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. 2022 Apr;27(2):e12364.
doi: 10.1111/jspn.12364. Epub 2021 Dec 8.

Dispersion of daily physical activity behaviors in school-age children: A novel approach to measure patterns of physical activity

Melissa M Klamm  1 , Angela A Duck  1 , Michael A Welsch  2 , Yonghua Yan  3 , Elisa R Torres  1 , Breanna Wade  2 , Mary W Stewart  1 , Jill Clayton  1 , Lei Zhang  1
Affiliations

Dispersion of daily physical activity behaviors in school-age children: A novel approach to measure patterns of physical activity

Melissa M Klamm et al. J Spec Pediatr Nurs. 2022 Apr.

Abstract

Purpose: The objectives of this paper are (1) to examine patterns of physical activity (PA) and sedentary behavior; (2) to describe development of a method to quantify movement dispersion; and (3) to determine the relationship between variables of movement (i.e., volume, intensity, and dispersion), volume of sedentary behavior, and estimated cardiorespiratory capacity in school-aged children.

Design and methods: A secondary analysis of an existing data set with raw accelerometer data identified PA patterns of movement dispersion in school-aged children. Bar graphs visually depicted each participant's daily vector magnitude counts. The research team developed a dispersion variable-movement dispersion-and formula to provide a new quantification of daily PA patterns. Total movement dispersion represents both intensity and distribution of movement, whereas pure movement dispersion refers to the distribution of movement during the wear time, independent of intensity. Kendall's tau examined the relationship between several variables: body mass index percentile, average minutes of sedentary behavior, average minutes of light PA, average minutes of moderate-vigorous PA (MVPA), derived VO2 max, total movement dispersion, and pure movement dispersion.

Results: Three participants' activity graphs were presented as examples: (1) active, (2) inactive, and (3) mixed. The more active participant had the highest values for pure and total movement dispersion. The inactive participant had much lower pure and total movement dispersion values compared to the active participant. The mixed participant had high average minutes of MVPA yet lower pure and total movement dispersion values. Total movement dispersion had a significant correlation with average minutes of light PA (r = .406, p = .016) and average minutes of MVPA (r = .686, p < .001). Pure movement dispersion was significantly correlated with average minutes of light PA (r = .448, p = .008) and average minutes of MVPA (r = .599, p < .001). Average minutes of sedentary behavior (SB) were not significantly correlated with total (r = .041, p = .806) or pure movement dispersion (r = .165, p = .326).

Practice implications: Movement dispersion may provide another tool to advance knowledge of PA, potentially leading to improved health outcomes. Raw accelerometer data, such as that gathered at the elementary school in this study, offer opportunities to identify school-aged children at risk for obesity, SB, and lack of PA.

Keywords: accelerometers; children; distribution; health promotion; patterns; physical activity.

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Conflict of interest statement

Conflicts of Interests

The authors declare that there are no conflicts of interest.

Figures

Figure 1a.
Figure 1a.
Active: High PA/High dispersion *Line designates cut off for MVPA levels related to vector magnitude counts.
Figure 1b.
Figure 1b.
Inactive: Low PA/Low dispersion
Figure 1c.
Figure 1c.
Mixed: High PA/Lower dispersion
See this image and copyright information in PMC

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