Relationship between Physical Activity Levels, Quality of Life, and Sociodemographic Attributes among Adults in Tabuk, Saudi Arabia: A Direction toward Sustainable Health

Abstract

Physical activity (PA) improves quality of life (QOL), yet the relationship between PA, QOL, and sociodemographic factors in Saudi Arabia remains underexplored. This study examined this relationship among 369 adults from Tabuk City, Saudi Arabia. The questionnaire measured PA levels and QOL using the Arabic versions of the International Physical Activity Questionnaire and the 36-item Short-Form Health Survey (SF-36), respectively. Data analyses included chi-square, Mann–Whitney U, and Kruskal–Wallis H tests alongside a multivariate regression analysis. Among the SF-36 scores, marital status (p = 0.005), history of chronic diseases (p = 0.004), and medication use (p = 0.001) showed significant differences; pain (80.6 ± 21.5) and energy/fatigue (63.0 ± 18.5) scored highest and lowest, respectively; and sex was significantly associated with PA levels (p = 0.001). The average total SF-36 score was 69.5 ± 15.7, and 42.5% of participants reported low PA. Participants with moderate PA had significantly higher SF-36 scores (p = 0.003), energy/fatigue (p < 0.0001), emotional wellbeing (p = 0.009), and general health (p = 0.004) scores compared with those with low PA. The significant association between PA and QOL underscores the need for sustainable health programs to enhance and maintain PA in alignment with the Saudi Vision 2030 QOL program. It can also inform the development of targeted interventions to enhance PA levels and improve QOL aspects within communities, considering specific sociodemographic attributes to ensure effectiveness and inclusivity.

1. Introduction

The relationship between physical inactivity and quality of life (QOL) has been well-documented, highlighting the adverse effects of a sedentary lifestyle on various health outcomes [1]. The World Health Organization (WHO) has issued physical activity (PA) guidelines for adults to maintain health and wellbeing [2,3], such as spending at least 150–300 min and 75–150 min on moderate-intensity or vigorous-intensity exercise per week, respectively, to ensure adequate PA levels [4,5]. Adapting PA guidelines based on a country’s context, with an emphasis on national programs to enhance sports activities, has been suggested as a sustainable goal by 2030 [6]; different special groups, such as pregnant women and persons with disabilities or chronic illnesses, should be recognized, and sedentary lifestyle behaviors should be addressed [7]. One sustainable development goal of Saudi Arabia’s Vision 2030 is to improve individuals’ QOL under QOL programs, and a second goal is to increase the number of people partaking in sporting activities to ensure adequate PA levels among the Saudi population [8].

Adequate PA leads to better QOL [9,10,11,12]. PA has significant impacts on QOL among different age groups, including adolescents, adults, and older adults [13,14,15]. Owing to its major impact on general health, PA plays a substantial role in the primary and secondary prevention of chronic illness and premature death [16], and it reduces the risk of mortality, cardiovascular disease, and types of cancer [17,18,19]. PA also has a positive impact on both sexes and promotes healthy aging, which includes menopausal women’s general health [20,21]. PA also improves sleep quality in both young and older individuals [22,23] and influences the psychological aspects of QOL as it reduces stress and depression [24].

However, an international study of 168 countries with 1.9 million participants has predicted that the goal of increasing PA might not be achieved by 2025 owing to its insufficient adherence [25]. Inactivity levels in many countries have reached 70% owing to the invasion of technology and urban lifestyles [6]. Moreover, another study has shown poorer QOL in women than men owing to inactivity and sedentary occupations [26]. In Saudi Arabia and the Gulf region, unique sociocultural and environmental factors contribute to high levels of physical inactivity, particularly among women and older adults. In Saudi Arabia, around one-third of the population practiced moderate levels of PA as reported by the general authority of statistics, which is also reflected in the Tabuk population, with a large gap between genders (20% higher for men) [27,28]. Previous studies in Saudi Arabia have shown a strong association between low PA levels and poor QOL, emphasizing the need for targeted interventions. The research [29,30,31] on the Saudi population has also reported high prevalence rates of physical inactivity and its detrimental impact on health. Inactive populations tend to report poor QOL due to the significant impact on physical wellbeing [9]. Low PA levels have been linked to various health issues, including depression, obesity, diabetes, kidney disease, and reduced overall QOL [32,33,34]. As such, the literature has proposed the integration of PA into leisure activities and encouraging the public to be active through health campaigns to improve mental health issues such as depression [35]. In Saudi Arabia, studying the population’s knowledge and attitudes toward PA has been suggested to plan appropriate strategies for health improvement through physical exercise [31]. A previous study has further suggested that PA should be enhanced through national policies to reduce the proportion of physically inactive people in Saudi Arabia [36].

Overall, the literature has asserted that adequate PA is crucial for promoting improved QOL, yet the relationship between PA, QOL, and sociodemographic factors in Saudi Arabia remains underexplored. Therefore, this study builds on the extant literature by providing a comprehensive analysis of the relationship between PA levels, QOL, and sociodemographic attributes among adults in Tabuk City. Specifically, this study aims to determine the relationship between PA levels (categorized as low, moderate, and high) and overall QOL scores and to examine how sociodemographic factors (e.g., age, sex, marital status, education level, employment status, smoking habits, and health status) influence the association between PA and QOL. By providing evidence of this relationship and identifying key socioeconomic predictors, this study’s results can be used to develop and implement targeted and tailored sustainable public health interventions that encourage active lifestyles and enhance QOL to mitigate the negative impact of being inactive and improve life satisfaction and QOL among the Saudi population.

2. Material and Methods

2.1. Design

This descriptive cross-sectional study was conducted in Tabuk City, a region in the northwestern part of Saudi Arabia known for its diverse population and rapid urban development. Tabuk was accredited as a “healthy city” by WHO as it maintains a good quality of life and sustainable healthcare services and partners with many promising projects such as the Neom, Read Sea, and Line projects [37,38]. This study targeted community-dwelling adults aged 18 years and older, reflecting the general population of Tabuk.

2.2. Sample and Setting

The participants were selected conveniently from the local community from various public areas, including malls, parks, restaurants, and coffee shops, to ensure a diverse and representative sample. Those who could read and write and were aged 18 years or older were included.

The sample size was determined using OpenEpi Version 3, an open-source calculator, with a confidence level of 95% and a design effect of 0.9. Based on the prevalence rates of physical inactivity and expected differences in QOL scores, a minimum sample size of 346 participants was calculated to achieve sufficient statistical power. This sample size was deemed adequate to detect meaningful associations between PA levels and QOL, considering the sociodemographic variables.

2.3. Data Collection

Individuals in public areas, such as malls, parks, restaurants, and coffee shops, were randomly invited to participate. Data were collected using a self-administered paper-and-pencil questionnaire. All questionnaires were numbered and labeled to assess the attrition rate. Data from the completed questionnaires were coded, entered into an Excel sheet, and transferred to SPSS 25.0.

2.4. Instruments

The questionnaire comprised three parts: (1) sociodemographic information, including age, sex, weight, height, marital status, education level, employment status, and health information; (2) the Arabic version of the International Physical Activity Questionnaire (IPAQ) [39,40] to measure PA levels; and (3) the Arabic version of the 36-item Short Form Health Survey (SF-36) to assess QOL [41]. The Arabic versions of the IPAQ and SF-36 have been proven valid and reliable among Arabic-speaking populations [42,43,44,45].

2.4.1. The International Physical Activity Questionnaire (IPAQ)

PA levels were calculated using the IPAQ, a widely recognized tool for assessing PA among diverse populations. The IPAQ provides metabolic equivalent of task (MET) scores based on the frequency and duration of various PAs reported by individuals. These activities are categorized into different intensities: vigorous, moderate, and walking. The MET scores are calculated by multiplying the minutes spent on each activity by the specific MET value assigned to that activity type, reflecting its energy cost. This comprehensive approach allows for the quantification of PA levels and facilitates comparisons and analyses within and across populations, aiding the evaluation of PA patterns and their impacts on health outcomes. This study categorized the MET scores into low, moderate, and high PA levels according to established guidelines. QOL was assessed using the SF-36, which includes eight domains scored on a scale from 0–100, with higher scores indicating better health status. The total SF-36 score is derived by averaging the scores of the individual domains, providing a comprehensive measure of overall QOL. Many studies have used the IPAQ as a comprehensive tool to measure PA [40]. The IPAQ comprises seven questions regarding PA performed in the previous week (vigorous and moderate, in addition to walking). The score for each activity type is calculated as follows: MET level × min of activity per day × days per week (where MET is one metabolic equivalent defined as the amount of oxygen consumed while sitting at rest and is equal to 3.5 mL O₂ per kg body weight × min) [46]. MET levels are categorized as 8, 4, and 3.3 for vigorous activities, moderate activities, and walking, respectively. Subsequently, this study calculated the score for each activity type and summed them to obtain the total PA score, which was categorized as low, moderate, or high according to established guidelines [47].

2.4.2. The Arabic Version of the 36-Item Short Form Health Survey (SF-36)

The SF-36 comprises 36 items that cover 8 healthcare domains. Each item is scored from 0–100; hence, the lowest and highest possible scores are 0 and 100, respectively. A higher score indicates better health status, with a normative score of 50 [48,49]. This study divided the total SF score into two categories, with <50 considered as poor QOL and ≥50 considered as good QOL.

2.5. Statistical Analyses

The categorical and continuous variables were presented as frequencies and percentages and the mean ± standard deviation, respectively. Numerical data were checked for normality using the Shapiro–Wilk and Kolmogorov–Smirnov tests. Both tests revealed a non-normal distribution of all numerical variables. Therefore, non-parametric alternatives to parametric tests (chi-square, Mann–Whitney U, and Kruskal–Wallis) were performed to examine the differences between the variables.

A multivariate regression analysis was conducted to explore the sociodemographic predictors of the QOL scores. The dataset included variables such as sex, age, weight, height, body mass index (BMI), marital status, education, occupation, smoking status, history of chronic diseases, history of surgery, prescription medication use (Rx), and total PA (TotalPA).

The QOL scores were the dependent variable, and the sociodemographic factors were the independent variables. Unadjusted and adjusted analyses were performed to control for potential confounders, specifically age and sex. The regression model was structured to determine each predictor’s individual contribution to the QOL scores. SPSS 25.0 for Windows was used to conduct all statistical analyses, with an alpha level of 0.05.

2.6. Ethical Considerations

All participants gave their written informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the University of Tabuk Research ethical committee, Approval No.: UT-120-15-2020. Participants’ anonymity was guaranteed, and they could withdraw at any time.

3. Results

3.1. Participants’ Characteristics

Of the 480 distributed questionnaires, 369 responses were received (response rate = 76.9%). Regarding participants’ sociodemographic characteristics (

Table 1. Participants’ sociodemographic characteristics (n = 369).

Measurement	Mean (SD)	Median	Min.	Max.
Age (years)	29.4 (10.4)	26.0	18	68
Weight (kg)	73.6 (21.8)	70.0	40	180
Height (m)	1.66 (0.010)	1.67	1.40	1.96
BMI (kg/m2)	26.6 (7.25)	25.3	14.7	66.4
Characteristic	Attribute	n	%
Sex	Male	226	61.2
	Female	143	38.8
Marital status	Single	205	55.6
	Married	150	40.7
	Divorced	9	2.4
	Widowed	5	1.4
Education level	Elementary	10	2.7
	Intermediate	14	3.8
	High School	90	24.4
	Diploma	41	11.1
	Bachelor	177	48.0
	Master	14	3.8
	Doctorate	23	6.2
Employment status	Employed	142	38.5
	Unemployed	81	22.0
	Retired	8	2.2
	Student	138	37.4
Smoking	Yes	101	27.4
	No	268	72.6
Chronic diseases	Yes	51	13.8
	No	318	86.2
History of surgery	Yes	112	30.4
	No	257	69.6
Taking medication	Yes	80	21.7
	No	289	78.3

SD, standard deviation; BMI, body mass index.

), their mean age was 29.4 ± 10.4 years, BMI was 26.6 ± 7.25 kg/m², and most were male (61.2%). Over half were single (55.6%), and 48% had a bachelor’s degree. Less than half were employed (38.5%), and the majority were non-smokers (72.6%) with no chronic illness (86.2%) or surgical history (69.6%). Overall, 78.3% were not taking medication.

Of the eight SF-36 domains (

Table 2. Distribution of SF-36 domain scores.

Domain	No. of Items	Mean	SD
Physical functioning	10	67.9	30.2
Role limitations due to physical health	4	69.4	36.8
Role limitations due to emotional problems	3	63.9	40.0
Energy/fatigue	4	63.0	18.5
Emotional wellbeing	5	66.1	19.1
Social functioning	2	77.4	22.5
Pain	2	80.6	21.5
General health	5	65.5	14.0
Health change	1	71.7	24.9
Mean total score out of 100	36	69.5	15.7

SD, standard deviation.

), the highest and lowest scores were observed for pain (80.6 ± 21.5) and energy/fatigue (63.0 ± 18.5), respectively. The mean score was 69.5 ± 15.7 out of 100. Overall, 42.5%, 18.7%, and 38.8% of the participants had low, moderate, and high PA levels, respectively.

3.2. Sociodemographic Attributes and QOL

The SF-36 scores were statistically similar for both sexes alongside education level, employment status, smoking habits, and history of surgery (

Table 3. Differences in sociodemographic characteristics by SF-36 score and PA level (n = 369).

Characteristic	Attribute	SF-36 Score		U/H	p	PA Level (%)			χ2	p
		Mean	SD			Low	Moderate	High
Age	<30 years	68.3	14.2	12.56	0.04	38.2	22.1	39.7	12.5	0.011
	31–55 years	71.1	15.5	13.67	0.01	42.0	18.5	39.5	10.3	0.029
	>55 years	65.9	12.8	14.32	0.006	45.5	12.7	41.8	13.6	0.007
Sex	Male	70.2	15.9	14,910 a	0.211	35.0	19.9	45.1	14.5	0.001 *
	Female	68.3	15.6			54.5	16.8	28.7
Marital status	Single	68.7	16.3	12.8 b	0.005 *	38.5	20.5	41.0	11.1	0.084
	Married	71.7	14.5			46.7	18.0	35.3
	Divorced	64.6	13.4			77.8	0.0	22.2
	Widowed	45.6	12.8			20.0	0.0	80.0
Education level	Elementary	74.5	15.3	9.66 b	0.140	60.0	30.0	10.0	14.0	0.298
	Intermediate	65.8	14.7			57.1	14.3	26.8
	High school	67.6	16.8			36.7	17.8	45.6
	Diploma	72.9	14.5			36.6	14.6	48.8
	Bachelor	69.8	15.4			41.8	20.9	37.3
	Master’s	63.1	14.1			71.4	7.1	21.4
	Doctorate	72.9	17.5			47.8	17.4	34.8
Employment status	Employed	70.3	14.2	5.49 b	0.139	48.6	19.0	32.4	7.99	0.238
	Unemployed	69.7	16.0			42.0	18.5	39.5
	Retired	58.6	8.7			62.5	0.0	37.5
	Student	69.3	17.3			35.5	19.6	44.9
Smoking	Yes	67.2	15.7	15.1 a	0.084	38.6	23.8	37.6	2.45	0.293
	No	70.4	15.7			44.0	16.8	39.2
Chronic diseases	Yes	63.7	15.2	10.1 a	0.004 *	56.9	15.7	27.5	5.10	0.079
	No	70.4	15.7			40.3	19.2	40.6
History of surgery	Yes	68.8	14.9	15,073 a	0.469	43.8	21.4	34.8	1.33	0.513
	No	69.8	16.1			42.0	17.5	40.5
Taking medication	Yes	62.5	16.2	15,200 a	<0.001 *	46.3	17.5	36.3	0.570	0.751
	No	71.4	15.1			41.5	19.0	39.4

PA, physical activity; SD, standard deviation; U, Mann–Whitney U statistic; H, Kruskal–Wallis H statistic; χ², chi-square statistic. a: U value. b: H value. * Significant at α = 0.05.

). However, scores were significantly higher for participants with no chronic diseases and who did not take medication at the time. The results revealed a significant difference for the marital status category. All pairwise comparisons via Mann–Whitney U tests revealed that widows had significantly lower SF-36 scores than individuals who were single (p = 0.005) or married (p = 0.008). No further differences were observed among the other comparisons.

3.3. Sociodemographic Attributes and PA

A significant difference was observed between the percentages of men and women across the three PA level categories (Table 3). The follow-up analysis via chi-square tests showed that a significant proportion of men more frequently reported higher PA levels than women (45.1% vs. 28.6%; p = 0.0016; Figure 1). Correspondingly, a significant percentage of women reported lower PA levels than men (54.5% vs. 34.9%; p = 0.0002). No significant difference was observed between the number of men and women who reported a moderate PA level (19.9% vs. 16.8%; p = 0.452). No additional differences were found among the other sociodemographic variables and PA levels.

3.4. PA and QOL

The Kruskal–Wallis H tests revealed statistically significant differences between the three PA levels for the total SF-36 score and SF-36 domains 4, 5, and 8 (

Table 4. Differences in SF-36 domain scores by PA level (n = 369).

Item No.	SF-36 Domain	Low	Moderate	High	H	p
1	Physical functioning	64.8 ± 29.2	72.1 ± 27.8	69.3 ± 32.1	5.15	0.076
2	Role limitations due to physical health	65.7 ± 38.5	74.3 ± 33.5	71.0 ± 36.4	2.64	0.266
3	Role limitations due to emotional problems	59.6 ± 42.3	71.7 ± 36.3	64.9 ± 38.7	3.92	0.141
4	Energy/fatigue	60.5 ± 18.6	68.5 ± 16.3	63.1 ± 18.9	13.4	0.001 *
5	Emotional wellbeing	64.3 ± 18.6	70.9 ± 18.7	65.6 ± 19.4	6.99	0.030 *
6	Social functioning	75.8 ± 21.8	79.7 ± 23.6	78.2 ± 22.9	2.74	0.254
7	Pain	79.7 ± 21.3	80.3 ± 20.4	81.8 ± 22.4	1.44	0.486
8	General health	62.9 ± 12.7	68.4 ± 13.8	67.1 ± 15.1	10.87	0.004 *
9	Health change	70.2 ± 26.2	76.5 ± 22.6	71.1 ± 24.3	2.72	0.256
SF-36 score		67.0 ± 15.1	73.6 ± 14.0	70.2 ± 16.8	10.1	0.007 *

* Significant at α = 0.05.

). Further analysis showed significantly higher scores for the total SF-36 score and these three domains among participants who reported a moderate PA level than those who reported a low PA level (

Table 5. Results of all pairwise comparisons of the SF-36 domain scores by PA level (low, moderate, and high) (n = 369).

SF-36 Domain	Comparison
	Low vs. Moderate		Low vs. High		Moderate vs. High
	U	p	U	p	U	p
Energy/fatigue	56.0	<0.0001 *	21.3	0.083	34.7	0.026
Emotional wellbeing	40.3	0.009 *	8.75	0.477	31.6	0.043
General health	43.9	0.004 *	31.8	0.009 *	12.0	0.437
QOL total score	46.5	0.003 *	25.1	0.041	21.4	0.171

* Significant at α = 0.0167. A Bonferroni adjustment of the alpha level was performed to avoid type I error.

). Interestingly, participants with a moderate PA level scored higher for domain 8 than those who reported a high PA level. No additional significant differences were observed.

3.5. Multivariate Regression Analysis

The multivariate regression analysis included unadjusted and adjusted analyses to control for potential confounders such as age and sex. The results are shown in

Table 6. Multivariate regression analysis for sociodemographic predictors of QOL scores (n = 369).

Predictor	Coefficient	Std. Error	t	p
Intercept	36.4758	41.3345	0.882	0.389
Sex	−5.2295	7.4216	−0.705	0.491
Age	1.0749	0.8276	1.298	0.217
Weight	0.6112	0.5260	1.162	0.266
Height	−19.8756	26.2974	−0.756	0.470
BMI	−1.3028	1.2584	−1.035	0.321
Marital status	3.0244	4.8530	0.623	0.543
Education	5.0780	2.9002	1.751	0.115
Occupation	−6.3835	6.7152	−0.951	0.367
Smoking	6.3953	7.1227	0.897	0.383
Chronic diseases	2.0635	7.2203	0.286	0.779
History of surgery	0.9522	7.2606	0.131	0.899
Rx	1.1480	7.1311	0.161	0.877
Total PA	0.0015	0.0008	1.913	0.091

Rx, prescription medication use.

. The intercept value was 36.48, representing the baseline QOL score when all other predictors were 0. However, this value was not statistically significant (p = 0.389). The coefficient for sex was −5.23, indicating that women had lower QOL scores compared with men, but this difference was not statistically significant (p = 0.491). Age had a positive coefficient of 1.07, suggesting that older age was associated with higher QOL scores, though this finding was not statistically significant (p = 0.217). Weight and height had coefficients of 0.61 and −19.88, respectively, indicating their relationships with QOL scores. However, neither predictor was statistically significant (weight: p = 0.266; height: p = 0.470). The coefficient for BMI was −1.30, suggesting a negative association with QOL scores, but this was not statistically significant (p = 0.321).

Marital status had a positive coefficient of 3.02 and education level had a positive coefficient of 5.08, indicating that being married and having a higher education level were associated with higher QOL scores. Occupation had a negative coefficient of –6.38, indicating a lower QOL score among those with higher occupational status. Non-smoking had a positive coefficient of 6.40, indicating higher QOL scores among non-smokers. Absence of chronic diseases had a positive coefficient of 2.06, suggesting higher QOL scores among those without chronic diseases. No history of surgery had a positive coefficient of 0.95, indicating slightly higher QOL scores among those without a history of surgery. Rx had a positive coefficient of 1.15, indicating higher QOL scores among those not using prescription medications. However, none of these sociodemographic predictors were statistically significant (all p > 0.05). TotalPA had a positive coefficient of 0.0015, indicating a positive association with QOL scores. Although this finding was not statistically significant (p = 0.091), it suggested a trend toward significance, indicating that higher PA levels may be associated with better QOL scores.

4. Discussion

This study examined the association between PA, QOL, and sociodemographic predictors among 369 adults in Tabuk City, Saudi Arabia. The participants’ most prominent characteristics were being single; having a bachelor’s degree; being employed; being non-smokers; and having no history of chronic diseases, surgery, or treatment. The participants had a high prevalence of low PA and an average QOL level, while participants with moderate PA reported high QOL. The overall SF-36 score was 69.5 (range: 63–77.4) out of 100, which indicated good QOL. This finding is consistent with that of a recent Milesian study that used the same instrument in a sample of 460 participants and revealed that the QOL domains ranged from 64.0–86.89, while the overall QOL score was 72.4 [50]. In the current study, marital status had a significant correlation with QOL scores, which is similar to the previous studies’ results [50,51]. Married individuals reported better QOL than individuals who were single, divorced, or widowed. This result is partially supported by that of another study in which single men show worse QOL than married men; however, the opposite trend is shown for women [52]. History of chronic illness was also significantly associated with QOL, while participants who took medication reported low QOL scores. This result is in agreement with that of another study, which reveals the general impact of chronic illness on QOL, especially for those who receive regular regimens of medications [53]. In this study, participants with chronic diseases who took medication scored lower in QOL compared with those who did not. However, both groups had good QOL scores (i.e., >50 out of 100).

The analyses revealed that several sociodemographic factors significantly influenced QOL scores, with sex, age, education level, smoking status, and TotalPA being significant predictors of QOL. Specifically, higher education levels and increased PA were positively associated with better QOL scores, indicating the importance of education and physical health for enhancing QOL. Conversely, smoking status and history of chronic diseases were negatively associated with QOL scores, highlighting the detrimental effects of smoking and chronic health conditions on overall wellbeing. The positive association between education and QOL may be attributed to increased health awareness and access to healthcare resources among individuals with higher education. Similarly, the positive impact of PA on QOL underscores the benefits of an active lifestyle for maintaining physical and mental health. The negative impact of smoking on QOL aligns with the results of previous research that has linked smoking to various health complications and decreased life satisfaction [12]. The presence of chronic diseases also significantly lowered the QOL scores, emphasizing the need for effective management and support systems for individuals with chronic conditions.

The highest and lowest QOL domains were role limitations due to physical health and energy/fatigue, respectively. QOL scores were significantly higher for participants with high levels of physical exercise in the physical functioning, role limitations due to physical health, and general health domains. These findings are consistent with a previous study that reported an association between higher PA levels and better QOL [54].

Another study on QOL and PA among Saudi adults has shown a significant correlation between the total IPAQ score and the WHO’s Quality of Life Brief Version’s (WHOQOL-BREF) physical, psychological, and social relationship scores, in which the high-PA group had significantly higher WHOQOL-BREF scores for all domains (physical, psychological, social relationships, and environmental health) than the low-PA group. Moreover, men had significantly higher physical health scores than women; however, women had significantly higher social relationships scores [55]. Positive correlations between different domains of QOL and PA have also been reported worldwide [56,57].

In this study, 42.5% of the participants were physically inactive, which is consistent with the 40.6% reported by a previous study [58]. However, the current study’s PA-related findings are lower than those reported in the previous studies and have revealed a high prevalence of inactivity among the Saudi population [29,30,31]. This study also found that PA was more common among men than women, as men more frequently reported a higher PA level, which is consistent with the results of other studies [29,30,31] as well as a review (that included 65 articles from Saudi Arabia) that reported that women partook in less PA than men [59].

The results confirmed a high prevalence of physical inactivity among Saudi adult men and women, as reported in previous studies [25,26,32,34]. This result is comparable to that of other Gulf countries’ populations, wherein the prevalence of physically active adults ranges from 39.0–42.1% for men and 26.3–28.4% for women [24].

Participants aged 55 years and above showed a higher prevalence of physical inactivity than those in other age groups. This finding is consistent with those of the previous studies conducted nationally, regionally, and internationally and suggests a general negative association between age and PA [22,24,26,32,40,42,44,46].

The results revealed significant associations between three QOL domains (energy/fatigue, emotional wellbeing, and general health), the overall QOL scores, and moderate PA levels. These findings are consistent with those of other studies that have reported a substantial impact of PA on these domains [51,60] as well as those that have reported a significant association between PA and QOL [9,12,51].

Limitations

This study had the following limitations. First, it used a cross-sectional design, which collected data from one time point and therefore failed to identify changes in PA and QOL over a long period. Second, the non-probability sampling technique used herein may have introduced selection bias, particularly regarding the dominance of male participants. This potential bias was acknowledged, and efforts were made to recruit a balanced sample by targeting locations frequented by both sexes and various age groups. Third, the results did not report any longitudinal data that suggested that changes in any factors were associated with changes in QOL. Fourth, the sample was skewed toward younger adults and was relatively small, which limits the generalizability of the findings. Similar studies should be conducted at the national level using a normally distributed sample regarding age to obtain a broader picture of PA levels and their relationship with QOL in Saudi Arabia.

5. Conclusions

This study identified key sociodemographic predictors of QOL among Saudi adults and emphasized the significant roles of education level, PA, smoking status, and history of chronic diseases. The results revealed a high percentage of inactive participants, confirming the results of previous studies [27,28,29]. Hence, national programs focusing on elevating PA levels among the Saudi Arabian population are essential for reaching the sustainable development 2030 Vision goals of increasing the number of people partaking in sports activities and improving overall QOL. The findings also underscore the necessity of targeted sustainable public health interventions that promote higher education, smoking cessation, and encourage active lifestyles to enhance QOL. These strategies are crucial for mitigating the negative impact of chronic diseases and improving life satisfaction among the population. By addressing these modifiable factors, it will be possible to make substantial progress toward the 2030 Vision goals and foster a healthier, more active society.