Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis

• Published in: Journal of medical Internet research Vol. 25; no. 5; p. e48754
• Main Authors: Abd-alrazaq, Alaa, AlSaad, Rawan, Harfouche, Manale, Aziz, Sarah, Ahmed, Arfan, Damseh, Rafat, Sheikh, Javaid
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 08.11.2023; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications
• Subjects: Accuracy; Advertising executives; Algorithms; Analysis; Anxiety; Anxiety - diagnosis; Anxiety Disorders; Artificial Intelligence; Automation; Bias; Clinical assessment; Clinical standards; Databases, Factual; Digital libraries; Evaluation; Extraction; Fear & phobias; Health aspects; Humans; Machine learning; Medical research; Medicine, Experimental; Mental depression; Mental disorders; Mental health; Meta-analysis; Multimedia; Neuroimaging; Performance evaluation; Quality assessment; Questionnaires; Risk assessment; Self evaluation; Smartwatches; Systematic review; Textiles; Wearable computers; anxiety; wearable devices; machine learning; systematic review; mobile phone; artificial intelligence
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/48754

Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety. Wearable artificial intelligence (AI), the combination of AI technology and wearable devices, has been widely used to detect and predict anxiety disorders automatically, objectively, and more efficiently. This systematic review and meta-analysis aims to assess the performance of wearable AI in detecting and predicting anxiety. Relevant studies were retrieved by searching 8 electronic databases and backward and forward reference list checking. In total, 2 reviewers independently carried out study selection, data extraction, and risk-of-bias assessment. The included studies were assessed for risk of bias using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-Revised. Evidence was synthesized using a narrative (ie, text and tables) and statistical (ie, meta-analysis) approach as appropriate. Of the 918 records identified, 21 (2.3%) were included in this review. A meta-analysis of results from 81% (17/21) of the studies revealed a pooled mean accuracy of 0.82 (95% CI 0.71-0.89). Meta-analyses of results from 48% (10/21) of the studies showed a pooled mean sensitivity of 0.79 (95% CI 0.57-0.91) and a pooled mean specificity of 0.92 (95% CI 0.68-0.98). Subgroup analyses demonstrated that the performance of wearable AI was not moderated by algorithms, aims of AI, wearable devices used, status of wearable devices, data types, data sources, reference standards, and validation methods. Although wearable AI has the potential to detect anxiety, it is not yet advanced enough for clinical use. Until further evidence shows an ideal performance of wearable AI, it should be used along with other clinical assessments. Wearable device companies need to develop devices that can promptly detect anxiety and identify specific time points during the day when anxiety levels are high. Further research is needed to differentiate types of anxiety, compare the performance of different wearable devices, and investigate the impact of the combination of wearable device data and neuroimaging data on the performance of wearable AI. PROSPERO CRD42023387560; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387560.