| Title: | Association of walking and osteoporosis risk among 432,493 UK participants: A prospective cohort study |
| Journal: | Public Health |
| Published: | 28 Apr 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42054719/ |
| DOI: | https://doi.org/10.1016/j.puhe.2026.106308 |
Publication 17810
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
OBJECTIVE: This study aimed to investigate the optimal dosage of walking duration for osteoporosis prevention.</p>
STUDY DESIGN: This study represents a prospective cohort analysis based on data derived from the UK Biobank.</p>
METHODS: Utilizing prospective data from the UK Biobank (n = 432,493), we analyzed osteoporosis incidence in adults aged 38-73 years. Walking duration was assessed via validated questionnaires. Cox proportional hazards models adjusted for demographics, comorbidities, and lifestyle factors. Restricted cubic splines characterized nonlinear associations.</p>
RESULTS: Participants in all walking groups exhibited significantly lower osteoporosis risk versus the non-walking group. In fully adjusted models, the hazard ratios (HR) (95% confidence intervals) for osteoporosis were: 0.72 (0.65-0.79; P < 0.001) for the <180 min/week of walking group, 0.73 (0.65-0.81; P < 0.001) for the 180-360 of walking group, and 0.74 (0.67-0.83; P < 0.001) for the >360 min/week of walking group, respectively. A U-shaped dose-response relationship was demonstrated (P for nonlinearity <0.001).</p>
CONCLUSION: Regular walking demonstrates significant osteoporosis protection, with optimal benefits at 180-360 min/week. This aligns with global physical activity guidelines while revealing an upper threshold beyond which additional walking yields no further risk reduction. Our findings support tailored exercise prescriptions for osteoporosis prevention.</p>
5 Authors
- Zhehan Yang
- Donglin Luo
- Haichao Jiang
- Yahai Wang
- Haixia Qi
1 Application
| Application ID | Title |
|---|---|
| 75732 | Application of machine learning for classification of late-life diseases and their outcome prediction |
Enabling scientific discoveries that improve human health