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Journal of medical Internet research···
Links Between Digital Health Measures and Anxiety
Updated
Abstract
A total of 44 studies were reviewed, with sample sizes ranging from 17 to 170,320 participants.
- Meta-analyses of sleep metrics indicated no significant associations with anxiety symptoms.
- Qualitative analyses suggested that lower levels of physical activity and higher heart rates may be linked to greater anxiety.
- Machine learning studies showed varied performance in predicting anxiety when using only wearable data.
- Combining wearable data with other sources, such as self-reports, could enhance predictive performance regarding anxiety.
Simplified
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 generalized anxiety disorder and anxiety symptoms, remains unknown.
OBJECTIVE: This systematic review and meta-analysis examined the association between digital biomarkers of health obtained from wrist-worn wearables and anxiety in adults.
METHODS: Systematic literature searches were conducted across 6 databases, including unpublished gray literature. The final search was done on September 21, 2025. 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 National 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 or trait) and age group. Otherwise, studies were synthesized narratively.
RESULTS: A total of 44 studies from 42 articles were eligible. Among these, 36 studies used inferential statistical approaches for analysis (21 reporting sleep characteristics, 8 reporting physical activity, 2 reporting heart rate variability, and 5 reporting more than 1 type), and 8 studies used machine learning approaches. Sample size ranged from 17 to 170,320. Meta-analyses on 4 sleep metrics found no associations: sleep efficiency (Fisher z=-0.07, 95% CI -0.14 to 0.002; P=.06; PI -0.19 to 0.05), wake after sleep onset (Fisher z=0.13, 95% CI -0.04 to 0.30; P=.11; PI -0.15 to 0.41), total sleep time (Fisher z=0.009, 95% CI -0.01 to 0.03; P=.28; PI -0.02 to 0.03), and sleep onset latency (Fisher z=0.04, 95% CI -0.07 to 0.15; P=.08; PI -0.19 to 0.27). Qualitative syntheses revealed that lower physical activity levels and higher heart rate were associated with greater anxiety symptoms. Machine learning studies using wrist-worn wearable data alone showed varied performance, with predictive performance improving when wearable data were combined with other data sources.
CONCLUSIONS: This is the first review to synthesize evidence from inferential statistical (mostly fair quality) and machine learning studies examining association between wearable-derived digital biomarkers and anxiety. Meta-analyses found no associations between sleep metrics and anxiety. Although based on limited studies, lower physical activity levels and elevated heart rate were associated with greater anxiety symptoms. Digital biomarkers may be more useful when integrated with other data sources (eg, self-report and clinical data) rather than used as stand-alone screening tools.
TRIAL REGISTRATION: PROSPERO CRD42023409995; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023409995.
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