Occupational differences for nutrient intake and physical-activity levels in young and middle-aged men

Abstract

BACKGROUND: Physical demands may vary between white collar and blue collar occupations when evaluating specific physiological differences such as one’s weight, body mass index and body fat percentage.

OBJECTIVE: To explore nutritional, anthropometric, and physical-activity differences between young and middle-aged WC and BC workers.

METHODS: Ninety-one male participants aged 19–64 that were either WC and/or BC workers completed a 3-day food recall, modified Baecke Questionnaire, and had their height, weight, body mass index and body fat percentage measured. Participants were classified as: young-white-collar, young-blue-collar, middle-white-collar or middle-blue-collar workers.

RESULTS: No nutritional differences within ages or between occupations were reported. Body mass index and body fat percentage were lower for young vs. middle-aged workers. Lastly, physical-activity at work was higher for young and middle-aged Blue collar workers, while White collar workers participated in more physical activity outside of work.

CONCLUSIONS: The present findings suggest that the observed increases in body fat percentage and body mass index with age, are independent of occupation.

Keywords

Age blue collar calories macronutrients work index

List of Abbreviations

Symbol

Name

Kcal

Kilocalorie

PRO

Protein

CHO

Carbohydrate

FAT

Total Fat

SATFAT

Saturated Fat

FIB

Fiber

SUG

Sugar

EAA

Essential Amino Acid

Wat

Water

Chol

Cholesterol

Calcium

Sodium

BMI

Body Mass Index

BF%

Body Fat Percentage

Fat Mass

FFM

Fat Free Mass

RDA

Recommended Daily Allowance

MWC

Middle While-Collar

MBC

Middle Blue-Collar

YWC

Young White-Collar

YBC

Young Blue-Collar

TEE

Total Energy Expenditure

Work Index

Sports Index

LTI

Leisure Time Activities

RDI

Recommended Daily Intake

White Collar

Blue Collar

1 Introduction

White collar (WC) and blue collar (BC) workers vary in occupational demands (i.e. non-manual vs. manual-workers). There is growing support for the claim that BC workers may have increased health risks for coronary heart disease (CHD) and heart attacks in comparison to WC workers [1, 2]. A limited number of previous studies have reported that decreased health, or CHD for BC workers may be attributed to the monotony and inability to learn new tasks throughout their profession [3]. For example, Samilova et al. [3] reported female shift workers who performed repetitive job tasks were three-to-five times more likely to develop CHD than individuals who performed less repetitive self-paced work. Additionally, poor nutrition could also increase the susceptibility for CHD or mortality [4]. Specifically, Sasaki et al. [4] suggested that consuming high amounts of sodium (Na) and/or saturated fatty acids may lead to greater health concerns (hypertension, stroke). Occupation type has been shown to influence physical activity and overall health; the norm of the work environment (sitting a lot, moving around a lot) can be a large contributor to one’s physical activity levels throughout the work day [5]. Taken together, these findings suggest occupation and nutritional intake are factors that may negatively affect one’s health. However, there is little data evaluating American WC and BC workers’ nutrition and related physiological markers. To our knowledge, Kachan et al. [6] is the only author to report macro- and micronutrient intakes among WC and BC workers; suggesting BC workers consumed greater amounts of cholesterol (Chol) and calories, while WC workers consumed greater amounts of fiber (FIB), NA, and fat (FAT). Accordingly, due to these populations failing to abide by nutritional recommendations they may exhibit an increased susceptibility to obesity and stroke [2, 4]. However, limited data exist quantifying the nutritional intake between occupations.

Recommended daily allowances (RDA’s) for adults advise that an average person’s proportion of nutrient intake should consist of consuming roughly 45–65% carbohydrates (CHO), 10–35% protein (PRO), and 20–35% of FAT [7]. However, it is suggested that a majority of Americans, either under or over consume these dietary recommendations [6 , 8–10]. These findings are supported by Kimmons et al. [10] who suggest that only 10% of Americans consume adequate amounts of fruits and vegetables. In addition, a recent occupational study from Kachan et al. [6] reported that United States WC and BC workers only met the RDA’s for all macronutrient variables 32–34% of the time. Because over- and/or under consumption of macronutrients may be directly related to body mass and body composition changes [11], future examination of nutritional compliance may be needed. Although it has been suggested that active males live longer than non-active males [12], limited research is available evaluating activity at work and the effect it has on one’s health. To assess these questions, a habitual activity pattern questionnaire (Modified Baecke Questionnaire) was designed to assess physical activity levels during work (WI) and anything outside of work such as sport (SI), and leisure time activities (LTI) [13]. Although there is limited literature regarding occupational differences for the Modified Baecke Questionnaire, Kannel et al. [12] suggests that in general, sedentary males 35–64 years are at greater risk for CHD compared to physically active males. Socioeconomic status has also been shown to impact leisure activity time, with those in a higher socioeconomic status performing more activity [14]. Although these findings did not account for occupational differences, recent literature suggests that BC workers are at risk for obesity, CHD risks, high blood pressure, and metabolic syndrome (females only) [2 , 16]. While no studies have directly investigated occupational classification and aging with nutrient intake and physical activity levels, recent research has suggested that nutrient intake varies between WC and BC workers [6]. Thus, the purpose of the present study was to extend the findings of previous studies by examining occupational classification, nutrient intake, and physical activity levels in young and middle-aged men.

2 Materials and methods

2.1 Subjects

Ninety-one male participants aged 19–64 years volunteered to participate in this study which was conducted in Stillwater, Oklahoma. Descriptive characteristics of the participants are presented in Table 1. The participants were solicited from the local community and university setting. Participants from the WC group were randomly selected, whereas participants from the BC group were purposively selected from BC businesses. This study was approved by the University Institutional Review Board, and all participants completed and signed an informed consent.

2.2 Procedures

Participants visited the laboratory on two occasions, separated by seven days (±1 d). During the first session, participants completed an occupational questionnaire, which allowed the researcher to classify the participants based upon their occupation, underwent a series of anthropometric measurements, and were instructed on how to appropriately record their dietary food record procedures. The second session involved collection of the food record and verification of its completion and/or clarification of any incomplete or uncertain recordings. All demographic, anthropometric, and nutritional information occurred between November 2010 – November2011.

2.3 Anthropometric and body composition assessments

The participants height and body mass were measured using a balance beam scale (Detecto Eye-Level Physician, Webb City, MO) and stadiometer (Detecto Manual Physician, Webb City, MO), respectively. The assessments were taken in minimal clothing with all heavy items (e.g., shoes, jackets, sweaters, and belts) removed [17]. The skinfold method was used to estimate body composition, and all measurements were taken on the right side of the body with a calibrated Lange caliper by the same experienced technician (BJT) for all participants. Skinfold thickness was measured to the nearest 0.5 mm and each participant was measured twice, within a range of 10%, and the average was used for all subsequent analyses [18]. Body density was calculated according to the recommendations of Jackson and Pollock [19] for a three-site measurement, at the sites of abdomen, chest and thigh. Body fat percentage (BF%) was calculated from body density using population specific formulas [20]. Fat mass (FM) and fat-free mass (FFM) were subsequently calculated from total body mass and the derived BF% values.

2.4 Occupational classification

Occupational information including work history, status, and job description was collected via questionnaire and interview format, during the participants’ initial visit. Occupational classification was determined using only the job the participants performed the longest throughout the duration of their occupational or active life [21]. Participants were classified according to their occupation based on a previously used two categorical widespread job classification system, where workers are grouped into broad categories based on similar types of occupations, similar to the procedures of previous studies [15, 21]. The two general categories consisted of either WC or BC occupations. Specifically, WC workers included occupations categorized as managers, administrators, scientific or research professionals (i.e., professors, technicians, intellectuals), clerical employees, full-time students, and market or sales workers. BC workers included occupations categorized as skilled workers in agricultural, manufacturing, or construction industries, machine installers or operators, assemblers, mechanics, grounds crew and unskilled laborers. Participants were further categorized by age into either a young (19–30 yrs) or middle-age (45–64 yrs) group; yielding four groups: young WC (YWC), young BC (YBC), middle-aged WC (MWC), and middle-aged BC (MBC) workers.

2.5 Baecke questionnaire

The previously validated modified Baecke physical activity questionnaire was used to asses physical activity levels during WI, SI and LTI [13]. Likert scores ranging from 1–5 were given for every question excluding name of occupation, sport played, and leisure time activities. When scoring for the three aforementioned questions, similar procedures were used as reported by Baecke et al. [22] to appropriately score the participants. When calculating for WI, questions 1–8 were added then divided by eight, SI consisted of questions 9–12 which were added then divided by four, and LTI consisted of questions 13–19 which were also added then divided by seven. The WI and SI were further used, in conjunction with the Harris-Benedict equation to estimate total energy expenditure (TEE) [23, 24], with a higher SI assigned an activity coefficient of 1.55, and a higher WI assigned a 1.375 coefficient.

2.6 Dietary assessment

During the initial laboratory visit, detailed verbal instructions were provided to each participant on how to complete a 3-day dietary record. Specifically, participants were instructed to write down everything they consumed for two non-consecutive days during the week and one weekend day. Guidance notes were provided, in which directions were given detailing the dietary record procedures used to assist participants in describing portion sizes and household measures. Visual illustrations were also provided as a means of aiding in the estimation of portion sizes [25]. The dietary record directions instructed participants to write down all food and drink in the order it was consumed and included all meals, snacks, gum and candy. Participants were instructed to be as specific as possible, including details such as item brand names as well as the method of food preparation (baked, fried, broiled, etc.). Additionally, all supplements were to be recorded, including the name of the supplement and the amount taken. Dietary intake data were collected and analyzed using Nutrition Data System for Research software version NDSR 2012, developed by the Nutrition Coordinating Center at the University of Minneapolis, MN. These data were used to derive total calories (KCAL); protein (PRO), carbohydrate (CHO), total fat (FAT), saturated fat (SATFAT); fiber (FIB), sugar (SUG), essential amino acids (EAA) and water (Wat) in grams; and cholesterol (Chol), calcium (CA), and sodium (NA) in milligrams. Dietary intakes from the 3-day diet records were averaged and compared to national recommendations for occupation groups. The following ranges for recommended dietary intake guidelines, according to the Institute of Medicine [26], the US Departments of Agriculture and Health and Human Services [27], and the American College of Sports Medicine and American Dietetics Association [28], were used: percentage of total calories from PRO (% PRO) 10–14% daily calories, percentage CHO (% CHO) 52–64% daily calories, FAT (% FAT)≤30% of daily calories, saturated fat (% SATFAT)≤10% daily calories, FIB≥25 g/d; Chol≤300 mg/d, CA≥1,000 mg/d, and NA≤2,000 mg/d. Participants were subsequently trichotomized as follows: fell below, met, or above recommended daily intake.

2.7 Statistics

Frequencies and descriptive statistics were calculated on all nutrient variables stratified by occupation. To evaluate the primary purpose of the study, a one way analysis of variance (ANOVA) was used to evaluate mean nutrient intake, body composition (BMI, FM, FFM, BF%), and physical activity (WI, SI, LTI,) variables between all four occupational groups, with modified Bonferroni post hoc comparisons. Crosstabs descriptive statistics were used to identify individuals who consumed below, met, or above RDA guidelines for % CHO, % PRO, % FAT, % SATFAT, Chol, FIB, Ca, and Na to allow for further description of occupational differences among nutrient intake. A paired samples t-test was used to compare total energy intake and total energy expenditure estimations between occupational groups. To further examine the relationships among BMI, BF%, TEE, and nutritional intake variables across occupation, Pearson-product moment correlations were calculated. All data were analyzed using SPSS Version 20.0 (Chicago, IL, USA). An alpha level of 0.05 was set a priori.

3 Results

3.1 Nutrient intake

Mean ± standard deviation for each macronutrient and % RDA are presented in Table 2 and 3. There were no significant group differences for total caloric intake (p = 0.823) or macronutrient intakes: PRO (p = 0.351); CHO (p = 0.937); FAT (p = 0.764); SATFAT (p = 0.717); FIB (p = 0.154), SUG (p = 0.698), EAA (p = 0.397), or Wat (p = 0.068). Additionally, there were no significant differences for alcohol (p = 0.760), Chol (p = 0.378), Ca (p = 0.460) or Na (p = 0.128).

3.2 Adherence to recommended intakes

Table 3 depicts the percentage of participants who were adherent to dietary guidelines for each nutrient. Overall for % FAT, all groups exceeded the RDA. Additionally, all groups over consumed SATFAT, with MWC workers exceeding intake recommendations 77% of the time. For CHO, the majority fell below (mean: 78.2%) the RDA, except for MBC in which 23.8% fell below and 66.7% exceeded the recommendations. For PRO and Ca, the YWC cohort had approximately 50% within and 50% above; whereas the majority of the YBC and middle-aged cohorts were above the recommendations. For FIB, the majority of all cohorts were above the recommendations, with YWC reporting the greatest number of people above FIB intake (81.8%). All cohorts were above the recommended intake for Na. For Chol, the majority of the subjects in all groups were within normal range.

3.3 Body composition

Significant group differences for BMI (p = 0.023); FM (p = 0.001), and BF% (p = 0.0001) were demonstrated, with no significant differences for FFM (p = 0.872) (Table 1). Post hoc comparisons showed a significant difference between YWC and MBC for BMI (p = 0.018), FM (p = 0.008); and BF% (p = 0.001) and significant difference from MWC for FM (p = 0.001) and BF% (p = 0.001). These results from the present study indicated that YWC workers had a lower BMI, FM and BF% compared to MBC workers, and lower FM and BF% from MWC workers. Additionally, YBC workers had significantly lower FM (p = 0.001 and p = 0.008) and FFM (p = 0.001) compared to MWC and MBC workers respectively, while no differences were observed between young cohorts. There were no significant differences between MWC and MBC for all variables (P > 0.05).

Percent body fat was significantly correlated with BMI for all groups (p = 0.001) with the strongest relationship demonstrated for the YBC (r = 0.818) and MBC (r = 0.829). BMI was also significantly moderately correlated with CHO for the YWC (p = 0.011; r = –0.532) and MBC (p = 0.047; r = –0.450) groups. For the YWC, BMI was also strongly correlated with TEE (p = 0.001; r = 0.662) and moderately correlated with SUG (p = 0.043; r = –0.435). For the YBC cohort, BMI was strongly correlated with TEE (p = 0.001; r = 0.923) only. Correlations for the MWC cohort showed a strong relationship for BMI and TEE (p = 0.001; r = 0.785) and a moderate association with Na (p = 0.018; r = 0.512). For the MBC, BMI was strongly correlated with TEE (p = 0.001; r = 0.668); kcal (p = 0.008; r = –0.573) and moderately correlated with CHO (p = 0.047; r = –0.450). No other correlations were significant.

3.4 Physical activity

There was a significant difference between groups for WI (p = 0.001) and SI (p = 0.004), with no difference between LTI (p = 0.091). Post hoc comparisons demonstrated a significantly greater WI for both BC groups in comparison to WC workers (p = 0.001), but there were no intragroup differences. For SI, the YWC were significantly greater than both YBC (p = 0.030) and MBC (p = 0.014) workers, with no intragroup differences.

Total energy expenditure calculated from activity equivalents from the WI and SI responses, was compared with actual energy intake. Results demonstrated a lower actual intake for all groups, however, significant differences were only revealed for the YWC (–652.5 ± 737.9 kcal; p = 0.001) and MWC (–388.0 ± 608.4; p = 0.008) groups. The YBC and MBC groups had a discrepancy of 246.4 ± 1133 kcal (p = 0.298) and –277.5 ± 849.7 kcal (p = 0.160), respectively.

4 Discussion

The primary findings of the present study revealed no differences in macronutrient intake between or within occupations. Surprisingly, evaluation of adherence to recommended dietary intakes (RDI), demonstrated that few participants in either occupation fell within recommendations. While total caloric intake was below the estimated required intake for weight maintenance, results demonstrated caloric intake was above the RDI (2380.68±110.47). Anthropometric differences were seen between groups for BMI, BF% and FM; and there was also a significant difference for WI and SI between groups. Additionally, BMI was correlated with BF% (r = 0.823; p = 0.001), macronutrient intake (r = 0.321; p = 0.002), and TEE (r = 0.785; p = 0.001) for all groups, potentially supporting the use of BMI as a practical indicator of health and diet in this population.

To our knowledge, this is the first study to evaluate age and occupation with respect to calories, macronutrients, and other health parameters (body composition, physical activity). The present study showed no macronutrient intake differences between or within occupations. In contrast, Morikawa et al. [29] reported a significant difference in nutrient intake across young (20–29 yrs), middle (40–49 yrs), and old (50–59 yrs) blue collar workers. The differences in macronutrient values between Morikawa et al. [29], and the present study may be due to varying occupational status, with Morkiawa et al. [29] evaluating factory light metal workers versus a variety of BC professions in the present study. Cultural differences may also play a role, with the current study focusing on U.S. workers. Additionally, Morikawa et al. [29] reported lower kcal values for YBC and MBC Japanese workers, in comparison to the YBC and MBC American workers studied in the present study (2068–2501 vs. 2146–2360 kcal, respectively). Kachan et al. [6] used a 2-day dietary record, and reported significant differences in macronutrient intakes between white (2,244 kcal) vs. blue collar workers (2,330 kcal). Although no differences were reported between occupation types for the present study, mean caloric intakes were similar to Kachan et al. [1]. It is evident that poor eating behaviors occur for occupational workers (Table 3) with a majority of people under consuming Kcal and CHO, and over consumption of Na, Chol, and SATFAT. Under consumption of Kcal and CHO may have subsequent negative effects on endurance, thermoregulation and skeletal muscle contractions [30, 31]. Importantly, a chronic under consumption of Kcal and CHO in workers with labor-intensive occupations may reduce muscular strength, endurance as well as activities of daily living (ADL) [32]. Additionally, over consumption of Na, Chol, and SATFAT could increase susceptibility for cardiovascular disease or mortality [4]. Although the present study did not directly assess caloric intake and health risks, future studies evaluating caloric intake and chronic health issues with WC and BC populations would be valuable.

It is known that BF% and FM increases across the lifespan, and as much as a 10% increase in BF% may occur between males aged 20 – 50 [30, 31], which is in line with the age-related differences presented in the current study. Body composition resulted in significant differences for BF% and FM between YWC and YBC vs. MWC and MBC (p = 0.001) workers. Although there were no differences in FM between occupations (WC, BC), differences were seen between the young and middle aged males (∼ 10 kg) in the current study. Furthermore, MWC men tended to have more FM (Δ2.4 kg) compared to MBC. Previous data suggest that occupational demands may influence body composition due to different physical activity levels, resting metabolic rate, and ADL [30, 33]. It has been suggested that manual workers perform more physical movements/activity throughout the day compared to non-manual workers [34]. This was supported in the present study, in which the WI was greater for BC compared to WC workers (p = 0.001), showing that the BC workers performed more physical activity while at work compared to the WC workers. As FM increases with age, there is a concomitant decrease in FFM. Forbes and Reina [35] suggest by the age of 65, males will have 12 kg less FFM than they did at the age of 25. For the present study, the middle-aged workers had significantly higher amounts of FM than the younger workers and FFM was similar between groups. These findings are key as no significant FFM differences, within or between ages and occupations, were observed.

The importance of nutrition on BF% is widespread. However, little data exists on the potential role of macro and micronutrient intake in BC and WC workers [36 –38]. Kachan et al. [6] reported that nutrient intake (fiber, sodium, calories, protein, carbohydrates and saturated fat) vary by BC and WC workers. Additionally, Mayer et al. [39] assessed nutrient intake differences between various occupations (sedentary, light work, medium work, heavy work, and very heavy work) and suggested that “ ... the increase in weight associated with inactivity appears to be of significance in relation to the problem of obesity” (pg. 174). Although limited research is available to compare WC and BC workers, the present study observed similar macronutrient intakes between and within occupation types. These findings refute Kachan et al. [6] who observed macronutrient intake differences for FIB, and Na between WC and BC workers. The macronutrient intake differences between the studies may be attributed to the overall sample size (6,879 vs. 91), specific occupation type (4,495 vs. 44 WC), (2,384 vs 47 BC) and grouping of ages in all one cohort vs. different age groups within the same occupation. However, future studies that evaluate both nutrient intake and physical activity levels would allow for a better understanding within and between groups of ages and occupations.

The current study revealed a significant correlation between BF% and BMI for all groups (r = 0.823; p = 0.001). According to the group means for BMI, the individuals in the study were either overweight (young) or obese (middle-age). These findings may suggest that assessing BMI in BC workers could have utility in occupational-based settings, being highly reflective as a measurement of BF%. These findings may have similar implications as Rona et al. [40] who suggested that BMIs≥30 were accurate for assessing obesity in young military personnel. For instance, YWC had a lower BMI vs. MBC (26.93 vs. 31.68 kg/m²; p = 0.018) and a similar result for BF% (YWC: 17.14% BF; MBC: 25.09% BF; p = 0.001). However, due to the strong relationship between BMI and BF% in the current population, future researchers may have success using BMI to categorize one’s health in BC workers.

Findings of the present study resulted in associations for BMI between occupations and ages. For example, although BMI was correlated with macronutrient intake and TEE for all groups, specific macronutrient intake differences were reported for CHO between YWC and MBC workers (r = –0.532, R = –0.435). In addition, MWC workers consumed the highest amounts of Na. These findings contrast Kachan et al. [6] who reported BC workers consumed higher amounts of Na compared to WC workers. Lastly, the present study observed that YWC workers consumed the highest amounts of SUG. Although the present study observed moderately increased SUG intake for YWC workers, to the author’s knowledge, we are aware of no previous studies that have examined different ages and occupations in regards to SUG intake. Therefore, future studies should utilize more comprehensive nutritional assessments to elucidate more macronutrient and caloric differences that may be responsible for caloric intake differences displayed between and within different occupationalgroups.

A unique aspect of this study was the comprehensive analysis of the WI, and SI, which revealed dissociations between WI and SI for ages and occupations. Specifically, WI was higher for both YBC, and MBC workers, which is expected to be due to the increased work-related demands that a BC worker may commonly face compared to a WC worker [34]. Lastly, SI was greater for YWC vs. both BC groups. These findings suggest that YWC workers may be more recreationally active compared to BC workers. However, because of the limited studies in regards to occupational types and SI, future research is warranted to substantiate and extend upon the present findings.

5 Conclusion

The results of the present study revealed no macronutrient intake differences within ages or between occupations. However, differential effects for age were observed, such that BMI and BF% were lower for the young workers compared to the middle-aged workers. Additionally, although not significant, YWC workers consumed the greatest amounts of CHO and SUG, while MWC workers consumed the greatest amounts of Na. In regards to the RDA guidelines, 23–84% of participants in all groups were below the RDA for CHO, while 54.5–77% of participants in all groups were above the RDA for % FAT, SAT, and PRO intake. Lastly, WI and SI revealed dissociations, in which WI was higher for both young and middle-aged BC workers, while SI was greater for only YWC workers. Taken together, future studies should further evaluate the implications of occupation, age, and nutrition on strength and functionality, as well as other indicators of health.

Conflict of interest

The authors have no conflict of interest to report.

Footnotes

Acknowledgments

We would like to thank the Nutrition Obesity Research Center (NORC) at The University of North Carolina at Chapel Hill for performing the food logs in this study (NIH DK56350). The project described was also supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grants 1KL2TR001109 and 1UL1TR001111. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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