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

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 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. Objective This systematic review and meta-analysis aims to assess 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. 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. Results 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. Conclusions 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. Trial Registration PROSPERO CRD42023387560; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387560

The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis

“…To elaborate, although the pooled mean in this review was 85.6%, the average accuracy estimates in the previous reviews were 87.6% (50%-100%) [ 28 ], 87% (70.8%-99%) [ 34 ], 86% (60%-100%) [ 15 ], 85.4% (64.5%-98.3%) [ 35 ], 84.2% (51.2%-100%) [ 24 ], and 82.9% (53%-99%) [ 13 ]. Furthermore, this review showed findings comparable with those of previous studies on the performance of wearable AI in detecting depression [ 74 ] and anxiety [ 75 ]. Specifically, the pooled mean accuracies of wearable AI in detecting depression and anxiety were 89% [ 74 ] and 82% [ 75 ], respectively.…”

“…Furthermore, this review showed findings comparable with those of previous studies on the performance of wearable AI in detecting depression [ 74 ] and anxiety [ 75 ]. Specifically, the pooled mean accuracies of wearable AI in detecting depression and anxiety were 89% [ 74 ] and 82% [ 75 ], respectively.…”

Wearables and Their Potential to Transform Health Management: A Step towards Sustainable Development Goal 3

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“…According to the American Psychiatric Association (APA) [49] and the Global Burden of Disease (GBD) [50], anxiety disorders are among the most common mental illnesses in the world, with 4% of the world population suffering from the condition. Usually, physicians use self-assessment questionnaires or interview-based evaluation techniques to measure anxiety symptoms [48]. These procedures can be subjective, time-consuming, and difficult to replicate.…”

“…The effects of the SMA on health and wellbeing management are mainly centred around mental wellness and reduced anxiety [95,96], which is important to maintain general health. Particularly, anxiety, which is reported as a rising mental health issue [49,50], can benefit from the wearable SMA through the continuous objective collection of wearers' stress-related metrics and by providing relevant interventions [48,51].…”