Association between digital biomarkers, loneliness and social isolation: a systematic review and meta-analysis

Question

What is the current evidence base for the association between digital biomarkers from wrist-worn wearables, loneliness and social isolation in adults?

Study selection and analysis

We systematically searched six databases from inception to 24 ^th September, 2024. We narratively synthesised findings and pooled effect sizes using random-effects meta-analyses where possible.

Findings

We included 14 studies from 12 articles (12 assessing loneliness, two assessing social isolation). Eight studies used sleep metrics, four used physical activity metrics, and two studies used machine learning approaches. Three meta-analyses were conducted: worse sleep efficiency (SE), but not total sleep time or sleep onset latency, was associated with higher loneliness (Fisher’s z = −0.20, 95% CI −0.34 to −0.06, p = 0.006). Two studies examined wake after sleep onset (WASO), and found longer periods of WASO were associated with higher loneliness. These findings on loneliness were echoed in the study examining social isolation. One study found that lower total physical activity was associated with higher levels of loneliness and social isolation, while other activity intensities showed mixed evidence. Machine learning studies demonstrated high accuracy in predicting loneliness, though models using digital biomarkers from smartphones provided better accuracy.

Conclusions

Worse SE, more WASO, and lower total physical activity were associated with loneliness and social isolation, particularly in middle- and older-age. Digital biomarker-based machine learning studies are sparse but show potential in predicting loneliness. Leveraging digital biomarkers as proxy markers of loneliness and social isolation could facilitate early detection of these conditions.

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