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  Table of Contents 
Year : 2016  |  Volume : 20  |  Issue : 2  |  Page : 109-113

Association of quality of life and job stress in occupational workforce of India: Findings from a cross-sectional study on software professionals

1 Public Health Foundation of India, Bengaluru, Karnataka, Indi; Department of Epidemiology, University of California Los Angeles, Los Angeles, California, USA
2 Department of Clinical Psychology, NIMHANS, Bengaluru, Karnataka, India
3 Department of Epidemiology, University of California Los Angeles, Los Angeles, California, USA
4 Bihar Technical Support Program, CARE India Solutions for Sustainable Development, Patna, Bihar, India
5 Institute of Mental Health, University of Nottingham, Jubilee Campus, Nottingham, NG7 2TU, UK
6 Hergest Unit, Betsi Cadwaladr University Health Board, Ysbyty Gwynedd, Bangor, North Wales; Abraham Cowley Unit, Surrey and Borders Partnership NHS Trust, Chertsey, UK

Date of Web Publication4-Jan-2017

Correspondence Address:
Giridhara R Babu
Public Health Foundation of India, IIPH-H, Bangalore Campus, SIHFW Premises, Beside Leprosy Hospital, 1st Cross, Magadi Road, Bengaluru - 560 023, Karnataka, India

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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0019-5278.197544

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Background: There is limited scientific evidence on the relationship of job stress with quality of life (QoL). Purpose: This study aims to explore different domains of job stress affecting IT/ITES professionals and estimate the levels of stress that these professionals endure to reach positive levels of QoL given that other determinants operating between these two variables are accounted for. Materials and Methods: We estimated levels of stress that software professionals would have endured to reach positive levels of QoL considering that other factors operating between these two variables are accounted for. The study participants comprised 1071 software professionals who were recruited using a mixed sampling method. Participants answered a self-administered questionnaire containing questions on job stress, QoL, and confounders. Results: All the domains (physical, psychological, social, and environmental) of QoL showed statistically significant positive associations with increasing stress domains of autonomy, physical infrastructure, work environment, and emotional factors. Conclusions: The respondents clearly found the trade-off of higher stress to be acceptable for the improved QoL they enjoyed. It is also possible that stress might actually be responsible for improvements in QoL either directly or through mediation of variables such as personal values and aspirations. "Yerkes-Dodson law" and stress appraisal models of Folkman and Lazarus may explain the plausible positive association.

Keywords: Developing countries, eustress and distress, job stress, quality of life

How to cite this article:
Babu GR, Sudhir PM, Mahapatra T, Das A, Rathnaiah M, Anand I, Detels R. Association of quality of life and job stress in occupational workforce of India: Findings from a cross-sectional study on software professionals. Indian J Occup Environ Med 2016;20:109-13

How to cite this URL:
Babu GR, Sudhir PM, Mahapatra T, Das A, Rathnaiah M, Anand I, Detels R. Association of quality of life and job stress in occupational workforce of India: Findings from a cross-sectional study on software professionals. Indian J Occup Environ Med [serial online] 2016 [cited 2022 Jul 6];20:109-13. Available from:

  Introduction Top

Quality of life (QoL) is defined as the "individuals' perceptions of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns." [1],[2] Hence, QoL denotes subjective contexts of physical, psychological, cultural, social, and environmental perceptions from a holistic perspective. World Health Organization Quality of Life-BREF (WHOQOL-BREF) is a validated and widely used instrument for determining the impact of several diseases on QoL. A limited number of studies carried out in India have studied QoL, however, none of these have assessed its association with job stress among professionals working in Information Technology (IT) and Information Technology Enabled Services (ITES). [3],[4] In common parlance, QoL is perceived as having better access to amenities and services. Based on contextual settings, the perceived meaning of QoL and the means of attaining a higher level of QoL may vary. In the process of attempting to achieve perceived higher levels of QoL, individuals may be willing to endure/tolerate greater levels of stress. "Job stressors" are defined as "working conditions that may lead to acute reactions, or strains in the worker." It is plausible that professionals can subject themselves to greater degrees of stress in their occupational settings in order to achieve better QoL. There have been no attempts in India, thus far, to explore the psychosocial determinants (such as the amount of job stress one is willing to endure) of attaining a higher level of QoL. This study aims to explore the domains of job stress among IT/ITES professionals and estimate their associations with attainment of higher/positive levels of QoL, accounting for other potential determinants of job stress and QoL.

  Materials And Methods Top

We recruited 1071 IT/ITES professionals for the current study from the IT and ITES sectors. A detailed description of study methods including sample size and method of recruitment have been reported elsewhere. [5] In brief, we included workers from the IT and ITES industry who were 20-59 years old and had been working for at least 1 year prior to inclusion in the study. After obtaining informed consent, participants were requested to complete a self-administered questionnaire that contained items on QoL and job stressors.


Quality of Life

The original version of the QoL instrument, developed by the WHO, WHOQOL-100, allows a thorough evaluation of individual facets related to QoL. The WHOQOL-BREF is the shorter version of the same tool that examines domain level profiles. The WHOQOL-BREF contains 26 questions in total, consisting of one item from each of the 24 facets present in the WHOQOL-100, along with one item to assess the overall QoL and another to examine health in general. The WHOQOL-BREF estimates the overall QoL through four domain scores. The four domain scores denote an individual's perception about his/her QoL in each particular domain, with higher scores denoting better QoL. The mean score of items within each domain denotes the score for that particular domain. The scores are multiplied by 4 to rescale the domain scores to the level of WHOQOL-100. For the current study, we calculated the QoL scores using an SPSS syntax file, obtained from the WHOQOL SRPB Coordinator, Mental Health: Evidence and Research, Department of Mental Health and Substance Dependence, CH-1211 Geneva 27, Switzerland. The WHO-QoL, [6] a generic measure of health-related quality of life (HRQoL) was used to assess and classify health status of workers according to job titles held by them.

Job stress

To estimate job stress we utilized stress domains identified from a preceding qualitative study. [7] The stress domains included time pressure, length of experience, shift, income, job control, autonomy, appreciation, physical environment, work environment, and emotional stressors.

Potential confounders

We adjusted for waist-to-hip circumference ratio, past medical history, gender, age (continuous), socioeconomic status (continuous), marital status, tobacco use, alcohol, and regular exercise (for at least 20 minutes/day) as confounders. A detailed description of measurement of above confounders is provided in earlier publications. [8],[9]


Variables were recoded in increasing order of contextual stress and increasing levels of QoL. Newly coded variables were created in the dataset for further analysis. The data from the cross-sectional survey was analyzed using SAS 9.1.3104. [10] Detailed analysis plan has been described elsewhere. [8],[10] In brief, we employed ordinal logistic regression for testing associations between the QoL domains and job stressors. For this purpose, tertiles of stress domain scores (Y) were used as ordinal categories - namely low, moderate, and high levels of stress. We did not use polytomous logistic regression because such an analysis plan would not make use of the information about ordering of variable categories, and hence, would be comparatively inefficient. On the other hand, ordinal logistic regression model, using cumulative probabilities, takes into account the ordering obtained from contextual stress domains. [11] The cumulative ordinal logistic model for outcome having ordinal categories can be employed when the coefficients of the predictor variables do not depend on i, and it can be assumed that there is one common parameter βij for each covariate. Based on above assumption, an example of cumulative odds model will be [Supplement equation] [Additional file 1] .

This model suggests that the k odds for each cut-off category i will differ only with regard to the intercepts ai.

  Results Top

The details of variable selection and data completeness are provided in our earlier paper. [12] In brief, data from 1071 participants were included for analyses on the variables of interest. Based on WHO-QoL, out of 1071 participants, 55% were found to have moderate quality of physical life. In the psychological domain of QoL, nearly 40% belonged to the moderate category, whereas 35% were classified as having poor QoL. Regarding quality of environmental life, 46% and 40% of the participants had moderate and poor QoL, respectively. Sixty percent of the participants fell into category of moderate quality of social life [Table 1].
Table 1: Descriptive table of domains of quality of life

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Being in the "good" category for physical and psychological QoL was found to be significantly associated with presence of higher occupational stress related to autonomy in both unadjusted and adjusted models. In addition, participants with moderate and good quality of environmental life were more likely to have higher autonomy related stress - both before and after adjustment of confounders. Having moderate and good social QoL was found to be associated with higher autonomy-related job stress in the unadjusted analysis; however, in the adjusted model, only good category of social QoL showed significant positive association with higher autonomy related job stress [Table 2].
Table 2: Estimates of domains of quality of life and autonomy stressors

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In comparison to workers reporting poor QoL, workers having moderate and good physical, psychological, environmental, and social QoL were found to have significantly higher odds of occupational stress related to physical environment in both unadjusted and adjusted models [Table 3].
Table 3: Estimates of domains of quality of life and physical environment stressors

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Similarly, in both unadjusted and adjusted analyses, except for the moderate category of psychological QoL, participants belonging to moderate and good category, compared to those from poor category, in all four domains of QoL had significantly higher odds of occupational stress related to work environment [Table 4].
Table 4: Estimates of domains of quality of life and work environment stressors

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[Table 5] depicts the results of crude and adjusted analyses of associations between QoL and occupational stress related to emotion. In comparison to workers having poor QoL, those with moderate and good score in any of the four QoL domains were found to have higher odds of occupational stress related to emotion in both unadjusted and adjusted models.
Table 5: Estimates of domains of quality of life and emotional stressors

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  Discussion Top

The present study aimed to explore the association between QoL and job stress among individuals working in the IT sector. Various researchers have previously studied the association between computer-centric work environment and different health parameters. These studies mostly focused on assessing physical and mental health in workplace settings. [13],[14],[15],[16] However, there is a paucity of published literature on association between job stress and QoL. [17]

The results of the present study indicate a significant positive association between QoL and increasing stress in the domains of autonomy, physical infrastructure, work environment, and emotional factors. All the QoL domains (physical, psychological, social, and environmental) showed statistically significant positive associations with increasing stress in the domains of autonomy, physical infrastructure, work environment, and emotional factors. First, many researchers have suggested the presence of an inverse relationship between QoL and stress. In particular, "Yerkes-Dodson law" states that "As the difficultness of discrimination is increased the strength of that stimulus which is most favorable to habit formation approaches the threshold." [18],[19],[20],[21],[22],[23],[21] Interpretation of this law suggests that there is an inverted U-shaped relationship between QoL achieved through efficiency of coping and arousal due to stress. [18],[19],[20],[21] Deducing from this logic, the peak accomplishment of QoL probably occurs by stimulus of moderate-to-high levels of job stressors, which facilitate transformation. [18],[19],[20],[21] Further, very low levels of job stress might lead to mix-up of extraneous and pertinent cues, leading to very little or no change toward better QoL. [18] The term "eustress" or good stress was coined by Hans Selye. "Eustress" or good stress, [22] concept suggests that there may be some common benefits accrued due to stress until it reaches a certain level. Beyond such cut-off, stress is likely to have negative effects, turning into "distress." Second, it is a possibility that workers experiencing highest levels of stress had to drop out of the study, probably even leave their jobs, and the ensuing survivor bias might have affected the results. In such a scenario, those who could handle stress well continued to work, got promoted in their jobs, and possibly reported higher levels of QoL.

Further, stress (eustress of distress) may not be a simple consequence of exposure to stressors alone because varying perception of stressors by different individuals plays an important role in this phenomena. [23],[24],[25] Stress appraisal models by Folkman and Lazarus can be more apt in this context as an explanation for the observations. [26],[27] These models offer a view based on the perception, coping, and interpersonal attitudes.

Fourth, it is possible that extraneous variables connecting job stress and QoL might be responsible for the observed association. The five possible domains that have important bearing on the determination of QoL are health of the individual, his/her general level of satisfaction, personal values, income, and aspirations. Health of an individual depends on his/her physical, mental, social, and emotional well-being. Self-perceptions of QoL is another important determinant. One may be perfectly healthy and still may consider him/herself sick because of the inability to appreciate the healthiness of self. In general, the professionals working in IT/ITES industry appear to be satisfied with their life. [28] Personal values and aspirations also play a significant role in determining QoL. Professionals who reach the top or perform well in their filed are often the individuals who can cope successfully with work pressures. As a result, they can be expected to have better QoL in most aspects. There is evidence suggesting that social support may reduce conflict, time pressure, and ambiguity. [29] Parental demands, satisfaction in marital relations, and family conflicts interact with job stressors and influence overall satisfaction with life. [30]

  Conclusions Top

The current study had some major strengths. A large sample size allowed us to capture the experiences of participants from different background and to adjust for several confounders simultaneously. Moreover, use of a standardized instrument for assessing occupational stress and QoL improves validity and permits external comparison of study findings.

Nonetheless, few important limitations affected this study. Because of the cross-sectional nature of data collection, it is not possible to rule out temporal ambiguity and as a result observed positive associations cannot be interpreted as causal. Moreover, we did not measure coping mechanisms such as sense of coherence and personality characteristics, which could be potential mediators in the pathway between stress and QoL. As with any observational study, we cannot rule out the presence of selection bias and confounding as well as their possible role in the reported association.

Notwithstanding the limitations, our research provides an important insight into a relatively less explored area of occupational epidemiology. Job stress is reportedly associated with higher income, higher control job categories that also pay better. The respondents in this study seemed to find the trade-off of higher stress to be acceptable in lieu of corresponding improvement in their QoL. It is also possible that "eustress" might actually be responsible for betterment in QoL - either directly or through mediation of variables such as personal values and aspirations, personality characteristics, income, and others. Large-scale future studies, preferably prospective in design, can be helpful in providing conclusive evidences in this regard.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Group W. Development of the WHOQOL: Rationale and current status. Int J Mental Health 1994;23:24-56.  Back to cited text no. 1
Group W. The development of the World Health Organization quality of life assessment instrument (the WHOQOL). Quality of life assessment: International perspectives Heidelberg: Springer Verlag; 1994. p. 41-60.  Back to cited text no. 2
Jha A, Sadhukhan SK, Velusamy S, Banerjee G, Banerjee A, Saha A, et al. Exploring the quality of life (QOL) in the Indian software industry: A public health viewpoint. Int J Public Health 2012;57:371-81.  Back to cited text no. 3
Kesavachandran C, Rastogi S, Das M, Khan AM. Working conditions and health among employees at information technology-enabled services: A review of current evidence. Indian J Med Sci 2006;60:300.  Back to cited text no. 4
[PUBMED]  Medknow Journal  
Babu GR, Mahapatra T, Detels R. Application of mixed methods for exploration of the association of job stress and hypertension among software professionals in Bengaluru, India. Indian J Occup Environ Med 2013;17:41.  Back to cited text no. 5
[PUBMED]  Medknow Journal  
(WHO) WHO. WHOQOL: Measuring quality of life. Division of Mental Health and Prevention of Substance Abuse, World Health Organization; 1997.  Back to cited text no. 6
Giridhara R Babu RD. Chapter. 3. A Qualitative study about work-environment of software professional in Bengaluru, India [Papers]. Los Angeles: University of California Los Angeles; 2012.  Back to cited text no. 7
Borle A, Gunjal S, Jadhao A, Ughade S, Humne A. Musculoskeletal morbidities among bus drivers in city of Central India. Age 2012;46:28-57.  Back to cited text no. 8
Giridhara R Babu RD. Chapter. 2. Methods of IT/ITES study in Bengaluru, India [Papers]. Los Angeles: University of California Los Angeles; 2012.  Back to cited text no. 9
Institute S. SAS software: Version 9.1. SAS Institute Cary, NC; 2002.  Back to cited text no. 10
McCullagh P. Regression models for ordinal data. J Royal Stat Soc B 1980:109-42.  Back to cited text no. 11
Babu GR, Mahapatra T, Detels R. Job stress and hypertension in younger software professionals in India. Indian J Occup Environ Med 2013;17:101.  Back to cited text no. 12
[PUBMED]  Medknow Journal  
Commissaris D, Douwes M, Schoenmaker N, de Korte E, editors. Recommendations for sufficient physical activity at work 2007.  Back to cited text no. 13
Work EAfSaHa. Health and safety at work in Europe (1999-2007): A statistical portrait. Luxembourg: European Union; 2010.  Back to cited text no. 14
Bhattacharya S, Basu J. Distress, wellness and organizational role stress among IT professionals: Role of life events and coping resources. J Indian Acad Appl Psychol 2007;33:169-78.  Back to cited text no. 15
Chaturvedi S, Kalyanasundaram S, Jagadish A, Prabhu V, Narasimha V. Detection of stress, anxiety and depression in IT/ITES professionals in the Silicon Valley of India: A preliminary study. Primary Care Community Psychiatry 2007;12:75-80.  Back to cited text no. 16
Albrecht GL, Devlieger PJ. The disability paradox: High quality of life against all odds. Soc Sci Med 1999;48:977-88.  Back to cited text no. 17
Teigen KH. Yerkes-Dodson: A law for all seasons. Theory Psychol 1994;4:525-47.  Back to cited text no. 18
Broadhurst P. The interaction of task difficulty and motivation: The Yerkes-Dodson law revived. Acta Psychol 1959;16:321-38.  Back to cited text no. 19
Winton WM. Do introductory textbooks present the Yerkes-Dodson Law correctly? American Psychologist; Am Psychol 1987;42:202.  Back to cited text no. 20
Broadbent DE. A REFORMULATION OF THE YERKES-DODSON LAW. Br J Math Stat Psychol 1965;18:145-57.  Back to cited text no. 21
Selye H. On the real benefits of eustress. Psychol Today 1978;11:60-70.  Back to cited text no. 22
Fevre ML, Matheny J, Kolt GS. Eustress, distress, and interpretation in occupational stress. J Managerial Psychol 2003;18:726-44.  Back to cited text no. 23
Selye H. Confusion and controversy in the stress field. J Human Stress 1975;1:37-44.  Back to cited text no. 24
Selye H. Selye′s guide to stress research: Van Nostrand Reinhold Company; 1983.  Back to cited text no. 25
Folkman S, Lazarus RS, Dunkel-Schetter C, DeLongis A, Gruen RJ. Dynamics of a stressful encounter: Cognitive appraisal, coping, and encounter outcomes. J Person Soc Psychol 1986;50:992.  Back to cited text no. 26
Folkman S, Lazarus RS. If it changes it must be a process: Study of emotion and coping during three stages of a college examination. J Person Soc Psychol 1985;48:150.  Back to cited text no. 27
Kanwar YPS, Singh AK, Kodwani AD. Work-Life Balance and Burnout as Predictors of Job Satisfaction in the IT-ITES Industry. Vision 2009;13:1-12.  Back to cited text no. 28
Carlson DS, Perrewé PL. The role of social support in the stressor-strain relationship: An examination of work-family conflict. J Management 1999;25:513-40.  Back to cited text no. 29
Bedeian AG, Burke BG, Moffett RG. Outcomes of work-family conflict among married male and female professionals. J Management 1988;14:475-91.  Back to cited text no. 30


  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]

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