Evidence map›Paper›PMID 34065620›Full record

SynthesisSensors (Basel, Switzerland)2021

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review.

Blake Anthony Hickey, Taryn Chalmers, Phillip Newton, Chin-Teng Lin, David Sibbritt, Craig S McLachlan, Roderick Clifton-Bligh, John Morley, Sara Lal

Registry-linked trialOpen access · goldAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07591935 (Artificial Intelligence and Mindfulness Meditation), which is not on this map. Cited by 112 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
112citing papers in PubMed, 7 pooled it
27.3field-weighted citation impact, top 1% of its field
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.

NCT07591935 nacompletednot on this mapstarted 2024, after this paper: background citation

Artificial Intelligence and Mindfulness Meditation: Innovative Methods to Reduce Stress Amongst Medical School Students

TypeinterventionalSponsorSaint James School of MedicineRan2024 to 2024Enrolled20ConditionsStressArmsMindfulness-Based Stress Reduction Course, Apollo Neuro Wearable
3 · Its place in the literature

Who cites it

112 citing papers in PubMed, 7 syntheses or guidelines pooled it, 295 citations in OpenAlex.

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52 more citing papers are in PubMed but not listed here.

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

9 authors at 4 institutions in 1 country.

Blake Anthony HickeyNeuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia.
Taryn ChalmersNeuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia.
Phillip NewtonSchool of Nursing and Midwifery, Western Sydney University, Penrith, NSW 2747, Australia.
Chin-Teng LinAustralian AI Institute, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia.ORCID 0000-0001-8371-8197
David SibbrittSchool of Public Health, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia.
Craig S McLachlanCentre for Healthy Futures, Torrens University, Sydney, NSW 2009, Australia.
Roderick Clifton-BlighKolling Institute for Medical Research, Royal North Shore Hospital, St Leonards, NSW 2064, Australia.
John MorleySchool of Medicine, Western Sydney University, Penrith, NSW 2747, Australia.
Sara LalNeuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia.
University of Technology Sydney · AUWestern Sydney University · AURoyal North Shore Hospital · AUTorrens University Australia · AU

Funding

NSW Defence Innovation Network and NSW State Government DINPP2019 S1-06
6 · The paper itself

Abstract

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.

Indexed as

Mental HealthWearable Electronic DevicesHeart RateMonitoring, PhysiologicReproducibility of Resultsanxietydepressionelectroencephalogramheart rate variabilitysmart technologywearable devices

Identifiers

PMID34065620
PMCPMC8156923
OpenAlexW3163763055

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.