Evaluating the Influence of Nutrition Determinants on Construction Workers’ Food Choices

Abstract

Nutritional knowledge as well as economic, social, biological, and cultural factors have been known to determine an individual’s food choices. Despite the existence of research on the factors which influence nutrition globally, there is little known about the extent to which these factors influence the food choices of construction workers, which in turn influence their health and safety during construction activities. The present article investigates the extent to which construction workers’ nutrition is influenced by nutritional knowledge, as well as economic, environmental, social, psychological, and physiological factors. A field questionnaire survey was conducted on site construction workers in the Gauteng Province of South Africa. Principal components analysis and multiple regression analysis were used to analyze the data. Findings revealed that consumption of foods termed alternative foods including dairy products, eggs, nuts, fish, and cereals, was influenced by nutritional knowledge and resources. Foods termed traditional core foods were influenced by cultural background; foods termed secondary core foods comprising fruits and vegetables were influenced by economic factors, resources, and cultural background; while foods termed core foods were mostly influenced by nutritional knowledge. By providing evidence of the factors which most influence selection and consumption of certain foods by construction workers, relevant nutrition interventions will be designed and implemented, taking cognizance of these factors.

Keywords

construction workers determinants nutrition

Introduction

Due to its invaluable role in productivity and health and safety improvements, nutrition has been a paramount concern for employers and organizations for more than a thousand years (Wanjek, 2005). Good nutrition can be attained from the consumption of a variety of foods from different classes of nutrients including proteins, carbohydrates, fats, vitamins, minerals, and water (Amare et al., 2012). Good nutrition helps in maintaining good physical and mental health, which in turn, allows for maximum concentration and alertness that are necessary to perform mentally and perpetually demanding tasks such as construction activities (Du Plessis, 2011).

According to the Ambekar Institute for Labour Studies (2012), the construction sector has disproportionately high incidences of accidents, injuries, and fatalities, compared with other sectors. Accidents and ill health have been a continuous source of worry in the construction industry (Musonda, 2012). As reported in Melia and Becerril (2009), poor health and safety on construction sites is partly attributable to workers’ unhealthy eating. The Cancer Prevention in the Workplace Writing Group (2014) and Groeneveld, van der Beek, Proper, Hildebrand, & van Mechelen (2011) reported prevalence of chronic disease among construction workers, which could be partly attributed to poor nutrition. As observed by Schulte et al. (2007), poor nutrition (unhealthy eating) is one of the health risk factors of chronic illnesses among construction workers. Fatigue, impaired concentration, and reasoning or reduced cognitive capabilities, partly as a result of unhealthy eating, can result in accidents and injuries (Bates & Schneider, 2008). Fatigue can lead to poorer performance on tasks which require attention, decision making, or high levels of skills, giving rise to increased risks especially in safety-critical tasks (Health & Safety Executive (HSE, 2009).

Furthermore, poor eating habits may lead to overweight and obesity (thereby exceeding weight limit for safety gear), which can also lead to occupational injury and illness (Schulte et al., 2007; Wanjek, 2005). Poor nutrition weakens the immune system and adversely affects an individual’s mental state resulting in depression and mental ill health (Du Plessis, 2011). Mental sluggishness leads to mistakes, lower output, and even deaths (Wanjek, 2005). The role of nutrition in combating disease and thus reducing injuries on worksites is unquestionable. Research on the nutrition of construction workers is therefore imperative.

Research on the nutrition of construction workers is also necessary since they are the most important assets in the construction industry (Smallwood, 2012). Since the nature of construction activities predisposes the workers to dangerous substances, falls, electrical wiring, unguarded equipment, and so forth, it is vital to reduce the risks posed by the inherently hazardous circumstances they are faced with on a daily basis. One way of reducing the risks is through improving nutrition. To improve nutrition and modify dietary patterns effectively, an understanding of the factors which influence food choice decisions is crucial (Steptoe, Pollard, & Wardle, 1995; European Food Information Council [EUFIC], 2005; Milosevic, Zezelj, Gorton, & Barjolle, 2012).

Research has been conducted on the factors which determine construction workers’ food choices (Du Plessis, 2011; Okoro, Musonda, & Agumba, 2014). However, the extent to which construction workers’ food choices are influenced by the determinants has not been studied. The present article focuses on the extent of the influence of nutrition determinants on food choices. By illustrating the extent to which construction workers’ food choices are influenced by the determinants, successful nutrition intervention programs can be designed, targeting the significant determinants.

The objective of the article is to establish the influence of nutrition determinants on construction workers’ food choices. The resulting research question was whether nutrition determinants influence food choices of construction workers. A general hypothesis was therefore postulated thus: “Nutrition determinants influence construction workers’ food choices.”

Literature Review

Measuring Nutritional Intake

There are different food intake methodologies used to determine nutritional intake, for example, 24-hour dietary recalls, food frequency questionnaires (FFQs), anthropometric measures, and measurement with biomarkers (Aich, Mahzebin, Fahriasubarna, & Hassan, 2014). Amare et al. (2012) used an FFQ and 24-hour dietary recalls to assess nutrient adequacy. However, the National Cancer Institute (2013) argued that FFQs asking about the frequency of food consumption, even without asking about portion sizes, is adequate for obtaining information about food intake. This view is supported by the Medical Research Council (n.d.), which reported that FFQs can be used to assess habitual diet by asking the frequency with which certain foods or specific food groups are consumed over a reference period. Quantitative information revealing the consumption pattern of a particular subject population can be obtained from FFQs. Which method one decides to use depends on the questions to be probed, the settings, the participants, and the outcomes required (Huang, Lee, Pan, & Wahlqvist, 2011).

FFQs may be based on an extensive list of food items or a relatively short list of specific foods, for example, meat, fish, eggs, fat-rich foods, dairy products, fruits, vegetables, and so forth, but should include (a) major sources of a group of nutrients of particular interest, (b) foods which contribute to the variability in intake between individuals in the population, and (c) foods commonly consumed in the study population (Medical Research Council, n.d.). FFQs should be able to provide the information for which they were intended, that is, frequency of food consumption, nutrient or dietary supplement intake, or specific dietary behavior, time period of interest (a week, a month, or a year), and so forth (Cade, Burley, Warm, Thompson, & Margetts, 2004). Single food items may be grouped to prevent excessive questionnaire length (Cade et al., 2004).

Determinants of Nutritional Intake

Food choice determinants have been hugely studied. Steptoe et al. (1995) validated a multidimensional measure of motives related to food choice. Nine motives were identified in the food choice questionnaire (FCQ), namely health, mood, convenience, sensory appeal, natural content, price, weight control, familiarity, and ethical concern. In a study among Japanese, Taiwanese, Malaysians, and New Zealanders, Prescott, Young, O’Neill, Yau, and Stevens (2002) opined that the factors identified by Steptoe et al. (1995) were important for a sample of U.K./European customers and this same selection may not necessarily reflect the most important factors in other cultures. The factor structure might have different connotations across different populations and cultures (Januszewska, Pieniak, & Verbeke, 2011; Milosevic et al., 2012; Pula, Parks, & Ross, 2014). Moreover, there may be other important factors that the FCQ is not addressing or indeed more appropriate questions within each factor, which makes the FCQ’s applicability limited in cross-cultural situations (Prescott et al., 2002). Furthermore, it was observed that some of the factors in Steptoe et al.’s (1995) study could be part of a larger factor. For instance, price could be classified as an economic factor. Following these cues, the FCQ was modified to include context, culture, and individual differences (Pula et al., 2014) as well as items related to food image such as product awareness created by media, experts, and the environment (Gagic, Jovicic, Tesanovic, & Kalenjuk, 2014).

Other food choice models revealed that life course events, influences and personal food systems (Sobal & Bisogni, 2009), economic and neighborhood (proximity) aspects (Rose et al., 2010) as well as product quality considerations, individual differences, and environmental factors determine food choices (Marreiros & Ness, 2009). In addition, other studies dwelt on factors influencing young construction apprentices (Du Plessis, 2011, 2012). Although there is evidence of a plethora of factors in extant literature related to food choice, a more comprehensive description of the factors influencing food choices in a broader sample of construction workers has not yet been conducted. It was unclear whether the questions in previously used questionnaires accurately represent the diversity of perceptions and capture the full range of the factors relevant to food selection among construction workers in South Africa (a culturally diverse country).

The framework of factors in Nie and Zepeda (2011), factor structures as seen in a review by the EUFIC (2005) as well as conclusions from other studies (as discussed below) were observed to be comprehensive and adaptable for the current study. Based on existing literature, the determinants of nutritional intake (food choice) can be grouped into nutritional knowledge, as well as economic, environmental, social, psychological, and physiological factors.

Nutritional Knowledge

According to Grunert, Wills, and Fernandez-Celemin (2010), nutritional knowledge is indicated by the ability to identify the healthiest foods from various sources or knowledge of what a healthy diet means, knowledge of the sources of nutrients, and knowledge of the health implications of eating or failing to eat particular foods. Food preparation and cooking skills were also reported to be useful indicators of nutritional knowledge (Chenhall, 2010; EUFIC, 2011), in addition to awareness of nutritional requirements for existing health status (Bruner & Chad, 2014), gender (Arganini, Saba, Comitato, Virgili, & Turrini, 2012; Nie & Zepeda, 2011), body size (Hassapidou & Papadopoulou, 2006), and age (Kinsey & Wendt, 2007; Nie & Zepeda, 2011).

Economic Determinants

Economic determinants include wages (Tiwary et al., 2012), cost, and availability of food (Du Plessis, 2011). In addition, price discounts influence purchase of fruits and vegetables sold at reduced prices (Waterlander, de Boer, Schuit, Seidell, & Steenhuis, 2013). Brand names, variety (Berger, Draganska, & Simonson, 2007), and marketing strategies (Kushi et al., 2006; Nie & Zepeda, 2011) were also identified to be food choice determinants.

Environmental Determinants

Environmental determinants of food choice and intake include physical elements of the environment (Ball, Timperio, & Crawford, 2006). The physical elements include seasonality, time (Kolbe-Alexander et al., 2008), and facilities provided on site for storing and preparing foods (Food and Agriculture Organization, n.d.). Work schedules (EUFIC, 2005; Queensland Government, 2012) and limited on-site catering facilities (e.g., a kitchen, canteen, microwave, etc.; Queensland Government, 2012; Smallwood & Deacon, 2015) are environmental factors on construction worksites which can influence workers’ eating lifestyle.

Social Determinants

Social determinants include colleagues and friends (Du Plessis, 2011), family traditions (Just, Heiman, & Zilberman, 2007), social belonging (Puoane, Matwa, & Bradley, 2006), and media (Gagic et al., 2014; McCluskey & Swinnen, 2011). Ellis (2013) argued that people make choices out of a need to gain and solidify social identity. In a way, this suggests that one can be peer-pressured into eating healthily or unhealthily (Barclay, Edling, & Rydgren, 2013). This agrees with the view that social support from within the household and from coworkers is positively associated with improvements in fruit and vegetable consumption (EUFIC, 2005). Pollard, Kirk, and Cade (2002) also reported that higher social pressure positively influences choice of consumption of certain foods.

Psychological Determinants

As reported in Babicz-Zielinska (2006), beliefs, habits, perceptions, attitudes, motives, and personality determine choices of foods. The author contended that some attitudes toward food usually stem from unfamiliarity of foods or their effects on health. Heartya, McCartya, Kearneyb, and Gibneya (2007) argued that individuals who perceive their eating habits to be healthy were more likely to comply with dietary guidelines than those who do not. Likewise, Petrovici and Ritson (2006) contended that health motivation and belief that healthy food can prevent diseases influence dietary health preventive behavior and healthy eating. Some meats may be avoided based on beliefs about preservation of health, for instance, prevention or control of high blood pressure (Petrovici & Ritson, 2006). Other psychological factors determining food choice are beliefs about the role of healthy eating in increasing productivity at work and in preventing accidents and injuries (Wanjek, 2005).

Physiological Determinants

Physiological determinants include biological and sensory mechanisms and needs of the body. According to Pollard et al. (2002) and EUFIC (2005), hunger, taste, appetite, and satiety (level of satisfaction) are essential prerequisites for choosing to consume particular foods. Quality and palatability/appearance of food also determine food choices (EUFIC, 2005; Pollard et al., 2002).

Method

An extensive literature review was initially conducted to identify food choices and measurements, as well as factors influencing food choices and previous categorization of the factors. Previously validated questionnaires were observed to be lacking in terms of applicability in diverse cultural settings and nationality (Januszewska et al., 2011; Milosevic et al., 2012; Pula et al., 2014; Steptoe et al., 1995) as well as in a broader sample of construction workers (Du Plessis, 2012). It was unclear whether the questions in previously used questionnaires accurately represent the diversity of perceptions and capture the full range of the factors relevant to food selection among construction workers in South Africa (which has a culturally diverse population). Moreover, it was observed that there may be other important factors that the FCQ is not addressing or indeed more appropriate questions within each factor, which makes the applicability of the FCQs used in these studies to be limited in cross-cultural situations (Prescott et al., 2002). A more comprehensive tool describing all potential factors influencing food choices in a broader sample of construction workers was not found.

A 5-point Likert-type scale questionnaire was therefore drafted with concepts emerging from the literature synthesis. The questionnaire was pilot-tested, reviewed, and revised by experts before the main study. The pilot study and expert revisions and validation served to enhance construct validity of the scales. Some of the terms were further simplified and some questions were revised to avoid misinterpretations. The questionnaire, which was developed based on an extensive literature survey, expert consultation, and advice from two supervisors and a methodology expert, as well as feedback from a pilot study (n = 19), was considered to be ethnically/culturally sensitive in recognition of different South African population groups.

The final questionnaire comprised two sections. The first section of the questionnaire consisted of 14 items relating to the frequency of consumption of a list of food items in a working week. The questions were adapted from the study by Amare et al. (2012) which validated the use of food items in collecting nutritional information. The response categories were coded 1 (never), 2 (rarely), 3 (sometimes), 4 (often), and 5 (always). The second section contained 42 items relating to food choice determinants, with responses ranging from 1 (strongly disagree) to 5 (strongly agree). The 42 items included 10 items indicating nutritional knowledge, 6 items for economic factors, 5 items for environmental factors, 4 items for social factors, 11 items for psychological factors, and 6 items for physiological factors.

The final questionnaire was self-administered to construction workers on construction sites. Eight construction sites (comprising five building construction and three road construction sites) were chosen by heterogeneity sampling. Attention was paid to including workers from various areas of Gauteng Province (which included Midrand, Centurion, Johannesburg, and Samrand) to obtain a representative population. The objective was to include views from different participants. A cover letter accompanied the questionnaire to explain the purpose of the study, assure anonymity of responses, and allow for voluntary participation. The participants, selected through convenience sampling, included workers who were actively engaged in the physical construction activities as opposed to the site managers and supervisors. This group was chosen as they were the most susceptible to poor nutrition and poor safety performance on construction sites. The participants were also purposively selected to include workers who could read and comprehend the simple-structured questions in English as the researcher is proficient in only one of the official languages (English) in South Africa. Out of a total of 220 questionnaires distributed, 183 were returned. The simplicity of the questions, the purposive selection of participants who could understand English, and the self-administration of the questionnaires during the workers’ break periods helped ensure a high rate of return.

Data were analyzed using Statistical Package for Social Sciences (SPSS) version 22 software. For the purpose of statistical analyses, the scales were scored using Likert’s method of summated ratings, whereby a number was assigned to each item response category, that is, 1, 2, 3, 4, and 5. Weights for the “unhealthy” food items including “a lot of fried foods,” “extra salt,” and “a lot of sugary foods” were reversed so that all items were weighted in the same direction (Grassi et al., 2007). With respect to the determinants, the response categories were assigned 1, 2, 3, 4, and 5 for strongly disagree, disagree, neutral, agree, and strongly agree, respectively. The scale scores were then computed by summing the weights assigned to the item responses and by linearly transforming scores from 0 to 100, where higher scores represent a higher quality of diet (more favorable eating behavior) and level of influence, for food choice and food choice determinants, respectively. Summated rating scales have the advantage of simplicity and can achieve high levels of reliability and validity (Grassi et al., 2007).

Principal components analysis (PCA) using principal axis factoring and oblimin rotation was conducted to examine underlying structures of the theorized measures and to reduce the large number of related variables prior to using them in other analyses. Preliminary considerations for PCA were assessed. The sample size requirement of 150+ was met (Pallant, 2013). Suitability of data for factor analysis was assessed using the Kaiser–Meyer–Olkin and Bartlett’s sphericity tests. Normality was checked. The data were normally distributed. Missing data and outliers were excluded before analysis. Multicollinearity was also checked. Outputs from the PCA, which contributed to the variance in the data sets were then adopted, retained, interpreted, and used for correlation analysis. Decisions on which factors to retain were made using the Kaiser’s criterion (retaining eigenvalues above 1), scree test (retaining factors above the “breaking point”), and Monte Carlo parallel analysis (retaining factors whose initial eigenvalues were larger than the criterion values from parallel analysis). The PCA also served to eliminate high correlations between the variables. Multicollinearity or high correlation between the independent variables in a regression model can make it difficult to correctly identify the most important contributors to a physical process (Sarkar, Mukhopadhyay, & Ghosh, 2014).

Cronbach’s alpha test was used to assess internal consistency reliability before PCA (Table 1). After PCA, Cronbach’s alpha and mean interitem correlations were used to examine internal consistency reliability (Table 2). The alpha values for the food categories ranged from “.43” to “.83.” Alpha values of >.4 are fairly acceptable. Where alpha values are low, it is more appropriate to report mean interitem correlations (Pallant, 2013). The mean interitem correlation values for food categories ranged from “.2” to “.4,” indicating good internal consistency (Pallant, 2013). Regarding the food choice determinants, the alpha values ranged from .62 to .85, indicating good internal consistency. Specific hypotheses were postulated based on the empirical framework emerging from the PCA.

Table 1.

Cronbach’s Alpha Values of Constructs Before Principal Components Analysis.

Constructs	α	Number of items
Nutritional intake measures	.758	14
Nutrition determinants
Nutritional knowledge	.775	10
Economic factors	.705	6
Environmental factors	.837	5
Social factors	.766	4
Psychological factors	.837	11
Physiological factors	.817	6

Table 2.

Cronbach’s Alpha Values and Interitem Correlations of Constructs After Principal Components Analysis.

Components	α	Number of items	Interitem correlations
Nutritional intake measures
1	.759	5	.389
2	.683	5	.298
3	.829	2	.709
4	.429	2	.274
Nutrition determinants
1	.850	6	.487
2	.817	6	.428
3	.623	4	.304
4	.841	10	.341
5	.740	4	.430
6	.797	7	.357
7	.713	4	.379

Multiple regression analysis was thereafter conducted to determine the extent to which nutritional choice is influenced by each determinant. To check the statistical significance and relative importance/contribution of the variables (components), the standardized beta weights of each predictive variable were examined. The beta values show how strongly each factor influences the dependent variable. Therefore, the higher the beta value, the greater the impact of the predictor variable on the dependent variable. The level of significance was set at p < .05, as this is the customary statistic for most studies of this nature, when working on statistical significance (Field, 2005). The null hypotheses were rejected when p < .05.

Results

Descriptive Statistics

Among the respondents, 89% was male, while 11% was female; 21% of the respondents was unskilled workers, 16% was bricklayers, 15% comprised glass fitters, painters, cleaners, and manhole specialists, 14% was electricians, 10% was carpenters and plumbers, 9% was steel fixers, and 5% was pavers.

Results from Principal Components Analysis

With regard to the food choices, four components, accounting for 61.45% of the total variance, explaining 26.32%, 15.44%, 10.96%, and 8.73% of the variance, respectively, were extracted and retained based on the Kaiser’s criterion and the scree test. Results from the Monte Carlo parallel analysis (Table 3) also supported retaining the four categories. Interpretation (Table 4) revealed different foods under the four categories. The four food categories were named based on their nature, level of importance, frequency of consumption, and universality among the study participants (Carmona, 2004; Passin & Bennett, 1943). Alternative foods described foods with much variation, individuality, and relative infrequency of occurrence (including dairy products, eggs, nuts, fish, and cereals). Traditional core was used to describe basic foods (with some degree of processing) and condiments used in food preparation (salty, sugary, and fried foods as well as pasta and grains like rice). Secondary core was used to denote foods that are widely consumed, more variable in use and form, but not universal (including fruits and vegetables). Core foods (including meat and corn meal) described basic, universal, and consistent foods for sustaining human life and which are relatively affordable (Carmona, 2004; Passin & Bennett, 1943).

Table 3.

Comparison of Initial Eigenvalues and Criterion Values.

Component	Initial eigenvalue from principal component analysis	Criterion value (random eigenvalue) from parallel analysis	Decision
1	3.685	1.4014	Accept
2	2.162	1.2653	Accept
3	1.535	1.2081	Accept
4	1.222	1.1082	Accept
5	0.850	1.0396	Reject
6	0.819	0.9110	Reject

Table 4.

Loading Matrix of Nutrition Components.

Item	Classifications
Item	Alternative foods	Traditional core foods	Secondary core foods	Core foods
Dairy products	.702	−.137	.029	.042
Eggs	.683	−.099	.014	.471
Nuts	.680	.105	.088	−.105
Fish	.590	.136	−.005	−.034
Cereals	.405	.353	.183	−.231
Extra salt	−.026	.725	−.281	.071
A lot of sugary foods	.014	.666	−.036	.167
A lot of fried foods	−.172	.609	.248	−.009
Pasta	.268	.466	.206	−.141
Grains like rice	.127	.420	−.018	−.036
Vegetables	−.002	−.083	.795	.166
Fruits	.120	−.018	.793	.052
Meat	.078	.010	.044	.597
Corn meal	−.049	.085	.075	.336

Figures in bold refer to item-loading on each classification.

Pertaining to the determinants of food choice, after repeated analysis, seven classifications, which accounted for 60.09% of the total variance, were revealed. Interpretation (Table 5) identifies that items loaded evenly on each component. However, one item (“what I am used to from home and family traditions”) had a notably low item loading (.279) and was therefore excluded from reliability testing and further analysis. All other items and constructs (categories) met convergent and discriminant criteria. Item discriminant validity requires that each of the items shares a higher correlation with items in its own construct than with other constructs (Guyonnet et al., 2008).

Table 5.

Loading Matrix of the Components of Nutrition Determinants After Rotation.

Measures	Component
Measures	Food context	Biological factors	Nutritional knowledge	Personal ideas and systems	Economic factors	Resources	Cultural background
Brand name	.726	.180	.065	.074	−.013	−.153	−.147
Food in season	.694	−.027	−.024	.084	.056	.024	.123
Time I have before work and during breaks	.551	.051	.017	−.067	.027	−.134	.373
Location of where the food is sold	.540	.046	−.065	.118	−.073	−.123	.064
Cooking skills	.482	−.029	.038	−.061	.078	.013	.369
The way the food is advertised or marketed	.469	.178	.020	.133	−.010	−.158	.121
What I am used to from home and family traditions	.279	.113	−.016	.129	.201	−.137	.106
The taste of the food	.156	.765	.283	−.093	−.030	.139	.110
My appetite for particular foods	.186	.623	−.007	.020	−.081	−.086	.054
How presentable the food is	−.002	.612	−.323	.067	−.043	−.243	.122
The feeling of fullness I get from the food	.015	.576	−.046	.005	.346	.060	.012
The quality of the food	−.096	.564	.009	.115	.031	−.142	−.061
How hungry I am	−.016	.507	.108	.149	.307	.158	.057
What I know will give me energy	−.177	.046	.786	.085	.172	.149	.074
What I know would give me different nutrients, for example, proteins, carbohydrates, vitamins, and minerals	−.123	.105	.721	.069	−.094	−.163	−.091
What I know can happen to my health if I eat or don’t eat particular foods	.228	.206	.427	−.128	.178	−.270	−.099
What I know an adult should eat in a day	.180	−.138	.404	−.043	−.030	−.086	.122
My eating habits	−.058	.256	−.124	.610	.023	−.010	.038
My idea that particular foods are advertised for the benefit of the sellers or advertisers	.142	−.206	.084	.574	.165	−.021	−.088
My mood, for example, happy, sad, stressed, and so forth	.196	.226	.018	.538	.110	.027	−.075
The fact that healthy food will help me concentrate on my work and avoid accidents and injuries	−.331	.020	.064	.521	.104	−.182	−.092
What my friends choose for us to eat	.104	.276	.011	.483	−.036	.075	.213
The need to belong to a particular social group	.002	.114	−.068	.471	.013	−.112	.248
Social media and networking	.315	.277	.032	.471	−.102	−.034	.005
My belief that avoiding meat will keep me healthier	.204	−.163	.080	.448	−.278	−.188	.313
My belief that killing animals for food is not good	.328	−.047	.159	.429	−.106	.043	.268
My belief that my current diet is adequate	.072	−.066	.258	.358	−.114	−.081	.093
The cost/price of the food	.049	−.168	.074	.118	.845	.051	−.127
The foods available	.062	.074	−.014	−.249	.729	−.198	.100
The wages I am paid/income I make	−.254	.069	.005	.079	.636	−.154	.233
The foods on special offers or discounts	.333	.122	.006	.204	.464	.190	.011
The facilities on site for storing and heating up my food	.466	.034	−.100	.106	.041	−.633	−.065
The eating facilities provided on site, for example, benches, tables, washing bowls/sinks, and so forth	.355	.033	.074	.120	.042	−.616	−.026
What I know my body needs for my current health status	.174	.036	.237	−.080	−.071	−.564	.138
What I know my body needs at my age	−.114	−.048	.151	.100	−.062	−.558	.300
The fact that healthy food will help increase my productivity at work	−.188	.131	.055	.073	.232	−.525	−.112
What I know my body size needs	.144	−.175	.212	−.059	.074	−.413	.263
My idea that I will add or lose weight with particular foods	.047	.173	−.131	.298	.110	−.318	.020
What I know I should eat as a man or woman	.202	.035	−.002	−.011	.014	.003	.652
What I know my body needs for the type of work I do	−.222	.232	.109	−.059	.091	−.062	.560
My belief that I should only eat food from my culture	.109	.027	.049	.396	.015	.022	.515
My belief that avoiding meat will save money	.251	−.206	−.252	.367	−.097	−.138	.427

Figures in bold refer to item-loading on each classification.

The seven classifications were named, based on the variables grouped in each category, as follows:

Food context (including brand name, food in season, time constraints, location, cooking skills and marketing strategies; Arganini et al., 2012; Sobal & Bisogni, 2009)

Biological factors (including taste of the food, appetite, appearance, quality, hunger, and satiety; Arganini et al., 2012; EUFIC, 2005)

Nutritional knowledge (including knowledge about food sources of energy, about sources of food nutrients, about health implications of consuming or not consuming particular foods, and about the daily dietary requirements; Grunert et al., 2010)

Personal ideas and systems (including eating habits, cynical attitude toward nutrition promotion, mood, the fact that healthy food help enhance concentration, peers/colleagues’ influence, the need to belong to a social group, social media and networking, belief that avoiding meat will keep one healthier, belief that killing animals for food is not good, and belief about adequacy of current diet; Eertmans, Baeyens, & van der Bergh, 2001)

Economic factors (including cost/price of food, availability of food, wages/income, and food discounts; EUFIC, 2005)

Resources (including on-site facilities for food storage and preservation, and heating up food, eating facilities such as benches, washing bowls, etc., knowledge of nutritional requirements for existing health conditions, for age and body size, the fact that healthy food will help increase productivity, and the fact that one will lose or add weight with certain foods; Sobal & Bisogni, 2009)

Cultural background (including knowledge of what to eat as a man or woman and what to eat for the type of work engaged in, belief that one should only eat food from their culture, and belief that avoiding meat will save money; Kulkarni, 2004)

Hypothesized Relationships

Specific hypotheses relating to food choice as predicted by each determinant were thereafter postulated based on the results from the PCA. The null (Hypothesis 0) and alternative hypotheses (Hypothesis 1), which were tested using multiple regression analysis, include the following:

Hypothesis 1: Relationship between nutrition determinants and choice of alternative foods: The hypothesized relationship between the determinants and choice of foods termed alternative foods was tested as follows:

Hypothesis 1a₀: Food context does not influence choice of alternative foods. Hypothesis 1a₁: Food context influences choice of alternative foods.

Hypothesis 1b₀: Biological factors do not influence choice of alternative foods. Hypothesis 1b₁: Biological factors influence choice of alternative foods.

Hypothesis 1c₀: Nutritional knowledge does not influence choice of alternative foods. Hypothesis 1c₁: Nutritional knowledge influences choice of alternative foods.

Hypothesis 1d₀: Personal ideas and systems do not influence choice of alternative foods. Hypothesis 1d₁: Personal ideas and systems influence choice of alternative foods.

Hypothesis 1e₀: Economic factors do not influence choice of alternative foods. Hypothesis 1e₁: Economic factors influence choice of alternative foods.

Hypothesis 1f₀: Resources do not influence choice of alternative foods. Hypothesis 1f₁: Resources influence choice of alternative foods.

Hypothesis 1g₀: Cultural background does not influence choice of alternative foods. Hypothesis 1g₁: Cultural background influences choice of alternative foods.

Hypothesis 2: Relationship between nutrition determinants and choice of traditional core foods: The following were hypothesized:

Hypothesis 2a₀: Food context does not influence choice of traditional core foods. Hypothesis 2a₁: Food context influences choice of traditional core foods.

Hypothesis 2b₀: Biological factors do not influence choice of traditional core foods. Hypothesis 2b₁: Biological factors influence choice of traditional core foods.

Hypothesis 2c₀: Nutritional knowledge does not influence choice of traditional core foods. Hypothesis 2c₁: Nutritional knowledge influences choice of traditional core foods.

Hypothesis 2d₀: Personal ideas and systems do not influence choice of traditional core foods. Hypothesis 2d₁: Personal ideas and systems influence choice of traditional core foods.

Hypothesis 2e₀: Economic factors do not influence choice of traditional core foods. Hypothesis 2e₁: Economic factors influence choice of traditional core foods.

Hypothesis 2f₀: Resources do not influence choice of traditional core foods. Hypothesis 2f₁: Resources influence choice of traditional core foods.

Hypothesis 2g₀: Cultural background does not influence choice of traditional core foods. Hypothesis 2g₁: Cultural background influences choice of traditional core foods.

Hypothesis 3: Relationship between nutrition determinants and choice of secondary core foods: The following were postulated under this specific hypothesis:

Hypothesis 3a₀: Food context does not influence choice of secondary core foods. Hypothesis 3a₁: Food context influences choice of secondary core foods.

Hypothesis 3b₀: Biological factors do not influence choice of secondary core foods. Hypothesis 3b₁: Biological factors influence choice of secondary core foods.

Hypothesis 3c₀: Nutritional knowledge does not influence choice of secondary core foods. Hypothesis 3c₁: Nutritional knowledge influences choice of secondary core foods.

Hypothesis 3d₀: Personal ideas and systems do not influence choice of secondary core foods. Hypothesis 3d₁: Personal ideas and systems influence choice of secondary core foods.

Hypothesis 3e₀: Economic factors do not influence choice of secondary core foods. Hypothesis 3e₁: Economic factors influence choice of secondary core foods.

Hypothesis 3f₀: Resources do not influence choice of secondary core foods. Hypothesis 3f₁: Resources influence choice of secondary core foods.

Hypothesis 3g₀: Cultural background does not influence choice of secondary core foods. Hypothesis 3g₁: Cultural background influences choice of secondary core food.

Hypothesis 4: Relationship between nutrition determinants and choice of core foods were hypothesized thus:

Hypothesis 4a₀: Food context does not influence choice of core foods. Hypothesis 4a₁: Food context influences choice of core foods.

Hypothesis 4b₀: Biological factors do not influence choice of core foods. Hypothesis 4b₁: Biological factors influence choice of core foods.

Hypothesis 4c₀: Nutritional knowledge does not influence choice of core foods. Hypothesis 4c₁: Nutritional knowledge influences choice of core foods.

Hypothesis 4d₀: Personal ideas and systems do not influence choice of core foods. Hypothesis 4d₁: Personal ideas and systems influence choice of core foods.

Hypothesis 4e₀: Economic factors do not influence choice of core foods. Hypothesis 4e₁: Economic factors influence choice of core foods.

Hypothesis 4f₀: Resources do not influence choice of core foods. Hypothesis 4f₁: Resources influence choice of core foods.

Hypothesis 4g₀: Cultural background does not influence choice of core foods. Hypothesis 4g₁: Cultural background influences choice of core foods.

Results From Multiple Regression Analysis

The influence of the nutrition determinants on alternative foods, traditional core foods, secondary core foods, and core foods, respectively, are presented hereunder. The significant relationships which emerged are depicted in Figure 1.

Figure 1.

Framework of significant relationships.

Influence of Nutrition Determinants on Choice of Alternative Foods (Dairy Foods, Eggs, Nuts, Fish, and Cereals)

Two nutrition determinants were identified to be statistically significant at the .05 level (Table 6). The determinants were nutritional knowledge (β = 0.16, p = .037) and resources (β = 0.31, p = .001). Of the two variables, resources made a larger significant unique contribution of 31%. The beta value for nutritional knowledge was lower at 16%, indicating that it made less of a unique contribution to food choice decisions. The null hypotheses that nutritional knowledge and resources do not influence choice of alternative foods, Hypothesis 1c₀ and Hypothesis 1f₀, respectively, can therefore be rejected. This means that the alternative hypotheses that nutritional knowledge and resources influence choice of alternative foods may be true.

Table 6.

Regression Coefficients—Influence of Nutrition Determinants on the Choice of Alternative Foods.

	Unstandardized		Standardized	Sig.	Zero-order correlations
	B	Standard error	β	Sig.	Zero-order correlations
Constant	1.561	0.355		.000
Food context	0.015	0.076	0.020	.843	.268
Biological factors	−0.012	0.081	−0.013	.879	.147
Nutritional knowledge	0.161	0.076	0.163	.037	.299
Personal ideas and systems	0.073	0.083	0.089	.376	.310
Economic factors	−0.062	0.064	−0.077	.335	.066
Resources	0.284	0.086	0.313	.001	.417
Cultural background	0.011	0.075	0.015	.880	.244

Values in bold are significant at .05 level.

Influence of Nutrition Determinants on Choice of Traditional Core Foods (Extra Salt, a Lot of Fried Foods, a Lot of Sugar, Pasta, and Grains Like Rice)

Only one determinant (cultural background) was significant at the .05 level (β = .23, p = .021), contributing 23% of the unique variance in the choice of traditional core foods, comprising extra salt, a lot of sugar, fried foods, pasta, and grains like rice (Table 7). Since only cultural background was identified to influence choice of traditional foods, Hypothesis 2g₀ can be rejected. Therefore, Hypothesis 2g₁, the alternative hypothesis that cultural background influences choice of traditional core foods may be true.

Table 7.

Regression Coefficients—Influence of Nutrition Determinants on the Choice of Traditional Core Foods.

Model	Unstandardized		Standardized	Sig.	Zero-order correlations
Model	B	Standard error	β	Sig.	Zero-order correlations
Constant	1.399	0.423		.001
Food context	0.058	0.090	0.063	.524	.342
Biological factors	−0.002	0.096	−0.002	.983	.188
Nutritional knowledge	−0.096	0.091	−0.081	.291	.092
Personal ideas and systems	0.102	0.098	0.103	.302	.355
Economic factors	0.098	0.076	0.101	.200	.194
Resources	0.166	0.103	0.152	.107	.344
Cultural background	0.206	0.089	0.229	.021	.395

Value in bold is significant at the .05 level.

Influence of Nutritional Determinants on Choice of Secondary Core Foods (Fruits and Vegetables)

Three determinants were identified to be significant at the .05 level. The three determinants included economic factors (β = −0.17, p = .039), resources (β = 0.24, p = .016), and cultural background (β = −0.38, p = .000; Table 8). Cultural background had the largest significant unique contribution (38%) of the variance, followed by resources (24%) and then economic factors (17%). Hypothesis 3e₀ to Hypothesis 3g₀ can therefore be rejected. This means that the alternative hypotheses that economic factors, resources, and cultural background may be true. On the other hand, Hypothesis 3a₀ to Hypothesis 3d₀ cannot be rejected; they may be true.

Table 8.

Regression Coefficients—Influence of Nutrition Determinants on the Choice of Secondary Core Foods.

	Unstandardized		Standardized	Sig.	Zero-order correlations
	B	Standard error	β	Sig.	Zero-order correlations
Constant	2.959	0.533		.000
Food context	0.156	0.114	0.138	.173	.109
Biological factors	−0.044	0.121	−0.031	.713	.022
Nutritional knowledge	0.213	0.114	0.147	.065	.202
Personal ideas and systems	0.207	0.124	0.170	.097	.142
Economic factors	−0.200	0.096	−0.169	.039	−.070
Resources	0.316	0.130	0.236	.016	.227
Cultural background	−0.421	0.112	−0.381	.000	−.085

Values in bold are significant at the .05 level.

Influence of Nutrition Determinants on Choice of Core Foods (Meat and Corn Meal)

From the regression coefficients (Table 9), it can be seen that only one item (nutritional knowledge; β = 0.27, p = .001) had a significant unique influence of 27% on the choice of foods termed core foods comprising meat and corn meal. Consequently, Hypothesis 4c₀ can be rejected. This means that the alternative Hypothesis 4c₁, that nutritional knowledge influences choice of core foods, may be true.

Table 9.

Regression Coefficients—Influence of Nutrition Determinants on the Choice of Core Foods.

	Unstandardized		Standardized	Sig.	Zero-order correlations
	B	Standard error	β	Sig.	Zero-order correlations
Constant	3.285	0.410		.000
Food context	0.078	0.088	0.092	.377	−.017
Biological factors	0.143	0.098	0.132	.147	.121
Nutritional knowledge	0.289	0.088	0.270	.001	.227
Personal ideas and systems	−0.124	0.096	−0.139	.196	−.101
Economic factors	0.055	0.073	0.064	.453	.117
Resources	−0.102	0.100	−0.103	.307	−.036
Cultural background	−0.131	0.087	−0.159	.132	−.137

Value in bold is significant at the .05 level.

Discussion

The extent to which food choices are determined by the determinants known to influence food choices has hardly been conducted empirically. Whether construction workers’ food choices are influenced by the known factors and to what degree are poorly documented. The present study was conducted to shed light on those questions. The significant relationships which emerged are depicted in Figure 1. The results support findings from other studies which have dwelt on food choice influencers. For instance, as viewed by Wanjek (2005), the availability of resources such as on-site facilities significantly influences the choice of foods eaten on construction worksites. The same view was shared in a study by Escoffery, Kegler, Alcantara, Wilson, and Glanz (2011) in which it was reported that foods such as eggs, fish, and dairy products (alternative foods) were more likely to be consumed where cafeterias, refrigeration, and microwaves were available. It is notable that most foods in this category require refrigeration because of their protein content, as viewed by Wanjek (2005). It would therefore mean that these foods will be consumed where there are proper storage and preservation facilities.

That nutritional knowledge is significant in influencing choice of alternative foods aligns with findings from Soederberg-Miller and Cassady (2012) which indicated that knowledge and understanding about nutrition enhances dietary modifications and allows for positive decision-making processes.

The present study is also corroborated by Kulkarni (2004) and Boyle and Holben (2012) which reported that people of various groups with strong attachments to their cultural orientation consumed foods categorized as traditional core foods, in this study, based on their beliefs. Traditional health beliefs, dietary customs, and cultural variations were identified to influence choice of salty and fatty foods. For instance, the traditional Mexican diet is low in fat and high in fiber (Kulkarni, 2004). Some American ethnic groups believe in materialism and in the notion that disease can be prevented (Boyle & Holben, 2012). On the other hand, some cultures deem that humans cannot control disease, staunchly believing in spirituality (Boyle & Holben, 2012). While the former group may likely consume healthy foods, the latter may indulge in unhealthy diets with the belief that the management and progression of their health is entirely out of their control. Nutrition intervention programs such as nutrition education should therefore emphasize consumption of healthy traditional diets which are, first and foremost, culturally acceptable by the target population.

Intake of fruits and vegetables (secondary core foods) was significantly influenced by economic factors such as cost, price discounts, wages, and availability of the food. Reduced prices of fruits and vegetables lead to increased rates of consumption of these foods (Waterlander et al., 2013). The result that wages influence nutritional intake also corroborates with findings from Tiwary et al. (2012), which reported that fruits and vegetable consumption among construction workers was rare, primarily due to the low wages they were paid. The studies by Du Plessis (2011, 2012) also corroborate that wages, availability of foods, and cultural background including cultural beliefs and gender–based distinctions have influence on intake of fruits and vegetables among construction workers.

The present study also concords with conclusions drawn in Wanjek (2005). The availability of on-site facilities encourages consumption of healthier foods. Fruits and vegetable consumption is especially improved with increased awareness of nutritional requirements. In Florindo et al.’s (2015) view, consumption of recommended quantities of fruits and vegetables is associated with the correct knowledge of the recommended quantities. Fruits and vegetable consumption also depends on cultural orientation and identity, as reported in Puoane et al. (2006). Cultural background and identity influence food choice among Black South Africans (Puoane et al., 2006).

That nutritional knowledge influences consumption of core foods (meat and corn meal) supports Kulkarni’s (2004) findings which revealed that knowledge, especially of the health implications of eating or not eating certain foods, determines intake of corn and meat. The conclusions of Crites and Aikman (2005) also suggest that nutritional knowledge influences health evaluations which in turn affect decisions and attitudes toward choice, preparation, and consumption of corn and meat.

The results are slightly different from the previous studies mentioned because none of the studies explored the structures of the factors known to influence food choices, as was done in the present study (using PCA). PCA defined the structures of the food categories and determinants by identifying those which are related and could be grouped together.

The findings in the current study are subject to some limitations. First, the focus was on construction workers in only one province in South Africa. While the findings could be generalized to construction workers in Gauteng, they may not be generalizable to workers in other geographical areas where beliefs, attitudes toward nutrition, and circumstances may differ from those of the sample population. Future research could therefore use samples in other parts of South Africa and the world to observe variances in results, if any. Second, the purposive sampling technique, which included only participants who could speak English, limited the possibility of including a broader sample and views and may have introduced some bias. Similar studies could adopt other official South African languages. Third, the study did not dwell on the nutritional contents or quantities consumed. Only FFQs were used to determine nutritional choices. Future studies could employ other food information–gathering methodologies to evaluate the relationship.

Conclusion

The study set out to establish the influence of nutrition determinants on nutritional choice and intake among construction workers. The study identified the most significant factors which determine the choices of foods as classified in the current study.

Knowledge of the factors which influence construction workers’ decision on food choice is invaluable in improving their nutrition because it will enable policy makers to direct improvement efforts toward the identified determinants. The study therefore provides a basis for future design of explicit, relevant, and effectual nutrition intervention programs targeted at construction workers, taking into consideration the identified significant factors. Interventions to improve nutrition of construction workers can result in more effective actions by government and construction employers.

Construction employers and managers could commit to healthy eating through environmental or organizational changes such as increasing the availability of healthy foods in vending machines or canteens, arranging with food vendors to sell healthy food options at reduced prices, and collaborating with organizations to provide healthy foods on-site. Supplementary feeding programs could be provided to help assuage the effects of factors which may be beyond construction workers’ control (e.g., low wages). Supplementary feeding on sites could help ensure that workers eat healthily.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: University of Johannesburg’s Global Excellence and Stature Scholarship granted to Chioma Sylvia Okoro for a Master’s degree in Construction Management.

References

Aich

Mahzebin

Fahriasubarna

Hassan

(2014). A study on socio-economic condition and nutritional profile of women workers in shrimp and agriculture sectors in selected two districts of Bangladesh. International Research Journal of Social Sciences, 3(3), 15-21.

Amare

Moges

Fantahun

Admassu

Mulu

Kassu

(2012). Nutritional status and dietary intake of urban residents in Gondar, Northwest Ethiopia. BMC Public Health, 12, 752.

Ambekar Institute for Labour Studies. (2012). Capacity building for the promotion of labour rights for vulnerable groups of workers: “Naka” workers (construction industry). Brussels, Belgium: European Council.

Arganini

Saba

Comitato

Virgili

Turrini

(2012). Gender differences in food choice and dietary intake in modern western societies. In Maddock

(Ed.), Public health: Social and behavioral health (pp. 83-102). Rijeka, Croatia: InTech.

Babicz-Zielinska

(2006). Role of psychological factors in food choice: A review. Polish Journal of Food and Nutrition Sciences, 15/56, 379-384.

Ball

Timperio

A. F.

Crawford

D. A.

(2006). Understanding environmental influences on nutrition and physical activity behaviors: Where should we look and what should we count? International Journal of Behavioral Nutrition and Physical Activity, 3, 33. doi:10.1186/1479-5868-3-33

Barclay

K. J.

Edling

Rydgren

(2013). Peer clustering of exercise and eating behaviours among young adults in Sweden: A cross-sectional study of egocentric network data. BMC Public Health, 13, 784.

Bates

G. P.

Schneider

(2008). Hydration status and physiological workload of UAE construction workers: A prospective longitudinal observational study. Journal of Occupational Medicine and Toxicology, 3, 21. doi:10.1186/1745-6673-3-21

Berger

Draganska

Simonson

(2007). The influence of product variety on brand perception and choice. Marketing Science, 26, 460-472.

10.

Boyle

Holben

(2012). Community nutrition in action: An entrepreneurial approach (6th ed.). Belmont, CA: Cengage Learning.

11.

Bruner

B. G.

Chad

K. E.

(2014). Dietary practices and influences on diet intake among women in a Woodland Creek community. Journal of Human Nutrition and Dietetics, 27(Suppl. 2), 220-229.

12.

Cade

J. E.

Burley

V. J.

Warm

D. L.

Thompson

R. L.

Margetts

B. M.

(2004). Food frequency questionnaires: A review of their design, validation and utilization. Nutrition Research Reviews, 17, 5-22.

13.

Cancer Prevention in the Workplace Writing Group. (2014). Cancer prevention and worksite health promotion: Time to join forces. Preventing Chronic Diseases, 11, 140127.

14.

Carmona

(2004). Statistical analysis of financial data in S-Plus. New York, NY: Springer Science + Business Media.

15.

Chenhall

(2010). Improving cooking and food preparation skills: A synthesis of the evidence to inform program and policy development. Ottawa, Ontario, Canada: Government of Canada.

16.

Crites

S. L.

Aikman

S. N.

(2005). Impact of nutritional knowledge on food evaluations. European Journal of Clinical Nutrition, 59, 1191-1200.

17.

Du Plessis

(2011). Diet and nutrition: A literature review of factors influencing blue-collar apprentices. Carlton, Victoria, Australia: Incolink and Australian Clearinghouse for Youth Studies.

18.

Du Plessis

(2012). Factors influencing Australian construction apprentices’ dietary behaviours. American Journal of Men’s Health, 6, 59-66.

19.

Eertmans

Baeyens

van der Bergh

(2001). Food likes and their relative importance in human eating behaviours: Review and preliminary suggestions for health promotions. Health Education Research, 16, 443-456.

20.

Ellis

(2013, December 31). Our food choices are influenced by social norms: Study suggests. Medical News Today. Retrieved from http://www.medicalnewstoday.com/articles/270722.php

21.

Escoffery

Kegler

M. C.

Alcantara

Wilson

Glanz

(2011). A qualitative examination of the role of small, rural worksites in obesity prevention. Preventing Chronic Disease, 8(4), A75. Retrieved from http://www.cdc.gov/pcd/issues/2011/jul/10_0185.htm

22.

European Food Information Council. (2005). The determinants of food choice. Retrieved from http://www.eufic.org/article/en/expid/review-food-choice/

23.

European Food Information Council. (2011). Can cooking skills be the key to health? (Food Today). Retrieved from http://www.eufic.org/article/en/artid/Cooking_skills_key_health/

24.

Field

(2005). Discovering statistics using SPSS (2nd ed.). London, England: Sage.

25.

Florindo

A. A.

Brownson

R. C.

Mielke

G. L.

Gomes

G. A. O.

Parra

D. C.

Siqueira

F. V.

. . . Hallal

P. C.

(2015). Association of knowledge, preventive counseling and personal health behaviors on physical activity and consumption of fruits and vegetables in community health workers. BMC Public Health, 15, 344. doi:10.1186/s12889-015-1643-3

26.

Food and Agriculture Organization. (n.d.). The food system and factors affecting household food security and nutrition. Retrieved from http://www.fao.org/docrep/w0078e/w0078e04.htm

27.

Gagic

Jovicic

Tesanovic

Kalenjuk

(2014). Motives for food choice among Serbian consumers. Economics of Agriculture, 61(1), 41-51.

28.

Grassi

Nucera

Zanolin

Omenaas

Anto

J. M.

Laynaert

(2007). Performance comparison of Likert and binary formats of SF-36 version 1.6 across ECRHS II adults populations. Value in Health, 10, 478-488.

29.

Groeneveld

I. F.

van der Beek

A. J.

Proper

K. I.

Hildebrandt

V. H.

van Mechelen

(2011). Short and long-term effects of a lifestyle intervention for construction workers at risk for cardiovascular disease: A ramdomized controlled trial. BMC Public Health, 11, 836.

30.

Grunert

K. G.

Wills

J. M.

Fernandez-Celemin

(2010). Nutrition knowledge, and use and understanding of nutrition information on food labels among consumers in the United Kingdom. Appetite, 55, 177-189.

31.

Guyonnet

Chassany

Picard

Guillemin

Meunier

Seignobos

. . . Trudeau

(2008). Perceived subject outcomes and impact on health-related quality of life associated with diet using the new Food Benefits Assessment (FBA) questionnaire: Development and psychometric validation. Public Health Nutrition, 11, 1163-1172.

32.

Hassapidou

Papadopoulou

S. K.

(2006). Food choice criteria in adolescents according to body image. Songklanakarin Journal of Science and Technology, 28(Suppl. 1), 165-169.

33.

Health and Safety Executive. (2009). Reducing error and influencing behaviour. Bootle, England: HSE Books.

34.

Heartya

A. P.

McCartya

S. N.

Kearneyb

J. M.

Gibneya

M. J.

(2007). Relationship between attitude towards healthy eating and dietary behavior, lifestyle and demographic factors in a representative sample of Irish adults. Appetite, 48, 1-11.

35.

Huang

Lee

Pan

Wahlqvist

M. L.

(2011).Validation of a simplified food frequency questionnaire as used in the Nutrition and Health Survey in Taiwan (NAHSIT) for the elderly. Asia Pacific Journal of Clinical Nutrition, 20, 134-140.

36.

Januszewska

Pieniak

Verbeke

(2011). Food choice questionnaire revisited in four countries: Does it still measure the same? Appetite, 57, 94-98.

37.

Just

D. R.

Heiman

Zilberman

(2007). The interaction of religion and family members’ influence on food decisions. Food Quality and Preference, 18, 786-794.

38.

Kinsey

Wendt

(2007). Do eating patterns follow a cohort or change over a lifetime? Minneapolis: University of Minnesota.

39.

Kolbe-Alexander

T. L.

Buckmaster

Nossel

Dreyer

Fiona

Noakes

T. D.

Lambert

E. V.

(2008). Chronic disease risk factors, healthy days and medical claims in South African employees presenting for health risk screening. BMC Public Health, 8, 228. doi:10.1186/1471-2458-8-228

40.

Kulkarni

K. D.

(2004). Food, culture and diabetes in the United States. Clinical Diabetes, 22, 190-192.

41.

Kushi

L. H.

Doyle

McCullough

Rock

C. L.

Demark-Wahnefried

Bandera

E. V.

. . . Gansler

(2006). American Cancer Society Guidelines on nutrition and physical activity for cancer prevention: Reducing the risk of cancer with healthy food choices and physical activity. CA: A Cancer Journal for Clinicians, 62, 30-67.

42.

Marreiros

Ness

(2009). A conceptual framework of consumer food choice behavior. Évora, Portugal: Centre for Advanced Studies in Management and Economics.

43.

McCluskey

Swinnen

(2011). The media and food-risk perceptions: Science and Society Series on Food and Science. European Molecular Biology Organization (EMBO) Reports, 12(7), 624-629. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3128959/

44.

Medical Research Council. (n.d.). Dietary assessment: Food frequency questionnaire. Retrieved from http://www.dapa-toolkit.mrc.ac.uk/dietary-assessment/methods/food-frequency-questionnaire/index.php

45.

Melia

J. L.

Becerril

(2009). Health behaviour and safety in the construction sector. Psicothema, 21, 427-432.

46.

Milosevic

Zezelj

Gorton

Barjolle

(2012). Understanding the motives for food choice in Western Balkan countries. Appetite, 58, 205-214.

47.

Musonda

(2012). Construction health and safety (H&S) performance improvement: A client-centred model (Doctoral dissertation). University of Johannesburg, South Africa.

48.

National Cancer Institute. (2013). Usual dietary intakes: NHANES Food Frequency Questionnaire (FFQ). Rockville, MD: Author.

49.

Nie

Zepeda

(2011). Lifestyle segmentation of US food shoppers to examine organic and local food consumption. Appetite, 57, 28-37.

50.

Okoro

C. S.

Musonda

Agumba

J. N.

(2014). A review of factors influencing construction workers’ nutritional uptake. In Proceedings of the DII-2014 Conference on Infrastructure Investments in Africa. Retrieved from https://ujdigispace.uj.ac.za/bitstream/handle/10210/12603/DII%20Conference%202014.pdf?sequence=5&isAllowed=y

51.

Pallant

(2013). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (5th ed.). Crows Nest, Australia: Allen & Unwin.

52.

Passin

Bennett

J. W.

(1943). Social process and dietary change: The problem of changing food habits (Report of the National Research Council Committee on Food Habits). Retrieved from http://www.nap.edu/read/9566/chapter/20

53.

Petrovici

D. A.

Ritson

(2006). Factors influencing consumer dietary health preventative behaviours. BMC Public Health, 6, 222.

54.

Pollard

Kirk

S. F. L.

Cade

J. E.

(2002). Factors affecting food choice in relation to fruit and vegetable intake: A review. Nutrition Research Reviews, 15, 373-387.

55.

Prescott

Young

O’Neill

Yau

N. J. N.

Stevens

(2002). Motives for food choice: A comparison of consumers from Japan, Taiwan, Malaysia and New Zealand. Food Quality and Preference, 13, 489-495.

56.

Pula

Parks

C. D.

Ross

C. F.

(2014). Regulatory focus and food choice motives: Prevention orientation associated with mood, convenience and familiarity. Appetite, 78, 15-22.

57.

Puoane

Matwa

Bradley

(2006). Socio-cultural factors influencing food consumption patterns in the black African population in an urban township in South Africa. Human Ecology, Special Issue 14, 89-93.

58.

Queensland Government. (2012). Healthy diet and lifestyle: Achieving a healthy lifestyle. Brisbane, Australia: Department of Justice and Attorney-General.

59.

Rose

Bodor

J. N.

Hutchinson

P. L.

Swalm

C. M.

(2010). The importance of a multi-dimensional approach for studying the links between food access and consumption. Journal of Nutrition, 140(6), 1170-1174.

60.

Sarkar

Mukhopadhyay

A. R.

Ghosh

S. K.

(2014). Developing a model for process improvement using multiple regression technique: A case example. TQM Journal, 26, 625-638.

61.

Schulte

P. A.

Wagner

G. R.

Ostry

Blanciforti

L. A.

Cutlip

R. G.

Krajnak

K. M.

. . . Miller

D. B.

(2007). Work, obesity and occupational safety and health. American Journal of Public Health, 97, 428-436.

62.

Smallwood

J. J.

(2012). Construction health and safety: The future. Durban, South Africa: Kwazulu-Natal Master Builders Association.

63.

Smallwood

J. J.

Deacon

(2015, September). Construction camps in building and civil engineering construction. Presented at the International Conference on Infrastructure Development and Investment Strategies for Africa, Livingstone, Zambia.

64.

Sobal

Bisogni

C. A.

(2009). Constructing food choice decisions. Annals of Behavioral Medicine, 38(Suppl. 1), S37-S46.

65.

Soederberg-Miller

L. M.

Cassady

D. L.

(2012). Making healthy food choices using nutrition facts panels: The roles of knowledge, motivation, dietary modifications, goals and age. Appetite, 59, 129-139.

66.

Steptoe

Pollard

T. M.

Wardle

(1995). Development of a measure of the motives underlying the selection of food: The food choice questionnaire. Appetite, 25, 267-284.

67.

Sultana

Ysmain

Chaklader

M. A.

Yasmin

(2014). Energy intake and energy expenditure of female ready-made Bangladeshi garment workers. International Public Health Forum, 1(3), 14-17.

68.

Tiwary

Gangopadhyay

P. K.

Biswas

Nayak

Chatterjee

M. K.

Chakraborty

Mukherjee

(2012). Socio-economic status of workers in the building and construction industry. Indian Journal of Occupational & Environmental Medicine, 16(2), 66-71.

69.

Wanjek

(2005). Food at work: Workplace solutions for malnutrition, obesity and chronic diseases. Retrieved from http://www.ilo.org/wcmsp5/groups/public/@dgreports/@dcomm/@publ/documents/publication/wcms_publ_9221170152_en.pdf

70.

Waterlander

W. E.

de Boer

M. R.

Schuit

A. J.

Seidell

J. C.

Steenhuis

I. H. M.

(2013). Price discounts significantly enhance fruit and vegetable purchases when combined with nutrition education: A randomized controlled supermarket trial. American Journal of Clinical Nutrition, 97, 886-895.