Corrigendum to “Associations Between 24-Hour Time Use and Academic Achievement in Australian Primary School

Abstract

Watson, A., Dumuid, D., & Olds, T. (2020). Associations between 24-hour time use and academic achievement in Australian primary school–aged children. Health Education & Behavior, 47(6), 905–913. https://doi.org/10.1177/1090198120952041

In this article, data from the CheckPoint module of the Longitudinal Study of Australian Children were used, and there was an error in how the accelerometry data were processed in the CheckPoint study. As a result, incorrect cut-points were used resulting in shorter physical activity estimates and longer sedentary time estimates. The authors repeated the analyses for this article and the corrected variables are highly correlated with the original variables. For the specific corrections, please see bolded text in sentences and shaded values in revised Tables 1 and 2:

Abstract: “Method. Participants for this study were drawn from two cohorts: the Australian arm of the cross-sectional International Study of Childhood Obesity, Lifestyle and the Environment (n = 452; mean age 10.7 years [SD = 0.4]; 54% female) and CheckPoint (n = 1,282; mean age 12 years [SD = 0.4]; 50% female), a cross-sectional study nested between Waves 6 and 7 of the Longitudinal Study of Australian Children.”

Data Analysis: “Zero MVPA was detected for no CheckPoint participants. As per published recommendations (Martín-Fernández et al., 2012), no zero replacement was needed to enable log-ratio transformation.”

Results: Participant Characteristics: “Participants with complete data for all variables were included in the analyses (ISCOLE: Literacy n = 294, Numeracy n = 290; and CheckPoint: Literacy n = 943, Numeracy n = 933). Participant characteristics are described in Table 1. The movement behavior composition was associated with literacy (p < .001 in both samples) and numeracy (ISCOLE p = .008, CheckPoint p = .006). The log-ratio coefficients from Table 2 indicate that lower LPA (relative to the remaining behaviors) was beneficially associated with both academic outcomes (Literacy: ISCOLE p = .002 and CheckPoint p = .002, Numeracy: ISCOLE p = .001 and CheckPoint p = .005). Higher sedentary time (relative to the remaining behaviors) was beneficially associated with literacy (ISCOLE p = .03, CheckPoint p = .002) but not with numeracy.”

Table 1.

Participant Characteristics.

Characteristics	ISCOLE (Adelaide)	CheckPoint
n ^a	294	943
Age, in years, M (SD)	10.7 (0.4)	12.0 (0.4)
Sex = Female, n (%)	161 (54.8)	451 (47.8)
Maturity offset, M (SD)	−1.8 (0.9)
Pubertal status, n (%)
Prepubertal		100 (10.6)
Early pubertal		242 (25.7)
Mid-pubertal		484 (51.3)
Late pubertal		113 (12.0)
Postpubertal		4 (0.4)
Highest parental education, n (%)
High School or lower	69 (23.5)
TAFE	100 (34.0)
University	71 (24.1)
Postgraduate	54 (18.4)
SEP z score		0.25 (1.00)
Movement behaviors, M (SD)
Sleep	566 (43)	567 (45)
Sedentary	383 (65)	552 (80)
LPA	440 (62)	250 (56)
MVPA	32 (18)	63 (35)
Movement behavior compositional means^d, % (sleep; sedentary; LPA; MVPA)	40; 27; 31; 2	40; 39; 17; 4
Literacy, M (SD)	497^b (60)	559^c (61)
Numeracy, M (SD)	489^b (65)^*n ^{= 290}	580^c (70)^*n ^{= 933}

Note. TAFE = technical and further education; SEP = socioeconomic position; LPA = light physical activity; MVPA = moderate-to-vigorous physical activity.

Number of participants with valid accelerometry data. ^bYear 5 academic data (2015). ^cYear 7 academic data (2015–2017). ^dCompositional means are the geometric mean of each behavior, linearly adjusted to collectively sum to 100%.

Table 2.

Relationships Between Movement Behaviors and Academic Achievement: Results From Compositional Models.

Test type	ISCOLE (Adelaide)				CheckPoint
Literacy	n	χ^a	p		N	Sum²	p
Overall composition	294	12.1	<.001		943	87,224	<.001
Pivot coordinates	Regression coefficient	SE	t	p	Regression coefficient	SE	t	p
Sleep vs. remaining	19.1	32.8	0.6	.56	−10.0	16.1	−0.6	.54
Sedentary vs. remaining	46.4	20.8	2.2	.03	36.9	12.0	3.1	.002
LPA vs. remaining	−69.7	22.3	−3.1	.002	−24.8	9.1	−2.7	.006
MVPA vs. remaining	4.2	6.8	0.6	.54	−2.2	3.8	−0.6	.57
Numeracy	N	χ	p		N	Sum²	p
Overall composition	290	11.8	.008		933	53,611	.006
Pivot coordinates	Regression coefficient	SE	t	p	Regression coefficient	SE	t	p
Sleep vs. remaining	50.0	34.7	1.4	.15	12.0	18.9	0.6	.53
Sedentary vs. remaining	16.7	22.0	0.8	.45	17.4	14.1	1.2	.22
LPA vs. remaining	−76.3	23.5	−3.2	.001	−29.8	10.7	−2.8	.005
MVPA vs. remaining	9.6	7.3	1.3	.19	0.4	4.5	0.1	.93

Note. Bold text indicates statistical significance (p < .05). Models adjusted for sex, age, maturity/puberty, highest parental education/household socioeconomic status. Remaining = the geometric mean of the remaining movement behaviors; LPA = light physical activity; MVPA = moderate-to-vigorous physical activity.

Multilevel models with random intercept for school were used for ISCOLE data to account for potential clustering because of the sampling frame. Multiple regression coefficient for multilevel models is presented as ChiSq, whereas the multiple regression coefficient for linear models is presented as SumSq.

Corrigendum to “Associations Between 24-Hour Time Use and Academic Achievement in Australian Primary School–Aged Children”

Abstract