Accelerometry-derived activity phenotypes discriminate metabolic risk in pediatric obesity

A. Gatti,^1,2 M. Vandoni,¹ C. Cavallo,^1,3 V. Carnevale Pellino,¹ E. Larnè,¹ A. Alberico,¹ E. Durando,¹ V. Rossi,^4,5 A. Odone,^6,7 G. Zuccotti,^4,5 V. Calcaterra^8,4 | ¹Laboratory of Adapted Motor Activity, Department of Public Health, Experimental Medicine and Forensic Science, University of Pavia, Italy; ²National PhD Programme in One Health approaches to infectious diseases and life science research, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Italy; ³Luxembourg Health & Sport Sciences Research Institute A.s.b.l., Differdange, Luxembourg; ⁴Pediatric Department, Buzzi Children’s Hospital, Milan, Italy; ⁵Department of Biomedical and Clinical Science, University of Milan, Italy; ⁶Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Italy; ⁷Medical Direction, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; ⁸Department of Internal Medicine and Therapeutics, University of Pavia, Italy

Abstract

Pediatric obesity is associated with early cardiometabolic alterations; however, moderate-to-vigorous physical activity (MVPA) duration alone may insufficiently describe behaviorally relevant variability. To identify multidimensional physical activity (PA) phenotypes in children and adolescents with obesity using discriminative dimensionality reduction via learning a tree (DDRTree) applied to accelerometry data, and to examine associations with body composition and cardiometabolic risk beyond MVPA. This cross-sectional study included 91 children and adolescents with obesity (39 girls; 43%), aged 6-17 years. Participants wore a thigh-mounted accelerometer for 7 consecutive days. A multidimensional set of accelerometry-derived features was extracted. DDRTree identified PA phenotypes. Anthropometry, insulin sensitivity indices, and a continuous metabolic syndrome risk score were compared across phenotypes using Analysis of Covariance (ANCOVA) adjusted for age and sex. DDRTree identified three PA phenotypes: Sedentary Pattern (24%), Light Activity Pattern (55%), and Higher Activity Pattern (21%). Only 3 participants (3.3%) met MVPA recommendations. Compared with the Sedentary Pattern, the Higher Activity Pattern accumulated approximately 122 min/day less sedentary time (p<0.0001) and 16.5 min/day more MVPA (p<0.001). The Higher Activity Pattern showed a lower fat mass percentage (38.8% vs. 41.6%; p=0.045), higher fat-free mass percentage (61.2% vs. 58.4%; p=0.037), higher insulin sensitivity (SPISE) (mean difference +0.90, 95% CI: 0.14, 1.66; p=0.016), and a lower metabolic risk score (mean difference −0.42, 95% CI: −0.78 to −0.06; p=0.017). Multidimensional PA phenotypes derived from accelerometry identify clinically meaningful differences in body composition and metabolic risk beyond MVPA duration, supporting phenotype-based approaches for pediatric obesity risk stratification and intervention design.

Keywords

• Pediatric obesity
• physical activity phenotypes
• accelerometry
• DDRTree
• sedentary behavior