research paper on quality of work life pdf

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Quality of Work Life and Organizational Performance: Workers’ Feelings of Contributing, or Not, to the Organization’s Productivity

João leitão.

1 Universidade da Beira Interior, 6201-001 Covilhã, Portugal; tp.ibu@anid (D.P.); [email protected] (Â.G.)

2 NECE–Research Center in Business Sciences, 6200-209 Covilhã, Portugal

3 CEG-IST–Centro de Estudos de Gestão do Instituto Superior Técnico, 1049-001 Lisboa, Portugal

4 ICS–Instituto de Ciências Sociais, 1600-189 Lisboa, Portugal

Dina Pereira

Ângela gonçalves.

This is a pioneering study on the relationship between quality of work life and the employee’s perception of their contribution to organizational performance. It unveils the importance of subjective and behavioral components of quality of work life and their influence on the formation of the collaborator’s individual desire to contribute to strengthening the organization’s productivity. The results obtained indicate that for workers: feeling their supervisors’ support through listening to their concerns and by sensing they take them on board; being integrated in a good work environment; and feeling respected both as professionals and as people; positively influence their feeling of contributing to organizational performance. The results are particularly relevant given the increased weight of services in the labor market, together with intensified automation and digitalization of collaborators’ functions. The findings also contribute to the ongoing debate about the need for more work on the subjective and behavioral components of so-called smart and learning organizations, rather than focusing exclusively on remuneration as the factor stimulating organizational productivity based on the collaborator’s contribution.

1. Introduction

Employee workplace performance is related to a set of factors affecting workers’ health, habits and environment, employees’ well-being and quality of work life (QWL). QWL is associated with job satisfaction, motivation, productivity, health, job security, safety and well-being, embracing four main axes: a safe work environment; occupational health care; appropriate working time; and an appropriate salary [ 1 ]. As originally stated in [ 2 ], the concept embraces the effects of the workplace on job satisfaction, satisfaction in non-work life domains, and satisfaction with overall life, personal happiness and subjective well-being. Moreover, improving employees’ QWL will positively affect the organization’s productivity, while augmented productivity will strengthen QWL [ 3 ].

In the literature of reference, there is an ongoing and fruitful discussion about the components of QWL [ 3 ] and its different associations with metrics of non-economic performance, namely satisfaction and fulfillment of physical conditions considered basic to ensure functionality, health and safety in the workplace [ 1 ].

The most sensitive components of the QWL, still unexplored, are intrinsically related to the socio-emotional and psychological needs of employees, which require the application of more behavioral lenses, in order to unveil the components that can most influence job satisfaction and motivation, but also productivity [ 4 , 5 ].

In the context of health organizations, the relationship between QWL and productivity was already investigated, suggesting the design of adequate strategies to reinforce the productivity in hospitals [ 6 ]. However, little is known about the different ways in which the behavioral and subjective components of the QWL can influence the employee’s feeling of contribution to the productivity of the organization that they integrate.

As stated before, there is still room to advance knowledge about the effects associated with subjective components of assessment of satisfaction with QWL on organizational performance, considering a response variable of particularly critical importance in the context of reducing investment in resources and simultaneous pressure to maximize results, i.e., productivity [ 7 ]. Therefore, it is particularly opportune to investigate the non-economic (that is, subjective or behavioral) motivations that lead to collaborators’ willingness to contribute to strengthening their organization’s productivity.

Following the Organisation for Economic Co-operation and Development (OECD)’s view of productivity indicators, there are plenty of productivity differences across organizations that require further studies to open up the organizational ‘black box’, concerning internal productivity determinants [ 8 ]. In fact, there is a need to advance knowledge about the individual determinants of organizational productivity. An example of this challenging task is the recent project launched by the Global Forum on Productivity (GFP), entitled: ‘The Human Side of Productivity’; considering a multidimensional approach applied to organizations, considering key people, such as workers, managers and owners [ 9 ].

Recently, in the context of public higher education, the role played by quality of life in determining satisfaction of internal stakeholders, such as students and collaborators (e.g., administrative staff, teachers and researchers), was also assessed. This opens up a research avenue concerning the lack of knowledge about the role played by the specificities of different organizational cultures in this type of institution, in influencing perception of academic quality of life by both internal and external stakeholders [ 10 ].

In this sense, there is still an open debate about the need for further understanding of the importance of organizational culture, using crossed perspectives on organizational and individual health, to be able to provide strategic lines for new organizational policies. These should be increasingly funded on a particular set of values and beliefs determining an organization’s behavioral objectives, aligned with the desired self-efficacy in terms of employees’ management and motivation [ 11 ].

Following this debate, the current study is particularly relevant, from the view that there is still limited knowledge about the necessary conditions to promote the subjective or behavioral components of satisfaction with QWL, focusing on each collaborator’s contribution to fostering the organization’s productivity. For example, a myth revisited here, through lack of thorough existing knowledge, is that productivity depends mainly on the remuneration attributed to performing certain functions. As yet unexplored subjective or behavioral factors, such as the collaborator feeling appreciated by the supervisor, the availability of jobs not subject to routines and where innovation is possible, promotion of continuous learning environments, the feeling of protection promoted by the supervisor, the feeling of having a really important and useful job, the possibility of the job allowing the development of new skills and reinforcing the conditions for personal and professional growth, are given special attention in this study. A data survey, which is pioneering in European terms, is followed by statistical and econometric treatment to shed new light on a little-explored relationship. i.e., the relationship between QWL and organizational performance, using a subjective measure of assessment of satisfaction expressed through collaborators’ feeling of contributing to organizations’ productivity.

Despite the limitations associated with the use of this dependent variable with subjective nature, its use seems to be justified, on the one hand, given the lack of studies using the behavioral lens to study the relationship between QWL and organizational performance. On the other hand, as it is not the objective of the present study to compare the relationships and the associated significance, using objective measures versus subjective measures, for the purposes of representing the dependent variable: organizational performance.

In turn, the current study aims to reveal employees’ satisfaction with the opportunities and conditions provided by their employer in six European countries, by looking after their QWL and their interests in pursuing a healthier, more satisfactory and happier lifestyle, as well as how the workplace can provide opportunities for them to improve productivity.

This study contributes to the literature on QWL and organizational performance in two ways, firstly, by identifying the determinant factors that can have a significant influence on employees’ understanding of their contribution to organizational performance, represented here by an alternative measure regarding the contribution to organizations’ productivity. Secondly, it provides new insights into complete fulfillment of the functions of human capital managers, revealing the importance of subjective and behavioral components of QWL that can help to design desirable collaborator behavior more likely to strengthen productivity in the organizational context.

The research partners involved in the survey design and administration developed an innovative tool to gather information for assessment of QWL. Afterwards, the survey was administered to 514 employees of local companies and public organizations in six European countries. Some highlights from the preliminary results obtained from the survey’s administration can be illustrated as starting points for the current study. Namely, 80% of respondents said they feel physically safe at work and more than 77% are satisfied with the fact that their workplaces are safe and sanitary. Almost 82% of respondents feel that their organization matches their skills with the needs of their jobs and 76% are satisfied with their workplaces’ maintenance/cleaning conditions. A substantial group (80%) of employees feel they are contributing to the organization’s productivity, and the great majority (83%) of employees revealed that having an important job is extremely important to be productive.

The first impression is that the collaborators seem to be aware of the importance of standard human capital management procedures and conditions oriented to the reinforcement of organizational performance. Nevertheless, it is worth noting that there is a need to address an organizational ‘black box’, an aim of the current study, that is, the set of subjective and behavioral components to promote QWL that can directly influence employees’ feeling of contribution to organizational performance, especially concerning productivity.

The remainder of the paper is structured as follows. After a literature review leading to formulation of the research hypotheses, the research methodology is presented. Next, the results are discussed, followed by the conclusions, limitations and implications.

2. Literature Review and Research Hypotheses

2.1. revealing the relationship between organizational performance and qwl.

There is no simple or universally recognized definition of what performance is at the level of an individual organization. Organizational performance is multidimensional, connected to its goals and objectives, and may be defined as an organization’s ability to use its resources efficiently, and to produce outputs that are consistent with its objectives and relevant for its users [ 12 ]. Analyzing organizational performance is a crucial step in the organizational assessment process [ 13 ]. In doing so, in the literature of reference, three main domains of organizational performance have been reported, namely: financial performance; operational performance; and organizational effectiveness [ 14 ]. Concerning the conceptualization of organizational performance, four main elements should be taken into consideration: effectiveness; efficiency; relevance; and financial viability [ 13 ].

People are the organization’s most important asset [ 15 ], and so the way an organization manages people’s impacts has a major influence on organizational performance [ 16 ].

Performance management is a continuous process of identifying, measuring and developing the performance of individuals and teams and aligning performance with the organization’s strategic goals [ 17 , 18 ]. The previous arguments are examples of cornerstone visions regarding the need to advance the knowledge available on subjective and behavioral components affecting the relationship between organizational performance and QWL.

Nevertheless, various performance management systems are found in the literature and these systems have some advantages, such as: increased motivation to perform; increased self-esteem; managers gain insights into subordinates; organizational goals are made clear; employee misconduct is minimized; organizational change is facilitated; motivation and commitment to stay in the organization are increased; and employee engagement is enhanced [ 19 ]. In fact, performance management systems are the source of information when making decisions about rewards and the allocation of resources, succession planning and staffing strategies [ 20 ].

Each employee’s emotional intelligence has an effect on behavior which ultimately affects achievements and performance in the workplace [ 21 ]. The satisfaction of employees’ needs through organizational development is at the core of the QWL movement [ 22 ]. Enhancing QWL will result in improved productivity, and in turn, gains in productivity will strengthen QWL [ 3 ].

Improving QWL and performance is of extreme importance, as productivity and innovation are part of the political agenda of European Union countries. With fewer people in the workforce due to an aging population there is a need to enhance labor productivity [ 23 ]. The quality of work life is covered in the guidelines for the employment policies of member states [ 24 ].

Previous applied empirical work [ 25 ] pointed out the existence of a positive and significant relationship between QWL and organizational performance, as well as a positive and significant association between QWL and employees’ job satisfaction.

Another study [ 26 ] found that employee commitment partially mediates the relationship between QWL and organizational performance; and also unveiled that work environment significantly affects employee commitment and thus organizational performance. It was also advocated that improving the QWL of an organization could achieve a heightened job satisfaction, commitment and also improved performance [ 27 ]. In order to achieve a higher employee commitment and consequently a better organizational performance, it is suggested for managers to pay attention to the different dimensions of QWL [ 26 ].

In contrasting terms, previous scholars [ 28 ] reported a negative but non-significant relationship between QWL and organizational performance, although it was also found a positive relationship between employee’s job satisfaction and organizational performance. This type of mixed evidences raises the interest for advancing knowledge about still unexplored subjective and behavioral components of the QWL and their influence on organizational performance.

2.2. Exploring Subjective and Behavioral Components of QWL

Quality of life is an elusive concept regarding the assessment of societal or community well-being from specific evaluation of individual or group cases [ 29 ]. The literature has associated a high quality of life with higher levels of productivity at the workplace. Therefore, increasing attention has been paid to the role played by occupational stress, including job demands, job control, job insecurity, organizational justice, intra-group conflict, job strain, effort-reward imbalance, employment level and shift work. In turn, this has been correlated with factors that negatively affect quality of life, namely insomnia, which results in impaired work performance and leads to significant productivity losses for organizations [ 30 ].

Quality of life is modulated by a wide range of factors, among them psychosocial parameters, health conditions and well-being in the workplace, as well as the adequacy of working resources and infrastructures provided. Policies and regulations created based on employees’ individualized considerations have suggested significant productivity improvement due to subjective components, such as trust, commitment, satisfaction and control. Nevertheless, the research opportunity remains to deepen knowledge about the role played by both subjective and behavioral components of QWL.

For instance, social support, reflecting individuals’ integration into a social group, has been reported as an important indicator of quality of life in occupational performance [ 31 ]. Infrastructures also have an important role in providing well-being in the workplace and therefore modulating the quality of life. It has been suggested that providing green lawns in urban areas enhances quality of life in the workplace, maximizing employees’ social interaction, physical activity and connection with nature [ 32 ]. Shiftwork has been reported as worsening the quality of life [ 33 ].

Cooperative decision making, adequate recognition and supportive supervisors are considered fundamental to QWL [ 34 ], with appropriate job performance feedback and favorable relations with supervisors being said to have a direct impact on QWL [ 35 ]. Another study [ 36 ] goes further and reveals that supervisory behavior is the most important component of QWL, contributing to the variance in the employee’s role efficacy by as much as 21%.

Considering the previous statements in the literature, the following research hypothesis is derived:

Workers who feel that they are supported and appreciated by their supervisors are more likely to feel that they contribute to the organization’s productivity.

QWL is considered a multi-dimensional construct with no clearly accepted definition of the term. This subjective definition means accurate measurement of its parameters is complex. QWL differs from job satisfaction [ 2 ], as job satisfaction is considered one of the outcomes of QWL. In turn, QWL is mainly associated with job satisfaction, motivation, productivity, health, job security, safety and well-being [ 37 ].

Following [ 1 ], QWL involves four major parts: a safe work environment; occupational health care; appropriate working time; and fitting salary. According to [ 2 ], QWL involves the effect of the workplace on satisfaction with the job, satisfaction in non-work life domains, and satisfaction with overall life, personal happiness and subjective well-being.

The factors relevant to employees’ QWL include the social environment within the organization, the relationship between life on and off the job, the specific tasks they perform and the work environment [ 38 ].

Providing safe and healthy working conditions aims to ensure the employee’s good health, thus, taking measures to improve QWL is expected to increase employee’s motivation ultimately leading to the enhancement of performance and productivity [ 38 ].

Accordingly, a work environment that is able to fulfill the employee’s personal needs will lead to an excellent QWL [ 39 ].

Thus, the following research hypothesis is considered:

Workers who feel that they are integrated in a good working environment are more likely than others to feel that they contribute to the organization’s productivity.

Researchers have proposed differentiated models concerning QWL. For example, in [ 39 ] a model is proposed in which the needs of psychological growth were connected to QWL. The same authors recognized several needs: skill variety; task identity; task significance; autonomy; and feedback.

In [ 2 ], a model is originally proposed founded on five critical key-factors concerning the satisfaction of workers’ needs, namely: (i) work environment; (ii) job requirements; (iii) supervisory behavior; (iv) ancillary programs; and (v) organizational commitment.

The second vision is highly valued in organizations committed to playing a responsible role in society, since QWL benefits the employee’s pride, social commitment, satisfaction and the organization’s contribution to society [ 11 , 40 ]; and can also be positively influenced by organizational support, for instance by relieving fatigue and enhancing self-efficacy [ 41 ].

QWL has been considered as the condition experienced by the individual in terms of the dynamic pursuit of their hierarchically organized goals within work domains, whilst reducing the gap separating the individual from these goals can have a positive impact on the individual’s general quality of life, organizational performance, and consequently on the overall functioning of society [ 42 ].

Furthermore, QWL is a phenomenon that can originate a change in terms of organizational culture, since the former corresponds to employees’ interpretation of all the conditions in a workplace and their perception of those conditions [ 43 ].

In a related vein, QWL can be approached as an indicator of the overall quality of the human experience at work [ 44 ]. The same author advocates that it creates a favorable workplace, which enhances employee well-being and satisfaction.

Employees that feel they are treated with respect by people they work with, and employees who feel proud of their job, increase their feeling of belonging to the company, thus feeling that they are an asset to the organization [ 45 ]. Studies [ 46 , 47 ] found that feeling respected is a predictor of QWL, together with self-esteem, variety in daily routine, challenging job, autonomy, safety, rewards and good future opportunities; and as already mentioned an improved QWL is expected to lead to a higher productivity [ 48 ].

Considering the previous vision, the QWL construct can be completed by incorporating subjective measures related with employee satisfaction, motivation, involvement and commitment with respect to their lives at work [ 49 ]. In the same vein, QWL corresponds to the degree to which individuals are able to satisfy their important personal needs while employed by the firm. This gives rise to the following research hypothesis:

Workers who are respected as professionals are more likely than others to feel that they contribute to the organization’s productivity.

Employees can experience a better QWL if they have a positive perception of the degree of responsibility of the organization they belong to [ 50 ]. A related study about perceived QWL in Croatia found that employees positively value non-competitive, co-operative work environments for improved quality of life [ 51 ]. In addition, factors like job security, human relations and work-life balance influence QWL positively [ 52 ]. The analysis of the first European Quality of Life Survey found also that positive aspects of work (good rewards, job security, favorable career prospects and interesting work) have a greater impact on life satisfaction and particularly job satisfaction [ 53 ]. In turn, it should be noted that a poor work-life balance lowers employees’ quality of life [ 53 ].

Work-life balance has been positioned in the reference literature as a key component of QWL [ 38 , 54 , 55 , 56 , 57 , 58 ], but it deserves to be noted that the employee’s level of emotional intelligence could influence his/her work-life balance [ 59 ].

It should be noted also that in a previous empirical study [ 60 ] no significant association, neither positive nor negative, between work-life balance and productivity was detected.

Nevertheless, Work-life balance plays an important role in overall life satisfaction and influences experiences in work life by increasing job satisfaction and organizational commitment [ 61 ]. A high level of engagement in work life is likely to produce a positive effect in work-life balance, which can be further enhanced by goal attainment in work life [ 62 ]. Accordingly, the following research hypothesis is derived:

Workers who have the possibility to enjoy the adoption of work-life balance practices in their organizations, are more likely than others to feel that they contribute to the organization’s productivity.

QWL involves acquiring, training, developing, motivating and appraising employees in order to obtain their best performance, in accordance with the organization’s objectives [ 28 ]. QWL is the foundation of employee well-being and leads to better performance [ 26 ].

Skills, occupational improvement and opportunity for training are considered sub components of QWL [ 45 , 63 , 64 ]. The development of skills and abilities can improve job satisfaction and overall QWL, and for its turn QWL can influence the employee’s performance [ 65 , 66 ]. Thus, employees expect to develop their skills and get promoted, ensuring a better performance for the organization [ 67 ]. In turn, training is an activity aimed at enhancing performance, by ensuring the opportunities for development of skills and encouragement given by the management team [ 38 ].

As previously revealed through the empirical evidence obtained in [ 68 ], both QWL and motivation influence employees’ performance positively. High levels of QWL lead to job satisfaction, which ultimately results in effective and efficient performance [ 49 ]. Considering the previous statements and empirical evidence, the following hypothesis is derived:

Workers who feel that their organizations invest in their careers, for example through continuous learning, the development of new skills or supporting professional growth, are more likely to feel that they contribute more than others to the organizations’ productivity.

3. Empirical Approach

3.1. methodology and data characterization.

The research methodology was developed using different questionnaires, which were designed taking into consideration a set of eleven selected international benchmarks, namely: (i) Health and well-being at work: a survey of employees, 2014, UK, Department for Work and Pensions; (ii) ACT Online Employee Health and Wellbeing Survey 2016, Australian Capital Territory Government; (iii) British Heart Foundation 2012, Employee survey; (iv) British Heart Foundation 2017, Staff health and wellbeing template survey; (v) Rand Europe (2015), Health, wellbeing and productivity in the workplace—Britain’s Healthiest Organization summary report; (vi) South Australia Health, Government of South Australia Staff needs assessment, Staff health and wellbeing survey; (vii) Southern Cross Health Society and BusinessNZ, Wellness in the Workplace Survey 2017; (viii) State Government Victoria, Workplace Health & Wellbeing needs survey; (ix) East Midlands Public Health Observatory, Workplace Health Needs Assessment for Employers, February 2012; (x) Tool for Observing Worksite Environments (TOWE). U.S. Department of Health & Human Services; and (xi) Measure of QWL, as originally proposed in [ 2 ].

The survey was conducted from April to July 2018. Twelve partners from Italy, Bulgaria, Cyprus, Portugal, Greece and Spain participated in data collection, by interviewing employees. The sample covers 15 private companies and five public entities or large firms per partner, involving two employees per organization and totaling 514 questionnaires. It was not intended to interview company owners or general managers to avoid bias in the responses.

A convenience sample procedure based on random selection was used. In each organization, a contact person was identified to ensure completion of the questionnaire, which was afterwards validated by the research team. The questionnaires were applied by personal interviews to ensure a maximum response rate.

The partners followed the following instructions in selecting interviewees: 15 companies among micro, small and medium-sized firms (10% of interviewees for each category—EU definition of SME), plus five among large firms and public entities.

The main aim of the study is to assess the influence of workers’ QWL on the perception of their contribution to organizational performance. The degree of novelty here lies in the innovative assessment of both subjective and behavioral components of workers’ QWL, embracing different types of organizations (e.g., public or private) with distinct dimensions and economic activities. A total of 514 questionnaires were collected involving organizations from the six European countries engaged in the data collection process.

The questionnaire includes two sections: (1) QWL (needs, work environment, work requisites, supervisor behavior, auxiliary programs inside the organization, organizational pressure, and organizational performance and commitment); and (2) sample characterization (gender, age, marital status, position in the organization, level of qualifications, organization’s sector of activity, size and age of the organization, type of employee contract and employee qualifications). In the first section, Likert scales (e.g., ranging from 1 to 7) were used to assess the level of agreement with a set of sentences in each sub-section, scales that had been transformed into binary considering the variables under analysis, namely the Feeling of contributing to productivity, Supervisors’ support, Good work environment, Professional respect and Work-life balance. In the second section, levels of answer were used. Below, the sample is characterized and a set of results for the whole sample is presented.

3.2. Sample Characterization

Sample and descriptive statistics.

Concerning respondents’ gender, 48% were women and 52% men. Relative to age, 9% were aged between 20 and 25, 34% between 26 and 35, 37% between 36 and 45, 14% between 46 and 55 and only 7% were older than 55. 35% were single, 59% married and almost 7% are in another non-defined situation. In terms of organizational role, 18% said they occupied a managerial role inside the organization, 67% a qualified role and 16% a non-qualified position. Regarding education, 51% have a college degree and 22% a post-graduate degree, 19% completed secondary education, 7% completed 9 years at school and only 1% completed 4 years. Concerning the sector of activity of the respondents’ organizations, almost 2% belong to the primary sector, 14% to the secondary, 77% to the tertiary and 7% to public organizations. The majority of respondents work in small and medium sized firms, 26% in companies with one to nine employees, 39% in firms with 10 to 49, 15% in companies with 50 to 249, 14% in companies with 250 to 1000 and 6% in companies with over 1000 employees. Concerning the organizations’ age, 16% are between 1 and 6 years old, 34% between 7 and 15 years, 25% between 16 and 29, almost 17% between 30 and 49 years and almost 8% have been in existence for more than 50 years. Concerning respondents’ contract type, 68% said they have a permanent contract, 11% a contract for a stipulated period, almost 9% were temporary, 5% were freelancers and 9% reported another sort of contract. Lastly, respondents were asked about their qualification inside the firm, with almost 7% saying they were senior managers, 10% intermediary managers, almost 17% staff in charge, 21% highly qualified employees, approximately 25% qualified, 6% semi-qualified and 8% non-qualified. In addition, 3% said they were apprentices and 1% said they did not know.

In descriptive terms, for the employees, it is observed that the items in which they feel more in agreement in their workplaces are professional respect as workers and people (70%), followed by the existence of a good work environment (65%), as seen in Table 1 presented below. For 62% of respondents having the supervisors’ support is essential. Approximately 37% denote the importance of having a work-life balance and 57% show that the organizations’ support for skills development is essential. Approximately 80% of the workers feel they really contribute to the organization’s productivity. Looking at the correlations matrix we can observe that the items most associated with the workers’ sense of contribution to the organizations’ productivity are professional respect, having a good work environment, and lastly supervisors’ support.

Descriptive Statistics and Correlation Matrix.

Source: Own elaboration. Significance levels: * p < 0.10. ** p < 0.05. *** p < 0.0. SME: Small and Medium-sized Enterprises.

The variables presented above were subsequently used in estimation processes, considering two distinct models: (1) an Ordinary Least Squares (OLS) model; and (2) a Multinomial Logit model; in order to reveal the set of subjective and behavioral components of QWL that influence the workers’ perception of contribution to productivity. The main reasons for using the two models are as follows: (i) estimation of the OLS model is justified by the dataset analyzed following normal distribution, considering a dependent variable represented in binary terms, which can determine the probability of the influence of a hypothetical set of independent variables arising from the literature review presented above; the dependent variable takes the value of 1, when the employee states they feel they contribute to productivity; and 0, otherwise; and (ii) estimation of the multinomial model can test a representation at level of the same dependent variable, which lets us, first, contrast the empirical evidence with Model 1, and secondly, determine the variability of the probability of influence of the same hypothetical set of independent variables, through comparison of the results between a baseline corresponding to: ‘not contributing to productivity’ (level 1); ‘contributing to productivity to some extent’ (level 2); and ‘totally contributing to productivity’ (level 3).

To do so, the log-odds for these two categories relative to the baseline are computed, and then the log-odds are considered as a linear function of the predictors. Several control variables were used, namely: gender; age; marital status; employee’s role; employee’s education; organization’s sector; organization’s size; organization’s age; and employee’s position in the organization. The operational model of analysis is as follows ( Figure 1 ):

QWL and Feeling of Contribution to Productivity: Operational model of analysis (Source: Own elaboration).

Table 2 below presents more details and description of the set of variables.

Variables description.

Source: Own elaboration.

4. Results and Discussion

Regarding the results of the OLS regression for the sample considered (see correspondent column of Model 1, in Table 3 ), which used as dependent variable the feeling of contribution to productivity, with the value of 1 when the worker declares they feel they contribute to productivity and 0 otherwise, the LR Chi 2 of 14.38 with a p -Value of 0.0000 indicates that the model as a whole is statistically significant.

QWL: Subjective and behavioral components influencing employees’ feeling of contribution to productivity.

Source: Own elaboration. Significance levels: * p < 0.10. ** p < 0.05. *** p < 0.0; Standard errors in brackets. LR Chi 2 : Likelihood Ratio (LR) Chi-Square test; Prob. > Chi 2 : The prob > chi2 statistic for the overall model is a test of the joint null hypothesis that all of the regression coefficients (other than the constant term) are zero.

As observed in Table 3 below, three statistically significant variables influence workers’ sense of contribution to productivity, namely: (i) professional respect; (ii) having a good work environment; and (iii) feeling supervisors’ support. Interestingly, work-life balance and the organization’s skills development support do not have any significant influence on the feeling of contribution to the organizations’ productivity.

Moreover, from the control variables tested in the first model, it should be noted that employees’ college education level has a significant and positive effect on their feeling of contribution to productivity.

In Model 2, the likelihood ratio quotient of 22.06 with a p -Value of 0.0002 signals that the model as a whole is statistically significant. Here, a set of predictors related to collaborators’ sense of contribution to productivity (computing a categorical variable with three levels: 1, not contributing to productivity; 2, contributing to productivity to some extent; and 3, totally contributing to productivity; are considered in the empirical application.

Regarding the sense of contributing to some extent to organizations’ productivity, only work-life balance denotes a significant, although negative, influence. Moreover, the older the workers are the more likely they are to feel somehow productive to their organizations. Concerning level 3, representing the feeling of totally contributing to the organization’s productivity, workers feeling respected by their companies, sensing that their organizations make them feel confident and value their contribution affects in a positive and significant way the high level of feeling they contribute to firms’ productivity. Workers who feel they are highly productive are also older and those occupying managerial roles and direction positions in their organizations

Contrasting the two estimation processes, we conclude that the OLS model reveals most predictors explaining workers’ feeling of contribution to productivity, by detecting positive and significant influences of 3 out of 6 subjective and behavioral components of QWL. Going deeper, it is important to crosscheck what predicts the collaborator’s feeling of lack of contribution to productivity, in order to improve the management capacity of human capital, following a behavioral approach.

Bearing in mind the set of research hypotheses under examination, new insights arise concerning the subjective and behavioral components of QWL influencing employees’ feeling of contribution to productivity.

Thus, model 1 gives support to H1a, as workers who feel they are supported and appreciated by their supervisors feel they contribute more to the organizations’ productivity than others. These findings are in line with prior findings of [ 30 ], stressing the importance of workers being supported and appreciated for increased productivity.

Model 1 supports H2, as we detect a significant and positive influence of good workplace environments, by being safe and sanitary, on workers’ feeling of productivity. Such results are aligned with prior studies which detected a positive association between job security, safety and well-being at the workplace and job productivity, satisfaction and motivation [ 37 ], and the existence of a safe work environment and its positive impact on productivity [ 1 ]. These results are aligned with prior literature, which found that by being involved in a socially supportive group inside the workplace, employees are more likely to contribute to organizational performance [ 31 ]. In the same line of reasoning, a study referred to previously, applied to the Croatian context [ 51 ], identified an important impact of co-operative working environments on QWL.

We found support for H3, as workers who feel respected as professionals (in Models 1 and 2) contribute more to organizations’ productivity than others. In Model 1, our empirical findings reveal a positive and significant influence of workers being professionally respected on the sense of feeling productive. Regarding the findings of Model 2, this influence is also important but only for the group of workers who feel they contribute greatly to the organization’s productivity. This corroborates the rationale of the model proposal found in [ 39 ], which outlined that the needs for psychological growth covering the different frameworks associated with professional valorization and respect (namely, skill variety, task identity and significance, autonomy and feedback) are connected with QWL and thus performance. Moreover, our results ratify the concluding remarks of previous scholars [ 11 , 40 ], who defended that employees’ sense of pride and commitment, in relation to being valued as professionals, increases their contribution. These visions are also in agreement with previous empirical findings denoting a positive effect of the worker being considered and taken into consideration in the organizations’ goals on performance [ 42 ].

Concerning H4, which states that workers who have the possibility to enjoy the adoption of work-life balance practices in their organizations, feel they contribute more to the organizations’ productivity than others, no significant evidence is found in Model 1. Moreover, in Model 2 we detect a significant, although negative, effect of employees’ feeling that the organization has a work-life balance vision on the feeling of contributing to productivity and so this hypothesis is rejected. This can be justified by the lack of work-life balance practices on the part of supervisors and the organization itself, as well as possible development of a negative emotion concerning the work-life balance allowance, which in certain organizational contexts could be interpreted as a mode of diminishing the potential leadership responsibilities given to target-workers.

The results are contrasting, but do not reject the previous findings in [ 52 ], which argued for a positive association between work-life balance and quality of work life, thus spurring productivity. In a similar vein, achieving a balance between private and professional life is expected to be positively associated with organizational commitment and, thus, with productivity at work [ 61 ]. In fact, the empirical findings obtained here not only do not contradict the previously identified positive association between work-life balance and QWL, but also shed some light on ‘invisible ceiling’ issues related with the gender leadership issue and supervisors’ behavior within the organizational context, which need to be further explored in future research concerned with organizational productivity based on the individual behavior (of supervisors and workers) and subjective well-being influenced in the scope of the organizational context’s boundaries.

We found no support for H5, stating that workers who feel their organizations invest in their careers and skills development, for example through continuous learning, the development of new skills or supporting professional growth, contribute more to organizations’ productivity than others. Interestingly, our findings do not seem to be related with prior work, for example, in [ 39 ], which pointed out an association between professional valorization (skill variety), QWL and performance, as well in [ 28 ], where positive argumentation was given to reinforcing investment in employees’ training, to be able to achieve better performance levels in the future. This contrasting result could be justified by the productivity measure used, being a subjective measure, concerning the perception of being productive. These results also contrast with prior literature defending a positive association between organizational investment in workers’ management and organizational performance [ 16 ], as well as paying attention to employee management systems, aligning the goals of the organization with career decisions, rewards, structured growth and thus impacting positively on workers and organizations’ performance.

5. Conclusions

This study analyses, in an innovative way, the influence of subjective and behavioral components of QWL on organizational performance, measured through collaborators’ feeling of contribution to the organization’s productivity. The empirical findings show the importance of factors related with workers having their supervisors’ support, integration in a good work environment and feeling respected both as professionals and as people.

One of the research challenges addressed here, in a pioneering way, is the use of a subjective measure of collaborators’ commitment to organizational productivity, attempting to provide new implications for organizational management, taking into account components that were hitherto unexplored empirically, various subjective and behavioral components that require greater knowledge to address, in an alternative way, improved organizational performance and behavioral drivers of productivity, rather than relying exclusively on increasing collaborators’ remuneration.

Adopting a more behavioral line of organizational management, and integrating the emerging literature on the QWL construct originally proposed in [ 7 ], this analysis contributes to the literature on QWL and organizational performance, bringing two axes of reasoning founded on new empirical evidence, namely: (1) identifying factors that can influence organizational performance, represented here by an alternative measure referring to the collaborator’s feeling of contributing to the organization’s productivity; and (2) proposing a new agenda for human capital managers, focusing on the importance of subjective and behavioral components of QWL, which can help to strengthen productivity in the organizational context, following a behavioral approach both at the company and individual level.

Regarding implications, the evidence obtained signals that human capital managers committed to reinforcing organizational productivity through changing the behavior of collaborators and the organization itself should seek to fulfill a new strategic action agenda with the following priorities: (1) fostering an organizational culture that values behavioral practices of supervisor respect for the collaborator (i.e., hierarchical subordinates) in the organizational context; (2) promoting positive emotions and feelings in collaborators that they are appreciated in the workplace; (3) ensuring that supervisors protect collaborators from hazardous conditions, to reduce feelings of uncertainty and risk; and (4) giving importance to the duties and tasks performed by collaborators.

Surprisingly, this study does not present additional evidence to the established view pointing towards the importance of having a work-life balance and companies’ support for workers’ skills development in the contribution to workers’ productivity. This may be justified, on the one hand, by the content of the research question included in the original survey used in the current study that allows us to point out a hypothetically negative feeling concerning the leadership responsibilities given to target workers, without valuing in a proper way the required work-life balance. Nevertheless, there is still great room for improvement as regards promoting the subjective conditions tending to strengthen behaviors oriented towards stimulating organizational productivity, especially, addressing gender issues, balanced management of the trade-offs between personal and professional life; and leadership responsibilities, per gender role.

The main limitations of the analysis concern the impossibility of carrying out a study with a time dimension, which could determine hypothetical relationships of causality (or precedence) between subjective and behavioral components and organizational performance. Another limitation is in relation to the response variable representing organizational productivity being based on a subjective measure of the collaborator’s perception of individual contribution to organizational productivity. Nevertheless, considering the difficulty in obtaining data of a subjective nature and the aims of this study, it seems acceptable to consider this alternative measure of the organization’s non-economic performance, which requires future exploration through additional research.

In a related vein, this opens an avenue for tracing further research endeavors, expanding both the number of objective and subjective metrics, in order to gauge the hypothetical differences in the relationships established between QWL’s components and organizational performance, “measured” in objective or subjective terms. This would imply the design of a new questionnaire targeted to assess the feelings of the leaders regarding the performance of workers, and, afterwards, it will be possible to produce a contrasting analysis.

For the future, more thorough study of the relationship between QWL and organizational productivity is suggested, by making a comparative analysis involving different profiles of organizational culture considering other contexts of organizational location, for example, in America, Asia, Europe, Africa and Australasia. In this line of analysis, it would also be interesting to pursue this topic considering different organizational and corporate governance contexts, for example, multinationals, family control, female management, management with ethnic diversity and management with values. Another avenue of future research would be the possibility, in the organizational context, of using new forms of organizational design and management able to change behavior in a subjective, inclusive and participatory way. It is necessary, therefore, to explore how design thinking, organizational gamification and co-creation can mobilize the collaborator to contribute effectively to improved organizational performance.

Acknowledgments

The authors acknowledge the highly valuable comments and suggestions provided by the editor and reviewers, which contributed to the improvement in the clarity, focus, contribution, and scientific soundness of the current study. A special debt of gratitude is also due to the track chair and participants in the 17th Conference of the International Society for Quality-of-Life Studies (ISQOLS), 2019, which took place in the University of Granada, Spain, for providing us with constructive feedback and positive incentives to improve the presentation of the research results, which are used as the empirical basis of this manuscript.

Author Contributions

Conceptualization, J.L.; Formal analysis, D.P. and Â.G.; Methodology, J.L. and D.P.; Writing—original draft, J.L., D.P. and Â.G.

This research has received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

• Corpus ID: 59333551

The Drivers of Quality of Working Life (QWL): A Critical Review

• Anwar Abd , Ahmad Salih Mheidi
• Published 2013
• Business, Psychology

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QUALITY OF WORK LIFE: A KEY TO IMPROVE ORGANIZATIONAL PERFORMANCE

Work is an integral part of our everyday life. On an average we spend around twelve hours daily in the work place, that is one third of our entire life; it does influence the overall quality of our life. It should yield job satisfaction, give peace of mind, a fulfilment of having done a task, as it is expected, without any flaw and having spent the time fruitfully, constructively and purposefully. A happy and a healthy employee will give better turnover, make good decisions and positively contribute to the organizational goal. The work-life balance must be maintained effectively to ensure that all employees are running at their peak potential and free from stress and strain. An assured good quality of work life will not only attract young and new talent but also retain the existing experienced talent. Quality of Work Life in an organization is essential for the smooth running and success of its employees.

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Work is a part of our daily life. We work on an average eight hours daily in business or career or livelihood. That means one third of our life is spending with work. It affects the quality of our life. A satisfied employee can put his best efforts towards achievement of organisational goals. The employer needs to provide a conducive environment in the organisation, to reach the goals. The term quality of work life (QWL) refers to the likeliness or unlikeliness of a job environment for people. A willing worker put his best efforts to achieve organisational goals. Retention of worker is a difficult task in complex environment where more stress is there in one side and opportunities are there on the other side. Satisfaction with pay and relationships with work colleagues, but also factors that broadly focuses on life satisfaction and general feelings of well-being. To retain a good talent in the organisation it is important for the organisation that he should have low stress level and quality of work life. This article focuses on detailed analysis of Quality of work life and its uses to employers and organisations.

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In the present era, organizations cannot sustain competitive advantage and growth unless they take care of the different needs of their employees. Suitable working conditions, monetary and non-monetary benefits, amicable working environment, appropriate working schedules, regular communication etc. are some of the factors which can be helpful in winning the trust and whole hearted commitment of workforce. Quality of work life (QWL) is related to those factors which influence the life of the individuals which they spend while working for their organisations. It encompasses certain subjective and objective indicators which are related to the well being of the employees in the organisation. QWL of employees in an organisation is the indicator of the significance which an organisation attaches to their employees. In this regard, the present paper attempts to examine the concept of QWL. The study discussed four main issues related with QWL. Firstly, individual versus organizational perspective is discussed. Secondly, subjective versus objective approach of QWL is presented. Thirdly, QWL is discussed as a domain of quality of life. In the last, the concept of customized QWL is discussed. The paper is based on secondary sources of data.

AARF Publications Journals

QWL has evolved as an important aspect, which affects an organizational efficiency and productivity. QWL is a multi-dimensional term which provides a good work life balance and gives a qualitative boost to total work environment of any organization. The Human Resources (HR) Managers constantly work on to train, engage and get the most from the valued employees. This leads to employee performance and commitment but ultimately results in pressure, stress and stretched time in the office. According to Richard and Loy, QWL is the degree to which members of a work organization are able to satisfy important personal needs through their experience in the organization. KEY WORDS:-Quality of work life, job satisfaction, Organization commitment, job security, participative management and salary. INTRODUCTION:-Quality of work life (QWL) refers to the favourableness or unfavourableness of a job environment for the people working in an organization. The period of scientific management which focused solely on specialization and efficiency, has undergone a revolutionary change. The traditional management (like scientific management) gave inadequate attention to human values. In the present scenario, needs and aspirations of the employees are changing. Employers are now redesigning jobs for better QWL.QWL is viewed as that umbrella under which employees feel fully satisfied with the working environment and extend their wholehearted cooperation and support to the management to improve productivity and work environment.QWL provides a more humanized work environment. It attempts to serve the higher order needs of workers as well as their more basic needs. Quality of work life denotes all the organizational inputs which aim at the employee's satisfaction and enhancing organizational effectiveness. Definition:-1.-QWL is a process of work organizations which enable its members at all levels to actively; participate in shaping the organizations environment, methods and outcomes. This value based process is aimed towards meeting the twin goals of enhanced effectiveness of organizations and improved quality of life at work for employees. ‖-The American Society of Training and Development.

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Quality of Work Life is the first and foremost factor to retain worthy employees and to attract talented people into the organization. The success of an organization largely depends on human resources and their skills with dedication. Dedication and determination are the outcomes of Quality of Work Life. QWL play a significant role in the success of an organization irrespective of its size and type whether it is a manufacturing or service. QWL concept is not static; it has several dimensions which determine the QWL. Several researchers have proposed different components of QWL which determines the QWL. The present study helps the managements to identify the right set of components of QWL and to enhance their employees' quality of work life intern achieve the organization's objectives and goals. The present study retrospects the different research articles and theses on QWL concept to identify the most influencing components of QWL and categorise them. The purpose of this paper is to suggest a wider and deeper understanding of most important dimensions of QWL.

IJSRD - International Journal for Scientific Research and Development

Quality of work life is the degree to which individuals are able to satisfy their important personal needs while employed by the firm. Quality satisfaction, motivation, involvement and commitment individuals experience with respect to their lives at work Quality of work life is a process in organizations, which enables its members at all levels to participate actively and effectively in shaping the organization environment, methods, and outcomes. The objective of the study is to help the organization to know the level of satisfaction of the workers and executives at various hierarchical levels, towards the facilities and welfare amenities provided by them and also to find out the challenges and difficulties faced by the management in providing better quality of Work life to the employees. Most of the employees covered under my study have not been found to be feeling any stress in their jobs and related working environment. It has been an interesting revelation that there is no employee in ORGANISATION, is working here just for the sake of the job and most of the employees are not only comfortable with ORGANISATION, but also feeling proud of being in the company There should be no communication gap between the team leader and group members. The communication flow must be improved to make it smooth to maintain cordial inter personal relations in the organization. The training and development programs have to be more effectively planned and implemented.

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Dr. Radha Yadav , Ashu Khanna

Quality of work life is becoming an imperative issue to achieve the goals of the organization in every sector whether it is education, service sector, banking sector, tourism, manufacturing, etc. Attrition, employees commitment, productivity etc. depend upon the dimensions of Quality of work life i.e. job satisfaction, organizational commitment, reward and recognition, participative management, work life balance, proper grievances handling, welfare facilities, work environment, etc. An organization provides a better QWL then it develops the healthy working environment as well as satisfied employee. High QWL can give a result in better organizational performance, effectiveness, innovativeness, etc. Consequently, to contribute better life for all those peoples whom organizational members serve and with whom they deal and interact. Today, quality of work life also affects the corporate social responsibility. Quality of work life is the corroboration between the employees and their organization it improves the family life as well as work life of the individual. This paper focuses and analyses the literature review on the quality of work life and their dimensions.

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isara solutions

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QWL (Quality of worklife) is the need of hour and every individual has got right over it. Human resource or workforce is the most valuable asset for the modern organisations today and therefore all the strategies, plans, policies etc. revolve around them in the organisations. As a result QWL has been continuously drawing attention of both organisations and employees. The time when QWL has gained this much significance in every field . It becomes the prime responsibility of organisations to offer their employees a good quality of worklife to affirm that in return employees will report high levels of performance and Job inducement. The objective of present study is to explore various factors affect quality of worklife of banking sector employees The study has identified various dimensions life work environment, compensation, training and development etc. which influence and have great impact on quality of worklife of various employees.

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• Introduction
• Conclusions
• Article Information

Participants continued to live in their home environment without any prescribed diet or physical activity during the 28 consecutive days of the study. Error bars are SEs of the mean. The vertical dashed line separates the two 2-week sleep periods.

A-D, Data are in ascending order of change in sleep duration for the control group and sleep extension group. E, Data were from 74 participants. All available data were used. The line represents the line of best fit from the linear regression model. One participant in the control group and 3 participants in the sleep extension group had missing data in change in sleep duration (ie, missing mean data in at least 1 of 2 study periods). One participant in the control group and 4 participants in the sleep extension group had missing data in change in energy intake. Overall, 1 participant in the control group and 5 participants in the sleep extension group had missing data in either change in sleep duration or change in energy intake.

Trial Protocol

eMethods. Participants, Inclusion and Exclusion Criteria

eReferences

eTable 1. Effect of Treatment on Actigraphy-Based Time in Bed and Sleep Duration on All Days, Workdays and Free Days

eTable 2. Effect of Treatment on Actigraphy-Based Outcomes

eTable 3. Baseline Characteristics of Participants With Complete vs Incomplete Data

eTable 4. Self-Reported Outcomes by Visual Analog Scales

Data Sharing Statement

• Good Sleep, Better Life—Enhancing Health and Safety With Optimal Sleep JAMA Internal Medicine Invited Commentary April 1, 2022 Mark R. Rosekind, PhD; Rafael Pelayo, MD; Debra A. Babcock, MD

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Tasali E , Wroblewski K , Kahn E , Kilkus J , Schoeller DA. Effect of Sleep Extension on Objectively Assessed Energy Intake Among Adults With Overweight in Real-life Settings : A Randomized Clinical Trial . JAMA Intern Med. 2022;182(4):365–374. doi:10.1001/jamainternmed.2021.8098

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Effect of Sleep Extension on Objectively Assessed Energy Intake Among Adults With Overweight in Real-life Settings : A Randomized Clinical Trial

• 1 Department of Medicine, The University of Chicago, Chicago, Illinois
• 2 Department of Public Health Sciences, The University of Chicago, Chicago, Illinois
• 3 Biotechnology Center, Department of Nutritional Sciences, University of Wisconsin–Madison, Madison
• Invited Commentary Good Sleep, Better Life—Enhancing Health and Safety With Optimal Sleep Mark R. Rosekind, PhD; Rafael Pelayo, MD; Debra A. Babcock, MD JAMA Internal Medicine

Question   What is the effect of sleep extension on objectively assessed energy intake in adults with overweight in their usual home environment?

Findings   In this randomized clinical trial of 80 adults with overweight and habitual sleep less than 6.5 hours per night, those randomized to a 2-week sleep extension intervention significantly reduced their daily energy intake by approximately 270 kcal compared with the control group. Total energy expenditure did not significantly differ between the sleep extension and control groups, resulting in a negative energy balance with sleep extension.

Meaning   The findings suggest that improving and maintaining adequate sleep duration could reduce weight and be a viable intervention for obesity prevention and weight loss programs.

Importance   Short sleep duration has been recognized as a risk factor for obesity. Whether extending sleep duration may mitigate this risk remains unknown.

Objective   To determine the effects of a sleep extension intervention on objectively assessed energy intake, energy expenditure, and body weight in real-life settings among adults with overweight who habitually curtailed their sleep duration.

Design, Setting, and Participants   This single-center, randomized clinical trial was conducted from November 1, 2014, to October 30, 2020. Participants were adults aged 21 to 40 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) between 25.0 and 29.9 and had habitual sleep duration of less than 6.5 hours per night. Data were analyzed according to the intention-to-treat principle.

Interventions   After a 2-week habitual sleep period at baseline, participants were randomized to either an individualized sleep hygiene counseling session that was intended to extend their bedtime to 8.5 hours (sleep extension group) or to continue their habitual sleep (control group). All participants were instructed to continue daily routine activities at home without any prescribed diet or physical activity.

Main Outcomes and Measures   The primary outcome was change in energy intake from baseline, which was objectively assessed as the sum of total energy expenditure and change in body energy stores. Total energy expenditure was measured by the doubly labeled water method. Change in body energy stores was computed using regression of daily home weights and body composition changes from dual-energy x-ray absorptiometry. Sleep duration was monitored by actigraphy. Changes from baseline were compared between the 2 groups using intention-to-treat analysis.

Results   Data from 80 randomized participants (mean [SD] age, 29.8 [5.1] years; 41 men [51.3%]) were analyzed. Sleep duration was increased by approximately 1.2 hours per night (95% CI, 1.0 to 1.4 hours; P  < .001) in the sleep extension group vs the control group. The sleep extension group had a significant decrease in energy intake compared with the control group (−270 kcal/d; 95% CI, −393 to −147 kcal/d; P  < .001). The change in sleep duration was inversely correlated with the change in energy intake ( r  = −0.41; 95% CI, −0.59 to −0.20; P  < .001). No significant treatment effect in total energy expenditure was found, resulting in weight reduction in the sleep extension group vs the control group.

Conclusions and Relevance   This trial found that sleep extension reduced energy intake and resulted in a negative energy balance in real-life settings among adults with overweight who habitually curtailed their sleep duration. Improving and maintaining healthy sleep duration over longer periods could be part of obesity prevention and weight loss programs.

Trial Registration   ClinicalTrials.gov Identifier: NCT02253368

Obesity is a major public health concern. 1 The obesity epidemic appears to coincide with a pattern of sleeping less that has been observed in society over the past several decades. For example, one-third of the US population reported not getting the recommended 7 to 9 hours of sleep per night. 2 - 4 Substantial evidence suggests that sleeping less than 7 hours per night on a regular basis is associated with adverse health consequences. 5 Particularly, insufficient sleep duration has been increasingly recognized as an important risk factor for obesity. 6 , 7 Prospective epidemiologic studies suggest that short sleep duration is an important risk factor for weight gain. 8 - 10 However, it remains unknown whether extending sleep duration can be an effective strategy for preventing or reversing obesity. Although sleep hygiene education is encouraged by obesity experts, 11 most health professionals and patients do not implement obtaining adequate sleep duration as part of the strategies to combat the obesity epidemic. 12

At the population level, the association between energy flux and body weight implicates that increased energy intake is the main factor in higher body weights in modern society. 13 According to dynamic prediction models, a sustained increase in energy intake of even 100 kcal/d would result in a weight gain of about 4.5 kg over 3 years. 14 , 15 Factors that underlie the observed persistent increase in energy intake and mean weight gain at the population level need to be better understood. One such factor is insufficient sleep duration. Short-term experimental laboratory studies have found that sleep restriction in healthy individuals is associated with an increased mean energy intake of about 250 to 350 kcal/d with minimal to no change in energy expenditure. 16 - 19 However, these laboratory studies do not represent real life. The magnitude of sleep restriction was extreme in most cases, and energy intake was ascertained from a single or a few meals. In a real-life setting in which participants continue their normal daily activities, multiple interacting factors (eg, social interactions and free-living physical activity) can influence energy intake or expenditure and weight.

To date, it remains unknown whether and to what extent an intervention that is intended to increase sleep duration in a real-life setting affects energy balance and body weight. We conducted a randomized clinical trial (RCT) to determine the effects of a sleep extension intervention on objectively assessed energy intake, energy expenditure, and body weight in real-life settings among adults with overweight who habitually curtailed their sleep duration.

This single-center, parallel-group RCT was conducted from November 1, 2014, to October 30, 2020. The protocol was approved by The University of Chicago Institutional Review Board, and participants provided written informed consent. The study protocol is available in Supplement 1 . We followed the Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline.

Adult men and women aged 21 to 40 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) between 25.0 and 29.9 and a mean habitual sleep duration of less than 6.5 hours per night were eligible. Individuals were required to have stable self-reported sleep habits for the past 6 months. They were recruited from the community and completed an initial online survey followed by a face-to-face interview. Race and ethnicity data were self-reported at this time and included the following race and ethnicity categories: Asian, Black or African American, Hispanic, and White. Those who met the inclusion criteria underwent laboratory screening (polysomnography, oral glucose tolerance test, and blood tests) to determine eligibility. Habitual sleep duration was confirmed by a 1-week screening wrist actigraphy at home. Those who had obstructive sleep apnea confirmed by laboratory polysomnography (apnea-hypopnea index >5), insomnia or history of any other sleep disorder, or night shift and rotating shift work (current or in the past 2 years) were excluded. Detailed eligibility criteria are provided in the eMethods in Supplement 2 .

After a 2-week habitual sleep period at baseline, participants were randomized to either 2-week sleep extension (sleep extension group) or 2-week continued habitual sleep (control group) ( Figure 1 ). Participants continued their daily routine activities at home without any prescribed diet or physical activity.

To blind participants to the sleep extension intervention, we described the study in the recruitment materials as follows: “we will collect information about sleep habits and metabolism.” The sleep extension group was blinded to randomization until after the 2-week baseline assessments, and the control group was blinded until the end of the 4-week study. This approach allowed us to capture habitual sleep-wake patterns without influencing participants' usual behavior or creating selection bias with only participants interested in improving sleep habits. After study completion, all participants were provided with information about the health benefits of optimal sleep duration. Block randomization, stratified by sex, was performed using computer-generated random numbers. Before the trial, randomization assignments were prepared by a biostatistician (K.W.) using opaque, sealed, and numbered envelopes and were given to the research coordinator (E.K.).

Sleep-wake patterns were continuously monitored at home by wrist actigraphy throughout the 4-week study. Participants were asked to wear an accelerometer (motion)-based monitor (Actiwatch Spectrum Plus; Philips) and to press a built-in event marker button when they went to bed to sleep each night and when they got out of bed each morning. Sleep was automatically scored (Actiware, version 6.0.9; Philips) using validated algorithms as the sum of all epochs that were scored as sleep during the total time spent in bed. 20 , 21

During the 2-week baseline, all participants were instructed to continue their habitual sleep patterns at home. On the morning of day 15, participants met with study investigators (E.T. and E.K.) in the research center. Those who were randomized to the sleep extension group received individualized sleep hygiene counseling through a structured interview (E.T.) (eMethods in Supplement 2 ). 22 At the end of the interview, participants were provided with individualized recommendations to follow at home for 2 weeks, with the aim of extending their bedtime duration to 8.5 hours. On day 22, participants returned for a brief follow-up visit. Actigraphy data from the first intervention week were reviewed, and further sleep counseling was provided as needed.

To minimize any imbalance in contact with the investigators between the 2 groups, we asked participants in the control group to meet with the study investigators on days 15 and 22. Actigraphy data of these participants were downloaded, but the participants did not receive any specific sleep recommendations and were instructed to continue their daily routine and habitual sleep behaviors until the end of the study.

For each 2-week period, the energy intake was calculated from the sum of total energy expenditure and change in body energy stores using the principle of energy balance. 14 , 23 , 24 Total energy expenditure was measured by the doubly labeled water method. 25 - 29 For each 2-week period, the change in body energy stores was computed from the regression (slope, grams per day) of daily home weights and change in body composition (ie, fat mass and fat-free mass) using dual-energy x-ray absorptiometry. Participants were provided a cellular-enabled weight scale (BodyTrace; BodyTrace Inc) and instructed to take their nude weights twice every morning after awakening before eating or drinking. Weight values were hidden from the participants to minimize potential influence on behavior. Changes in body composition were converted to changes in energy stores using 9.5 kcal/g as the energy coefficient of fat mass and 1.0 kcal/g as the energy coefficient of fat-free mass. 30 Resting metabolic rate was measured by indirect calorimetry for 30 minutes after fasting and for 4 hours after eating a standardized breakfast. Thermic effect of the meal was calculated, which was previously described elsewhere. 31 Activity energy expenditure was calculated by subtracting the resting metabolic rate and thermic effect of the meal from the total energy expenditure. 31 , 32 Additional details are provided in the eMethods in Supplement 2 .

The primary outcome was change in energy intake from baseline. A total final sample size of 80 participants (40 per group) was originally planned and provided 80% power to detect a true difference in energy intake between groups of 207 kcal/d using a 2-sided α = .05 significance threshold (trial protocol in Supplement 1 ). An intention-to-treat analysis was conducted in Stata, version 16 (StataCorp LLC) using 2-tailed tests with statistical significance set at P  < .05. Categorical data are presented as counts and percentages. Continuous data are presented as means and SDs. Linear mixed-effects models were fit to determine the treatment differences between the groups. 33 Models included the randomization group, 2-week baseline period (period 1) vs 2-week intervention (period 2) and their interaction, and random effects for each participant. The treatment effect (95% CI) was estimated by the treatment group and period interaction, which is equivalent to testing the difference in change from baseline (period 2 minus period 1) in the sleep extension group vs the control group. To confirm the robustness of primary findings, we fit additional models using the analysis of covariance approach with the period 2 value as the dependent variable, treatment group as the independent variable, and period 1 value as covariates.

In secondary analyses, mixed models that adjusted for sex or menstrual cycle were also fit; these covariates were chosen because of the known influence of menstrual cycle on short-term changes in weight. A Pearson correlation coefficient was calculated to assess the relationships between the changes from baseline in sleep duration and the changes from baseline in energy intake. No adjustments were made to P values or CIs for multiple comparisons. Baseline characteristics of participants with complete data were compared with those of participants with incomplete data using unpaired, 2-tailed t tests and Fisher exact tests. No imputation for missing values was performed.

Of the 210 adults who provided consent and were assessed for eligibility, 81 were randomized (41 to the control group and 40 to the sleep extension group) initially ( Figure 1 ). One participant in the control group revealed adhering to a weight loss regimen and thus did not meet the study inclusion criteria and was deemed ineligible after randomization. 34 The 80 participants had a mean (SD) age of 29.8 (5.1) years and consisted of 41 men (51.3%) and 39 women (48.7%). Baseline characteristics of participants were similar between randomization groups ( Table 1 ). None of the participants were using any antihypertensive or lipid-lowering agents or any prescription medication that can affect sleep or metabolism.

Figure 2 illustrates the mean nightly sleep duration by actigraphy in each group throughout the 4-week study. Participants in the sleep extension group had a significant increase from baseline in mean sleep duration by actigraphy compared with those in the control group (1.2 hours; 95% CI, 1.0-1.4 hours; P  < .001). The findings were similar with regard to change in sleep duration when only participants' workdays (1.3 hours; 95% CI, 1.0-1.5 hours; P  < .001) or free days (1.1 hours; 95% CI, 0.7-1.5 hours; P  < .001) were considered (eTable 1 in Supplement 2 ). No difference was found in change in sleep efficiency (percentage of time spent asleep during time in bed) between the 2 groups (–0.6 hours; 95% CI, –2.1 to 1.0 hours; P  = .48), confirming the success of the intervention (eTable 2 in Supplement 2 ).

Energy intake was statistically significantly decreased in the sleep extension group compared with the control group (−270.4 kcal/d; 95% CI, −393.4 to −147.4 kcal/d; P  < .001). Figure 3 A through D illustrates the changes from baseline in energy intake and the changes from baseline in sleep duration in individual participants. There was a significant increase in energy intake from baseline in the control group (114.9 kcal/d; 95% CI, 29.6 to 200.2 kcal/d) and a significant decrease in energy intake from baseline in the sleep extension group (−155.5 kcal/d; 95% CI, −244.1 to −66.9 kcal/d) ( Table 2 ). Considering all participants, the change in sleep duration was inversely correlated with the change in energy intake ( r  = −0.41; 95% CI, −0.59 to −0.20; P  < .001) ( Figure 3 E). Each 1-hour increase in sleep duration was associated with a decrease in energy intake of approximately 162 kcal/d (−162.3 kcal/d; 95% CI, −246.8 to −77.7 kcal/d; P  < .001).

No statistically significant treatment effect was found in total energy expenditure or other measures of energy expenditure ( Table 2 ). Participants in the sleep extension group had a statistically significant reduction in weight compared with those in the control group (−0.87 kg; 95% CI, −1.39 to −0.35 kg; P  = .001). There was weight gain from baseline in the control group (0.39 kg; 95% CI, 0.02 to 0.76 kg) and weight reduction from baseline in the sleep extension group (−0.48 kg; 95% CI, −0.85 to −0.11 kg) ( Table 2 ).

The findings on energy intake, energy expenditure, and weight were similar after adjustment for the effects of sex or menstrual cycle. No statistically significant differences in baseline characteristics were found between the 75 participants (93.8%) who had complete data on energy intake (primary outcome) vs participants with missing data on energy intake. The proportion of participants with complete data on energy intake was not significantly different between the sleep extension and control groups (90.0% vs 97.5%; P  = .36). When all reported outcomes were considered, no significant differences (except for depressive symptoms) in baseline characteristics were found between participants with complete data and participants with incomplete or missing data (eTable 3 in Supplement 2 ). The proportion of participants with complete data on all reported outcomes was similar between the sleep extension and control groups (82.5% vs 85.0%; P  > .99).

In this RCT of adults with overweight who habitually curtailed their sleep duration, sleep extension reduced energy intake and resulted in a negative energy balance (ie, energy intake that is less than energy expenditure) in real-life settings. To our knowledge, this study provides the first evidence of the beneficial effects of extending sleep to a healthy duration on objectively assessed energy intake and body weight in participants who continued to live in their home environment. Modest lifestyle changes in energy intake or expenditure are increasingly promoted as viable interventions to reverse obesity.

According to the Hall dynamic prediction model, a decrease in energy intake of approximately 270 kcal/d, which we observed after short-term sleep extension, would predict an approximately 12-kg weight loss over 3 years if the effects were sustained over a long term. 14 , 15 However, this study cannot infer how long healthy sleep habits may be sustained. Nevertheless, these modeling predictions on weight change suggest that continued adequate sleep duration and beneficial effect on energy intake could translate into clinically meaningful weight loss and help reverse or prevent obesity. Thus, the findings of this study may have important public health implications for weight management and policy recommendations.

The findings of decreased energy intake, negative energy balance, and weight reduction resulting from sleep extension are in agreement with the findings of short-term laboratory sleep-restriction studies showing increased energy intake and weight gain 17 as well as the findings of prospective epidemiologic studies linking sleep restriction to obesity risk. 8 A recent meta-analysis of randomized controlled laboratory studies found that short-term sleep restriction over 1 to 14 days of duration in healthy individuals was associated with increases of mean energy intake by approximately 253 kcal/d, as assessed during a single meal. 17 Another meta-analysis of prospective cohort studies found that the risk of obesity increased by 9% for each 1-hour decrease in sleep duration. 8 We did not observe a statistically significant change in total energy expenditure by doubly labeled water method or mean daytime activity counts by actigraphy (eTable 2 in Supplement 2 ). Although some laboratory sleep-restriction studies reported an increase in total energy expenditure of approximately 92 to 111 kcal/d, using a whole-room calorimeter, 35 , 36 other studies observed no change. 16 , 37 We found a modest reduction in weight after sleep extension, and the composition of weight change was primarily in fat-free mass, which is consistent with the short-term changes in body composition. 38 , 39 If sleep is extended over longer periods, weight loss in the form of fat mass would likely increase over time. A few observations suggest that sleeping 7 to 8 hours per night is associated with greater success in weight loss interventions. 40 - 43

In this RCT, we found an overall increase in objective sleep duration of approximately 1.2 hours in participants who habitually slept less than 6.5 hours per night. The change in sleep duration from baseline varied between participants and from night to night in the real-life setting. Overall, the sleep extension group compared with the control group had significantly higher subjective scores in obtaining sufficient sleep, with more daytime energy and alertness and better mood (eTable 4 in Supplement 2 ). Similar to a previous study of sleep extension, 22 the present RCT used an individualized counseling approach. Another study used bedtime extension in habitual short sleepers in real-life conditions but obtained variable benefits on sleep, likely because of a lack of an individualized approach or appropriate blinding. 44 None of these previous studies objectively measured energy intake.

Future similarly rigorous intervention studies of longer duration and using objective assessments of energy balance under real-life conditions are warranted to elucidate the underlying mechanisms and to investigate whether sleep extension could be an effective, scalable strategy for reversing obesity in diverse populations. Along with a healthy diet and regular physical activity, healthy sleep habits should be integrated into public messages to help reduce the risk of obesity and related comorbidities.

This study has several strengths. The major strengths are the randomized design and the objective tracking of energy intake and sleep in real-life settings. Most epidemiologic studies linking short sleep duration to body weight relied on self-reported dietary intake. 45 We did not collect self-reported dietary data because this method is subject to bias and has been shown to be inaccurate compared with the doubly labeled water method. 46 , 47 Most experimental studies that measured energy intake used a single meal under unnatural laboratory conditions. We used a validated method to objectively track energy intake by the doubly labeled water method and change in energy stores. 23 , 48 , 49 In this trial, we objectively quantified energy intake after sleep extension while individuals continued their daily routine in their usual environment. Participant blinding and use of actigraphy allowed us to capture true habitual sleep patterns at baseline. 22 , 50 In addition, we excluded insomnia and sleep apnea.

This study also has several limitations. We enrolled adults with overweight and used selective eligibility criteria, which may limit generalizability to more diverse populations. The increase in energy intake and weight from baseline that we observed in the control group may have contributed to the significant treatment effects. However, in RCTs, performing a between-group comparison, rather than separate tests against baseline within the groups, is strongly recommended. 51 The study did not provide information on how long healthy sleep habits could be maintained over longer periods. 44 We did not systematically assess the factors that may have influenced sleep behavior, but limiting the use of electronic devices appeared to be a key intervention among the participants (eTable 4 in Supplement 2 ). The doubly labeled water method has a precision of 5%, which may translate into some degree of uncertainty in the energy intake calculations. Although whole-room calorimeters can measure energy expenditure with a higher precision of approximately 1% to 2%, they do not represent real-life measurement and are not feasible over longer periods. We did not assess the underlying biological mechanisms of food frequency and the circadian timing of food intake. Multiple interrelated factors could contribute to the finding of decreased energy intake after sleep extension. 6 , 52 Evidence from laboratory sleep restriction studies suggests that increased hunger, alterations in appetite-regulating hormones, and changes in brain regions related to reward-seeking behavior are potential mechanisms that promote overeating after sleep restriction. 6 , 45

This RCT found that short-term sleep extension reduced objectively measured energy intake and resulted in a negative energy balance in real-life settings in adults with overweight who habitually curtailed their sleep duration. The findings highlighted the importance of improving and maintaining adequate sleep duration as a public health target for obesity prevention and increasing awareness about the benefits of adequate sleep duration for healthy weight maintenance.

Accepted for Publication: November 14, 2021.

Published Online: February 7, 2022. doi:10.1001/jamainternmed.2021.8098

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Tasali E et al. JAMA Internal Medicine .

Corresponding Author: Esra Tasali, MD, Department of Medicine, The University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637 ( [email protected] ).

Author Contributions: Author Dr Tasali and Ms Wroblewski had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Tasali, Schoeller.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Tasali, Schoeller.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Tasali, Wroblewski.

Obtained funding: Tasali.

Administrative, technical, or material support: Tasali, Kahn, Kilkus, Schoeller.

Supervision: Tasali.

Other - research coordination duties: Kahn.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was funded by grants R01DK100426, CTSA-UL1 TR0002389, and UL1TR002389 from the National Institutes of Health and by the Diabetes Research and Training Center at The University of Chicago.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement : See Supplement 3 .

Additional Contributions: Timothy Shriver, MS, University of Wisconsin–Madison, assisted with doubly labeled water measurements. Maureen Costello, MS, The University of Chicago, assisted with dual-energy x-ray absorptiometry scans. Becky Tucker, BA, Harry Whitmore, RPSGT, and Kristin Hoddy, PhD, RD, The University of Chicago, assisted with data collection. We thank the nurses, dieticians, and technicians at the Clinical Research Center at The University of Chicago for their expert assistance in data collection. We also thank the staff of the Sleep Research Center at The University of Chicago for their support. These individuals received no additional compensation, outside of their usual salary, for their contributions. We thank the volunteers for participating in this study.

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