Biomedical Research

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Research Article - Biomedical Research (2019) Volume 30, Issue 6

A study on musculoskeletal morbidity among professionals in information technology hub, Salt Lake City, Kolkata

Mukesh Kumar1, Shibani Dutta2, Indranil Saha3, Asim Saha4, Krishna Prasanth1*

1Department of Community Medicine, Sree Balaji Medical College and Hospital, Bharath Institute of Higher Education and Research, Chennai, India

2Department of PHA, All India Institute of Hygiene and Public Health, Kolkata, India

3Department of Community Medicine, IQ City Medical College and Hospital, Durgapur, West Bengal, India

4Regional Occupational Health Centre (E), Salt Lake, Kolkata, India

*Corresponding Author:
Krishna Prasanth
Epidemiologist and Assistant Professor
Department of Community Medicine
Bharath Institute of Higher Education and Research
Chennai, India

Accepted Date: November 28, 2019

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Abstract

Background: Long periods of working at a computer as most people do, can cause musculoskeletal
problems, eyestrain, obesity, occupational stress and overuse injuries of the hands and wrists
which can be reduced or eliminated with proper workstation design and improved posture. The
daily experience is of repetitive, intensive and stressful work, which frequently results in employee
"burnout”. Using the wrong chair or just sitting improperly in front of a computer for long time
can lead to chronic debilities such as stiffness, headache, and backache. Muscles and tendons can
become inflamed due to excess periods of sitting on computer. Carpal tunnel syndrome is a common
example of an overuse injury associated with computer work caused by pressure on the main nerve
that runs through the wrist. The fingers are also prone to overuse injury, particularly the finger that
clicks the mouse buttons. With this backdrop a study was conducted to find out the musculoskeletal
morbidity among professionals in Information Technology hub of sector V, Kolkata.
Methods: This was an observational, cross-sectional, institution based study was conducted to find
out morbidity among information technology professionals at Sector V, Salt Lake City, Kolkata,
from May 2015 to April 2016.
Results: Among 244 information technology professionals, their age ranges from 23 years to 39
years and mean age was 27.7 years. Male (186) 76.2 %, Female (58) 23.8 %, Married (65) 26.6%,
Unmarried (172) 70.5% and living together (07) 2.9%, Graduate (84) 34.4%, Post graduate (160)
65.6%. Smoker were (96) 39.3% and Alcoholic (113) 46.3%. Type of job among study participants
were; Management (08) 3.3%, Data processor (66) 27%, Call centre executive (37) 15.2%, Software
developer (133) 54.5%. Prevalence of musculoskeletal symptoms were; wrist/hands 25.8%, elbow
30.7%, shoulder 37.7%, neck 61.9%, upper back 33.2%, lower back 52.9%, hip/thigh 29.9%, knee
32%, and ankle/feet 29.1%. Musculoskeletal symptoms were increasing with age OR=2.62 (1.4-
5.1), male having more symptoms OR=7.2 (3.6-14.4), job duration more than 2 years had more
morbidity OR=2.2 (1.1-4.4), smoker were associated with more symptoms OR=9.4 (3.3-27.2) and
professionals, who had better ergonomic score were less morbid OR=0.86 (0.7-0.9).
Conclusion: On the basis of findings of study as young generation who are doing job in IT companies
lead to sedentary life, poor life style related behavior, musculoskeletal morbidity. Poor ergonomic
practices lead to musculoskeletal morbidity among older age group, male sex and who were working
for more than 2 years.

Keywords

VDT, IT professionals, Ergonomic practices, Nordic questionnaire.

Introduction

In twenty-first century, computers have become almost as ubiquitous as the humble pen and paper in many peoples’ daily life. There are approximately six computers per thousand populations with an installation of 18 million personal computers (PC’s) and their number is increasing all the time [1].

The computer is a vital tool in every dimension. However, the long periods of working at a computer as most people do, can cause musculoskeletal problems and overuse injuries of the hands and wrists which can be reduced or eliminated with proper workstation design and improved posture. The proliferation of video display terminals (VDT), in the modern office setting has generated concern related to potential health hazards associated with their use [2].

Using the wrong chair or just sitting improperly in front of a computer for long time can lead to chronic debilities such as stiffness, headache, and backache. Muscles and tendons can become inflamed due to excess periods of sitting on PC’s. Carpal tunnel syndrome is a common example of an overuse injury associated with computer work. This painful disorder of the hand is caused by pressure on the main nerve that runs through the wrist. The fingers are also prone to overuse injury, particularly the finger that clicks the mouse buttons [3].

Employees who start to feel the "pressure to perform" can get caught in a downward spiral of increasing effort to meet rising expectations with no increase in job satisfaction. The relentless requirement to work at optimum performance takes its toll in job dissatisfaction, employee turnover, reduced efficiency, illness and even death [4].

Absenteeism, illness, alcoholism, tobacco use "petty internal politics", bad or snap decisions, indifference and apathy, lack of motivation or creativity are all by-products of an over stressed workplace [5]. Chemical exposure from wellfurnished wall and furniture, smoking habits etc. may leads to respiratory problems. Sedentary activity, improper food eating habit with high prevalence of alcohol consumption and smoking lead to development of musculoskeletal, ocular, systemic disorder and non-communicable disease among IT workers.

India has been in the forefront in cyber world with IT industry developing into a major service provider. The primary service providers in Information technology (IT) industry are grouped into IT software industry; IT enabled service, Internet and e-commerce. There are approximately 916 IT providers registered with National Association of Software and Service Companies (NASSCOM) all over India.

Owing to recent bloom in the economy in Kolkata and also the state as a whole, West Bengal is now the 3rd fastest growing economy in the country and the city’s IT sector is growing at a rate of 70% per year; twice the national average. Around 2 lakhs people are employed in different companies in Kolkata.

IT professionals are emerging with the group of modern occupational diseases which are slowly taking their roots among them. Repetitive, intensive and stressful work daily results in employee burnout. This study is concerned with how the Information technology and the consequent change in job culture affect their work related morbidity among professionals. This is particularly relevant because jobs in Information technology are the most coveted one in modern India, and the most brilliant section of the youth are going for it. Even leaflets for their stretch and exercise breaks during work are available; they were not doing the same. As only few studies were done for musculoskeletal morbidity among IT professionals of Kolkata; this study was necessary to determine the prevalence of musculoskeletal morbidity along with associated factors among Information technology professionals and application of this knowledge to prevent the disease burden among them in Kolkata, West Bengal. With this backdrop a study was conducted to find out the musculoskeletal morbidity among professionals in Information Technology hub of sector V, Kolkata.

Materials and Methods

Type of study

This was an observational, cross-sectional, institution based study.

Study area

Sector V, planned hub of information technology is situated in Salt Lake City, Kolkata.

Study settings

The study was conducted in 3 information technology companies of sector V at Salt Lake City in Kolkata.

Study period

Data was collected for 1 year from May 2015 to April 2016.

Study population

People, who were engaged to computer related work from software developers, call center and data entry /processing, comprised our study population.

Selection criteria

Information technology professionals, male and female, who were working in different companies, at Sector V were considered.

The participants, who were working in the current job for past six months and also working on the computer for at least 3 h/day or 15 h/week, were included in the study.

Exclusion criteria

Subjects who did not give consent were excluded from the study.

Sample size

In a recent study done at Mumbai [6], it was found that prevalence of psycho-social problems in the form of stress among IT workers is 44% in India. This was the least among all the other morbidities suffered by the IT professionals. Now considering this prevalence with 20% allowable relative error, sample size becomes approximately 244 after applying the formula

Sample size = Z21-α/2pq/L2

Where, Z1-α/2= Standard normal deviate at a desired confidence level (95%); p = previous prevalence; q=100–p; L=allowable error, 20% of p taken. At 95% confidence level Z1-α/2 value is 1.96.

Sample size=122, since it was a multistage study, design effect of 2 was considered; so, final sample size came to 244.

Sampling design

Around 2 lakhs people are employed in different information technology (IT) companies at sector V and sector III of Salt Lake City in Kolkata. At first stage Sector V were selected randomly for selection of IT professionals. At second stage, 3 companies were selected out of 30 companies at Sector V (Simple Random Sampling without replacement) at Salt Lake City. First, second and third selected IT companies employed 600, 400 and 300 professionals respectively; among them 113, 75 and 56 adult professionals were chosen from each company by probability proportionate sampling with random table method to include in the study (Multi-stage sampling technique). Thus 244 participants were selected for the study [7,8].

Study Tools and Technique

Study tools

1. Pre-designed, pre-tested structured schedule (Two parts; part I elaborating the socio-economic and job related health behavior and part II deals instruments to elicit musculoskeletal morbidity (Nordic) questionnaire and ergonomic checklists).

2. Stethoscope.

3. Portable weighing machine (Digital).

4. Non stretchable measuring tape (Calibrated in 1 cm).

5. Sphygmomanometer.

6. Previous medical records.

7. List of employees obtained from the organization.

Technique

1. Interview of the study subjects.

2. Self-administered questionnaire

3. Clinical examination and investigations.

4. Record analysis (Reviewing the previous medical reports).

Data collection procedure

The purpose of the study was explained to all the participants and they were assured of confidentiality. After obtaining informed consent for participation in the study, the questionnaire was administered. The questionnaire was used to collect information regarding socioeconomic status, demographic and behavioural characteristics along with participant’s morbidity profile. Anthropometric data was collected along with blood pressure measurement [9,10].

Ergonomic scoring

An ergonomic checklist was adapted from various questionnaires (occupational health clinics for Ontario workers, WISHA services division, Washington state department of labor and industries, MMERT).

There were 43 questions related to ergonomic practices (keyboard, mouse, monitor, accessories, chair, and workstation design along with job satisfaction). Favorable practice is taken as 1 and unfavourable practice as 0. Maximum attainable score was 43 and minimum attainable score 0. Score ranges from 0 to 43, with higher scores indicating decreased level of musculoskeletal morbidity.

Statistical analysis and plan

Data was analysed using appropriate statistical methods and represented by various tables, graphs, diagrams etc. and various statistical significant tests were applied accordingly with the use of SPSS software (version 16.0).

Results

For the multivariable model, the Hosmer-Lemeshow test gave a Chi-square value of 8.67 (p=0.37, not significant) indicating good model fit. Nagelkerke R2 was 0.31.2 showing that the variables included in the model predicted 31% of the musculo skeletal morbidity.

Results are given in Tables 1-7.

Variables Frequency (n) Percentage (%)
Age (in years)
Mean ± SD
(27.7 ± 2.88)
21-25 48 19.7
26-30 158 64.8
31-35 33 13.5
>35 05 2.0
Sex Male 186 76.2
Female 58 23.8
Education level Graduate 84 34.4
Post graduate and above 160 65.6
Marital status Married 65 26.6
Unmarried 172 70.5
Others (living together) 07 2.9
Stay with family Yes
No
57
187
23.4
76.6
PCI (in rupees) 6001-10000 66 27.0
10001-14000 76 31.1
14001-18000 46 18.9
18001-22000 56 23.0

Table 1: Distribution of study subjects according to socio-economic and demographic characteristics (n=244).

Parameters Total
No. (%)
Management
No. (%)
Data processor No. (%) Call centre executive
No. (%)
Software developer No. (%)
Daily time spent on social network (in hours) [n=233]
= 3 173 (74.25) 04 (2.3) 47 (27.2) 22 (12.7) 100 (57.8)
>3 60 (25.75) 02 (3.3) 19 (31.7) 14 (23.3) 25 (41.7)
Use of transport predominantly
Own vehicle 45 (18.44) 01 (2.2) 14 (31.2) 11 (24.4) 19 (42.2)
Public transport 199 (81.56) 07 (3.5) 52 (26.1) 26 (13.1) 114 (57.3)
Eating habit of professionals at office
Home food 171 (70.08) 05 (2.9) 56 (32.8) 27 (15.8) 83 (48.5)
Outside/ street food 35 (14.34) 01 (2.9) 04 (11.4) 03 (8.6) 27 (77.1)
Canteen food 38 (15.58) 02 (5.3) 06 (15.8) 07 (18.4) 23 (60.5)
Substance use#
Smoking 96 (39.34) 03 (3.1) 36 (37.5) 14 (14.6) 43 (44.8)
Alcoholic 113 (46.31) 04 (3.5) 42 (36.8) 16 (14.1) 52 (45.6)
Not exercising 95 (38.93) 01 (1.1) 35 (36.8) 12 (12.6) 47 (49.5)

Table 2: Distribution of behavioral characteristics of study subjects (n=244).

Parameters Total
number
Management
No. (%)
Data processor No. (%) Call centre executive
No. (%)
Software developer No. (%)
Years of working
1 year 106 2 (1.9) 25 (23.6) 15 (14.2) 64 (60.3)
1 to 2 years 59 3 (5.1) 16 (27.1) 10 (17.0) 30 (50.8)
>2 years 79 3 (3.8) 25 (31.6) 12 (15.2) 39 (49.4)
Additional work in last 6 months
Office job at home 133 05 (3.8) 43 (32.3) 22 (16.5) 63 (47.4)
Night shift 51 - 19 (37.3) 06 (11.7) 26 (51.0)
Number of night shift in last 6 months *
<8 12 - 04 (33.3) 02 (16.7) 06 (50.0)
= 8 39 - 15 (38.5) 04 (10.3) 20 (51.3)
*51 subjects          

Table 3: Distribution of study subjects according to job characteristics (n=244).

Musculoskeletal discomfort * Last 12 months No. (%) Last 7 days
No. (%)
Any musculoskeletal problem (in either side)    
 Trouble in wrist/hands (in either side) 63 (25.8) 63 (25.8)
Trouble in elbow (in either side) 75 (30.7) 67 (27.5)
Trouble in shoulder (in either side) 92 (37.7) 77 (31.6)
Trouble in neck 151 (61.9) 147 (60.2)
Trouble in back, any site    
 Upper back 81 (33.2) 45 (18.4)             
 Lower back 129 (52.9) 120 (49.2)
Trouble in leg (in either side)    
 Hip/Thigh 73 (29.9) 60 (24.6)
 Knee 78 (32.0) 57 (23.4)
 Ankle/Feet 71 (29.1) 55 (22.5)

Table 4: Distribution of the IT professionals according to the musculoskeletal morbidity (n=244).

Variables Smoker
(n = 96)
[last 6 months]
No. (%)
Non smoker
(n=148)
[last 6 months]
No. (%)
Total
Age (years)*    
21-25 03 (3.1) 45 (30.4) 48
26-30 72 (75.0) 86 (58.1) 158
31-35 20 (20.8) 13 (08.8) 33
>35 01 (1.1) 04 (02.7) 05
Sex    
Male 83 (86.5) 103 (69.6) 186
Female 13 (13.5) 45 (30.4) 58
Type of work    
Management 03 (3.1) 05 (03.4) 08
Data processor 36 (37.5) 30 (20.3) 66
Call centre executive 14 (14.6) 23 (15.5) 37
Software developer 43 (44.8) 90 (60.8) 133
Marital status*      
Currently married 17 (26.2) 48 (73.8) 65
Never married 79 (45.9) 93 (54.1) 172
Others - 07 (100) 07
Night shift duty*      
Yes 35 (68.6) 16 (31.4) 51
No 61 (31.6) 132 (68.4) 193
Living with family*      
Yes 02 (3.5) 55 (96.5) 57
No 94 (50.3) 93 (49.7) 187
Smoking family history      
Yes 35 (43.8) 45 (56.2) 80
No 61 (37.2) 103 (62.8) 164

Table 5: Distribution of smoking habit with different variables among IT professionals (n=244).

Variables Alcoholic
(n = 113)
[last 6 months]
No. (%)
Non alcoholic
(n=131)
[last 6 months]
No. (%)
Total
Age (years)*    
21-25 09 (8.0) 39 (29.8) 48
26-30 79 (69.9) 79 (60.3) 158
31-35 23 (20.4) 10 (7.6) 33
>35 02 (1.7) 03 (2.3) 05
Sex    
Male 92 (81.4) 94 (71.8) 186
Female 21 (18.6) 37 (28.2) 58
Type of work    
Management 04 (03.5) 04 (03.1) 08
Data processor 42 (37.2) 24 (18.3) 66
Call centre executive 16 (14.2) 21 (16.0) 37
Software developer 51 (45.1) 82 (62.6) 133
Marital status*      
Currently married 24 (36.9) 41 (63.1) 65
Never married 89 (51.7) 83 (48.3) 172
Others - 07 (100) 07
Night shift duty*      
Yes 35 (68.6) 16 (31.4) 51
No 78 (40.4) 115 (59.6) 193
Living with family*      
Yes 11 (19.3) 46 (80.7) 57
No 102 (54.5) 85 (45.5) 187
Mostly which days of week      
All days of week 34 (30.1) ... 34
Weekend 79 (69.9) ... 79

Table 6: Distribution of alcohol consumption with different variables among IT professionals (n=244).

Variables MSD present (%) OR (CI) AOR (CI)
Age (years) median: 27      
= 27 122 (87.1) 2.62 (1.36-5.04)* 2.22 (1.02-4.81)*
< 27 75 (72.1) 1 1
Sex      
Male 166 (89.2) 7.22 (3.60-14.4)* 4.88 (2.27-10.51)*
Female 31(53.4) 1 1
Job duration (year)      
>2 84 (87.5) 2.17 (1.06-4.41)* 1.23 (0.54-2.82)
<= 2 113 (76.4) 1 1
Smoking      
Smoker 92 (95.8) 9.42 (3.26-27.24)* 5.15 (1.69-15.64)*
Non smoker 105 (70.9) 1 1
Ergonomic score ... -0.86 (0.76-0.96)* -0.92 (0.81-1.05)

Table 7: Association of musculoskeletal morbidity with different determinants (n=244), the variables already found significant in bivariate analysis were entered into a Multivariable Logistic model.

Discussion and Conclusion

Among 244 information technology professionals, their age ranges from 23 years to 39 years and mean age was 27.7 years. Similar finding were found in Hong Kong based study where mean age was 26.1 year. This study finding was: Male (186) 76.2%, Female (58) 23.8%, Married (65) 26.6%, Unmarried (172) 70.5% and living together (07) 2.9%, Graduate (84) 34.4%, Post graduate (160) 65.6%. Smoker were (96) 39.3% and Alcoholic (113) 46.3%. Kolkata, India based study 10 had different finding as Male 54.4%, Female 45.6%, Married 59.2%, Unmarried 39.8%, Graduate 91.7%, Post graduate 8.3%. Smoker was 33.5% and Alcoholic was 23.3% due to different study setting and design. Type of job among study participants were; Management (08) 3.3%, Data processor (66) 27%, Call centre executive (37) 15.2%, Software developer (133) 54.5% which was different as shown in Kolkata, India based study 10 i.e., Management 6.8%, Data processor 60.7%, Call centre executive 7.8%, Software developer 24.7% due to different socio economic condition of professionals and study settings. Prevalence of musculoskeletal symptoms were; wrist/hands 25.8%, elbow 30.7%, shoulder 37.7%, neck 61.9%, upper back 33.2%, lower back 52.9%, hip/thigh 29.9%, knee 32%, and ankle/ feet 29.1% which were similar to various studies as Kolkata, India 10 finger 67.5%, wrist 58.7%, elbow 53.3%, neck 54.4%, Germany, 200811 neck 55%, shoulder 38%, hand/ wrist 21%, elbow/forearm 15%, Sweden, 200712 Neck and shoulder 46%, Brazil, 200413 Neck/shoulder symptoms 43% and wrist/hand was 39% Denmark, 200414 Elbow, shoulder and low back symptoms were 10%, 18% and 23% respectively. Finland, 200315 Neck, shoulder, elbows, lower arms and wrist and fingers symptoms were 63%, 24%, 18%, 35% and 16% respectively.

Musculoskeletal symptoms were increasing with age OR=2.62 (1.4-5.1), male having more symptoms OR=7.2 (3.6-14.4), job duration more than 2 years had more morbidity OR=2.2 (1.1-4.4), smoker were associated with more symptoms OR=9.4 (3.3-27.2) and professionals, who had better ergonomic score were less morbid OR=0.86 (0.7- 0.9).

Ethics Committee Approval

Obtained ethics committee approval

Conflicts of Interest

None

References