Health Promotion Journal of Australia, 2015, 26, 105–114 http://dx.doi.org/10.1071/HE14092

Physical activity and sedentary behaviour among Asian and Anglo-Australian adolescents Claudia Strugnell A,E, Andre M. N. Renzaho B, Kate Ridley C and Cate Burns D A

School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, Vic. 3125, Australia. School of Social Sciences and Psychology, University of Western Sydney, Locked Bag 1797, Penrith, NSW 2751, Australia. C Centre for Sport, Health and Physical Education (SHAPE), School of Education, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia. D School of Dentistry and Health Services, Charles Sturt University, Locked Bag 588, Wagga Wagga, NSW 2678, Australia. E Corresponding author. Email: [email protected] B

Abstract Issue addressed: Evidence suggests that physical activity (PA) and sedentary behaviour (SB) participation varies among culturally and linguistically diverse (CALD) adolescents. The present study examined differences in PA and SB among a CALD sample of Chinese Australian, South-east Asian and Anglo-Australian adolescents. Methods: Data from 286 adolescents aged 12–16 years involved in the Chinese and Australian Adolescent Health Survey in metropolitan Melbourne, Australia, were analysed. Accelerometry outcomes included median activity counts per minute (counts.min–1) and minutes per day (min.day–1) spent in light-intensity PA (LPA), moderate-to-vigorous-intensity PA (MVPA) and sedentary time (ST). Kruskal–Wallis one-way analysis of variance and sequential multiple hierarchical linear regressions were used to examine CALD differences in PA and ST. Results: Multivariate analyses of accelerometry data found Chinese Australian and South-east Asian adolescents engaged in significantly less daily MVPA (5–8 min.day–1) and LPA (50–58 min.day–1; P < 0.05), but greater daily ST (40–41 min.day–1), than Anglo-Australian adolescents, after adjusting for age, gender and socioeconomic category. Conclusion: The results demonstrate lower engagement in daily MVPA and LPA and greater engagement in ST using accelerometry among Chinese Australian and South-east Asian adolescents compared with Anglo-Australian adolescents. These findings have important public health implications in furthering our understanding of CALD differences in PA and SB. So what? An understanding of the CALD differences in physical activity and sedentary behaviour among Australian adolescents has important implications for intervention planning and delivery as well as the wider health implications of these behaviours. This article furthers the current understanding of CALD adolescents’ participation in physical activity and sedentary behaviour, of which limited information is available.

Key words: adolescent, Chinese, ethnicity, exercise, sedentary lifestyle. Received 30 September 2014, accepted 25 March 2015, published online 4 June 2015

Introduction The Australian population is culturally and linguistically diverse, with approximately 26% of the entire population born overseas, many of whom come from non-English speaking countries.1 The term ‘culturally and linguistically diverse’ (CALD) is customarily used to refer to the wide range of cultural groups that represent the Australian population and recognises that groups and individuals differ according to ethnicity, language, race, religion and spirituality; it is used to describe groups that differ from the English speaking majority (non-CALD).2 In most culturally pluralist industrialised countries, Journal compilation  Australian Health Promotion Association 2015

inequalities exist in risk factors associated with poor physical and mental health outcomes, and families from CALD backgrounds are among the most affected.3,4 Despite the evidence that regular physical activity (PA) is associated with several health benefits, there is evidence that youth from CALD communities have less likelihood of engaging in sufficient PA to confer these benefits.5,6 Data to inform a conceptual framework to increase the participation of CALD communities in PA and decrease sedentary behaviours (SBs) are urgently needed. The many documented health benefits of PA for children and adolescents7 are commonly examined through risks associated with CSIRO Publishing

www.publish.csiro.au/journals/hpja

106

Health Promotion Journal of Australia

an absence or low levels of habitual PA, termed ‘physical inactivity’. Among youth, physical inactivity has been associated obesity and obesity-related diseases such as hypertension, metabolic syndrome and dyslipidaemia, as well as lower bone mineral density.7 SB, which is distinct from physical inactivity and is characterised by low levels of energy expenditure during waking hours,8 has also been linked to adverse health among youths (adiposity, decreased physical fitness, cardiovascular disease risk, lower self-esteem and behavioural problems).9 However, these associations are commonly observed in cross-sectional studies (thus, causality cannot be inferred) using reported (parent/proxy or self-report) duration spent in screen-based behaviours (television viewing, computer use and electronic gaming),8 and conjecture exists about the veracity of these relationships with health. Engagement in PA and SB is not uniform across child and adolescent populations. PA participation is typically higher among males,10,11 and among younger than older adolescents,10,12 and varies between ethnic/racial groups.10,13 Similarly, SB typically increases with age and has been shown to vary between ethnic/racial groups.8 Limited information exists regarding the PA or SB participation of adolescents from CALD backgrounds in Australia,14,15 and more research is needed. Estimates of PA and SB levels among Asian CALD youth in Australia and internationally indicate lower levels of PA participation5,6,16–22 and greater levels of SB5,17,18,21 compared with their non-CALD counterparts (predominantly European background). In contrast, two studies among Vietnamese Australian adolescents from a single private girls’ and boys’ schools, respectively, found no significant difference in self-reported moderate-to-vigorous-intensity PA (MVPA) between Vietnamese Australian and Anglo-Australian adolescents.23,24 It is not known whether the above trends are evident for all Asian subpopulations because of enormous variation in culture, language, biology, religion and heritage among people from the continent of Asia (e.g. Malaysia, Qatar, China, Japan, India and Sri Lanka).25 Therefore, an understanding of PA and SB participation and influences on these behaviours among specific Asian subpopulations are needed because these behaviours have implications for maintaining health throughout childhood and into adulthood. Chinese migration to Australia has a long and rich history. It began in the early colonial and gold-rush era, and has increased substantially over the past few decades,26 especially following the intensive marketing of Australian educational services to Asia.26 In the city of Melbourne, there is an important and growing community of Chinese Australian adolescents that represents between 4% and 6% of all adolescents (12–16 years of age).27 In comparison, migrants from South-east Asia are commonly considered to be those from countries south of China, east of India, west of New Guinea and north of Australian, including Cambodia, Laos, Burma, Thailand, Malaysia and Vietnam, and comprise approximately 1%–3% of Melbourne’s adolescents (12–16 years of age).27 With this understanding, the aim of the present study was to examine differences in self-reported and

S. Strugnell et al.

objectively measured PA and SB among a CALD sample of Chinese Australian, South-east Asian and Anglo-Australian adolescents.

Methods Study population, setting and design The present study was called the Chinese and Australian Adolescent Health Study (CAAHS) and was designed to examine the influences or correlates of PA, SB and overweight and obesity among CALD adolescents aged 12–16 years in metropolitan Melbourne. The CAAHS used a cross-sectional design with sample size calculations indicating 80 Chinese Australian and 80 Anglo-Australian adolescents were required to detect a 10 min.day–1 difference in objectively measured daily MVPA, assuming 80% power and an a level of 0.05.

Sampling A two-step sampling procedure was used to recruit sufficient numbers of Chinese Australian and Anglo-Australian adolescents. All Chinese weekend cultural schools in metropolitan Melbourne that had not been involved previously in a PA and SB questionnaire validation study28 and with adolescent enrolments 20 were invited to participate (n = 29). Anglo-Australian adolescents were recruited using a simple random sampling technique whereby six government and independent secondary schools from each of the 1st, 3rd and 5th Socioeconomic Index for Areas (SEIFA) quintiles (SEIFA Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD)29) were randomly invited to participate based on the schools’ postcode. Overall, six Chinese weekend cultural schools, comprising 12 separate campuses, and two secondary schools consented to participate. Data collection occurred between August–November 2009 and April–June 2010. Because Chinese weekend cultural schools are grouped into classes based on language ability rather than age, all students in language levels 7–10 who were aged 12–16 years were invited to participate. One thousand, one hundred and sixty-three students from Chinese weekend cultural schools were invited, with 200 consenting to participate (response rate = 17%). Eight hundred and thirty-six adolescents in Years 7–10 from government and independent schools were invited, with 120 consenting to participate (response rate = 14%). A parent and/or guardian for each participant provided written consent in addition to student assent. Prior to commencing the CAAHS, ethics approval was sought and granted from the Victorian Department of Education and Early Childhood Development and Deakin University’s Faculty of Health Human Ethics Advisory Group.

Measures and data management The modified Child and Adolescent Physical Activity and Nutrition Survey, Physical Activity (CAPANS-PA) questionnaire was used to examine the demographic characteristics of the participants and self-reported type and duration of PA and SB (previous 7 days).28 This questionnaire uses several items from existing questionnaires that have been described elsewhere.28 The CAPANS-PA questionnaire contained 11 demographic questions (date of birth, age, gender,

PA and SB among culturally diverse adolescents

suburb and postcode of residence and country of birth of the participant, biological mother, father, maternal grandmother and grandfather), 10 PA questions (school-based and non-school based PA and a 32-item PA checklist where type, frequency and duration was recalled) and a 14-item SB checklist where type and duration outside of school hours was recalled. The questionnaire had acceptable 7-day test–retest reliability for duration spent in weekday (Monday–Friday) MVPA (intraclass correlation (ICC) 0.78; 95% confidence interval (CI) 0.66, 0.85) and SB (ICC 0.64; 95% CI 0.58, 0.84) and low to moderate construct validity for daily MVPA (Monday– Sunday; r = 0.07, NS) and sedentary time (ST; r = 0.27, P < 0.05) compared with accelerometry among Chinese Australian youth.30 Self-reported MVPA and SB duration were calculated by summing the responses from the respective checklists and dividing by the number of days of recall. All activities in the PA checklist were of moderate (3.0–5.9 metabolic equivalent (MET)) or vigorous (6 MET) intensity based on the compendium of energy expenditure for youth.31 Extreme self-reported values were identified when the duration of MVPA exceeded 6 h.day–1and the SB duration exceeded 16 h.day–1. This was an arbitrary cut-off point and resulted in data from six participants (2%) being excluded (coded as missing) for MVPA and 46 participants (14%) for SB. Five categories of sedentary behaviour were generated (small screen recreation, education, travel, cultural activities, social activities) based on the categories used in the Adolescent Sedentary Activity Questionnaire.32 Participation in MVPA was compared with the current Australian National Physical Activity Guidelines for youth aged 12–18 years;33 where students who engaged in 60 min.day–1 MVPA using the ‘average  days’ method (total MVPA duration is summed and divided by the number of days of monitoring) were considered as meeting the recommendation.34 Objectively measured PA and SB were assessed using the ActiGraph GT1M (ActiGraph, Pensacola, FL, USA) activity monitor, which was worn for 7 days over the right hip. Activity counts were accumulated over 30-s intervals with recordings 500 min.day–1 for at least 3 days (including one weekend day) included in the analysis of average daily PA or ST (Monday–Sunday).35 Wear time was generated using the equation: [wear time (min) = 1440 min – non-wear time (min)]. Accelerometry outcome variables included activity counts per minute (count.min–1) and duration (min.day–1) spent in lightintensity PA (LPA; 1.50–2.99 MET) and MVPA (3.00 MET) based on the age-specific energy expenditure prediction equation (Table 1).36 Duration spent in ST (1.0–1.49 MET) was identified by activity counts 100 count.min–1.37 Objectively measured participation was summed and divided by the total number of days on which the accelerometer was worn. CALD (Chinese Australian and South-east Asian) and non-CALD (Anglo-Australian) categories were defined on the basis of selfreported demographic information. The Chinese Australian and South-east Asian categories were assigned if one of three conditions

Health Promotion Journal of Australia

107

Table 1. Accelerometer (counts min–1) range to determine activity intensity The counts min–1 ranges are based on the age-specific equation: metabolic equivalent (MET) = 2.757 + (0.0015  counts min–1) – (0.08957  age) – (0.000038  counts min–1  age), where age is in years, developed by Freedson et al.36 for light, moderate and vigorous activity Age (years) 12 13 14 15 16

Sedentary

Light activity

Moderate activity

Vigorous activity

0–100 0–100 0–100 0–100 0–100

101–1262 101–1398 101–1545 101–1705 101–1878

1263–4135 1399–4380 1546–4645 1706–4931 1879–5241

4136–20000 4381–20000 4646–20000 4932–20000 5242–20000

were met by the participant: (1) born in China (including Hong Kong, Macau and Taiwan) or South-east Asia themselves; (2) had both parents born in China or South-east Asia; or (3) had both maternal grandparents born in China or South-east Asia. The South-east Asian category was specifically assigned because 74% of participants in this category had either both parents born in Malaysia, Vietnam or Cambodia and 46% had maternal grandparents born in a South-east Asian country. The Anglo-Australian category comprised all remaining participants who largely had both parents and maternal grandparents born in Australia or Europe. High (1000) and low socioeconomic status (SES) (999) was defined using the Australian Bureau of Statistics SEIFA–IRSAD score from students’ self-reported residential postcode.29 The resulting score for a geographical area is an ordinal number that ranges from 500 to 1300 based on the 2006 Australian Census, with the median score being 1000 for the state of Victoria, Australia.29 A total of 320 participants consented to participate; however, fourteen participants were away on the day of testing and two participants were excluded from all analyses because they did not indicate their gender on the CAPANS-PA questionnaire and it was not apparent or translatable from their name (e.g. Sam, Bailey etc.). A further 18 participants were excluded from all analyses for the following reasons: five had missing questionnaire data, nine had missing anthropometric and PA data, three were 11 years of age at the time of data collection and one participant was physically impaired. The accelerometry analysis sample varied from a possible 286 participants with questionnaire data because seven participants lost their accelerometer, five monitors failed to download, 51 participants did not record at least 500 min.day–1 of monitoring over 3 days (including one weekend day) and 17 recorded spurious data (>4 million counts.day–1). There were no significant differences in age, gender, SES or CALD background among participants who met the accelerometry inclusion criteria.

Statistical analyses Statistical analyses were conducted using STATA 11.0 (STATA Corp., College Station, TX, USA). All accelerometry dependent variables

108

Health Promotion Journal of Australia

S. Strugnell et al.

(activity counts, ST, LPA, MVPA) were examined for normality using the sktest function in STATA. These dependent variables were found to be not normally distributed and were transformed (square root transformation for MVPA and LPA; square transformation for ST) before executing the multiple hierarchical regression analyses. Because the data were not normally distributed, non-parametric analyses were undertaken for the univariate analyses. Mann–Whitney U-tests (medians) were used to examine the effects of age, gender and SES on PA and SB participation. Differences in PA and SB activity in the CALD and non-CALD groups were examined using Kruskal–Wallis tests with post hoc multiple comparisons across ethnic groups. Significant differences in self-reported and objectively measured participation were compared using paired-sample Wilcoxon signedranked tests. For the transformed data, sequential multiple hierarchical regression analyses were conducted to examine CALD and non-CALD differences in PA and ST participation, controlling for demographic and socioeconomic covariates. The variables were entered into the regression models in the following sequence based on the evidence from the literature. In Model 1, only CALD/ non-CALD (category) was entered to obtain unadjusted coefficients for ethnicity. Model 2 contained Model 1 plus gender because being male is known to be positively associated with adolescent PA.11 Model 3 contained Model 2 plus age (continuous) because it is well established that PA typically declines with increasing age among youth.10,12 Model 4 was the full model containing all the variables in Model 3 plus SES (category) because SES is known to be negatively associated with SB among adolescents.11 All multivariate analyses were also adjusted for school-level clustering.

Results Participant demographic information and objectively measured daily PA and SB are presented in Table 2. Overall, few demographic

differences were observed between Anglo-Australian and Chinese Australian adolescents, although South-east Asian adolescents were slightly younger than Anglo-Australian adolescents and were typically from a low SES background (P < 0.001). When examining objectively measured PA and ST duration, Chinese Australian and South-east Asian adolescents had significantly lower median daily activity counts and LPA duration, and greater ST duration than AngloAustralian adolescents (P < 0.01). Daily MVPA participation ranged between 23 and 24 min.day–1 for all three groups. Multiple hierarchical regression analyses (Table 3) further examined the differences in daily PA and ST. Ethnicity explained 6% of the variance in ST and 28% of the variance in LPA (Model 1) with Chinese Australian and South-east Asian adolescents engaging in approximately 50–60 min.day–1 less LPA and 40 min.day–1 more SB than Anglo-Australian adolescents after adjusting for age, gender and SES category (Model 4). Further, ethnicity explained 1% of the variance in daily MVPA (Model 1), with the addition of gender (Model 2) and age (Model 3) explaining the greatest proportion of the variance, 13% and 12% respectively. After adjusting for age, gender and SES category (Model 4) ethnic differences in daily MVPA were significant, with Chinese Australian and South-east Asian adolescents engaging in approximately 5–8 min.day–1 less daily MVPA compared with Anglo-Australian adolescents (P < 0.01). These analyses also found that increasing age and gender (being female) were associated with lower MVPA but higher ST duration. Examination of self-reported type and duration spent in MVPA and SB provides further insight into the observed CALD differences in adolescent participation. Appendix I presents the 12 most popular MVPA activity types and duration spent in each activity among Anglo-Australian, Chinese Australian and South-east Asian adolescents, with physical and sport education, household chores and jogging and/or running being prominent among all three

Table 2. Demographic characteristics and differences in accelerometry measured daily physical activity and sedentary behaviour The Kruskal–Wallis test with post hoc multicomparison adjustment was used to compare median differences between Anglo-Australian, Chinese Australian and South-east Asian adolescents. Chi-squared tests were used to examine differences in proportions. IQR, interquartile range; SES, socioeconomic status; LPA, light physical activity; MVPA, moderate-to-vigorous physical activity n Age (years) Girls High SESD Valid accelerometer days Valid weekdays Valid weekend days Total valid days Daily activity countsC (min–1) Daily sedentaryC (min.day–1) Daily LPAC (min.day–1) Daily MVPAC (min.day–1)

Anglo-Australian Median (IQR)

n

110 66 83

14.0 (13.1, 15.3) 60.0% 76.9%

100 55 70

105 84 105 83 83 83 83

4 (4, 5) 2 (1, 3) 6 (5, 7) 441 (387, 526) 509 (460, 568) 225 (193, 249) 24 (17, 38)

84 84 87 68 68 68 68

Chinese Australian Median (IQR)

Chinese Australian adolescents differ significantly from Anglo-Australian adolescents (P < 0.01). South-east Asian adolescents significantly different from Anglo-Australian adolescents (P < 0.01). C Based on >500 min.day–1 monitoring over 3 days (including 1 weekend day). D High SES = index of relative socioeconomic advantage and disadvantage (IRSAD) 1000. A B

13.8 (13.1, 14.6) 55.0% 70.7% 5 (3, 5) 2 (1, 3) 6 (4, 7) 373A (297, 462) 546 A (502, 603) 164A (146, 188) 24 (17, 32)

n

South-east Asian Median (IQR)

76 40 36

13.8 (12.8, 14.4)B 52.6% 47.4%B

62 61 63 55 55 55 55

5 (3, 5) 2 (1, 3) 6 (5, 7) 353B (307, 459) 546B (503, 588) 170B (151, 198) 23 (12, 36)

Step 1. Ethnicity Anglo-Australian Chinese Australian South-east Asian Step 2. Ethnicity Anglo-Australian Chinese Australian South-east Asian Gender Boys Girls Step 3. Ethnicity Anglo-Australian Chinese Australian South-east Asian Gender Boys Girls Age (years) Step 4 Ethnicity Anglo-Australian Chinese Australian South-east Asian Gender Boys Girls Age (years) SES High SES Low SES

LPA R2 = 0.28 Reference –56.4*** (–79.34, –33.48) –47.9** (–74.48, –21.25) R2 = 0.28, DR2 = 0.00 Reference –56.6*** (–79.06, –34.19) –48.3** (–74.66, –21.94) Reference –6.1 (–21.61, 9.43) R2 = 0.29, DR2 = 0.01 Reference –58.8*** (–82.47, –35.15) –52.1** (–83.76, –20.46) Reference –6.1 (–21.99, 9.86) –4.6 (–10.62, 1.36) R2 = 0.30, DR2 = 0.01 Reference –58.2*** (–80.01, –36.47) –49.5** (–78.30, –20.61) Reference –6.4 (–21.78, 8.97) –4.8 (–10.88, 1.31) Reference –10.2 (–23.00, 2.54)

Sedentary

R2 = 0.06

Reference 34.9** (18.37, 51.36) 28.8** (13.04, 44.61)

R2 = 0.12, DR2 = 0.06 Reference 36.1*** (19.49, 52.69) 31.3*** (20.01, 42.66)

Reference 35.0*** (17.87, 52.10)

R2 = 0.15, DR2 = 0.03 Reference 40.7** (20.23, 61.20) 39.4** (20.86, 57.90)

Reference 34.9** (17.03, 52.81) 9.8 (–1.52, 21.09)

R2 = 0.15 DR2 = 0.00 Reference 40.7** (19.53, 61.94) 39.5** (18.96, 60.00)

Reference 34.9** (16.85, 52.96) 9.8 (–1.50, 21.06)

Reference –0.4 (–18.63, 17.84)

Reference –1.5 (–5.08, 2.17)

Reference –11.0*** (–15.01, –6.94) –4.3*** (–5.66, –2.91)

R2 = 0.26, DR2 = 0.00 Reference –5.4* (–10.48, –0.35) –7.9* (–14.38, –1.47)

Reference –10.9*** (–14.87, –6.97) –4.3*** (–5.60, –2.92)

R2 = 0.26, DR2 = 0.12 Reference –5.5* (–10.84, –0.15) –8.3* (–14.68, –1.94)

Reference –11.0*** (–14.50, –7.40)

R2 = 0.14, DR2 = 0.13 Reference –3.5 (–7.78, 0.81) –4.8 (–10.76, 1.15)

Reference –3.1 (–7.66, 1.47) –4.0 (–11.67, 3.63)

R2 = 0.01

MVPA

Reference 717.2 (–17805.58, 19240.04)

Reference 34830.0** (16791.98, 52867.95) 10475.0 (–903.32, 21853.30)

R2 = 0.15, DR2 = 0.00 Reference 44456.4** (22095.35, 66817.5) 41899.9** (21179.8, 62620.00)

Reference 34806.0** (17071.7, 52540.34) 10464.1 (–929.14, 21857.40)

R2 = 0.15 Reference 44496.3** (22711.00, 66281.61) 42086.1*** (23362.37, 60809.91)

Reference 34872.1** (18103.46, 51640.74)

R2 = 0.12 Reference 39556.0*** (21458.75, 57653.27) 33484.3*** (21962.53, 45006.15)

Reference 38328.7** (20349.99, 56307.42) 30979.5** (16017.47, 45941.53)

R2 = 0.06

Log-transformed sedentary

Reference –0.4 (–0.86, 0.11)

Reference –0.2 (–0.75, 0.34) –0.2 (–0.39, 0.04)

R2 = 0.31, DR2 = 0.03 Reference –2.1*** (–2.85, –1.33) –1.8** (–2.76, –0.76)

Reference –0.2 (–0.76, 0.38) –0.17 (–0.38, 0.05)

R2 = 0.30 Reference –2.1*** (–2.94, –1.29) –1.9** (–2.95, –0.77)

Reference –0.2 (–0.75, 0.36)

R2 = 0.28 Reference –2.0*** (–2.83, –1.24) –1.7** (–2.63, –0.81)

Reference –2.0*** (–2.84, –1.22) –1.7*** (–2.62, –0.79)

R2 = 0.28

Log-transformed LPA

Reference –0.2 (–0.53, 0.15)

Reference –1.1*** (–1.53, –0.72) –0.5*** (–0.59, –0.34)

R2 = 0.28, DR2 = 0.00 Reference –0.4* (–0.86, –0.00) –0.8* (–1.32, –0.18)

Reference –1.1*** (–1.51, –0.72) –0.5*** (–0.59, –0.34)

R2 = 0.28 Reference –0.4 (–0.90, –0.02) –0.8** (–1.35, –0.26)

Reference –1.1*** (–1.48, –0.76)

R2 = 0.15 Reference –0.2 (–0.58, 0.14) –0.4 (–0.93, 0.08)

Reference –0.2 (–0.57, 0.21) –0.3 (–1.02, 0.34)

R2 = 0.01

Log-transformed MVPA

Table 3. Hierarchical regression analyses examining differences between culturally and linguistically diverse (CALD) and non-CALD adolescents in daily (min.day–1) participation measured by accelerometry Data show b values with 95% confidence intervals in parentheses. LPA, light physical activity; MVPA, moderate-to-vigorous physical activity; SES, socioeconomic status. Analyses are based on 3 days of monitoring (including 1 weekend day). *P < 0.05, **P < 0.01, ***P < 0.001. SES, socioeconomic status; low SES, index of relative socioeconomic advantage and disadvantage (IRSAD) 999; High SES, IRSAD 1000; DR2, change in proportion of the variance explained from the previous model. All analyses were adjusted for school-level clustering

PA and SB among culturally diverse adolescents Health Promotion Journal of Australia 109

110

Health Promotion Journal of Australia

S. Strugnell et al.

groups. Table 4 presents the median duration spent in self-reported SB types, with consistently higher engagement in daily educationrelated SBs (homework/study, weekend cultural school and out-ofschool hours tutoring) observed among Chinese Australian and South-east Asian adolescents compared with Anglo-Australian adolescents (P < 0.01).

of PA5,6,16–22,38 and greater levels of SB5,17,18,21 among Chinese Australian and South-east Asian adolescents compared with their non-CALD Anglo-Australian counterparts. To the best of our knowledge, this is the first study to objectively investigate differences in PA and SB participation among specific Asian Australian populations and highlights several concerns that warrant attention.

Disparity between self-reported and objectively measured MVPA was evident with Anglo-Australian (median = 75.7, IQR = 50.0, 108.6 min.day–1), Chinese Australian (median = 75.6, IQR = 55.0, 117.1 min.day–1) and South-east Asian (median = 71.4, IQR = 44.3, 105.0 min.day–1) adolescents reporting greater daily participation in MVPA than was detected using accelerometry (Table 1). Based on selfreported participation, 65.7% of Anglo-Australian, 67.7% of Chinese Australian and 60.3% of South-east Asian adolescents met the national MVPA recommendations using the average  days method; in comparison, using accelerometry only 3% of all participants (n = 5 Anglo-Australians; n = 1 South-east Asian) met the recommendation.

In the CAAHS, accelerometry-measured MVPA was generally low for all adolescents (~24 min.day–1) and highlighted that most adolescents engaged in insufficient amounts to confer the health benefits.7 As evidenced in several systematic reviews, boys in the CAAHS were shown to engage in more MVPA than girls,10,11 as were younger compared with older adolescents.10,13 However, inconsistencies for the associations with age11,13 and gender13 have been documented.

Discussion Consistent with previous findings among Asian CALD youth in Australia and internationally, the present study found lower levels

The observed pattern of participation in MVPA among adolescents in the present study was typically lower than their Canadian (11–14 years) and American (12–15 years) counterparts.39,40 Adolescent boys and girls in the nationally representative Canadian Health Measures Survey (2007–09) typically engaged in 59 and 47 min.day–1 of accelerometry-measured MVPA, respectively,40 which was considerably higher than that in the present study (31 and

Table 4. Culturally and linguistically diverse (CALD) and non-CALD differences in self-reported daily sedentary behaviour (SB) duration (min.day–1) by SB type The Kruskal–Wallis test with post hoc multicomparison adjustment was used to compare differences between Anglo-Australian, Chinese Australian and South-east Asian adolescents. Median and interquartile range (IQR) participation was generated by summing participation in the 14-item sedentary behaviour checklist and dividing by the number of days of recall (= 7) n Small screen recreation Anglo-Australian Chinese Australian South-east Asian Education Anglo-Australian Chinese Australian South-east Asian Travel Anglo-Australian Chinese Australian South-east Asian Cultural activities Anglo-Australian Chinese Australian South-east Asian Social activities Anglo-Australian Chinese Australian South-east Asian All sedentary Anglo-Australian Chinese Australian South-east Asian

Weekday (Monday–Friday) Median (IQR)

Weekend (Saturday–Sunday) n Median (IQR)

n

90 81 60

84.0 (48.0, 114.0) 72.0 (52.0, 144.0) 114.0 (60.0, 180.0)

84 82 53

130.0 (60.0, 232.5) 180.0 (95.0, 295.0) 180.0 (90.0, 255.0)

90 83 60

102.9 (61.4, 154.3) 112.9 (68.6, 204.3) 124.3 (77.9, 180.0)

75 78 56

24.0 (12.0, 48.0) 60.0A (36.0, 96.0) 40.5B (24.0, 84.0)

47 79 57

60.0 (30.0, 120.0) 165.0A (105.0, 240.0) 150.0B (90.0, 180.0)

78 82 60

25.7 (8.6, 47.1) 85.0A (64.3, 135.0) 68.6B (52.5, 100.7)

57 57 40

24.0 (12.0, 36.0) 20.0 (10.0, 36.0) 20.0 (11.0, 40.0)

48 50 32

36.3 (21.3, 60.0) 26.3A (10.0, 45.0) 30.0B (12.5, 40.0)

69 60 44

20.0 (8.6, 37.1) 17.1 (8.6, 36.4) 17.1 (9.3, 37.9)

70 71 47

38.0 (15.0, 72.0) 48.0 (24.0, 96.0) 36.0 C (12.0, 58.0)

49 60 32

60.0 (40.0, 120.0) 60.0 (30.0, 120.0) 52.5 (25.0, 80.0)

72 73 49

38.6 (17.1, 72.9) 50.0 (25.7, 98.6) 30.0C (17.1, 51.4)

58 44 35

37.5 (12.0, 84.0) 28.5 (12.0, 62.0) 24.0 (12.0, 42.0)

49 44 31

90.0 (60.0, 240.0) 90.0 (35.0, 150.0) 60.0 (30.0, 90.0)

65 55 44

47.1 (17.1, 106.4) 34.3 (17.1, 77.1) 29.3B (8.6, 49.3)

91 83 60

213.0 (120.0, 318.0) 258.0A (177.6, 258.0) 233.0 (185.0, 363.5)

86 83 58

365.0 (205.0, 510.0) 540.0A (432.5, 637.5) 423.8C (267.5, 545.0)

91 83 60

251.4 (161.4, 359.3) 321.4A (264.3, 430.7) 286.8 (212.1, 397.1)

Chinese Australian adolescents differ significantly from Anglo-Australian adolescents (P < 0.01). South-east Asian adolescents significantly different from Anglo-Australian adolescents (P < 0.01). C South-east Asian adolescents significantly different from Chinese Australian adolescents (P < 0.01). A B

Daily (Monday–Sunday) Median (IQR)

PA and SB among culturally diverse adolescents

20 min.day–1, respectively). This finding was also true for American adolescents, with boys and girls engaging in 44.3 and 24.6 min.day–1 MVPA, respectively, on average.39 A wide variety of study factors are likely to have influenced these differences, particularly the age range of participants included, survey year, accelerometer inclusion criteria, cut-off point selection and the specific recruitment of CALD adolescents in the present study, which make direct comparisons impossible. Because there are no representative studies that have used accelerometry among Australian adolescents (state or national), definitive comparisons cannot be made. However, a longitudinal study of youth living in Melbourne (Children Living in Active Neighbourhoods) in 2004 found adolescent boys (13–15 years) engaged in 59.1  40.1 min.day–1 daily MVPA, compared with 40.9  25.9 min.day–1 for girls.41 This further highlights that the adolescents in the present study are a unique population, especially considering their CALD backgrounds, by engaging in less MVPA. Multivariate analyses of CALD differences in MVPA found Chinese Australian and South-east Asian adolescents engaged in significantly less daily MVPA (5–8 min.day–1) than their non-CALD (AngloAustralian) counterparts, representing 9%–13% of the recommended 60 min.day–1 MVPA.34 These findings are supported by the representative 2010 Schools Physical Activity and Nutrition Survey (SPANS) in New South Wales, Australia, which also found a significantly lower proportion of adolescents (~11–16 years) who spoke an Asian language at home met the national MVPA guidelines compared with their predominantly English-speaking (at home) counterparts.42 A possible explanation for the lower engagement in weekend MVPA and LPA for Chinese Australian and South-east Asian adolescents in the present study was attendance at weekend Chinese cultural schools. Because these classes run for a minimum of 3 h on a Saturday or Sunday, they limit the opportunities for adolescents to engage in MVPA or LPA. However, these weekend classes do present a unique PA intervention avenue because students are attending a formal education institution on the weekend. Significant CALD differences were again evident on daily duration spent in objectively measured LPA, with Chinese Australian and South-east Asian adolescents engaging in approximately 1 h less daily LPA than Anglo-Australian adolescents (P < 0.01). It is possible that Chinese Australian and South-east Asian adolescents are engaging in LPA in preference to the more exhausting MVPA, but further investigations are required to examine this hypothesis. In addition, further research is required to investigate whether CALD differences exist in the underlying psychosocial influences on PA participation, which may help explain the above findings. A South Australian study highlighted differences in perceived levels of social support between Vietnamese Australian and Anglo-Australian adolescent boys and girls.23,24 The researchers found Vietnamese Australian adolescent boys and girls perceived lower levels of paternal and maternal support compared with their Anglo-Australian counterparts.23,24 Fathers’ help and encouragement towards PA was positively associated with MVPA among Vietnamese Australian

Health Promotion Journal of Australia

111

boys and maternal co-participation was positively associated with Vietnamese Australian girls participation (P < 0.05), whereas these were not significant among their Anglo-Australian counterparts. Although it is not known whether these associations are specific for Vietnamese Australian youth or are generalisable to other Asian subpopulations in Australia, they highlight future lines of enquiry. Accelerometry-measured ST participation ranged between 8.5 and 9 h.day–1 for participants in the present study, which is consistent with American adolescents in the 2004–04 National Health and Nutrition Examination Survey (NHANES)43 and the 2007–09 Canadian Health Measures Survey40 of approximately 7.5 and 9.1 h.day–1 in accelerometry-measured ST, respectively. Multivariate analyses in the present study confirmed greater engagement in ST among Chinese Australian and South-east Asian adolescents compared with Anglo-Australian adolescents (~40 min.day–1). These findings are similar to those reported among South Asian children (9–10 years) in the UK Child Health Study in England (CHASE) study18 and AsianAmerican girls (11–12 years) in the Trial of Activity for Adolescent Girls (TAAG) study.21 In both studies, children from an Asian background engaged in significantly more accelerometry-measured ST (+22–39 min.day–1) than their non-CALD peers (predominantly European background).18,21 The 2010 Australian SPANS study also confirmed that adolescent boys and girls (~14–16 years) who predominantly spoke an Asian language at home engaged in significantly more weekday SB than adolescents who predominantly spoke English at home.5 Examination of self-reported duration in specific SB types revealed the key contributor to this discrepancy was education-related SBs in the present study. This finding is not surprising given the attendance at Chinese weekend cultural schools and is likely to be a reflection of the high value placed on education and occupational aspirations by parents of Asian Australian children (Chinese and Vietnamese).44,45 Interestingly, the 2010 NSW SPANS survey found no significant difference between Asian language (at home) adolescents compared with English-speaking adolescents in weekday duration spent in education-related SBs, but significantly greater duration on weekends for Year 8 Asian language boys and Asian language Year 10 girls.5 A wide variety of study factors (study design, recruited population, data analysis techniques and country-specific influences) may have contributed to the greater ST and SB engagement among Chinese Australian and South-east Asian adolescents in the CAAHS; however, the older age group of participants is likely to account for this discrepancy because SB increases with age.8 Several important limitations should be considered when interpreting the findings from the CAAHS. First, selection bias may have occurred because we targeted Chinese weekend cultural schools rather than all Chinese Australian adolescents in metropolitan Melbourne, and establishing the proportion of those who do not attend these weekend cultural schools is difficult. However, this approach was deemed necessary to recruit sufficient numbers of Chinese Australian adolescents and proved to be a

112

Health Promotion Journal of Australia

successful approach. Second, the use of accelerometry involved many data-handling and management decisions that are not standardised36 and can influence the reported duration spent in various activity intensities.46 The authors have opted for full disclosure of all data-handling and management decisions to enhance transparency and comparability with other studies. In the CAAHS, the desired sample size for accelerometry-derived MVPA CALD comparisons was not met and therefore these results are at risk of Type 2 error, however, because these calculations were based on the variable with the estimated smallest difference (MVPA), the analysis samples are believed to be sufficiently powered for LPA and SB analyses. As with previous studies, a disparity was evident between self-reported MVPA and SB and objectively measured MVPA and ST. These findings highlight potential social desirability bias,47 as well as limitations of the CAPANS-PA questionnaire, which is likely to have been influenced by the use of a non-concurrent recall period. The use of accelerometers is also not without limitation because several monitor types do not collect water-based, cycling and upper bodybased movements, in addition to the vast data-handling and management decisions, and this can lead to reduced estimates of total activity.48 Another key limitation of the study is the low response rate, although this was expected,49 especially when involving participants from a CALD background. Therefore, the influence of this selection bias on the generalisability and accuracy of the results, including ethnic comparisons, to the wider community cannot be ignored.

S. Strugnell et al.

References 1. 2. 3.

4.

5.

6.

7.

8. 9.

10.

11.

12.

13.

Conclusion 14.

Compared with Anglo-Australian adolescents, accelerometryderived daily MVPA and LPA was significantly lower and daily SB higher among Chinese Australian and South-east Asian adolescents, even after adjusting for a range of covariates. It is recommended that future studies investigate the underlying factors influencing participation among these CALD communities to highlight possible intervention targets.

15.

16.

17.

Acknowledgements The authors acknowledge Dr Jisheng Cui for his advice, assistance and statistical support throughout all aspects of the study, except for manuscript development and refinement. In addition, special thanks are due to Professor Jo Salmon and Associate Professor Anna Timperio who provided comments and feedback on the PhD thesis chapter relating to this study. The authors also thank the students, parents and staff of the Chinese weekend cultural schools in metropolitan Melbourne; without their support this study would not have been possible. The authors also acknowledge that the lead author was supported by a Deakin University Postgraduate Research Scholarship during this study. Professor Andre Renzaho is supported by an ARC Future Fellowship (FT110100345).

18.

19.

20.

21. 22.

23.

Australian Bureau of Statistics (ABS). Reflecting a nation: stories from the 2011 census, 2012–2013. In. Canberra: ABS; 2012. Dental Health Services Victoria. Culturally and linguistically diverse communities: resource kit. Melbourne: Victorian Government; 2010. O’Dea JA. Gender, ethnicity, culture and social class influences on childhood obesity among Australian schoolchildren: implications for treatment, prevention and community education. Health Soc Care Community 2008; 16: 282–90. doi:10.1111/j.1365-2524.2008.00768.x Rissel C, McLellan L, Bauman A. Social factors associated with ethnic differences in alcohol and marijuana use by Vietnamese-, Arabic- and English-speaking youths in Sydney, Australia. J Paediatr Child Health 2000; 36: 145–52. doi:10.1046/j.14401754.2000.00476.x Hardy L, King L, Espinel P, Cosgrove C, Bauman A. NSW schools physical activity and nutrition survey (SPANS 2010): full report. Sydney: NSW Ministry of Health; 2011. Booth M, Okely A, Denney-Wilson E, Hardy L, Yang B, Dobbins T. NSW schools physical activity and nutrition survey (SPANS 2004): full report. Sydney: Sydney: NSW Department of Health, 2006. Janssen I, Leblanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phys Act 2010; 7: 40–56. doi:10.1186/1479-5868-7-40 Pate RR, Mitchell JA, Byun W, Dowda M. Sedentary behaviour in youth. Br J Sports Med 2011; 45: 906–13. doi:10.1136/bjsports-2011-090192 Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ, Larouche R, Colley RC, et al. Systematic review of sedentary behaviour and health indicators in schoolaged children and youth. Int J Behav Nutr Phys Act 2011; 8: 98. doi:10.1186/14795868-8-98 Sallis JF, Prochaska JJ, Taylor WC. A review of correlates of physical activity of children and adolescents. Med Sci Sports Exerc 2000; 32: 963–75. doi:10.1097/00005768200005000-00014 Van Der Horst K, Paw MJ, Twisk JW, van Mechelen W. A brief review on correlates of physical activity and sedentariness in youth. Med Sci Sports Exerc 2007; 39: 1241–50. doi:10.1249/mss.0b013e318059bf35 Biddle S, Atkin A, Cavill N, Foster C. Correlates of physical activity in youth: a review of quantitative systematic reviews. Int Rev Sport Exerc Psychol 2011; 4: 25–49. doi:10.1080/1750984X.2010.548528 Uijtdewilligen L, Nauta J, Singh AS, van Mechelen W, Twisk JW, van der Horst K, et al. Determinants of physical activity and sedentary behaviour in young people: a review and quality synthesis of prospective studies. Br J Sports Med 2011; 45: 896–905. doi:10.1136/bjsports-2011-090197 Green J, Waters E, Haikerwal A, O’Neill C, Raman S, Booth ML, et al. Social, cultural and environmental influences on child activity and eating in Australian migrant communities. Child Care Health Dev 2003; 29: 441–8. doi:10.1046/j.1365-2214. 2003.00363.x Dudley DA, Okely AD, Pearson P, Cotton WG, Caputi P. Changes in physical activity levels, lesson context, and teacher interaction during physical education in culturally and linguistically diverse Australian schools. Int J Behav Nutr Phys Act 2012; 9: 114–23. doi:10.1186/1479-5868-9-114 Duncan MJ, Woodfield L, Al-Nakeeb Y, Nevill AM. Differences in physical activity levels between white and South Asian children in the United Kingdom. Pediatr Exerc Sci 2008; 20: 285–91. Brodersen NH, Steptoe A, Boniface DR, Wardle J. Trends in physical activity and sedentary behaviour in adolescence: ethnic and socioeconomic differences. Br J Sports Med 2007; 41: 140–4. doi:10.1136/bjsm.2006.031138 Owen CG, Nightingale CM, Rudnicka AR, Cook DG, Ekelund U, Whincup PH. Ethnic and gender differences in physical activity levels among 9–10-year-old children of white European, South Asian and African-Caribbean origin: the Child Heart Health Study in England (CHASE Study). Int J Epidemiol 2009; 38: 1082–93. doi:10.1093/ije/ dyp176 Duncan JS, Schofield G, Duncan EK. Pedometer-determined physical activity and body composition in New Zealand children. Med Sci Sports Exerc 2006; 38: 1402–9. doi:10.1249/01.mss.0000227535.36046.97 Duncan EK, Scott Duncan J, Schofield G. Pedometer-determined physical activity and active transport in girls. Int J Behav Nutr Phys Act 2008; 5: 2–11. doi:10.1186/14795868-5-2 Pate RR, Stevens J, Pratt C, et al. Objectively measured physical activity in sixth-grade girls. Arch Pediatr Adolesc Med 2006; 160: 1262–8. doi:10.1001/archpedi.160.12.1262 McGuire MT, Hannan PJ, Neumark-Sztainer D, Cossrow NH, Story M. Parental correlates of physical activity in a racially/ethnically diverse adolescent sample. J Adolesc Health 2002; 30: 253–61. doi:10.1016/S1054-139X(01)00392-5 Wilson AN, Dollman J. Social influences on physical activity in Anglo- and Vietnamese-Australian adolescent males in a single sex school. J Sci Med Sport 2007; 10: 147–55. doi:10.1016/j.jsams.2006.05.025

PA and SB among culturally diverse adolescents

24. Wilson AN, Dollman J. Social influences on physical activity in Anglo-Australian and Vietnamese-Australian adolescent females in a single sex school. J Sci Med Sport 2009; 12: 119–22. doi:10.1016/j.jsams.2007.10.012 25. Australian Bureau of Statistics (ABS). The Australian census of population and housing 2006. Canberra: ABS; 2008. 26. Commonwealth Department of Immigration and Citizenship. Community information summary: China*-Born. Canberra: National Communications Branch of the Department of Immigration and Citizenship; 2007. 27. Australian Bureau of Statistics (ABS). The Australian census of population and housing 2011. Canberra: ABS; 2012. 28. Strugnell C, Renzaho A, Ridley K, Burns C. Reliability of the modified child and adolescent physical activity and nutrition survey, physical activity (CAPANS-PA) questionnaire among chinese-australian youth. BMC Med Res Methodol 2011; 11: 122–33. doi:10.1186/1471-2288-11-122 29. Australian Bureau of Statistics (ABS). Socio-economic indexes for areas (SEIFA): technical paper, 2006. Report no. 2039.0.55.001. Canberra: ABS; 2008. 30. Strugnell C. Correlates of physical activity and obesity among a sample of ChineseAustralian adolescents. PhD Thesis, Deakin University, Australia, 2013. 31. Ridley K, Olds TS. Assigning energy costs to activities in children: a review and synthesis. Med Sci Sports Exerc 2008; 40: 1439–46. doi:10.1249/MSS.0b013e 31817279ef 32. Hardy LL, Booth ML, Okely AD. The reliability of the Adolescent Sedentary Activity Questionnaire (ASAQ). Prev Med 2007; 45: 71–4. doi:10.1016/j.ypmed.2007. 03.014 33. Commonwealth Department of Health and Ageing. Australia’s physical activity recommendations for 12–18 year olds. Canberra: Department of Health and Ageing, Commonwealth of Australia; 2004. 34. Olds T, Ridley K, Wake M, Hesketh K, Waters E, Patton G, et al. How should activity guidelines for young people be operationalised? Int J Behav Nutr Phys Act 2007; 4: 43–9. doi:10.1186/1479-5868-4-43 35. Steele RM, van Sluijs EM, Sharp SJ, Landsbaugh JR, Ekelund U, Griffin SJ. An investigation of patterns of children’s sedentary and vigorous physical activity throughout the week. Int J Behav Nutr Phys Act 2010; 7: 88–96. doi:10.1186/14795868-7-88 36. Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sports Exerc 2005; 37(Suppl): S523–30. doi:10.1249/01.mss.0000185658.28284.ba 37. Pate RR, O’Neill JR, Lobelo F. The evolving definition of ‘sedentary’. Exerc Sport Sci Rev 2008; 36: 173–8. doi:10.1097/JES.0b013e3181877d1a

Health Promotion Journal of Australia

113

38. Hardy LL, King L, Hector D, Baur LA. Socio-cultural differences in Australian primary school children’s weight and weight-related behaviours. J Paediatr Child Health 2013; 49: 641–8. doi:10.1111/jpc.12263 39. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008; 40: 181–8. doi:10.1249/mss.0b013e31815a51b3 40. Colley RC, Garriguet D, Janssen I, Craig CL, Clarke J, Tremblay MS. Physical activity of Canadian adults: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Health Rep 2011; 22: 7–14. 41. Carver A, Timperio A, Hesketh K, Ridgers ND, Salmon J, Crawford D. How is active transport associated with children’s and adolescents’ physical activity over time? Int J Behav Nutr Phys Act 2011; 8: 126–32. doi:10.1186/1479-5868-8-126 42. Hardy L, King L, Espinel P, Cosgrove C, Bauman A. NSW Schools physical activity and nutrition survey (SPANS 2010): full report. Sydney, NSW: NSW Ministry of Health; 2011. 43. Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, et al. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol 2008; 167: 875–81. doi:10.1093/aje/kwm390 44. Dandy J, Nettelbeck T. The relationship between IQ, homework, aspirations and academic achievement for Chinese, Vietnamese and Anglo-Celtic Australian school children. Educ Psychol 2002; 22: 267–75. doi:10.1080/01443410220138502 45. Zhao D, Singh M. Why do Chinese-Australian students outperform their Australian peers in mathematics: a comparative case study. Int J Sci Math Educ 2011; 9: 69–87. doi:10.1007/s10763-010-9214-7 46. Mâsse LC, Fuemmeler BF, Anderson CB, Matthews CE, Trost SG, Catellier DJ, et al. Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc 2005; 37(Suppl): S544–54. doi:10.1249/01.mss.0000185674.09066.8a 47. Jago R, Baranowski T, Baranowski J, Cullen K, Thompson D. Social desirability is associated with some physical activity, psychosocial variables and sedentary behaviour but not self-reported physical activity among adolescent males. Health Educ Res 2007; 22: 438–49. doi:10.1093/her/cyl107 48. Rowlands AV. Accelerometer assessment of physical activity in children: an update. Pediatr Exerc Sci 2007; 19: 252–66. 49. Galea S, Tracy M. Participation rates in epidemiologic studies. Ann Epidemiol 2007; 17: 643–53. doi:10.1016/j.annepidem.2007.03.013

Physical and sport education class Travel by walking to school carrying load Play with pets

Walk for exercise Jogging or running Bike riding Bounce on trampoline Tennis/table tennis Walk the dog Basketball Swimming laps

2

5 6 7 8 9 10 11 12

4

3

Household chores

Activity

1

No.

93 92 93 94 92 89 95 99

93

94

95

93

38.7 38.0 35.5 33.0 27.2 27.0 24.2 24.2

53.8

54.3

71.6

77.4

75.0 (30.0, 120.0) 50.0 (20.0, 95.0) 90.0 (30.0, 180.0) 32.5 (17.5, 62.5) 60.0 (40.0, 180.0) 40.0 (30.0, 120.0) 67.5 (35.0, 135.0) 60.0 (50.0, 120.0)

50.0 (15.0, 95,0)

100.0 (30.0, 150.0)

122.0 (75.0, 200.0)

60.0 (30.0, 130.0)

Anglo-Australian n % Yes Total weekly duration (min week–1)

88 93 90 93 94 93 95 93

89

92

89

96

42.1 39.8 37.8 36.6 30.9 30.1 27.4 22.6

53.9

55.4

69.7

90.1

40.0 (20.0, 80.0) 60.0 (40.0, 100.0) 60.0 (30.0, 120.0) 67.5 (45.0, 120.0) 60.0 (30.0, 95.0) 30.0 (15.0, 60.0) 75.0 (60.0, 100.0) 35.0 (20.0, 60.0)

75.0 (50.0, 100.0)

60.0 (35.0, 110.0)

42.5 (20.0, 90.0)

120.0 (90.0, 165.0)

Chinese Australian n % Yes Total weekly duration (min week–1)

Travel by walking to school carrying load Walk for exercise Soccer Bike riding Basketball Swimming laps Tag/chasey Tennis/table tennis Four square/down ball

Household chores

Physical and sport education class Jogging or running

Activity

Travel by walking to school carrying load Tennis/table tennis Walk for exercise Tag/chasey Soccer Basketball Swimming laps Bike riding Four square/down ball

Jogging or running

Physical and sport education class Household chores

Activity

71 71 70 72 71 69 70 70

72

71

71

73

39.4 38.0 34.3 33.3 32.4 29.0 27.1 24.3

48.6

54.9

73.2

87.7

60.0 (30.0, 140.0) 45.0 (25.0, 100.0) 30.0 (15.0, 50.0) 60.0 (60.0, 120.0) 60.0 (40.0, 120.0) 60.0 (30.0, 95.0) 50.0 (30.0, 70.0) 20.0 (10.0, 30.0)

50.0 (25.0, 90.0)

55.0 (20.0, 100.0)

40.0 (20.0, 70.0)

100.0 (70.0, 150.0)

South-east Asian n % Yes Total weekly duration (min week–1)

Appendix I. The 12 most popular moderate-to-vigorous-intensity physical activity types and self-reported duration over the previous 7 days (Monday–Sunday) Unless indicated otherwise, values are given as the median with the interquartile range (IQR) in parentheses. Median participation and IQR were generated by summing participation in the identified activity type Monday–Sunday for those who indicated (Yes) that they had participated in the activity in the previous 7 days. The Kruskal–Wallis test with post hoc multicomparison adjustment were used to compare differences between Anglo-Australian, Chinese Australian and South-east Asian adolescents; no significant differences in duration were observed

114 Health Promotion Journal of Australia S. Strugnell et al.

www.publish.csiro.au/journals/hpja