Evidence map›Paper›PMID 36743720›Full record

ArticleComputer methods and programs in biomedicine update2023

Wearable devices for anxiety & depression: A scoping review.

Arfan Ahmed, Sarah Aziz, Mahmood Alzubaidi, Jens Schneider, Sara Irshaidat, Hashem Abu Serhan, Alaa A Abd-Alrazaq, Barry Solaiman, Mowafa Househ

Registry-linked trialOpen access · goldAbstract readScoping Review
In one paragraph

Article in Computer methods and programs in biomedicine update, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06843187 (Lemborexant for the Treatment of Residual Insomnia in Adequately Treated Major Depressive Disorder), which is not on this map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
8.3field-weighted citation impact, top 2% 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.

NCT06843187 phase2terminatednot on this mapstarted 2024, after this paper: background citation

Lemborexant for the Treatment of Residual Insomnia in Adequately Treated Major Depressive Disorder: A Pilot Randomized Controlled Trial

TypeinterventionalSponsorUnity Health TorontoRan2024 to 2026Enrolled30ConditionsMajor Depressive Disorder(MDD), Insomnia Comorbid to Psychiatric DisorderArmsLemborexant, Placebo
3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.

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  8. DCEM-TCRCN: an innovative approach to depression detection using wearable IoT devices and deep learning.International journal of computer assisted radiology and surgery · 2025
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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 3 institutions in 2 countries.

Arfan AhmedAI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
Sarah AzizAI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
Mahmood AlzubaidiCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Jens SchneiderCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Sara IrshaidatJordan University Hospital, Amman, Jordan.
Hashem Abu SerhanJordan University Hospital, Amman, Jordan.
Alaa A Abd-AlrazaqAI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
Barry SolaimanCollege of Law, Hamad Bin Khalifa University, Doha, Qatar.
Mowafa HousehCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Hamad bin Khalifa University · QAWeill Cornell Medical College in Qatar · QAJordan Hospital · JO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices. Objective: This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression. Methods: Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized. Results: From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies ( Conclusion: The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.

Indexed as

AA, Arfan AhmedAnxietyDepressionDigital mental healthHA, Hashem Abu SerhanMA, Mahmood AlzubaidiMental healthSA, Sarah AzizSelf-careSI, Sara IrshaidatWearable devices

Identifiers

PMID36743720
PMCPMC9884643
OpenAlexW4318473324

What OpenQuestion holds

Textmetadata
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.