Background: Digital biomarkers are gaining interest as proxy markers for mental health as they enable passive and continuous data collection. However, the association between digital biomarkers of health and anxiety, both generalised anxiety disorder and anxiety symptoms, remains unknown. Objective: This systematic review and meta-analysis aimed to examine the association between digital biomarkers of health obtained from wrist-worn wearables and anxiety in adults. Methods: Systematic literature searches were conducted across six databases, including unpublished grey literature. The final search was done on 24th September, 2024. Cross-sectional or longitudinal studies investigating the association between digital biomarkers from wrist-worn wearables and anxiety were eligible. Studies using inferential statistics or machine learning methods were both eligible. Studies were excluded if participants received diagnoses of neurodegenerative disorders or physical health conditions. Two risk of bias tools were used: the Nationale Heart, Lung and Blood Institute assessment tool for inferential statistical studies, and the modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 for machine learning studies. Whenever possible, effect sizes were combined across studies, for each digital biomarker of health separately, using random-effects meta-analyses. Sensitivity analyses were performed to assess whether results differed according to anxiety type (state, trait) and age group. Otherwise, studies were synthesised narratively. This study is registered on PROSPERO, CRD42023409995. Results: 32 studies from 30 articles were eligible. 25 studies used inferential statistical approaches for analysis (17 reporting sleep characteristics, four reporting physical activity, two reporting both, and two reporting heart rate variability), and seven studies used machine learning approaches. Sample size range from 17 to 60,235. Meta-analyses were conducted for four sleep metrics: sleep efficiency, wake after sleep onset, total sleep time, and sleep onset latency. Sleep efficiency (K = 8, N = 3,627, Fisher’s z = -0.08, 95% confidence interval CI = -0.15 to -0.01, P = .026) and wake after sleep onset (K = 6, N = 3,291, Fisher’s z = 0.13, 95% CI = 0.01 to 0.24, P = .029) were associated with anxiety symptoms. Total sleep time and sleep onset latency were not associated with anxiety symptoms. A qualitative synthesis of the limited number of studies examining physical activity metrics revealed that more sedentary behaviour was associated with greater anxiety symptoms. Conclusions: Worse sleep efficiency, longer wake after sleep onset, and more sedentary behaviour were associated with greater anxiety symptoms. Relationships between different physical activity intensities and anxiety remain unclear due to the limited number of studies, warranting further investigation. Studies using machine learning to predict anxiety from wrist-worn wearable data had limited accuracy, potentially due to the exclusion of demographics as predictors. Future employment of metrics measured from wrist-worn wearables and machine learning techniques could potentially be used as a screening tool for anxiety.
Lau et al. (Wed,) studied this question.