Physical activity phenotypes from volume-based versus intensity-based wearable metrics: discordant classification and divergent metabolomic, body composition, and cardiac magnetic resonance associations.

Abstract

BACKGROUND: Consumer wearables report direct volume-based metrics (steps, distance, floors climbed) alongside device-derived metrics generated by manufacturer algorithms (activity-intensity minutes, energy expenditure). Both are widely used to characterize individuals into activity phenotypes, yet whether these representations yield concordant activity phenotypes and biological associations is unclear. OBJECTIVE: To compare activity phenotypes derived from direct accelerometry with those derived from device-derived intensity metrics, and to evaluate each against metabolomic, body composition, and cardiac magnetic resonance (CMR) phenotypes. METHODS: Using the SingHEART cohort, two hierarchical cluster analyses using Ward linkage yielded five phenotypes each for volume-based and intensity-based metrics. Agreement was quantified by the adjusted Rand index. For analyses, the two high-variability phenotypes were combined, giving four groups: sedentary, average movers, regular exercisers, and high-variability movers. Serum ¹H-NMR metabolomics was analyzed by PERMANOVA with Holm adjustment across pairwise contrasts. Body and CMR associations were assessed using linear regression with false discovery rate correction. RESULTS: In total, 984 adults (mean age 45.6 years, 48.3% male) wore accelerometers for a mean of 7.1 valid days. The two classifications agreed poorly: adjusted Rand index was 0.18, with only 46.2% assigned identically. Volume-based phenotypes differed in metabolomic profiles (P < .001). Active participants (high-variability movers plus regular exercisers) differed from sedentary participants across 106 of 250 biomarkers (FDR q < 0.05), including higher HDL, lower VLDL, LDL and lower GlycA. Conversely, although intensity-based phenotypes showed a nominally significant omnibus metabolomic association (P = .003), no metabolomic difference was observed between active and sedentary groups defined by intensity-based phenotyping (P = .29). Across 16 body composition and CMR outcomes, volume-based phenotypes were associated with 11 and intensity-based phenotypes with 6, in divergent directions: volume-based high-variability movers showed lower body fat, higher skeletal muscle, and larger ventricular volumes and mass, whereas intensity-based high-variability movers showed higher body mass index and waist circumference. CONCLUSIONS: Volume-based metrics and intensity-based metrics assign the same individuals to discordant activity phenotypes. Volume-based phenotypes showed more consistent associations across metabolomic, body composition, and cardiac imaging measures. These findings suggest that the choice of wearable-derived metrics substantially influences digital activity phenotyping and its biological interpretation.

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