially attractive for detection of mental disorders and discovering respective digital biomarkers [8-11].”

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References 233 · Cited by 136

Research works printed but not linked: 1

  1. Saeb S Lonini L Jayaraman A 2016b Voodoo Machine Learning for Clinical Predictions bioRxiv 059774

Authors
Reinertsen, E., & Clifford, G. D.
Affiliations
Georgia Institute of Technology (US)
Corresponding author
Clifford, Gari D
Venue
Physiological Measurement
Published
2018-05-01
Volume
39
Issue
5
Pages
05TR01
ISSN
  • 0967-3334 (ISSN-L)
  • 1361-6579
OpenAlex
W2801374094
Cited by
136
References
233
Citations by year
2026: 11 · 2025: 17 · 2024: 17 · 2023: 12 · 2022: 12 · 2021: 19 · 2020: 34 · 2019: 10 · 2018: 4
FWCI
1.97
Type
journal article
Language
en
Open access
Locations
  • Physiological Measurement (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Heart, Lung, and Blood Institute (R01HL136205, P50HL117929)
  • National Institute of General Medical Sciences (T32GM008169)
  • National Institute of Environmental Health Sciences (K01ES025445)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords
neuropsychiatric disorders, passive monitoring, digital sensors, wearable accelerometers, actigraphy, smartphone sensors, multimodal sensing, depression, bipolar disorder, schizophrenia, post-traumatic stress disorder, Alzheimer's disease, Parkinson's disease, stress, heart rate, tone of voice, social activity, remote patient monitoring, patient severity assessment
MeSH terms
Smartphone, Wearable Electronic Devices, Behavior, Mental Disorders, Humans, Monitoring, Physiologic, Electrocardiography, Ambulatory

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