Adequate nutrition is fundamental to operational readiness for military personnel( Reference Hill, Fallowfield and Price 1 ). A healthy diet is therefore imperative for sustaining performance and optimising general health in this population( Reference Tharion, Lieberman and Montain 2 , Reference Montain, Carvey and Stephens 3 ). A wide range of individual (beliefs, knowledge, attitudes, skills and genetics), social (interaction with friends, family and community) and environmental factors are known to influence dietary intake( Reference Worsley 4 – Reference Brug, Kremers and Lenthe 8 ). Despite this complex network of influences, interventions designed to alter dietary behaviour in the military are typically designed around the manipulation of single factors such as improving nutrition knowledge or changing the food environment (e.g. dining hall programmes). Interventions aimed at increasing nutrition knowledge are particularly attractive as they are easy to implement; however, this approach is based on the assumption that increased knowledge translates to positive behaviour change.
Nutrition knowledge is reported to be influenced by a range of factors such as sex, age, level of education and socio-economic status. Previous studies have consistently found that men have a lower level of nutrition knowledge than women, a factor that may be related to women having a stronger interest in nutrition( Reference Parmenter, Waller and Wardle 9 – Reference Trent 13 ). Age is also an important demographic factor when assessing nutrition knowledge. Middle-aged individuals usually possess greater knowledge than younger and older participants in community( Reference Parmenter, Waller and Wardle 9 , Reference Hendrie, Coveney and Cox 11 ), athlete( Reference Heaney, O’Connor and Michael 14 ) and military groups( Reference Trent 13 , Reference Conway, Hervig and Vickers 15 , Reference Bovill, Tharion and Leiberman 16 ). A positive relationship between nutrition knowledge and level of education is also evident across a number of studies in general community samples( Reference Parmenter, Waller and Wardle 9 , Reference Hendrie, Coveney and Cox 11 ). In studies of military personnel from the USA, rank( Reference Bovill, Tharion and Leiberman 16 ) and education( Reference Trent 13 ) have been shown to influence nutrition knowledge, with officers’ nutrition knowledge reported as superior than that of soldiers. This relationship is not surprising, as a higher level of education is generally also associated with a range of other positive health behaviours or attributes, including a lower prevalence of smoking and obesity( Reference Parmenter, Waller and Wardle 9 , Reference Ball, Crawford and Mishra 17 ).
A number of studies have assessed the relationship between nutrition knowledge and dietary intake( Reference Heaney, O’Connor and Michael 14 , Reference Spronk, Kullen and Burdon 18 ). A recent systematic review from our group identified twenty-nine studies investigating the relationship between nutrition knowledge and dietary intake( Reference Spronk, Kullen and Burdon 18 ). Of these studies, nineteen reported a significant, positive relationship between nutrition knowledge and dietary intake. However, these associations were generally weak (r<0·5), with positive associations for nutrition knowledge most often reported for fruit and vegetable intakes. No studies have examined this relationship in military personnel. A number of weaknesses were identified in this review; importantly, nutrition knowledge and dietary intake were often not measured using validated instruments or methodology. The overall study quality ranged widely, and there was a low representation from men, with only 23 % of the participants in the combined study sample being male.
Although no studies linking nutrition knowledge and food intake have been conducted in military personnel, numerous studies( Reference Skiller, Booth and Coad 19 – Reference DeBolt, Singh and Day 25 ) have reported dietary intake in this population to be inconsistent with public health and/or military nutrition recommendations. Characteristics of studies reporting poor dietary intake in this population include excessive consumption of fat, particularly SFA, unbalanced eating patterns with increased serves of ‘extras’ or ‘treat’ foods and inadequate fruit, vegetable and whole-grain consumption. Some studies report substantially low intakes of certain nutrients including Ca, Mg and Zn. One factor that complicates dietary assessment in active individuals such as military personnel includes high energy consumption, which is required to meet the elevated needs of occupational activity. Individuals with high energy intakes may meet nutrient reference values more easily because of this increased food consumption, but may not consume diets consistent with dietary guidelines. Alternative approaches to dietary assessment may therefore be a more effective means of not only assessing the quality of diet intake in these populations but also for examining the relationship between dietary intake and nutrition knowledge( Reference Heaney, O’Connor and Michael 14 ).
Recently, an Australian Recommended Food Score (ARFS) was developed to evaluate diet quality within the Australian population( Reference Collins, Young and Hodge 26 ). The ARFS, modelled on the Recommended Food Score developed by Kant & Thompson( Reference Kant and Thompson 27 ), is derived primarily from dietary intakes measured by an Australian FFQ( Reference Giles and Ireland 28 ). The ARFS is known to be influenced by demographic factors including age, self-related health status, level of physical activity and education( Reference Collins, Young and Hodge 26 ). Studies thus far limited to female cohorts have found that a higher ARFS is associated with positive dietary attributes including frequent consumption and greater variety of vegetables, fruits, whole-grains, nuts, legumes, meats and reduced-fat dairy products( Reference Collins, Young and Hodge 26 ). A higher ARFS is also associated with a lower frequency intake of foods considered unhealthy( Reference Collins, Young and Hodge 26 ).
Adequate nutrition is a key component of operational readiness for military personnel. Although various approaches exist to influence military personnel dietary habits, interventions to increase knowledge are often the primary approach. The aim of this study was to evaluate the relationship between nutrition knowledge and dietary intake in an Australian military sample using a well-validated tool to assess nutrition knowledge( Reference Parmenter and Wardle 29 , Reference Hendrie, Cox and Coveney 30 ) and the ARFS( Reference Collins, Young and Hodge 26 ) to assess diet quality. The present study also aimed to examine the association and underlying demographic factors linking nutrition knowledge and diet quality in this population.
Methods
Participants and study design
A convenience sample of male Australian Regular Army personnel participated in this study. To ensure a cross-section of occupations and ranks, personnel were invited to participate from both Brigade (field-based) and Headquarter (desk-based) environments. Participants completed a general nutrition knowledge questionnaire (GNKQ) and FFQ administered via ‘paper and pencil’ or online, depending on participant computer access. Demographic information collected included the following: sex, age, level of education, living arrangement (family v. non-family arrangement), living environment (live on base v. off base), rank, self-rated nutrition knowledge and previous nutrition education at school or during military service. Two specific questions on eating patterns were also included. The first examined how often during an average week (including weekends) participants skipped breakfast, lunch and/or dinner. Response options included ‘always’, ‘often’, ‘sometimes’ or ‘never’. The second examined on average how many times per week participants ate dinner away from home – for example, from a restaurant, takeaway or frontline (military canteen). Participants were required to provide a numerical value of occasions per week. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and was approved by the Australian Defence Human Research Ethics Committee. The National Statement on Ethical Conduct in Human Research (developed jointly by the National Health and Medical Research Council, Australian Research Council and the Australian Vice Chancellors Committee, March 2007) has been adhered to. Written informed consent was obtained from all participants before participation.
General nutrition knowledge questionnaire
The GNKQ and local adaptations have been previously used in a number of Western populations( Reference Parmenter, Waller and Wardle 9 , Reference Wardle, Parmenter and Waller 10 , Reference De Vriendt, Matthys and Verbeke 31 – Reference Jovanovic, Kresic and Zezelj 33 ) including Australian cohorts( Reference Hendrie, Coveney and Cox 11 , Reference Spendlove, Heaney and Gifford 34 ). The GNKQ has also been psychometrically validated( Reference Parmenter and Wardle 29 , Reference Hendrie, Cox and Coveney 30 , Reference Spendlove, Heaney and Gifford 34 ) and is designed to measure a spectrum of nutrition knowledge (content validity), including dietary guideline recommendations (Section A), nutrient content of foods (Section B), everyday food choice (Section C) and links between diet and disease (Section D). When administered to cohorts with varying degrees of nutrition knowledge, significantly different scores are achieved (construct validity). Internal consistency and test–re-test reliability of the GNKQ have also been conducted( Reference Spendlove, Heaney and Gifford 34 , Reference Kline 35 ). Minor modifications that have been subsequently validated were made to the original GNKQ for the Australian population, for example, the use of Australian terminology, resulting in a maximum of 113 points compared with the original version, which contained 110 points( Reference Hendrie, Cox and Coveney 30 ).
Assessment of dietary intake
Dietary intake was assessed using the Dietary Questionnaire for Epidemiology Studies (DQES) version 2, a FFQ developed and validated by Cancer Council Victoria( Reference Giles and Ireland 28 ). The DQES is designed to measure usual intake over the preceding 12-month period, including the frequency of seventy-four food items and six alcoholic beverages using a 10-point frequency scale. The food items are grouped into four categories: cereal foods, sweets and snacks; dairy products, meat and fish; fruits; and vegetables. Additional questions about the number of serves of different types of fruits, vegetables, bread, dairy products, eggs, fat spreads and sugar are used to probe diet quality. The DQES has been validated in a number of cohorts and performs well against numerous other dietary assessment methods( Reference Collins, Young and Hodge 26 ).
Diet quality and Australian Recommended Food Score
The ARFS was used to assess diet quality from FFQ data. The ARFS was originally modelled on the recommended food score (RFS) by Kant & Thompson( Reference Kant and Thompson 27 ) and re-modelled by Collins et al.( Reference Collins, Young and Hodge 26 ). This tool is intended to measure the frequency of consumption of foods independent of amounts owing to the associated measurement error with amounts. The ARFS is based on the nutrition and dietary recommendations from the Dietary Guidelines for Australian Adults ( 36 ) and The Australian Guide to Healthy Eating ( Reference Kellett, Smith and Schmerlaib 37 ). The maximum possible score is 74. For more detailed information on ARFS scoring, refer to the study by Collins et al.( Reference Collins, Young and Hodge 26 ). Previous studies that utilised similar scores, that is RFS and Alternate Healthy Eating Index, applied this same method( Reference Kant and Thompson 27 , Reference McCullough, Feskanich and Stampfer 38 – Reference Guenther, Kirkpatrick and Reedy 40 ).
Statistical analysis
χ 2 Tests for homogeneity were used to compare officers and soldiers with regard to categorical demographic variables (age groups, education), rank and whether the military personnel lived in a family environment. Two sample t tests were used to compare the GNKQ total score and sub-section scores for officers and soldiers. To adjust for demographic variables, ANCOVA were used to compare GNKQ total and sub-section scores for officers and soldiers. For meat/poultry, fish, fats and alcohol ARFS component scores, Fisher’s exact χ 2 was used to compare officers and soldiers, whereas the median test was used for vegetables, fruit, grains, eggs/nuts/beans/soya and dairy ARFS component scores. To adjust ARFS component scores for demographic variables and total GNKQ score, ordinal logistic regressions were used. The strength of a relationship between two quantitative variables is reported using Spearman’s rank correlation coefficient. Data are reported as mean values and standard deviations; significance was set at P<0·05.
Results
Participant characteristics
In all, 211 male military personnel ranging in rank from Private (lowest) to Colonel (highest) participated in this study (Table 1). The mean age of the participants was 28·7 (sd 8·9) years. The group consisted predominantly of junior soldiers (Private, Corporal and Lance Corporal) (78·2 %), senior soldiers (Sergeants and Warrant officers) (9·5 %) and officers (12·3 %). Most participants were from Combat Arms, for example, infantry, armoured and artillery (60·5 %), Combat Service Support, for example, ordinance, cooks, medics and transport (23·2 %), and Combat Support, for example, engineers (16·3 %). The majority lived off base (74·4 %), with approximately half of the total sample living with their families (55·0 %). Most of the participants self-rated their nutrition knowledge as average (67·5 %), with a smaller number rating their nutrition knowledge as above (22·5 %) or below average (10·0 %). A substantial proportion reported previous nutrition education throughout school (61·1 %) and/or throughout their military service (51·9 %). A large proportion of participants reported never skipping breakfast (43·6 %), with others skipping sometimes (32·2 %), often (19·0 %) or always (5·2 %). In addition, 16·4 % of participants reported eating their evening meal from a restaurant, takeaway or Frontline≥3 times/week.
Participant demographic characteristics (Numbers and percentages; mean values and standard deviations)
* Baseline categorical data analysed by χ 2 test for homogeneity (except age: analysed by two sample t test), significance set at P<0·05.
† Includes participants in the final 4 years of high school, year 8–12 (14–18 years).
‡ Includes TAFE, university, private college.
§ Includes living with partner/spouse and/or children.
|| Includes living alone/with peers/flat mates/on base.
General nutrition knowledge scores
The mean GNKQ score was 52·7 % (Table 2). Overall, participants scored the highest (58·5 %) on Section A (knowledge of dietary recommendations) and the lowest (31·0 %) on Section D (diet–disease relationships). When considering rank, officers scored significantly higher than soldiers overall (P=0·001), as well as for Section B (sources of nutrients; P=0·009) and D (diet–disease relationships; P=0·001). They also scored non-significantly higher for Section A (knowledge of dietary recommendations; P=0·259) and Section C (choosing everyday foods; P=0·130). The highest scoring sections by rank were Section B (63·1 %) for officers and Section A (58·5 %) for soldiers. Both groups scored the worst on Section D (officers: 39·5 %, soldiers: 30·0 %).
Nutrition knowledge scores on the general nutrition knowledge questionnaire (Mean values, standard deviations, percentages and 95 % confidence intervals)
* Significant difference set at P<0·05.
† P-value comparison between officer and soldier group means – differences assessed using ANCOVA.
‡ Adjusted P-value for general linear model with predictors age, education, living arrangement.
When adjusting for potential confounders, age was the only demographic characteristic significantly associated with the GNKQ total score (P<0·0005) and three of the four sub-section GNKQ scores (A: P=0·069, B: P=0·003, C: P=0·005, D: P=0·027). Age was significantly and weakly, positively correlated to the overall (r 0·307; P<0·0005) and all sub-section GNKQ scores (A: r 0·140, P=0·042; B: r 0·279, P<0·0005; C: r 0·178, P=0·010; D: r 0·272; P<0·0005) but only explained 25·4 % of the variance for the overall GNKQ score. No significant associations were observed for any of the other demographic characteristics (e.g. level of education, living arrangement).
The effect of self-rated nutrition knowledge (‘below average’, ‘average’, ‘above average’) was also evaluated. Personnel who self-rated their nutrition knowledge as ‘above average’ (mean GNKQ: 58·5 (sd 10·9) %) scored significantly higher (P<0·0005) than those who self-rated as ‘average’ (mean GNKQ: 52·4 (sd 9·8 %) or ‘below average’ (mean GNKQ: 42·9 (sd 9·9) %). Self-rated nutrition knowledge explained 13·8 % of the variance in the overall GNKQ score. Previous nutrition education in the Army had no significant influence on the overall GNKQ score (P=0·109). Regarding meal skipping and eating outside home, participants who reported never skipping breakfast (43·6 %) scored significantly higher overall and in Section B and Section D of the GNKQ score than those who skipped breakfast sometimes, often or always (56·4 %) (Overall 56·2 v. 51·2 %, P=0·001; A: 60·8 v. 57·8 %, P=0·174; B: 60·8 v. 55·6 %, P=0·004; C: 58·9 v. 55·0 %, P=0·308; D: 35·2 v. 29·9 %, P=0·019). Those who ate their evening meal outside home ≥3 times a week (16·4 %) had significantly lower GNKQ scores than those reporting they did this <3 times a week (Overall 47·6 v. 54·6 %, P=0·000; A: 51·8 v. 60·8 %, P=0·003; B: 52·9 v. 58·9 %, P=0·006; C: 50·3 v. 58·0 %, P=0·014; D: 25·7 v. 33·5 %, P=0·003). No significant associations were found for skipping other meals.
Australian Recommended Food Score
The average ARFS of the entire cohort was 37·6 (sd 7·7) (50·8 %) (Table 3). Demographic factors such as age, level of education and living arrangement were not significantly associated with total ARFS. However, officers achieved a significantly higher total ARFS than soldiers (officers: 40·5 (sd 8·0), 54·7 % v. soldiers: 37·2 (sd 7·6), 50·3 %; P=0·040). Officers also achieved a non-significantly higher sub-component ARFS for vegetables, fruits, grains, meat/poultry, fish, dairy products and fats, with scores being similar for protein foods (eggs/nuts/beans/soya). Officers also had a better score for alcohol than soldiers (P=0·011), indicating an intake more consistent with the Australian recommendations for alcohol consumption( 41 ). The only demographic factor significantly associated with sub-section ARFS was living in a family environment (as opposed to living alone), which resulted in a better ARFS for fat (P=0·028). With respect to skipping breakfast, those who never skipped breakfast (43·6 %) scored significantly higher total ARFS than those who skipped breakfast sometimes, often or always (56·4 %) (ARFS: 53·7 v. 49·1 %, P=0·001). No significant associations were found for skipping other meals. No significant differences in ARFS were observed between those who ate their evening meal outside home ≥3 times a week v. <3 times/week.
Australian Recommended Food Scores (ARFS) (Mean values, standard deviations and percentages)
* Differences between officer and soldier group means. Significant difference set at P<0·05.
The total GNKQ score had a significant, positive but weak relationship with total ARFS (r 0·179; P=0·009) and explained 3·8 % of the variance. For every 10-point increase in GNKQ, ARFS increased by 1·4 points (P=0·001). With respect to ARFS component scores, a significant, positive but weak correlation was found for GNKQ total score and the sub-component ARF score for vegetables (r 0·23; P=0·001) and dairy products (r 0·20; P=0·004). The mean GNKQ score was also higher (54·5 v. 51·2 %) for participants scoring ‘1’ v. ‘0’ for the fat sub-component of ARFS (P=0·038). There were no other significant correlations between GNKQ total score and sub-component scores of the ARFS.
Discussion
This is the first study to investigate the relationship between nutrition knowledge and dietary intake in military personnel. The overall nutrition knowledge score in this study was similar to an Australian community sample of men( Reference Hendrie, Coveney and Cox 11 ). Although officers were found to have significantly higher nutrition knowledge than soldiers, this was still below mixed-sex, Australian community samples( Reference Hendrie, Cox and Coveney 30 , Reference Spendlove, Heaney and Gifford 34 ). Overall performance on the different knowledge domains was similar to most other studies using the same instrument, with participants performing best on knowledge of dietary guidelines and worst on diet–disease relationships( Reference Hendrie, Coveney and Cox 11 , Reference Parmenter and Wardle 29 , Reference Hendrie, Cox and Coveney 30 ). Age was the only demographic factor significantly influencing nutrition knowledge. The diet quality of both soldiers and officers in this cohort was relatively low, although slightly higher than that reported by a large study of Australian women( Reference Collins, Young and Hodge 26 ), which also used the ARFS. As with nutrition knowledge, officers had significantly higher overall diet quality than soldiers. The relationship between nutrition knowledge and diet quality was found to be significant, positive but weak, consistent with the findings reported by a recent systematic review( Reference Spronk, Kullen and Burdon 18 ). Initiatives to improve nutrition knowledge and encourage healthy eating, particularly in soldiers, are warranted.
The overall GNKQ score in this study was 52·7 %, which was below the recently reported data from Australian, mixed-sex community (64·1 %)( Reference Hendrie, Cox and Coveney 30 ) and athlete samples (57·6 %)( Reference Spendlove, Heaney and Gifford 34 ) who also completed the GNKQ. Although it might be expected that military personnel would have better nutrition knowledge because of the link between diet and physical performance, this study indicates that nutrition knowledge at least in male soldiers is similar to a cohort of Australian men (52·8 %)( Reference Hendrie, Coveney and Cox 11 ), suggesting that higher scores in mixed-sex groups are driven by better nutrition knowledge in women, a finding often reported in other cohorts internationally( Reference Parmenter, Waller and Wardle 9 – Reference Trent 13 ). Historically, women have assumed the major responsibility for household food provision( Reference Parmenter, Waller and Wardle 9 ), a role which nurtures better nutrition knowledge( Reference Hendrie, Coveney and Cox 11 ). Nutrition education in the Australian military is generally limited to a 1-h lecture and a short dining hall orientation delivered during the early recruitment training period. There is minimal progressive or occupation-specific nutrition education, and this is the most likely reason for the levels of nutrition knowledge being similar to the community. In contrast to previous studies( Reference Parmenter, Waller and Wardle 9 , Reference Wardle, Parmenter and Waller 10 , Reference Trent 13 , Reference Bovill, Tharion and Leiberman 16 , Reference Obayashi, Bianchi and Song 42 ), there was no effect of rank, level of education or living arrangement on nutrition knowledge in this study. In fact, the best predictor of nutrition knowledge was self-rated competency, with those self-rating nutrition knowledge ‘above average’ achieving significantly higher scores on the GNKQ than those who self-rated nutrition knowledge as ‘below average’.
Performance on sub-sections of the GNKQ revealed a pattern consistent with other studies using the same tool( Reference Wardle, Parmenter and Waller 10 , Reference Hendrie, Coveney and Cox 11 , Reference Parmenter and Wardle 29 , Reference Hendrie, Cox and Coveney 30 , Reference De Vriendt, Matthys and Verbeke 31 , Reference Spendlove, Heaney and Gifford 34 ), with the sample performing the best on Section A (dietary recommendations) and the worst on Section D (diet–disease relationships). Better performance on Section A may be due to current dietary guideline messages (e.g. eat less fat, eat more fibre) being frequently disseminated in the media and community in general. The scores on Section D (diet–disease relationships) in this study were particularly low (31·0 %) in comparison with other Australian community and athlete groups (range: 35·0–50·0 %)( Reference Hendrie, Coveney and Cox 11 , Reference Hendrie, Cox and Coveney 30 , Reference Spendlove, Heaney and Gifford 34 ); however, they were similar to that of a UK study of predominantly male university students (31·0 %)( Reference Parmenter and Wardle 29 ). Lower scores for this section are consistently reported across studies. Given that the majority of this military sample was relatively young, with 47·4 % under the age of 25 years, it may be that the relationship between diet and disease seems less relevant to the participants at this life stage. Section D (diet–disease relationships) was also the only open-ended section requiring participants to respond in writing rather than via multiple choice, potentially making this section more challenging to answer and/or may have resulted from response fatigue as this part of the instrument is completed last. The scores on Section B and C were similar to those of other Australian men( Reference Hendrie, Coveney and Cox 11 ). Section C focused on procedural knowledge (i.e. ‘how to’ plan, purchase and prepare healthy food). In the present study, both officers and soldiers scored higher than Australian men, but substantially lower than mixed-sex community groups on this section. This type of food knowledge is important for understanding how to choose healthier options when presented with a wide range of foods; therefore, finding ways to focus more on this type of knowledge may be key in changing behaviour.
Although it might be expected for military personnel who are required to maintain occupational physical fitness standards to have high-quality diets, the mean total AFRS in this military sample was low (50·8 %). Although officers achieved a significantly higher total ARFS than soldiers (officers: 40·5 (sd 8·0), 54·7 % v. soldiers: 37·2 (sd 7·6), 50·3 %; P=0·040), their scores also remained low. The low total ARF scores in this study were mostly attributed to a lack of variety and inadequate intake of fruits and vegetables, low consumption of high-fibre breads and cereals, poor intake of low-fat dairy foods, higher than recommended consumption of alcohol and dietary fat intakes inconsistent with dietary guidelines. Studies using the ARFS in Australia have been in female cohorts( Reference Collins, Young and Hodge 26 , Reference Collins, Patterson and Fitzgerald 43 – Reference Aljadani, Sibbritt and Patterson 45 ), making it difficult to compare our results, given that the present study was an all-male sample. However, the Australian National Nutrition Survey identified the diets of young men in particular to follow a similar pattern of low fruit and vegetable intake and high levels of alcohol consumption( 46 ). This pattern was also shown in a group of young Australian male athletes using an adapted version of the ARFS, with the diets of male athletes inferior to their female counterparts( Reference Spronk, Heaney and Prvan 47 ). The only factors significantly associated with total ARFS and sub-section ARFS in this study were breakfast consumption and living in a family environment. This is in contrast with previous studies using healthy eating index approaches( 48 – 50 ), which have found age and education to be associated with higher diet-quality scores, but consistent with research that indicates skipping breakfast to be associated with poor nutrient intake and diet quality and associated weight gain( Reference Smith, Dotson and Young 22 ). It is possible that the lack of association between demographic factors such as education and diet quality was due to the limited power to assess this because of the smaller number of officers than soldiers. With regard to breakfast skipping, military personnel are required to participate in physical training on arrival at work most days of the week. Possible explanations for skipping breakfast could therefore be time constraints, individual preference to not perform physical activity on a full stomach and inflexible dining facility hours to enable post-training breakfast consumption.
This study found a significant relationship between general nutrition knowledge (GNKQ score) and diet quality (ARFS). A recent systematic review from our group( Reference Spronk, Kullen and Burdon 18 ) also found either null or weak (r<0·5) relationships when examining this relationship. ARFS component scores for vegetables, dairy products and fat in this study were significantly correlated with GNKQ scores and the relationship was weakly positive. Other studies using the same GNKQ have also reported a positive relationship between nutrition knowledge and intakes of vegetables as well as fruits, fibre, fish and low-fat products( Reference Wardle, Parmenter and Waller 10 , Reference De Vriendt, Matthys and Verbeke 31 , Reference Gambaro, Raggio and Dauber 32 , Reference Jovanovic, Kresic and Zezelj 33 ). Similarly, significant relationships between general nutrition knowledge and dairy( Reference Guthrie and Fulton 51 , Reference Sharma, Gernand and Day 52 ) and fat( Reference Dallongeville, Marecaux and Cottel 53 ) intakes have been observed by studies examining nutrition knowledge utilising other general knowledge questionnaires.
Numerous theoretical models have been developed to better understand eating behaviour( Reference Baranowski, Cullen and Nicklas 54 ). These models hypothesise that health behaviours (e.g. food choice) are determined by individual, social and environmental factors such as one’s ability to make healthy choices, their motivation to do so and environmental opportunities( Reference Brug 7 , Reference Brug, Kremers and Lenthe 8 ). One of the earliest models that would seem pertinent to this study is the knowledge–attitude–behaviour (KAB) model. The KAB model is based on the hypothesis that an increase in nutrition knowledge improves attitudes towards healthy eating and subsequently improves eating behaviour( Reference Baranowski, Cullen and Nicklas 54 ). However, given the weak relationship found between nutrition knowledge and diet quality observed in this study, it appears that this model has limited capacity to predict eating behaviour in male military personnel.
More recently, social ecological models of behaviour have been used to explain eating behaviour in military populations( Reference Carins and Rundle-Thiele 55 ). These models move beyond just intrapersonal factors and emphasise interpersonal (e.g. peer and family influences) and environmental factors on eating behaviour( Reference Sallis and Owen 56 ), which may have greater application to the unique military setting. For example, military personnel spend a substantial proportion of their career away from the home environment, with many living in shared accommodation facilities on base; thus, the influence of military peers and the physical eating environment in barracks (e.g. the dining hall) and in the field (e.g. combat ration packs) have the potential to influence eating attitudes and behaviours. In terms of the physical environment (i.e. availability of healthy and unhealthy choices), restricted or inappropriate menus provided on Army bases were unlikely to have been a contributing factor to the low ARFS, given that >50 % of personnel never consume meals in the mess. On average, only 10·0 % of military personnel reported eating ≥15 meals (i.e. breakfast or lunch or dinner) in the mess during a 7-d week (Kullen unpublished results). However, difficulty accessing the dining facilities due to inflexible operating hours may have influenced personnel to seek more convenient but potentially unhealthier food options elsewhere.
From a practical perspective, recruitment into the military from a young age and/or directly from the family home where meals may have been prepared by others may limit exposure to practical skills such as shopping for and cooking food, a factor shown to be associated with increased consumption of fast food( Reference Thornton, Jeffery and Crawford 57 ). In addition, research examining factors that influence food selection in young people have found taste to be the most important influence, followed by convenience (availability), cost and then nutritional/health value of the food( Reference Hebden, Chan and Louie 58 ). Noting the age of the study population, it is possible that these factors may explain to some extent the present diet-quality scores.
Eating, drinking and making food choices are some of the most frequent human behaviours. Although seemingly simple, a complex web of personal, social and environmental factors influence these behaviours( Reference Köster 59 ). Future research therefore needs to better understand the aspirations of military personnel regarding their health, fitness and food-related goals, how they acquire food-related knowledge and what social and environmental factors need to be considered when designing future nutrition initiatives. The potential for social ecological models and other recognised behavioural change models such as the Health Belief Model, Social Cognitive Theory and Theory of Planned Behaviour therefore warrant further investigation in military populations, as little research currently exists in the area of eating behaviour. Future research of this type will, however, need to ensure that the behaviours being tested are within the person’s control (e.g. healthy food choices will need to be available in the dining facility).
Although some nutrition education is likely central to the success of these models, single-factor interventions focusing on just knowledge or just the food service environment are unlikely to be effective in positively changing eating behaviour. The military setting does, however, provide a unique environment in which nutrition education and dietary interventions can embrace a multidisciplinary approach. Opportunities exist for traditional methods (e.g. seminars, face-to-face workshops), practical skill development (e.g. cooking lessons), but also potentially for novel approaches using technology such as web or smart phone applications. There is also significant potential to exploit passive interventions such as food labelling and social marketing at points of food service. Clearly the interventions need to go beyond improving nutrition knowledge as the relationship with diet quality is weak.
Strengths of this present study include the use of validated tools to assess general nutrition knowledge and diet quality. The study makes a useful contribution to the limited body of literature in this area and is the first to determine the relationship between nutrition knowledge and diet quality in a military population. The present sample was of moderate size but sufficiently large to find significant associations between nutrition knowledge and diet quality and also assess differences between officer and soldier sub-groups. However, larger military samples with a wider range of nutrition knowledge may be needed to more comprehensively assess the strength of the associations reported in this study. Most importantly, our results cannot be generalised to female military personnel. Although females form a small part of the Australian military (approximately 10–15 %), evidence from one study in military personnel( Reference Trent 13 ) demonstrates that women have superior dietary intake/diet quality.
In conclusion, this study demonstrates a positive, weak relationship between nutrition knowledge and diet quality in military personnel. Despite officers achieving higher nutrition knowledge and diet-quality scores than soldiers, this study shows that many young male military personnel have limited nutrition knowledge. It also demonstrates that their diets are lacking in variety and many are failing to meet basic dietary guidelines, particularly in relation to fruits, vegetables, grains and dairy products. Military populations provide a unique setting to conduct ‘worksite’ nutrition interventions; thus, further research in these cohorts, particularly soldiers, who appear to possess lower nutrition knowledge and diet quality is warranted. This study supports the need for well-designed interventions in the Australian military to improve dietary intake. These interventions need to go beyond the provision of nutrition knowledge and should examine the predictive capacity of eating behaviour models to assist in delineating the most effective approach for achieving and maintaining eating behaviour change in this population.
Acknowledgements
The authors thank the Australian Regular Army personnel who participated in this study, Captain Clay Baker for support during data collection and Laura Iredale for assistance with data collection and data entry. This work was supported by Headquarters Forces Command.
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Conception and design (C. J. K., H. T. O’C.), data collection (C. J. K.), data interpretation and analysis (C. J. K., H. T. O’C., T. P., J.-L. F.), writing and editing of the manuscript (C. J. K., H. T. O’C., T. P., J.-L. F.).
The authors declare that there are no conflicts of interest.
The opinions expressed in this research herein are those of the authors and do not necessarily reflect those of Defence or any extant policy.
