JMIR Publications

ABSTRACT

Background:

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 utilizing 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 exploited to detect and predict anxiety disorders automatically, objectively, and more efficiently.

Objective:

This systematic review and meta-analysis aimed at assessing the performance of wearable AI in detecting and predicting anxiety.

Methods:

Relevant studies were retrieved by searching 8 electronic databases and backward and forward reference list checking. Two 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 Quality Assessment of Studies of Diagnostic Accuracy-Revised (QUADAS-2). Evidence was synthesized using a narrative (i.e., texts and tables) and statistical approach (i.e., meta-analysis), as appropriate.

Results:

Of the 918 records identified, 21 studies were included in this review. A meta-analysis of results from 17 studies revealed a pooled mean accuracy of 0.82 (95% confidence interval (CI) 0.71 to 0.89). Meta-analyses of results from 10 studies showed a pooled mean sensitivity of 0.79 (95% CI 0.57 to 0.91) and a pooled mean specificity of 0.92 (95% CI 0.68 to 0.98). Subgroup analyses demonstrated that the performance of wearable AI was not moderated by algorithms, aims of AI, used wearable devices, status of wearable devices, data types, data sources, reference standards, and validation methods.

Conclusions:

Although wearable AI has the potential in detecting anxiety, it is not yet advanced enough for clinical use. Until further evidence shows an ideal performance of wearable AI, it should be used alongside other clinical assessments. Wearable device companies need to develop devices that can promptly detect anxiety and identify specific timepoints in the day where 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.