Growing Up in Scotland: Sweep 3 Food and Activity Report

Chapter 6
Conclusion

Improving the eating habits and nutrition of children, and increasing the amount of physical activity that children participate in, remain key policy priorities as part of the Scottish Government's wider strategic objective for a healthier Scotland. The challenge facing policy makers remains considerable in the context of rising levels of childhood overweight and obesity and socio-economic health inequalities. Although more limited in scope than dedicated diet and nutrition studies, GUS is nevertheless able to provide useful information on the range of food types - both healthy and unhealthy - eaten by young children on a typical day, and the reported physical and sedentary activity levels of these children. Moreover, the study has the additional benefit of being able to examine parental views and experiences in relation to their children's eating and activity. In combination, these allow for the exploration of choice, influence and culture in early years provision and consumption of food, and in access to and participation in activities, across socio-economic groups. The wide focus of data collection on GUS also allows for exploration of these topics in relation to breastfeeding and BMI.

It was encouraging to see that almost all children had at least one type of fruit a day (96%) and 86% had at least one type of vegetable a day. However, children from certain households were eating a wider variety of fruit and vegetable than others: those children whose mothers had higher educational qualifications and who knew a lot about healthy eating, as well as children in managerial and professional households, were more likely to be eating a wider variety of fruit and vegetable on the average day. A more worrying finding though, was that consumption of unhealthy sugary and salty foods was also high, with almost half of children eating sweets or chocolates once a day or more often and 43% drinking sugary soft drinks once a day or more often, and again this was heavily patterned by socio-demographic factors.

While almost all children had eaten a meal prepared with fresh ingredients in the last week, 32% had eaten a takeaway meal, such as Chinese food or fish and chips, and 26% had eaten a fast food meal, such as McDonald's. Again, this was seen to be socially patterned, with almost twice as many children in the most deprived areas having eaten a fast food meal in the previous week (36%), compared with 20% in the least deprived areas.

Another aspect of the food culture debate is the concern surrounding the potential erosion of family life, family meals and family time as a result of longer working hours for parents and changes in the size and make up of the average family unit. However, GUS found little basis for this concern. Overall, the majority of children (82%) 'mostly' ate their main meal at the same time as the rest of the family with only 5% only occasionally or never eating with the rest of the family. Whilst 62% of children ate in the kitchen or dining room, 32% ate in the living room, although these findings did vary considerably by a number of factors including age of mother at time of birth, income and deprivation. Encouragingly, the majority of parents (52%) felt that mealtimes gave the family time to talk to each other and almost half (48%) said that mealtimes are mostly enjoyable.

Again, it was encouraging to see that, although sedentary activity levels are high (85% watching TV every day and a quarter playing on a computer or games console every day), participation levels in physical activities were also high (the vast majority having participated in at least one physical activity in the past week, and two-thirds having participated in five or more). Once more, socio-demographic factors had an effect, with children in the highest activity group being more likely to be in households with higher incomes and in managerial and professional occupations. However, there also appeared to be a neighbourhood effect, which having a car only compensated for to some extent. One of the biggest impacts though seemed to be from both parents' example setting and beliefs about the importance of exercise.

There was a correlation between diet and activity, with less active children being less likely to eat a wide variety of vegetables and being more likely to consume unhealthy foods or drinks on a daily basis. Interestingly, the correlation was not seen with regards to fruit consumption. Furthermore, GUS data reinforced previous research findings that children who had a higher preference for energy-dense foods and fatty foods also had a stronger preference for sedentary activities, such as watching TV and playing computer games.

An exploration was carried out of the impact of breastfeeding on later eating habits, which found that, although there was little difference between the proportions of breastfed and non-breastfed children who snacked on breakfast cereal, sweets or chocolate, cakes or biscuits or bread, breastfed children were more likely to snack on fruit (75% vs. 64%) and savoury snacks (51% vs. 40%), and were less likely to eat crisps (38% vs. 51%). Breastfed children were also more likely to have an overall 'good' diet: 17% vs. 7% of non-breastfed children. This remained the case even when controlling for socio-demographic factors.

Interestingly, children of different BMI levels varied little in their diet and activity scores, probably due to the young age of the children.

Children's health, their diet and their physical activity is now a firm element on the Scottish policy agenda. The results from GUS suggest that while nutrition and activity are separately important elements in the healthy development of children, there is a close relationship between nutrition and activity, and future policy initiatives may want to focus on the very relationship between what and how children eat, and what activities children engage in.

Appendix A Explanation of Bmi cut-offs

While the BMI measure has come under some scrutiny for not always being accurate, it remains the best non-invasive measure for obesity. Furthermore, a review of the measure by (Reilly et al., 1999) in the British Medical Journal suggests that the BMI is more likely to understate, rather than overstate, the true levels of obesity, as has been discussed by Prentice (Prentice, 1998) and Barlow and Dietz (Barlow & Dietz, 1998).

The main child overweight and obesity prevalence estimates in this section have been produced using the International Obesity Taskforce cut-offs. These cut-offs are based on BMI reference data from six different countries around the world (over 190,000 subjects in total aged 0 to 25 from UK, Brazil, Hong Kong, the Netherlands, Singapore, and the United States). In summary, the BMI percentile curves that pass through the values of 25 and 30 kg/m ² (standard adult cut-off points for overweight and obesity, respectively) at age 18 were smoothed for each national dataset and then averaged. The averaged curves were then used to provide age and sex-specific BMI cut-off points for children and adolescents aged 2 to 18. By averaging the distribution curves from each reference country, the international cut-offs for children purport to be representative of the countries but independent of the overweight or obesity level in each country. One of the benefits of using these international standards is the possibility of making international comparisons. However, the international classification is not without problems: international reference data differ from those for the UK population, and this is reflected in the sex-specific overweight and obesity estimates produced by the International classification.

In light of this lack of consensus on its use, key results have also been produced using the 85th (overweight cut-off)/95th (obesity cut-off) BMI percentiles of the UK reference curves (referred to as the National BMI percentiles classification). The National BMI percentiles classification has been used in the past to describe childhood overweight and obesity prevalence trends in the UK. However, the National BMI percentiles classification were not selected as the primary measure in this report as they are based on the arbitrary assumption that the prevalence of overweight and obesity at the point when the reference data was compiled was 15% and 5%, respectively. Furthermore, there seems to be no indication that these cut-off points relate directly or indirectly to any physiological outcomes or health or disease risks. It is worth noting that the UK component of the international classification used the same sample as that used to construct the UK reference BMI data.

Appendix B References

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Arenz, S., Rückerl, R., Koletzko, B., and von Kries, R., (2004) 'Breast-feeding and childhood obesity - a systematic review', International Journal of Obesity 28, 1247-1256. doi:10.1038/sj.ijo.0802758 Published online 17 August 2004.

Barlow, S.E., and Dietz W.H. (1998) Obesity evaluation and treatment: Expert Committee recommendations. The Maternal and Child Health Bureau, Health Resources and Services Administration and the Department of Health and Human Services, Pediatrics. 1998 Sep; 102(3): E29

Bradshaw, P., Cunningham-Burley, S., Dobbie, F., MacGregor, A., Marryat, L., Ormston, R. and Wasoff, F. (2008), Growing Up in Scotland: Sweep 2 Overview Report, Scottish Government.

Bromley, C., Chaudhury, M., Craig, R., Deverill, C., Erens, B., Fuller, E., Gray, L., Herrick, K., Hirani, V., Kelly, Y., Leyland, A., MacGregor, A., Moody, A., Prescott, A., Pickering, K., Primatesta, P., Scholes, S., Shelton, N., Speight, S., Stamatakis, E., Wardle, H. and Zaninotto, P. (2005), The Scottish Health Survey - Volume 3: Children, Scottish Executive.

Bullo, M., Casas-Agustench, P., Amigo-Correig, P., Aranceta, J. and Salas-Salvado, J. (2007), 'Inflammation, obesity and comorbidities: the role of diet', Public Health Nutrition, 10, 1164-1172.

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Food Standards Agency (2008) Survey of sugar intake among children in Scotland, Aberdeen: Food Standards Agency Scotland.

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Appendix C Regression Tables

Table 6.1 Regression of the extent to which diet explains activity

Source	Df1	Df2	Wald F	Sig.
(Corrected model)	25.000	41.000	20.787	0.000
(Intercept)	1.000	65.000	12868.845	0.000
DcFdietsco	25.000	41.000	20.787	0.000

Model Summary ^a

R Square

.029

a. Model: DcAsco01 = (Intercept) + DcFdietsco

Table 6.2 Regression of the extent to which activity explains diet

Source	Df1	Df2	Wald F	Sig.
(Corrected model)	48.000	18.000	21.501	0.000
(Intercept)	1.000	65.000	12269.932	0.000
DcFdietsco	48.000	18.000	21.501	0.000

Model Summary ^a

R Square

.046

a. Model: DcFdietsco = (Intercept) + DcAsco01

Table 6.3 Regression exploring the association between BMI at sweep 2 and activity score at sweep 3

Source	Df1	Df2	Wald F	Sig.
(Corrected model)	16.000	50.000	5.136E15	0.000
(Intercept)	1.000	65.000	4616.502	0.000
BMIval_S	16.000	50.000	5.136E15	0.000

Model Summary ^a

R Square

.013

a. Model: DcAsco01 = (Intercept) + bmival_S

Table 6.4 Regression exploring the effects of breastfeeding on BMI

Source	Df1	Df2	Wald F	Sig.
(Corrected model)	1.000	65.000	4.784	0.032
(Intercept)	1.000	65.000	121787.37	0.000
MaBFDe01	1.000	65.000	4.784	0.032

Model Summary ^a

R Square

.003

a. Model: bmival = (Intercept) + MaBFDe01

Table 6.5 Regression exploring the effects of breastfeeding on diet score

Source	Df1	Df2	Wald F	Sig.
(Corrected model)	1.000	65.000	107.281	0.000
(Intercept)	1.000	65.000	4071.804	0.000
MaBFDe01	1.000	65.000	107.281	0.000

Model Summary ^a

R Square

.063

a. Model: DcFdietsco = (Intercept) + MaBFDe01

Table 6.6 Regression exploring the effects of breastfeeding on diet score controlling for socio-demographic characteristics

Source	Df1	Df2	Wald F	Sig.
(Corrected model)	61.000	5.000	5.625	0.030
(Intercept)	1.000	65.000	2083.782	0.000
DcAsco01	51.000	15.000	9.185	0.000
Bmival_S	21.000	45.000	9.9123	0.000
MaBFDe01	1.000	65.000	60.249	0.000
DcADsco3	1.000	65.000	13.503	0.000
DcMedu04	1.000	65.000	54.669	0.000

Model Summary ^a

R	Square .147

a. Model: DcFdietsco = (Intercept) + DcAsco01 + bmival_S + MaBFDe01 + DcADsco3 + DcMedu04