Abd-alrazaq, Alaa; AlSaad, Rawan; Aziz, Sarah; Ahmed, Arfan; Denecke, Kerstin; Househ, Mowafa; Farooq, Faisal; Sheikh, Javaid (2023). Wearable artificial intelligence for anxiety and depression: Scoping review Journal of Medical Internet Research, 25(25), e42672. JMIR Publications 10.2196/42672
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Background: Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of AI and wearable devices (wearable artificial intelligence (AI)) have been exploited to provide mental health services. Objective: The current review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. Methods: We searched 8 electronic databases (MEDLINE, PsycINFO, EMBASE, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar). Then, we checked studies that cited the included studies, and screened studies that were cited by the included studies. Study selection and data extraction were carried out by two reviewers independently. The extracted data were aggregated and summarized using the narrative synthesis. Results: Of the 1203 citations identified, 69 studies were included in this review. About two thirds of the studies used wearable AI for depression while the remaining studies used it for anxiety. The most frequent application of wearable AI was diagnosing anxiety and depression while no studies used it for treatment purposes. The majority of studies targeted individuals between the ages of 18 and 65. The most common wearable devices used in the studies were Actiwatch AW4. The wrist-worn devices were most common in the studies. The most commonly used data for model development were physical activity data, sleep data, and heart rate data. The most frequently used dataset from open sources was Depresjon. The most commonly used algorithms were Random Forest (RF) and Support Vector Machine (SVM). Conclusions: Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals as a pre-screening assessment of anxiety and depression. Further reviews are needed to statistically synthesize studies’ results related to the performance and effectiveness of wearable AI. Given its potential, tech companies should invest more in wearable AI for treatment purposes for anxiety and depression.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
School of Engineering and Computer Science > Institute for Patient-centered Digital Health School of Engineering and Computer Science |
Name: |
Abd-alrazaq, Alaa; AlSaad, Rawan; Aziz, Sarah; Ahmed, Arfan; Denecke, Kerstin  0000-0001-6691-396X; Househ, Mowafa; Farooq, Faisal and Sheikh, Javaid |
Subjects: |
Q Science > Q Science (General) R Medicine > R Medicine (General) |
ISSN: |
1438-8871 |
Publisher: |
JMIR Publications |
Language: |
English |
Submitter: |
Kerstin Denecke |
Date Deposited: |
14 Dec 2022 12:34 |
Last Modified: |
25 Oct 2023 13:41 |
Publisher DOI: |
10.2196/42672 |
Related URLs: |
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ARBOR DOI: |
10.24451/arbor.18390 |
URI: |
https://arbor.bfh.ch/id/eprint/18390 |
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