8 8. questionnaire results (1): nutrition, physical ... · questionnaire results (1): nutrition,...

8. Questionnaire results (1): Nutrition, physical activity and lifestyle

The previous chapter analysed diabetes risk in relation to birthweight and childhood growth.

Even if the evidence to support an association between child growth and subsequent diabetes

risk had been strong, features of the adult environment must also play a role. Their influence

may be through being at odds with a childhood environment (such as in the programming

hypothesis), or through exerting their influence early on in life and establishing a trajectory

(such as overnutrition promoting perhaps too rapid weight growth among children). This

chapter explores the proximal causes of diabetes, such as nutritional quality, level of physical

activity and other lifestyle factors of alcohol and tobacco consumption, through the responses to

the surveys that were conducted as part of the current study. These are discussed in terms of

diabetes control and prevention within the community. The development of the survey

questions and the coding of responses were presented in Section 5.4.

8

Responses to the food frequency questionnaire were analysed in relation to participant category:

diagnosed (D), high-risk (H), low-risk (L) for females and males, and females who had been

diagnosed at some stage with gestational diabetes (G). Food frequency questionnaire (FFQ) and

lifestyle responses were also analysed in relation to FBSL, BMI, waist circumference, systolic

and diastolic pressure and age. These were divided into quintiles for analysis. This risk factor

analysis was performed for four different sub-groups of participants: all females, all males,

never-diagnosed females and never-diagnosed males.

Physical activity (leisure, occupational and total activity) levels, as determined by the method

described in Section 5.4.1, were assessed in relation to diabetes risk. Patterns of alcohol

consumption (frequency, quantity, and frequency * quantity) were tested to determine if there

were differences relating to specific diabetes risk factors among never-diagnosed participants.

Tobacco consumption was also assessed.

Where nutrition and lifestyle responses from diagnosed participants differ from those who are

high-risk, it is likely that diagnosis has informed and shaped their choices. Where those who

high-risk provide similar responses to those who are diagnosed, but different from those who

are low-risk, it is likely that the lifestyle factors may be contributing to diabetes risk. Unless

specified otherwise, the significance tests performed on the data are one-way ANOVAs. All

frequency data (such as food frequency and alcohol consumption) were log transformed to

Nutrition, physical activity and lifestyle 245

better fit a normal distribution (ANOVA assumes a normal distribution). Details of statistical

results are provided in Appendices M to P.

8.1. Nutrition results

The means for each group for the items in the FFQ are shown in Table 8.1, in descending order

of overall frequency.

Table 8.1 Mean weekly frequencies of foods eaten, by diabetes category and by sexa

Females Males

D H L G All ♀ D H L All ♂ All

Fresh vegetables 6.77 7 7.2 6.35 6.93 5.58 5.48 5.5 5.53 6.32

White bread 5.67 7.64 6.54 4.4 6.08 4.92 5.63 7.96 6.41 6.23

Add table salt to cooked food 6.09 7.61 6.24 4.56 6.6 5.52 5.93 6.81 6.18 6.17

Whole milk, ice-cream, hard cheese, butter 4.73 5.85 5.24 4.54 5.02 4.52 5.64 6.3 5.49 5.23

Fresh fruit 4.77 3.71 4.32 4.08 4.4 4.22 3.21 3.49 3.73 4.11 Red meats (beef, pork,

lamb, lunch meats) 2.69 4.65 4.41 3.08 3.64 4.22 3.96 4.85 4.46 4

Wholegrain cereals (e.g. Weetbix / porridge) 3.98 3.5 3.7 2.02 3.6 3.8 2.71 5.17 4.24 3.88

Soft drink (non ‘diet’) 1.34 3.02 3.71 3.16 2.68 1.98 3.02 3.11 2.66 2.67

Wholemeal / wholegrain bread 2.67 3.1 2.37 2.32 2.55 3.41 2.04 1.92 2.51 2.53

Poultry (chicken) 2.12 1.91 2.41 3.21 2.5 2.37 1.8 2.05 2.13 2.34

Salty snacks (chips, peanuts, corn chips) 1.83 3.02 2.55 1.04 2.16 1.86 1.59 2.41 2.06 2.12

Pastries (pie, cake, biscuits, sweet rolls,

doughnuts) 1.48 2.03 2.34 0.94 1.83 1.7 1.63 2.17 1.9 1.86

The fat on meat 2.03 2.67 1.73 1.52 1.92 2.08 0.75 1.86 1.76 1.85

Take-away / fast food 1.69 1.81 2.44 1.02 1.93 1.57 1.5 1.88 1.7 1.83

Diet soft drink 2.17 2.72 0.94 2.09 1.72 2.71 0.8 0.99 1.64 1.68

Reduced-fat / low-fat dairy products 2.17 1.55 1.12 0.85 1.53 2.92 0.57 1.46 1.88 1.68

Frozen meals (e.g. McCain’s) 1.04 0.98 1.31 1.04 1.14 1.23 0.34 1.63 1.27 1.2

Fish and seafood 0.6 1.79 1.26 2.02 1.15 0.84 1.05 1.37 1.11 1.14

Bush tucker 0.55 1.1 0.6 0.67 0.74 1.02 0.39 0.69 0.76 0.75 a Mean number of times per week a particular food is consumed (not log transformed), listed in descending order of overall mean frequency. The number of responses in each category for each food item are given in Figures 8.1 to 8.19.


8.1.1. Food frequencies and overall diabetes risk

Each item on the food frequency questionnaire was analysed to determine whether there were

any differences in the frequencies of consumption of certain foods between categories of risk

(for example: diagnosed, high and low). Results of frequency comparisons are presented in the

order in which they appeared in the survey, so that related foods, such as fruit and vegetables,

are discussed together (Figures 8.1 to 8.19).

Salt

Salt was added to cooked food very frequently, the overall mean weekly frequency was 6.17

(Table 8.1). There was very little difference between any of the groups (Figure 8.1), with

females reporting slightly more frequent salt use than males. People with diagnosed diabetes

reported slightly lower salt usage than the average. None of these differences between groups

were significant (p=0.072).

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Figure 8.1. Weekly frequency (log) of adding table salt to cooked food (95% confidence interval of the mean).

‘Adding table salt to cooked meals’ does not include any salt use during cooking, but this is

likely to parallel salt use after cooking, as both depend on how liberal attitudes are towards salt

use; those who add salt to meals are unlikely to be averse to its use in cooking.

Soft drink

On average, people with diagnosed diabetes consumed less regular soft drink than other groups,

and were about twice as likely to consume diet soft drink as regular soft drink (Table 8.1). As

diagnosed participants consumed diet soft drink at about the same rate as the overall average

consumption of regular soft drink, this suggests they were substituting diet soft drink for regular


rather than choosing a different style of beverage. People who had never been diagnosed with

diabetes consume regular soft drink two to three times as often as diet soft drink.

Women with diagnosed diabetes consume soft drink significantly less frequently than low-risk

women (p=0.001) (Figure 8.2). Diagnosed women and men drink diet soft drink more often

than others (p<0.001). Diabetes may actually promote a preference for sweet tastes which

increases the desire for sweet drinks (Perros et al. 1996). This may explain why soft drink

consumption remains so high among those who have been diagnosed, although a switch has

been made to diet versions.

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Figure 8.3. Weekly frequency (log) of diet soft drink consumption (95% confidence interval of the mean).

Figure 8.2. Weekly frequency (log) of regular (non-diet) soft drink consumption (95% confidence interval of the mean).


The confidence interval was largest for women in the high-risk group, which may reflect

greatest variability in consumption in addition to small sample size. It is possible that some of

the high-risk women see themselves as being at risk due to their higher BMIs, and have acted on

the message to cut down on sugar consumption. This is supported on closer examination.

Among women who had never been diagnosed, those whose BMI fell into the highest quintile

were drinking diet soft drink significantly more often (log transformed frequency = –0.32) than

those in the lower two quintiles (quintile 1: frequency = –3.97, quintile 2: frequency = –4.19,

p=0.037 and 0.044 respectively). However, as higher-BMI women are also drinking the same

amount of non-diet soft drink as other women (p=0.999), it could be that they are attempting to

improve their diet; they may have been drinking more previously but have now substituted some

of their soft drink consumption with a perceived healthier option.

Fish and seafood

Fish or seafood were consumed about once per week on average (Table 8.1). Although men on

average reported eating more fish than women, differences between categories were not

significant (p=0.069) (Figure 8.4). Participants with diagnosed diabetes consumed fish about

half as frequently as those who had never been diagnosed or had gestational diabetes (Table

8.1).

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Figure 8.4. Weekly frequency (log) of fish and seafood consumption (95% confidence interval of the mean).


Vegetables and fruit

There were no significant differences in vegetable consumption frequencies between categories

(p=0.395) (Figure 8.5), although overall, women ate vegetables significantly more frequently

than men (p=0.035). Frequency of vegetable consumption was higher than that of fruit across

all groups. The differences between categories in fruit consumption were not significant

(p=0.467) and females and males reported similar frequencies overall (p=0.643), although on

average, diagnosed men also reported eating fruit more often than other males.

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Figure 8.5. Weekly frequency (log) of fresh vegetable consumption (95% confidence interval of the mean).

Figure 8.6. Weekly frequency (log) of fresh fruit consumption (95% confidence interval of the mean). 3946 141212 3342N =

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ruit

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Frozen meals

Frozen meals were included as a category in the FFQ as they represent a convenience food, but

one that is generally less unhealthy than take-away. Frozen meals were reported to be

consumed on average between one and two times a week for males and females. Low-risk

participants reported the most frequent consumption, followed by the diagnosed group and

women who had had gestational diabetes (Figure 8.7). Aside from women with gestational

diabetes diagnosis, this high-risk group, particularly men, had the lowest consumption

frequency. These differences however were not found to be significant (p=0.424).

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Pastries

Those in the diagnosed group reported eating pastries less often than

Overall, women ate pastries slightly less frequently than men, altho

group reported eating them more frequently than men in the h

consumption was between one and two times per week. These diffe

(p=0.327).

Figure 8.7. Weekly frequency (log) of frozen meal consumption (95% confidence interval ofthe mean).

other groups (Figure 8.8).

ugh those in the low-risk

igh-risk group. Overall

rences were not significant


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Poultry

On average, women who had gestational diabetes ate poultry more frequently than other groups,

at more than three times per week (Table 8.1). Participants in the low-risk group were the next

greatest consumers, eating poultry on average around 2.5 times per week. They were followed

by those with diagnosed diabetes, and high-risk participants ate it the least often, less than twice

per week. None of these differences were found to be significant (p=0.442) (Figure 8.9).

Figure 8.8. Weekly frequency (log) of pastries consumption (95% confidence interval of the mean).

Figure 8.9. Weekly frequency (log) of poultry consumption (95% confidence interval of the mean).

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Red meat

Red meats (such as beef, pork, lamb, lunch meats) were consumed much more often than

poultry by all groups, and by men more frequently than women except among high-risk

participants (Table 8.1). The most frequent consumers of red meat were the men in the low-risk

group, averaging about five times per week overall. Both low- and high-risk women ate red

meat about 4.5 times per week, high-risk men and diagnosed men around four times, the

gestational diabetes group around 3 times, and diagnosed women around 2.5 times. Differences

between groups were significant (p=0.045) (Figure 8.10).

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Salty snacks

Salty snacks (chips, peanuts, corn chips) were reported to be eaten on

twice a week (Table 8.1). There was little difference between males

the high-risk group women reported eating salty snacks twice as ofte

men in that group. Women in the low-risk group also reported eating

than low-risk men, while for the diagnosed group there was very littl

had gestational diabetes consumed salty snacks less frequently on av

these differences were significant (p=0.286).

Figure 8.10. Weekly frequency (log) of red meat consumption (95% confidence interval of the mean).

average a little more than

and females overall, but in

n (three times per week) as

snacks slightly more often

e difference. Women who

erage than others. None of


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Take-away / fast food

Overall, women eat take-away foods more frequently than men, with

the gestational diabetes group who are the lowest frequency consum

(Table 8.1). The highest frequency consumers were the low-risk wo

times per week. Men overall were averaging about 1.5 times per we

the remaining women. These intergroup differences were significant

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Figure 8.11. Weekly frequency (log) of salty snacks consumption (95% confidence interval ofthe mean).

the exception of women in

ers (about once per week)

men, averaging nearly 2.5

ek, slightly less often than

(p=0.022) (Figure 8.12).

Figure 8.12. Weekly frequency (log) of take-away consumption (95% confidence interval ofthe mean).


The fat on meat

Asked how often they eat the fat on meat, participants with diagnosed diabetes gave the highest

mean consumption frequency, apart from women in the high-risk group who reported eating

meat fat nearly three times per week (Table 8.1). In contrast, men in the high-risk group ate

meat fat the least often at less than once per week. In general there was little difference between

men and women, none of the intergroup variation was found to be significant (p=0.546) (Figure

8.13).

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Figure 8.13. Weekly frequency (log) of meat fat consumption (95% confidence interval of the mean).

Wholemeal / wholegrain bread and white bread

Diagnosed participants overall reported more frequent consumption of wholemeal or wholegrain

bread than either high-risk or low-risk participants (Table 8.1) although these differences were

not significant (p=0.501). White bread was consumed much more frequently than wholemeal

bread by all groups, and those with diagnosed diabetes consume white bread less frequently

than other participants (p=0.005).


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Wholegrain cereals

Participants with diagnosed diabetes and those in the low-risk g

frequency of consumption of wholegrain cereals, such as Weet-Bi

among men (Table 8.1). The lowest frequencies were reported by m

and among women who had had gestational diabetes. These differ

(p=0.357) (Figure 8.16).

Figure 8.14. Weekly frequency (log) of wholemeal / wholegrain bread consumption (95% confidence interval ofthe mean).

Figure 8.15. Weekly frequency (log) of white bread consumption (95% confidence interval of the mean).

roup reported the highest

x and porridge, especially

en in the high-risk group

ences were not significant


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Dairy foods

Diagnosed women and men were the least frequent consumers of

most frequent consumers of reduced-fat dairy (Table 8.1), suggesting

they had made since diagnosis. Among those diagnosed, reduc

average twice a week for women and three times a week for men, v

other groups. There were no significant differences between groups

(Figure 8.17), but differences were significant for reduced-fat dairy (

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Figure 8.16. Weekly frequency (log) of wholegrain cereals consumption (95% confidence interval ofthe mean).

full-fat dairy foods but the

this was a substitution that

ed-fat dairy was eaten on

ersus about once a week for

for full-fat dairy (p=0.609)

p=0.024) (Figure 8.18).

Figure 8.17. Weekly frequency (log) of full-fat dairy products consumption (95% confidence interval of the mean).


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Bush tucker

Reported consumption of bush tucker for most groups was about once a fortnight, with male

diagnosed participants reporting frequency about once a week and female high-risk at nearly

twice a week (Table 8.1). Both low-risk and diagnosed women reported less frequent

consumption than men in the same groups. None of these differences were found to be

statistically significant (p=0.103) (Figure 8.19).

Figure 8.18. Weekly frequency (log) of reduced-fat / low-fat dairy products consumption (95% confidence interval of the mean).

Figure 8.19. Weekly frequency (log) of bush tucker consumption (95% confidence interval of the mean).

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Combined food groups and ratios

Some food groups were then combined for further analysis to determine whether broader

categories differed between groups. Wholemeal bread, white bread and cereals were combined

to create a new category of total cereal consumption (Figure 8.20). Low-fat and full-fat dairy

were combined into total dairy foods, and fish, poultry and red meat were combined to form a

total meat group (Figures 8.21 and 8.22). There were no significant differences between groups

on any of these three measures (cereals: p=0.194, dairy: p=0.697, meat: p=0.055). For meat

consumption, the difference in mean frequency between total meat consumption of those

diagnosed and those who had never been diagnosed (high- and low-risk combined) was

however significant (p=0.021), suggesting that diagnosis had led to a minor drop in the

frequency of meat consumed.

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Figure 8.20. Weekly frequency (log) of total cereal consumption (breads plus cereals) (95% confidence interval of the mean).


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Figure 8.21. Weekly frequency (log) of total dairy consumption (full-fat plus low-fat) (95% confidence interval of the mean).

Figure 8.22. Weekly frequency of total meat consumption (fish, poultry, red meat) (95% confidence interval of the mean). 3744 141112 3342N =

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2.6

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.8

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Further combinations included all meat with all dairy to produce a total animal foods group, and

breads and cereal with fruit and vegetables to produce a total plant foods group (Figures 8.23

and 8.24). Total plant foods provides an indication of relative fibre consumption, potentially

important as low fibre intake has been associated with elevated risk of diabetes (Salmerón et al.

1997a; Salmerón et al. 1997b), while total animal foods provides an indication of relative

protein and fat intake. Variation between risk categories, however, was not significant for either

plant foods or animal foods (total animal foods p=0.407, total plant foods p=0.610).


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Figure 8.23. Weekly frequency (log) of total animal foods consumption (total meat plus total dairy) (95% confidence interval of the mean).

Figure 8.24. Weekly frequency of total plantfoods consumption (total cereals plus vegetables and fruit) (95% confidence interval of the mean). 3945 141011 3240N =

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Ratios between certain food groups were also examined to determine whether these differed

systematically between groups and to give an indication of how much of a change in diet might

have been made after diabetes diagnosis. The ratios that were analysed were wholemeal bread

to white bread, low-fat dairy to full-fat dairy, total cereals to total meat, total cereals to total

animal foods and total plant foods to total animal foods (Figures 8.25 to 8.29). The frequency

ratio between low-fat and full-fat dairy foods was significantly higher among people who had

been diagnosed than those who had never been diagnosed (p=0.002), suggesting that this was a

major dietary change they had implemented. No significant differences were found among the


remaining ratios (bread: p=0.135, cereals to meat: 0.197, cereals to animal foods: 0.579, plant

food to animal foods: p=0.410), although diagnosed diabetics had a higher cereal to meat

frequency ratio than non-diagnosed participants, but this did not reach significance (p=0.201).

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Figure 8.25. Weekly frequency (log) ratio ofwholemeal bread to white bread consumption (95% confidence interval of the mean).

Figure 8.26. Weekly frequency (log) ratio oflow-fat to full-fat dairy foods consumption (95% confidence interval of the mean).

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Figure 8.27. Weekly frequency ratio of total cereals to total meat consumption (95% confidence interval of the mean).

Figure 8.28. Weekly frequency ratio of total cereal to animal foods consumption (95% confidence interval of the mean).

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8.1.2. Food frequencies and specific diabetes risk factors

The quintile range for each diabetes risk factor are provided in Appendix M. Where significant

(<0.05) associations or a non-significant tendency (<0.10) were found using ANOVA, the

relationships are graphed. These illustrate that not all significant differences between risk

quintile groups exhibit a clear, unidirectional association with food frequency, such as

increasing risk with increased consumption frequency of a particular food.

Fasting blood sugar level

The food frequency items which were both significantly associated (p<0.05) and those with a

non-significant tendency towards association (p<0.1) with FBSL are shown in Table 8.2. The

relationship between these food items and FBSL quintiles are illustrated in Figure 8.30.

Table 8.2. Food items (including combined and ratios) that were significantly associated with FBSL. Items with a non-significant association (p<0.10) are included.

Females Males All females Never-diagnosed All males Never-diagnosed

significant (p<0.05)

soft drink (0.002)

diet soft drink (0.014)

salt (0.033)

wholemeal bread (0.025)

wholemeal: white bread ratio

(0.006)

fat on meat (0.026)

trend (p<0.10)

salt (0.064)

take-away (0.063)

cereal: meat (0.058)

fat on meat (0.062)

white bread (0.084)

low-fat dairy (0.065)

frozen meals (0.061)

take away (0.088)

wholemeal bread(0.073)

Figure 8.29. Weekly frequency ratio of plant foods to animal foods consumption (95% confidence interval of the mean).


Females (all)

fasting BSL quintiles

54321

mea

n so

ft dr

ink

frequ

ency

(log

)

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

Females (all)


54321

mea

n sa

lt fre

quen

cy (l

og)

2.0

1.5

1.0

.5

0.0

-.5

Females (all)

54321

mea

n di

et s

oft d

rink

frequ

ency

(log

) -1.0

-1.5

-2.0

-2.5

-3.0

-3.5

-4.0

fasting BSL quintilesFemales (all)


54321

mea

n ta

ke a

way

freq

uenc

y (lo

g)

.5

0.0

-.5

-1.0

-1.5

Females (never diagnosed)2.5

fasting BSL quintilesMales (all)


54321mea

n w

hole

mea

l to

whi

te b

read

ratio

(log

)

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

-3.0

fasting BSL quintilesMales (all)


54321

mea

n w

hole

mea

l bre

ad fr

eque

ncy

(log) 1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

Females (all)

54321

mea

n ce

real

to m

eat r

atio

1.4

1.2

1.0

.8

.6

.4

.254321

mea

n sa

lt fre

quen

cy (l

og) 2.0

1.5

1.0

.5

0.0

-.5

-1.0

Figure 8.30. Food items associated with FBSL amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10) (continued next page).

Nutrition, physical activity and lifestyle

265

Figure 8.30 continued. Food items associated with FBSL amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10).

Males (all)


54321

mea

n fa

t on

mea

t fre

quen

cy (l

og)

-.5

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5

-4.0

Males (all)


54321

mea

n lo

w fa

t dai

ry fr

eque

ncy

(log)

-.5

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5

-4.0

log

al fr

eq

roze

m

)

eque

nc

l bre

a

who

l

Males (all)


54321

mea

n w

hite

bre

ad fr

eque

ncy

(log)

2.2

2.0

1.8

1.6

1.4

1.2

1.0

.8

.6

Males (never diagnosed)


54321

mea

n fa

t on

mea

t fre

quen

cy (l

og)

0.0

-1.0

-2.0

-3.0

-4.0

-5.0



54321

mea

n ta

ke a

way

freq

uenc

y (lo

g)

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0



54321

ean

fn

me

unec

y (

)

-.5

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5

-4.0



54321

mea

nem

ead

fry

(log .5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5


BMI

The food frequency items which were significantly associated with BMI and those showing a

non-significant tendency towards an association are shown in Table 8.3. The relationship

between these items and BMI quintiles are illustrated in Figure 8.31.

Table 8.3. Food items (including combined and ratios) that were significantly associated with BMI. Items with a non-significant trend association (p<0.10) are included.

Females Males

All females Never-diagnosed All males Never-diagnosed




total cereal (0.005)

total plant foods (0.002)

total dairy foods (0.040)

soft drink (0.002)

trend (p<0.10) fruit

(0.062) cereals (0.087)

Females (all)

BMI quintiles

54321

mea

n di

et s

oft d

rink

frequ

ency

(log

) -.5

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5

-4.0

Females (never diagnosed)

BMI quintiles

54321

mea

n di

et s

oft d

rink

frequ

ency

(log

) -1.0

-2.0

-3.0

-4.0

-5.0


BMI quintiles

54321

mea

n to

tal p

lant

food

s (lo

g)

3.4

3.2

3.0

2.8

2.6

2.4


BMI quintiles

54321

mea

an to

tal c

erea

ls fr

eque

ncy

(log)

3.0

2.8

2.6

2.4

2.2

2.0

1.8

1.6

Figure 8.31. Food items associated with BMI amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10) (continued next page).


Males (all)

BMI quintiles

54321

tota

l dai

ry fo

ods

frequ

ency

(log

)2.5

2.0

1.5

1.0

.5

0.0

Males (all)

BMI quintiles

54321

mea

n fru

it fre

quen

cy (l

og)

1.5

1.0

.5

0.0

-.5

-1.0


BMI quintiles

54321

mea

n so

ft dr

ink

frequ

ency

(log

)

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5


BMI quintiles

54321

mea

n ce

real

s fre

quen

cy (l

og)

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

Figure 8.31 continued. Food items associated with BMI amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10).

Waist circumference

The food frequency items which were significantly associated with waist circumference and

those tending towards association are shown in Table 8.4. The relationship between these

items and waist circumference quintiles are illustrated in Figure 8.32.


Table 8.4. Food items (including combined and ratios) that were significantly associated with waist circumference. Items with a non-significant trend association (p<0.10) are included.

Females Males



soft drink (0.028)


salt (0.029)


white bread (0.027)

bush tucker (0.004)

total cereals (0.007)


fat on meat (0.044)

trend (p<0.10)

low-fat: regular dairy

(0.062)

poultry (0.072)

salt (0.080)

white bread (0.098)

)

log

uenc

y (

req

rink

f

sof

t d

ean

m

(log

)

ratio

airy

ular

d

reg

fat t

o

n lo

w

mea

Females (all)

waist quintiles

54321

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

Females (all)

waist quintiles

54321

mea

n di

et s

oft d

rink

frequ

ency

(log

) -.5

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5

-4.0

-4.5

Females (all)

waist quintiles

54321

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5

-4.0


waist quintiles

54321

mea

n sa

lt fre

quen

cy (l

og)

2.5

2.0

1.5

1.0

.5

0.0

-.5

-1.0

Figure 8.32. Food items associated with waist circumference amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10) (continued next page).


y (lo

g

k fre

q

et s

o

me

ncy

(

cker

f

an b

u


waist quintiles

54321

mea

n w

hite

bre

ad fr

eque

ncy

(log)

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0


waist quintiles

54321

an d

ift

drin

uenc

) 0.0

-1.0

-2.0

-3.0

-4.0

-5.0


waist quintiles

54321

mea

n to

tal c

erea

ls fr

eque

ncy

(log)

2.8

2.6

2.4

2.2

2.0

1.8

1.6


waist quintiles

54321

me

sh tu

requ

elo

g)

0.0

-1.0

-2.0

-3.0

-4.0

-5.0


waist quintiles

54321

mea

n po

ultry

freq

uenc

y (lo

g)

1.5

1.0

.5

0.0

-.5

-1.0


waist quintiles

54321

mea

n to

tal p

lant

food

s fre

quen

cy (l

og) 3.4

3.2

3.0

2.8

2.6

2.4

Figure 8.32 (continued). Food items associated with waist circumference amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10) (continued next page).


Males (all)

waist quintiles

54321

mea

n sa

lt fre

quen

cy (l

og)

2.0

1.5

1.0

.5

0.0

-.5

Males (all)

waist quintiles

54321

mea

n w

hite

bre

ad fr

eque

ncy

(log)

2.2

2.0

1.8

1.6

1.4

1.2

1.0

.8

.6


waist quintiles

54321

fat o

n m

eat f

requ

ency

(log

)

1.0

0.0

-1.0

-2.0

-3.0

-4.0

-5.0

Figure 8.32 (continued). Food items associated with waist circumference amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10).

The food frequency items which were significantly associated with systolic blood pressure, and

those tending towards association, are shown in Table 8.5. The relationship between these

items and systolic pressure quintiles are illustrated in Figures 8.42 to 8.45.


Table 8.5. Food items that were significantly associated with systolic pressure. Items with a non-significant trend association (p<0.10) are included.

Females Males



red meat (0.041)

total animal foods (0.005)

cereals: animal foods (0.019) plant: animal foods (0.008)

cereals (0.030)

trend (p<0.10)

salt (0.089) pastries (0.091)

fruit (0.087) cereals (0.086)

bush tucker (0.064)

low-fat: regular dairy (0.095)


salty snacks (0.093) cereals (0.062)

total meat (0.063)

pastries (0.061)


Females (all)

systolic pressure quintiles

54321

mea

n re

ad m

eat f

requ

ency

(log

)

1.5

1.0

.5

0.0

-.5

-1.0



54321

mea

n sa

lt fre

quen

cy (l

og)

2.5

2.0

1.5

1.0

.5

0.0

-.5



54321

mea

n pa

strie

s fre

quen

cy (l

og)

1.5

1.0

.5

0.0

-.5

-1.0

-1.5



54321

mea

n fru

it fre

quen

cy (l

og)

2.0

1.5

1.0

.5

0.0

-.5

-1.0

Figure 8.33. Food items associated with systolic blood pressure amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicatessignificant association (p<0.05), dotted line indicates a trend association (p<0.10) (continued next page).


tio (l

al fo

als

to

mea

n

og)

eque

n

ean



54321

mea

n bu

sh tu

cker

freq

uenc

y (lo

g)

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5

-4.0

Males (all)


54321

mea

n to

tal a

nim

al fo

ods

frequ

ency

(log

) 3.0

2.8

2.6

2.4

2.2

2.0

1.8



54321

mea

n ce

real

freq

uenc

y (lo

g)

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0



54321

mea

n lo

w fa

t to

regu

lar d

airy

ratio

(log

) -1.5

-2.0

-2.5

-3.0

-3.5

-4.0

-4.5

Males (all)


54321

cer

e a

nim

ods

raog

) .4

.2

-.0

-.2

-.4

-.6

Males (all)


54321

mea

n pl

ant t

o an

imal

food

s ra

tio (l

og) 1.0

.8

.6

.4

.2

0.0

Males (all)

54321

mea

n pa

strie

s fre

quen

cy (l

og)

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

Males (all)

54321

m s

alt f

rcy

(l

2.5

2.0

1.5

1.0

.5

0.0

-.5

-1.0

systolic pressure quintilessystolic pressure quintiles

Figure 8.33 (continued). Food items associated with systolic blood pressure by all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10) (continued next page).


Figure 8.33 (continued). Food items associated with systolic blood pressure amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10).

Males (all)


54321

mea

n ce

real

freq

uenc

y (lo

g)

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

Males (all)


54321

mea

n sa

lty s

nack

s fre

quen

cy (l

og)

.5

0.0

-.5

-1.0

-1.5

-2.0



54321

mea

n ce

real

freq

uenc

y (lo

g)

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

Males (all)


54321

mea

n to

tal m

eat f

requ

ency

(log

)

2.4

2.2

2.0

1.8

1.6

1.4



54321

mea

n to

tal p

lant

food

s fre

quen

cy (l

og) 3.4

3.2

3.0

2.8

2.6

2.4



54321

mea

n pa

strie

s fre

quen

cy (l

og)

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

-3.0

Diastolic blood pressure

The food frequency items which were significantly associated with diastolic blood pressure, and

those showing a trend towards association, are shown in Table 8.6. The relationship between

these items and diastolic pressure quintiles are illustrated in Figure 8.34.


Table 8.6. Food items that were significantly associated with diastolic pressure. Items with a non-significant trend association (p<0.10) are included.

Females Males


significant (p<0.05) salt

(0.032)

salt (0.031)

soft drink (0.029) cereals (0.002)



pastries (0.025) cereals (0.004)



trend (p<0.10) poultry

(0.096) total cereals: meat (0.051)


diastolic pressure quintiles

54321

mea

n sa

lt fre

quen

cy (l

og)

2.5

2.0

1.5

1.0

.5

0.0

-.5



54321

mea

n po

ultry

freq

uenc

y (lo

g)

1.0

.5

0.0

-.5

-1.0

Males (all)


54321

mea

n ce

real

freq

uenc

y (lo

g)

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

Males (all)


54321

mea

n to

tal c

erea

ls (l

og)

3.0

2.8

2.6

2.4

2.2

2.0

1.8

1.6

Figure 8.34. Food items associated with diastolic blood pressure amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10) (continued next page).


Males (all)


54321

mea

n to

tal p

lant

food

s fre

quen

cy (l

og) 3.4

3.2

3.0

2.8

2.6

2.4

Males (all)


54321

mea

n ce

real

s to

mea

t rat

io (l

og)

1.0

.8

.6

.4

.2

0.0



54321

mea

n pa

strie

s fre

quen

cy (l

og)

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5



54321

mea

n ce

real

freq

uenc

y (lo

g)

2.0

1.0

0.0

-1.0

-2.0

-3.0



54321

mea

n to

tal c

erea

ls fr

eque

ncy

(log)

3.2

3.0

2.8

2.6

2.4

2.2

2.0

1.8

1.6



54321

mea

n to

tal p

lant

food

s fre

quen

cy (l

og) 3.4

3.2

3.0

2.8

2.6

2.4

Figure 8.34 (continued). Food items associated with diastolic blood pressure amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10).

Age

As some of these differences could be in part due to cohort effects, and intergenerational

changes in food preferences, food frequency was also analysed in terms of age (Table 8.7).

Where age is related to frequency of certain foods, frequencies may still be affected by

diagnosis status (diagnosed participants may be more likely to have altered their diets) or food


frequencies that vary by age could simultaneously be affecting other diabetes risk factors. Food

frequencies more affected by cohort alone are more likely to be those found significant amongst

participants who had never been diagnosed with diabetes, rather than in all participants.

Relationships between food frequency and age are shown in Figure 8.35.

Table 8.7. Food items that were significantly associated with participants’ age. Items with a non-significant trend association (p<0.10) are included.

Females Males



soft drink (0.024)

salty snacks (0.045)

take-away (0.021) cereals (0.004)

plant: animal foods (0.024)

take away (0.040) cereals (0.013)

soft drink (0.049)

fat on meat (0.012)

low-fat dairy (0.033)

total plant: animal foods (0.012)

bush tucker (0.025)

trend (p<0.10)



take away (0.094)

regular dairy (0.083)

bush tucker (0.094)

total dairy (0.088)

low-fat: regular dairy (0.088)

pastries (0.063)



g)

quen

t dai

mea

n

og)

eque

n

ean

s


age quintiles

54321

mea

n fre

quen

cy ta

keaw

ay (l

og)

1.0

.5

0.0

-.5

-1.0

-1.5


age quintiles

54321

mea

n ce

real

freq

uenc

y (lo

g)

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

Males (all)

age quintiles

54321

mea

n so

ft dr

ink

frequ

ency

(log

)

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

Males (all)

age quintiles

54321

mea

n fa

t on

mea

t fre

quen

cy (l

og)

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5

Males (all)

age quintiles

54321

low

fary

fre

cy (l

o

-.5

-1.0

-1.5

-2.0

-2.5

-3.0

-3.5

-4.0

Males (all)

age quintiles

54321mea

n to

tal p

lant

to a

nim

al fo

ods

ratio

(log

)

1.0

.8

.6

.4

.2

0.0

Males (all)

age quintiles

54321

mal

t fr

cy (l

2.0

1.5

1.0

.5

0.0

-.5

Males (all)

age quintiles

54321

mea

n pa

strie

s fre

quen

cy (l

og)

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

Figure 8.35. Food items associated with participant age amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10) (continued next page).


Males (all)

age quintiles

54321

mea

n ta

keaw

ay fr

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s fre

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3.3

3.2

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2.9

2.8

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2.5

Figure 8.35 (continued). Food items associated with participant age amongst all females (red), never-diagnosed females (purple), all males (blue) and never-diagnosed males (green). Solid line indicates significant association (p<0.05), dotted line indicates a trend association (p<0.10).


Even where specific food item frequencies differ significantly according to risk quintiles occur,

there are very few instances where a monotonic relationship is apparent. The exceptions to this

are summarised in Table 8.8.

Table 8.8. Summary of foods that appear to exhibit a dose-response relationship with specific diabetes risks. Based on significant differences between quintiles in combination with visual assessment of Figures 8.30 to 8.35.

Relationship (inverse / positive)

Risk Food item Males

salt positive FBSL

Females

fat on meat positive

BMI diet soft drink positive

salt positive waist circumference diet soft drink positive

total animal foods inverse systolic pressure

cereals inverse

cereals inverse

total cereal foods inverse diastolic pressure

total plant foods inverse

age take away inverse

Furthermore, although some themes do emerge, particularly in the consumption of certain food

types according to diagnosis status (in diet soft drink, meat and low-fat dairy), these distinctions

are not consistently found to be statistically significant. This suggests that although these

themes probably do represent ‘true’ patterns of food consumption, the food frequency

questionnaire that was used was not specific enough to produce significant results where they

might otherwise be expected. For example, ‘vegetables’ did not distinguish between green leafy

vegetables and root vegetables such as potatoes, which have very different nutrient profiles;

‘reduced fat dairy’ did not distinguish between types of dairy foods – reduced fat cheese and

ice-cream are not as low in fat as reduced fat milk. Using broad item categories may have

masked such differences in types and frequencies of foods consumed by different participant


groups. A food diary approach, or some more targeted method, may be more suited to tease out

finer differences in a future study.

The results of the food frequency questionnaire indicate that diabetes diagnosis does make

some, albeit subtle, difference to a person’s diet. The main differences seem to be in

substituting diet for regular soft drink, low-fat for full-fat dairy foods, and reducing total meat

consumption.

These findings have interesting implications for the modified thrifty genotype hypothesis

introduced in Section 2.3.2, and the evidence among some Aboriginal groups for higher protein

consumption and lower carbohydrate consumption improving metabolic control (for example,

O’Dea 1981), rather than the reverse which seems to be being practised. Those who have been

diagnosed with diabetes have apparently cut down on their meat consumption based on medical

advice to avoid fatty foods. Alternatively, perhaps their higher intake of carbohydrate foods in

relation to meat intake was a contributing factor to their development of diabetes, given that

never-diagnosed women in the highest BMI quintile also reported more frequent cereal intake.

Missed meals

The majority of participants in each group report missing meals regularly, but the frequency and

the reasons for missing meals differ from group to group (Table 8.9). Fewer diagnosed women

and men habitually miss meals, probably a reflection of educative advice to try to eat regularly.


Table 8.9. Missed meals Females Males

D (n=49)

H (n=15)

L (n=47)

G (n=12)

D (n=38)

H (n=15)

L (n=40)

Regularly miss meals 69.4% 73.3% 89.4% 100.0% 65.8% 86.7% 75.0%

Most common

frequency of missed meals

Every day

(28.1%)

Few times a

week and every day

(both 30.0%)

Few times a week

(26.8%)

Every day

(63.6%)

Few times a week

(40.0%)

Few times a week

(41.7%)

Few times a week

(38.7%)

Most common

reason for missed meals

Too busy / rushed (50.0%)

Not hungry (70.0%)

Not hungry (68.4%)

Not hungry (75.0%)

Not hungry (84.0%)

Not hungry (76.9%)

Not hungry (48.4%)

Some other reasons given for missing meals included drinking or being ‘grog sick’, being away

from home, playing cards, being stressed, getting up late, or being too tired.

Of those who missed meals, women in the gestational diabetes category missed meals more

frequently than any other group (p=0.006) (Figure 8.36). Participants with diagnosed diabetes

were, on average, less likely to miss meals than others. Participants may be missing meals as a

deliberate dietary strategy, perceiving that it would help them to lose weight or to not gain

weight. Baturka et al. (2000) for example found the women in their study believed that missing

meals would help them to lose weight.

3141 121011 2532N =

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CI m

iss

mea

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8.0

7.0

6.0

5.0

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Figure 8.36. Weekly frequency of missed meals (95% confidence interval of the mean).


Whether people regularly missed meals was unrelated to any diabetes risk factor, other than

negatively to age among all females (t-test: p=0.005). This most likely reflects diabetes

diagnosis rather than age as there were no differences according to age among never-diagnosed

women.

Nutrition security

Nutrition security at the household level is apparently lacking, with a substantial proportion of

participants reporting that they sometimes worry about not getting enough food: 14.3%(D♀),

6.7%(H♀), 14.9%(L♀), 31.6%(D♂) and 20.0%(L♂). This is higher than a more general

Queensland survey on food insufficiency, in which 11.3% of respondents reported insufficiency

at either individual or household level (Radimer et al. 1997a). None of the women who had had

gestational diabetes nor any of the high-risk men reported worrying about not getting enough

food.

Dietary beliefs

Approximately 20% of participants did not know whether they were eating a healthy diet overall

or not (Table 8.10). Never-diagnosed males were more likely than others to believe they eat a

healthy diet, while roughly half of all participants thought their diet was generally healthy. That

approximately 20% of diagnosed diabetics had no idea whether their diet was healthy or not

highlights an important gap in diabetes education. That a similar proportion think their diet is

unhealthy suggests that there are other barriers to improving diet in addition to a lack of

knowledge, perhaps motivational issues or food availability. These are discussed further in

Chapter Ten.

Table 8.10. Participants’ thoughts on whether their diet is healthy overall Females % Males %

D (n=49)

H (n=15)

L (n=47)

G (n=12)

D (n=38)

H (n=15)

L (n=40)

Yes 44.9 40.0 51.1 41.7 44.7 60.0 60.0

No 22.4 20.0 23.4 25.0 13.2 20.0 15.0

Don’t know 20.4 20.0 23.4 33.3 28.9 20.0 20.0

Among women who had never been diagnosed, those who thought their diet was generally

healthy had lower BMIs on average than women who thought their diet was unhealthy (means

27.3 and 31.7) but the difference was not statistically significant. There was no difference

among men. Perhaps women have greater understanding about healthy foods, or they may have


responded to this question by thinking about their own body size in relation to others and made

their decision based on that comparison. Many women may recognise that their diet is

unhealthy, but may lack the motivation or the means to change it.

Where people shop

The vast majority of people do most of their food shopping in Murgon: 81.6%(D♀), 86.7%(H♀),

93.6%(L♀), 100%(G♀), 71.1%(D♂), 93.3%(H♂) and 97.5%(L♂). A few people in each group

also rated Cherbourg as where they do most of their shopping in addition to Murgon, but this

was less than 3% in each group.

Many people said they would choose to shop somewhere else (for example, Kingaroy rather

than Cherbourg or Murgon) if suitable transport were available: 36.7%(D♀), 46.7%(H♀),

53.2%(L♀), 50.0%(G♀), 15.8%%(D♂), 53.3%(H♂) and 47.5%(L♂). Relatively few diagnosed

males feel that transport availability is a problem probably because older males are the group

with the highest car ownership level. This is supported too by the most common means of

getting to the shops (Table 8.11). Most people report that they do most of their own shopping.

Table 8.11. Main transport used to do the shopping Females %a Males %a

D (n=49)

H (n=15)

L (n=47)

G (n=12)

D (n=38)

H (n=15)

L (n=40)

Someone else shops 4.1 0.0 8.5 0.0 7.9 13.3 17.5

Own car 34.7 20.0 25.5 33.3 53.3 40.0 30.0

Borrowed car 8.1 26.7 10.6 16.7 2.6 13.4 12.5

Someone else drives in their car 26.5 26.7 42.5 25.0 13.1 26.7 32.5

Walk 10.2 0.0 2.1 16.7 7.9 0.0 7.5

Bicycle 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Other (e.g. taxi) 6.1 20.0 8.5 8.3 2.6 6.7 7.5 a Columns may sum to over 100% due to multiple responses


8.2. Lifestyle results

8.2.1. Physical activity

Twenty-two-point-six percent of female and 15.1% of male study participants reported that they

do no leisure time physical activity at all, even light activity. This compares 15% of Australians

as a whole (AIHW 2002a). A further 66.1% of females and 48.8% of males reported only light

activity, compared with 28% of Australians overall (AIHW 2002a).

By far the least recreationally active group are diagnosed women (Table 8.12). Only half of this

group regularly do any activity, such as walking, gardening or some other light outdoor activity,

and only one diagnosed women regularly plays sport. Very few people participate in regular

sporting activities. Men (36.1%) are far more likely to play sport than women (11.3%). Many

participants report walking regularly, but the speed of walking (and therefore the intensity of the

activity) may be low, and may therefore not have a great effect on reducing diabetes risk. For

example, Hu et al. (2001) found that faster usual walking speed was associated with reduced

risk from CVD among women.

Table 8.12. Participation in regular physical activity Females Males

D (n=49)

H (n=15)

L (n=47)

G (n=12)

D (n=38)

H (n=15)

L (n=40)

Regular light activity 53.1 73.3 89.4 75.0 68.4 80.0 80.0

Regular sporting activity 2.0 13.3 23.4 25.0 34.2 53.3 42.5

Of those who do play sport, it is most often played on three to four days per week. The main

sports played by participants include netball for women and rugby for men, with golf gaining

popularity rapidly since the successes of Tiger Woods.64

64 Tiger Woods (African-American professional golfer) was viewed as a role model by many of the golfers in the community. Golf seemed to be dominated by men – I did not hear of any women playing. It is likely that many of the men, especially the older men, who reported that they played regular sport were referring to golf, hence activity levels may be somewhat overestimated for some men as golf would be better classed as a light activity.


Overall, men are more physically active than women (Figures 8.37, 8.38, 8.39). People with

diagnosed diabetes have much lower activity levels, both leisure and occupational, than others

of their sex (Table 8.13). Diagnosed women had the lowest sporting activity participation rate.

Table 8.13. Mean activity levels: leisure, occupational and total activitya

Females Males

D (n=49)

H (n=15)

L (n=47)

G (n=12)

D (n=38)

H (n=15)

L (n=40)

Leisure activity 1.65 1.86 2.15 2.17 2.33 2.64 2.46

Occupational activity 1.72 1.83 1.95 1.92 1.91 2.20 2.57

Total activity 3.37 3.58 4.11 4.08 4.24 4.79 5.05 a Leisure and occupational activity are each scored out of 4, with the total a score out of 8, calculated as described in Section 5.4.1.

This quantification of activity levels corresponds fairly well to the categories of none, light,

moderate and vigorous used by Armstrong et al. (2000). In the results of the 1999 physical

activity survey, Armstrong et al. (2000) report that at least 150 minutes per week of moderate

activity (such as brisk walking) are required to confer a health benefit. Although participants

were not asked in the Cherbourg survey how much time they spent on each activity, the number

of days per week gives an indication of the amount of time likely to be involved.

Only 4.3% of women were categorised as having a leisure activity of 4, while approximately

35% of women in Australia generally undertake vigorous activity, 23% of Cherbourg men were

assessed as having a leisure activity level of 4 while just over 40% of men in Australia reported

undertaking vigorous activity (Armstrong et al. 2000).

Those with lowest risk of diabetes have the highest activity levels for their sex, suggesting that

exercise rather than diet might be more important in reducing risk of diabetes, which supports

previous studies (for example, Eriksson and Lindgärde 1991, Helmrich et al. 1991).


3946 141412 3343N =

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CI l

eisu

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ctiv

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ccup

atio

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ctiv

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vel

3.0

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h

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Figure 8.37. Leisure activity level (95% confidence interval ofthe mean).

Figure 8.38. Occupational activity level (95% confidence interval of the mean).


3744 141212 3343N =

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otal

act

ivity

leve

l

6.0

5.5

5.0

4.5

4.0

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d

g

h

l

Figure 8.39. Total activity level (95% confidence interval of the mean).

As with food frequency, diabetes risk factors were also analysed in relation to physical activity

levels for participants who had never been diagnosed with diabetes. Diagnosed participants

were omitted from these analyses as diagnosis may itself have an impact on how people both

perceive their ability to undertake certain types of activity and their motivation to do so.

Among females, most diabetes risk factors declined with increasing level of physical activity

(Figure 8.40). These differences were only significant for waist and age (Appendix N),

although there was also a trend towards significance for diastolic blood pressure (ANOVA:

p=0.045, 0.021 and 0.093 respectively). Among never-diagnosed males, there was an overall

pattern of declining risk with increased activity, but differences in risk between activity

approached significance only for age (ANOVA: p=0.065) (Figure 8.41). Although observed

declining risk may be attributable to younger age of more active participants, age among never-

diagnosed females was found to be related to diastolic blood pressure only and not waist

circumference, and was not related to any diabetes risk factor among never-diagnosed males

(Section 6.2.1). Physical activity may therefore reduce risk of diabetes among women chiefly

by reducing risk of central obesity, but not necessarily BMI. For example, Ball et al. (2001)

found that higher physical activity was associated with normal BMI and lower body fat among

women but not among men.


288

leisure activity level

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1.6
Figure 8.40. Means of individual risk factors according to level of leisure activity (never-diagnosed females).


289


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Figure 8.41. Means of individual risk factors according to level of leisure activity (never-diagnosed males).


For every risk factor, other than age (and possibly FBSL in women), there is a change in

direction of risk that occurs at activity level 4. This could mean that no additional gains are

made with physical activity beyond a certain level, or it could mean that the method of

estimating physical activity became less accurate beyond level 3. For example, if men reported

participating in sport three days a week they were categorised as having a leisure activity level

of 4, but the sport they were most likely to be playing was golf, rather than a moderate to

vigorous activity (for example, cycling or squash).

It could also be that the physical activity questions were not precise enough to tease out more

subtle differences between groups, as only intensity and frequency were investigated, and not

duration of activity as Pols et al. (1998) recommend. Neither were they objective rather than

subjective measures such as doubly-labelled water or movement sensors, as recommended by

Wareham and Rennie (1998), methods which would not have been feasible in the present

study.65 Frequency and intensity, however, should be sufficient in this case to provide a relative

measure of activity between individuals and groups.

A further reason for the absence of statistical significance is again the small sample size. Table

8.14 provides estimates of the sample sizes required to produced statistically significant results.

65 Such methods are both more intrusive and could serve to increase people’s perceptions of feeling like guinea-pigs and reduce participation. For example, during my fieldwork, permission was refused by the Cherbourg Health Action Committee for a proposed study of body fat and fat-free mass using bioelectrical impedance. This study was primarily intended as a trial to test the method, and offered no obvious benefit to the community.


Table 8.14. Estimates of sample sizes required to produce statistically significant results for physical activity level with the magnitude of differences observed in the sample availablea

Risk factor Females Males

Fasting BSL (mmol) 860 2390

BMI 44 115

Waist circumference (cm) 50 880

Systolic pressure (mmHg) 410 1260

Diastolic pressure (mmHg) 118 635

a Estimates calculated using PS Power and Sample Size Calculations version 2.1.30. Estimates were derived for physical activity as dichotomous (low-high) using an alpha level of 0.05 and a power level of 0.8 and are rounded to the nearest 5.

In general, men were much more likely than women to consider themselves to be both more

active and fitter than others of the same age and sex (Table 8.15).

Table 8.15. Subjective comparison of activity level and fitness level to others of the same age and sex

Females Males D

(n=49) H

(n=15) L

(n=47) G

(n=12) D

(n=38) H

(n=15) L

(n=40)

More active 12.2 13.3 10.6 16.7 31.6 20.0 32.5

About as active 44.9 40.0 59.6 41.7 34.2 53.3 40.0

Less active 30.6 33.3 25.5 41.7 29.1 20.0 25.0

Fitter 2.0 6.7 4.3 8.3 18.4 20.0 22.5

About as fit 51.0 53.3 63.8 41.7 39.5 60.0 55.0

Less fit 30.6 26.7 25.5 50.0 26.3 20.0 20.0

There were no significant differences in diabetes risk factors between the comparison groups

(less active/fit, about as active/fit, more active/fitter) for either women or men (Appendix N),

although there was an overall pattern for those who thought themselves to be more active and


fitter to have lower risk factor measurements, suggesting that perceptions of relative activity and

fitness may be fairly accurate (Figures 8.42 to 8.45). This pattern was less obvious among men.

Interestingly, subjective rating of activity level increases with age among females, suggesting

either that older women are either more active than younger women or they think that other

older women are less active than themselves. The reverse occurs among males, with older men

more likely to consider themselves less active than their peers, while younger men consider

themselves more active. This may be due to overconfidence among young men regarding their

health, and is possibly related to why so many of them are at greater risk of undiagnosed

diabetes (Section 6.3.2).


155834N =

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Figure 8.42. Confidence interval of the means of diabetes risk factors according to subjective comparison of activity levels with others of the same age and sex (females). Figure 8.43. Confidence interval of the means of diabetes risk factors according to subjective comparison of activity levels with others of the same age and sex (males).

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100

90

80

70

6056533N =

fitte

r

ab

ou

t a

s fit

less

fit

95%

CI sy

stolic

pre

ssure

(m

mH

g)

160 150 140 130 120 110 100

90 80

Figure 8.44. Confidence interval of the means of diabetes risk factors according to subjective comparison of fitness levels with others of the same age and sex (females). of fitness levels with others of the same age and sex (females). 10

8

7

6

5

150

140

130

120

Figure 8.45. Confidence interval of the means of diabetes risk factors according to subjective comparison of fitness levels with others of the same age and sex (males). Figure 8.45. Confidence interval of the means of diabetes risk factors according to subjective comparison of fitness levels with others of the same age and sex (males).

174116N =

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ou

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s f

it

less f

it

95%

CI w

ais

t circum

fere

nce (

cm

)

120

110

100

90

80

194621N =

fitt

er

ab

ou

t a

s f

it

less f

it

95%

CI age (

years

)

48

46

44

42

40

38

36

34

32

184116N =

fitt

er

ab

ou

t a

s f

it

less f

it

95

% C

I B

MI

34

32

30

28

26

24174117N =

fitt

er

ab

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less f

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95%

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sting B

SL

9

174017N =

fitt

er

ab

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95%

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ressure

(m

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174017N =

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less f

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95%

CI dia

sto

lic p

ressure

(m

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98

96

94

92

90

88

86

84

82


Perceived relative fitness seems to have very little to do with objective measures of body shape

(BMI and waist circumference) among women (Figure 8.44), but men who think they are fitter

than their peers also have slightly lower mean BMI and waist circumference (Figure 8.45). The

much larger confidence interval for means of women who think they are fitter than their peers is

probably due to the very small number in this category (5).

8.2.2. Alcohol consumption

Many participants reported that they never drink alcohol, especially women (Table 8.16), and

most of those who consume alcohol do so on fewer than two days per week. On a day when

alcohol is used, however, the number of drinks consumed may be very high. In Australia as a

whole, approximately 17% of adults do not drink alcohol (AIHW 2002a), substantially fewer

than among participants in the present study where 45% of all females and 26% of all males

reported that they did not drink alcohol.

The proportion of non-drinkers is not static across groups. Reported alcohol frequency and

amount, in addition to anecdotal evidence from study participants, suggest that diagnosis

impacts considerably on people’s pattern of alcohol consumption. However, although the

proportion of diagnosed people who do not consume alcohol is high, those who do drink alcohol

continue to drink at ‘risky’ or ‘high-risk’ levels (see below) on a day when they consume

alcohol. Among participants who had never been diagnosed, although fewer women drink

alcohol than men, those who do report drinking both more frequently and consuming more per

drinking day than men. Frequently used alcoholic drinks include beer, spirits and fortified wine

(sold by the flagon), which may be consumed in tumblers or mugs or from the bottle.


Table 8.16. Alcohol consumption frequency and quantity Females % Males %

D (n=49)

H (n=15)

L (n=47)

G (n=12)

D (n=38)

H (n=15)

L (n=40)

Never 75.6 28.6 28.3 25.0 39.4 21.4 16.2

Less than once a week 17.1 7.1 10.9 58.3 33.3 28.6 18.9

1 or 2 days per week 2.4 50.0 23.9 16.7 18.2 21.4 40.5

3 or 4 days per week 0.0 0.0 21.7 0.0 9.1 14.3 13.5

5 or 6 days per week 2.4 0.0 8.7 0.0 0.0 0.0 5.4

Usu

al fr

eque

ncy

Everyday 2.4 14.3 6.5 0.0 0.0 14.3 5.4

1 or 2 drinks 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3 or 4 drinks 0.0 0.0 0.0 11.1 10.0 0.0 3.1

5 to 8 drinks 30.0 0.0 3.0 11.1 10.0 0.0 3.1

9 to 12 drinks 10.0 0.0 9.1 44.4 25.0 36.4 9.4

13 to 20 drinks 10.0 10.0 12.1 11.1 5.0 9.1 15.6

Usu

al n

umbe

r of d

rinks

per

dr

inki

ng d

aya

More than 20 drinks 50.0 90.0 75.8 22.2 50.0 54.5 68.8

a Of the participants who consume alcohol. ‘Number of drinks’ was not standardised, e.g. one can of beer contains approximately 1.4 ‘standard’ drinks, and so the number of standard drinks may actually be much higher than reported number of drinks. In Australia, one standard drink contains 10g of alcohol. The amount of alcohol in a standard drink varies internationally (Food Standards Australia New Zealand 2002).

In the Australian population as a whole, 12% of females and 15% of males drink one or more

times per week at levels considered either ‘risky’ or ‘high-risk’ in the short-term (defined for

females as five or six and seven or more drinks respectively per drinking day, and for males

seven to 10 and 11 or more drinks respectively) (AIHW 2002a). Among female study

participants, approximately 8.1% were drinking at risky levels and 85.4% (93.5% combined)

were drinking at high-risk levels. Among males, these rates were 4.8% and 90.4% respectively

(95.2% combined). When non-drinkers are included, 52.1% of all females and 70.6% of all

males were engaging in short term risky or high-risk drinking.


Of those who drink, women’s long-term drinking behaviour is on the whole riskier than men’s.

Long-term ‘risky’ and ‘high-risk’ levels of drinking (defined as: 15 to 28 and 29 or more drinks

per week for females; 29 to 42 and 43 or more for males) (AIHW 2002a) are apparent for 4.5%

and 31.9% of all women (36.4% combined), and 23.8% and 15.6% of all men (39.4%

combined).

A previous national study on indigenous obesity found that those who do not drink alcohol tend

to have higher BMIs on average than those who do (Cunningham and Mackerras 1994). This

could be because heavy drinking often correlates with heavy smoking as well as limiting the

intake of foods, hence the heaviest drinkers may be poorly nourished. Indeed, a common reason

reported by participants for missing meals was because they had been drinking or were ‘grog

sick’. It could also be that the Cunningham and Mackerras (1994) study did not consider the

potential behaviour-modifying effects of diagnosis with diabetes, that diagnosed people may

both have higher BMIs and be less likely to consume alcohol than people without diagnosed

diabetes.

There were no significant correlations between any of the measures of alcohol consumption and

specific risks of diabetes among never-diagnosed women, although a negative association

between alcohol frequency and BMI approached significance (Pearson correlation: p=0.091)

which supports the findings from Cunningham and Mackerras (1994). A positive (but non-

significant) relationship was also found between diastolic blood pressure and both alcohol

frequency and overall alcohol consumption (Pearson correlation: p=0.064 and 0.065

respectively) (Appendix O). Means of BMI and diastolic pressure according to alcohol

frequency are shown in Figure 8.45. There was a mean difference of nearly 12mmHg in

diastolic blood pressure between women who did not consume alcohol and women who

consumed alcohol every day. The group which showed the highest measurements for most

diabetes risk factors were women who drink on one to two days per week (Appendix O).

Among never-diagnosed males, both alcohol frequency and total alcohol consumption were

significantly positively related to diastolic blood pressure (Pearson correlation: p=0.005 and

0.012 respectively) and showed a non-significant positive association with systolic blood

pressure (Pearson correlation: p=0.053 and 0.077 respectively). These means are shown in

Figure 8.46. Among males, increased blood pressure related to alcohol consumption seems to

commence where consumption reaches three to four days per week. Below this frequency, there


appears to be very little difference in blood pressure associated with alcohol frequency. Alcohol

consumption was not found to be correlated with age in either women or men.

number of usual drinking days per week

7.05.53.51.5.50m

ean

dias

tolic

pre

ssur

e (m

mH

g)

94

92

90

88

86

84

82

80

78

76


7.05.53.51.5.50

mea

n BM

I

34

32

30

28

26

24

22

20

Figure 8.45. Mean BMI and mean diastolic pressure according to frequency of usual weekly alcohol consumption (days per week): females.


7.05.53.51.5.50

mea

n di

asto

lic p

ress

ure

(mm

Hg)

110

105

100

95

90

85

80


7.05.53.51.5.50

mea

n sy

stol

ic p

ress

ure

(mm

Hg)

146

144

142

140

138

136

134

132

130

128

Figure 8.46. Mean systolic and diastolic pressures according to frequency of usual weekly alcohol consumption (days per week): males.

Given the absence of associations between most diabetes risk factors and patterns of alcohol

consumption, and the large proportion of the community that does not drink alcohol, these

findings suggest that alcohol consumption alone is not responsible for the high rates of diabetes

in the community. The patterns of alcohol consumption may, however, be very important in


contributing to enhanced CVD risk among those who drink, through increasing blood pressure

(especially among males).

8.2.3. Smoking

Women are more likely to be current tobacco smokers than men, and those with diagnosed

diabetes are less likely to smoke than those who have never been diagnosed (Table 8.17).

Smoking among men occurs at twice the rate of Australian men overall (45% Cherbourg, 21%

Australia), while among women the difference is much greater, at four times the rate for

Australian women (73% Cherbourg, 18% Australia)66 (AIHW 2002a).

Table 8.17. Participants who are regular smokers Females % Males %

D (n=49)

H (n=15)

L (n=47)

G (n=12)

D (n=38)

H (n=15)

L (n=40)

Current smokers 42.9 73.3 66.0 83.3 23.7 53.3 70.0

Mean number of cigarettes per day a 17.7 26.7 20.9 20.0 20.4 20.0 20.3

a Calculation based on current smokers only.

Of those who do smoke, diagnosed women consume on average the fewest cigarettes each day

(17.7), so perhaps they have tried to reduce their tobacco consumption. High-risk women

smoke the most (26.7) while other groups smoke approximately 20 cigarettes per day. These

differences were not significant (ANOVA: p=0.772) (Appendix P).

Only a small proportion of non-smokers reported that they had been regular smokers in the past

(between 2% and 8%), and so behavioural change due to diagnosis of diabetes is unlikely to be

the main reason for the differences between the diagnosed and never-diagnosed groups.

Instead, the difference is probably due to age, with a very high uptake of smoking among young

people, especially women, contributing to the high rates reported among those without

diagnosed diabetes.

Among never-diagnosed participants, mean BMI was slightly lower among female smokers than

non-smokers (28.9 versus 29.6) but this difference was not significant (p=0.728, two-tailed

66 Australian rates include those aged over 14 years.


t-test). Among never-diagnosed men, current smokers had a significantly lower BMI (25.4

versus 32.0 among non-smokers, p<0.001). Being male and a smoker may combine with other

factors that limit nutritional intake, such as frequent and/or heavy alcohol use, and may depress

appetite.

8.2.4. Recent lifestyle change

Recent weight change

Is recent weight change associated with diabetes risk? Gaining or losing weight over the

previous 12 months was reported by a number of participants. In general, women’s weight

appears less stable than men’s, as a greater number reported recent weight change (Table 8.18).

Table 8.18. Reported weight change over previous 12 months

Females % Males %

D (n=49)

H (n=15)

L (n=47)

G (n=12)

D (n=38)

H (n=15)

L (n=40)

Gained weight 14.3 40.0 25.5 41.7 18.4 26.7 22.5

Lost weight 34.7 13.3 27.7 41.7 15.8

0.0 0.0

13.3

40.0 15.0

Both lost and gained 0.0 0.0 2.6 0.0 0.0

No change 12.2 17.0 0.0 23.7 20.0 25.0

Don’t know 24.5 26.7 25.5 16.7 21.1 13.3 35.0

Mean change in weight in kilos a -2.8 . -2.3 -2.8 -1.57 +3.0 +2.6

Range of weight change in kilos (na)

-12 to +5 (10) .

-20 to +20 (8)

-30 to +8 (5)

-20 to +10 (7)

-5 to +10 (4)

-10 to +20 (9)

a Of those reporting weight change who knew how much they had gained or lost. No high-risk woman knew by how much her weight had changed.

The changes reported in weight suggest that some of those with diagnosed diabetes are having

some success in reducing their weight, as they are less likely to report weight gain than their

never-diagnosed counterparts. These changes to weight are for most, however, very small.

Insulin resistance can enhance capacity for weight gain, and this may make it much harder for

some people to deliberately lose weight. Unfortunately, these patterns of weight change may

not accurately reflect overall change in the community, as many people did not know how much

their weight had changed over the previous year, just whether they had lost or gained weight.


Recent changes to diet and exercise

Diagnosed diabetics, especially men, were more likely to have made changes to their diets

within the last five years in order to be healthier (Table 8.19). To improve their health, many

people have also made recent changes to how much exercise they do, but the group reporting

the lowest rate for changes to exercise is diagnosed women. This group also expressed the least

willingness to change their exercise regime (see below).

Table 8.19. Whether participants had made changes to diet and exercise in the previous five years

Females % Males % D

(n=49) H

(n=15) L

(n=47) G

(n=12) D

(n=38) H

(n=15) L

(n=40)

Yes 30.6 20.0 29.8 25.0 47.4 26.7 30.0

Die

t

No 57.1 73.3 68.1 75.0 39.5 73.3 67.5

Yes 18.4 26.7 27.7 25.0 28.9 20.0 40.0

Exer

cise

No 69.4 60.0 68.1 66.7 57.9 73.3 57.5

8.2.5. Willingness to change diet and exercise

Both women and with diagnosed diabetes are more willing to change their diet than the amount

of exercise they do (Table 8.20). Men overall expressed greater willingness than women to

change how much exercise they do. It appears that diagnosis with diabetes is a great motivator

towards women’s willingness to change what they eat, with diagnosed women reporting more

willingness and less unwillingness than never-diagnosed women. Men who have been

diagnosed are more willing than women to change both their diet and exercise regime, but less

willing than never-diagnosed men. The unwillingness expressed by some males may be related

to their belief that they are already eating well (Table 8.10 above). Gender-related differences

in barriers to changing diet and exercise patterns are explored in Chapter Ten.


Table 8.20. Willingness to make changes to diet and exercise to be healthier Females Males

D (n=49)

H (n=15)

L (n=47)

G (n=12)

D (n=38)

H (n=15)

L (n=40)

Willing 59.2 33.3 46.8 58.3 52.6 66.7 52.5

Not willing 20.4 26.7 36.2 25.0 15.8 20.0 35.0 Die

t

Don’t know 8.2 33.3 12.8 16.7 18.4 13.3 7.5

Willing 36.7 33.3 46.8 58.3 52.6 60.0 50.0

Not willing 24.5 13.3 25.5 16.7 18.4 20.0 22.5

Exer

cise

Don’t know 24.5 33.3 23.4 25.0 15.8 13.3 22.5

8.3. Discussion and conclusions

There are few statistically significant dietary differences between categories of participants as

measured by the FFQ, nor are there consistent differences associated with particular risk factors.

Where there are differences, they relate more to diagnosis of diabetes rather than to the level of

risk among those without diabetes, suggesting that diagnosis motivates people to some extent to

consume what is perceived as a more nutritious diet, but that having well-publicised risk factors,

such as obesity, does not.

Associations between individual risk factors and food frequency appear to be positive for salt,

fat on meat and diet soft drink for FBSL, BMI and waist circumference, but inverse for total

animal foods, cereals and plant foods in relation to blood pressure.

That there is little difference in food frequencies associated with BMI may also mean that level

of physical activity may in fact be more important in determining diabetes risk than diet is, as

activity levels were inversely associated with risk factors associated with diabetes. As people

were in general more willing to change their diet than their levels of activity, a focus on the

benefits of increasing even incidental physical activity could have a profound impact on the risk

of diabetes in the community. This is discussed further in Chapter 10.

Nutrition security at the household level could be improved, with up to a third of participants

reporting that they sometime worry about not getting enough food. Nutrition education could

also be improved, as 20% of participants do not know whether their diet is healthy overall,

while between 40% and 60% of participants think that they do eat a healthy diet overall. There


are further issues that constrain food choices, including a ‘captive market’; up to half the

participants stating that they would shop somewhere else if they had transport.

Physical activity levels in the community are low, women are less active than men, and women

with diagnosed diabetes are the least active of all. The importance of increasing physical

activity to have improved health was highlighted by the lower risk factor measurements among

women who were more active. This supports the widely accepted relationship between higher

levels of physical activity and reduced diabetes risk factors (for example, Reaven et al. 1996,

Samara et al. 1997 and Wannamethee et al. 2000). Perceived physical fitness relative to peers

was not associated with BMI among women.

Gregg et al. (1996) concluded from a study of Pima Indians with diabetes that physical activity

level was associated with perceived locus of control; those with an internal locus of control, i.e.

people who perceive everyday events to be more within their personal control than under the

control of external factors, such as government, were more physically active than those with an

external locus of control. Given the historical context of Cherbourg, especially the restrictions

placed on Aboriginal people and the removal of responsibilities and freedoms under legislation

until fairly recently, it is not surprising if participants tend to view their lives as not under their

personal control – the expectation of many, for example, is that they will get diabetes no matter

what they do. This could be further enhanced by both gender and age differences (see Section

10.3).

A large proportion of participants do not consume alcohol, but among those who do consume

alcohol, levels of consumption on drinking days are extremely high. BMI was slightly higher

among people who do not consume alcohol, which may be due to the large proportion of non-

drinkers who are diagnosed diabetics and have higher BMIs. A large number of these

participants may also be ex-drinkers, who have given up alcohol subsequent to diabetes

diagnosis. BMI was unrelated to alcohol consumption among never-diagnosed participants. As

obesity is the main modifiable risk factor associated with diabetes, it is unlikely that alcohol

consumption is contributing directly to diabetes risk. The absence of a positive association

between alcohol and BMI may also be due to co-occurrence of heavy alcohol consumption,

eating irregularly and smoking; such factors associated with heavy consumption of alcohol may

therefore act to keep weight down, hence reducing diabetes risk as it was measured in this study,

while risk to overall health remains high. Increased consumption of alcohol is related to higher

blood pressure rather than to other diabetes risk factors, suggesting that alcohol could play a


major role in CVD. It may also be that those who are heavy consumers of alcohol are potential

candidates for pancreatitis (Toskes 2001), so that even if their body weight remains low their

risk of diabetes is increased.

Smoking rates in the Cherbourg community are more than double the rate for Australia as a

whole; smoking prevalence is extremely high, especially among young women in Cherbourg.

That approximately 70% of participants categorised as ‘low-risk’ smoke cigarettes does not

bode well for their future health; even if their current risk of diabetes is low, they are at

increased risk from CVD, respiratory diseases and cancers (AIHW 2002a). Perhaps the high

rate of smoking among women is partially due to perceived usefulness as a means for weight

control, as suggested by Abbey and Stewart (2000).

Methodological issues

Similar questionnaires to the FFQ used in the present study have been shown to be highly

repeatable in a range of subjects (Horwarth and Worsley 1990; Willett 1994; Franceschi et al.

1995; Lazarus et al. 1995; Smith et al. 1998). The FFQ emphasises overall patterns of

consumption rather than what was eaten over the previous few days and this may be most

advantageous in terms of understanding the development of diabetes.

Underreporting of food frequencies may be greater among those with higher BMIs (Johansson

et al. 2001), which would mask some possible dietary associations with BMI. Some dietary

studies exclude under-reporters from analyses (for example, see Smith et al. 1994). Neither the

presence or extent of underreporting was determined in the present study, however, as

comparison with actual energy requirements could not be made given, that the frequency

questionnaire provides an indication of dietary habits rather than quantifying nutrient intake.

One regular criticism of studies based on self-reporting is that participants are prone to tell the

researcher what they think they want to hear, or they might try to portray themselves in a

favourable light; promoting a social desirability bias (Radimer et al. 1997b). In the case of

food, this might manifest in under-reporting of consumption of foods that are perceived to be

‘bad’, unhealthy or socially undesirable (Johansson et al. 2001), and an over reporting of those

that are perceived to be ‘good’, healthy or socially desirable (Cook et al. 2000).

This is unlikely to be an issue in the present study. Participants seemed very open to reporting

‘unhealthy’ behaviours throughout the survey (for example, smoking and heavy alcohol

consumption, Section 8.2.2) and gave no impression of trying to give an answer they thought I


might want. A fairly common response to questions about the consumption of ‘bad’ foods, such

as the fat on meat or take-away, was for the participant to sigh and say conspiratorially, ‘oh, I do

like the fat’ or ‘I do get take-away sometimes because it’s easy’. Participants therefore seemed

willing to admit to eating a particular type of food ‘more than they should’. They might know a

food to be unhealthy, but did not see that as a reason to hide their consumption of it. As the

voluntary and confidential nature of the survey were of course also emphasised to the

participants, this might also have reduced the bias away from undesirable responses.

Recall bias – that some participants might systematically be more or less likely to recall eating

certain types of foods – could potentially have occurred (Liu et al. 2000). Those diagnosed with

diabetes for instance may be more likely to recall eating foods they have been educated to eat

more or less of. Examining ratios of reported frequencies (for example, ratios of wholemeal to

white bread) might have controlled for any such bias. In addition, it is unlikely that systematic

effects of bias would have occurred between the high-risk and the low-risk groups in relation to

diabetes significant foods, given that they would not have received such targeted advice.

Using a fixed list of foods in a frequency questionnaire may be somewhat limiting (Guest and

O'Dea 1993), although the foods that were included were chosen after consultation with the

community nutritionist67 as to the sorts of foods that would be most appropriate given

community pattern of consumption.

Striking a balance between accuracy and convenience, the use of this simple FFQ was suitable

for the purpose of this study, to establish general patterns of consumption within the community

and determine if there are overall differences between particular groups; attaining relative

validity, rather than absolute validity, is more important in epidemiological nutrition assessment

(Block 1982). A greater number of items may have increased specificity and helped tease out

intergroup differences.

The following chapter explores risk on its social context, in particular the contributions made by

gender to diabetes risk.

67 Tarita Fisher

8 8. questionnaire results (1): nutrition, physical ... · questionnaire results (1): nutrition,...

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