Edited by: Donato Angelino, University of Teramo, Italy
Reviewed by: Cinzia Ferraris, University of Pavia, Italy; Giorgia Vici, University of Camerino, Italy
This article was submitted to Nutritional Epidemiology, a section of the journal Frontiers in Nutrition
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The factors influencing an individual's food choice are numerous and include, amongst others, habits, practical skills, cultural or environmental factors as well as motives like taste, convenience or price (
As reported in a systematic review by Spronk et al. (
A limitation of previous studies is the challenging measurement of nutrition knowledge. The General Nutrition Knowledge Questionnaire (GNKQ) has been well studied, validated, and adapted to different populations (
Instruments that cover different types of nutrition knowledge are also rare (
In Germany, the National Nutrition Survey (NVS) II (
Therefore, the aims of the current study were to investigate the declarative and procedural nutrition knowledge in the German population, its determinants and its associations with food consumption based on the NEMONIT study (
This cross-sectional analysis is based on the last survey year data of the NEMONIT study (2014/15). NEMONIT was designed as a longitudinal study to assess changes in food consumption and nutrient intake in Germany. Besides the repeated measurement of food consumption, the annual surveys also included questions on specific nutritional topics (e.g., nutrition knowledge) allowing further cross-sectional analyses. A detailed description of NEMONIT has been published previously (
The NEMONIT sample (
Information on socio-demographic characteristics and lifestyle factors was obtained in CATIs. Socio-demographic characteristics included sex, age, school education (highest school-leaving qualification, recoded to years spent in school), and socio-economic status (SES). SES was an index based on three characteristics (participants' education, net household income and employment status of the principal earner of the household; possible range: 3–25 points) and categorized into low, medium, or high (
With regard to lifestyle factors, questions on specific diets (e.g., vegetarian diet, dieting), self-rated healthiness of the own diet, self-rated health status, and smoking status were included. Body mass index (BMI) was calculated based on self-reported body weight and height and categorized according to the cut-off points provided by the World Health Organization (WHO) (
Nutrition knowledge was measured using an adapted version of the consumer nutrition knowledge scale (CoNKS) by Dickson-Spillmann et al. (
Items of the adapted version of the consumer nutrition knowledge scale (CoNKS) and response behavior of the participants in the NEMONIT study 2014/15
If you have eaten high-fat foods, you can reverse the effects by eating apples | F | 91 | 4 | 5 |
A healthy meal should consist of half meat, a quarter vegetables and a quarter side dishes | F | 78 | 21 | 1 |
Fat is always bad for your health; you should therefore avoid it as much as possible | F | 71 | 28 | 1 |
A balanced diet implies eating all foods in the same amounts | F | 69 | 29 | 1 |
For a healthy nutrition, dairy products should be consumed in the same amounts as fruit and vegetables | F | 64 | 31 | 4 |
Brown sugar is much healthier than white sugar | F | 60 | 31 | 9 |
To eat healthily, you should eat less fat. Whether you also eat more fruit and vegetables does not matter | F | 55 | 42 | 2 |
Oily fish (salmon, mackerel) contain healthier fats than red meat | T | 85 | 9 | 6 |
Lentils contain only few useful nutrients, therefore their health benefit is not great | F | 81 | 8 | 10 |
T | 76 | 16 | 9 | |
T | 73 | 17 | 10 | |
Skimmed milk contains fewer minerals than full-fat milk | F | 64 | 24 | 12 |
The health benefit of fruit and vegetables lies alone in the supply of vitamins and minerals | F | 64 | 33 | 3 |
T | 37 | 44 | 19 | |
If cream is whipped it contains less calories than in its liquid form | F | 91 | 5 | 4 |
Bacon contains more calories than ham | T | 80 | 15 | 4 |
Fat contains fewer calories than the same amount of fiber | F | 79 | 11 | 11 |
The same amount of beef steak and chicken breast contains equally many calories | F | 66 | 23 | 11 |
A sandwich with mozzarella contains as many calories as the same sandwich with Emmental/Swiss cheese | F | 64 | 21 | 15 |
The same amount of sugar and fat contains equally many calories | F | 60 | 25 | 15 |
The nutrition knowledge items were asked in randomized order during the CATI at the end of the second 24-h recall. For scale construction, items were recoded with correct answers taking the value “1” and incorrect answers, “don't know” answers and missing values taking the value “0.” Nutrition knowledge (CoNKS Total) was calculated as the sum of the 20 items, yielding a range of 0 to 20 points. Internal consistency of the scale was measured with Cronbach's Alpha (α = 0.59).
To analyse different types of knowledge, three subscales were formed based on content considerations: one scale for procedural nutrition knowledge (7 items, hence 0–7 points), one scale for declarative knowledge on nutrient contents (7 items, 0–7 points) and one scale for declarative knowledge on calorie content (6 items, 0-6 points) (
Food consumption (g/d) was assessed with two 24-h recalls conducted on randomly drawn non-consecutive days (at least 1 week apart) by phone using the software EPIC-Soft (
Diet quality was evaluated using the Healthy Eating Index-NVS II (HEI-NVS II) adapted to 24-h recalls. The HEI-NVS II compares ten components of food consumption or nutrient intake [e.g., “fruit/fruit products,” “meat/meat products”„ “fat (% of energy intake)”] with food-based dietary guidelines of the German Nutrition Society (
Descriptive statistics are provided as means with standard deviations (SD) for metric variables and percentages for categorical variables. Since nutrition knowledge was not normally distributed, differences in nutrition knowledge between groups were tested using Mann–Whitney
Data analysis was performed using SAS 9.3 (SAS Institute, Inc.) and level of significance for all analyses was set at
Socio-demographic and lifestyle characteristics of NEMONIT study 2014/15
Men | 638 | 42.4 |
Women | 867 | 57.6 |
Mean | (56.8) | |
SD | (14.2) | |
22–34 years | 130 | 8.6 |
35–50 years | 370 | 24.6 |
51–64 years | 501 | 33.3 |
65–80 years | 504 | 33.5 |
Up to 9 years | 364 | 24.2 |
10 years | 503 | 33.4 |
12 or 13 years | 638 | 42.4 |
Mean | (15.0) | |
SD | (3.5) | |
Low | 170 | 11.3 |
Medium | 768 | 51.0 |
High | 567 | 37.7 |
Vegetarian (incl. pesco-vegetarian) diet | 47 | 3.1 |
Dieting (e.g., to lose weight or due to an illness) | 106 | 7.0 |
Very healthy | 111 | 7.4 |
Predominantly healthy | 1,237 | 82.2 |
Less healthy/not healthy | 155 | 10.3 |
Missing | 2 | 0.1 |
Mean | (67.9) | |
SD | (10.0) | |
Good (>88 points) |
26 | 1.7 |
In need of improvement (>55 and ≤88 points) |
1,326 | 88.1 |
Poor (≤55 points) |
153 | 10.2 |
Mean | (26.0) | |
SD | (4.6) | |
Underweight | 16 | 1.1 |
Normal weight | 681 | 45.2 |
Preobese | 587 | 39.0 |
Obese | 221 | 14.7 |
Good | 1,163 | 77.3 |
Moderate | 301 | 20.0 |
Poor | 39 | 2.6 |
Missing | 2 | 0.1 |
Inactive | 441 | 29.3 |
Active, below recommendations | 391 | 26.0 |
Active, in agreement with recommendations | 654 | 43.5 |
Missing | 19 | 1.3 |
Smoker | 165 | 11.0 |
Occasional smoker | 42 | 2.8 |
Ex-smoker | 521 | 34.6 |
Non-smoker | 777 | 51.6 |
On average, items of the nutrition knowledge scale were answered correctly by 70% of the participants. All except one item were answered correctly by more than half of the participants, indicating relatively easy items (in terms of scale construction). Participants' mean was 14.1 points for the CoNKS Total (SD 3.0, IQR 4), 4.9 points for the subscale procedural knowledge (SD 1.6, IQR 2), 4.8 points for knowledge on nutrients (SD 1.4, IQR 1), and 4.4 points for knowledge on calories (SD 1.2, IQR 2) (
Nutrition knowledge (CoNKS Total and subscales) by socio-demographic group
Total sample | 14.1 | 4.9 | 4.8 | 4.4 | ||||
Sex | <0.001 | <0.001 | 0.006 | 0.689 | ||||
Males | 13.6 | 4.6 | 4.7 | 4.4 | ||||
Females | 14.4 | 5.1 | 4.9 | 4.4 | ||||
Age groups | <0.001 | <0.001 | <0.001 | <0.001 | ||||
22–34 years | 15.3 | 5.6 | 5.0 | 4.7 | ||||
35–50 years | 14.7 | 5.2 | 5.0 | 4.5 | ||||
51–64 years | 14.4 | 5.0 | 4.9 | 4.5 | ||||
65–80 years | 13.0 | 4.3 | 4.5 | 4.2 | ||||
School education | <0.001 | <0.001 | <0.001 | <0.001 | ||||
Up to 9 years | 12.5 | 4.1 | 4.2 | 4.1 | ||||
10 years | 14.2 | 4.9 | 4.8 | 4.4 | ||||
12 or 13 years | 15.0 | 5.3 | 5.1 | 4.5 | ||||
SES class | <0.001 | <0.001 | <0.001 | <0.001 | ||||
Low | 12.2 | 4.1 | 4.1 | 4.1 | ||||
Medium | 13.9 | 4.8 | 4.7 | 4.4 | ||||
High | 14.9 | 5.3 | 5.1 | 4.5 |
Some areas for knowledge enhancement can be identified when looking at the single items (
Nutrition knowledge differed significantly between socio-demographic groups (
Nutrition knowledge was higher among individuals following a vegetarian diet, having a normal weight and being physically active (
Nutrition knowledge (CoNKS Total and subscales) by nutrition and health behaviour
Vegetarian (incl. pesco-vegetarian) diet | <0.001 |
<0.001 |
<0.001 |
0.604 | ||||
Yes | 16.1 | 6.1 | 5.6 | 4.5 | ||||
No | 14.0 | 4.9 | 4.8 | 4.4 | ||||
Dieting (e.g., to lose weight or due to an illness) | 0.016 | 0.036 | 0.131 | 0.083 | ||||
Yes | 13.3 | 4.5 | 4.6 | 4.2 | ||||
No | 14.1 | 4.9 | 4.8 | 4.4 | ||||
Healthiness of diet | 0.182 | 0.105 | 0.173 | 0.923 | ||||
Very healthy/ predominantly healthy | 14.1 | 4.9 | 4.8 | 4.4 | ||||
Less healthy/ not healthy | 13.8 | 4.7 | 4.7 | 4.4 | ||||
Subjective health status | 0.096 | 0.044 | 0.200 | 0.646 | ||||
Very good/ good | 14.2 | 4.9 | 4.8 | 4.4 | ||||
Moderate/ poor/very poor | 13.8 | 4.7 | 4.7 | 4.4 | ||||
Body mass index | <0.001 |
<0.001 |
<0.001 |
0.655 | ||||
Normal weight | 14.6 | 5.2 | 5.0 | 4.4 | ||||
Underweight, overweight | 13.7 | 4.7 | 4.7 | 4.4 | ||||
Sport activities | <0.001 |
<0.001 |
<0.001 | 0.022 | ||||
Active | 14.3 | 5.0 | 4.9 | 4.4 | ||||
Inactive | 13.5 | 4.6 | 4.6 | 4.3 | ||||
Smoking status | 0.079 | 0.052 | 0.059 | 0.355 | ||||
Non-smoker/ex-smoker | 14.1 | 4.9 | 4.8 | 4.4 | ||||
Smoker/occasional smoker | 13.7 | 4.7 | 4.6 | 4.4 |
The significant results of the bivariate analysis were generally confirmed in multiple linear regressions controlling for sex, age, and SES (data not shown).
Nutrition knowledge was positively associated with the consumption of cereals/cereal products, vegetables, fruit/fruit products, and dairy products and negatively with the consumption of potatoes/potato products and meat/meat products (
Association between nutrition knowledge (CoNKS Total and subscales) and food consumption (g/d)
Bread | 0.00 | 0.958 | −0.03 | 0.227 | 0.03 | 0.227 | −0.00 | 0.984 |
Cereals and cereal products | 0.11 | <0.001 | 0.14 | <0.001 | 0.07 | 0.006 | 0.02 | 0.394 |
Potatoes and potato products | −0.06 | 0.029 | −0.05 | 0.046 | −0.00 | 0.860 | −0.07 | 0.004 |
Vegetables |
0.09 | <0.001 |
0.11 | <0.001 |
0.11 | <0.001 |
−0.04 | 0.173 |
Fruit and fruit products | 0.10 | <0.001 |
0.10 | <0.001 |
0.09 | <0.001 |
0.01 | 0.630 |
Milk, dairy products, and cheese | 0.12 | <0.001 |
0.07 | 0.009 | 0.13 | <0.001 |
0.06 | 0.023 |
Eggs | 0.01 | 0.590 | −0.01 | 0.635 | 0.03 | 0.300 | 0.02 | 0.514 |
Meat, meat products | −0.14 | <0.001 |
−0.14 | <0.001 |
−0.11 | <0.001 |
−0.05 | 0.070 |
Fish, fish products, and seafood | 0.03 | 0.194 | 0.03 | 0.259 | 0.05 | 0.077 | −0.01 | 0.588 |
In multiple linear regressions, the associations between nutrition knowledge and its subscales with consumption of vegetables, fruit/fruit products, dairy products, and meat/meat products were largely confirmed.
With increasing values on nutrition knowledge, respondents also had increasing values on the HEI-NVS II (Spearman's Rho correlation coefficient:
Similar to nutrition knowledge, HEI-NVS II was higher among women and among groups with higher school education and higher SES. However, HEI-NVS II increased with age while nutrition knowledge decreased with age. Therefore, multiple linear regressions were performed again to examine whether the association between nutrition knowledge and HEI-NVS II was independent of sex, age, and SES (
Associations of socio-demographic characteristics and nutrition knowledge (CoNKS Total and selected subscales) with HEI-NVS II
Female sex | 2.986 | <0.001 |
Age (in years) | 0.055 | 0.003 |
SES index | 0.177 | 0.019 |
Female sex | 2.391 | <0.001 |
Age (in years) | 0.080 | <0.001 |
SES index | 0.023 | 0.760 |
CoNKS Total | 0.620 | <0.001 |
Female sex | 2.407 | <0.001 |
Age (in years) | 0.077 | <0.001 |
SES index | 0.065 | 0.400 |
Procedural knowledge | 0.924 | <0.001 |
Female sex | 2.630 | <0.001 |
Age (in years) | 0.064 | <0.001 |
SES index | 0.052 | 0.500 |
Knowledge on nutrients | 1.289 | <0.001 |
This analysis of nutrition knowledge in adults based on NEMONIT and using an adapted version of the CoNKS showed several key results:
Areas for knowledge enhancement could be observed in the assessment of the health benefits of fruit and vegetable consumption, in the understanding of the concept of a balanced diet as well as regarding the knowledge on saturated fatty acids.
Nutrition knowledge was higher among individuals who were female, younger, had higher SES or showed a more health conscious lifestyle.
Nutrition knowledge was positively associated with a favorable food consumption.
Analyses of subscales of nutrition knowledge yielded similar results for procedural nutrition knowledge and knowledge on nutrients but not for knowledge on calories.
In accordance with the results from Dickson-Spillmann et al. (
Also in agreement with Dickson-Spillmann et al. (
Additionally, knowledge on saturated fatty acids seems relatively low. This result reinforces international findings ascertaining knowledge deficits with regard to types of dietary fats (
Although this research identified some important areas to address in nutrition education, it simultaneously indicates that an increase in nutrition knowledge alone will not substantially improve dietary behavior (see below).
That nutrition knowledge is higher in women, normal weight and physically active persons as well as among those with higher socio-economic status (or its indicators such as education or employment status) has already been observed in a number of earlier studies and was discussed previously (
The relationship between age and nutrition knowledge, however, was contradictory across studies (
In this study, self-defined vegetarians (including pesco-vegetarians) compared to non-vegetarians had a higher nutrition knowledge. Up to now, there are only few and inconsistent studies on differences in nutrition knowledge among vegetarians and non-vegetarians (
Participants with higher nutrition knowledge ate more favorable (e.g., vegetables, fruit/fruit products) and less unfavorable foods (e.g., meat/meat products) and showed a higher diet quality overall. Although significant associations in the expected direction were observed, the correlations between nutrition knowledge and food consumption were low in this study, also when compared to the validation study of the CoNKS (
The separate analysis of procedural knowledge and knowledge on nutrients provided similar results as the analysis of nutrition knowledge in total. Knowledge on calories, however, seems to be a different kind of knowledge. Research on different types of knowledge is rare, but Grunert et al. (
According to our results, knowledge on calories does not seem helpful in making healthy food choices. Contrary to what we would theoretically expect, it was not associated with BMI either. Knowledge on the caloric content of macronutrients, foods and meals might be too technical to be translated into a diet with adequate energy intake. Our results suggest that it might be advisable to include more information on, for e.g., the contribution of a meal to a balanced diet, in the commonly used media rather than just information on calories.
This study has several strengths. First, it explored nutrition knowledge based on a large sample of the German adult population. This allowed using multivariate analyses to examine group differences and associations independent from socio-demographic factors. Second, the study used a scale which showed a good ability to distinguish between nutrition-literate and lay respondents. Although some essential modifications were made, low associations between nutrition knowledge and dietary behavior are unlikely to result from incapacity of the scale to distinguish between participants with different grades of nutrition knowledge. The measurement also allowed investigating both declarative and procedural nutrition knowledge. Low associations therefore cannot be attributed to a mere assessment of declarative knowledge, which was assumed by some authors to have a lower relevance for dietary behavior (
Some limitations of the study also need to be considered. First, the study sample was biased toward women, older persons and persons with a higher SES (
Second, the measurement of nutrition knowledge includes some uncertainties. Although nutrition knowledge was measured using a previously validated instrument, Cronbach's Alpha, which is used to assess the internal consistency of a scale, was low (α = 0.59). The value could not be substantially increased by deleting an item and the correlations between some items were very low. This could indicate that nutrition knowledge is a heterogeneous construct with different dimensions. Another possible explanation for a low Cronbach's Alpha would be a very homogenous sample. As previously mentioned, NEMONIT respondents might consistently have a higher interest in nutrition topics. Nutrition knowledge in the sample was high with a relatively low standard deviation. This might restrict the ability of the study to find large associations between nutrition knowledge and dietary behavior.
Finally, it is important to note that no causal relationships can be implied from the cross-sectional analysis.
The present study identified areas for knowledge enhancement in the assessment of the health benefits of fruit and vegetable consumption, in the understanding of the concept of a balanced diet as well as regarding the knowledge on saturated fatty acids. These topics might be most relevant for future nutrition education efforts. However, this study also supports a number of previous studies observing significant but weak associations between nutrition knowledge and dietary behavior. This indicates that an increase in nutrition knowledge through nutrition education alone is unlikely to provoke large improvements in dietary behavior. From health and sustainability literature, it is well-known that knowledge is usually not directly translated into action. Instead, behavior is complex and influenced by a number of different factors. Research should find ways to address the complexity of dietary behavior and to identify the most important factors that need to be addressed to improve dietary behavior of the population.
The datasets presented in this article are not readily available because according to the regulations for the use of NVS II- and NEMONIT-study data, the datasets of the NEMONIT study are only available for universities, public and/or publicly funded scientific research institutions. Furthermore, a general essential prerequisite is the pure scientific use of the data, excluding any commercial use of the data and of the derived results. Requests to access the datasets should be directed to the corresponding author,
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.
FK analyzed and interpreted the data and drafted the manuscript. IH was involved in data interpretation and manuscript preparation. EC initiated and conceptualized the research, was involved in data interpretation and manuscript preparation and was responsible for the final content. All authors read and approved the final version of the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.