ergonomic analysis of nrega work

“VALIDATION AND COMPARISON OF ERGONOMIC RISK

ASSESSMENT METHODS IN MANUAL LABOUR WORK OF

MGNREGA”

Presented by:

Manan Shukla

(2012PMM5265)

Supervisor:

Prof. A. Bhardwaj

DEPARTMENT OF MECHANICAL ENGINEERING

MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY

JAIPUR

1

CONTENT

• INTRODUCTION

• RESEARCH METHODOLOGY

• DATA COLLECTION AND ANALYSIS

• RESULT AND DISCUSSION

• CONCLUSION

INTRODUCTION

• Work related musculoskeletal disorders are a major concern in many manual labour work

• There are two ways by which WMSDs can be detected:

1. by those who are trained in the field of ergonomics

2. By using Ergonomic Risk Assessment Method

BACKGROUND OF THE PROBLEM

• MGNREGA was launched in 200 districts on 2.2.2006. Presently, Mahatma Gandhi NREGA is being implemented in all the notified rural areas of the country

• After checking the attendance record and taking survey it has been found that work related musculoskeletal injuries caused increase in absenteeism and lose of wages

OBJECTIVE OF STUDY

• To determine the prevalence of musculoskeletal disorders among MGNREGA workers and implementation and validation of the ergonomic risk assessment methods.

• To compare the results of ergonomic risk assessment method by determining the level of agreement among tools.

ERGONOMIC TOOLS USED IN THE STUDY

• The Ergonomic tools used in this study are

1. Rapid Upper Limb Assessment

2. Rapid Entire Body Assessment

3. Strain Index

RAPID UPPER LIMB ASSESSMENT (RULA)

• RULA (rapid upper limb assessment) is a survey method developed for use in ergonomics investigations of workplaces where work-related upper limb disorders are reported.

• The method uses diagrams of body postures and three scoring tables to provide evaluation of exposure to risk factors.

STEPS TO CONDUCT RULA ANALYSIS

• The development of the method for recording working postures

• Development of the system for grouping the body part posture scores

• Development of the grand score and action list

RULA SCORE SHEET

UPPER ARM

LOWER ARM

WRIST

WRIST TWIST

A

+ + =

B

+ + =

POSTURE

SCORE

(A)

MUSCLE

USE

FORCE

SCORE

C

NECK

TRUNK

LEGS

POSTURE

SCORE

(B)

MUSCLE

USE

FORCE SCORE

D

GRAND

SCORE

RAPID ENTIRE BODY ASSESSMENT (REBA)

• REBA was developed with six main goals: • Develop a postural analysis system sensitive to

musculoskeletal risksin a variety of tasks. • Divide the body into segments to be coded individually,

with reference to movement planes. • Provide a scoring system for muscle activity caused by

static, dynamic, rapid changing or unstable postures. • Reflect that coupling is important in the handling of loads

but may not always be via the hands. • Give an action level with an indication of urgency. • Require minimal equipment-pen and paper method.

(Hignett & McAtamney, 2000, p. 201)

REBA SCORING SHEET

GROUP A GROUP B

+ +

+

=

TRUNK

NECK

LEG

USE

TABLE A

LOAD/

FORCE

SCORE

A

R

UPPER ARM L

R

LOWER ARM

L

R

WRIST

L

L

USE

TABLE A

COUPLING

SCORE

B

SCORE C

ACTIVITY

SCORE

GRAND REBA SCORE

STRAIN INDEX

• The Strain Index (SI) was published by Moore and Garg in 1995. It was designed to be an objective method of designating the risk for developing distal upper extremity disorders. It is not designed to predict disorders outside the distal upper extremity.

SI SCORE

• An SI score is calculated from the measurement or estimation of six task variables:

(a)intensity of exertion, (b) duration of exertion per cycle, (c) efforts per minute, (d) wrist posture, (e) speed of exertion, and (f) duration of task per day.

SI SCORING SHEET

RESEARCH METHODOLOGY

• The procedure undertaken for the study is given below

1. Observed the subject for several cycles

2. Fill the Standard Nordic Questionnaire by the feedback from the worker and it was determined that there is prevalence of Musculoskeletal Disorder (MSD) among MGNREGA workers

3. Determined fundamental tasks of the job by recording it using Flow Process Chart; and confirmed with the worker that the selection of tasks was indicative of "normal operations”.

4. Video graphing the fundamental tasks.

PROCEDURE (CONTD.)

• The postures selected for analysis were those identified as the most demanding (awkward posture and/or force application) at the workstation.

• Applying RULA, REBA and SI analysis to the posture selected in order to get the respective score and then these scores are classified into low, medium, high and very high risk levels.

• The chi square test is performed

PROCEDURE (CONTD.)

• After checking the validity of the ergonomic risk assessment methods, the comparison is done between the three methods by developing 4*4 agreement matrix.

• The level of agreement is determined by the kappa coefficient analysis.

• Determining the reason for level of agreement/disagreement between the three methods.

DATA ANALYSIS

CATEGORIZATION OF OUTPUTS

• The risk level used for comparing the ergonomic risk assessment methods are divided into four categories as shown

(Marie-Ève Chiasson, Daniel Imbeau, Karine Aubry, Alain Delisle, 2012)

Tool Ranges of

Score

Category Assignment Remarks

Low Medium High Very High

Strain Index 0.0625-20+ 1-2.99 3-4.99 5-7 7+ Categories follow 4 levels

of "risk" in Strain Index

RULA 1 to 7 1&2 3&4 5&6 7 Categories follow 4 "action

levels" given in RULA

REBA 1 to 15 1 to 3 4 to 7 8 - 10 11 to 15 Action level 0 and 1

lumped together

CHI SQUARE TEST

• The chi-square independence test is used to find out whether there is an association between a row variable and column variable in a contingency table constructed from sample data

• In the study chi square test has been used to determine the validity of each ergonomic assessment tool in manual labour work.

NULL/ALTERNATIVE HYPOTHESIS

• The Null Hypothesis and Alternative Hypothesis taken in this case are

• H0= There is no association between the given method risk level and the pain and ache reported by the workers.

• H1= There is association between the given method risk level and the pain and ache reported by the worker

PROCESSES SELECTED FOR STUDY

• The ergonomic analysis is done on two process of MGNREGA

1. Digging work

2. Construction of bund

• These processes are selected because maximum no. of worker who are affected by WMSDs are from these processes

• 35 Workers from digging work and 30 worker from bund work are selected for study

Assessment of the Validity of RULA in MGNREGA (Digging) Work

• A tool is considered valid when it measures what it intends to measure

• In this study basically the predictive validity of the ergonomic risk assessment methods is evaluated

S.No. Name of

worker

RULA

score

Risk

Level

Pain/No

Pain

S.No. Name of

worker

RULA

score

Risk

Level

Pain/No

Pain

1. MALA 2.0 1.0 No Pain 21 SUSHEELA 5.0 3.0 Pain

2. SAJJO 2.0 1.0 No Pain 22 CHENA

RAM

5.0 3.0 Pain

3. SUNITA 2.0 1.0 No Pain 23 MATA DIN 6.0 3.0 No Pain

4. GHASI 5.0 3.0 Pain 24 HUKUM

SINGH

6.0 3.0 No Pain

5. KAALU 5.0 3.0 Pain 25 VINA 5.0 3.0 No Pain

6. SANJU 5.0 3.0 Pain 26 RAM

JEEVAN

5.0 3.0 No Pain

7. GENDI

BAI

7.0 4.0 Pain 27 JYOTI 5.0 3.0 Pain

8. VIMALA 7.0 4.0 Pain 28 SHAILU 4.0 2.0 No Pain

9. MEENA 7.0 4.0 Pain 29 SHANTI 4.0 2.0 No Pain

10. KAMALA 7.0 4.0 Pain 30 SEEMA 4.0 2.0 No Pain

11. NIRMALA 7.0 4.0 Pain 31 NANKU 4.0 2.0 No Pain

12. NATHU

LAL

7.0 4.0 Pain 32 SEETA

DEVI

4.0 2.0 Pain

13. MOOLI

DEVI

4.0 2.0 Pain 33 MAANA 4.0 2.0 Pain

14. MAINA

BAI

4.0 2.0 Pain 34 REKHA 4.0 2.0 Pain

15. GANGA 4.0 2.0 Pain 35 ASHA BAI 4.0 2.0 Pain

16. PARVATI 6.0 3.0 Pain

17. SHILA 6.0 3.0 Pain

18 KAMLA 3.0 2.0 Pain

19. PAPPU 3.0 2.0 Pain

20. SAVITRI 5.0 3.0 Pain

RULA score and reported pain in “Digging the

pit using “Geti” by operator”

RESULT OF CHI SQUARE TEST

TABLE 5.4: Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

RISK LEVEL * PAIN/NO

PAIN 70 100.0% 0 .0% 70 100.0%

TABLE 5.5 Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 14.972a 3 .002

Likelihood Ratio 15.572 3 .001

Linear-by-Linear Association 13.578 1 .000

N of Valid Cases 70

a. 0 cells (.0%) have expected count less than 5. The minimum

expected count is 5.80.

FIG : Clustered Bar Chart between Reported Pain & Risk level

CHI SQUARE TEST RESULTS FOR RULA IN BUNDING WORK

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

RISK LEVEL * PAIN/NO

PAIN 60 92.3% 0 0% 60 100.0%

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 15.264a 3 .002

Likelihood Ratio 16.878 3 .001

Linear-by-Linear Association 11.969 1 .001

N of Valid Cases 60

CHI SQUARE TEST RESULTS FOR REBA IN BUNDING WORK

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

RISK LEVEL * PAIN/NO

PAIN 60 100.0% 0 .0% 60 100.0%

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 10.247a 3 .017

Likelihood Ratio 10.658 3 .014

Linear-by-Linear Association 7.645 1 .006

N of Valid Cases 60

CHI SQUARE TEST RESULTS FOR SI IN BUNDING WORK

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

RISK LEVEL * PAIN/NO

PAIN 60 100.0% 0 .0% 60 100.0%

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 11.094a 3 .011

Likelihood Ratio 12.356 3 .006

Linear-by-Linear Association 9.371 1 .002

N of Valid Cases 60

a. 0 cells (.0%) have expected count less than 5. The minimum

expected count is 5.20.

Level of Agreement between the RULA & REBA

using Kappa Analysis

• The kappa coefficient is determined using SPSS 16.0 for windows and the final result of the analysis is as shown below:

• Since kappa value is coming to be 0.67, it can be inferred

that there is substantial correlation between RULA and REBA. The p value is also coming out to be p<0.05 which signifies that the relationship between RULA and REBA is statistically significant.

Symmetric Measures

Value

Asymp. Std.

Errora Approx. Tb Approx. Sig.

Measure of Agreement Kappa .670 .061 10.849 .000

N of Valid Cases 130

• Since kappa value is coming to be 0.474, it can be inferred that there is moderate agreement between RULA and SI. The p value is also coming out to be p<0.05 which signifies that the relationship between RULA and SI is statistically significant

• Since kappa value is coming to be 0.526, it can be inferred that there is moderate agreement between RULA and SI. However the association is of REBA and SI is stronger as compared to RULA and SI. The p value is also coming out to be p<0.05 which signifies that the relationship between REBA and SI is statistically significant.

RESULTS AND CONCLUSIONS

• RULA and REBA were expected to show the greatest degree of agreement. Since kappa value is coming to be 0.67

• The primary reasons for this are

1. The method to take observations are very similar.

2. The distal upper extremities are evaluated in the same manner

• RULA only allows a wrist posture score of 1 when the wrist is bent 0° (perfectly straight). A wrist score of 1 for REBA is allowed for extension or flexion up to 15°.

• RULA with just two classes of body balance (leg postures) produced more discriminatory power regardless of balanced and unbalanced leg postures, as compared to SI and REBA with more classes for leg postures

• Due to above reasons there is more chance that RULA score will be higher as compared to REBA and SI

• The existence of “force exertions” in SI give a major emphasis to that risk factor while RULA does not give it a similar weight.

• SI also highlights “force” in three of its six variables (effort’s intensity, effort’s duration and efforts per minute).

• RULA presents different “force” scores once the importance to the application of force below 2kg is substantial nevertheless the anatomical zone of force exertion (arm or fingers).

• Due to the incorporation of activity score there is higher level of agreement between SI and REBA which is not available in case of REBA

• From above it can be concluded that RULA and REBA should not be used simultaneously since level of agreement is higher.

FUTURE SCOPE

• Intermediate scores could be compared instead of just final scores as well as each score or rating that contributes to the overall score

• It is also recommended to investigate different cut-points for risk categories for each of the tools to see if there is another system of dividing the tools into risk categories that gives a reasonable amount of agreement