Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression

Abstract

Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases.

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.…”

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

“…To address this gap, this review aimed to examine the performance of wearable AI in detecting and predicting anxiety. It is worth noting that this review is built upon and differs from our previous reviews [22,23]. Specifically, the first study [22] was a scoping review to explore the features of wearable AI used for anxiety and depression and identify the research gaps in this area.…”

Developing Advanced AI Ecosystems to Enhance Diagnosis and Care for Patients with Depression

“…smartwatches) could enable continuous monitoring of (bio)markers and signals as well as environmental influences, which could help to overcome traditional assessment limitations and detect signs of depression in real time. This combination could help improve the accuracy and timeliness of mental health monitoring and enable individuals to proactively maintain their wellbeing [14,15].…”