Before-school physical activity levels and sociodemographic correlates among Australian adolescents: A cross-sectional study

ABSTRACT

Understanding adolescents’ physical activity levels and underpinning contextual factors is crucial for health promotion. This cross-sectional study, using 24-hour time use diaries and sociodemographic variables from the Longitudinal Study of Australian Children, addressed gaps in understanding of physical activity in the before-school segment (the time between waking up and commencing classes). The study examined a) adolescents’ time spent in before-school physical activity, focusing on location and shared presence, and b) sociodemographic correlates of before-school physical activity. Completed diaries by 12–13 year-olds (n = 3,201) revealed that adolescents reported an average of 10.8 minutes of daily before-school physical activity (average segment length: 114 min), mostly classified as active transport (5.7 min). Most before-school physical activity occurred in a location other than home or school (6.1 min) and with peers (6.1 min). Notably, 51% of boys and 60% of girls did not report any before-school physical activity. Through two-part regression, we found that boys, adolescents from single-parent households, and those with longer before-school segments are more likely to report before-school physical activity compared to their counterparts. Before-school initiatives should promote active transport and diverse opportunities in other settings. Research into barriers and facilitators may inform more inclusive and effective promotion strategies, including school-based initiatives.

KEYWORDS:

• Adolescence
• before-school
• physical activity
• time-use

Introduction

Physical activity (PA) engagement by adolescents is associated with numerous health benefits, including improved cardiovascular and metabolic health (Poitras et al., 2016). However, international data indicate that average engagement in physical activity during adolescence is lower than that recommended to observe these benefits (Cooper et al., 2015). An estimated 81% of adolescents worldwide are insufficiently active (Guthold et al., 2020), with physical activity significantly declining between the ages of 10 and 14 years (Brooke et al., 2016). In 2018 in Australia, 84% of secondary school students reported not meeting guidelines of 60 minutes of moderate-to-vigorous physical activity (MVPA) every day (Cancer Council Australia, 2018).

A recommended strategy to address these concerns is to implement school-based multicomponent approaches, allowing a coordinated, whole-of-school effort to physical activity promotion (Colabianchi et al., 2015; McMullen et al., 2015). Such approaches include physical activity opportunities outside of, but adjacent to, school hours (i.e., during the before and after school periods). To identify potential avenues to enhance physical activity participation, studies have sought to investigate how adolescents accumulate physical activity across segments of the school day, such as before- and after-school. While the after-school segment has been identified as a critical period of discretionary time and has consequently been the setting of several interventions (Arundell et al., 2015; Atkin et al., 2008; Mears & Jago, 2016), the before-school segment is not as well understood (Woodforde et al., 2022). This is partly due to challenges related to the measurement of physical activity in this segment (Woodforde et al., 2023). For instance, the omission of a specific before-school item in commonly used self-report tools leads to a gap in data collection (Kowalski et al., 2004). Furthermore, examining the before-school literature reveals a range of segment definitions, and some studies have reported assigning inexact start and end times to the segment in the absence of wake time and school start time data (Woodforde et al., 2023). For the analyses in the current study, we define the before-school segment as the time between waking up and commencing school lessons.

Within the before-school physical activity literature, preliminary evidence exists to suggest that this segment, like the after-school segment, may be an important contributor to overall youth physical activity (Fairclough et al., 2012). This is evident in findings showing that the most active youth accumulate significantly more before-school physical activity than the least active youth (Fairclough et al., 2012; Garriguet & Colley, 2012). Throughout the day, notable increases in physical activity, measured as a proportion of time, typically occur in the periods immediately preceding and following school hours (Jago et al., 2010; Lopes et al., 2023). Although peaks are observed before and after school, objective physical activity data from a representative sample of US adolescents demonstrate that the before-school segment (defined as 12:00 am − 7:59am) may contribute fewer minutes to daily MVPA than the after-school segment (Long et al., 2013). In part, this is likely due to the before-school segment incorporating a shorter period of the day, though average before-school accelerometer wear time in the abovementioned study exceeded 60 minutes (Long et al., 2013). Therefore, the before-school segment is opportune to target for enhancing total physical activity in youth.

Most studies examining the before-school segment have assessed physical activity using devices (e.g., accelerometers) (Woodforde et al., 2023). Although self-report methods such as 7-day recall questionnaires have also been used, time use diaries represent a novel approach in this context (Woodforde et al., 2023). Time use diaries typically require participants to record the timing and event description of sequential activities completed during an observation period (e.g., 24 hours) (Bauman et al., 2019). These instruments have demonstrated moderate validity against device-based measures of physical activity and high test-retest reliability in adolescents, and can be administered to large samples of participants (Hofferth et al., 2008; Ridley et al., 2006). The distinctive ability of time use diaries to capture contextual information, such as the social setting (e.g., presence of family or peers) and activity type, aspects not addressed by common self-report questionnaires and device-based measures, makes them a valuable tool for examining factors in an individual’s social-ecological environment in which physical activity occurs (Bauman et al., 2019).

Examining social-ecological factors related to participation in physical activity, and establishing a deeper contextual understanding of physical activity in specific segments is critical for developing targeted interventions (Ortega et al., 2020; Sallis & Owen, 2015). At the level of the built environment, one contextual element that has been the subject of scholarly attention is the location in which physical activity occurs (Kelso et al., 2021). In the before-school segment, this requires differentiating active transport to school from physical activity at school, which previous work has sought to examine by assigning standardised time cut-offs (Saint-Maurice et al., 2018). However, this approach is prone to misclassification, which may be addressed with the supplementary information on participant location and activity classification available in time use diaries. Examination of before-school physical activity with respect to co-presence (i.e., who adolescents are with) is also warranted, given known influences on youth physical activity at the social-environment level, and this information is also available in time use diaries (Cheng et al., 2014; Fitzgerald et al., 2012).

Sociodemographic factors known to correlate with physical activity in adolescents include gender, socioeconomic status and family income (Sterdt et al., 2013). However, few studies have investigated correlates of physical activity specific to the before-school segment. In this segment, research has found that adolescent boys accumulate more MVPA than girls, which may be a result of environmental factors (e.g., perceived neighbourhood safety) or external support (e.g., from family and peers), and boys are more likely to access activity areas (e.g., outdoor play areas and marked court spaces) (Aibar et al., 2014; McKenzie et al., 2000). The relationship between additional sociodemographic characteristics, such as socioeconomic status, ethnicity, and family structure, and before-school physical activity among adolescents is not as well understood. By examining a broader range of sociodemographic factors, this study can identify subgroups of adolescents who may benefit from the development of before-school intervention programmes (Sterdt et al., 2013).

Accordingly, by using time use diary data, the aims of this study were a) to examine adolescents’ time spent in physical activity in the before-school segment, by classification (structured, unstructured, transport-related), location (home-, school- or otherwise-based) and by shared presence (alone, family, peers or other individuals), and b) to examine sociodemographic correlates of physical activity participation before school. This study addresses gaps in existing knowledge and uses methods not previously applied in this time segment to provide new insights into the before-school physical activity levels of adolescents, to explore the context of where and with whom adolescents are active, and to identify key sociodemographic correlates. The findings of this study may subsequently strengthen the existing case for targeted before-school physical activity interventions.

Methods

Data source and participants

This study uses data from the Longitudinal Study of Australian Children (LSAC), which began in 2004 (Wave 1) with two nationally representative cohorts (B cohort, initially aged 0–1 years; and K cohort, initially aged 4–5 years). Sampling followed a two-stage clustered design, whereby postcodes were first selected, then individual children. In LSAC, a wave of data collection takes place approximately every 2 years. Data from both cohorts, when participants were aged 12–13 years, have been used in this study. The relevant data were collected in 2012 for the K cohort and in 2016 for the B cohort. Of the 10,090 Wave 1 participants, 66% from the B cohort and 79% from the K cohort provided data at this timepoint. Among those who provided data, the K cohort and the B cohort had time use diary response rates of 94% and 95%, respectively, for a combined total of 6,705 participants.

LSAC has ethical approval from the Australian Institute of Family Studies Ethics Committee. For the current study, data access was granted by the National Centre for Longitudinal Data [#704291] and exemption from human research ethics review was approved by The University of Queensland [2021/HE000431]. Full details of the LSAC methodology have been published elsewhere (Soloff et al., 2005).

Measures

Before-school physical activity

Time spent in physical activity was derived from time use diaries, as described in previous studies using this LSAC data component (Del Pozo-Cruz et al., 2019; Sanders et al., 2015). The day before their scheduled interview, adolescents were asked to complete the time use diary, recording their activities in the order that they occurred in a paper diary, including start and end times, over a 24-hour period. During the computer-assisted interview with the adolescent, interviewers inputted the adolescent’s diary information to be coded according to a framework of activities. Participants were prompted to provide additional details when necessary. This method aligns with recommended protocols for collecting time-use data from adolescents (Chatzitheochari et al., 2018). The activity codes categorised as physical activity, and their respective classifications as “structured”, “unstructured” or “transport-related”, are listed in Supplementary File 1. There is a difference in the granularity of activity coding between Waves 5 and 7. Wave 7 used more specific activity codes (e.g., unstructured ball sports), whereas Wave 5 employed broader categories (e.g., unstructured active play). Additional information on the time use diary data was obtained during the survey interviews, including who the adolescent was with (co-presence) and where the adolescent was during each activity reported (Mullan, 2014). Participants who completed the time use diary for a weekend or other non-school day (e.g., public holiday) were excluded, so that only participants whose diary day was a weekday on which they attended school remained in the analyses for this study.

Before-school physical activity was calculated by summing the duration (in minutes) of all physical activity reported (as a main or secondary activity) between self-reported wake time and school start time. As school bell times were not explicitly captured by LSAC, school start times were established for each adolescent as the time they first reported “school lessons” as their main activity in their time use diary. For adolescents who did not use this activity code, school start was set to coincide with the start time of the first activity reported to take place at school. To ensure these times were consistent with Australian norms, we excluded participants for whom school start was determined to occur before 8:15 am or after 9:30 am (Short et al., 2013; Woodforde et al., 2023).

To reduce missing location data attached to before-school physical activities within the time use diary, we imputed the location for transport-related activities (i.e., walking or cycling to school) as “other” (not at home nor school). Additionally, to limit the influence of outliers on the analyses, we adopted methods applied elsewhere (Del Pozo-Cruz et al., 2019) by topcoding the before-school physical activity duration variable so that values above the 99^th percentile (75 minutes) were replaced with the 99^th percentile value.

Predictor variables

Potential sociodemographic predictors of participation in before-school physical activity were selected according to correlates of child and adolescent physical activity identified in the broader physical activity literature for this population, including earlier studies based on LSAC (Del Pozo-Cruz et al., 2019; Kemp et al., 2021; Sterdt et al., 2013). These potential predictor variables, derived from LSAC surveys, included the adolescent’s age at the time of the survey (months), gender (boy/girl), Aboriginal and/or Torres Strait Islander background (yes/no), area-level socio-economic status (national quintiles, Index of Relative Socio-Economic Advantage and Disadvantage; IRSAD) (Australian Bureau of Statistics, 2018b), weekly parental income (adjusted to 2016 values using the Consumer Price Index for equivalence between cohorts), main language spoken at home (English/other), residence in a major city of Australia (yes/no, Australian Bureau of Statistics Remoteness Areas) (Australian Bureau of Statistics, 2018a), and whether the adolescent had two parents in the home (yes/no) or a sibling in the home (yes/no). For socio-economic status, IRSAD scores were divided into quintiles for analysis, where quintile 1 encompasses the lowest 20% of scores, representing the most disadvantaged areas, while quintile 5 comprises the highest 20% of scores, indicating the most advantaged areas. Further, the analyses included before-school segment duration (the difference between an individual’s wake time and school start time, in hours) as a predictor variable, and a dummy variable for cohort was included to account for potential differences between the two cohorts.

Statistical analysis

Data processing and analyses were conducted using Stata (version 17.0) (StataCorp, 2021). Group differences between the participants eligible for analysis and those ineligible were examined using t-tests and chi-square tests for all examined sociodemographic characteristics. To examine differences in before-school physical activity minutes between location categories (home, school, other location), co-presence groups (alone, parent, sibling, peer, other adult) and activity classifications (structured, unstructured, transport-related), Friedman tests followed by post-hoc pairwise Dunn-Bonferroni tests were conducted. The relative contributions of physical activity in the home, school and other locations, and structured, unstructured and transport-related physical activity were calculated as percentages of total before-school segment duration.

A two-part gamma generalised linear model (GLM) was used to examine correlates of before-school physical activity, with data pooled across both cohorts. Following this approach, a probit regression model is first fitted to determine whether someone engages in a behaviour, followed by a gamma GLM to determine the degree of engagement among those who participate. This modelling specification is suited to the data at hand, given the large number of participants reporting zero minutes of before-school physical activity (58% of total sample) and the positively skewed distribution of activity durations among those who reported engaging in physical activity (Akram et al., 2023; Baldwin et al., 2016). In our study, the first step modelled whether participants engaged in physical activity before school (yes/no), and the second step modelled the degree of engagement in before-school physical activity (minutes per day). Missing data for predictor variables were minimal (0.25% of all cases), therefore we used listwise deletion in our regression. Location-specific and activity-classification-specific supplementary analyses were conducted by repeating these methods. This was done to model before-school physical activity that occurred at school and before-school physical activity that occurred outside of school, as well as before-school physical activity classified as structured, unstructured, and transport-related (see Supplementary File 2).

Results

Sample characteristics

Following exclusions of 3,504 participants who provided data at the timepoint of interest, the analysis sample comprised 3,201 participants. Exclusions were necessary for various reasons: 3,154 participants completed their time use diary for a weekend or non-school-day, and therefore did not provide data for a before-school segment; 5 participants had incomplete time use diary data (e.g., one single activity reported for the whole day); and 345 participants were excluded on the basis of missing (i.e., despite being coded as a school day, no activities were tagged as occurring at school from which to determine school start time) or abnormal (i.e., outside of 8:15am-9:30am) school start times. The analysis sample included a slightly larger proportion of adolescents residing in a major city (65.3%) than the excluded sample (62.6%) (χ² = 5.2, p = .023). No further statistically significant differences in sociodemographic characteristics were observed between included and excluded participants (Supplementary File 3).

Among the analysis sample, 672 participants completed the diary on a Monday, 694 on a Tuesday, 603 on a Wednesday, 554 on a Thursday and 678 on a Friday. The mean wake time was 6:50 am and mean school start time was 8:45am. Table 1 shows the characteristics of participants from each cohort. The mean age of participants from the pooled cohorts was 12.9 years (range 12.2–13.9). Fifty-one percent of participants were girls, and two-thirds resided in a major city of Australia. The mean score for the IRSAD, a summary of socioeconomic status within a local area, was 1013 (range 730–1190), slightly above this measure’s standardised mean of 1000.

Table 1. Sample characteristics.

	B cohort, Wave 7 (n = 1377)	K cohort, Wave 5 (n = 1824)	Total (n = 3201)
Predictor variable
Segment duration, h:mm [mean (SD)]	1:56 (0:36)	1:53 (0:34)	1:54 (0:35)
Age, months [mean (SD)]	155.4 (3.95)	155.1 (3.73)	155.2 (3.83)
Gender, n (%)
Boys	709 (51.49)	873 (47.86)	1582 (49.42)
Girls	668 (48.51)	951 (52.14)	1619 (50.58)
Aboriginal and/or Torres Strait Islander, n (%)	31 (2.25)	39 (2.14)	70 (2.19)
Socioeconomic status (IRSAD) quintile, n (%)
1	176 (12.78)	267 (14.64)	443 (13.84)
2	257 (18.66)	347 (19.02)	604 (18.87)
3	301 (21.86)	377 (20.67)	678 (21.18)
4	298 (21.64)	399 (21.88)	697 (21.77)
5	345 (25.05)	431 (23.63)	776 (24.24)
Weekly parental income, ’000 AUD [mean (SD)]	2.6 (1.73)	2.5 (1.77)	2.5 (1.75)
English is main language spoken at home, n (%)	1255 (91.14)	1690 (92.65)	2945 (92.03)
Residence in major city, n (%)	910 (66.09)	1179 (64.64)	2089 (65.28)
Two parents in home, n (%)	1157 (84.02)	1509 (82.73)	2666 (83.44)
Sibling in home, n (%)	1252 (90.92)	1639 (89.86)	2891 (90.49)

IRSAD, Index of Relative Socio-Economic Advantage and Disadvantage; SD, standard deviation.

Before-school physical activity

Forty percent of girls and 49% of boys reported engaging in at least one activity before school that was classed as physical activity (Table 2). Mean daily before-school physical activity was 9.2 minutes for girls and 12.5 minutes for boys. By activity classification, the major contributor to before-school physical activity time was transport-related activity (5.7 min, for both girls and boys). Boys spent significantly more time in unstructured physical activity (e.g., informal, non-organised ball sports) (5.3 min) than structured physical activity (e.g., organised sports training or competition) (1.5 min), whereas girls spent equal time in structured and unstructured physical activity (1.8 min). For both boys and girls, most before-school physical activity occurred in a location other than home or school (6.0 min girls, 6.2 min boys), followed by school (2.6 min girls, 5.4 min boys), then home (0.3 min girls, 0.5 min boys). Most physical activity was accumulated in the presence of peers (5.1 min girls, 7.1 min boys), followed by alone (2.5 min girls, 3.6 min boys) and with siblings (1.5 min girls, 1.8 min boys). Results of post-hoc tests comparing pairs of locations, co-presence groups, and activity classifications are displayed in Supplementary File 4.

Table 2. Before-school physical activity engagement.

	Participants reporting before-school physical activity, n (%)			Before-school physical activity minutes, mean (SD)
	Girls (n = 1619)	Boys (n = 1582)	Total sample (n = 3201)	Girls (n = 1619)	Boys (n = 1582)	Total sample (n = 3201)
Location^a,b
Home	22 (1.36)	36 (2.28)	58 (1.81)	0.3 (2.65)	0.5 (3.81)	0.4 (3.28)
School	128 (7.91)	296 (18.71)	424 (13.25)	2.6 (10.60)	5.4 (13.08)	4.0 (11.97)
Other	515 (31.81)	537 (33.94)	1052 (32.86)	6.0 (13.48)	6.2 (12.22)	6.1 (12.87)
Missing	35 (2.16)	40 (2.53)	75 (2.34)	0.3 (2.91)	0.5 (3.64)	0.4 (3.29)
Co-presence^a,b
Alone	268 (16.55)	348 (22.00)	616 (19.24)	2.5 (7.61)	3.6 (9.23)	3.0 (8.46)
Parent	30 (1.85)	25 (1.58)	55 (1.72)	0.3 (3.01)	0.4 (3.74)	0.3 (3.39)
Sibling	148 (9.14)	148 (9.36)	296 (9.25)	1.5 (6.72)	1.8 (6.81)	1.7 (6.76)
Peer	280 (17.29)	404 (25.54)	684 (21.37)	5.1 (14.71)	7.1 (14.87)	6.1 (14.82)
Other adult	42 (2.59)	31 (1.96)	73 (2.28)	1.3 (9.27)	0.8 (6.24)	1.0 (7.92)
Activity classification^a,b
Structured	59 (3.64)	63 (3.98)	122 (3.81)	1.8 (10.85)	1.5 (8.72)	1.6 (9.86)
Unstructured	125 (7.72)	311 (19.66)	436 (13.62)	1.8 (7.24)	5.3 (12.67)	3.5 (10.44)
Transport	518 (32.00)	533 (33.69)	1051 (32.83)	5.7 (11.67)	5.7 (10.78)	5.7 (11.24)
Total	646 (39.90)	769 (48.61)	1415 (44.20)	9.2 (17.17)	12.5 (18.21)	10.8 (17.77)

Significant differences (p < .001) in before-school physical activity minutes between locations, co-presence categories and activity classifications denoted by^a for girls and^b for boys. See Supplementary File 4 for results of post-hoc pairwise tests.

Location: “Other” includes all locations outside of the home or school, for example parks, shopping centres and travel routes. Co-presence: Participants were able to report co-presence with multiple people during a single activity. “Other adult” may include teachers or adult family members other than parents or siblings.

SD, standard deviation.

Examining contributions of before-school physical activity to total segment duration (Figure 1), on average, girls spent 7.5% of before-school time in physical activity, and boys spent 10.9% of the segment in physical activity. The majority of this physical activity time took place in a location away from home or school (5% of the segment for girls, 5.6% of the segment for boys). By activity classification, the majority of before-school physical activity time was classed as transport-related (4.9% of the segment for girls, 5.3% of the segment for boys).

Figure 1. Mean percentages of total before-school segment duration in each reported a) activity classification, and b) location.

Predictors of before-school physical activity

Table 3 displays the results for the two-part regression model. Following previous studies using similar methods (Desgeorges et al., 2021; Mahalik et al., 2013), for ease of interpretation, regression coefficients have been exponentiated into odds ratios in the probit model. For the linear model, findings are reported based on average marginal effects, to be interpreted as the change in before-school physical activity minutes per day with a one unit increase in the predictor variable. In the probit part of the model (probability of engaging in before-school physical activity), segment duration, gender, and number of parents in the home were statistically significantly related to adolescents reporting before-school physical activity. For every additional hour of segment length, the odds of engaging in before-school physical activity increase by 38% (OR 1.38, p = <.001, 95% CI: 1.27, 1.49). An increase in the odds of engaging in physical activity was also observed for boys compared to girls (OR 1.27, p < .001, 95% CI: 1.17, 1.39). Adolescents with two parents in the home were 20% (OR 0.80, p = .001, 95% CI: 0.70, 0.91) less likely to engage in before-school physical activity than those without.

Table 3. Predictors of before-school physical activity.

	Probit model (odds of engaging in before-school physical activity)				Linear model (volume of before-school physical activity)
Variables	b	SE	OR	95% CI	b	SE	AME	95% CI
Segment duration	0.319***	0.040	1.38	1.27, 1.49	0.409***	0.032	9.87	8.22, 11.53
Age	−0.006	0.006	0.99	0.98, 1.01	−0.007	0.005	−0.17	−0.39, 0.05
Weekly parental income	0.016	0.015	1.02	0.99, 1.05	−0.001	0.012	−0.03	−0.58, 0.53
Socioeconomic status (IRSAD) quintile	0.008	0.019	1.01	0.97, 1.05	−0.009	0.016	−0.21	−0.96, 0.54
Gender
Boy	0.241***	0.045	1.27	1.17, 1.39	0.178***	0.037	4.25	2.50, 6.00
Girl (ref)
Aboriginal and/or Torres Strait Islander, yes	−0.105	0.158	0.90	0.66, 1.23	0.188	0.135	4.96	−2.68, 12.60
English as main language, yes	0.142	0.086	1.15	0.97, 1.37	−0.112	0.075	−2.84	−6.77, 1.09
Residence in major city, yes	0.027	0.052	1.03	0.93, 1.14	0.077	0.044	1.82	−0.21, 3.86
Two parents in home, yes	−0.223**	0.067	0.80	0.70, 0.91	−0.042	0.053	−1.03	−3.60, 1.54
Sibling in home, yes	0.095	0.078	1.10	0.94, 1.28	0.031	0.066	0.74	−2.30, 3.79

N = 3193; *p < .05, **p < .01, ***p < .001. Model is adjusted for study cohort membership. A zero-inflated negative binomial regression model (available on request) yielded a consistent pattern of results and was discarded in favour of this two-part gamma GLM for ease of interpretation.

AME, average marginal effect (expressed in minutes, representing the average change in before-school physical activity minutes for a one-unit increase in the predictor variable); IRSAD, Index of Relative Socio-Economic Advantage and Disadvantage; OR, odds ratio.

In the linear model predicting the volume of before-school physical activity for active participants, segment duration and gender were statistically significantly associated with before-school physical activity. With an extended segment length of 1-hour, daily volume of before-school physical activity was estimated to be 9.9 minutes higher (p < .001, 95% CI: 8.2, 11.5). The volume of physical activity was 4.3 minutes higher for boys than girls (p < .001, 95% CI: 2.5, 6.0).

Supplementary, location-specific regression analyses (presented in full in Supplementary File 2) identified some significant and notable findings regarding before-school physical activity at school and in other locations. Age emerged as a predictor of before-school physical activity at school, with older adolescents significantly less likely to report engagement. Further, boys were significantly more likely than girls to report before-school physical activity on school grounds, and having two parents in the home was associated with lower odds of reporting before-school physical activity outside of school.

In regression analyses by activity classification (structured, unstructured, and transport, Supplementary File 2), boys were more likely than girls to report engagement in unstructured physical activity during the segment. Among adolescents who reported structured physical activity before school, boys reported significantly more minutes of this activity classification than girls. Participants residing in a major city were less likely to report unstructured physical activity, but more likely to report transport-related physical activity, compared to those residing outside of major cities. Additionally, adolescents with two parents in the home were less likely to report engagement in before-school physical activity related to transport than those without.

Discussion

This study examined the time spent in before-school physical activity of two large cohorts of Australian adolescents, in various locations, and identified sociodemographic correlates of physical activity in this segment. The study’s findings contribute an important advancement to the limited literature on before-school physical activity by investigating contextual factors and sociodemographic correlates that can inform targeted interventions, and by using methods not previously applied in the before-school segment (Woodforde et al., 2023). We found that the majority of adolescents reported that they did not participate in any physical activities before school on the day of recall, suggesting there is potential to intervene to increase physical activity during this segment of the day (Beck et al., 2016; Saint-Maurice et al., 2018). The results also showed that most before-school physical activity was transport-related and correspondingly took place away from the home and school environments. Factors such as the adolescent’s gender, family structure, and before-school segment duration were found to be statistically significant correlates of before-school physical activity.

Adolescents spent 10.8 minutes (girls: 9.2 minutes, boys: 12.5 minutes) on average in physical activity before school. While no studies using time use diaries to measure physical activity specific to the before-school segment are available for direct comparison, this amount can be interpreted relative to physical activity levels reported by previous Australian studies using device-based measures. For example, Carver and colleagues reported 12.4 minutes of before-school MVPA in girls and 15.5 minutes in boys (Carver et al., 2010), while Strugnell and colleagues found 6.6 minutes in girls and 9.6 minutes in boys (Strugnell et al., 2016). Although these accelerometer studies used slightly younger, non-representative samples, all three studies demonstrate quite similar amounts of time spent in before-school physical activity, which gives confidence in the time use diary measure.

As little is currently known about the context in which before-school physical activity takes place, a unique contribution of our study comes from having examined who adolescents were with when engaging in physical activity before-school. Our finding that most before-school physical activity occurred in the presence of peers reflects what we know about the overall importance of peers for health and activity behaviours (Fitzgerald et al., 2012). Although adolescents in the 12–13-year-old age group spend a similar amount of time with parents as they do with other young people outside of school on weekdays (Baxter, 2018), by this age, peers may be becoming more influential on physical activity behaviours (Fitzgerald et al., 2012). Further, by this age, adolescents are more likely to spend weekday time with their parents in the evening than in the morning, and the proportion of adolescents reporting time spent alone during waking hours is at its peak in the before-school segment (Baxter, 2018). This indicates that mornings before school may be a time of greater independence from family for many adolescents. Indeed, our analyses found that more before-school physical activity took place alone than with parents, siblings or other adults.

We also found that active transport contributed to over half of the time spent in before-school physical activity, and that one-third of participants reported engaging in active transport in the segment. While it is likely this reflects travel to school, this could not be determined from the time use diary. Alternative methods for assessing location of physical activity have been used in previous studies by combining accelerometers, geographical information systems and global positioning system data. One such study demonstrated similar results, whereby before-school physical activity (measured as counts per minute) was twice as high on the journey to school than in the school playground, and MVPA was three times higher (Cooper et al., 2010). Another study used similar methods to compare the home, school and transport environments, finding that most before-school physical activity took place in the home, followed by in transport, then at school (Remmers et al., 2020). Reflecting on our findings and those from related research, the substantial contribution of active transport to before-school physical activity is clear. Our findings do, however, highlight opportunities to support more adolescents to engage in active travel, and to expand before-school opportunities to other settings to provide a more inclusive and comprehensive approach to enhancing physical activity.

Previous intervention research targeting the before-school segment has predominantly focused on promotion of active transport to school, and there have been limited studies addressing interventions on school grounds during this segment (Jones et al., 2019; Larouche et al., 2018; Woodforde et al., 2022). Our results highlight an opportunity to examine how school-based interventions could enhance overall before-school physical activity levels, in addition to physical activity accumulated during the trip to school. Low amounts of physical activity at school in the before-school segment may be partially explained by limited available opportunities. In the US, school practices supportive of student physical activity, such as providing equipment and supervision in areas designated for physical activity, have been found to be limited in the before-school segment compared to other segments of the day (Cheung et al., 2019; McKenzie et al., 2010). Moreover, schools sometimes enact policies that prohibit students from being physically active before school begins (Katapally et al., 2018; Lounsbery, 2017). However, as an example of a resource-effective option for schools seeking to promote physical activity, a preliminary efficacy study has shown that meaningful amounts of MVPA can be accumulated in before-school running and walking programmes, lasting as little as 15 minutes (Stylianou et al., 2016).

Our regression model showed that boys were 27% more likely than girls to engage in any before-school physical activity, and that volume of physical activity was 4.3 minutes higher among boys. Inconsistencies exist in the literature with regard to gender differences in before-school physical activity. Carver and colleagues found that boys were significantly more active than girls before school in a group of Australian children, yet no significant difference was detected in a group of older adolescents (Carver et al., 2010). In a study of US children and adolescents, Saint-Maurice and colleagues found that boys were slightly more active than girls before school (Saint-Maurice et al., 2018). A revealing finding of our breakdown of activity classification was the gender discrepancy in unstructured physical activity. While mean time spent in both structured physical activity and active transport was similar between boys and girls, boys engaged in significantly higher amounts of unstructured physical activity, a substantial contributor to the overall difference. Additionally, our supplementary analyses indicated that boys were 81% more likely than girls to engage in before-school physical activity on school grounds, whereas gender was not a statistically significant predictor of participation in other locations. These findings are consistent with literature that indicates that total time spent in MVPA from active transport is similar between boys and girls (Olds et al., 2011). However, in contrast to our before-school findings, total daily time in unstructured play is similar in young adolescent boys and girls, and unequal participation in structured sports explains most of the gender difference in MVPA (Olds et al., 2011). Therefore, given differences among adolescents in how physical activity is accrued before school, it is important that a range of activity options are supported. Further, our results call attention to a need for future research that examines factors limiting girls’ participation in school-based and unstructured before-school physical activity.

Family structure was also found to be a significant predictor of before-school physical activity in our analyses. Adolescents who live with two parents in the home were 20% less likely to engage in physical activity before school. This is in contrast to the literature examining the whole day, where living in a single-parent household has been found to be negatively associated with daily MVPA (Langøy et al., 2019). Other research has found no influence of family structure on leisure-time physical activity (Morton et al., 2012). Our finding may be explained by higher car ownership and a lower likelihood for adolescents from dual-parent households to actively travel to school (Bjerkan & Nordtømme, 2014), although information relating to vehicle ownership was not collected in LSAC surveys. It is important to continue efforts to promote physical activity through active transport for adolescents from all household types where practicable. However, as parents may instead choose passive transport for their children due to safety concerns, convenience, and as active transport may not be feasible when youth reside a far distance from their school (Aranda-Balboa et al., 2020), the opportunity exists for schools to support on-site before-school physical activity.

The length of the before-school segment, and therefore the window of opportunity to be physically active before school, is variable between contexts, given differences in wake times and school start times. In our study, longer segment durations were positively associated with engagement in before-school physical activity. In practical terms, an extension of 30 minutes was associated with an additional 4.9 minutes of physical activity. Although a complex process, one way to increase available time in the before-school segment is through delayed school start times. While research indicates that later wake times may follow such an adjustment, the delay in wake times is typically not directly proportional to the delay in school start times (Chan et al., 2018; Gariépy et al., 2017). Therefore, it is still likely to result in a net increase in the duration of the before-school segment, providing additional time for physical activity. We observed an average school start time of 8:45 am, typical of the Australian context. This differs to other countries, for example the US where the school day may start as early as 7:30 am (Wheaton et al., 2015), or Latin American countries, where multiple school shifts may be offered including afternoon shifts (Estevan et al., 2018). Within the broader discussion of how school start times may influence factors such as sleep and academic performance (Bowers & Moyer, 2017; Wheaton et al., 2016), further research on the interplay between school bell times, sleep, and physical activity before school and throughout the day is warranted. The current evidence is limited and demonstrates inconsistent effects of modest delays to school start time, yet these delays do not appear to negatively interfere with daily MVPA (Patte et al., 2019).

This study utilised data from a large, nationally representative dataset to examine adolescents’ before-school physical activity. Although 48% were eligible for analysis, the sociodemographic characteristics of included and excluded participants were similar, with only slight differences in metropolitan-nonmetropolitan residence status. This is a strength of our study, as studies of adolescents’ physical activity often use purposive sampling. However, there are limitations to consider relating to the use of time use diaries to measure physical activity. From the time use diaries used in the present study, we were unable to determine intensity of physical activity, preventing differentiation between light, moderate, and vigorous activities. Another significant limitation is the potential under-reporting of short bouts of activity (Chau et al., 2019). While a large proportion of the sample did not report engagement in any physical activity in the before-school segment, device-based measures may have identified bouts of physical activity (e.g., short walks from a car into school grounds) that were overlooked or deemed too trivial to record by these participants. Additionally, the single 24-hour recall period used in the study may not be representative of an individual’s usual behaviour (van der Ploeg HP et al., 2010). Despite this, 24-hour time use diaries have shown moderate validity and high reliability (Hofferth et al., 2008; Ridley et al., 2006), and overcome limitations of device-based measures by capturing contextual details. By using time use diaries, we were able to investigate contextual factors (i.e., location and co-presence) not previously captured for the before-school segment among large samples of adolescents.

Taken together, the findings of this study indicate that a large proportion of adolescents may not be participating in physical activities as part of their before-school routine. Among those who are active before school, most of their physical activity is transport-related. Our analyses indicate that boys, adolescents from single-parent households, and those with longer before-school segments (as a function of wake time and school start time) are more likely than their comparators to participate in before-school physical activity. Our study provides insights into before-school physical activity within a critical but narrow age range of adolescents. Extending this research through longitudinal studies across a broader age spectrum could provide a more comprehensive understanding of evolving activity patterns and influences. Additional future research should focus on examining barriers and facilitators of participation in before-school physical activity, particularly on-site at school, and strategies to increase before-school physical activity among less active groups, including strategies encouraging girls’ physical activity on school grounds.

Availability of data

The datasets analysed for this study are available in the Australian Data Archive repository: https://dataverse.ada.edu.au/dataverse/ada

Acknowledgments

We acknowledge the data custodians, investigators, and study participants of Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC). LSAC is conducted in partnership with the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS). The Australian Bureau of Statistics (ABS) were also partners of the study until 2022, with Roy Morgan joining at this point.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online https://doi.org/10.1080/02640414.2024.2326353

Additional information

Funding

JW is supported by Australian Government and University of Queensland Research Training Programme Scholarships. JS is supported by a National Health and Medical Research Council Leadership Level 2 Fellowship [APP 1176885].