Validation of pure and total movement dispersion variables as measures of the distribution of daily activity using accelerometers: a secondary analysis of a prospective cohort

Abstract Aim Physical activity (PA) positively impacts health, but the optimal way to achieve PA remains unclear. Pure and total movement dispersion are novel metrics that quantify the spread of movement across the day, with (total) or without (pure) intensity. The purpose of this study is to evaluate whether pure and total movement dispersion are positively associated with established PA metrics and inversely associated with sedentary behavior (SB) metrics. Subject and methods This study involves a secondary analysis of accelerometer data collected within the Multicenter AIDS Cohort Study (MACS) from 2018 to 2020 in four US cities (Boston, Chicago, Los Angeles, and Pittsburgh) as part of a sleep sub-study. Of the data received on 638 participants who wore an ActiGraph GT9X on their wrist for a period of 7 days, 594 (93%) completed at least four valid wear days (≥ 10 h/day) and were included in the analyses. Pure and total movement dispersion were calculated from raw accelerometer data utilizing an author-developed algorithm on a supercomputer and compared with average duration and bout length of moderate- to vigorous-intensity PA (MVPA) and SB, determined using the ActiLife platform with Montye cut points for adult wrist-worn activity, and active-to-sedentary (ASTP)/sedentary-to-active transition probability (SATP), previously calculated as part of the MACS sleep sub-study. Due to the skewness of the data, Spearman’s rank correlations were computed to assess the relationships between pure and total movement dispersion and the established measures of PA and SB. Study activities were approved by institutional review boards at each participating site, and all participants provided written informed consent for their data to be used in future studies. Results Participants ( n = 594) had a mean age of 45 years (SD = 12), and 78% were living with HIV. Pure movement dispersion demonstrated significant positive associations with PA measures (MVPA minutes: r s = .4056, p < .0001; MVPA bout: r s = .1708, p < .0001; SATP: r s = .5011, p < .0001) and negative associations with SB metrics (SB minutes: r s = −.0853, p = .0377; SB bout: r s = −.382, p < .0001; ASTP: r s = −.3837, p < .0001). Total movement dispersion exhibited stronger associations with PA measures (MVPA minutes: r s = .5719, p < .0001; MVPA bout: r s = .2844) and SB metrics (SB minutes: r s = −.2183, p < .0001; SB bout: r s = −.5039, p < .0001; ASTP: r s = −.4707, p < .0001). The strength of the relationships between the measures was weaker in those under 40 years of age, with pure movement dispersion and SB minutes ( r s = −.1758, p = .2174) and MVPA bout ( r s = .2451, p = .0829) and with total movement dispersion and MVPA bout ( r s = .2565, p = .0692) no longer significantly associated. Conclusion Both measures displayed expected associations with duration, intensity, and transition of PA and SB, supporting their convergent validity as a combined movement metric. These metrics can complement established measures to provide a richer context for PA behaviors and potentially explore the impact of varying dispersion levels on health outcomes. Future work will focus on establishing optimal ranges for both metrics that relate to positive health outcomes, such as changes in cardiovascular risk. Because the data are cross-sectional, causal inference cannot be established. Additionally, wrist-based accelerometer placement may bias intensity classification, and residual confounding from unmeasured lifestyle factors cannot be excluded.