1. Introduction
According to the WHO [
1], stress as a psychosocial work factor has both psychological and physical effects. Among the psychological effects can be anxiety, lack of concentration, and difficulty in making decisions, tiredness, and depression and sleep problems. Conversely, prestigious authors recognized that physical effects include heart diseases, digestive disorders, augmented blood pressure, headaches, and/or musculoskeletal disorders [
2,
3,
4,
5,
6,
7,
8,
9,
10,
11]. Work stress has become a public health problem due to new forms of work organization, new relationships and new employment patterns [
12]. Theoretically, work stress requests are based on the way that human beings adapt to the demands of work [
13]; other authors mention that work stress is the organism’s response to external demands [
14]. In recent years, its definition is attached to the studies made by Karasek who establishes that work stress is present when the demands of work overcome humans’ control capacity [
8,
14]. Lately, this disorder has increased due to the constant changes generated by the creation of new companies and tasks, all of which affect workers’ health in various ways.
In addition to work stress, obesity is another important public health problem in the industrial sector due to its magnitude, rapid growth, and negative effect on the health of the population suffering from it. Obesity is known to be a systemic, chronic and multicausal disease found not only in economically developed countries [
15]. The industrial sector faces significant challenges in dealing with obesity despite the various alternatives to prevent and counteract this disorder. Among other measures, Klein proposes a change of lifestyle, an increase in physical activity, and the integration of healthy eating habits into the daily routine [
16].
Indeed, the variables studied in this work may be having combined effects while acting together and may also be related to each other. In recent years, illnesses caused by psychosocial factors have been more frequent and could be having an effect on employees’ wellbeing. On the other hand, fatigue, chronic stress, and harassment at work, among others, could result in an increase in absenteeism in the industrial sector [
17]. Therefore, it is important to observe and study the harmful effects of work stress and obesity as they have increased cardiovascular, respiratory, and gastrointestinal problems, thus affecting both the physical and mental health of employees. In addition, the work stress and obesity-related mortality index in industrial employees has been increasing recently [
18]. The reason is that the effects of work stress and obesity can also increase the risk of diabetes and some types of cancer [
15] as well as of cardiovascular diseases, which are the main cause of death worldwide [
19].
Among the issues mentioned, engaging in physical activity has been identified as one of the variables that most benefits the decrease and management of both stress and obesity. The Mexican Social Security Institute [
20] defines physical activity as body motion that puts the muscles to work and demands energy exertion. Some authors recognize that exercise contributes to the generation of a more positive response to work stress [
21]. In addition, not only does physical activity improve health, but it also helps individuals’ sense of humor and the ability to withstand difficult situations [
22].
In relation to the context of this work, the industrial growth in Mexico has entailed the development of habits that lead to obesity, for example, a sedentary lifestyle, company-cooked fast food, and the lack of a fixed eating schedule. Besides, the factors that affect obesity have to do with job design. Hence, if work is deficiently designed, employees may experience an imbalance between job demands and their abilities.
Furthermore, mental health disorders related to work stressors are on the increase both in the Mexican context and abroad. Managerial job positions, specifically, are exposed to numerous work stressors, which explains their being considered among the most stressful occupations in industry [
23]. Additionally, such positions are known to be highly sedentary. On the other hand, although doing physical activity may lower the effects of work stress and may contribute to reducing BMI, the relationships between physical activity, work stress and body mass index (BMI) among obese employees have received little attention [
24].
Due to the above, this paper has focused on studying more extensively the relationships between these three variables as they have been deemed insufficiently addressed in the available literature. The most important contribution of this paper lies on the analysis of the relationships among work stress, physical activity and their effects on the BMI of such a vulnerable and neglected population as are the obese managers in the manufacturing industry. Additionally, the widely known Karasek’s demand–control work stress model, including the job content dimensions utilized in this research, has scarcely been related to physical activity dimensions to determine the effects of both variables (work stress and physical activity) on obese individuals. Moreover, the number of studies relating such work and physical activity dimensions to other public health problems, such as obesity, is limited. The contributions of this paper may also increase the knowledge and understanding of the complexity of the work stress and obesity issues in manufacturing environments.
3. Materials and Methods
The aim of this research will be achieved through three structural equation models. The first will determine the relationships between the dimensions of the work stress by JCQ and BMI. The second will determine the relationship between the dimensions of the Baecke’s physical activity questionnaire and BMI. Finally, the third integrative model will determine the relationship between the significant dimensions of both work stress by JCQ and physical activity and BMI.
3.1. Participants and Characteristics of the Sample
The study was conducted in 16 manufacturing companies in Ciudad Juarez, Chihuahua, Mexico from November through May 2019. The duration of the study was of three months, as sufficient time was needed to collect and debug data; the analytical process required approximately one month. A sample of 255 staff in middle and higher management from the maquiladora industry participated in this study. Their age range was 31–40 years, and 67.1% of the workers were male and 32.9% were female. Regarding their schooling, 55.7% held a bachelor’s degree, 23.7% had finished high school studies only, 13.2% had a master’s degree, 6.6% had only finished middle school, 0.6% had a Ph.D., and 0.1% had only finished elementary school. As for their marital status, 55.4% were married, 30.1% single, 8.4% lived in free union, 5.3% were divorced, and 0.9% were widowed. According to the hours worked per week, 52.5% worked a total of 45 h a week, 26.9% worked 48 h, 12.8% worked 42 h, and 7.8% worked 52 h or more a week. The information regarding their current position and categorization showed the following composition: Managers made up 9.9% of the sample, supervisors 25.3%, technicians 25.9%, group leaders 11.3%, and others such as administrative staff 27.7%.
This section offers a summary of statistics regarding the percentage of managers who suffer from work stress and the percentage of managers who suffer from obesity to any degree. Additionally, a sedentary criterion was found for the physical activity indices.
In the case of work stress, 25% of managers show evidence of suffering from it according to the JCQ Job Strain Index [
48].
Concerning the prevalence of obesity among managers, 72.94% of them feature first degree obesity, 17.64% show it in second degree, and 9.41% of the participants suffer from the third degree type.
In reference to the physical activity, indices obtained from managers offered the diagnosis of a sedentary lifestyle in all three dimensions (physical sports activity, physical activity at work, and physical activity in leisure time). Interpretation of these indices and reference values were available from Baecke et al. [
49]. As abbreviations, these variables were renamed (sports, work and leisure-time), respectively, in the structural models.
3.2. Materials
The instruments of Job Content Questionnaire [
48] in its Spanish version and Baecke’s short Physical Activity Questionnaire [
49] were used; lastly, a sociodemographic factors questionnaire was utilized.
3.2.1. Job Content Questionnaire
Cedillo [
50] created the Spanish version of the instrument used in this research that consists of 27 items divided into 6 dimensions (job demands, supervisor support, co-workers support, skill discretions, decision-making authority, and job insecurity.) The instrument is answered by means of a Likert scale with different values. The reliability of the questionnaire in our sample was checked using Cronbach’s alpha and the result was statistically reliable with a value of 0.827.
3.2.2. Baecke’s Physical Activity Questionnaire
Created by Baecke [
49], this instrument makes it possible to obtain physical activity indices in different dimensions such as work, sports and leisure time. The questionnaire for measuring physical activity consists of 23 questions; one of these questions leads to other questions to obtain more information about the dimension being assessed. Questions are answered by means of a Likert scale with different values for each item. The results presented in this questionnaire are the physical activity index at work, the physical activity index in sports, and the physical activity index during leisure time; such indices are then calculated using equations pre-established by the author. The reliability of the instrument for this specific sample was verified using Cronbach’s alpha, and the result was statistically acceptable as it featured the value of 0.774.
3.2.3. The Sociodemographic Data Questionnaire
This questionnaire was designed to obtain more information about the characteristics of the sample. It features sociodemographic data such as age, gender, marital status, current job position, seniority in the company, hours worked per week, and other data such as weight and height, which were obtained by companies’ medical staff.
3.3. Methods
3.3.1. Sample Identification
A non-probabilistic convenience snowball sampling was used for this study. The researchers presented the project to the members of the medical committee from the manufacturing sector. Those companies that agree to participate were visited to collect data. Several factors were considered to identify the sample for this research; first, the need found in the literature regarding the effects of work stress and Job Content on employees of the manufacturing industry, and then the concern about the obesity grades in middle and upper management in this same industry. It is also necessary to highlight that the people occupying these job positions are key to the development and performance of the organization, a reason why their physical and mental health is relevant. In addition, work stress and limited physical activity may lead to poor performance; they may increase the risk of developing chronic diseases, which increases costs due to losses in productivity and medical expenses [
51,
52]. On the other hand, the reason this sample was studied in Ciudad Juarez is the location’s relevance as one of the top three industrial cities in Mexico and the seventh largest manufacturing center of North America. The city hosts 327 manufacturing “
maquiladora” industries with over 300,000 direct and indirect employees [
53]. Additionally, this Ciudad Juarez, Chihuahua, Mexico-El Paso Texas, United States of America (USA) border region offers a unique geographic point that gives direct entry to the USA market and represents the largest bi-national urban zone in North America.
3.3.2. Database Debugging
At this stage of data processing, descriptive analyses are performed to detect lost values and outliers. Since ordinal values are used (Likert scale) mostly for the questionnaires, the lost values should be replaced by the median; however, Hair [
54] points out that the amount of replaced values should not exceed 10% of total data per variable. As for the weight and height, the lost values can be replaced by the mean value as that is a nominal scale. Subsequently, the outliers should be analyzed through box charts using software IBM Statistics SPSS 21
® (IBM Software Group, Attention: Licensing, 233 S. Wacker Dr., Chicago, IL 60606, USA). This procedure helps reduce errors caused by deficient data input or observation.
3.3.3. Generation and Validation of the Structural Equation Models
At this stage of the methodology, structural equation models were generated and validated. Likewise, the following variables were stated for these models: in terms of the JCQ, the work control dimensions were skill discretion, decision-making authority, and social support (coworkers support and supervisor support). In reference to job demand dimensions, the variables stated were job demands and job insecurity; in terms of physical activity, the considered variables were work physical activity, sport physical activity and leisure-time physical activity; and, finally, in terms of BMI (BMI >= 30 kg/m2). The relationships of the dimensions raised in the research hypotheses were analyzed by means of the WarpPLS 6.0® (ScriptWarp Systems P.O. Box 452428 Laredo, Texas, 78045 USA) software, and the criterion for the acceptance or rejection of the hypotheses was based on the p-values.
3.3.4. Direct Effects
Direct effects were obtained for each established model and for the participating latent variables. The direct effects are those given per segment from one (latent) variable to another and in which the hypotheses posed are validated. The directs effects are presented and explained for each model separately in the results section.
3.3.5. Model Efficiency Indices
The proposed models are analyzed using the following general efficiency indices provided by WarpPLS 6.0
® after processing the hypothetical model. Kock [
55] highlights the following indices: average path coefficient (APC), average R-square (ARS), average adjusted R-square (AARS), average block variance inflation factor (AVIF), average full collinearity (AFVIF), Tenenhaus goodness of fit (GoF), Simpson’s paradox (SPR), the R square contribution ratio (RSCR) and the statistical suppression ratio (SSR). Model efficiency indices are presented and explained for each model separately in the results section.
3.3.6. Coefficients of Latent Variables
The coefficients used to validate the questionnaires are the values of R square, Q square, adjusted R square, composite reliability coefficient, Cronbach’s alpha, and the VIF and AFVIF values for latent variables. Coefficients of latent variables are presented and explained for each model separately in the results section.
3.3.7. Sum of the Model’s Total Effects
In this step, the values that made up the regression value of R2 of each dimension were analyzed as was the case, this was to analyze what percentage of each variable explained the other. Indirect effects were obtained through other latent dimensions or variables with two or more segment paths. The sum of these effects yielded the total effects or sum of total effects. The sum of total effects is the sum of both direct and indirect effects among the latent variables.
5. Discussion
The results of the first two models show significant relationships between the skill discretion and job demands dimensions contained in the job content variable and BMI, as well as between the leisure-time physical activity contained in the physical activity variable and BMI. Lastly, the results of the integrative model infer that even work stress and physical activity have direct negative effects on BMI.
The first model relates the Job Content dimensions (work stress by JCQ) with BMI, its results showing that three of the four JCQ dimensions used were statistically significant. The variable with the highest explanatory power over BMI is decision-making authority, which can mean that when managers feel limited regarding the job decisions they can make, they become hesitant towards how to perform it; similarly, when they feel that their opinions at work are neglected, their stressful perceptions show an impact on their BMI. Thus, when this variable (decision making-authority) increases by one unit, BMI increases by 0.21 units.
On the other hand, the study found that the skill-discretion dimension is inversely related to BMI. This could be associated with the fact that when workers are able to use their learned skills and abilities, their sense of control regarding their job increases. This increase in the sense of control is a variable that may reduce the feeling of stress. Considering that managers tend to have stable characteristics (unless some disorder is present), a greater sense of control could result in healthier behavior, which would have an impact on BMI. Regarding the demand scale, a negative relationship with BMI was found, reflecting that the greater the work demands, the lower the BMI; this means that these employees’ workload can lead to a lower BMI. This is an interesting aspect since stress is associated with physiological activation, which would normally result in a lower BMI than that of workers with fewer demands. Thus, these results lead us to agree with Muñoz [
36] and Santana’s [
39] studies, which show the relationship between stress and BMI, except that in our case the sample comprises middle and upper managers featuring obesity.
Conversely, the second model on physical activity and BMI showed interesting results since the physical activity during the leisure-time dimension was the only variable directly related to BMI. Thus, reduced physical activity, or sedentarism, outside of work contributes to the increase in BMI as described by Quirantes’ study [
57], in whose sample most of the obese people led a sedentary lifestyle. The rest of Baecke’s questionnaire dimensions were not significant. In this regard, it can be confirmed that employees’ work demands have resulted in long work hours and practically non-existing physical activity, both of which show a relationship with BMI. That means that the variable of physical activity at work (work) is of no relevance when explaining BMI. However, this study found that physical activity during leisure-time (leisure) is the only variable significantly related in a direct though inverse way with BMI. Perhaps an increase in these workers’ physical activity during their working hours would result in a significant relationship with BMI. Therefore, it would be important to increase physical activity during the working day by instructing workers to change their posture several times during the workday as well as implementing physical activity through workplace gymnastics or opening gym facilities in the workplace to positively influence the reduction of BMI, especially in obese individuals. The promotion of physical activity at work is an essential, although not unique, aspect of the increase in energy consumption among workers with sedentary work.
Finally, the integrative model shows the relationship between physical activity, work stress by job content, and BMI among obese managers in the Mexican manufacturing industry. Although the model features a relatively low explanation, this relationship is indeed a significant one. In this regard, it must be considered that there are multiple factors influencing BMI prediction (i.e., genetic, biological, physiological, etc.). However, few studies analyze the role of collective aspects (such as work stress) in BMI as an obesity indicator. These results, then, support the scarce literature found, as is the case with the study by Azofeifa et al. [
58], which finds that the greater the lack of physical activity, the higher the level of stress that a person can show. In this study, although variables such as work stress or physical activity offer but a small explanation of the percentage of BMI variance, they are still considered relevant. The effects of work-stress on BMI have been generally ignored, especially among high and middle managers, even though, according to the results obtained in this study, those factors do seem to be directly related to it. In this sense, considering that, for managerial positions, exposure to work stress is habitual and long-lasting, it should be highlighted that addressing stress itself could be a beneficial factor for these workers’ health.
This work is theoretically supported by previous research that has studied some of these problems, as well as their dimensions. In turn, it provides the theoretical framework to support the hypotheses of models that confirm to some extent the relationships between job stress, physical activity and BMI among obese managers. In addition, the industrial sector would benefit from the awareness that serious diseases such as type II diabetes, hypertension, cholesterol, fatty liver, coronary, vascular and respiratory diseases and even some types of cancer may be associated with obesity. In general, a low-calorie diet is recommended for obese patients. Patients suffering from occupational stress generally have a high intake of saturated fats, sugars and carbohydrates, and this affects their health and contributes to the generation of these chronic diseases [
59,
60]. Therefore, an additional recommendation is to promote good eating habits among patients with obesity and fatty liver problems: a diet consisting of fruits, vegetables, vitamins (C, D and E), some foods, such as fish containing Omega 3 and olive oil for cooking, which is a healthier option. These and other foods help fight and reduce the above-mentioned diseases [
61].
Additionally, it should be mentioned that this research has been financially supported to study the relationship between work stress and obesity among the vulnerable and commonly disregarded population of Mexico’s middle and higher management. The results and analyses of such a complex problem, considering its different variables and aspects, have been studied in our previous work. Consequently, the results earlier obtained led us to conclude that other factors must be included in the study to better explain the relationship work stress, obesity and/or overweight since the explanatory power found by means of the structural models developed was very low. Therefore, the scope of this research was expanded to include physical activity as a factor that may help explain the variation of BMI variable as an indicator of obesity and overweight among Mexican managers.
The contribution of this work lies in the lack of studies that relate such variables together to explore the relationship among work stress by job content dimensions, physical activity, and BMI in Mexican managers specifically.
Regarding this study’s limitations, it can be said that it has included important variables that explain the variation in BMI, for example, physical activity, while other factors that could help describe BMI have been excluded. Consequently, trying to cover other factors might help explain BMI. Another limitation relates to the different instruments that can be used to measure stress and physical activity since this research used only one instrument for each variable. Therefore, it would be interesting to obtain closer day-to-day measures (by diaries, for instance), of both work stress and physical activity, which might allow for the observation of workers’ behavior on a regular basis.
6. Conclusions
This research aimed to determine the relationship and effects of job content and physical activity on BMI among obese managers of the Mexican manufacturing industry.
Accordingly, the conclusions of the first model about the relationship between JCQ dimensions and BMI in the obese managers’ sample is that the four dimensions involved are statistically significant, three directly and one indirectly. Thus, the skill-discretion dimension is directly significant with a β = −0.12 and a p-value of 0.03, and, through this dimension, the social support is indirectly significant. Additionally, the job demands variable is directly significant, showing a β = −0.15 and a p-value < 0.01; and, finally the decision-making authority variable is directly significant with a β = 0.21 and a p-value < 0.01. Additionally, the explanatory power of the model is medium and corresponds to an R2 = 0.08 value.
In relation to the second model, conclusions of the relationships between the physical activity dimensions and BMI in the obese managers’ sample, are that the three dimensions of the physical activity questionnaire are significant, although only the leisure-time variable is directly significant, showing a value of β = −0.19 and a value of p < 0.01. While physical activity at work and sport physical activity are both indirectly significant, the three variables manage to obtain a medium explanatory power of the model and correspond to an R2 = 0.03.
Finally, the conclusions of the integrating model, between job content (work stress by JCQ), physical activity, and BMI in the obese managers’ sample are that both the variable of work stress by JCQ and the variable of physical activity are significantly related to BMI. In the case of the JCQ work stress variable, the variable has a β = −0.11 and a p-value of 0.03; and the variable of physical activity presents a β = −0.15 and a p-value < 0.01, resulting in low explanatory power and correspond to and R2 = 0.04.
6.1. Industrial Implications
Recently, in an attempt to decrease work stress in Mexico, the Mexican government has issued new occupational health regulations for the industrial sector. These are related to the psychosocial risks at work, where the topic of work stress is included. This is where the relevance of this research can be highlighted, especially when occupational health problems suffered by middle and high managers of the manufacturing industry are studied. These important job positions are usually underestimated and have been insufficiently addressed by the available literature. Additionally, obesity is considered a national priority problem in Mexico since the country’s population occupies the top positions in this health problem worldwide. Consequently, knowledge about the relationship between work stress and obesity may offer organizations a new perspective on how to manage both of these problems effectively. Companies around the world could develop strategies to increase and promote employees’ engagement in physical activity at work and to increase the practice of sports activities, especially by middle and higher managers. Formal organizational programs fostering healthy habits, psychological assistance, and work stress management techniques at work are also common practices among world-class companies pursuing high performance based on their employees’ wellbeing.
The results of this research help expand the knowledge about these occupational health problems in the industrial manufacturing sector. Such knowledge can be used to inform and encourage companies to intensify their attention to health-related problems, not only physical health but also mental wellbeing. Additionally, companies may increase their interest in the consequences of work stress and encourage aid through proposals for stress-coping strategies as well as human and organizational development to prevent these health problems in the light of new approaches.
6.2. Future Research
The results of this research pave the way for new causal research into overweight and obesity. As future work, we will seek to integrate other factors into the models, including eating habits and sociodemographic considerations, in addition to including other jobs, such as manufacturing operators. The objective of refining the models would be to provide literacy and a clearer understanding of the variance of BMI that can be related to obesity. Thus, it is intended to extend our research by developing software and cognitive systems to obtain knowledge and support the diagnoses and effective management on these issues in industrial environments.