Wearable devices for anxiety assessment: a systematic review

Abstract Background There is growing interest in using biosignals from wearable devices to assess anxiety disorders. Among these, electrocardiography is the most widely used due to its ability to monitor cardiovascular activity. Other signals, such as respiratory, electrodermal activity, and photoplethysmography, also show promise. This review aims to evaluate how these signals, individually and in combination, have been used for anxiety detection. Methods We systematically reviewed 26 studies published between 2014 and 2024 that used wearable devices to collect signals for anxiety detection. Extracted information included study design, signal types, features, classification methods, and accuracy outcomes. Pooled accuracies were calculated to compare single-signal and multi-signal approaches. Results Here we show that approaches combining multiple signals outperform those using a single signal, with a pooled accuracy of 81.94% compared to 76.85%. Electrocardiography was the most reliable individual signal, with a pooled accuracy of 80.34% across 12 studies. However, the limited number of single-sensor studies and methodological variability limit conclusions about the superiority of any one modality. The most common features included mean heart rate and heart rate variability for electrocardiography, the mean inspiratory-to-expiratory time ratio for respiratory signals, mean skin conductance for electrodermal activity, and the mean heart rate for photoplethysmography. Support vector machine was the predominant classifier. Conclusions This review underscores the clinical potential of wearable devices for anxiety detection, emphasizing the value of multimodal approaches. Future research should focus on refining algorithms, expanding sample sizes, and exploring diverse contexts to improve the accuracy and generalizability of these methods.