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Journal of Peking University (Health Sciences) ›› 2026, Vol. 58 ›› Issue (3): 551-559. doi: 10.19723/j.issn.1671-167X.2026.03.015

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

Jiaqi GAO1,2, Wenpeng LI1, Xiaoyi LI3, Yinxi TAN1, Yixin LI1, Liping DUAN2, Tao WU3,4, Dafang CHEN3,4, Yonghua HU3,4, Mengying WANG1,3,*()   

  1. 1. Department of Nutrition and Food Hygiene, Peking University School of Public Health, Beijing 100191, China
    2. Department of Gastroenterology, Peking University Third Hospital, Beijing 100191, China
    3. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China
    4. Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Beijing 100191, China
  • Received:2026-02-26 Online:2026-06-18 Published:2026-04-22
  • Contact: Mengying WANG
  • Supported by:
    the National Key Research and Development Program of China(2024YFC3606700)

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Abstract:

Objective: 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. Methods: 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. Results: 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%). Conclusion: 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.

Key words: Older adults, Wearable electronic devices, Physical activity patterns, Gastrointestinal microbiome, Microbial risk score

CLC Number: 

  • R153.3

Table 1

Baseline characteristics of the study participants by age group"

Characteristics Age group Total (n=743) P value
60-74 years (n=381) ≥75 years (n=362)
Age/years 69.7±3.6 80.0±3.5 74.7±6.2 < 0.001
Sex (female) 284 (74.5) 224 (61.9) 508 (68.4) < 0.001
Education level 0.201
  Primary school or below 48 (12.6) 42 (11.6) 90 (12.1)
  Junior high school 116 (30.4) 79 (21.8) 195 (26.2)
  High school 207 (54.3) 231 (63.8) 438 (59.0)
  College or above 10 (2.6) 10 (2.8) 20 (2.7)
Dietary habits 0.198
  Meat-predominant 239 (62.7) 242 (66.9) 481 (64.7)
  Plant-predominant 74 (19.4) 82 (22.7) 156 (21.0)
  Balanced 66 (17.3) 37 (10.2) 103 (13.9)
  Others 2 (0.5) 1 (0.3) 3 (0.4)
Smoking/(cigarettes/d) 0.5±2.7 0.1±0.9 0.3±2.1 0.066
History of hypertension 140 (36.7) 166 (45.9) 306 (41.2) 0.042
History of diabetes 93 (24.4) 86 (23.8) 179 (24.1) 0.987
History of heart disease 96 (25.2) 123 (34.0) 219 (29.5) 0.032
History of hyperlipidemia 96 (25.2) 88 (24.3) 184 (24.8) 0.954
BMI/(kg/m2) 23.9±2.8 23.5±2.7 23.7±2.7 0.243
SBP/(mmHg) 124.5±10.3 127.6±9.5 126.0±10.1 < 0.001
DBP/(mmHg) 72.5±6.9 70.4±7.2 71.5±7.1 < 0.001
Fasting blood glucose/(mg/dL) 120±14 120±12 120±13 0.101
Mean daily steps/(steps/d) 7 300±2 900 6 400±2 600 6 900±2 800 < 0.001
Coefficient of variation of steps 0.50±0.17 0.50±0.17 0.50±0.17 0.998
Proportion of active days 0.97±0.05 0.97±0.06 0.97±0.06 0.81

Figure 1

DDRTree classification of physical activity patterns and comparison of physical activity characteristics between patterns A, DDRTree manifold projection; B, distribution of core objective features. * * * *P < 0.000 1. DDRTreed, discriminative dimensionality reduction via learning a tree; CV, coefficient of variation; Dim, dimension."

Table 2

Comparison of baseline characteristics across physical activity patterns identified by DDRTree"

Characteristics Irregular (n=188) Moderate (n=375) Active (n=180) Total (n=743)
Age/years 75.4±6.6 74.7±6.1 74.2±5.9 74.7±6.2
Sex (female) 129 (68.6) 271 (72.3) 108 (60.0) 508 (68.4)
Education level
  Primary school or below 20 (10.6) 48 (12.8) 22 (12.2) 90 (12.1)
  Junior high school 40 (21.3) 108 (28.8) 47 (26.1) 195 (26.2)
  High school 124 (66.0) 209 (55.7) 105 (58.3) 438 (59.0)
  College or above 4 (2.1) 10 (2.7) 6 (3.3) 20 (2.7)
Dietary habits
  Meat-predominant 111 (59.0) 238 (63.5) 132 (73.3) 481 (64.7)
  Plant-predominant 44 (23.4) 82 (21.9) 30 (16.7) 156 (21.0)
  Balanced 33 (17.6) 52 (13.9) 18 (10.0) 103 (13.9)
  Others 0 (0) 3 (0.8) 0 (0) 3 (0.4)
Smoking/(cigarettes/d) 0.6±2.8 0.2±1.4 0.3±2.3 0.3±2.1
History of hypertension 89 (47.3) 158 (42.1) 59 (32.8) 306 (41.2)
History of diabetes 49 (26.1) 80 (21.3) 50 (27.8) 179 (24.1)
History of heart disease 77 (41.0) 110 (29.3) 32 (17.8) 219 (29.5)
History of hyperlipidemia 138 (73.4) 273 (72.8) 148 (82.2) 559 (75.2)
BMI/(kg/m2) 24.1±3.0 23.5±2.8 23.7±2.3 23.7±2.7
SBP/(mmHg) 127.6±10.1 126.0±9.7 124.4±10.6 126.0±10.1
DBP/(mmHg) 73.3±6.2 70.9±7.2 70.9±7.4 71.5±7.1
Fasting blood glucose/(mg/dL) 123.1±22.3 118.6±19.3 116.3±18.4 118.9±19.8
Mean daily steps/(steps/d) 3 924.2±1 317.6 6 557.7±1 300.3 10 569.4±1 817.3 6 863.2±2 766.6
Coefficient of variation of steps 0.72±0.17 0.46±0.08 0.36±0.07 0.50±0.17
Proportion of active days 0.91±0.09 0.99±0.02 0.99±0.02 0.97±0.06

Figure 2

Key differential bacterial genera (A) and microbial risk scores (B) across different physical activity patterns *P < 0.05; * *P < 0.01. DDR, discriminative dimensionality reduction; MRS, microbial risk score; ns, no significance."

Figure 3

Microbial co-occurrence networks identified by DDRTree classification A, irregular group; B, moderate group; C, active group. DDRTreed, discriminative dimensionality reduction via learning a tree."

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