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Jiahe Cui

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Meta-analysis Open access Aug 2026

Physical activity as a protective factor for insomnia: systematic review, meta-analysis, and quality assessment of observational studies

Background While meta-analyses of randomised controlled trials have demonstrated the therapeutic effects of physical activity (PA) on insomnia, the real-world associations between different levels and domains of PA and insomnia symptoms remain unclear. We aimed to examine these associations in the general population. Methods We conducted a systematic review and meta-analysis of observational studies. We searched PubMed, MEDLINE, EMBASE, APA PsycINFO, CINAHL, and the Cochrane Library up to 5 August 2024, including studies that examined the association between PA and insomnia symptoms in the general population. We calculated pooled effect sizes using a random-effects model and performed subgroup analyses by PA intensity and domain. We assessed publication bias using funnel plots. Results We included 27 risk estimates. Compared with low PA, high PA was not significantly associated with insomnia risk (odds ratio (OR) = 0.96; 95% confidence interval (CI) = 0.85–1.09). Moderate PA was associated with a 7% lower risk of insomnia (OR = 0.93; 95% CI = 0.87–1.00). Subgroup analyses showed significant variation across activity domains, with occupational PA associated with a 21% higher risk of insomnia (OR = 1.21; 95% CI = 1.02–1.43). There was no evidence of publication bias. Conclusions Moderate levels of PA are associated with a modest reduction in insomnia risk, whereas occupational PA may increase the risk. These findings highlight the importance of considering both the intensity and domain of PA in relation to sleep health. Registration PROSPERO: CRD42023417826.

Chaoqun Xie, Fangfang Xie, Jiahe Cui et al. · 0 citations
Review Open access Jul 2026

Human-Centered AI in Sleep Health Management: Scoping Review of Stakeholder Perspectives and Co-Design Practices

Unlike existing reviews prioritizing algorithmic performance metrics over usability, clinical workflow integration, and patient trust, this study systematically maps these essential sociotechnical factors and innovatively applies the HCAI framework to the sleep AI lifecycle.

Dacheng Dai, Fangfang Xie, Jiahe Cui et al. · 0 citations

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