Understanding musculoskeletal disorders among

International Journal of Industrial Ergonomics 67 (2018) 32–40

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International Journal of Industrial Ergonomics journal homepage: www.elsevier.com/locate/ergon

Understanding musculoskeletal disorders among Iranian apple harvesting laborers: Ergonomic and stop watch time studies

T

Ehsan Houshyara,∗, In-Ju Kimb a b

Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Jahrom University, PO BOX 74135-111, Jahrom, Iran Department of Industrial Engineering and Engineering Management, College of Engineering, University of Sharjah, PO Box 27272, Sharjah, United Arab Emirates

A R T I C LE I N FO

A B S T R A C T

Keywords: Apple harvesting laborers Ergonomic interventions Occupational safety and health Time study Work measurement

Background: Musculoskeletal disorders (MSDs) are usually caused by bad working postures and habits. There is a great demand to correctly estimate laborers' work-related musculoskeletal disorders (WMSDs) during physically intensive works. This study aimed to suggest a practical solution with ergonomic principles and time studies on the reduction of WMSDs for the apple harvesting laborers in Sepidan gardens at the Fars Province, Islamic Republic of Iran (Iran). Methods: Prevalence of MSDs was evaluated by the Nordic Standard Questionnaire surveys and the laborers’ workload was assessed by the rapid entire body assessment postural analysis tool. A stop watch time study method was employed to estimate rest time allowances. Data from the time study was also recorded by a questionnaire validated from a panel of experts. Thirty laborers from two age groups: a young (20–35 years of old) and old (36–55 years old) were sampled from 10 gardens. Results: The stop watch time study revealed that the frequencies of apple harvesting works were 95 vs. 119 times for the old group and young one, respectively. The prevalent disorders were related to specific body regions such as the lumber, knee, neck and shoulder areas. The percentages of disorders were significantly reduced when ergonomically corrected postures were applied with suitable rest time allowances. Conclusion: Fruit harvesting works may need to improve their work-rest time intervals to prevent WMSD developments and productivities with time managements. With a correct estimation of the desired number of laborers, apple harvesting jobs can be performed on time, and by implementing appropriate ergonomic postures, occupational health and safety problems can be lessened in the apple harvesting workers.

1. Introduction Agriculture is regarded as a job containing high risks in many countries (Fathallah et al., 2010). Human resources are one of the most crucial capitals and the main production factors in the agriculture industry. Its management is a major determinant for higher productivities. However, farmers and agricultural laborers are exposed to various diseases and hurt such as musculoskeletal disorders (MSDs) (Amitabha and Robendranaz, 1992). Work-related musculoskeletal disorders (WMSDs) are one of the main causes of working time losses and employee injuries around the world (Bon and Daim, 2010). Thus, control and reduction of WMSDs amongst laborers represent a major ergonomic problem. This issue is so critical that the prevention of WMSDs is considered as a national priority in many countries (Hosseini et al., 2010). Poor awareness on WMSDs is a major problem amongst the farm workers who use inappropriate postures during their agricultural works

∗

including harvesting. It is well documented that WMSDs can be avoided by providing continuous educations (intervention) and utilizing proper ergonomic methods (Krystosik-Gromadzińska, 2017; Subramanian, 2017). Effective applications of ergonomic principles and tools in working environments can result in the balance between worker protections and task demands (Petit et al., 2014). To increase productivity and prevent work-related injury problems, the workers need to complete their work in a reasonable time. WMSD's can be lowered by the perception of work types and employment of an appropriate number of workers depending on the workload. The workload is usually measured by three methods: “activity sampling”, “work sampling”, and “time study” (Zahedi and Najjari, 2008). Amongst the three methods, it is suggested that the time study method is most suited to those jobs which are repetitive in nature and required to be performed according to a clearly definable method (Nair, 2004). Time study is the development of a standard time by observing a task and analyzing it with the use of a stop watch (Russell and Taylor,

Corresponding author. E-mail addresses: [email protected], [email protected] (E. Houshyar), [email protected] (I.-J. Kim).

https://doi.org/10.1016/j.ergon.2018.04.007 Received 31 October 2017; Received in revised form 5 April 2018; Accepted 16 April 2018 0169-8141/ © 2018 Published by Elsevier B.V.


E. Houshyar, I.-J. Kim

2005). A stop watch time study measures how long an average worker takes to complete a task at a normal pace (Yusoff et al., 2012). A “normal” operator is defined as a qualified and thoroughly experienced operator who is working under conditions as they customarily perform the workstation at a pace that is neither fast nor slow, but representative of an average (Nakayama, 2002). Although time study and work measurement are useful tools for the enhancement of work efficiency, they are not widely used in the agriculture study and even within the industry (Zain and Rajamony, 2014). Amitabha and Robendranaz (1992) studied a relationship between a work speed and a heart pulse in rice production farms. They reported that the average speed of workers was 1.39 m/sec., which was higher than the expected value. Many other studies have focused on ergonomics interventions to reduce WMSDs (Krystosik-Gromadzińska, 2017; Petit et al., 2014; Guan et al., 2013), but no study has been found on estimating the best working time durations to reduce WMSDs. In this sense, the present study seems to be the first trial to measure the best working times with the consideration of environmental and caring conditions under a given time interval whilst WMSDs are mitigated. Fruit harvesting in Iran is highly dependent on human resources due to lack of mechanized fruit harvesting machines. Fig. 1 shows an example of apple harvesting works from a laborer using a traditional apple bucket (or bag) and container. As shown in Fig. 1, typical harvesting jobs include picking apples, carrying a full apple bucket, transferring an apple container, and unloading an apple container for sorting. These activities require the laborer to perform a number of awkward postures, ranging from leaning far to one side whilst standing to hold both hands over the head for prolonged periods, which increase the likelihood of muscle and joint strain injuries. The demand for laborers to reap fruits during the harvesting period forces the gardeners to pay higher salaries, especially when enough laborers are not available. On the other hand, in some cases, the lack of enough laborers imposes extra costs such as fruit damages and wastes due to the difficulty of rapid harvesting. Moreover, laborers are sometimes compelled (or even pressured) to work harder and faster to meet tight time schedules and/or insufficient workforces. Such working conditions may cause to further developments of MSDs and other injury symptoms such as mental stress and fatigue. Therefore, this study had three main goals to investigate laborers’ MSD prevalence: 1) The first goal was to estimate required working times for traditional apple harvesting practices and find an optimal number of laborers for on-time apple harvesting. A stop watch time study technique was employed to estimate correct working times for each task. 2) The second goal was to assess the current working poses for apple harvesting works from an ergonomic point of view and identify corrective worker postures. The Nordic Standard Questionnaire (NSQ) assessment tool was used to evaluate the prevalence of MSDs in various body areas of laborers. The laborers' workload was also assessed by the rapid entire body assessment (REBA) postural analysis tool. Based on the assessment results, an ergonomically principled postural method was suggested to training the harvesting laborers. 3) The third goal was to validate the effect of combining ergonomic principles and time study on the reduction of WMSDs.

Fig. 1. Photographic images for three main apple harvesting works from various angles and steps: (a) picking apples from an apple tree and put them in a carrying basket, (b) carrying a filled apple box to a garden, and (c) placing the box down and unloading apples for sorting, respectively (Waseem Andrabi/HT Photo, 2017).

in Sepidan town at Fars Province in Iran. The Sepidan town is the main city for apple growing in the province and within the country. The highest record of apple yield per hectare is around 152 tons/ha (Ministry of Jihad-e-Agriculture, 2016). Many laborers work to harvest apples in the gardens without any machinery aid. Thus, correct working posture is one of the essential concerns which may, in turn, reduce MSDs. To collect data for the study, ten out of 43 apple gardens were randomly selected using random sampling method without replacement. Three laborers from each garden were invited for the study. Totally 30 laborers were tested in the time study and ergonomic experiments. To consider age effects, the laborers were divided into two groups: a

By the accurate estimation of the needed number of laborers, apple harvesting works will be performed on time, and by implementing appropriate ergonomic postures, occupational health and safety problems will be reduced. Findings from this study may provide a conceivable solution to prevent MSD developments for the labor-intensive industries such as fruit harvesting and farming business. 2. Materials and methods This study was carried out during 45 days of apple harvesting times 33



young group (20–35 years of old) and an old group (36–55 years of old), respectively. Each group included 15 laborers and all the participants were offered face-to-face interviews. Based on results of the time study and interviews, a questionnaire was developed. For the apple harvesting jobs, only men laborers were employed in this study. A panel of 24 experts from the agriculture industry participated in this study to check the validity of the questionnaire. The experts were asked to score each question within a range of 1–4 (1: poor, 2: fair, 3: good, and 4: excellent, respectively). A question was considered as valid when more than 80% of the experts had scored it as good or excellent. The invalid questions were removed or revised to obtain the necessary score. Similarly, the overall validity of the entire questionnaire was examined. Reliability of the questionnaires was assessed using Cronbach's alpha coefficient which reached 0.81 after several revisions of the questionnaires, showing that the developed questionnaire was highly reliable. 2.1. Stop watch time study Stop watch time study method was used in this study. The basic procedures for stop watch time study are as follows: 1) Firstly, apple harvesting works were broken into small task elements. Overall harvesting work process was formulated by a flowchart model with task elements as drawn in Fig. 2. The model shows the detailed workflows for apple harvesting tasks with the time study technique. The entire sequence of apple harvesting tasks are W1: Bringing an empty box to the farm; W2: Putting the box near an apple tree; W3: Primary inspecting of the tree; W4: Putting the box in a suitable place near the tree in order to harvesting apples; W5: Picking apples; W6: Inspecting apples; W7: Putting apples in the box; and W8: Putting the filled box out of the farm, respectively. 2) Secondly, the time required for all the task components from each worker during 1 h was calculated with three repetitions by a digital chronometer. Since apple harvesting involved some repetitive and labor intensive tasks, the workers were easily tired. Thus, 1 h time interval was chosen to ensure that time recording of each task was correctly viewed. An average value was used as "observed time" (Sable, 2013). 3) Thirdly, three types of times: optimum time (OT), most likely time (MLT), and pessimistic time (PT) were measured to estimate expected time (ET) of each task. OT is the working time estimated when all the required factors and resources are available. This time was recorded by a timer under the best working conditions. PT refers to the time of work completed under unfavorable conditions such as dusty weather. Most likely time was recorded under common working conditions. After recording these three times, the ET was estimated using the following formula (Irannejad and Sassangohar, 2005):

ET =

OP +PT + 4MLT 6

Fig. 2. The operation flowchart model of harvesting apple including W1: Bringing empty box to farm; W2: Putting box near tree; W3: Primary inspecting of tree; W4: Putting box in a suitable place near tree in order to harvesting apple; W5: Harvesting the fruit; W6: Fruit inspecting; W7: Putting fruit in the box; and W8: Putting filled box out of farm, respectively. Circle stands for working and square denotes inspection and control.

(1)

Although the ET included awkward working conditions; the PT was not a useful guide since the work could not be done at a constant speed without any long period of rest times. Accordingly, some allowances should be considered as discussed in the next section.

light intensity, dust, sound, etc. The percentage of allowance is then determined by the estimated scores. For instance, for an average physical attempt (e.g. carrying a 12 kg box), fatigue score is 17 and respective percentage of allowance is 11% (Aliahmadi, 2014). For the task of “picking apples”, which includes kneeling and standing, focus on the task and picking, in addition to working area conditions, a score of 49 was obtained. Carrying filled boxes scored 54. With these scores, allowances were extracted from the standard tables in the next step. For example, when a score is 49 and a respective percentage of allowance is 24%, then, the “final coefficient”, “modified time”, and “work completion time” regarding the required allowance were calculated by the following equation (Yousefi et al., 2014):

2.2. Determination of allowances Extra allowance times need to include for the laborer's fatigue recovery and personal needs such as toilet break, equipment breakdowns, and information delays (Aliahmadi, 2014). To determine a number of necessary rest allowances for each task, delay or fatigue scores were extracted from work-study standard tables (Aliahmadi, 2014). There are several tables assigning scores for work conditions based on physical pressures, required mental focuses on a given task, temperature,

Final coefficient = 1 + allowance 34

(2)



Modified time = expected time × final coefficient

The allowed rest time (allowances) differed amongst the laborers. In other words, there were no equal rest times for all the laborers. After implementing such an integrative approach for 12 days, the NSQ was completed once more to assess the combined effects of ergonomic corrections and rest times MSDs reduction.

(3)

Working time per hour = modified time × repetition of the work per hour (4) Under certain conditions, however, a laborer may need a higher level of allowance. For example, a laborer with a disability or wearing glasses may need extra allowances.

2.5. Statistical analysis This study included four continuous variables: age, weight, height, and BMI, as well as nine ordinal variables from the NSQ: feet and ankle, knee, thigh, lumbar, upper back, wrist, elbow, shoulder and neck disorders, respectively. In order to compare the two groups of laborers, independent sample T-test (parametric) and Mann-Whitney U tests were employed to find the statistical differences between the continuous and ordinal data. Correlations were also examined to find any possible relationships amongst the variables. Pearson and Spearman correlation coefficients were reported for the continuous and ordinal variables, respectively. Finally, linear regression for the continuous data and logistic regression for the ordinal data were performed using IBM SPSS Statistics 22. The logistic regression was employed since it is the most appropriate model to estimate a dichotomous dependent variable (Greene, 1997). In the current study, the dichotomous codes (“1” and “0”) correspond to “yes” and “no”, respectively in the NSQ. In the logistic regression model, a special disorder was considered as the “dependent variable” while the others were defined as the “predictor variables”.

2.3. Ergonomic analysis of apple harvesting works Prevalence of MSDs in the various parts of workers’ bodies was evaluated by the Nordic Standard Questionnaire (NSQ) survey. At this survey, the general and specific information was collected by the following two steps: 1) Before the period of apple harvesting: to clarify the prevalence of disorders during last 12 months. 2) After 12 days of apple harvesting works: to identify how the harvesting works change the prevalence of WMSD's. After the primary evaluations from the NSQ surveys, the worker's workload was further assessed by the REBA postural analysis tool (Hignett and McAtamney, 2000). The REBA analysis was repeated three times during two days of apple harvesting. In this method, different parts of the body were divided into two groups: A and B. The limbs in group A include body, neck, and feet whilst the limbs in group B include arms, wrists, and forearms which create 31 different combinations of physical modes (Nakayama, 2002). Based on the scores A and B, score C was estimated from the REBA standard table, and a final score was obtained by summing it up with activity score. For calculations of the final score, the level of risk and priority of posture correction were determined for the tasks. Score 1 shows the weak necessity for any postural corrections; whereas scores 2 and 3 denote low risk and probability of posture change. Scores 4 to 7 show average risk (and the need for change of posture in the near future); scores 8 to 10 displays high risk (and the need for immediate change of posture), and scores higher than 11 indicate very high risk and necessity of immediate change in the body position (Arvidsson and Arvidsson, 2006). The weight and height of laborers were gathered to calculate body mass index (BMI) to understand probable relationships between these factors and WMSDs (Sarkar et al., 2012). Laborers were photographed during the apple harvesting works from various angles and steps such as bringing the box into the garden, placing the box down, and picking the apples up.

3. Results 3.1. Time study of harvesting apple The process of apple harvesting included eight steps (w1 to w8) as shown in Fig. 2 with time study symbols. The laborers started their works with bringing empty boxes to the garden area and ended with them carrying the filled boxes out of the garden area. The flowchart model was drawn in detail by the time study technique. The entire sequence of apple harvesting tasks were W1: Bringing an empty box to the farm; W2: Putting the box near an apple tree; W3: Primary inspecting of the tree; W4: Putting the box in a suitable place near the tree in order to harvesting apples; W5: Harvesting apples; W6: Inspecting apples; W7: Putting apples in the box; and W8: Putting the filled box out of the farm. Each element of the harvesting works was measured to determine how much time was needed. Table 1 shows that the frequencies of all apple harvesting tasks were almost similar for the two groups of laborers. However, the task Nos. five to seven were different between the two groups. These tasks were the main and repetitive tasks which had significant effects on the speed of apple harvesting. In other words, the frequency of task W5 was 119.2 for the young group and was 95.3 for the old group. The variation of all the data given in Table 1 was below 0.06%. Accordingly, the ranges are not shown in Table 1. The pessimistic time (PT) revealed that some tasks might be delayed to finish as compared with the optimistic time (OT) under the hostile conditions such as hot weather. The time for completing task W3 (primary tree inspecting) was increased by around 100–120% for the two groups where the work conditions were not suitable (OT vs. PT). Some tasks demanded higher allowances than others; such as W8 (taking filled boxes out of the garden) versus W3 (primary inspection of trees). The former required 54% of time allowances but the latter only 12%. Totally, around 53 min per hour was required to performing the apple harvesting tasks whilst all the necessary allowances were considered. Nevertheless, the speed of performing some tasks was clearly different between the two groups. For example, the tasks W5 to W7 were repeated for 119 times for the young group whilst 95 times for the old one. Accordingly, the speed of work and amount of apples harvested were dissimilar.

2.4. Combined effects of ergonomic interventions and time study Using illustrative scientific training pamphlets, a proper harvesting method based on ergonomics principles was used to train the harvesting workers for two days. After a first day of the training, almost all laborers harvested apples correctly. However, trainings and inspections were continued for two days to ensure that all the apple harvesting jobs were conducted correct manners. In order to investigate the combined effects of suitable postures and allowed rests, the laborers’ jobs were examined twice by the following steps: 1) After 12 days of apple harvesting works, the laborers were asked to complete the NSQ in order to measure whether the training education was effective enough to reduce the risk of MSDs during the harvesting practices. 2) After 12 days of the ergonomic training session, the laborers were granted to use allowances (or allowable rest times) which were determined by the stop watch time study method.

35



Table 1 Results of the stop watch time study for the apple harvesting works. Age (Years)

20-35

Harvesting task elementsa

W1 W2 W3 W4 W5 W6 W7 W8

Frequency (No. hr−1)

1 3.4 4.2 3.5 119.2 119.2 116.4 1

Times to do the task (min hr−1) OT

PT

MLT

ET

3.72 0.16 0.08 0.08 0.09 0.03 0.03 8.55

4.60 0.25 0.16 0.17 0.17 0.09 0.08 10.57

4.35 0.23 0.08 0.15 0.12 0.05 0.05 9.65

4.29 0.22 0.09 0.14 0.12 0.05 0.05 9.62

Sum 36-55

W1 W2 W3 W4 W5 W6 W7 W8

1 3.7 4.1 3.5 95.3 95.3 91.5 1

3.95 0.15 0.08 0.11 0.09 0.03 0.03 9.35

4.95 0.29 0.19 0.19 0.26 0.08 0.06 11.30

4.82 0.27 0.11 0.16 0.14 0.06 0.05 10.25

4.70 0.25 0.12 0.16 0.15 0.06 0.05 10.28

Sum

ET × Frequency

Allowances-%

Time to doing task considering allowances- (min hr−1)

4.29 0.75 0.39 0.50 14.70 6.36 6.01 9.62

15.41 12.43 12.50 16.75 24.10 14.90 16.80 50.10

4.95 0.85 0.44 0.58 18.24 7.30 7.02 14.44

42.62

–

53.83

4.70 0.94 0.49 0.55 14.45 5.56 4.42 10.28

15.99 12.50 12.60 17.10 25.27 15.23 18.40 52.10

5.45 1.05 0.55 0.64 18.11 6.41 5.24 15.63

41.38

–

53.07

a W1: Bringing empty box to farm; W2: Putting box near tree; W3: Primary inspecting of tree; W4: Putting box in a suitable place near the tree in order to harvesting apple; W5: Harvesting the apple; W6: Inspecting the apple; W7: Putting apple in the box; W8: Putting filled box out of farm.

results revealed that allowances had positive effects on the reduction of MSDs by around 5–21%. Fig. 3 shows that allowances on the old laborers were more effective than the younger ones in almost all body regions. It was also important to note that postural corrections without adequate rest times had few effects on the prevalence reduction of MSDs whilst the effects were significantly higher when suitable postures were combined with suitable allowances, particularly on the neck, shoulder, and lumbar areas. These findings highlight that allocation of rest times during agricultural tasks is a necessary issue, but it is currently ignored.

3.2. Prevalence of MSDs The key findings from the Nordic Standard Questionnaire (NSQ) surveys are summarized in Fig. 3. It clearly shows that 10–55% of the laborers had MSDs at least in one body region during the last 12 months before starting apple harvesting works. Lumbar, knee(s), and neck symptoms were found to be the most frequently suffering body areas amongst the laborers studied. The symptom of MSDs amongst the old laborers was higher than that of younger ones by around 10–20%. The NSQ survey was completed for the second time after commencing the apple harvesting works. The results showed that the sign of MSDs was increased after the 12 days of apple harvesting works in both groups of laborers. Disorders in the feet and ankles, elbows, and thighs were noticed with no prevalent pains. However, the lumber, knee, neck, and shoulder areas showed the highest number of complaints. Although the disorders were existed in all body regions before starting apple harvesting, some disorders increased substantially after the 12 days of apple harvesting works, especially in the old group of laborers. For instance, neck and lumbar disorders increased around 30% and knee disorders increased around 19%, respectively.

3.5. Statistical analysis The result of one-way ANOVA showed that the two groups of laborers had similar heights and BMIs, whilst the weights and ages were significantly different between the groups. The average weight of old laborers was around four kgs. heavier than the younger ones: 64.37 kg for the young group and 68.55 kg for the old one, respectively. The average BMI was not much difference between the two groups: 21.52 kg/m2 for the young group and 22.89 kg/m2 for the old one. The Mann-Witney U test displayed significant differences in body disorders before and after apple harvesting works between the two groups of laborers (Table 3). Lumbar and neck disorders were not significantly different after 12 days of postural corrections. Nonetheless, the lumbar, neck and shoulder disorders were significantly different between the two groups of laborers after 12 days of the postural corrections and the time study method. This difference demonstrated that performing ergonomic interventions and appropriate rest times had positive effects on all the laborers with different ages especially when the effects were much higher on the aged laborers. The result of correlation analyses is summarized in Table 4. There are high correlations found after postural corrections through a combination of the postural corrections and the time study. Although the reductions in disorders of certain body areas were higher for the aged laborers, correlations between the disorder reductions and personal variables (age, weight, height and BMI) were not significant for this group in comparison with the younger ones. For instance, reduction in the lumbar disorder was as high as 25% after postural corrections with

3.3. Analysis of suitable working postures The result of postural analyses by the REBA assessment tool showed different levels of risks for each element of apple harvesting works (Table 2). The variation of data in Table 2 was below 2% for the groups. The final REBA scores were not different between the two groups of laborers since both groups had similar postures during the apple harvesting works. The REBA score for tasks W5 and W8 reached to 11, displaying a “very high risk”. This finding indicated that immediate changes in the work postures were necessary. However, other tasks from the apple harvesting works showed average or low risks. 3.4. Combined effect of suitable postures and allowances According to the result of stop watch time study to clarify the combined effects of corrected postures and allowances, suitable postures were applied to the laborers with some rest times (allowance). The rest times were different between the two groups of laborers. The 36



Fig. 3. Results of prevalence of musculoskeletal disorders from the Nordik Standard Questionnaire surveys amongst the apple harvesting laborers.

Table 2 Summary of the REBA scores for apple harvesting tasks. Harvesting apple task elementsa

Score A

Score B

REBA Score

Risk Level

W1 W2 W3 W4 W5 W6 W7 W8

5 5 3 4 10 3 5 11

5 4 3 5 6 3 6 7

6 5 3 5 11 3 7 11

Average Average Low Average Very high Low Average Very high

Table 3 Summary of two statistical comparisons for the body disorders before and after the apple harvesting works between the two groups of laborers. Knee

Lumbar

Shoulder

Neck

330.000 340.000 360.000 405.000 0.045 0.042 0.069 0.430

345.000 330.000 415.000 315.000 0.047 0.045 0.651 0.037

360.000 350.000 435.000 450.000 0.069 0.049 0.767 1.000

320.000 325.000 310.000 315.000 0.040 0.041 0.035 0.037

C1 C2 C3 C4 C1 C2 C3 C4

a

Mann-Whitney U test

Asymp. Sig. (P values)

a W1: Bringing empty box to farm; W2: Putting box near tree; W3: Primary inspecting of tree; W4: Putting box in a suitable place near the tree in order to harvesting apple; W5: Harvesting the apple; W6: Inspecting the apple; W7: Putting apple in the box; W8: Putting filled box out of farm.

C1: 12 last months before harvesting apple; C2: 12 days after harvesting apple; C3: 12 days after postural corrections; C4: 12 days after combination of postural corrections and time study method.

the time study. Higher correlations were found amongst the lumbar, knee and shoulder disorders, and personal variables for the younger laborers (Table 4). The higher correlations indicated that the younger laborers were inclined to adopt suitable rest times and ergonomic postures. Since the highest correlations were found after the postural corrections through the combination of postural corrections and time

studies, the linear regression analysis was performed for the further analysis. From the linear regression analysis, the age factor can be predicted by the elements of knee disorder and weight for the aged laborers (Table 5). The result of logistic regression revealed that the lumbar disorder can be predicted by knee and shoulder disorders for the young group of laborers as shown in Table 6. The coefficient 2.883 shows that the

a

37



Table 4 Summary of the Spearman correlation amongst the weight, height, BMI and disorder variables. Shoulder disorder

Neck disorder

Knee disorder

Lumbar disorder

BMI

0.51, 0.54, 0.66, 0.74, 0.42, 0.31, 0.36, 0.28, 0.46, 0.42, 0.40, 0.37, 0.43, 0.38, 0.38, 0.45, 0.79, 0.77, 0.83, 0.91, 0.53, 0.57, 0.46, 0.59, 0.61, 0.65, 0.65, 0.63,

0.71, 0.57, 0.68, 0.69, 0.24, 0.23, 0.36, 0.41, 0.31, 0.38, 0.47, 0.52, 0.27, 0.31, 0.41, 0.46, 0.51, 0.42, 0.53, 0.51, 0.52, 0.53, 0.53, 0.61, 1 – – –

0.65, 0.61, 0.65, 0.73, 0.36, 0.34, 0.61, 0.52, 0.38, 0.41, 0.40, 0.47, 0.41, 0.38, 0.52, 0.49, 0.81, 0.78, 0.80, 0.89, 1 – – – – – – –

0.51, 0.53, 0.72, 0.78, 0.28, 0.31, 0.63, 0.72, 0.23, 0.21, 0.24, 0.29, 0.34, 0.36, 0.32, 0.41, 1 – – – – – – – – – – –

0.24, 0.24, 0.24, 0.24, 0.81, 0.81, 0.81, 0.81, 0.78, 0.78, 0.78, 0.78, 1 – – – – – – – – – – – – – – –

0.49 0.57 0.41 0.54 0.37 0.41 0.39 0.46 0.51 0.49 0.34 0.41 0.43 0.45 0.36 0.44 0.36 0.25 0.31 0.38 0.61 0.60 0.39 0.50 0.60 0.58 0.59 0.61

0.67 0.48 0.53 0.58 0.21 0.21 0.35 0.38 0.37 0.42 0.40 0.49 0.29 0.35 0.37 0.44 0.45 0.34 0.38 0.56 0.56 0.45 0.56 0.60

0.84 0.81 0.88 0.90 0.25 0.36 0.38 0.51 0.42 0.39 0.45 0.49 0.31 0.34 0.41 0.49 0.63 0.57 0.54 0.61

0.49 0.49 0.62 0.71 0.30 0.24 0.41 0.52 0.31 0.33 0.32 0.31 0.28 0.39 0.22 0.23

0.38 0.38 0.38 0.38 0.83 0.83 0.83 0.83 0.84 0.84 0.84 0.84

Height

Weightb

Age

Time of study

0.22, 0.22, 0.22, 0.22, 0.76, 0.76, 0.76, 0.76, 1 – – – – – – – – – – – – – – – – – – –

0.54, 0.54, 0.54, 0.54, 1 – – – – – – – – – – – – – – – – – – – – – – –

1 – – – – – – – – – – – – – – – – – – – – – – – – – – –

C1 C2 C3 C4 C1 C2 C3 C4 C1 C2 C3 C4 C1 C2 C3 C4 C1 C2 C3 C4 C1 C2 C3 C4 C1 C2 C3 C4

0.34 0.34 0.34 0.34 0.71 0.71 0.71 0.71

0.79 0.79 0.79 0.79

a

Age

Weight

Height

BMI

Lumbar disorder

Knee disorder

Neck disorder

a

Time of study: C1: 12 last months; C2: 12 days after harvesting apple; C3: 12 days after postural corrections; C4: 12 days after combination of postural corrections and time study method. b The first number is for 20–35 years old labors and the second number is for 36–55 years old labors.

practical solution for the reduction of work-related injuries. This study found out that awkward working postures, manual harvesting and handling as well as long hours of standing and bending postures such as the task elements: W5 (picking apples), W7 (putting apples in the box), and W8 (putting the filled box out of the farm) were the main causes of prevalent disorders. These task elements were critical when suitable postures were not performed during the work. The recent studies showed that repetitive work tasks, short cycles, awkward posture, working with arms above the shoulder height, heavy lifting and lack of recovery were associated with neck and shoulder disorders (Stambolian et al., 2016; Linaker and Walker-Bone, 2015; David et al., 2008). The stop watch time study identified that certain environmental conditions such as hot, windy, and dusty weathers were specific reasons of the higher PT. The total multiplication of frequencies of each apple harvesting element by its respective ET showed that around 42–43 min per hour were required for the laborers to perform tasks without any allowances. Whereas, the allowances were necessary because a given task could not be continuously conducted for a long period of time without rests or rest times. However, the old group needed more allowance times than the young one. Since the estimated allowances were different for the two groups of laborers (young and old), each group used different rest times during their apple harvesting works. The NSQ survey results also showed the reductions of 5–25% in disorders amongst different parts of the laborers’ bodies (see, Fig. 3). However, the old laborer group showed higher NSQ results, suggesting postural corrections. This finding was consistent with a recent study, which reported an educational impact on MSD reductions (Bulduk et al., 2016). Detailed investigations in the NSQ surveys uncovered that postural corrections were even more effective for the laborers with high BMIs. The lumbar and knee disorders were reduced by 27% in some aged cases with high BMIs (around 27 kg/m2). As shown in Table 4, there was a significant correlation (93.5% inverse relation) between the frequencies of tasks W5 and ET. This

Table 5 Summary of statistical analyses for the variables and their coefficients in the linear regression model for the old laborer group (36–55 years of old). Sig.

t

Standard Error

B Coefficient

Variablesa

0.004 0.000 0.036

1 1 1

0.813 0.067 3.949

2.599 0.557 −8.704

Knee disorder (KD) Weight (W) Constant

a

Dependent Variable: Age.

Table 6 Summary of statistical analyses for the variables and their coefficients in the logistic regression model for the young laborer group (20 to −35 years of old). Exp(B)

Sig.

df

Wald

Standard Error

B Coefficient

Variablesa

0.034 0.056

0.041 0.003

1 1

8.923 9.058

0.930 0.958

2.231 2.883

3.250

0.039

1

4.249

0.572

1.179

Knee disorder (KD) Shoulder disorder (SD) Constant

a

Dependent Variable: Lumbar disorder.

effects of the shoulder disorder on the lumbar disorder are more than the knee disorder whereas both variables have significant effects on the lumbar disorder. 4. Discussion Workers in the gardens and agricultural industry are exposed to several physical and mental risk factors. The current research focused on the apple harvesting laborers as a case study and conducted an ergonomic analysis with a stop watch time study technique to explore their jobs. Ergonomic principles were applied to correctly estimate actual working hours for the apple harvesting works and to provide a 38



finding indicated that all the laborers worked at a suitable speed. In other words, there was no very high-speed or very low-speed worker. Furthermore, the correlation between the estimated allowances and frequencies was also significantly high (94% inverse relation). It was assessed that six laborers were needed from the young group or seven laborers from the old group to harvest 25,000 kgs. of apples in 15 working days (8 h/day). This finding recognized that employing a suitable number of laborers allowed them to use extra rest times (allowance) which could reduce fatigues and MSDs. With these outcomes, the garden owners were asked to employ the required numbers of laborers. In this study, however, the discrete effect of allowances on the reduction of MSD's was not investigated since the period of apple harvesting was short. This effect can be analyzed by other agricultural and/ or industrial operations that have no limitations in their time of working. Furthermore, the effectiveness of extended allowances for the aged laborers or laborers with some physical limitations needs to be investigated using medical factors such as a heart impulse rate, and blood pressure level. This study also recognized that the laborers had no regular rest times during their apple harvesting works because the tasks were performed by two time schedules: 7 a.m.–12 p.m. and 2 p.m.–5 p.m., respectively. Accordingly, the effects of postural corrections were not useful as expected. To provide beneficial solutions on this issue, the following two ideas are suggested to reduce workloads with postural corrections:

can be negotiated to the better performed laborers. By accurately estimating the required number of laborers, apple harvesting works will be performed on time, and by implementing appropriate ergonomic postures, occupational health and safety problems will be reduced. Findings from this study may provide a practicable answer to prevent MSD developments for the labour-intensive industries such as fruit harvesting and farming business. Acknowledgements This work was funded by Jahrom University. The experts and laborers participating in the study are highly appreciated. Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx. doi.org/10.1016/j.ergon.2018.04.007. Conflicts of interest We have no potential conflicts of interest to disclose. References Aliahmadi, A.R., 2014. Work Study. Iran University Science & Technology (In Farsi). Amitabha, D., Rabindranath, S., 1992. A work measurement method for application in Indian agriculture. Int. J. Ind. Ergon. 10, 285–292. Arvidsson, I., Arvidsson, M., Axmon, A., Hansson, G.-Å., Johansson, C.R., Skerfving, S., 2006. Musculoskeletal disorders among female and male air traffic controllers performing identical and demanding computer work. Ergonomics 49, 1052–1067. Bon, A.T., Daim, D., 2010. Time motion study in determination of time standard in manpower process. In: Proceedings of EnCon 2010 3rd Engineering Conference on Advancement in Mechanical and Manufacturing for Sustainable Environment, Kuching, Sarawak, Malaysia. Bulduk, S., Bulduk, E.Ö., Süren, T., 2017. Reduction of work-related musculoskeletal risk factors following ergonomics education of sewing machine operators. Int. J. Occup. Saf. Ergon. 23 (3), 347–352. David, G., Woods, V., Li, G., Buckle, P., 2008. The development of the quick exposure check (QEC) for assessing exposure to risk factors for work-related musculoskeletal disorders. Appl. Ergon. 39, 57–69. Edet Ekpenyong, C., Clement Inyang, U., 2014. Associations between worker characteristics, workplace factors, and work-related musculoskeletal disorders: a cross-sectional study of male construction workers in Nigeria. Int. J. Occup. Saf. Ergon. 20 (3), 447–462. Fathallah, F.A., 2010. Musculoskeletal disorders in labor-intensive agriculture. Appl. Ergon. 41, 738–743. Greene, W.H., 1997. Econometric Analysis, third ed. Prentice Hall. Guan Ng, Y., Shamsul Bahri, M.T., Irwan Syah, M.Y., Mori, I., Hashim, Z., 2013. Ergonomics observation: harvesting tasks at oil palm plantation. J. Occup. Health 55, 405–414. Hignett, S., McAtamney, L., 2000. Rapid entire body assessment (REBA). Appl. Ergon. 31 (2), 201–205. Hosseini, M., Shaker, H., Ghafouri, H.B., Shokraneh, F., 2010. Journal of Health Administration 13 (40), 13–23 (In Farsi). Irannejad, P.M., Sassangohar, P., 2005. Organization and Management Theory and Practice. Iran Banking Institute, Iran 500 PP. [Book in Persian]. Krystosik-Gromadzińska, A., 2017. Ergonomic assessment of selected workstations on a merchant ship. Int. J. Occup. Saf. Ergon. 1–9. http://dx.doi.org/10.1080/10803548. 2016.1273589. Linaker, C.H., Walker-Bone, K., 2015. Shoulder disorders and occupation. Best Pract. Res. Clin. Rheumatol. 29, 405–423. Ministry of Jihad-e-Agriculture, 2016. Annual Agricultural Statistics, Department of Agronomy. www.maj.ir/ Accessed December 2016. Nair, N.G., 2004. Work Measurement and Production Standard, Production and Operations Management. Chapter 9. Tata McGraw-Hill Education Pvt. Ltd, pp. 285–323. Nakayama, S., 2002. A study on setting standard time using work achievement quotient. Int. J. Prod. Res. 40 (15), 3945–3953. Petit, A., Ha, C., Bodin, J., Parot-Schinkel, E., Ramond, A., Leclerc, A., Imbernon, E., Roquelaure, Y., 2014. Personal, biomechanical, organizational and psychosocial risk factors for neck disorders in a working population. J. Occup. Health 56, 134–140. Russell, R.R., Taylor, B.W., 2005. Operations Management: Quality and Competitiveness in a Global Environment, fifth ed. J. Wiley, New York. Sable, S.R., 2013. Stop Watch Time Study and MOST: Work Measurement Techniques. http://shodhganga.inflibnet.ac.in/bitstream/10603/13108/9/09_chapter%204.pdf/, Accessed date: 11 October 2017. Sarkar, A., Aronson, K.J., Patil, S., Hugar, L.B., Vanloon, G.W., 2012. Emerging health risks associated with modern agriculture practices: a comprehensive study in India.

1) Changing of working hours from 7–12 a.m. to 7–11 a.m., and from 2–5 p.m. to 2–6 p.m., respectively. Five hours of working times in the morning session can be reduced to 4 h and the laborers' fatigue level will be reduced as well. This is an important concern since muscular strains due to fatigues are a prominent cause for those who conduct manual handling activities (Shikdar and Sawaqed, 2004). 2) Planning for allocating suitable rest times or rest allowances during working hours. This is an essential issue since there are positive relationships amongst working time, age, and gender with mental health (Edet Ekpenyong and Clement Inyang, 2014; ŻołnierczykZreda, 2012). Because other critical factors were determined to disorders in specific body areas such as the shoulder, knee, and neck, more studies are also recommended to clarify the effect of open working area conditions such as temperatures, dusts, wind speeds, and seasonal issues on the laborers’ pains to obtain workers' satisfaction, job performance enhancement, and safety and health improvement. 5. Conclusions Ergonomic principles and time studies were applied to reduce WMSDs for the apple harvesting laborers in Sepidan gardens at the Fars Province, Iran. Findings from this study clearly identified that postural corrections had positive effects on the reduction of MSDs. This effect was even more effectual when suitable rest time allowances were combined with right ergonomic postures. Time study application in the current study evidently showed that the old laborers harvested apples with lower speeds than the younger ones. This finding suggested that appropriate numbers of laborers should be employed for a given task so that laborers can have enough rest times without physical and psychological pressures. Changing in the current working hours and balancing in the working times between morning and evening sessions may reduce laborers' fatigues. Young and old laborers require different rest times during their work hours. Thus, it is recommended that suitable rest time allowances should be allocated for each work with consideration of environmental conditions (working area). It also needs to mention that employers should not disadvantage the laborers who are using necessary allowances. To moderate this issue, higher salaries or incentives 39



application of a work measuring model in Hospital. J. Payavard Salamat 8 (1), 79–89. Yusoff, N., Jaffar, A., Abbas, N.M., Saad, N.H., 2012. Work measurement for process improvement in the car seat polyurethane injection manufacturing line. Procedia Eng 41, 1800–1805. Zahedi, S., Najjari, R., 2008. Human resource productivity and knowledge management. Peike Noor 6 (1) (In Farsi). Zain, T.Y.Z.M., Rajamony, B., 2014. ASEAN industrial engineering and work measurement practices phenomenon. In: Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia, pp. 7–9 January. Żołnierczyk-Zreda, D., Bedyńska, S., Warszewska-Makuch, M., 2012. Work time control and mental health of workers working long hours: the role of gender and age. Int. J. Occup. Saf. Ergon. 18 (3), 311–320. http://dx.doi.org/10.1080/10803548.2012. 11076947.

Environ. Res. 115, 37–50. Shikdar, A.A., Sawaqed, N.M., 2004. Ergonomics, and occupational health and safety in the oil industry: a managers' response. Comput. Ind. Eng. 47, 223–232. Stambolian, D., Eltoukhy, M., Asfour, S., 2016. Development and validation of a three dimensional dynamic biomechanical lifting model for lower back evaluation for careful box placement. Int. J. Ind. Ergon. 54, 10–18. Subramanian, G.C., Arip, M., Saraswathy Subramaniam, T.S., 2017. Knowledge and risk perceptions of occupational infections among health-care workers in Malaysia. Safety and Health at Work. Saf Health Work 8 (3), 246–249. Waseem Andrabi/HT Photo, 2017. Available at:: http://www.hindustantimes.com/ photos/india-news/photos-apple-harvesting-begins-in-kashmir-s-orchards/photovYqDn2NAkT1yhHy7OZIoaI.html (accessed 11 October 2017). Yousefi, M., Ahmadi, M., Fazaeli, S., 2014. Staff management based on performance:

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