Effects of multiple risk factors on upper limb musculoskeletal disorders among workers in inner Brazil

Abstract

BACKGROUND:

The literature discussing musculoskeletal diseases of inner northeastern workers is scarce, although 67,559 cases were reported in Brazil between 2007 and 2016.

OBJECTIVE:

This study aimed to evaluate the effect of multiple risk factors that influence the symptoms of work-related musculoskeletal disorders (WMSDs) in wrists, elbows, and shoulders in workers from four different economic sectors.

METHODS:

A sample included 420 workers from the inner regions of the Brazilian states of Alagoas and Bahia. The Nordic Musculoskeletal Questionnaire was used to capture pain symptoms on both sides of the body (left and right). Sociodemographic variables, items from the biomechanical exposure and organizational conditional, in addition to other questionnaires (JCQ, COPSOQ II, ERI) were used to assess the characteristics and occupational risks of the respondents. Ordinal logistic regression model was using to identify the relationship between symptoms and factors.

RESULTS:

This study highlights the psychosocial, biomechanical, occupational, and sociodemographic variables contributed to development of WMSDs. Use of hand-vibrating tool increased the likelihood of symptoms manifesting on the body. On the other hand, high job control and high job insecurity reduced the likelihood of developing symptoms. On the other hand, high job control and job satisfaction reduced the likelihood of developing symptoms. Factors such as age, curved spine, high job insecurity and excessive commitment contributed to the development of WMSDs only on one side of the body.

CONCLUSIONS:

The development of WMSDs is multifactorial. Sociodemographic, occupational, biomechanical, and psychosocial factors may commonly contribute to WMSD manifesting only on one side of the body more than on both sides.

Keywords

1 Introduction

Work-related musculoskeletal disorders (WMSDs), which represent a public health problem, are characterized by local or generalized pain, a feeling of heaviness, or muscle fatigue. These injuries originate or are aggravated by work activities, and can affect passive structures (bones and joints) and active structures (muscles, tendons, ligaments, and peripheral nerves) [1, 2] due to excessive use of body parts without adequate recovery. It is known that the origin of WMSDs is multifactorial [3 –5]. Further, they are influenced by the characteristics of the work environment and the way the work is performed [3]. WMSDs is associated with higher presenteeism and sick leave days [6] and opioid consumption approximately 10% of the time [7]. In Brazil, 67,559 cases were reported between 2007 and 2016 [8].

As a rule, biomechanical factors (physical demands or abduction of the arm), individual factors (age or body mass index [BMI]), or stress act directly on WMSDs, in the same way that organizational factors (automatic machine speed and different requirements for work according to market demand) and psychosocial factors (psychological demands, decision latitude and social support) act indirectly [9]. Meanwhile, the literature presents many other factors associated with WMSDs, which can only be correctly understood in light of the work conditions.

Biomechanical aspects such as maintaining static postures, repetitive movements, excessive load lifting, and using vibrating tools are associated with WMSDs [2 , 10–12]. Psychosocial factors, such as low reward, low control at work, and motivation [10 –13] have been better studied recently, as they contribute to an increase in muscle tension, perceived exertion, and stress [14, 15]. Other authors consider that psychosocial factors influence the adoption of awkward postures, contributing to the illness of the workers [16]. Further, gender, education, and physical activity are associated with WMSDs [12], particularly in developing countries [17].

Prolonged and diversified exposure to factors that contribute to the increase in occupational diseases is responsible for approximately 71% of injuries classified as work accidents, especially diseases of the musculoskeletal system, which correspond to 16% of the benefits granted to employees during the period of absence from work [18]. Such accidents entail budgetary costs to the Social Security of Brazil (PSB in Portuguese). Approximately 5.75% of the benefits issued in 2019 were destined for the payment of sickness benefits and accidental benefits resulting from work, with the PSB incurring a cost of approximately R$ 4 billion. Of this total, 25.72% was destined for Brazilian northeastern, the second region of the country with the highest number of requests based on the PSB Statistical Bulletin of 2019 [19].

In the first four-month period of 2020— the second follow-up to the Worker’s Health Goal in the National Health Plan 2020/2023 [20]— the state of Alagoas had the second-highest coefficient of incidence of diseases and injuries related to work in the country (a total of 245.8). The state of Bahia had one of the lowest coefficients in the country (80.8). Although both are in the northeast region and have similar economic and population data, the results show significant differences that can be justified by the organization of work in each state [21]. The official data on the incidence of occupational diseases are limited in scope as a study basis since they exclude workers not linked to the general social security [22], especially workers in the informal sector, who represent 41.40% of the employed population in Brazil [23].

The inner region of these states is characterized by climatic vulnerability, scarcity of water resources, and, in general, low soil fertility [24]. The evolution of the states’ work policies was mostly developed to solve the drought problem and boost the technological development of the inner northeastern region [25]. Even today, the sectors that stand out in the economy are linked to this aspect, including agribusiness, family farming, textile production, tourism, and industry for the state of Alagoas [24]. However, industry, agriculture, and services have greater participation in the state of Bahia. The state tends to follow the Brazilian economy cyclically [26] and contrasts with the state of Alagoas, which sticks to its main economic activity.

The commercialization of productive activity in these places led to an accumulation of income that directly interferes with labor relations and results in high labor turnover, low wages, precarious bonds, intense competition among workers, unhealthy working hours, and labor exploitation [21]. These work characteristics are still linked to illiterate or poorly educated workers living at risk and with a minimum basic social infrastructure [26]. The literature associated with the occupational health of inner northeastern workers is still scarce, especially when it comes to musculoskeletal diseases and their related factors.

Thus, to analyze the profiles of the workers and verify the information listed, this study aims to conduct a multifactorial assessment of sociodemographic variables and occupational risks and their relationship with symptoms of WMSDs in the wrists, elbows, and shoulders of Brazilian inner northeastern workers from the health, education, trade, and industrial sectors.

2 Material and methods

2.1 Ethical declaration

The Research Ethics Committee of the Federal University of Alagoas previously approved all methodological procedures of this research under CAAE number 35014720.6.0000.5013.

2.2 Participants

A total of 13 small- and medium-sized establishments in the region of the states of Alagoas and Bahia allowed their workers to answer the research instrument, choosing the sectors of health, education, services, and industry. Moreover, to facilitate understanding, the following steps were taken: (i) description of the venues visited and the sample, (ii) questionnaire for collecting information, and (iii) statistical analysis of the collected data.

The companies visited were located in two cities in the inner northeastern region, one in the state of Alagoas and the other in the state of Bahia. In both cities, questionnaires were administered to health professionals. There were 167 respondents, with the participation of doctors, nurses, pharmacists, and nursing assistants, among other professionals who work in three hospitals, a clinic, and two health centers in the network of the studied sites.

Industry workers were analyzed from three companies, two in Bahia city and one in Alagoas city. The employees who participated in the survey were from the production sector, with 59 participants. For the education and commerce sectors, the survey was conducted in Alagoas city due to its ease of access. In the education sector, 13 institutions allowed the participation of employees for the research, with nine schools in the municipality, three state schools, and one federal institution of higher education; 159 responses were obtained, notably teachers, librarians, and professionals with management positions, among others, were interviewed. Further, in the commerce sector, 35 service employees in five private chain stores were interviewed.

For participation in the research, the pre-established criteria were: (i) voluntary worker participation, (ii) being at least 18 years old, and (iii) having an effective contract with the company. The exclusion criteria were as follows: (1) < 18 years old, (2) apprenticeship contracts, (3) pregnant, (4) recent surgical procedures, (5) hypertension, (6) history of neuromusculoskeletal diseases, and (7) sick leave. Consequently, the survey included a sample of 420 workers, according to the availability of each respondent; therefore, the definition of the sample quantity occurred through non-probabilistic means.

2.3 Data collection tools

The questionnaire for collecting information consisted of two parts related to the independent variables (sociodemographic, biomechanical, occupational/organizational, and psychosocial factors) and the dependent variables (symptoms in the elbows, shoulders, and wrists). Thus, data were collected via a self-administered questionnaire applied on the spot.

The Nordic Musculoskeletal Questionnaire (NMQ) [27], translated and validated into Portugese in Brazil [28], was used to capture, measure, evaluate, or report musculoskeletal pain symptoms in the shoulders, wrists, and elbows on both sides of the workers’ bodies. The original version of the NMQ has three items related to last 12 months symptoms, last 7 days symptoms, and last 12 months disability. The item “pain in the last 7 days” was used to capture pain symptoms for the shoulders, elbows and wrists. The responses were coded on a five-point scale (1 = no pain, 2 = mild pain, 3 = moderate pain, 4 = severe pain, 5 = extreme pain), which allowed workers to report their perception of pain.

Regarding sociodemographic factors, information was collected on gender (male and female), age (years), BMI (kg/m²), education (incomplete elementary, complete elementary, incomplete high school, complete high school, incomplete higher education, complete higher education, and post-graduation), marital status (single or married), and whether they had children (yes or no). BMI was classified as underweight (less than 18.5 kg/m²), normal weight (between 18.5 and 24.9 kg/m²), overweight (between 25.0 and 29.9 kg/m²), type I obese (30.0 and 34.9 kg/m²), type II obese (between 35.0 and 39.9 kg/m²), and type III obese (more than 40 kg/m²) [29, 30]. Age was dichotomized into (1) less than or equal to 45 years and (2) older than 45 years [31].

Biomechanical exposure was assessed by asking how many hours per day the worker was exposed to certain situations [32]. The situations considered were as follows: (1) standing work; (2) sitting work; (3) squatting; (4) working with the upper limbs in an uncomfortable position; (5) working with the lower limbs in an uncomfortable position; (6) working with a curved spine; (7) working with a twisted spine; (8) working using hands and fingers; (9) workload loads of up to 6 kg; (10) workload loads between 6 and 15 kg; (11) workload loads above 15 kg; (12) working by performing repetitive movements; and (13) working using hand tools. The factors were categorized as (1) seldom (< 1 hr/d), (2) often (1–6 hr/d), and (3) always (> 6 hr/d) [32]. The perception of physical demands and effort were assessed using items from the Job Content Questionnaire (JCQ) [33] and the effort-reward imbalance (ERI) questionnaire [34], respectively. JCQ and ERI have been translated and validated in Brazil by Araújo and Karasek [35] and by Chor et al. [36], respectively. A five-level response scale (1 = almost never, 2 = never, 3 = rarely, 4 = sometimes, and 5 = frequently) was used as an alternative to the ERI and JCQ items. The average scores of physical demands and effort were used to classify workers with high or low exposure.

The occupational and organizational factors considered in this study were the professional category (health professionals, education, industry, or commerce), type of employment relationship (public or private), time worked at the company (years), time worked per week (hours), the working time between vacations (months), and if they had another job (yes or no). The perception regarding the variation of activities was measured with the help of one item in the Copenhagen Psychosocial Questionnaire II (COPSOQ II) [37], translated and validated in Brazil by Luna and Gondim [38]. The average scores of variation in activities were used to classify workers with high or low variation.

Finally, psychosocial factors, the meaning of work, commitment to the workplace, job satisfaction, and work–family conflict were assessed using COPSOQ II [37]. The perception of workers regarding psychological demands, control over work, job insecurity, support from co-workers, and support from supervisors were measured through the JCQ by Karasek et al. [33]. The perception of workers’ rewards and excessive commitment was verified using the items of the ERI questionnaire [34]. An item on motivation (I feel motivated in this work) was added to the research instrument. All items presented a scale with five response categories (1 = almost never, 2 = never, 3 = rarely, 4 = sometimes, and 5 = frequently). The mean of the scores was adopted to classify workers with high or low exposure to psychosocial factors [39].

2.4 Statistical analysis

The data collected via the JCQ, ERI, and COPSOQ II items had internal consistency and reliability assessed via Cronbach’s alpha (α) and McDonald’s omega (ωt). Similarly, the adjustment of the data to the exploratory factor analysis (EFA) was verified by the Bartlett sphericity test and the Kaiser-Meyer-Olkin test (KMO). Items with a factor load (F) and commonality (h2) below 0.30 and 0.20, respectively, were excluded from the study. The sum of the product between F and the individual’s response was used to calculate the scores for each factor evaluated.

A brief descriptive statistic was built to characterize the sample, indicate the degree of exposure of each occupational group to certain risk factors, and analyze the reported WMSD symptoms. The homogeneity of the responses of the occupational groups was verified using the chi-square test with a significance level of 5%. Multicollinearity between risk factors was assessed using generalized variance inflation factors (GVIF).

The relationship between symptoms and factors was verified using an ordinal logistic regression model. Through the odds ratio (OR) extracted from the models, it is possible to express the increase or decrease in the potential of developing WMSDs by workers when exposed to a given risk factor. Outliers were excluded from the models when they were points of leverage; that is, when the observations were influential and inconsistent simultaneously in the model built. Cordeiro and Demétrio [40] define observation as inconsistent if its standardized residue is outside the range [–2; 2]. It is influential when its value is greater than twice the quotient of the number of variables, regardless of the sample size. Finally, the accuracy of the models was estimated, with a value greater than 50% sufficient to indicate good precision for ordinal logistic regression models [41]. All statistical procedures were performed using R software [42], version 3.6.3.

3 Results

The items in COPSOQ II, JCQ, and ERI showed α= 0.70, ωt = 0.77; α= 0.75, ωt = 0.80; and α= 0.75, ωt = 0.79, respectively, indicating good internal consistency and reliability of the data collected via the questionnaire, given that both parameters are greater than or equal to 0.70, with ωt > α [43] Bartlett’s sphericity test yielded χ² = 63.55 [p = 0.000], χ² =211.11 [p = 0.000], and χ² = 38.49 [p = 0.002]; and KMO = 0.73, 0.74, and 0.76, respectively, for the items of COPSOQ II, JCQ, and ERI. This indicates good adjustments of the data to EFA [44] items with F and h2 values less than 0.30 and 0.20, respectively, were excluded from the study (Table 1).

Table 1
Results of exploratory factor analysis

Independent variables F h2 F^* h2^*

Biomechanical factors

Physical demands 0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

Effort 0.75 0.57 0.76 0.57

0.75 0.57 0.75 0.56

–0.28 0.08 – –

0.55 0.30 0.55 0.30

0.33 0.11 – –

0.46 0.22 0.46 0.22

Psychosocial factors

Meaning of work 0.55 0.30 0.55 0.30

0.86 0.74 0.87 0.75

0.66 0.44 0.66 0.44

Commitment to the workplace 0.64 0.40 0.67 0.44

0.71 0.51 0.74 0.55

0.34 0.12 – –

Psychological demands 0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

Control over work 0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

Job satisfaction 0.50 0.25 0.50 0.25

0.42 0.20 0.42 0.20

0.78 0.61 0.78 0.61

0.82 0.67 0.82 0.67

Job insecurity 0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

Supervisors’ social support 0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

Social support from co-workers 0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

0.45 0.20 0.45 0.20

Reward 0.72 0.51 0.71 0.51

0.95 0.90 0.95 0.91

0.70 0.50 0.70 0.49

0.43 0.20 0.42 0.20

0.05 0.00 – –

0.08 0.01 – –

Excessive commitment 0.43 0.20 0.44 0.21

0.57 0.32 0.57 0.33

0.23 0.05 – –

0.59 0.35 0.60 0.36

0.84 0.71 0.83 0.68

0.71 0.50 0.71 0.50

Family work control 0.70 0.50 0.70 0.50

0.90 0.82 0.90 0.82

0.57 0.33 0.57 0.33

Motivation 0.45 0.20 0.45 0.21

Occupational factors

Variation in work 0.45 0.20 0.45 0.20

Independent variables	F	h2	F^*	h2^*
Biomechanical factors
Physical demands	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
Effort	0.75	0.57	0.76	0.57
	0.75	0.57	0.75	0.56
	–0.28	0.08	–	–
	0.55	0.30	0.55	0.30
	0.33	0.11	–	–
	0.46	0.22	0.46	0.22
Psychosocial factors
Meaning of work	0.55	0.30	0.55	0.30
	0.86	0.74	0.87	0.75
	0.66	0.44	0.66	0.44
Commitment to the workplace	0.64	0.40	0.67	0.44
	0.71	0.51	0.74	0.55
	0.34	0.12	–	–
Psychological demands	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
Control over work	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
Job satisfaction	0.50	0.25	0.50	0.25
	0.42	0.20	0.42	0.20
	0.78	0.61	0.78	0.61
	0.82	0.67	0.82	0.67
Job insecurity	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
Supervisors’ social support	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
Social support from co-workers	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
	0.45	0.20	0.45	0.20
Reward	0.72	0.51	0.71	0.51
	0.95	0.90	0.95	0.91
	0.70	0.50	0.70	0.49
	0.43	0.20	0.42	0.20
	0.05	0.00	–	–
	0.08	0.01	–	–
Excessive commitment	0.43	0.20	0.44	0.21
	0.57	0.32	0.57	0.33
	0.23	0.05	–	–
	0.59	0.35	0.60	0.36
	0.84	0.71	0.83	0.68
	0.71	0.50	0.71	0.50
Family work control	0.70	0.50	0.70	0.50
	0.90	0.82	0.90	0.82
	0.57	0.33	0.57	0.33
Motivation	0.45	0.20	0.45	0.21
Occupational factors
Variation in work	0.45	0.20	0.45	0.20

Note 1: F^* and h2^* stands for F and h2 after excluding items. Note 2: Excluded items have F and h2 values in bold.

Table 2 summarizes the sociodemographic characteristics of the participants. In general, the sample was composed of women (72.38%), aged up to 45 years (72.14%), normal weight (51.67%), high school level (34.05%), married (52.38%), without children (69.05%), and who practiced physical activity (52.14%). As for the occupational group, the sample was heterogeneous in terms of gender, BMI, education, and children. In trade, health, and education, the sample mostly comprised women; in industry, the number of men was higher. Regarding BMI, education workers were predominantly of normal weight; in the other groups, the prevalence of overweight individuals was high. Concerning education, health and education professionals completed higher education. Meanwhile, in industry and trade, the individuals had high school level education. Most workers in industry and trade had children; this was not the case for education and health workers.

Table 2

Synthesis of the sociodemographic factors

Variables	General (n = 420) n (%)	Health (n = 167) n (%)	Industry (n = 59) n (%)	Education (n = 159) n (%)	Commerce (n = 35) n (%)	X² (p-value)
Gender
Female	304 (72.38)	134 (80.23)	21 (35.59)	122 (76.73)	27 (77.14)	47.00 (0.000)
Male	116 (27.62)	33 (19.77)	38 (64.41)	37 (23.27)	8 (22.86)
Age
18 to 45 years	304 (72.38)	126 (75.44)	48 (81.36)	112 (70.44)	31 (88.57)	6.52 (0.089)
Older than 45 years	116 (27.62)	41 (26.56)	11 (18.64)	47 (29.56)	4 (11.43)
BMI
Under weight	9 (2.14)	3 (2.00)	1 (1.69)	2 (1.26)	3 (8.57)	38.88 (0.001)
Normal weight	217 (51.67)	64 (38.32)	33 (55.93)	103 (64.78)	17 (48.57)
Overweight	135 (32.14)	65 (38.92)	20 (33.9)	38 (23.90)	12 (34.29)
Type I obesity	45 (10.71)	24 (14.37)	5 (8.47)	13 (8.18)	3 (8.57)
Type II obesity	10 (2.38)	7 (4.19)	0 (0.00)	3 (1.89)	0 (0.00)
Type III obesity	4 (0.95)	4 (2.40)	0 (0.00)	0 (0.00)	0 (0.00)
Education
Incomplete elementary	28 (6.67)	5 (2.98)	19 (32.2)	0 (0.00)	4 (11.43)	299.92 (0.000)
Complete elementary	19 (4.52)	4 (2.40)	11 (18.64)	2 (1.26)	2 (5.71)
Incomplete high school	15 (3.57)	1 (0.60)	9 (15.25)	0 (0.00)	5 (14.29)
Complete high school	143 (34.05)	84 (50.3)	18 (30.51)	19 (11.95)	22 (62.86)
Incomplete higher education	36 (8.57)	9 (5.39)	0 (0.00)	27 (16.98)	0 (0.00)
Complete higher education	135 (32.14)	60 (35.93)	2 (3.40)	71 (44.65)	2 (5.71)
Post graduation	44 (10.48)	4 (2.40)	0 (0.00)	40 (25.16)	0 (0.00)
Marital status
Single	200 (47.62)	81 (48.5)	26 (44.07)	74 (46.54)	19 (54.29)	1.05 (0.790)
Married	220 (52.38)	86 (51.5)	33 (55.93)	85 (53.46)	16 (45.71)
Children
Yes	130 (30.95)	81 (48.5)	51 (86.44)	58 (36.48)	24 (68.57)	47.81 (0.000)
No	290 (69.05)	86 (51.5)	8 (13.56)	101 (63.52)	11 (31.43)
Physical activity
No day	201 (47.86)	71 (42.51)	25 (42.37)	85 (53.46)	20 (57.14)	5.83 (0.120)
1 day or more days	219 (52.14)	96 (57.49)	34 (57.63)	74 (46.54)	15 (42.86)

Note: Significant values are shown in bold.

Table 3 presents the organizational data, and most workers were public servants (69.05%) who had no other job (71.43%), had worked for up to 15 years (58.33%), worked up to 40 hours a week (57.14%), and with more than 11 months between vacations (53.10%). All factors were heterogeneous within occupational groups. Most health and education workers were public servants. As for time with the company, many health and retail workers had been in the role for less than a year; while in education, many had been in the job for more than 16 years. Trade workers worked fewer hours a week, while industry workers worked more hours. Most industrial and commercial workers took vacations after 11 months of work, while a significant portion of health and education workers had taken vacations before 11 months of work. It is evident that a much larger number of workers in industry and commerce had no other jobs. Furthermore, education and trade workers had a more significant variation in performance activities.

Table 3

Summary of occupational factors

Variables	General (n = 420) n (%)	Health (n = 167) n (%)	Industry (n = 59) n (%)	Education (n = 159) n (%)	Commerce (n = 35) n (%)	X² (p-value)
Work environment
Private	130 (30.95)	34 (20.35)	59 (100.00)	2 (1.26)	35 (100.00)	284.06 (0.000)
Public	290 (69.05)	133 (79.65)	0 (0.00)	157 (98.74)	0 (0.00)
Worked time in the company
Less than or equal to 1 year	74 (17.62)	35 (20.95)	3 (5.08)	20 (12.58)	16 (45.71)	82.11 (0.000)
Between 2 and 15 years	245 (58.33)	99 (59.28)	54 (91.53)	75 (47.17)	17 (48.57)
Between 16 and 30 years	78 (18.57)	24 (14.37)	2 (3.39)	60 (37.74)	2 (5.71)
More than 30 years	23 (5.48)	9 (5.39)	0 (0.00)	5 (3.14)	0 (0.00)
Worked hours in the week
Less than or equal to 15 hours	62 (14.76)	2 (1.20)	16 (27.12)	19 (11.95)	25 (71.43)	244.27 (0.000)
Between 16 and 40 hours	240 (57.14)	114 (68.26)	0 (0.00)	126 (79.25)	0 (0.00)
Between 41 and 60 hours	114 (27.14)	49 (29.34)	43 (72.88)	12 (7.55)	10 (28.57)
More than 60 hours	4 (0.95)	2 (1.20)	0 (0.00)	2 (1.26)	0 (0.00)
Working time between holidays (months)
Less than or equal to 6 months	33 (7.86)	2 (1.20)	0 (0.00)	31 (19.50)	0 (0.00)	80.79 (0.000)
Between 7 and 11 months	164 (39.05)	88 (52.69)	16 (27.12)	57 (35.85)	3 (8.57)
More than 11 months	223 (53.10)	77 (46.11)	43 (72.88)	71 (44.65)	32 (91.43)
Variation in activities
Yes	209 (49.76)	93 (55.69)	34 (57.63	68 (42.77)	14 (40.00)	8.25 (0.041)
No	211 (50.24)	74 (44.31)	25 (42.37)	91 (57.23)	21 (60.00)
Other job
Yes	120 (28.57)	55 (32.93)	2 (3.39)	59 (37.11)	4 (11.43)	30.61 (0.000)
No	300 (71.43)	112 (67.07)	57 (96.61)	100 (62.89)	31 (88.57)

Note: Significant values are shown in bold.

Regarding biomechanical risk factors, Table 4 shows that most workers spent more than six hours in a standing position (62.38%) and using their hands and fingers (55.71%). Further, many of the individuals worked up to six hours a day sitting (59.05%), with the upper limbs (42.14%) and lower limbs (40.48%) in an uncomfortable position, with a curved spine (45.71%) and carrying 16 to 25 kg (45.71%). For up to one hour a day, these workers squat (87.14%), with a twisted spine (50.00%), carrying up to 6 kg (55.95%) or between 6 and 15 kg (79.05%), performing repetitive movements (37.38%), and using hand tools (88.10%). For most workers, effort (56.67%) and physical demands (52.14%) were low. However, heterogeneity was observed. A much larger number of health and industry workers work on their feet, and many industry workers sit for less than an hour. Additionally, a more significant proportion of industry workers endure for more than six hours with their upper and lower limbs in an uncomfortable position, as well as being bent or twisted, lifting loads between 6 to 25 Kg, performing repetitive movements, using fingers and hands, and making use of hand tools.

Table 4

Synthesis of the biomechanical factors

Variables	General (n = 420) n (%)	Health (n = 167) n (%)	Industry (n = 59) n (%)	Education (n = 159) n (%)	Commerce (n = 35) n (%)	X² (p-value)
Work in a standing position
Seldom (Less than 1 hour)	17 (4.05)	8 (4.79)	4 (6.78)	3 (1.89)	2 (5.71)	49.4 (0.000)
Often (Between 1 and 6 hours)	141 (33.57)	43 (25.75)	3 (5.08)	81 (50.94)	14 (40.00)
Always (More than 6 hours)	262 (62.38)	116 (69.46)	52 (88.14)	75 (47.17)	19 (54.29)
Work in a sitting position
Seldom (Less than 1 hour)	89 (21.19)	15 (8.98)	51 (86.44)	14 (8.81)	9 (25.71)	181.09 (0.000)
Often (Between 1 and 6 hours)	248 (59.05)	114 (68.26)	4 (6.78)	111 (69.81)	19 (54.29)
Always (More than 6 hours)	83 (19.76)	38 (22.75)	4 (6.78)	34 (21.38)	7 (20.00)
Work in a squatting position
Seldom (Less than 1 hour)	366 (87.14)	143 (85.63)	57 (96.61)	133 (83.65)	33 (94.29)	11.01 (0.088)
Often (Between 1 and 6 hours)	49 (11.66)	21 (12.57)	1 (1.69)	25 (15.72)	2 (5.71)
Always (More than 6 hours)	5 (1.20)	3 (1.80)	1 (1.69)	1 (0.63)	0 (0.00)
Upper limbs in an uncomfortable position
Seldom (Less than 1 hour)	177 (42.14)	75 (44.91)	28 (47.46)	52 (32.70)	22 (62.89)	61.517 (0.000)
Often (Between 1 and 6 hours)	177 (42.14)	68 (40.72)	9 (15.25)	95 (59.75)	5 (14.29)
Always (More than 6 hours)	66 (15.72)	24 (14.37)	22 (37.29)	12 (7.55)	8 (22.86)
Lower limbs in an uncomfortable position
Seldom (Less than 1 hour)	156 (37.14)	66 (39.52)	21 (35.59)	48 (30.19)	21 (60.00)	75.159 (0.000)
Often (Between 1 and 6 hours)	170 (40.48)	66 (39.52)	6 (10.17)	93 (58.49)	5 (14.29)
Always (More than 6 hours)	94 (22.38)	35 (20.96)	32 (54.24)	18 (11.32)	9 (25.71)
Curved spine
Seldom (Less than 1 hour)	162 (38.57)	57 (34.13)	23 (38.98)	59 (37.11)	23 (65.71)	55.564 (0.000)
Often (Between 1 and 6 hours)	192 (45.71)	82 (49.10)	12 (20.34)	89 (55.97)	9 (25.71)
Always (More than 6 hours)	66 (15.72)	28 (16.77)	24 (40.68)	11 (6.92)	3 (8.57)
Twisted spine
Seldom (Less than 1 hour)	210 (50.00)	84 (50.30)	12 (20.34)	84 (52.83)	30 (85.71)	171.55 (0.000)
Often (Between 1 and 6 hours)	146 (34.76)	62 (37.13)	7 (11.86)	73 (45.91)	4 (11.43)
Always (More than 6 hours)	64 (15.24)	21 (12.57)	40 (67.80)	2 (1.26)	1 (2.86)
Taking a load of up to 6 Kg
Seldom (Less than 1 hour)	235 (55.95)	99 (59.28)	38 (64.41)	75 (47.17)	23 (65.71)	43.065 (0.000)
Often (Between 1 and 6 hours)	133 (31.67)	52 (31.14)	4 (6.78)	71 (44.65)	6 (17.14)
Always (More than 6 hours)	52 (12.38)	16 (9.58)	17 (28.81)	13 (8.18)	6 (17.14)
Taking a load from 6 Kg to 15 Kg
Seldom (Less than 1 hour)	332 (79.05)	137 (82.04)	52 (88.14)	110 (69.18)	33 (94.29)	22.005 (0.001)
Often (Between 1 and 6 hours)	77 (18.33)	27 (16.17)	4 (6.78)	44 (22.67)	2 (5.71)
Always (More than 6 hours)	11 (2.62)	3 (1.80)	3 (5.08)	5 (3.14)	0 (0.00)
Taking a load from 16 Kg to 25 Kg
Seldom (Less than 1 hour)	162 (38.57)	134 (80.24)	48 (81.36)	146 (91.82)	34 (97.14)	30.777 (0.000)
Often (Between 1 and 6 hours)	192 (45.71)	24 (14.37)	3 (5.08)	13 (8.18)	1 (2.86)
Always (More than 6 hours)	66 (15.72)	9 (5.39)	8 (13.56)	0 (0.00)	0 (0.00)
Repetitive movements
Seldom (Less than 1 hour)	157 (37.38)	68 (40.72)	2 (3.39)	70 (44.03)	17 (48.57)	134.13 (0.000)
Often (Between 1 and 6 hours)	110 (26.19)	34 (20.36)	1 (1.69)	68 (42.77)	7 (20.00)
Always (More than 6 hours)	153 (36.43)	65 (38.92)	56 (94.92)	21 (13.21)	11 (31.43)
Work using hands and fingers
Seldom (Less than 1 hour)	42 (10.00)	19 (11.38)	0 (0.00)	18 (11.32)	5 (14.29)	73.1 (0.000)
Often (Between 1 and 6 hours)	144 (34.29)	52 (31.14)	2 (3.39)	84 (52.83)	6 (17.14)
Always (More than 6 hours)	234 (55.71)	96 (57.49)	57 (96.61)	57 (35.85)	24 (68.57)
Hand tools
Seldom (Less than 1 hour)	370 (88.10)	144 (86.23)	42 (71.19)	150 (94.34)	34 (97.14)	53.993 (0.000)
Often (Between 1 and 6 hours)	30 (7.14)	19 (11.38)	4 (6.78)	6 (3.77)	1 (2.86)
Always (More than 6 hours)	20 (4.76)	4 (2.40)	13 (22.03)	3 (1.89)	0 (0.00)
Effort
Low effort	238 (56.67)	77 (46.11)	31 (52.54)	78 (49.06)	20 (57.14)	1.7837 (0.618)
High effort	182 (43.33)	90 (53.89)	28 (47.46)	81 (50.94)	15 (42.86)
Physical demands
Low demands	219 (52.14)	88 (52.69)	22 (37.29)	84 (52.83)	19 (54.29)	4.9805 (0.173)
High demands	201 (47.86)	79 (47.31)	37 (62.71)	75 (47.17)	16 (45.71)

Note: Significant values are shown in bold.

Table 5 presents information about psychosocial factors. Most workers perceive high significance (68.33%), commitment (56.90%) and control (53.81%), and job satisfaction (53.81%). Psychological demands (51.43%), support from co-workers (56.19%) and supervisors (52.38%), reward (51.19%), excessive commitment (50.71%), and motivation (58.10%) were also considered high. The perception of most workers was low for job insecurity (55.71%) and work–family conflict (52.86%). Heterogeneity was observed only for the factors meaning of work and control over the work. Health and education workers more easily perceive the meaning of their work for society and have greater autonomy and freedom of decision regarding job procedures.

Table 5

Synthesis of the psychosocial factors

Variables	General (n = 420) n (%)	Health (n = 167) n (%)	Industry (n = 59) n (%)	Education (n = 159) n (%)	Commerce (n = 35) n (%)	X² (p-value)
Meaning of work
Low meaning	133 (31.67)	32 (19.16)	30 (50.85)	56 (35.22)	15 (42.90)	25.052 (0.000)
High meaning	287 (68.33)	135 (80.84)	29 (49.15)	103 (64.78)	20 (57.10)
Commitment to the workplace
Low commitment	181 (43.10)	78 (46.71)	23 (38.98)	65 (40.88)	15 (42.90)	1.614 (0.656)
High commitment	239 (56.90)	89 (53.29)	36 (61.02)	94 (59.12)	20 (57.10)
Psychological demands
Low demands	204 (48.57)	92 (55.09)	30 (50.85)	84 (52.83)	24 (68.60)	3.306 (0.347)
High demands	216 (51.43)	75 (44.91)	29 (49.15)	75 (47.17)	11 (31.40)
Control over work
Low control	194 (46.19)	65 (38.92)	52 (88.14)	55 (34.59)	22 (62.90)	57.832 (0.000)
High control	226 (53.81)	102 (61.08)	7 (11.86)	104 (65.41)	13 (37.10)
Job satisfaction
Low satisfaction	194 (46.19)	76 (45.51)	20 (33.90)	84 (53.82)	14 (40.00)	6.977 (0.073)
High satisfaction	226 (53.81)	91 (54.49)	39 (66.10)	75 (47.17)	21 (60.00)
Job insecurity
Low tenure	234 (55.71)	100 (59.88)	28 (47.46)	89 (55.97)	17 (48.60)	3.533 (0.316)
High tenure	186 (44.29)	67 (40.12)	31 (52.54)	70 (44.03)	18 (51.40)
Supervisor support
Low support	200 (47.62)	73 (43.71)	27 (45.76)	79 (49.69)	21 (60.00)	3.526 (0.317)
High support	220 (52.38)	94 (56.29)	32 (54.24)	80 (50.31)	14 (40.00)
Support from coworkers
Low support	184 (43.81)	66 (39.52)	20 (33.90)	82 (51.57)	16 (45.70)	7.546 (0.056)
High support	236 (56.19)	101 (60.48)	39 (66.10)	77 (48.43)	19 (54.30)
Reward
Low reward	205 (48.81)	79 (47.31)	19 (32.30)	78 (49.06)	17 (48.60)	5.327 (0.149)
High reward	215 (51.19)	88 (52.69)	40 (67.80)	81 (50.94)	18 (51.40)
Excessive commitment
Low commitment	207 (49.29)	85 (50.90)	30 (50.85)	70 (44.03)	22 (62.90)	4.571 (0.206)
High commitment	213 (50.71)	82 (49.10)	29 (49.15)	89 (55.97)	13 (37.10)
Motivation
High motivation	244 (58.10)	74 (44.31)	15 (25.42)	72 (45.28)	15 (42.90)	7.739 (0.052)
Low motivation	176 (41.90)	93 (55.69)	44 (74.58)	87 (54.72)	20 (57.10)
Work-family conflict
Low conflict	222 (52.86)	95 (56.89)	30 (50.85)	76 (47.80)	21 (60.00)	3.533 (0.316)
High conflict	198 (47.14)	72 (43.11)	29 (49.15)	83 (52.20)	14 (40.00)

Note: Significant values are shown in bold.

Regarding the results for WMSDs, Table 6 shows that most responses were “no pain” for all regions assessed. However, a substantial number of workers had WMSD symptoms, with 20.00% and 21.67% of workers reporting mild symptoms in the left and right shoulders, respectively. Moderate symptoms were reported by more than 8.00% of workers in the elbows. Likewise, 9.29% of workers reported intense symptoms in the left wrist. The distribution of symptoms was si

Table 6

Levels of musculoskeletal discomfort and risk factors

Body parts	General (n = 420) n (%)	Health (n = 167) n (%)	Industry (n = 59) n (%)	Education (n = 159) n (%)	Commerce (n = 35) n (%)	X² (p-value)
Left shoulder
No pain	238 (56.67)	109 (65.27)	32 (54.24)	77 (48.43)	20 (57.14)	18.02 (0.115)
Mild pain	84 (20.00)	25 (14.97)	13 (22.03)	37 (23.27)	9 (25.71)
Moderate pain	51 (12.14)	15 (8.98)	10 (16.95)	21 (13.21)	5 (14.29)
Severe pain	35 (8.33)	14 (8.38)	4 (6.78)	16 (10.06)	1 (2.86)
Extreme pain	12 (2.86)	4 (2.40)	0 (0.00)	8 (5.03)	0 (0.00)
Right shoulder
No pain	222 (52.86)	99 (59.28)	33 (55.93)	69 (43.40)	21 (60.00)	17.34 (0.137)
Mild pain	91 (21.67)	31 (18.56)	12 (20.34)	39 (24.53)	9 (25.71)
Moderate pain	51 (12.14)	13 (7.78)	9 (15.25)	25 (15.72)	4 (11.43)
Severe pain	43 (10.24)	18 (10.78)	5 (8.47)	19 (11.95)	1 (2.86)
Extreme pain	13 (3.10)	6 (3.59)	0 (0.00)	7 (4.40)	0 (0.00)
Left elbow
No pain	332 (79.05)	138 (82.63)	45 (76.27)	121 (76.10)	28 (80.00)	8.36 (0.757)
Mild pain	27 (6.43)	10 (5.99)	4 (6.78)	11 (6.92)	2 (5.71)
Moderate pain	34 (8.10)	11 (6.59)	6 (10.17)	13 (8.18)	4 (11.43)
Severe pain	19 (4.52)	6 (3.59)	4 (6.78)	8 (5.03)	1 (2.86)
Extreme pain	8 (1.90)	2 (1.20)	0 (0.00)	6 (3.77)	0 (0.00)
Right elbow
No pain	320 (76.19)	135 (80.84)	43 (72.88)	113 (71.07)	29 (82.86)	11.27 (0.506)
Mild pain	31 (7.38)	10 (5.99)	3 (5.08)	16 (10.06)	3 (8.57)
Moderate pain	37 (8.81)	13 (7.78)	8 (13.56)	15 (9.43)	2 (5.71)
Severe pain	22 (5.24)	7 (4.19)	5 (8.47)	9 (5.66)	1 (2.86)
Extreme pain	10 (2.38)	4 (2.0)	0 (0.00)	6 (3.77)	0 (0.00)
Left wrist
No pain	247 (58.81)	106 (63.47)	41 (69.49)	77 (48.43)	23 (65.71)	19.10 (0.086)
Mild pain	79 (18.81)	26 (15.57)	9 (15.25)	38 (23.90)	7 (20.00)
Moderate pain	36 (8.57)	16 (9.58)	4 (6.78)	12 (7.55)	4 (11.43)
Severe pain	39 (9.29)	13 (7.78)	4 (6.78)	21 (13.21)	1 (2.86)
Extreme pain	19 (4.52)	7 (4.19)	1 (1.69)	11 (6.92)	0 (0.00)
Right wrist
No pain	258 (61.43)	108 (64.67)	39 (66.10)	88 (55.35)	23 (65.71)	10.97 (0.532)
Mild pain	84 (20.00)	32 (19.16)	10 (16.95)	36 (22.64)	7 (20.00)
Moderate pain	43 (10.24)	16 (9.58)	6 (10.17)	16 (10.06)	5 (14.29)
Severe pain	17 (4.05)	4 (2.40)	3 (5.08)	10 (6.29)	0 (0.00)
Extreme pain	18 (4.29)	8 (4.79)	1 (1.69)	9 (5.66)	0 (0.00)

Table 7 shows the results of the ordinal logistic regression model for the body parts. Although the distribution of symptoms in terms of their intensity is similar among the body’s dimensions for all the anatomical parts analyzed, they were not necessarily the same risk factors contributing to WMSD symptoms. Biomechanical factors such as the use of vibrating tools (for more than six hours a day), keeping the upper limbs in an uncomfortable position (between one and six hours a day), and psychosocial factors such as job satisfaction and work–family conflict had an impact on the likelihood of developing WMSD symptoms in both shoulders. Meanwhile, the (public) work environment and job insecurity increased the possibility of symptoms only in the right shoulder. Likewise, the age factor (older than 45 years) increased the likelihood of symptoms only in the left shoulder.

Table 7

Ordinal logistic regression model

Variables	Right shoulder (n = 420) OR (95% CI)	Left shoulder (n = 419) OR (95% CI)	Right wrist (n = 420) OR (95% CI)	Left wrist (n = 417) OR (95% CI)	Left elbow (n = 420) OR (95% CI)	Right elbow (n = 416) OR (95% CI)
Sociodemographic factors
Age
Less than 45 years	–	1.00 (Reference)	–	–	1.00 (Reference)	–
Older than 45 years	–	1.60 (1.04–2.47) ^*	–	–	1.89 (1.07 –3.33) ^*	–
Occupational factors
Workplace
Private	1.00 (Reference)	–	1.00 (Reference)	1.00 (Reference)	–	–
Public	1.74 (1.08–2.80) ^*	–	3.16 (1.87–5.37) ^***	1.99 (1.20–3.33) ^**	–	–
Other job
Yes	–	–	–	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
No	–	–	–	0.61 (0.39–0.96) ^*	0.45 (0.25 –0.81) ^**	0.41 (0.23 –0.73) ^**
Variation in work
Low variation	–	–	–	–	–	1.00 (Reference)
High variation	–	–	–	–	–	0.51 (0.34–0.77) ^**
Biomechanical factors
Use of hand-vibrating tools
Seldom (Less than 1 hour)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	–	1.00 (Reference)
Often (Between 1 and 6 hours)	0.71 (0.33–1.54)	0.80 (0.37–1.75)	0.49 (0.21–1.15)	0.51 (0.22–1.18)	–	0.30 (0.07 –1.40)
Always (More than 6 hours)	2.70 (1.18–6.18) ^*	2.63 (1.17–5.89) ^*	2.78 (1.20–6.45) ^*	5.74 (2.51–13.15) ^***	–	4.35 (1.64 –11.59) ^**
Upper limbs in an uncomfortable position
Seldom (Less than 1 hour)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	–	–	–
Often (Between 1 and 6 hours)	2.79 (1.80–4.31) ^***	2.24 (1.44–3.47) ^***	1.63 (1.04–2.56) ^*	–	–	–
Always (More than 6 hours)	1.42 (0.77–2.62)	2.37 (1.31–4.32) ^**	1.94 (1.05–3.56) ^*	–	–	–
Lower limbs in an uncomfortable position
Seldom (Less than 1 hour)	–	–	–	–	1.00 (Reference)	1.00 (Reference)
Often (Between 1 and 6 hours)	–	–	–	–	2.68 (1.24–5.77) ^*	3.55 (1.81–6.99) ^***
Always (More than 6 hours)	–	–	–	–	3.18 (1.33–7.64) ^*	2.38 (1.11–5.13) ^*
Curved spine
Seldom (Less than 1 hour)	–	–	–	–	1.00 (Reference)	–
Often (Between 1 and 6 hours)	–	–	–	–	2.31 (1.23–4.34) ^**	–
Always (More than 6 hours)	–	–	–	–	0.89 (0.35 –2.29)	–
Taking load of up to 6kg
Seldom (Less than 1 hour)	–	–	–	–	1.00 (Reference)	1.00 (Reference)
Often (Between 1 and 6 hours)	–	–	–	–	2.04 (1.04–3.99) ^*	1.20 (0.62 –2.34)
Always (More than 6 hours)	–	–	–	–	4.85 (2.23–10.55) ^***	3.24 (1.56–6.73) ^**
Psychosocial factors
Control at work
Low control	1.00 (Reference)	–	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	–
High control	0.83 (0.74–0.92) ^***	–	0.88 (0.79–0.99) ^*	0.88 (0.79–0.97) ^*	0.74 (0.64–0.85) ^***	–
Job satisfaction
Low satisfaction	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
High satisfaction	0.83 (0.74–0.92) ^*	0.77 (0.68–0.87) ^**	0.83 (0.73–0.95) ^**	0.86 (0.76–0.99) ^*	0.79 (0.66–0.95) ^*	0.70 (0.58–0.84) ^***
Physical demands
Low demand	–	–	1.00 (Reference)	1.00 (Reference)	–	1.00 (Reference)
High demand	–	–	1.12 (1.02–1.24) ^*	1.17 (1.06–1.29) ^**	–	1.25 (1.09 –1.43) ^**
Job insecurity
Low insecurity	1.00 (Reference)	–	–	–	–	–
High insecurity	1.20 (1.04–1.38) ^*	–	–	–	–	–
Work-family conflict
Low conflict	1.00 (Reference)	1.00 (Reference)	–	–	–	–
High conflict	1.13 (1.04–1.24) ^**	1.17 (1.07–1.28) ^***	–	–	–	–
Excessive commitment
Low commitment	–	–	–	–	1.00 (Reference)	–
High commitment	–	–	–	–	1.22 (1.10–1.36) ^***	–

Note 1: ^*, ^** and ^*** indicate a significant relationship (in bold) with a p-value less than 0.05, 0.01 and 0.001, respectively. Note 2: The model for the left shoulder, left wrist and right elbow had one, three and four leverage points, respectively. Note 3: The model for the right shoulder, left shoulder, right wrist, left wrist, left elbow and right elbow showed accuracy equal to 57.75%, 54.05%,61.34%, 79.05% e 76.61%, respectively. Note 4: The highest Generalized Variance Inflation Factor (GVIF) values were 1.26, 1.21, 1.25, 1.10, 1.66 and 1.48 for the models of the right shoulder, left shoulder, right wrist, left wrist, left elbow and right elbow, respectively. Note 5: Factors that were not significant for any of the regions analyzed were not considered in this table.

This phenomenon was repeated when analyzing the results of the models for the wrists and elbows. Factors such as the (public) work environment and the maintenance of superiors in an uncomfortable position increased the potential of symptoms only in the right wrist. Further, having another job reduced the likelihood of symptoms only in the left wrist. Factors such as age (greater than 45 years), working with the spine curved (between one and six hours a day), control over the work, and excessive impairment influence only on symptoms in the left elbow. Similar to the possibility of variation in work activities, the use of tools that vibrate the hands and the perception of physical demands had repercussions on symptoms only in the right elbow.

Different factors increased the likelihood of developing symptoms in both dimidia of the analyzed body parts. Biomechanical factors include the maintenance of the upper limbs in an uncomfortable position. The use of tools that vibrate the hands increased the probability of symptoms in the right and left shoulders by twice. The work environment (public) and the use of tools that vibrate in the hands increased the likelihood of symptoms in the right and left wrist by three and five times, respectively. Lifting loads (up to 6 kg) and the need to keep the lower limbs in an uncomfortable position increased the potential of symptoms in the left and right elbows by four and three times, respectively.

4 Discussion

This study obtained evidence of the risk factors that influence upper limb work-related musculoskeletal disorders (in shoulders, wrists, and elbows) in the pre-selected workers in different sectors in the inner northeastern region of Brazil. The regions were specifically in the internal areas of the states of Bahia and Alagoas. This issue has great relevance because recognizing these factors benefits the academic research community and workers and employers, as well as helps formulate internal policies to improve working conditions [45]. To the best of our knowledge, this is the first study carried out in this specific population.

Another significant finding was that these factors could contribute to WMSD symptoms on both sides. Table 6 shows the similarity between the symptoms of both dimensions for the shoulders, wrist, and elbows. Nonetheless, the regression model results (Table 7) showed that some factors might have repercussions in symptoms in only one of the dimensions. Therefore, when considering symptoms only on the right side, for example, one can run the risk of ignoring that a factor can act in isolation on the left side. Such findings can be helpful in the development of policies focused on reducing WMSDs, as it presents more precisely the actual effect, right or indirect, of the risk factors on each side of the parts of the body.

The multifactorial role of risk factors in WMSDs on the shoulders, wrists, and elbows was also evidenced. Bovenzi et al. [46] stated that during the performance of activities with the upper limbs, such parts of the body (the shoulders, elbows, and wrists) work together as biomechanical links. Their study also defended the existence of multifactorial data, such as demographic, socioeconomic, biomechanical, physical, psychological/psychosocial, and organizational risk factors in the workplace, which can assist in developing WMSDs. However, others point out that these diseases are due to the sum of these risk factors, time of exposure [47], excessive use of the locomotor system, lack of time for recovery [8], repetitive and forced movements [48], and mental stress [49, 50]. Additionally, Leite et al. [47] reported that repetitive movements are dominant factors in developing these work pathologies, especially in monofunctional workers, so that the variation of activities tends to reduce the likelihood of WMSD.

Among the analyzed members, the shoulders received more indications of pain, with the education sector being the most affected by the symptoms. Further, there was a high incidence of discomfort in the wrists for this sector, almost equivalent to that of the shoulder. Cheng et al. [51] pointed out the existence of WMSD in workers in the education sector, with the shoulders and wrists being the most affected parts, with a WMSD prevalence of 63.4% (n = 246) and 56.7% (n = 220), respectively, in their studies. Likewise, Ng et al. [10] pointed out that relationships were found between psychosocial factors and musculoskeletal disorders, mainly among professionals in the education sector. Their research also showed that the most significant experience of WMSD among the participants was on the wrist (93.2%). Musculoskeletal and psychosocial problems increased during the COVID-19 pandemic due to online job [52], so studies on the relationship between musculoskeletal disorders and psychosocial factors will be increasingly necessary.

Biomechanical factors dramatically increased the potential of developing symptoms in the shoulders of workers. The use of manual vibrating tools for an extended period (more than six hours per day) had a two-fold increase in the likelihood of developing symptoms in the shoulders. The relationship between vibration and shoulder pain has already been observed among male workers [53]. For shorter periods, no risk was found for any of the shoulders. Similarly, the need to keep the upper limbs in an uncomfortable position for more than an hour a day proved to be a risk factor for the shoulders, increasing the likelihood of musculoskeletal symptoms by up to twice.

The use of tools that vibrate the hands contributed to the development of symptoms in all joints of the upper extremities. Similarly, Bovenzi et al. [46] pointed to a greater risk for workers with musculoskeletal disorders in the upper limbs exposed to vibrations transmitted by the hands, with the results being more significant for the occurrences in the elbow, forearm, and wrist or hand. Furthermore, in the same study, it was found that the increase in vibrations transmitted by the hand increased the probability of developing WMSDs. Exposure to vibration changes depending on the body part and the distance from the source, and its impact can also be dichotomous [47].

It was found that when using vibrating tools for a long time, there was a two-fold increase in the likelihood of symptoms occurring in the right wrist. Meanwhile, the potential becomes greater (five times) when analyzing the left wrist. This increase is believed to be related to the need for left-handed individuals to use tools idealized for right-handed people, especially in the health sector [54]. The absence of left-handed tools has already been demonstrated [55]. In the industrial sector, left-handers improvise on how to perform work because of inappropriate tools, which leads many workers to adopt new standards for carrying out labor operations, with current tools and machines not being readily adapted to the needs of these workers [56]. Such findings reinforce the need to build models for each dimidium of the body parts.

The age factor (> 45 years) increased the likelihood of symptoms of only the left shoulder by 60.00%. Recent findings by Bodin et al. [9] did not find that age > 45 years was a direct risk factor for shoulder WMSDs. However, the difference in realities experienced by inner northeastern Brazilian and French workers can explain the contrast in the results of the studies. Unlike the French, country workers mostly carry out their work activities under Fordist/Taylorist molds (a man, an activity, and a workplace) with tasks that are still extremely manual and with little technological solutions. While the work presented by Bodin et al. [9] did not clarify which shoulder was considered in the construction of the structural equation model, assuming that it was the right shoulder (usually selected, since most of the population is right-handed), there would be no difference with the findings of this study. Thus, the age factor (> 45 years) was only considered and identified as being at risk for shoulder symptoms because isolated models were built for the right and left sides. Meanwhile, review and meta-analysis papers indicated that older workers have a greater risk of shoulder WMSDs [31, 57]. Therefore, further studies should explore this relationship to clarify the relationship between age and shoulder symptoms.

Regarding psychosocial factors, there is evidence that musculoskeletal symptoms are lower when workers are extremely satisfied with their work [58, 59], indicating that psychological and social aspects influence WMSDs. Some studies have suggested that the influence of psychosocial factors is indirect, but still acts on the perception of stress [9]. Others associate psychosocial factors with adopting bad work postures [60] and inappropriate movements [16], leading to WMSDs. Among the psychosocial factors presented in the models, we highlighted the relationship between control and symptoms in the right shoulder (OR = 0.83), job satisfaction and symptoms in the left (OR = 0.79) and right (OR = 0.70) elbows, and satisfaction at work and symptoms in the left (OR = 0.86) and right (OR = 0.83) wrists. Therefore, psychosocial factors can also mitigate the occurrence of WMSDs. The findings of this research are in line with those of the studies by Eatough et al. [61] on self-control at work and shoulder symptoms, Silva et al. [41] on job dissatisfaction and symptoms in the elbows, and Veisi et al. [62] on dissatisfaction and wrist symptoms. Job satisfaction improves the performance and motivation among the staff [63], and should be improved in the workplace.

This study has several limitations. First, this was a cross-sectional study. Longitudinal data are known to be more reliable and accurate. Second, pain originating in other regions of the body was not considered to radiate to the parts included in the study; however, symptoms in the neck may reflect pain in the shoulders. The third limitation is associated with not using a structural equation model, which could indicate the factors that act directly and indirectly on pain symptoms. The fourth limitation is associated with the absence of data on hand dominance. The fifth limitation is comparisons between groups were compromised by the asymmetry between sample sizes from different occupational sectors. Future studies may verify interactions between risk factors and the development of WMSD symptoms, and should also contain a greater discussion of recent work in the area, emphasizing the importance of the proposed study. They could also analyze the relationship between hand dominance and the occurrence of WMSDs due to the use of inappropriate work tools in a symmetrical sample from different occupational sectors.

5 Conclusion

This study sheds light on the factors that contribute to upper extremity musculoskeletal symptoms in workers from the inner regions of the states of Alagoas and Bahia. The absence of previous studies on workers in this population makes it difficult to compare the results in different scenarios. A series of policies aimed at preserving the musculoskeletal condition of workers can utilize the findings of this study as a starting point.

It was reinforced that the origin of WMSDs is multifactorial, with sociodemographic, occupational, biomechanical, and psychosocial factors influencing the development of symptoms. Further, the importance of building models for body dimensions is evident. Age, for example, generated significant results only for the left shoulder and left elbow; however, the same relationship was not confirmed for the right shoulder and right elbow. Likewise, different risk factors have a more significant impact depending on the size of the analyzed body part. Therefore, prevention strategies for WMSDs, designed by local enterprises, must consider that each part of the body, whether on the right or left sides, has its own risk factors. By ignoring such phenomenon, risk factors can be wrongly disregarded in ergonomic and work safety interventions.

Footnotes

Acknowledgments

The authors want to thank the Group of Ergonomics and New Tools (GENT/UFAL), and the Community Circles of Extension Activities Program (ProCCAExt/UFAL) for encouraging this research.

Conflict of interest

None to report.

References

Ali

, Ellis

, Woolf

, Hamilton

, Fenton

, Developing partnerships and a whole-system approach for the prevention of musculoskeletal conditions in England [Internet], Public Health Panorama. 2020;4(3):407–14 [cited Oct 10]. Available from https://apps.who.int/iris/handle/10665/324935.

Kozak

, Wirth

, Verhamme

, Musculoskeletal health, work-related risk factors and preventive measures in hairdressing: A scoping review, J Occup Med Toxicol 2019;14(24):1–14. doi:doi: 10.1186/s12995-019-0244-y.

Nambiema

, Bertrais

, Bodin

, Fouquet

, Aublet-Cuvelier

, Evanoff

, Descatha

, Roquelaure

, Proportion of upper extremity musculoskeletal disorders attributable to personal and occupational factors: Results from the French Pays de la Loire study, BMC Public Health 2020;20(456):1–13. doi:doi: 10.1186/s12889-020-08548-1.

Lee

, Kim

, Jung

, Kim

, Lee

, The association between long working hours and work-related musculoskeletal symptoms of Korean wage workers: Data from the fourth Korean working conditions survey (a cross-sectional study), Ann Occup Environ Med 2018;30(67):1–11. doi: 10.1186/s40557-018-0278-0.

Dias

, Tirloni

, Reis

, Moro

ARP

, Risk of slaughterhouse workers developing work-related musculoskeletal disorders in different organizational working conditions, Int J Ind Ergon 2020;76:102929. doi: 10.1016/j.ergon.2020.102929.

Coledam

DHC

, Silva

, Predictors of health-related absenteeism, presenteeism and sick leave among brazilian elementary school teachers: A cross-sectional study, Work 2020;67(3):709–19. doi: 10.3233/WOR-203320.

Hanson

, Cooper

, Tegarden

, Tipton

, Freeman

, Davis

, Gillespie

, Huston

, The impact of emergency responder musculoskeletal injuries in the State of Ohio, Work 2021;68(4):1001–8. 10.3233/WOR-205065.

Brasil. Ministério da Saúde, LER e DORT são as doenças que mais acometem os trabalhadores, aponta estudo [Internet]. [cited Oct 7]. Governo Federal do Brasil. Available from 2020 https://www.gov.br/saude/pt-br/assuntos/noticias/ler-e-dort-sao-as-doencas-que-mais-acometem-os-trabalhadores-aponta-estudo.

Bodin

, Garlantézec

, Costet

, Descatha

, Viel

, Roquelaure

, Shoulder pain among male industrial workers: Validation of a conceptual model in two independent French working populations, Appl Ergon 2020;85:1030–75. doi: 10.1016/j.apergo.2020.103075.

10.

, Voo

, Maakip

, Psychosocial factors, depression, and musculoskeletal disorders among teachers, BMC Public Health.234 2019;19(1):234. doi: 10.1186/s12889-019-6553-3.

11.

Hämmig

, Work- and stress-related musculoskeletal and sleep disorders among health professionals: A cross-sectional study in a hospital setting in Switzerland, BMC Musculoskelet Disord 2020;21. 319. doi: 10.1186/s12891-020-03327-w.

12.

Kaliniene

, Ustinaviciene

, Skemiene

, Vaiciulis

, Vasilavicius

, Associations between musculoskeletal pain and work-related factors among public service sector computer workers in Kaunas County, Lithuania, BMC Musculoskelet Disord 2016;17(1):420doi: 10.1186/s12891-016-1281-7.

13.

Hurtado

ÁMR

, Buelvas

, Cardona

, Martínez

, Health-related quality of life of patients with rotator cuff injuries, Cofee Triangle, Colombia, Rev Bras Orto 2018;53(3):364–72. doi: 10.1016/j.rboe.2018.03.018.

14.

Lundberg

, Granqvist

, Hansson

, Magnusson

, Wallin

, Psychological and physiological stress responses during repetitive work at an assembly line, Work Stress 1989;3(2):143–53. doi: 10.1080/02678378908256940.

15.

Bathman

, Almond

, Hazi

, Wright

, Effort–reward imbalance at work and pre-clinical biological indices of ill-health: The case for salivary immunoglobulin A, Brain Behav Immun 2013;33:74–9. doi: 10.1016/j.bbi.2013.05.010.

16.

Govindu

, Babski-Reeves

, Effects of personal, psychosocial and occupational factors on low back pain severity in workers, Int J Ind Ergon 2014;44(2):335–41. doi: 10.1016/j.ergon.2012.11.007.

17.

Karimi

, Moghimbeigi

, Motamedzade

, Roshanaei

, Evaluation of related risk factors in number of musculoskeletal disorders among carpet weavers in iran, Saf Health Work 2016;7(4):322–25. doi: 10.1016/j.shaw.2016.04.004.

18.

Bezerra

, Arantes

, Shimizu

, Hamann

, Ramalho

, Workers’ health in brazil: Accidents recorded by social security from 2008 to 2014, Rev Bras Enferm 2020;73(6):e20180892. doi: 10.1590/0034-7167-2018-0892.

19.

Ministério da Economiaoletim Estatístico da Previdência Social Secretaria Especial de Previdência e Trabalho [Internet]. [cited Nov 10]. 40 Available from 2020 https://sa.previdencia.gov.br/site/2019/12/Beps1112019_trab_Final1_portal.pdf.

20.

Ministério da SaúdeMeta da Saúde do Trabalhador no Plano Nacional de Saúde /Rede Nacional de Atenção Integral à Saúde do Trabalhador [Internet], [cited Nov 10] 20 Available from 2020 https://renastonline.ensfiocruz.br/sites/default/files/arquivos/recursos/resultados_1_quadrimestre_regiao_nordeste.pdf.

21.

Oliveira

, Trabalho no Nordeste em perspectiva histórica, Estud Av 2016;30(87):49–73. doi: 10.1590/S0103-40142016.30870004.

22.

Pinto

, Tendência na incidência de acidentes e doenças de trabalho no Brasil: Aplicação do filtro Hodrick-Prescott, Rev Bras Saúde Ocue10 2017;42: doi: 10.1590/2317-6369000003016.

23.

Pesquisa Nacional por Amostra de Domicílios Contínua - PNAD Mercado de Trabalho Brasileiro, Instituto Brasileiro de Geografia e Estatística [Internet]. [cited Set 5]. 55 Available from2020 https://agenciadenoticias.ibge.gov.br/media/com_mediaibge/arquivos/07182068b89dcffa9ffde7c6aa5c18ff.pdf.

24.

Santos

, Correia

, Almeida

, Canal do Sertão de Alagoas: Território destinado para o agronegócio, Diversitas J 2020;5(1):153–61. doi: 10.17648/diversitas-journal-v5i1-1067.

25.

Lima

SLS

, Organização socioeconômica e o papel do estado na configuração territorial do sertão nordestino, Campo-território [Internet]. [cited Set 6]140-66. Available from 2020;4(7):140–66. https://www.seer.ufu.br/index.php/campoterritorio/article/view/11891.

26.

Sousa Filho

, Silva

KCM

, Fonseca

, Caires

FOC

, Análise setorial da produtividade do fator trabalho na Bahia a partir dos anos 2000. Rev Polít e Planej Reg [Internet], [cited Set 10]2020;6(3):324–42. Available from: https://www.revistappr.com.br/conteudo.php?m=MzY3&l=tx.

27.

Kuorinka

, Jonsson

, Kilbom

, Vinterberg

, Biering-Sørensen

, Andersson

, Jørgensen

, Standardized Nordic questionnaire for the analysis of musculoskeletal symptoms, Appl Ergon.233-7 1987;18(3):233–7. doi: 10.1016/0003-6870(87)90010-x.

28.

Pinheiro

, Tróccoli

, Carvalho

, Validação do questionário nórdico de sintomas osteomusculares como medida de morbidade, Rev Saúde Pública.307-12 2002;36(3):307–12. doi: 10.1590/S0034-89102002000300008.

29.

World Health Organization (WHO) Expert Committee. Physical Status: The use and interpretation of anthropometry. WHO Technical Report Series no. 854 [Internet]. 1995 [cited 2020 Set 22]. Available from: https://apps.who.int/iris/bitstream/handle/10665/37003/WHOTRS854.pdf?sequence=1&isAllowed=y.

30.

World Health Organization (WHO). Obesity: Preventing and managing the global epidemic. 2000 Report of aWHO consultation. World Health Organization Technical Report Series 2000 [Internet]; 2000 [cited 2020 Set 22];894:i–xii,1-268Available from: https://apps.who.int/iris/handle/10665/42330.

31.

Djade

, Porgo

, Zomahoun

, Perrault-Sullivan

, Dionne

, Incidence of shoulder pain in 40 years old and over and associated factors: A systematic review, Eur J Pain.39-50 2020;24(1):39–50. doi: 10.1002/ej1482.

32.

Widanarko

, Legg

, Devereux

, Stevenson

, The combined effect of physical, psychosocial/organisational and/or environmental risk factors on the presence of work-related musculoskeletal symptoms and its consequences, Appl Ergon 2014;45(6):1610–21doi: 10.1016/j.apergo.2014.05.018.

33.

Karasek

, Brisson

, Kawakami

, Houtman

, Bongers

, Amick

, The job content questionnaire (JCQ): An instrument for internationally comparative assessments of psychosocial job characteristics, J Occup Health Psychol 1998;3(4):322–55. doi: 10.1037/1076-8998.3.4.322.

34.

Siegrist

, Adverse health effects of high-effort/low-reward conditions, J Occup Health Psychol 1996;1(1):27–41.doi: 10.1037/1076-8998.1.1.27.

35.

Araújo

, Karasek

, Validity and reliability of the job content questionnaire in formal and informal jobs in Brazil, Scand J Work Environ Health.52-9. Available from 2008;34(6):52–9.https://www.sjweh.fi/show_abstract.php?abstract_id=1251.

36.

Chor

, Werneck

, Faerstein

, Alves

MGM

, Rotenberg

, The Brazilian version of the effort-reward imbalance questionnaire to assess job stress, Cad Saúde Pública 2008;24(1):219–24.doi: 10.1590/S0102-311X2008000100022.

37.

Pejtersen

, Kristensen

, Borg

, Bjorner

, The second version of the copenhagen psychosocial questionnaire, Scand J Public Health 2010;38(3 Suppl):8–24. doi: 10.1177/1403494809349858.

38.

Luna

ADF

, Gondim

SMG

, Fatores de risco psicossocial no trabalho: Adaptação e evidências de validade do copsoq II para o contexto brasileiro, Rev Laborativa [Internet]. [cited Mar 29]5-25. 2021;8(1):5–25. Available from https://ojs.unesbr/index.php/rlaborativa/article/view/2756.

39.

Souza

, Carvalho

, Araújo

, Koifman

, Porto

, Depressão em trabalhadores de linhas elétricas de alta tensão, Rev Bras Epidemiol. [Internet] [cited Mar 29]235-45. 2021;15(2):235–45. Available from https://www.scielosorg/article/rbepid/2012.v15n2/235-245/pt/#ModalArticles.

40.

Cordeiro

, Demétrio

CGB

, Modelos lineares generalizados e extensões [Internet], Departamento de Ciências Exatas, ESALQ, USP: São Paulo, Brasil, Available from 2008, https://docs.ufpr.br/ taconeli/CE22517/LivClarice.pdf.

41.

Silva

JMN

, Silva

, Gontijo

, Relationship between psychosocial factors and musculoskeletal disorders in footwear industry workers, Production. 2016;27:e20162315. doi: 10.1590/0103-6513.231516.

42.

R Core Team. R: A language and environment 892 for statistical computing. Vienna: R Foundation for Statistical Computing. Version 3.6.3 [software]. 2016 [cited 2020 Set 26]. Available form: https://www.R-project.org/.

43.

Zinbarg

, Revelle

, Yovel

, Li

, Cronbach’s α, Revelle’s β, and McDonald’s ωH): Their relations with each other and two alternative conceptualizations of reliability, Psychometrika 2005;70(1):123–33. doi: 10.1007/s11336-003-0974-7.

44.

Hair

Jr , William

, Babin

, Anderson

Análise multivariada de dados. 6th ed. Porto Alegre: Bookman; 2009.

45.

Shafiei

, Ghasemian

, Eslami

, Najoomi

, Rajabi-Vardanjani

, Risk factors and control strategies for silicotuberculosis as an occupational disease, New Microbes New Infect 2019;27:75–7. doi: 10.1016/j.nmni.2018.11.002.

46.

Bovenzi

, Prodi

, Mauro

, A longitudinal study of neck and upper limb musculoskeletal disorders and alternative measures of vibration exposure, Int Arch Occup Environ Health 2016;89(6):923–33. doi: 10.1007/s00420-016-1131-9.

47.

Leite

WKS

, Silva

, Souza

, Fernandes

, Colaço

, Risk of WMSDs in monofunctional and multifunctional workers in a Brazilian footwear company, Production 2017;27:e20172318.. doi: 10.1590/0103-6513.231817.

48.

Roman-Liu

External load and the reaction of the musculoskeletal system –A conceptual model of the interaction, Int J Ind Ergon 2013;43(4):356–62. doi: 10.1016/j.ergon.2013.04.002.

49.

de Almeida

, Vieira

, Zaia

, Santos

BMO

, Lourenço

ARV

, Quemelo

PRV

, Musculoskeletal disorders and stress among footwear industry workers, Work 2017;56(1):67–73. doi: 10.3233/WOR-162463.

50.

Almhdawi

, Alrabbaie

, Kanaan

, Alahmar

, Oteir

, Mansour

, Obeidat

, The prevalence of upper quadrants work-related musculoskeletal disorders and their predictors among registered nurses, Work 2021;68(4):1035–47. doi: 10.3233/WOR-213434.

51.

Cheng

, Wong

, Yu

, Ju

Work-related musculoskeletal disorders and ergonomic risk factors in special education teachers and teacher’s aides, BMC Public Health 2016;16:137. doi: 10.1186/s12889-016-2777-7.

52.

Kayabınar

, Kayabınar

, Önal

, Zengin

, Köse

, The musculoskeletal problems and psychosocial status of teachers giving online education during the COVID-19 pandemic and preventive telerehabilitation for musculoskeletal problems, Work 2021;68(1):33–43. doi: 10.3233/WOR-203357.

53.

Herin

, Vézina

, Thaon

, Soulat

, Paris

, Predictive risk factors for chronic regional and multisite musculoskeletal pain: A 5-year prospective study in a working population. Pain. 2014;155(5):937–43. doi: 10.1016/j.pain.2014.01.033

54.

Savetsky

, Cammarata

, Kantar

, Diaz-Siso

, Avashia

, Rohrich

, Saadeh

, The left-handed plastic surgery trainee: Perspectives and recommendations, Plast Reconstr Surg Glob Open 2022;8(5):e2686. doi: 10.1097/GOX.0000000000002686.

55.

Burdett

, Theakston

, Dunning

, Goodwin

, Kendall

, Left-handed surgical instruments –a guide for cardiac surgeons, J Cardiothorac Surg 2016;11(1):135doi: 10.1186/s13019-016-0497-9.

56.

Flatt

, Is being left-handed a handicap? the short and useless answer is “Yes and No”, Proc (Bayl Univ Med Cent) 2008;21(3):304–7. doi: 10.1080/08998280.2008.11928414.

57.

Leong

, Fu

, He

, Oh

, Yamamoto

, Hang

, Risk factors for rotator cuff tendinopathy: A systematic review and meta-analysis, J Rehabil Med 2019;51(9):627–37.doi: 10.2340/16501977-2598.

58.

Baek

, Yang

, Lee

, Chung

, The association of workplace psychosocial factors and musculoskeletal pain among korean emotional laborers, Saf Health Work 2018;9(2):216–23.doi: 10.1016/j.shaw.2017.09.004.

59.

Bulduk

, Bulduk

EÖ

, Güler

, Job satisfaction among aircraft baggage handlers and their exposure to risk factors for work-related musculoskeletal disorders: A case study, Work 2017;56(2):301–8.doi: 10.3233/WOR-172494.

60.

Mehralizadeh

, Dehdashti

, Kashani

, Structural equation model of interactions between risk factors and work-related musculoskeletal complaints among Iranian hospital nurses, Work 2017;57(1):137–46.doi: 10.3233/WOR-172534.

61.

Eatough

, Way

, Chang

C-H

, Understanding the link between psychosocial work stressors and work-related musculoskeletal complaints, Appl Ergon 2012;43(3):554–63. doi: 10.1016/j.apergo.2011.08.009.

62.

Veisi

, Choobineh

, Ghaem

, Kujerdi

, Barazandeh

, Upper extremity musculoskeletal symptoms among Iranian hand-woven shoe workers, Work 2020;67(1):129–39.doi: 10.3233/WOR-203258.

63.

Soltaninejad

, Khammar

, Aminizadeh

, NabiAmjad

, Raei

, Hami

, Poursadeqiyan

, Shift working disorders among nurses of Tehran hospital and its related factors in 2016. Work 2020;66(1):213–9.doi: 10.3233/WOR-203165.