Thin adolescents: Who are they? What do they do? Socio-demographic and use-of-time characteristics

Preventive Medicine 51 (2010) 253–258

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Preventive Medicine j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y p m e d

Thin adolescents: Who are they? What do they do? Socio-demographic and use-of-time characteristics Katia Ferrar ⁎, Tim Olds Health and Use of Time Group, Sansom Institute, University of South Australia, Australia

a r t i c l e

i n f o

Available online 11 July 2010 Keywords: Youth Thinness Socio-demographics Use-of-time

a b s t r a c t Objectives. Examine: (1) the anthropometric, socio-demographic and use-of-time characteristics of thin adolescents, and (2) compare these characteristics to other weight status categories. Methods. Data were from the 2007 National Children's Nutrition and Physical Activity Survey which collected data on a random sample of 2200 9 to 16 year old Australians from February to August 2007. Seven socio-demographic variables, anthropometric data (height and weight were measured) and nine use-of-time variables were used, and compared across the weight status categories. Physical activity was measured using pedometers and the Multimedia Activity Recall for Children and Adults. Results. 5.3% of adolescents were classified as thin, a percentage which did not significantly vary by age, sex, indigenous status, household income, education level or family structure. Relative to other adolescents, thin adolescents were shorter and lighter. Thin adolescents were less active than their normal weight peers, but walked further and accumulated significantly less screen and TV time than obese adolescents. Conclusion. Thin adolescents were found in similar proportions across all socio-demographic bands. Thin adolescents recorded similar physical activity levels to their normal weight peers, but were more active than obese adolescents. The findings from the study support in part the theory of thinness related developmental delay. © 2010 Elsevier Inc. All rights reserved.

Introduction The topic of adolescent thinness in developed countries is much under-researched, especially when compared to the vast amount of research regarding obese adolescents. Extremes in weight status have been associated with poor health outcomes both during adolescence and into adulthood (Burke et al., 2004; Frisch, 1994; Luder and Alton, 2005; Nielsen and Andersen, 2003; Sur et al., 2005). Available research regarding thinness investigates predominantly under-nourished adolescents from developing countries, where malnutrition is a major issue. The data that are available regarding thin adolescents from developed countries such as Australia focus mainly on clinical populations such as anorexics or Aboriginals. Studies of the correlates of weight status have either ignored thinness, or have used inconsistent definitions, making comparisons difficult. Thin adolescents have tended to be classified as ‘normal weight’ or ‘non-overweight’, thus potentially confounding the normal weight data. The development of a standard definition of thinness in youth (Cole et al., 2007) based on age- and sex-specific BMI cut-offs, combined with the definitions of overweight and obesity (Cole et al., ⁎ Corresponding author. School of Health Sciences, University of South Australia, Centenary Building, Room C7-42, City East Campus, North Terrace, Adelaide SA 5000, Australia. E-mail address: [email protected] (K. Ferrar). 0091-7435/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.ypmed.2010.07.001

2000), allows for accurate investigation of not only thinness characteristics but also variability across the weight status spectrum. Like obesity, thinness is probably attributable to a combination of both genetic and external factors (Bulik and Allison, 2001). In the same way as a “thrifty gene” may exert influence to the development of obesity (Barness et al., 2007), a genetic predisposition towards thinness has been explored (Bulik and Allison, 2001). Various other etiological factors are associated with thinness, including illness, malnutrition and dieting (Luder and Alton, 2005). Being a thin adolescent has been linked to various health conditions including delayed puberty, negative body image and fatigue (Luder and Alton, 2005), and may also predict an increased risk of osteoporosis and reduced fertility as an adult (Frisch, 1994; Luder and Alton, 2005). Thin adolescents may be shorter, leaner and weaker than peers of the same age and sex (Malina, 1986). While thinness is associated with malnutrition and low socio-economic position (SEP) in developing countries (Malina et al., 2004; Walgate, 2002) no data are available on the socio-demographic characteristics of thin Australian adolescents. Nor have the activity characteristics of thin Australian adolescents been explored. This present study aims to provide the first comprehensive picture of thin Australian adolescent characteristics. We aimed to investigate whether thin Australian adolescents varied in anthropometric (height, weight and BMI), socio-demographic (sex, age, household income, parental education, remoteness, indigenous status and family

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structure) and use-of-time (physical activity level, use-of-time and sleep) characteristics when compared to their normal weight, overweight and obese peers. Methods

three variables. The first related to marriage status of the primary caregiver (married, other, or single). The second described the difference between the mid-caregiver age and that of the target child (median split; younger ≤ 31 years, older N 31 years). The third denoted age difference between the target child and the sibling closest in age (small ≤3 years, large N 3 years, or singleton).

Sample and design The participants for this study were 2200 randomly selected Australian boys and girls aged between 9 and 16 years, who were interviewed as part of the National Children's Nutrition and Physical Activity Survey (NCNPAS), conducted between March and August 2007 (Department of Health and Ageing, 2008a). The response rate was 41% (Department of Health and Ageing, 2008a). Participant characteristics are shown in Table 1. Protocol approval was obtained from the ethics committees of the University of South Australia and the Commonwealth Scientific and Industrial Research Organisation, and written informed consent was obtained from the parents of participants, and from participants if they were 14 years and older. Outcome measures Socio-demographic data were gathered during a computer-assisted faceto-face interview. Height and body mass were measured (standiometer and Tanita HD332 electronic scales) using standard methodologies (Marfell-Jones et al., 2006) by trained interviewers in the participants' homes. All 58 interviewers demonstrated inter-tester technical errors of measurement (TEMs) of ≤ 2% and intra-tester TEMs of ≤ 1.5% (Department of Health and Ageing, 2008b). Adolescents completed four 24 h use-of-time diaries, two at the time of the face-to-face interview, and two during the follow-up telephone interview (one to three weeks later). Wherever possible, use-oftime data were collected on at least one school day and one non-school day. Use-of-time data were collected using the Multimedia Activity Recall for Children and Adults (MARCA) (Ridley et al., 2006), a computerised 24h recall. The software allows young people to recall everything they did on the previous day from a list of about 250 activities. The MARCA has a same-day test–retest reliability of r = 0.84–0.92 for major outcome variables [moderate to vigorous physical activity (MVPA), physical activity level (PAL) and screen time (minutes spent watching television, playing videogames and using a computer)], and convergent validity with reference to pedometry of rho = 0.54 for PAL (Olds et al., 2010). The participants wore New Lifestyles 1000 pedometers which have been shown to have excellent validity and reliability (Schneider, 2000). Data treatment

Anthropometric data Weight status was calculated as thin (Grade I, II and III combined), normal, overweight and obese according to the criteria of Cole et al. (2000; 2007). Statistical analysis Comparisons between the weight status categories were conducted using one-factor factorial ANOVA for the anthropometric and use-of-time variables, and X2 for the socio-demographic variables. Post-hoc tests using Fisher's PLSD test (ANOVA) and Fisher's Exact Test (X2) were also conducted. Multivariate analysis of use-of-time variables and those socio-demographic variables found significant after univariate analysis was conducted using multifactorial ANOVA. Sequential Bonferroni correction was applied to all use-oftime analyses to allow for alpha slippage.

Results

Socio-demographic data Reported household income was stratified into four bands which represent approximate quartiles for this sample: N AUD104,000; AUD75,000–104,000; AUD52,000–75,000; and b AUD52,000. The highest education level achieved by either caregiver was categorised as (1) high school only, (2) some post-secondary education (e.g. certificate or diploma), or (3) university degree. Remoteness was determined using ARIA+, the standard Australian Bureau of Statistics endorsed geographic measure of Australian remoteness [About ARIA + (Accessibility/Remoteness Index of Australia (n.d.))], and stratified into four bands: major city, inner regional, outer regional and remote. Aboriginal and Torres Strait Islander status was determined by direct questioning. Family structure was characterised using

Table 1 Sample size, decimal age and anthropometric characteristics of the sample as a whole, and by each weight status category, (study conducted in Australia, in 2007).

All Thin Normal Overweight Obese P values

Use-of-time data Each activity in the MARCA is associated with an energy expenditure (Ridley et al., 2008) so that an overall estimate of daily energy expenditure (PAL, in METs) could be determined by multiplying the duration of each activity by the associated energy cost. For example, a PAL of 1.7 would mean that a child uses 1.7 times the amount of energy required to sit still all day. MVPA was defined as minutes of activity requiring ≥ 3 METs, and vigorous physical activity (VPA) ≥ 6 METs (Pate et al., 1995). Minutes of sport, TV, videogame and screen time were calculated. Sleep duration was converted to waking time [1440 min/day − sleep (min)], as it was deemed a more appropriate representation of behaviour in one day. Pedometer records (average steps/day) with fewer than 1000 steps/day on any day, and those removed for more than four hours a day were excluded (10 h average daily wear time). Because there are differences in school and non-school activity patterns (Ridley et al., 2006) and because children spend approximately one day in two in school, use-of-time variables were quantified as the average of school and non-school day averages. Use-of-time data were corrected for age and sex by regressing the values against age for boys and girls separately, fitting a fourth order polynomial and using the residuals in analysis. The age and sex-adjusted values are reported.

n (%)

Age (SD)

Mass (kg)

Height (cm)

2200 (100) 116 (5.3) 1522 (69.2) 419 (19.0) 143 (6.5)

13.4 13.3 13.5 13.3 13.3 0.5

53.7 (15.9) 38.3 (9.6)a 49.9 (12.3)a 63.5 (14.0)a 79.0 (18.4)a b 0.0001

159.5 155.6 159.5 160.2 160.1 0.01

(2.2) (2.3) (2.2) (2.2) (2.3)

Figures are shown as mean (SD) or %. Values with same superscript are significantly different.

(13.8) (14.5)abc (14.0)c (13.0)b (12.5)a

Anthropometric characteristics Thin adolescents were significantly shorter (p = 0.01) and lighter (p b 0.001) than the adolescents from all other weight status groups. Thin adolescents were 3.9 cm shorter than normal weight adolescents, and 11.6 kg lighter (Table 1). Socio-demographics Overall, 5.3% of adolescents were classified as thin. The distribution of thin adolescents was strikingly consistent across socio-demographic categories (Table 2). The percentage of adolescents classified as thin did not vary by sex (5.3% of boys, 5.2% of girls), indigenous status (5.9% of indigenous and 5.3% of non-indigenous adolescents), or age band (varying from 4.4% for 13-year olds to 5.9% for 9-year olds). Use-of-time characteristics There was a consistent inverted J relationship between weight status and PAL and activity variables, with the highest values generally being recorded by the normal and overweight adolescents (Fig. 1), except for pedometer steps where the thin and normal adolescents recorded the highest values. Thin adolescents' daily PAL (1.61 METs) was significantly lower than normal weight adolescents'

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Overall P value

time, TV, PAL and pedometer steps. Following sequential Bonferroni correction, weight status was only independently associated with screen and TV time (Table 3). Fisher's PLSD post-hoc test showed both screen time and TV viewing time were significantly less in thin than in obese adolescents.

6.4 9.1 6.6 6.9 4.9 5.7 7.1 5.7

0.74

Discussion

17.6 20.5

5.8 7.2

0.15

70.0 68.2 60.7 66.4

18.2 19.0 21.4 20.9

6.6 6.5 5.6 8.2

0.84

4.2 5.7 6.0 6.0

74.0 69.6 67.7 64.1

19.0 18.2 17.4 21.8

2.8 6.5 8.9 8.1

0.0004

388 934 875

4.1 4.8 6.3

64.9 67.5 72.9

23.7 19.6 16.5

7.2 8.1 4.3

0.0005

Indigenous status Indigenous Non-indigenous

68 2129

5.9 5.2

54.4 69.7

23.5 18.8

16.2 6.2

0.004

Marriage status Married Other (incl. defacto) Single parent

1621 184 395

5.4 4.9 4.8

70.6 64.7 65.3

18.3 20.1 21.8

5.7 10.3 8.1

0.07

Parent–child age difference Small (≤ 31 years) 1154 Large (N 31 years) 1044

5.4 5.2

68.1 70.3

19.6 18.5

6.9 6.0

0.69

Sibling age difference Small (≤ 3 years) Large (N 3 years) Singleton

5.6 5.5 4.4

70.7 66.0 68.1

18.4 19.4 20.3

5.4 9.1 7.2

0.14

Table 2 Results of X2 analysis of socio-demographic variables stratified by the four weight status categories, (study conducted in Australia, in 2007). %

%

%

%

Thin

Normal

Overweight

Obese

202 219 243 221 225 401 410 279

5.9 5.9 4.9 5.9 4.4 4.5 5.9 5.0

67.8 65.8 65.8 68.5 72.1 72.4 65.6 74.9

20.3 19.2 22.6 18.7 18.6 17.4 21.5 14.3

Sex Boy Girl

1086 1114

5.3 5.2

71.3 67.1

Remoteness Major city Inner regional Outer regional Remote

1222 475 393 110

5.2 6.3 4.3 4.5

Income band N AUD104,000 AUD75,000–104,000 AUD52,000–75,000 b AUD52,000

616 401 436 618

Education High school Diploma or certificate University

n Age category Nine Ten Eleven Twelve Thirteen Fourteen Fifteen Sixteen

1261 397 542

Bolded mean percentage values indicate post-hoc testing attributed significance (N2 or b -2 standardised residuals). AUD = Australian dollars. $1AUD = $0.87 American (Reserve Bank of Australia 2010).

PAL (1.66 METs) and higher than obese adolescents' (1.55 METs). Thin adolescents walked significantly further than obese adolescents with the mean values of 10,916 steps/day and 9552 steps/day respectively. As weight status increased, so did screen-based activity times, with the lowest values recorded by the thin adolescents (except for video watching duration) and the highest times recorded by the obese adolescents (Fig. 1). Thin adolescents accumulated significantly less screen time, and watched significantly less TV than obese adolescents. Thin adolescents experienced less time awake (min/day) than all other adolescents and hence more sleep in a 24 h period, but these differences were not significant (Fig. 1). Use-of-time multivariate analysis Table 3 shows that there was a significant effect for weight status independent of parental education and household income for screen

This is one of the first studies to provide a description of thin Australian adolescents. Thin adolescents were shorter and lighter when compared to their peers; in general had lower physical activity involvement than normal weight but higher levels than obese adolescents; had much lower level screen and TV time than obese adolescents; and the prevalence of thinness did not vary across sociodemographic categories. Before considering the findings further, it is important to highlight the strengths and limitations of the current study. As participation was voluntary it is possible that families with children of a certain weight status were either more or less willing to participate, which would affect the weight status prevalence rates but not the characteristic differences. Yet when the weight status prevalence are compared to data from other Australian studies (state level) (Abbott et al., 2007; O'Dea, 2008; Wake, 2008), they appear similar. The Socio-Economic Indexes for Areas (SEIFA) Index of Relative Socioeconomic Disadvantage, which has a national mean of 1000 and a SD of 100 was 1001 (65) for the participants in the study (Australian Bureau of Statistics, 2006), suggesting the subjects are a reasonable representation of Australia's socio-economic mix. A further limitation may be related to the use of self-reported activity data using the MARCA. It has been suggested that overweight and obese individuals may over-report certain types of activity (McMurray et al., 2008; Slootmaker et al., 2009) but these findings are not conclusive. However, the MARCA has high reliability, validity similar or superior to most self-report instruments for young people (Olds et al., 2010) and provides a 24 h recall unlike other measurement tools. Overall, 5.3% of adolescents were classified as thin. Interestingly, this figure is similar to that reported by international studies from developed countries using the same definition, with 6.9% of 9–10 year olds from Liverpool (Boddy et al., 2009) and 3.9% and 4.8% of Spanish 13–18 year old boys and girls respectively (Artero et al., 2009) reported as thin. Who are they? Thinness appears to be largely consistent across all sociodemographic variables tested. This surprising consistency (4.1% to 6.3% range) leads to speculation that either a) there are different types of thinness related to different socio-demographic variables, or b) thinness is associated with a genotype which manifests itself in a particular phenotype which is relatively unaffected by the environment. It is conceivable that while thinness prevalence is largely evenly distributed across the socio-demographic spectrum, the etiological factors associated with the development of thinness may vary across socio-demographic strata. The SEP variables may be associated with the etiological factors of thinness but not the resultant prevalence. Genetic tendency toward thinness, which is most likely to be independent of socio-demographic stratification, has been posited as a possible cause of thinness (Bulik and Allison, 2001). Thin adolescents may also be biologically immature. Pubertal development is characterised in both sexes by linear growth, fat accumulation in girls and muscle bulk development in boys (Rowland, 2005). This study showed that thin adolescents were shorter and lighter than their normal weight peers, possibly reflecting pubertal delay. Unfortunately, no data regarding biological age (such as Tanner stage) were collected. Growth age, an indicator of biological age, can

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K. Ferrar, T. Olds / Preventive Medicine 51 (2010) 253–258 Table 3 Results of multivariate factorial ANOVA comparing weight status categories and use-oftime variables, (study conducted in Australia, in 2007).

Activity

Sedentary Waking

257

Table 4 Differences between growth age and biological age for thin and normal adolescents, (study conducted in Australia, in 2007).

Variables

Weight status category

Education

Income

Boys

MVPA VPA Sport Pedometer PAL Screen TV Waking time

0.26 0.08 0.22 0.03 0.03 0.003a 0.005a 0.93

0.03 0.62 0.45 0.35 0.07 0.23 0.76 0.87

0.04 0.005 0.03 0.12 0.003 0.03 0.06 0.45

Normal

Thin

Normal

Thin

0.38 (0.92) 0.51 (1.23)

− 0.51 (0.68) − 0.68 (0.91)

0.63 (1.20) 0.84 (1.60)

− 0.25 (0.80) − 0.34 (1.06)

Bolded p values indicate Fisher's PLSD significant p b 0.05. a Indicate significance remains after Sequential Bonferroni Correction (weight status category only).

be calculated using two different equations (Riegerová and Przeweda) (Sedlak, 2007).

growth age =(a + b + 2c) / 4 (Riegerova) growth age =(a + b + c) / 3 (Przeweda) a = height age i.e. chronological age represented by the child's height as the 50th percentile b =body mass age i.e. chronological age represented by the child's body mass as the 50th percentile c = chronological age The differences between growth age and biological age for thin and normal adolescents were calculated and compared using t-test for boys and girls separately. The thin adolescents (n = 116) and a randomly selected subset of normal weight boys (n = 78) and girls (n = 75) were used for the analysis. Negative findings suggest that the biological age is younger than the chronological age (Table 4). Using the equations, thin and normal values were significantly different for both boys and girls (p b 0.001). These findings support the theory that thin adolescents may be biologically immature.

Riegerová Przeweda

Girls

Values are shown in years as mean (SD).

Studies have reported differences in cardiorespiratory fitness of early and late pubescent girls (Ortega et al., 2008) and boys more pubertally advanced participated in higher levels of weekend exercise (Simon et al., 2003). Yet Murdey et al. (2004) found no associations between pubertal timing and sleep or sedentary behaviours. Pubertal timing and weight status research has focused primarily on obesity, with obese adolescents tending to be pubertally advanced (Semiz et al., 2008; Styne, 2004). Sleep decreases with age, so if thin adolescents are developmentally delayed, they are likely to sleep longer than their peers. Screen time peaks at approximately age 14, so again it is possible that thin adolescents show screen time patterns typical of younger children. However MVPA falls with age, a pattern not reflected in the findings if thin adolescents are considered developmentally delayed. The findings from the study support in part the theory of thinness related developmental delay. Conclusion The data from this study provide a picture of thin Australian adolescents. This study shows that thin Australian adolescents have near normal activity and sedentary behaviour levels and are equally prevalent across socio-demographic bands.

Conflict of interest statement The authors declare that there are no conflicts of interest.

What do they do? Acknowledgments Thin adolescents were less active than normal weight adolescents (PAL). This finding is surprising if weight status is in part mediated by physical activity levels and burning of kilojoules. One may have expected the thin adolescents to display increased activity levels when compared to normal weight adolescents, resulting in lower body weight. Yet, physical activity levels and the interactions with factors such as kilojoule intake must not be ignored. Unfortunately no dietary data were available for this analysis. Thinness has been associated with illness (Luder and Alton, 2005), which may explain the difference in activity levels. Basic carerreported health data was recorded as part of the NCNPAS. The question identified medical conditions (17 categories) that had lasted or were likely to last for six months or more. Analysis (data not shown) indicated thin adolescent health status was not significantly different to that of normal weight adolescents. Therefore, it is unlikely that the health status of thin adolescents contributed to activity levels. Different levels of activity across the weight status categories may be associated with weight status specific biological maturity. There is evidence to suggest that “gender-related patterns of time use vary greatly within adolescence” (Olds et al., 2009), and it is likely these patterns reflect a mix of both biological (pubertal) and social factors.

The survey on which this study was based was supported by the Australian Commonwealth Department of Health and Ageing; the Department of Agriculture, Fisheries and Forestry; and by the Australian Food and Grocery Council. References Abbott, R.A., Macdonald, D., Mackinnon, L., Stubbs, C.O., Lee, A.J., Harper, C., Davies, P.S. W., 2007. Healthy Kids Queensland Survey 2006 — Summary Report. Queensland Health, Brisbane. About ARIA + (Accessibility/Remoteness Index of Australia), n.d. GISCA. (Viewed on14 August 2008), www.gisca.adelaide.edu.au/. Artero, E., España-Romero, V., Ortega, F., Jiménez-Pavón, D., Ruiz, J., Vicente-Rodríguez, G., Bueno, M., Marcos, A., Gómez-Martínez, S., Urzanqui, A., 2009. Health-related fitness in adolescents: underweight, and not only overweight, as an influencing factor. The AVENA study. Scand. J. Med. Sci. Sports Jun 23, [Epub ahead of print]. Australian Bureau of Statistics, 2006. Information Paper: An Introduction to SocioEconomic Indexes for Areas (SEIFA). Australian Bureau of Statistics. Catalogue No: 2039.0. Barness, L.A., Opitz, J.M., Gilbert-Barness, E., 2007. Obesity: genetic, molecular, and environmental aspects. Am. J. Med. Genet. A 143A, 3016–3034. Boddy, L.M., Hackett, A.F., Stratton, G., 2009. The prevalence of underweight in 9–10year-old schoolchildren in Liverpool: 1998–2006. Public Health Nutr. 12, 953–956. Bulik, C.M., Allison, D.B., 2001. The genetic epidemiology of thinness. Obes. Rev. 2, 107–115.

Fig. 1. Use-of-time behaviours across the four weight status categories (study conducted in Australia, in 2007), a) MVPA, b) VPA, c) sport, d) pedometer, e) PAL, f) screen, g) TV, h) video and i) waking. Values are age- and sex-adjusted transformed mean values. All units are min/day except PAL in METs. Values with the same superscript letters are significantly different. The error bars are the 95% confidence interval. T = thin, N = normal, OW = overweight and O = obese. MVPA = moderate to vigorous physical activity, VPA = vigorous physical activity, PAL = physical activity level. *Indicates significance following Sequential Bonferroni Correction.

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