Comparability of accelerometer signal aggregation metrics across placements and dominant wrist cut points for the assessment of physical activity in adults

Large epidemiological studies that use accelerometers for physical behavior and sleep assessment differ in the location of the accelerometer attachment and the signal aggregation metric chosen. This study aimed to assess the comparability of acceleration metrics between commonly-used body-attachment locations for 24 hours, waking and sleeping hours, and to test comparability of PA cut points between dominant and non-dominant wrist. Forty-five young adults (23 women, 18-41 years) were included and GT3X + accelerometers (ActiGraph, Pensacola, FL, USA) were placed on their right hip, dominant, and non-dominant wrist for 7 days. We derived Euclidean Norm Minus One g (ENMO), Low-pass filtered ENMO (LFENMO), Mean Amplitude Deviation (MAD) and ActiGraph activity counts over 5-second epochs from the raw accelerations. Metric values were compared using a correlation analysis, and by plotting the differences by time of the day. Cut points for the dominant wrist were derived using Lins concordance correlation coefficient optimization in a grid of possible thresholds, using the nondominant wrist estimates as reference. They were cross-validated in a separate sample (N = 36, 10 women, 22-30 years). Shared variances between pairs of acceleration metrics varied across sites and metric pairs (range in r(2) : 0.19-0.97, all p amp;lt; 0.01), suggesting that some sites and metrics are associated, and others are not. We observed higher metric values in dominant vs. non-dominant wrist, thus, we developed cut points for dominant wrist based on ENMO to classify sedentary time (amp;lt;50 mg), light PA (50-110 mg), moderate PA (110-440 mg) and vigorous PA (amp;gt;= 440 mg). Our findings suggest differences between dominant and non-dominant wrist, and we proposed new cut points to attenuate these differences. ENMO and LFENMO were the most similar metrics, and they showed good comparability with MAD. However, counts were not comparable with ENMO, LFENMO and MAD.Funding Agencies|MINECO/FEDER [DEP2013-47540, DEP2016-79512-R, RYC-2011-09011]; Spanish Ministry of Education, Culture and Sport [FPU15/02645, FPU16/02760]; Spanish Ministry of Economy and Competitiveness [BES-2014-068829, IJCI-2017-33642]; Alicia Koplowitz Foundation; Strategic Research Area Health Care Science, Karolinska Institutet/Umea University; NIHR Leicester Biomedical Research Centre; Collaboration for leadership in Applied Health Research and Care (CLAHRC) East Midlands; Research Council of NorwayResearch Council of Norway [249932/F20]; University of Granada; University of Granada, UGR Research and Knowledge Transfer Fund (PPIT) 2016; Regional Government of Andalusia, Regional Ministry of Economy, Knowledge, Entreprises and University; European Regional Development Fund (ERDF)European Union (EU) [SOMM17/6107/UGR, RD16/0022]; SAMID III network, RETICS - PN I + D + I 2017-2021 (Spain); ISCIII-Sub-Directorate General for Research Assessment and Promotion; EXERNET Research Network on Exercise and Health in Special Populations [DEP2005-00046/ACTI]; European UnionEuropean Union (EU) [667302]