Musculoskeletal disorders in relation to computer workstation ergonomics and sleep quality among university employees

Abstract

Musculoskeletal disorders (MSDs) are among the most common work-related health problems and are frequently associated with prolonged sitting, poor computer workstation ergonomics, and inadequate sleep. This study aimed to determine the prevalence of MSD complaints and examine their associations with computer workstation ergonomics and sleep quality among employees at Jazan University, Saudi Arabia. A cross-sectional study was conducted between December 2024 and April 2025 among 460 university employees selected using a multistage cluster random sampling technique. Data were collected using the Nordic Musculoskeletal Questionnaire, the National Institutes of Health Computer Workstation Ergonomic Self-Assessment Checklist, and the Pittsburgh Sleep Quality Index. Data were analyzed using IBM SPSS Statistics, version 27. MSDs were highly prevalent, with the low back (65.9%), neck (65.0%), shoulders (57.2%), and upper back (46.3%) being the most commonly affected body regions, whereas the elbows were the least affected (16.7%). Male employees had significantly lower odds of hip pain (odds ratio [OR] = 0.22, 95% confidence interval [CI]: 0.07–0.66) and ankle pain (OR = 0.34, 95% CI: 0.13–0.90) than female employees. Compared with employees from the College of Arts and Humanities, those from the College of Science had significantly higher odds of wrist pain (OR = 2.12, 95% CI: 1.07–4.20), elbow pain (OR = 2.45, 95% CI: 1.09–5.53), and ankle pain (OR = 2.48, 95% CI: 1.15–5.33). Poor sleep quality was independently associated with increased odds of neck pain (OR = 1.13, 95% CI: 1.03–1.24), upper-back pain (OR = 2.47, 95% CI: 1.08–5.67), and knee pain (OR = 4.28, 95% CI: 1.70–10.80). In addition, poor workstation ergonomics were independently associated with several MSDs after adjustment for potential confounders. MSDs were highly prevalent among university employees and were independently associated with poor workstation ergonomics and poor sleep quality. These findings highlight the importance of implementing workplace ergonomic interventions and employee wellness programs to reduce the burden of MSDs. Future longitudinal studies are needed to evaluate the effectiveness of ergonomic and sleep-related interventions and to further investigate the contributions of occupational stress and screen time to MSD risk.

1. Introduction

Musculoskeletal disorders (MSDs) are widespread occupational health challenges that affect the workforce and countless individuals in today’s fast-paced society. MSDs affect muscles, tendons, bones, and joints that support the body and enable movement.^[1,2] They are the leading cause of disability globally, with low-back pain ranking as the primary contributor across 160 countries.^[3] In modern workplaces, factors such as prolonged computer use, extended exposure to visual display terminals, and poor ergonomic practices are strongly associated with the development of MSDs.^[4–8] University employees, including teaching and administrative staff, represent a particularly vulnerable group due to sustained computer use, high cognitive demands, and multitasking requirements. In Saudi Arabia, the prevalence of work-related MSDs is notably high, ranging from 42.5% to 55%, with the low back and neck being the most commonly affected regions.^[4–6]

Ergonomic workstation design is a critical determinant of musculoskeletal health. Key elements such as adjustable seating, appropriate desk height, optimal monitor positioning, and adequate lighting are essential for minimizing physical strain. Conversely, poor ergonomic practices, such as improper posture, inadequate support, and misaligned workstations, have been strongly associated with the development and progression of MSDs. Evidence suggests that such deficiencies may increase MSD prevalence by up to 94%, underscoring the importance of ergonomic interventions in occupational health strategies. Overall, the existing literature consistently supports an association between poor workstation ergonomics and an increased risk of MSDs.^[4,9–11]

In addition to physical risk factors, sleep quality has emerged as an important, yet often underexplored, contributor to musculoskeletal health. Sleep quality is a multidimensional construct encompassing sleep initiation, continuity, duration, depth, and subjective restoration.^[12] Poor sleep quality has been linked to adverse outcomes, including fatigue, impaired daytime functioning, and increased pain perception.^[13,14] Notably, a high prevalence of poor sleep quality has been reported among working populations in Saudi Arabia; for example, nearly two-thirds of teaching staff in Jeddah were found to experience inadequate sleep.^[15]

Growing evidence suggests a complex relationship between sleep quality and MSDs. Some studies indicate that poor sleep may contribute to the onset and persistence of musculoskeletal pain through mechanisms such as increased pain sensitivity, delayed tissue recovery, and systemic inflammation.^[13–16] Conversely, chronic musculoskeletal pain may disrupt sleep patterns, leading to insomnia and reduced sleep quality.^[17] This bidirectional relationship highlights the need to consider both factors concurrently when examining occupational health outcomes.

Despite the established associations between workstation ergonomics, sleep quality, and MSDs, several gaps remain in the literature. First, most studies have examined these factors in isolation rather than within an integrated framework. Second, there is limited evidence from Middle Eastern populations, particularly among university employees, who face unique occupational demands. Third, few studies have simultaneously assessed ergonomic conditions and sleep quality as combined predictors of MSDs within a single population.

Addressing these gaps is essential for developing comprehensive and context-specific interventions aimed at improving employee health and productivity. Therefore, this study aimed to assess the prevalence of MSD complaints and their associations with computer workstation ergonomics and sleep quality among university employees at Jazan University. By integrating these factors, the study sought to provide a more holistic understanding of MSD risk and inform targeted workplace health strategies.

2. Methods

2.1. Study design, setting, and participants

This cross-sectional study was conducted at Jazan University between December 2024 and April 2025. Jazan University, located in southwestern Saudi Arabia, is the main university in the region. The university comprises 26 colleges and has 4727 employees.

2.2. Sampling technique

We adopted a multistage cluster random sampling technique because workloads differ among colleges. In the first stage, Jazan University was categorized into 4 clusters of colleges: medical, paramedical, scientific, and literary. In the second stage, 1 college was randomly selected from each cluster. Finally, all teaching and administrative staff employed at the selected colleges were invited to participate in the study. Participants read and agreed to participate in the study. Informed consent was obtained before participants completed the interviewer-assisted questionnaire, which took 15 to 20 minutes to complete. Participants were observed during a typical working day to complete the National Institutes of Health (NIH) Computer Workstation Ergonomic Self-Assessment Checklist to ensure an accurate assessment of their workstation practices.

2.3. Sample size calculation

We used the Raosoft sample size calculator to determine the minimum sample size required to achieve the study objectives.^[18] The Raosoft calculator is a widely used and accepted online tool for estimating sample size in cross-sectional studies based on standard statistical parameters (confidence level, margin of error, and population proportion). Based on a total population of 4727, a 95% confidence level, and a 5% margin of error, the minimum required sample size was 356 participants. To account for a potential 20% nonresponse rate, the final estimated sample size was adjusted to 428 participants (calculated as 356 × 1.2 ≈ 428).

2.4. Data collection tool

After obtaining ethical approval, data were collected through structured interviews using a 64-item Google Form questionnaire. The questionnaire was administered using an interview-assisted approach, in which trained researchers provided guidance when necessary to ensure participants’ full comprehension, irrespective of their familiarity with Google Forms. The questionnaire was divided into 4 sections, each designed to gather specific information relevant to the study objectives.

The first section collected sociodemographic data, including sex, age, height, weight, occupation, income, marital status, nationality, and average daily hours spent using a computer.

The second section assessed MSDs using the Arabic version of the Nordic Musculoskeletal Questionnaire to evaluate musculoskeletal discomfort in the neck, shoulders, upper back, low back, elbows, wrists/hands, hips/thighs, knees, and ankles/feet. Each region was scored as 0 (no discomfort) or 1 (presence of discomfort), with higher scores indicating poorer musculoskeletal health. The questionnaire captured variables such as the frequency, severity, and duration of pain or discomfort, as well as the extent to which symptoms affected daily activities or work. It assessed the prevalence of these symptoms over 4 timeframes: present (daily), monthly, annually, and lifetime.^[19,20]

The third section evaluated ergonomic practices using a translated Arabic version of the NIH Computer Workstation Ergonomic Self-Assessment Checklist. This checklist examined various workstation aspects, including chair design and positioning, keyboard and mouse arrangement, work surface conditions (such as monitor placement and lighting), and the availability of desks and laptop accessories. We also assessed the frequency of breaks and movement patterns. The overall ergonomic quality was expressed as a percentage score to quantify workstation conditions.^[21]

The fourth section assessed sleep quality using the Arabic version of the Pittsburgh Sleep Quality Index (PSQI). This instrument comprises 7 components, each scored from 0 (no difficulty) to 3 (severe difficulty). The total score ranges from 0 to 12, with higher scores indicating poorer sleep quality. A total score >5 reflects significant sleep disturbances. The PSQI evaluates the following 7 variables: sleep duration, sleep disturbances, sleep latency, daytime dysfunction, use of sleep medications, sleep efficiency, and overall sleep quality.^[22,23]

2.5. Pilot study

Twenty-six male and female employees of Jazan University were invited to participate in this pilot study. The results of this pilot study were excluded from the primary analysis. Instead, these data were examined to assess the questionnaire’s validity and reliability and to evaluate how well the participants understood the questions. Participants also provided feedback on the clarity and comprehensibility of the questionnaire. Based on their evaluations (Cronbach’s alpha ≈ 0.86), necessary modifications were made to improve the questionnaire before it was used in the main study.

2.6. Data management and analysis

After data collection, responses were exported to Microsoft Excel (version 2016; Microsoft Corporation, https://office.microsoft.com/excel) for initial preprocessing, including the identification and correction of missing values, inconsistencies, and errors. The cleaned data were coded and analyzed using IBM SPSS Statistics for windows (version 27.0; IBM Corp.).

For categorical data such as yes/no questions, their answers were scored (e.g., Yes = 1, No = 0). Ordinal data, such as the frequency and duration of activities, were numerically coded. Dummy variables were created when required from variables with more than 2 categories. Measures used included means, standard deviations, numbers, and percentages for all variables. Multiple logistic regression was performed to examine the associations among MSDs, computer workstation ergonomics, and sleep quality. The general equation for multiple logistic regression is given as log(P/1 − P) = β₀ + β₁X₁ + β₂X₂ + ... + β_kX_k, where P denotes the probability of the event. The odds ratio (OR) was calculated through the exponential of the regression coefficient; an OR of >1 signifies that there is an increased risk of the event, while an OR of <1 shows a decreased risk of the event.

A 95% confidence interval (CI) was used to indicate the uncertainty of the OR estimates. A P-value of <.05 was considered statistically significant.

2.7. Study limitations, bias, and generalizability

This study had several limitations. First, the cross-sectional design does not allow for the establishment of causal relationships between MSDs and the associated factors identified; however, this design is well suited for estimating disease prevalence, which was the primary objective of the study. Second, this study was conducted at a single university, which may have limited the generalizability of the findings. Nevertheless, Jazan University shares many organizational, occupational, and ergonomic characteristics with other universities in Saudi Arabia and the broader Arab region, particularly those in the eastern part of the continent. Accordingly, these findings may be applicable to similar teaching settings. Finally, although the study did not receive external funding, single-center studies play an important role in highlighting the magnitude of occupational health problems, attracting the attention of policymakers and funding bodies, and providing a foundation for larger multicenter and intervention-based studies in the future.

Regarding study bias, we addressed sampling bias by using the multistage cluster random sampling technique. Questionnaire bias was addressed through an interviewer-assisted questionnaire to help participants understand any unclear points.

2.8. Ethical considerations

This study was conducted according to the Declaration of Helsinki and was approved by the Standing Committee for Scientific Research at Jazan University (Reference Number: REC-46/06/1274) on December 24, 2024. All participants provided informed consent after receiving a detailed explanation of the study, including voluntary participation and withdrawal at any time, confidentiality, and data protection. All data were collected anonymously, and data access was strictly limited to the research team.

3. Results

As described in the methodology, in the fourth stage of the sampling technique, we distributed the questionnaire to all employees in the selected colleges. The total number of employees was 513. A total of 460 employees agreed to participate and completed the questionnaire, with a response rate of approximately 90%.

The demographic characteristics of the 460 Jazan University teaching and administrative staff are summarized in Table 1. The participants had an average age of 43 ± 7 years, an average height of 165 ± 13 cm, and an average weight of 75 ± 16 kg, with an approximately equal gender distribution. The majority of respondents (63.7%) were teaching staff, and most of them held a master’s degree or higher (60%; Table 1). In addition, 80.7% of participants were married, and 74.3% reported a monthly income of 10,000 Saudi Riyal or more. Approximately 60% of the participants used computers for 5 to 8 hours per day.

Table 1.

Sociodemographic characteristics of the study population.

Variables	Mean	Standard deviation	Count	N, %
Age	43	7
Height	165	13
Weight	75	16
College
Arts and Humanities			120	26.1
Nursing and Health Sciences			120	26.1
Medicine			100	21.7
Science			120	26.1
Gender
Female			220	47.8
Male			240	52.2
Educational level
High school diploma			13	2.8
Bachelor’s degree			145	31.5
Master’s degree or above			276	60.0
Others			26	5.7
Job
Administrators			167	36.3
Teaching staff			293	63.7
Marital status
Divorced			29	6.3
Married			371	80.7
Single			57	12.4
Widow			3	0.7
Family monthly income
<5000 SAR			4	0.9
5000–10,000 SAR			114	24.8
10,000–15,000 SAR			156	33.9
>15,000 SAR			186	40.4
How many hours do you spend working on the computer each day?
0–4 h			148	32.2
5–8 h			275	59.8
>8 h			37	8

SAR = Saudi Riyal.

The low back (65.9%) and neck (65.0%) were the most commonly affected sites of musculoskeletal complaints, followed by the shoulders (57.2%) and upper back (46.3%; Fig. 1).

Figure 1.

Frequency and sites of musculoskeletal complaints among Jazan University employees over the past 12 months.

The key findings of the ergonomic assessment of participants’ computer workstations are shown in Table 2. Although 83% of the participants reported having adjustable office chairs, only 63% indicated that their chairs provided adequate low back support. Additionally, 89% found their mouse comfortable to use, and 87% reported that their keyboard, mouse, and work surface were positioned at elbow height. However, only 37% of participants reported taking regular eye breaks, and only 44% of them took postural breaks every 30 minutes. The use of ergonomic accessories was also limited, with only 44.1% using document holders and 22% using sloping desk surfaces. Among those who used laptops for extended periods, 34.6% did not use external keyboards, and 52% did not use workstations. These findings indicate substantial gaps in workstation ergonomics and break-taking practices, which may contribute to the high prevalence of musculoskeletal complaints among the employees at Jazan University.

Table 2.

Alignment of computer workstations among Jazan University employees with ergonomic standards.

Ergonomic variables	Participant, N = 460
	Yes n (%)		No n (%)		N/A n (%)
Office chair
Can the height, seat, and back of the chair be adjusted to achieve the posture outlined below?	383 (83)	62 (13)		15 (3)
Absence of a footrest for foot support	380 (83)	74 (16)		6 (1)
Does your chair provide support for your lower back?	151 (63)	85 (35)		4 (2)
When your back is supported, are you able to sit without feeling pressure from the chair seat on the back of your knees?	168 (70)	64 (27)		8 (3)
Do your armrests allow you to get close to your workstation?	347 (75)	94 (20)		19 (4)
Keyboard and mouse
Are your keyboard, mouse, and work surface at your elbow height?	402 (87)	47 (10)		11 (2)
Are frequently used items within easy reach?	371 (81)	67 (15)		22 (5)
Is the keyboard close to the front edge of the desk, allowing space for the wrists to rest on the desk surface?	326 (71)	107 (23)		27 (6)
When using your keyboard and mouse, are your wrists straight and your upper arms relaxed?	389 (85)	39 (8)		32 (7)
Is your mouse at the same level and as close as possible to your keyboard?	371 (81)	52 (11)		37 (8)
Is the mouse comfortable to use?	408 (89)	38 (8)		14 (3)
Work surface
Is your monitor positioned directly in front of you?	154 (64)	84 (35)		2 (1)
Is your monitor positioned at least an arm’s length away?	186 (78)	50 (21)		4 (2)
Is your monitor height slightly below eye level?	215 (90)	21 (9)		4 (2)
Are your monitor and work surface free from glare?	354 (89)	34 (9)		12 (3)
Do you have appropriate light for reading or writing documents?	216 (54)	173 (43)		11 (3)
Are frequently used items located within the usual work area and items which are only used occasionally in the occasional work area?	228 (57)	160 (40)		12 (3)
Breaks
Do you take postural breaks every 30 min?	175 (44)	199 (50)		26 (7)
Do you take regular eye breaks from looking at your monitor?	147 (37)	238 (60)		15 (4)
Accessories
Is there a sloped desk surface or angle board for reading and writing tasks if required?	101 (22)	322 (70)		37 (8)
Is there a document holder either beside the screen or between the screen and keyboard if required?	203 (44.1)	227 (49.3)		30 (6.5)
Are you using a headset or speakerphone if you are writing or keying while talking on the phone?	177 (38.5)	265 (57.6)		18 (3.9)
Laptop
In the event of using a laptop computer for prolonged periods of time, do you use a full-sized external keyboard and mouse?	254 (55.2)	159 (34.6)		47 (10.2)
In the event of using a laptop computer for prolonged periods of time, do you use a docking station with a full-sized monitor or a laptop stand?	170 (37)	239 (52)		51 (11.1)
Are you provided with time, support, and supervision to make the above adjustments?	336 (73)	88 (19.1)		36 (7.8)

Figure 2 illustrates that, based on the PSQI, 34.3% of Jazan University employees reported poor sleep quality (PSQI > 5), which may contribute to the development of MSDs and reduced work productivity.

Figure 2.

Sleep quality among Jazan University employees. PSQI = Pittsburgh Sleep Quality Index.

Multiple demographic, occupational, ergonomic, and sleep-related factors were significantly associated with MSD complaints among Jazan University employees (Table 3).

Table 3.

Multivariate logistic regression analysis of the relation between musculoskeletal disorders, computer workstation ergonomics, and sleep quality among Jazan University employees.

Risk factor	Neck	Shoulder	Upper back	Elbows	Wrist/hand	Lower back	Hip/thighs	Knees	Ankle/feet
	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Age	1.03 (1.00–1.06)	1.03 (0.99–1.06)	1.06 (1.03–1.1)	1.06 (1.02–1.11)	1.03 (1.0–1.07)	1.00 (0.97–1.03)	1.04 (1.0–1.09)	1.01 (0.98–1.05)	1.02 (0.98–1.07)
Height (cm)	0.99 (0.96–1.02)	0.99 (0.96–1.02)	0.98 (0.95–1.01)	0.95 (0.91–0.99)	0.98 (0.96–1.01)	1.01 (0.99–1.03)	0.99 (0.96–1.01)	0.95 (0.92–0.99)	0.99 (0.96–1.01)
Weight (kg)	0.98 (0.96–1.00)	0.98 (0.96–1)	0.99 (0.97–1.01)	1.00 (0.97–1.02)	0.99 (0.97–1.01)	1.03 (1.01–1.05)	1.01 (0.99–1.03)	1.03 (1.01–1.04)	1.02 (1–1.04)
Male compared with female	0.64 (0.29–1.43)	0.56 (0.29–1.07)	0.53 (0.24–1.19)	0.82 (0.25–2.67)	0.56 (0.24–1.32)	0.49 (0.22–1.07)	0.22 (0.07–0.66)	1.02 (0.45–2.34)	0.34 (0.13–0.9)
Marital status
Single (ref.)
Married	0.24 (0.05–1.17)	0.25 (0.05–1.21)	1.94 (0.69–5.43)	1.31 (0.32–5.29)	1.28 (0.43–3.77)	0.78 (0.25–2.49)	3.32 (0.67–16.38)	1.05 (0.37–2.99)	0.85 (0.27–2.67)
Divorced	0.27 (0.05–1.48)	0.28 (0.05–1.53)	1.43 (0.45–4.53)	1.42 (0.31–6.41)	1.37 (0.42–4.46)	1.31 (0.35–4.83)	3.73 (0.7–19.98)	1.60 (0.51–5.07)	0.69 (0.19–2.47)
College
Arts and Humanities (ref.)
Nursing and Health Sciences	0.48 (0.26–0.90)	0.51 (0.27–0.95)	0.66 (0.36–1.21)	1.29 (0.55–3.02)	1.82 (0.92–3.57)	0.80 (0.44–1.46)	0.59 (0.28–1.27)	0.84 (0.46–1.52)	1.63 (0.76–3.52)
College of Medicine	0.64 (0.30–1.38)	0.55 (0.26–1.15)	0.57 (0.27–1.21)	1.56 (0.51–4.77)	1.23 (0.45–3.34)	1.30 (0.62–2.7)	1.21 (0.41–3.58)	0.92 (0.41–2.06)	0.91 (0.22–3.78)
College of Science	0.70 (0.37–1.33)	0.74 (0.39–1.39)	0.58 (0.32–1.06)	2.45 (1.09–5.53)	2.12 (1.07–4.2)	1.31 (0.71–2.44)	0.91 (0.44–1.92)	1.03 (0.57–1.86)	2.48 (1.15–5.33)
Educational level
High school diploma (ref.)
Bachelor’s degree	1.43 (0.33–6.17)	1.40 (0.33–5.92)	0.85 (0.21–3.47)	0.84 (0.15–4.73)	1.78 (0.33–9.7)	1.95 (0.48–7.98)	1.34 (0.23–7.81)	1.90 (0.42–8.56)	1.14 (0.21–6.3)
Master’s degree or above	0.99 (0.23–4.22)	1.06 (0.26–4.36)	0.44 (0.09–2.13)	0.37 (0.05–2.78)	1.55 (0.24–10.22)	0.76 (0.16–3.65)	1.05 (0.14–7.75)	0.86 (0.16–4.77)	0.71 (0.1–4.97)
Others	1.38 (0.25–7.56)	1.46 (0.27–7.86)	0.41 (0.08–2.19)	0.00 (!*)	1.39 (0.19–9.99)	1.02 (0.2–5.22)	0.73 (0.08–6.38)	0.57 (0.09–3.78)	1.75 (0.23–13.33)
Job
Teaching staff compared with administrators	1.45 (0.62–3.35)	2.47 (1.08–5.67)	1.82 (0.8–4.11)	2.25 (0.69–7.31)	0.69 (0.28–1.75)	2.09 (0.9–4.84)	1.35 (0.47–3.9)	4.28 (1.7–10.8)	1.94 (0.68–5.55)
Monthly income
<5000 SAR (ref.)
5000–10,000 SAR	0.64 (0.33–1.23)	0.63 (0.33–1.21)	0.66 (0.35–1.24)	0.89 (0.40–1.98)	1.27 (0.65–2.48)	0.88 (0.46–1.69)	1.21 (0.58–2.53)	0.45 (0.24–0.83)	1.71 (0.84–3.51)
10,000–15,000 SAR	0.89 (0.46–1.73)	0.84 (0.44–1.62)	0.76 (0.40–1.44)	0.95 (0.42–2.15)	1.56 (0.78–3.11)	0.73 (0.38–1.41)	0.78 (0.35–1.73)	0.45 (0.24–0.86)	0.86 (0.4–1.84)
Adjustable office chair	0.82 (0.41–1.65)	0.87 (0.44–1.71)	1.28 (0.64–2.56)	1.35 (0.59–3.07)	0.84 (0.41–1.74)	0.82 (0.41–1.62)	3.05 (1.44–6.5)	1.11 (0.57–2.16)	2.27 (1.07–4.8)
Comfortable mouse and keyboard	0.78 (0.41–1.47)	0.73 (0.39–1.36)	0.87 (0.48–1.60)	0.74 (0.35–1.58)	1.00 (0.52–1.91)	0.65 (0.34–1.23)	1.88 (0.81–4.36)	0.89 (0.49–1.61)	0.600 (0.29–1.22)
Comfortable work surface	0.96 (0.50–1.83)	1.34 (0.84–2.14)	0.81 (0.42–1.55)	1.57 (0.56–4.43)	0.67 (0.30–1.49)	0.80 (0.41–1.54)	1.61 (0.56–4.59)	1.01 (0.50–2.00)	0.63 (0.24–1.63)
Regular breaks	1.32 (0.83–2.11)	1.55 (0.97–2.49)	1.25 (0.79–1.98)	0.89 (0.49–1.61)	0.86 (0.52–1.42)	0.86 (0.54–1.37)	1.27 (0.70–2.28)	1.04 (0.65–1.65)	1.07 (0.60–1.90)
Accessories ergonomic setup	1.51 (0.94–2.42)	1.04 (0.95–1.14)	1.17 (0.74–1.87)	2.03 (1.07–3.85)	0.99 (0.59–1.65)	1.42 (0.88–2.27)	2.27 (1.21–4.26)	1.37 (0.85–2.20)	2.10 (1.14–3.88)
Duration of computer use	1.04 (0.95–1.14)	1.70 (0.99–2.90)	1.11 (1.01–1.21)	1.07 (0.95–1.19)	1.05 (0.95–1.15)	1.04 (0.95–1.13)	1.01 (0.91–1.13)	1.03 (0.94–1.12)	1.01 (0.91–1.13)
PSQI score	1.13 (1.03–1.24)	0.00 (0.00–0.00)	2.47 (1.08–5.67)	2.25 (0.69–7.31)	0.69 (0.28–1.75)	2.09 (0.90–4.84)	1.35 (0.47–3.90)	4.28 (1.7–10.8)	1.94 (0.68–5.55)

Bold font represents significant results P < .05.

CI = confidence interval, OR = odds ratio, PSQI = Pittsburgh Sleep Quality Index, SAR = Saudi Riyal.

^*Indicates that the value could not be calculated by the program due to an excessively large value.

Increasing age was significantly associated with higher odds of elbow complaints (OR = 1.06, 95% CI: 1.02–1.11) and upper-back MSD complaints (OR = 1.06, 95% CI: 1.03–1.10). It was also demonstrated that taller participants had lower odds of elbow (OR = 0.95, 95% CI: 0.91–0.99) and knee MSD complaints (OR = 0.95, 95% CI: 0.91–0.99). Increasing body weight was significantly associated with increased odds of developing MSD complaints in the low back (OR = 1.03, 95% CI: 1.01–1.05) and knee (OR = 1.03, 95% CI: 1.01–1.04).

Compared with employees from the College of Arts and Humanities, those from the College of Science had significantly higher odds of reporting elbow (OR = 2.45, 95% CI: 1.09–5.53), wrist/hand (OR = 2.12, 95% CI: 1.07–4.20), and ankle/foot (OR = 2.48, 95% CI: 1.15–5.33) MSD complaints. Comparing teaching and administrative staff showed that teaching staff had significantly higher odds of shoulder (OR = 2.47, 95% CI: 1.08–5.67) and knee (OR = 4.28, 95% CI: 1.70–10.80) MSD complaints.

Ergonomic factors were also significantly associated with MSD complaints. The absence of an ergonomically designed workstation was associated with higher odds of elbow (OR = 2.03, 95% CI: 1.07–3.85), hip/thigh (OR = 2.27, 95% CI: 1.21–4.26), and ankle/foot (OR = 2.10, 95% CI: 1.14–3.88) MSD complaints. Likewise, the absence of an adjustable office chair was associated with increased odds of hip/thigh (OR = 3.05, 95% CI: 1.44–6.50) and ankle/foot (OR = 2.27, 95% CI: 1.07–4.80) MSD complaints. Longer daily computer use was significantly associated with upper-back MSD complaints (OR = 1.11, 95% CI: 1.01–1.21).

Unexpectedly, participants who reported taking regular breaks had higher odds of hip/thigh (OR = 2.27, 95% CI: 1.21–4.26) and ankle/foot (OR = 2.10, 95% CI: 1.14–3.88) MSD complaints. In light of the study’s cross-sectional nature, it would appear that the results show an inverse causal relationship where people exhibiting signs of MSD take breaks to cope.

Poor sleep quality, as assessed by the PSQI, was significantly associated with higher odds of neck pain (OR = 1.13, 95% CI: 1.03–1.24), upper-back MSD (OR = 2.47, 95% CI: 1.08–5.67), and knee MSD (OR = 4.28, 95% CI: 1.70–10.80).

Overall, these findings underscore the multifactorial nature of MSDs among Jazan University employees, highlighting the combined influence of demographic characteristics, occupational factors, ergonomic workstation design, and sleep quality.

4. Discussion

This study investigated the association between MSDs, computer workstation ergonomics, and sleep quality among employees at Jazan University. The findings demonstrated a high prevalence of MSDs, particularly involving the lower back, neck, and shoulders. Poor workstation ergonomics were significantly associated with an increased risk of MSDs, emphasizing the importance of appropriate workstation design, including suitable chairs, desks, and monitor positioning. Furthermore, poor sleep quality was independently associated with neck pain (OR = 1.13, 95% CI: 1.03–1.24) and upper-back pain (OR = 2.47, 95% CI: 1.08–5.67), suggesting that inadequate sleep may contribute to the development or persistence of musculoskeletal symptoms. Collectively, these findings highlight the importance of integrating ergonomic interventions with strategies to improve sleep quality in order to enhance employee health and workplace productivity.

The higher prevalence of several MSDs among employees from the College of Science may reflect differences in occupational responsibilities and workplace environments. In addition to routine computer-based work, teaching and technical personnel in scientific disciplines frequently perform laboratory activities requiring repetitive manual tasks, prolonged standing, and repeated transitions between laboratory and office settings. These occupational demands may increase mechanical stress on both the upper and lower extremities, thereby elevating the risk of MSDs. However, because detailed information on job-specific tasks and laboratory exposures was not collected, this interpretation should be considered speculative and warrants further investigation.

The prevalence of MSDs observed in this study was particularly high for the lower back (65.9%), neck (65.0%), shoulders (57.2%), and upper back (46.3%). Comparable patterns have been reported among university employees and computer users in Bangladesh and office workers in Thailand.^[5,11,24,25] Although direct comparisons should be interpreted cautiously because of differences in study populations, occupational settings, and working conditions, the overall distribution of MSDs across computer-intensive occupations appears remarkably consistent. Studies conducted among university teachers and computer operators in Bangladesh included populations similar to ours,^[5,25] whereas Thai studies primarily involved office workers from various organizations.^[24] More broadly, evidence from occupational health research indicates that prolonged computer use remains a common risk factor for work-related MSDs across different professions.^[11] Conversely, lower prevalence rates have been reported among workers in Egypt,^[26] possibly reflecting greater ergonomic awareness or differences in occupational exposures. In contrast, substantially higher rates have been documented in Jordan, highlighting regional and occupational variability in MSD prevalence.^[27]

The high prevalence of neck, shoulder, and lower back complaints in our study may be explained by prolonged sedentary work, inadequate workstation design, sustained static postures, and occupational stress. The inclusion of both teaching and administrative staff likely captured a wide range of ergonomic exposures. Moreover, limited implementation of structured ergonomic programs and inadequate awareness of appropriate body mechanics may have further contributed to the observed burden of MSD complaints.

Although most participants reported having adjustable chairs (83%) and workstations positioned at approximately elbow height (87%), only 63% had adequate lumbar support. Furthermore, fewer than half reported taking regular postural breaks every 30 minutes (44%), and only 37% routinely took visual breaks. These findings suggest that the mere availability of ergonomic equipment does not ensure its effective utilization. Similar observations have been reported among university employees in Saudi Arabia and healthcare workers in Botswana, where ergonomic resources were available but were not consistently used.^[28,29] A recent meta-analysis also identified similar gaps between ergonomic resource availability and their practical implementation.^[30]

Our findings further indicate that providing ergonomic equipment alone is unlikely to prevent MSDs unless accompanied by organizational support and behavioral changes. Factors such as the absence of institutional ergonomic policies, insufficient managerial supervision, and high workloads may discourage employees from consistently adopting healthy ergonomic practices. In demanding work environments, employees may prioritize productivity over maintaining appropriate posture or taking regular breaks, thereby reducing the effectiveness of available ergonomic resources.

Approximately one-third (34.3%) of university employees reported poor sleep quality. Similar prevalence estimates have been reported in Turkey (38.9%),^[31] Malaysia (49%–51%), and Iran (49%).^[32,33] The slightly higher prevalence reported in these studies may reflect additional psychological or occupational stressors that were not assessed in the present investigation. Other studies have documented even higher rates exceeding 60% among Malaysian and Brazilian populations,^[33,34] potentially owing to heavier workloads and limited opportunities for rest. Within Saudi Arabia, poor sleep quality has been reported among 75% of teaching staff in Dammam (Eastern Saudi Arabia)^[35] and 70.4% of physicians and nurses working in a university hospital in Jeddah (Western Saudi Arabia).^[36]

These variations are likely attributable to differences in occupational stress, institutional culture, workload, and study populations. Unlike investigations limited to teaching or clinical personnel, our study included both teaching and administrative employees, who may experience different occupational demands and stress levels. In addition, psychological factors such as anxiety and depression were not evaluated and may partly explain the comparatively lower prevalence of poor sleep quality observed in our study.

Our regression analysis revealed several significant associations between ergonomic factors and MSDs. For instance, those employees who did not have any ergonomic accessories experienced higher odds of MSDs related to the elbow joint (OR = 2.03, 95% CI: 1.07–3.85), hips/thighs (OR = 2.27, 95% CI: 1.21–4.26), and ankle/feet (OR = 2.10, 95% CI: 1.14–3.88). Likewise, those who did not possess any adjustable office chair faced higher risks of MSD complaints related to hips/thighs (OR = 3.05, 95% CI: 1.44–6.50) and ankles/feet (OR = 2.27, 95% CI: 1.07–4.80). On the contrary, higher daily hours spent working on computers were associated with an increased likelihood of upper-back discomfort (OR = 1.11, 95% CI: 1.01–1.21). Similar associations have been reported in numerous studies that have identified poor ergonomics, prolonged sitting, and awkward postures as key risk factors for MSDs. Interventional studies also support these findings, with regular breaks and workstation adjustments reducing musculoskeletal complaints^[37] and proper desk and chair height adjustments significantly lowering pain levels.^[38] Other studies have confirmed that ergonomic training and continuous workstation adjustments can substantially reduce MSD prevalence.^[25,39]

Our analysis showed a significant association between poor sleep quality and MSDs, particularly in the upper back, neck, and knees. Employees engaged in sedentary and computer-intensive work may be particularly vulnerable to the combined effects of poor sleep and prolonged sitting time. This aligns with the growing body of literature that indicates a bidirectional relationship between sleep disturbances and MSDs. For instance, a survey of public school teachers found strong links between poor sleep and widespread MSD. The study showed that teachers with poor sleep quality were approximately twice as likely to report thoracic pain (OR = 2.16 [95% CI = 1.12–4.16]), wrist pain (OR = 3.28 [95% CI = 1.18–9.07]), low-back pain (OR = 3.09 [95% CI = 1.29–7.41]), and ankle/foot pain (OR = 2.83 [95% CI = 1.32–6.08]).^[38] Another Turkish study among office workers demonstrated that extended computer use combined with inadequate sleep increased musculoskeletal complaints. The Turkish researchers showed that MSDs were prevalent among 83.3% of the office workers. Most of them (74.7%) had poor sleep quality. Knee pain (P = .016, OR = 3.670, 95% CI: 1.280–10.342) and lower back pain (P = .003, OR = 4.380, 95% CI: 1.680–11.517) were significant predictors of poor sleep quality. There was a moderate positive correlation between the number of body areas where pain was reported and the PSQI score (r: 0.367, P < .001). The presence of poor sleep quality was significantly higher among those who reported musculoskeletal pain than among those who did not (P = .001).^[40]

These variations are likely attributable to differences in occupational stress, institutional culture, workload, and study populations. Unlike investigations limited to teaching or clinical personnel, our study included both teaching and administrative employees, who may experience different occupational demands and stress levels. In addition, psychological factors such as anxiety and depression were not evaluated and may partly explain the comparatively lower prevalence of poor sleep quality observed in our study.^[41] Similar findings have been reported among patients with chronic pain,^[42] emergency nurses,^[43] and workers across multiple occupational sectors in Sweden, where severe sleep disturbances independently predicted the progression from occasional to chronic low-back pain. The study showed that high job strain (OR 1.5), active jobs (OR 1.3), and severe sleep disturbances (OR 3.0) were significant independent prognostic factors for individuals with occasional back pain to develop troublesome low-back pain.^[44,45] Collectively, this evidence supports the interpretation that sleep is a key modifiable factor that influences musculoskeletal health.

Our findings also suggest that occupational role may influence MSD risk. Teaching staff may experience greater physical demands because of prolonged standing during teaching, repetitive instructional activities, and heavier workloads, whereas administrative personnel typically perform more sedentary and desk-based tasks. These differences in occupational exposure may partly explain the variation in MSD patterns observed between employee groups.

This study has several strengths. The use of interview-based data collection allowed for real-time clarification of responses and improved data accuracy. Additionally, validated instruments, including the Nordic Musculoskeletal Questionnaire, NIH Ergonomic Assessment Checklist, and PSQI, ensured methodological rigor. The pilot study enhanced reliability, and multistage cluster random sampling improved the representativeness across university departments. However, this study had some limitations. MSDs were assessed by body region only, without distinguishing between right- and left-sided symptoms, which limited the analysis of laterality. In addition, marital status and educational qualifications were adjusted for in the multivariable model but showed no consistent association with musculoskeletal outcomes. The cross-sectional design limits causal inferences between ergonomics, sleep, and MSDs. Recall bias is also possible, as participants reported symptoms over extended periods. Despite these limitations, this study provides valuable insights into how workstation ergonomics and sleep quality jointly influence musculoskeletal health among university employees. These findings highlight the need for institutional ergonomic education programs and workplace health policies to promote both musculoskeletal well-being and productivity.

5. Conclusion and Recommendations

MSD complaints are highly prevalent among Jazan University employees and are influenced by demographic, ergonomic, and sleep-related factors. Inadequate ergonomic practices and poor sleep quality are significant modifiable contributors to MSD risk. Addressing these factors requires a comprehensive approach that extends beyond providing ergonomic equipment to include behavioral, organizational, and health-promotion strategies.

Universities in the Arab region should implement structured ergonomic training, conduct regular workstation assessments, promote scheduled postural and visual breaks, and support effective workload management. Workplace health programs should also incorporate sleep health education and screening for sleep disturbances while encouraging active work practices, such as regular movement breaks and the use of sit–stand workstations. Future longitudinal and interventional studies are needed to establish causal relationships, examine workload differences across employee groups in different colleges, and evaluate the effectiveness of targeted preventive interventions.

Acknowledgments

The authors would like to express their gratitude to the Jazan University employees who participated in the study and supported this research through their valuable insights.

Author contributions

Conceptualization: Maged El-Setouhy, Amani Abdelmola, Zenat A. Khired, Mohamed M. Ahmed, Sharifah A. Komeit, Remaz R. Remely, Shuruq A. Hakami, Naseem A. Moafa, Abeer H. Ghalib, Ebhar G. Magrashi, Retaj M. Shawish, Azza A. Alareefy, Ahmad A. Alharbi, Tawfeeq Altherwi, Mohammad Zaino.

Data curation: Maged El-Setouhy, Amani Abdelmola, Mohamed M. Ahmed, Sharifah A. Komeit, Remaz R. Remely, Shuruq A. Hakami, Naseem A. Moafa, Abeer H. Ghalib, Ebhar G. Magrashi, Retaj M. Shawish, Mohammad Zaino.

Formal analysis: Maged El-Setouhy, Mohammad Zaino.

Investigation: Maged El-Setouhy, Amani Abdelmola, Zenat A. Khired, Mohamed M. Ahmed, Ebhar G. Magrashi, Azza A. Alareefy, Ahmad A. Alharbi, Tawfeeq Altherwi.

Methodology: Maged El-Setouhy, Amani Abdelmola, Zenat A. Khired, Mohamed M. Ahmed, Sharifah A. Komeit, Remaz R. Remely, Shuruq A. Hakami, Naseem A. Moafa, Abeer H. Ghalib, Ebhar G. Magrashi, Retaj M. Shawish, Azza A. Alareefy, Ahmad A. Alharbi, Tawfeeq Altherwi, Mohammad Zaino.

Project administration: Maged El-Setouhy, Amani Abdelmola, Zenat A. Khired.

Supervision: Maged El-Setouhy, Amani Abdelmola, Mohammad Zaino.

Software: Mohammad Zaino.

Validation: Maged El-Setouhy, Mohamed M. Ahmed, Sharifah A. Komeit, Remaz R. Remely, Shuruq A. Hakami, Naseem A. Moafa, Retaj M. Shawish, Mohammad Zaino.

Visualization: Mohamed M. Ahmed, Sharifah A. Komeit, Remaz R. Remely, Shuruq A. Hakami, Abeer H. Ghalib, Ebhar G. Magrashi, Retaj M. Shawish, Azza A. Alareefy, Ahmad A. Alharbi, Tawfeeq Altherwi.

Writing – original draft: Maged El-Setouhy, Amani Abdelmola, Zenat A. Khired, Mohamed M. Ahmed, Sharifah A. Komeit, Remaz R. Remely, Shuruq A. Hakami, Naseem A. Moafa, Abeer H. Ghalib, Ebhar G. Magrashi, Retaj M. Shawish, Ahmad A. Alharbi, Tawfeeq Altherwi, Mohammad Zaino.

Writing – review & editing: Maged El-Setouhy, Amani Abdelmola, Zenat A. Khired, Mohamed M. Ahmed, Sharifah A. Komeit, Remaz R. Remely, Shuruq A. Hakami, Naseem A. Moafa, Abeer H. Ghalib, Ebhar G. Magrashi, Retaj M. Shawish, Azza A. Alareefy, Ahmad A. Alharbi, Tawfeeq Altherwi, Mohammad Zaino.