Assessing dietary intake: Who, what and why of under-reporting

Under-reporting of food intake is one of the fundamental obstacles preventing the collection of accurate habitual dietary intake data. The prevalence of under-reporting in large nutritional surveys ranges from 18 to 54% of the whole sample, but can be as high as 70% in particular subgroups. This wide variation between studies is partly due to different criteria used to identify under-reporters and also to non-uniformity of under-reporting across populations. The most consistent differences found are between men and women and between groups differing in body mass index. Women are more likely to under-report than men, and under-reporting is more common among overweight and obese individuals. Other associated characteristics, for which there is less consistent evidence, include age, smoking habits, level of education, social class, physical activity and dietary restraint. Determining whether under-reporting is specific to macronutrients or food is problematic, as most methods identify only low energy intakes. Studies that have attempted to measure under-reporting specific to macronutrients express nutrients as percentage of energy and have tended to find carbohydrate under-reported and protein over-reported. However, care must be taken when interpreting these results, especially when data are expressed as percentages. A logical conclusion is that food items with a negative health image (e.g. cakes, sweets, confectionery) are more likely to be under-reported, whereas those with a positive health image are more likely to be over-reported (e.g. fruits and vegetables). This also suggests that dietary fat is likely to be under-reported. However, it is necessary to distinguish between under-reporting and genuine under-eating for the duration of data collection. The key to understanding this problem, but one that has been widely neglected, concerns the processes that cause people to under-report their food intakes. The little work that has been done has simply confirmed the complexity of this issue. The importance of obtaining accurate estimates of habitual dietary intakes so as to assess health correlates of food consumption can be contrasted with the poor quality of data collected. This phenomenon should be considered a priority research area. Moreover, misreporting is not simply a nutritionist's problem, but requires a multidisciplinary approach (including psychology, sociology and physiology) to advance the understanding of under-reporting in dietary intake studies.

Associations among overeating, overweight, and attention deficit/hyperactivity disorder: a structural equation modelling approach

BACKGROUND

Some recent studies have reported strong links between obesity and ADHD in adults; however, to date, little work has focussed on possible behavioural mechanisms that could account for this association.

METHOD

This study used structural equation modelling (SEM) in a sample of healthy adult women to test the predictions that ADHD symptoms predict aspects of overeating, including binge eating and emotionally-induced eating, which in turn are positively correlated with Body Mass Index.

RESULTS

The SEM produced a non-significant chi-square and both the measurement model and the structural model fit the data very well.

CONCLUSIONS

Plausible mechanisms are discussed to help explain how the symptomatology of ADHD could foster different forms of overeating.

Diet, nutrition and the prevention of excess weight gain and obesity

OBJECTIVES

To review the evidence on the diet and nutrition causes of obesity and to recommend strategies to reduce obesity prevalence.

DESIGN

The evidence for potential aetiological factors and strategies to reduce obesity prevalence was reviewed, and recommendations for public health action, population nutrition goals and further research were made.

RESULTS

Protective factors against obesity were considered to be: regular physical activity (convincing); a high intake of dietary non-starch polysaccharides (NSP)/fibre (convincing); supportive home and school environments for children (probable); and breastfeeding (probable). Risk factors for obesity were considered to be sedentary lifestyles (convincing); a high intake of energy-dense, micronutrient-poor foods (convincing); heavy marketing of energy-dense foods and fast food outlets (probable); sugar-sweetened soft drinks and fruit juices (probable); adverse social and economic conditions-developed countries, especially in women (probable). A broad range of strategies were recommended to reduce obesity prevalence including: influencing the food supply to make healthy choices easier; reducing the marketing of energy dense foods and beverages to children; influencing urban environments and transport systems to promote physical activity; developing community-wide programmes in multiple settings; increased communications about healthy eating and physical activity; and improved health services to promote breastfeeding and manage currently overweight or obese people.

CONCLUSIONS

The increasing prevalence of obesity is a major health threat in both low- and high income countries. Comprehensive programmes will be needed to turn the epidemic around.

Snack patterns of older Europeans

OBJECTIVE

To describe the snack consumption of older people from several European towns. Subjects with similar snack patterns are clustered into groups to explore the contribution of snacks to daily energy and micronutrient intake.

DESIGN

Data from the 1993 Survey in Europe on Nutrition and the Elderly: A Concerted Action (SENECA) follow-up study were collected from a random, age-stratified sample of inhabitants of small traditional towns in Europe. Food intake data collected by the 3-day estimated record method were used for grouping snack foods into 15 food groups. From these data daily energy intake and intake of calcium; iron; and vitamins B-1, B-2, B-6, and C were calculated. Additional self-reported data were collected for health status, presence of chronic diseases, and activity level.

SUBJECTS/SETTING

The study population consisted of 379 men and 428 women aged 74 to 79 years and who were inhabitants of the following towns: Haguenau, France; Romans, France; Padua, Italy; Culemborg, The Netherlands; Yverdon, Switzerland; and Marki, Poland.

STATISTICAL ANALYSES

Cluster analysis was used to classify subjects into groups based on similarity in snack patterns.

RESULTS

In general, older people from the various European towns consumed the same snack types. Five distinct snack patterns emerged from our analyses. The large group light snackers had a low snack use and low energy and micronutrient intakes. Alcohol drinkers and dairy snackers had a high snack use and high intakes of energy and several vitamins and minerals. Fruit and vegetable snackers and sweet drinkers often had intake values between the other 3 groups.

APPLICATIONS

Our study indicates the existence of identifiable snack patterns that coincide with different intakes of energy and micronutrients. Especially in countries in which people derive high percentages of energy through snacking, the identification of snack patterns can improve dietary advice, gearing it to personal needs.

A medium-term intervention study on the impact of high- and low-fat snacks varying in sweetness and fat content: large shifts in daily fat intake but good compensation for daily energy intake

Thirty-six normal-weight, habitual snackers (eighteen males, eighteen females) completed a medium-term intervention study designed to examine the tendency of four different types of snacks, varying in nutrient (low- (LF) or high-fat (HF) and sensory properties (sweet (SW) or non-sweet (NSW)), to influence the control of appetite and to adjust daily energy intake. Subjects were exposed to each snack category for a 3-week period and were asked to consume a minimum number of snacks each day so that at least 25% of their daily energy intake would be derived from the test snacks. Energy and macronutrient intakes from the test snacks were calculated every day and also from other eating episodes (using 3 d food diary records) during the third week of snack exposure. Subjects consumed more energy/d from the SW snacks than from the NSW snacks, with most energy being consumed from the HF/SW snacks (3213 kJ) and least energy from the LF/NSW snacks (1628 kJ). This differential snack intake remained stable across the whole snack exposure period. Total daily energy intake did not differ significantly during exposure to any of the four snack types. Furthermore, the encouragement to eat freely from the test snacks did not lead to daily overconsumption of energy when compared with pre-study intakes. Hence, the level of snack consumption was largely compensated for by the energy consumed from the rest of the eating pattern. Although daily energy intake during exposure to the HF snacks was an average of 364 kJ higher (NS) than that during exposure to the LF snacks, the clearest and most significant effect of snack consumption was on daily macronutrient intake. Appreciable consumption of the HF snacks raised the percentage of total daily energy intake consumed as fat from 37 to 41% (P < 0.01). In contrast, the LF snacks reduced daily fat intake to 33.5% (LF/SW, P < 0.05; LF/NSW, NS) of total daily energy. The results, therefore, suggest that, in habitual snackers, generous consumption of LF snacks, when compared with HF snacks, is an effective strategy to reduce fat intake so that it approaches the recommendations of dietary guidelines without increasing total daily energy intake.