Employees’ health and well-being are vital for achieving organizational goals and promoting productivity to meet market needs. According to previous research, healthy employees are likelier to have higher work productivity and contribute more to their communities (Centers for Disease Control and Prevention, 2022; Harvard Business Review, 2016; Institute for Health and Productivity Studies, Johns Hopkins Bloomberg School of Public Health, & Transamerica Center for Health Studies, 2015). However, university employees’ physical, psychological, and social well-being are impacted by job demands, such as the need for constant self-improvement by participating in innovative teaching, research, and outreach programs, or by engaging in administrative or management work and collaboration across institutions (Sanchez et al., 2019). Research is needed to understand how to increase employees’ and organizations’ productivity, address health issues, and improve employees’ quality of life (Biman et al., 2021).

 

According to the World Health Organization (1996), quality of life is a person’s perception of their position in life in relation to their goals, expectations, standards, concerns, and the cultural context and value systems among which they live. Due to high work pressure, higher education employees are vulnerable to psychological side effects such as anxiety, stress, depression, and burnout, as well as physical health problems such as headaches, obesity, hypertension, and cardiovascular diseases (Ismail et al., 2013; Khalilzadeh et al., 2020; Liu et al., 2015). In China, the quality of life of college faculty members has been found to be lower than that of the general population (Ge et al., 2011). Moreover, a reduced quality of life among Malaysian university employees was found to compromise students’ quality of education, thus negatively affecting the lives of students and others (Abdul Manaf et al., 2021). Given that the employees of higher education institutions form the foundation of the organizational structure, their quality of life and related factors must be investigated to understand how their performance is affected.

 

Various factors can impact an individual’s quality of life. According to Kontoangelos et al. (2023) and Shandu et al. (2023), individuals with chronic illnesses or who are obese experience a marked decline in their quality of life. These conditions not only impair physical health but also affect psychological well-being, leading to increased instances of depression and anxiety; furthermore, smoking has been identified as a significant aggravating factor, compounding the negative effects of obesity and chronic diseases on individuals’ overall health and quality of life (Shandu et al., 2023).

 

In contrast, regular physical exercise has been established as an essential factor for enhancing quality of life, particularly among those with obesity and chronic health conditions (Shandu et al., 2023). Nduaguba et al. (2019) emphasized that systematic engagement in physical activity not only aids in weight management but also improves physical functioning and mental health, enhancing overall quality of life. By fostering resilience against the negative impacts of both obesity and smoking, exercise serves as a pivotal component in the multifaceted approach to improving the quality of life for these populations.

 

As quality of life is central to societal development, Saudi Arabia’s Vision 2030 government program (Saudi Vision 2030, n.d.) is an ambitious plan aiming to improve the country’s quality of life via programs and initiatives bolstering people’s biopsychosocial well-being through participation in cultural, entertainment, sports, tourism, urban, and other related activities. Saudi Arabia introduced the Quality of Life Program in 2018 to improve citizens’, residents’, and visitors’ participation, experiences, and quality of life (Saudi Vision 2030, n.d.). The initiative has broadened participants’ perspectives in several areas directly affecting everyday experiences, such as entertainment, sports, and culture. Despite remarkable developments in quality of life across the country, research (especially academic research) on the topic in Saudi Arabia is scarce. The existing literature on quality of life among Saudi university employees provides valuable insights into the multifaceted nature of this phenomenon. Al-Mulla and Musa (2024) and Alyousef (2022) highlighted the significant roles of psychological well-being, job satisfaction, and workplace conditions in shaping these individuals’ overall quality of life. However, while these studies effectively outline general quality-of-life levels, they have failed to address critical sociodemographic factors, such as years of experience at work, and health predictors, particularly obesity (Abdul Manaf et al., 2021; Sriutaisuk, 2014).

 

Understanding the interplay between sociodemographic factors and quality of life is essential, as it may reveal disparities in the experiences and challenges employees face in various roles. For instance, faculty members may experience different levels of psychological well-being compared to administrative staff or support personnel. Additionally, the implications of health predictors like obesity and the presence of chronic conditions are increasingly important to consider, especially given the rising prevalence of lifestyle-related health issues in Saudi Arabia (Mahmood et al., 2024). Exploring how specific sociodemographic factors and health indicators impact quality of life among Saudi university employees will not only enhance understanding of the current landscape but also inform targeted interventions that promote improved psychological health and workplace conditions tailored to the diverse needs of university staff. By broadening the scope of investigation, my goal was to foster a more comprehensive view of quality of life that spans both individual and contextual variables.

 

Given the lack of academic research in Saudi Arabia, this study examined (a) quality of life among employees of Jazan University, (b) its influencing factors, and (c) the associations between employees’ quality of life, sociodemographic factors, and health indicators. I hypothesized that employees’ quality-of-life scores would be associated with their sociodemographic factors and health predictors.

This cross-sectional study combined descriptive and analytical methods to investigate employees of Jazan University from September to December 2023 using a simple random sampling method. I calculated the minimum necessary sample size using the following formula with a 95% confidence interval (CI), nominal alpha of .05, and 50% population proportion:

 

Finite population: n’ = n/(1 + (z^2 _× p  ̂(1 − p ̂))/ɛ²N)

 

The calculated required sample size was 393. I distributed the survey among all university employees via official university emails and social media, ensuring random participant selection, until I had reached the target number of 393. This threshold was set to guarantee that the data collected would be adequate for thorough analysis and validation of the hypothesis. All responses were complete and retained, with no data discarded during the analysis. This study included academic and nonacademic university employees aged over 18 years. Workers on parental leave, sabbaticals, scholarships, or taking other types of leave for over 2 months were ineligible.

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Jazan University (protocol code REC-45/02/738). Informed consent was obtained from all respondents. I created a three-part, anonymous, self-administered survey in Arabic and English. Participants’ sociodemographic data (gender, age, marital status, type of occupation, and length of service) were queried in Section 1. There were two categories of occupation: academic and nonacademic. Academic professionals included demonstrators, lecturers, assistant professors, associate professors, and professors. Those not engaged in academic teaching or learning activities, such as administrators, technicians, or clinicians, were classified as nonacademic.

 

Section 2 queried information on health predictors, including body mass index (BMI), smoking status, chronic conditions, and physical activity. BMI was computed by dividing the individual’s weight by their height in m² (kg/m²). Respondents were classified using the guidelines specified by the World Health Organization International Consortium in Psychiatric Epidemiology (2000; i.e., BMI [kg/m²] of < 18.5 = underweight, 18.5–24.9 = normal weight, 25.0–29.9 = overweight, 30.0–34.9 = obese, and ≥ 35 = extremely obese).

 

I used a single item to measure participants’ current smoking status. Participants who had smoked at least 100 cigarettes in their lifetime and were currently smoking were considered current smokers (Centers for Disease Control and Prevention, 2017). This item was evaluated using a binary response (yes/no). Moreover, a single-item, self-reported measure with a binary response (yes/no) regarding diagnosis by a medical professional served as the basis for assessing chronic conditions: “Have you been diagnosed with diabetes, hypertension, or hypercholesterolemia by a medical doctor, or are you currently taking medications for these conditions?”

 

I used the physical activity subscale of the Health Promoting Lifestyle Profile II (Walker et al., 1987) to assess participants’ physical activity levels. This subscale comprises eight positively stated questions, the mean of which determines the final score. Higher scores indicate increased participation in physical activity. Previous research has established the validity and reliability of the English (Walker et al., 1987) and Arabic (Alkhawaldeh, 2014) versions of this subscale.

 

The primary outcome measure, quality of life, was assessed in the questionnaire’s last section using the validated Arabic and English versions of the World Health Organization Quality of Life Scale–Brief Version (World Health Organization, 1996). This 26-item questionnaire assesses quality of life in four domains: physical health (seven items e.g., “To what extent do you feel that physical pain prevents you from doing what you need to do?”), psychological health (six items, e.g., “How much do you enjoy life?”), social relationships (three items, e.g., “How satisfied are you with your relationships?”), and environment (eight items, e.g., “How satisfied are you with your transport?”). Two further items address overall quality of life and general health: “How would you rate your quality of life?” and “How satisfied are you with your health?” These items are rated on a 5-point Likert scale ranging from 1 = very poor to 5 = very good for overall quality of life, and from 1 = very dissatisfied to 5 = very satisfied for general health satisfaction. Three negatively stated items (Items 3, 4, and 26) were reverse coded. Each domain’s total score was subsequently multiplied by 4 for the scores to be directly comparable with those derived from the World Health Organization Quality of Life Scale. Respondents’ total quality-of-life score was calculated by multiplying the mean of the four domains by 4, with higher domain scores indicating a higher quality of life.

SPSS 25.0 was employed for data analysis. I used a one-sample t test to estimate the population mean and calculated 95% CIs for the interval variables, using a nonparametric one-sample test for the categorical variables. The null hypothesis was that the mean quality-of-life score for the sample would be 47. This score was selected based on previous research conducted among employees of Saudi universities (Al-Mulla & Musa, 2024). I evaluated the strength and direction of the variables’ relationships using two-tailed Pearson correlations (r) at a .05 significance level. Multiple regression analysis was used to assess the impact of sociodemographic characteristics and health indicators on quality of life. The cutoff point for statistical significance was p < .05.

Table 1 lists the sample characteristics. This study included 393 participants, of whom 252 (64.1%) were women and 141 (35.9%) were men. Their mean age was 42.42 years (SD = 6.19) and their mean years of experience at work was 12.66 (SD = 5.49). Most employees were married (n = 321, 81.7%) and academics (n = 240, 61.1%). Their mean exercise participation score was 2.05 (SD = 0.80). Among the respondents, 210 (53.4%) had at least one chronic health condition, 15 (3.8%) were smokers, and 102 (26%) had a body weight classed as normal according to their BMI.

On average, the participants reported high total quality-of-life scores (see Table 2). The quality-of-life subscales were further analyzed to determine the highest and lowest domains. Among the domains, social relationships had the highest mean, whereas environment had the lowest. A significance level of alpha = .05 (p < .001) was reached for all estimated population means.

I examined the relationships between the variables using Pearson correlation coefficients. Table 3 shows the associations between the quality-of-life domain scores, sociodemographic factors, and health indicators. Total quality of life was significantly associated with age, years of experience at work, chronic health conditions, exercise behaviors, and smoking status. Statistically significant associations were observed between the quality-of-life subscales, sociodemographic factors, and health indicators. Both physical and psychological health were significantly greater among employees who were older, had more work experience, had no chronic diseases, and engaged in physical exercise. Social relationship scores were greater among employees who were older, had more experience, had no chronic diseases, engaged in physical exercise, and were nonsmokers. Finally, environment scores were higher among employees who were older, women, had more work experience, had a lower BMI, had no chronic diseases, engaged in physical exercise, and were nonsmokers.

Multiple regression analyses were performed to evaluate the effect of the sociodemographic factors and health indicators (age, gender, marital status, years of experience at work, BMI, chronic conditions, exercise behavior, and smoking status) on total quality of life and each quality-of-life subscale. Table 4 shows that the predictors explained 19% of the variance in total quality of life. Age, years of experience, and exercise behavior positively predicted total quality of life, while having at least one chronic condition negatively predicted it.

 

The model revealed that 13% of the variance in the physical health subscale was explained by the predictors. Age, gender, and having at least one chronic condition were all significant predictors of physical health. For the psychological health subscale, a significant proportion (21%) of the variance was explained by the independent variables. Age, years of experience at work, and exercise behavior positively predicted psychological health, while BMI and the presence of at least one chronic condition negatively predicted it. Moreover, the predictors explained 10% of the variance in the social relationship subscale. Age, years of experience at work, and having at least one chronic condition were all significant predictors of social relationships. For the environment subscale, a significant proportion (16%) of the variance was explained by the independent variables. Age, years of experience at work, and exercise behavior positively predicted environmental health, while smoking status and the presence of at least one chronic condition negatively predicted it.

This study examined the factors associated with quality-of-life scores among public university employees in Saudi Arabia. Quality-of-life domain scores were marginally higher, and employees’ overall quality of life was significantly higher than those obtained in other studies of Saudi universities (Al-Mulla & Musa, 2024) or university staff in Malaysia (Abdul Manaf et al., 2021). Scores for the social relationship subscale were the highest, whereas those for the environment domain were the lowest among the employees in my sample. This is consistent with previous research conducted in Brazil (Oliveira Filho et al., 2012) and Malaysia (Abdul Manaf et al., 2021). A high social relationship score could be attributed to social activities within the university, cultural factors that extend beyond societal values and customs, and the Saudi Vision 2030 programs and initiatives for improving quality of life. In contrast, the university’s structure, behavior, and climate could explain the low environmental domain score.

 

In line with previous research, sociodemographic factors and health indicators directly impacted employees’ quality of life (Abdul Manaf et al., 2021; Oliveira Filho et al., 2012; Sriutaisuk, 2014). Specifically, age demonstrated a positive association with all quality-of-life domains. Older individuals may face different health challenges and life circumstances than their younger counterparts (Abdul Manaf et al., 2021). Similarly, years of experience at work and participation in exercise contribute to psychological health, social relationships, and overall well-being, influencing how employees perceive their roles and workplaces (Abdul Manaf et al., 2021; Sriutaisuk, 2014). The interaction between years of experience at work and personal well-being is essential; employment offers financial stability and plays a significant role in psychological and social fulfillment. Previous studies have indicated that long-term employment histories, especially those marked by consistent work patterns, are correlated with a higher quality of life in older adults (Abdul Manaf et al., 2021; Wahrendorf, 2015). Although many factors influence well-being, this relationship is significant because it underscores the value of sustained employment. In addition, regular physical activity has been linked to improved mental and physical health, contributing positively to an individual’s quality of life (Nduaguba et al., 2019).

 

In contrast, individuals with long-term health issues and those who are smokers may experience limitations in their daily activities, affecting both their personal and professional lives (Krawczyk-Suszek & Kleinrok, 2022; Noto, 2023; Oliveira Filho et al., 2012). Having chronic health conditions and smoking can severely diminish one’s quality of life (Nduaguba et al., 2019; Noto, 2023).

 

The transformation of university education has increased pressure on academic institutions to generate globally competitive and motivated graduates. To remain competitive in university ranking systems, universities worldwide have adopted accelerated research activities, community outreach programs, scholarly publications, and efficient delivery systems as fundamental metrics (Alves et al., 2019; Sanchez et al., 2019). In response to national aspirations for a knowledge-based economy and global trends in higher education, Saudi universities are undergoing significant reforms to enhance their global rankings and meet the demands of a highly skilled workforce (Al-Mubaraki, 2011). The push for a knowledge economy necessitates that institutions adapt their curricula and teaching methods to align with industry needs, particularly in emerging fields like data science and artificial intelligence (Thomran & Alshammari, 2023). The pressure of such work increases the strain on employees, inducing negative psychological effects and competitiveness at work, leading to a lower quality of life (Alves et al., 2019; Ge et al., 2011; Sanchez et al., 2019). Al-Mulla and Musa (2024) reported a significant association between psychological burnout and university employees’ quality of life, attributing this to work pressure and the organizational environment. Consistent with these results, in this study the environmental domain emerged as the lowest scoring dimension, followed by psychological health. This trend raises significant concerns regarding quality of life and sustainability. The low scores indicate pressing challenges that employees face, including—but not limited to—issues related to the physical environment, management style, workload, and resource depletion. These issues not only impact physical health but also limit excellence opportunities and contribute to a sense of disconnection from work, further exacerbating overall dissatisfaction with life (Al-Mulla & Musa, 2024; Alyousef, 2022; Kaewpan et al., 2017). Therefore, for universities to keep pace with the Saudi Vision 2030 plan, it is necessary to enhance environmental sustainability and psychological well-being through policy reforms and community initiatives while simultaneously prioritizing mental health resources and support systems (Al-Mulla & Musa, 2024). By taking a holistic approach that recognizes the interconnectedness of environmental and psychological factors, stakeholders can work toward fostering healthier and more productive individuals.

To my knowledge, this is the first study to examine the quality of life of Saudi university employees in the context of Saudi Vision 2030. I found that quality-of-life scores were linked with several sociodemographic factors and health indicators. University environments and climates require more attention to achieve optimal quality of life. This study’s results may help university administrators create initiatives and preventative programs to improve employees’ quality of life. Such programs and initiatives could also help healthcare professionals and nursing researchers improve their quality of life at work.

Limitations of the present study include the use of a cross-sectional design and the nonnational representativeness of participants drawn from a single public university. Further, the difference in quality-of-life scores between my study and that of Al-Mulla and Musa (2024), who also examined employees of Saudi universities, may reflect differences in sampling. A nationwide longitudinal study with a larger sample size is required to address these limitations.

This study found that sociodemographic factors and health indicators were significantly associated with employees’ quality-of-life scores. Age and chronic conditions were the only variables with statistically significant positive correlations across all four quality-of-life domains. Length of experience at work was significantly associated with all quality-of-life domains except the physical domain, whereas gender was associated only with the physical domain. Exercise behavior and BMI were significantly associated with the psychological and environmental domains. Finally, smoking status was associated only with the environmental domain. This study is the first to thoroughly analyze variables that influence Saudi university employees’ quality-of-life scores. These findings may help to develop suitable behavioral interventions within organizational structures to improve employees’ quality of life.

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