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Karl Smith-Byrne

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#software testing Open access Sep 2026

Towards harmonised accelerometer-derived physical activity: a comparability study across wrist, thigh, and hip wear locations

Wearable devices are widely used in epidemiological studies, but differences in wear locations make data harmonisation and pooled meta-analyses challenging. We aimed to (i) assess the comparability of measures of physical activity and sedentary behaviour derived from wrist, thigh, and hip accelerometers in adults during free-living conditions, and (ii) derive and validate calibration equations to enable harmonisation across wear locations. We analysed accelerometer data from UK adults from the SMART Work and Life (SWAL) study (concurrent wrist- and thigh-worn devices), Australian adults from the Raine Study Generation 1 26-year follow-up (concurrent wrist- and hip-worn devices), and an independent sample of UK adults wearing devices at all three locations. From each wear location, we derived overall activity (mean acceleration), step count, peak 1-minute cadence, moderate-to-vigorous physical activity, light physical activity, and sedentary behaviour. Cross-location comparability was assessed using correlation coefficients, intraclass correlation coefficients (ICCs), Bland-Altman analyses, and equivalence testing. Calibration equations between wear locations were derived using standardised major axis regression and were externally validated in the independent sample. Analyses included valid data from 658 SWAL participants (mean age 44.8 years; 72% women) and 901 Raine Study participants (mean age 56.7 years; 57% women). Cross-location correlations were strong, ranging from 0.66 to 0.89 between the wrist and thigh, and from 0.66 to 0.90 between the wrist and hip, with approximately linear relationships across the observed range of values. Absolute agreement was generally higher for wrist-thigh comparisons (ICCs 0.49–0.89) than for wrist-hip comparisons (ICCs 0.35–0.75), with larger systematic biases in the latter. In external validation in 42 UK adults (mean age 38.4 years; 64% women), calibrated thigh- and hip-derived estimates showed moderate-to-good agreement with wrist-derived estimates (ICCs 0.53–0.97 for wrist-thigh and 0.55–0.88 for wrist-hip comparisons), though confidence intervals for some measures extended into the poor agreement range. Accelerometer-derived measures from wrist, thigh, and hip locations are strongly correlated. These findings provide evidence to support pooled meta-analyses across wear locations and the development of location-agnostic processing software.

C. Zisou, B. Maylor, A. Acquah et al. · 0 citations
Open access Aug 2026

Polygenic score for sleep duration in relation to the risk of Alzheimer’s disease: results from the UK biobank

Studies have suggested that sleep duration may be associated with Alzheimer’s disease risk; however, findings based on self-reported sleep duration are likely to be influenced by reverse causation and residual confounding bias. We derived weights for genetic variants associated with wearable-derived sleep duration using the LDpred2-auto method in 77,770 white British participants from the UK Biobank, following the generation of new genome-wide association summary statistics. We then used these weights to generate polygenic scores (PGSs) for the remaining 264,746 white British participants for the association analysis, independent of the sample used to develop PGS weights. We assessed the association of fifths between genetically predicted sleep duration and the risk of Alzheimer’s disease (1,451 cases/264,746 individuals over a median 12.5 years of follow-up). The PGS explained approximately 2% of the variation in device-measured sleep duration. Compared with individuals in the middle fifth of PGSs, those in the highest fifth (indicating approximately 15 min/day longer sleep) had a lower risk of Alzheimer’s disease (hazard ratio (HR) = 0.79[95%CI, 0.67–0.94]). Our results indicate that genetic predisposition to relatively long sleep duration is associated with a lower Alzheimer’s disease risk.

Angel T. Y. Wong, S. Floud, G. Reeves et al. · 0 citations

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