[Association between wearable-derived physical activity patterns and gut microbiota in older adults]

To identify real-world physical activity patterns in older adults using objective measurements from wearable devices, and to analyze the associations between these patterns and gut microbiota composition.

Based on data collected from a real-world health management project, a total of 743 participants from Eastern, Central, and Northern China were enrolled between January 2018 and June 2025. A 180-day objective physical activity dataset prior to fecal sampling was collected via smart wearable devices to extract features including mean daily steps, coefficient of variation of steps, and the proportion of active days. Fecal samples underwent 16S ribosomal RNA (rRNA) gene (V3-V4 region) amplicon sequencing to obtain genus-level relative abundance matrices. Covariates, including demographics, lifestyle, and chronic disease history, were collected via questionnaires and physical examinations. The discriminative dimensionality reduction via learning a tree (DDRTree) algorithm combined with K-means clustering was applied to identify physical activity phenotypes. Alpha diversity was evaluated using the Shannon index (Kruskal-Wallis test), and beta diversity was assessed using covariate-adjusted permutational multivariate analysis of variance (PERMANOVA) based on Bray-Curtis distance. Multivariable linear regression with false discovery rate (FDR) correction was used to screen differential taxa. A microbial risk score (MRS) was constructed based on taxa with a raw P < 0.05, defined as the difference between the standardized abundance of beneficial and harmful taxa. Co-occurrence networks were constructed to evaluate micro-ecological topological structures.

The cohort comprised 381 (51.3%) individuals aged 60-74 years and 362 (48. 7%) aged ≥75 years. Compared with the 60-74 group, the ≥75 group had higher prevalences of hypertension (45.9% vs. 36.7%, P=0.045) and heart disease (34.0% vs. 25.2%, P=0.032), higher systolic blood pressure (median 130 mmHg vs. 120 mmHg, P < 0.001), and fewer mean daily steps (median 6 200 steps vs. 7 000 steps, P < 0.001). Clustering identified three activity patterns: active group (n=143, 19.2%; high steps, low variation, high adherence), moderate group (n=429, 57.7%), and irregular group (n=171, 23.0%; low steps, high variation, low adherence). The active group exhibited the lowest prevalences of hypertension (35.0%) and heart disease (21.7%), and the lowest systolic blood pressure (mean 124.4 mmHg), whereas the irregular group showed the highest values (51.5%, 40.4%, and 127.6 mmHg, respectively). Alpha diversity showed no significant differences among the groups. After adjusting for covariates, physical activity patterns showed no statistically significant effect on beta diversity (R²=0.003 7, P=0.115). Compared with the irregular group, two genera in the active group showed significant differences (P < 0.05). Specifically, the relative abundance of Roseburia in the active group was significantly lower than that in the irregular group (P < 0.05), and the relative abundance of Butyricimonas was also significantly lower than that in the moderate group (P < 0.01). However, these differences did not remain statistically significant after FDR correction. The MRS exhibited a significant gradient distribution across the groups, with the active group scoring the highest (P < 0.001). Co-occurrence network analysis revealed that the active group had the highest network density and proportion of positive correlations (84.5%), whereas the irregular group had the lowest (60.3%).

Physical activity patterns identified from wearable device data are associated with gut microbiota composition and ecological network characteristics in older adults. Active and regular physical activity patterns indicate a higher MRS and more stable microbial co-occurrence networks, suggesting potential associations between activity regularity and gut microbial ecology, though causal inference requires longitudinal confirmation.