Smartphone addiction and its association with accident risk in Iranian adults

Abstract

In recent years, the widespread use of smartphones has led to concerns about smartphone addiction, which is now recognized as a major social and public health issue. This addiction has resulted in numerous negative consequences, the most significant of which is an increase in different types of accidents. The study aimed to investigate the association between smartphone addiction and the occurrence of different types of accidents. This nationwide population-based study was conducted in Iran in 2024. The target population comprised individuals aged 18 to 65 years. The Smartphone Addiction Scale (SAS) questionnaire and extra 8 questions about the occurrence of accidents were used to data collection. A total of 983 individuals responded to approximately 5,000 electronically distributed questionnaires (response rate: ~19.7%), which may have introduced selection bias due to the online recruitment method. Data were analyzed using descriptive statistics, the Mann-Whitney test, the Kruskal-Wallis test, and multivariate logistic regression in SPSS 24. The average age of the respondents was 33.59 years, and the majority (approximately 69%) were women. The average score of smartphone addiction was estimated to be 123.27 (31.7). Traffic related accidents were the most common (about 70%). About 56% of people reported the experience of slipping while using a smartphone. The results showed that smartphone addiction is high among Iranian adults and it is a significant predictor of the occurrence of all kinds of assessed accidents (P < 0.05). The high score of smartphone addiction and their association with various accidents; particularly road traffic accidents and falls; highlight the urgent need for research that reflects Iran’s unique social and infrastructural conditions. Key contributing factors include heavy urban traffic, limited public education on safe smartphone use, inadequate infrastructure, and social norms that encourage constant mobile phone engagement. These elements must be addressed to design effective and culturally relevant prevention strategies.

Introduction

Smartphone usage is rapidly increasing worldwide, becoming an integral part of daily life^1–3. As an advanced communication device that integrates the features of earlier media, smartphones have created new opportunities for both users and policymakers^4,5. In addition to their numerous beneficial applications, smartphones can also lead to adverse consequences⁶. Due to their widespread use, multimedia capabilities, and engaging applications, many users have developed a dependency on these devices, which researchers identify as one of the most prevalent effects of smartphone usage⁷. Findings from various studies highlight the high prevalence of smartphone addiction^8–10. Moreover, individuals addicted to smartphones are at a greater risk of experiencing health issues, including physical conditions such as cranial disorders, dry eyes, carpal tunnel syndrome, and headaches, as well as mental health challenges such as fear, sadness, anger, psychological distress, depression, and anxiety^11–16. Additionally, excessive smartphone use can result in various social harms, including the erosion of values, decreased social interactions, premature maturity, identity loss, consumerism, and threats to both personal and social security^17,18. One of the most significant consequences of smartphone addiction is distraction, which increases the risk of accidents^19,20. Growing concerns have emerged regarding the heightened accident risk among individuals who talk, text, or listen to music on their smartphones while driving²¹. Recent findings suggest that smartphone-addicted users may be more susceptible to accidents due to their reduced ability to identify potentially hazardous or unsafe conditions²². A study conducted by Kim et al. in Korea in 2017 reported a significant difference in the incidence of various types of accidents; including slips, falls, and traffic collisions; between individuals addicted to smartphones and those without addiction²². Additionally, findings from previous studies indicate a significantly higher rate of accidents among smartphone-addicted individuals compared to regular users^23,24. Given that addicted users not only pose risks to themselves but also to others, and considering the substantial burden of injuries in different societies^25,26examining the relationship between smartphone addiction and accident occurrence is of critical importance.

Research on smartphone addiction has increased in recent years. A meta-analysis examining this issue across 24 countries found that, while studies have been conducted in both developing and developed nations, problematic smartphone use appears to be more prevalent in developing countries. The study identified Iran; alongside countries such as China, Saudi Arabia, and Malaysia; has the highest scores of problematic smartphone use⁸.

In recent years, Iran has reported a significant increase in smartphone usage. By early 2025, the number of active mobile phone users had reached 152 million (166% of the total population) and internet coverage had increased to 79.6 million²⁷. This rapid expansion of the internet network, coupled with challenges such as heavy urban traffic and limited public safety education, has increased the risks associated with distracted behaviors, including using smartphones while driving. Studies report alarming statistics. A study conducted in the capital of Iran (Tehran) found that 88% of drivers use their mobile phones while driving, and 51% of them reported near-accident experiences²⁸. Another study in Zahedan found that 47.1% of drivers involved in road accidents were using their phones at the time of the accidents²⁹.

Despite these findings, there is still a lack of comprehensive research on the relationship between smartphone addiction and accidents among Iranian adults. This study aims to address this research gap by investigating the association between smartphone addiction and various types of accidents in Iranian adults. Specifically, it seeks to estimate smartphone addiction scores among adults, identify the determinants of smartphone addiction, and assessing the impact of smartphone addiction on the occurrence of various types of accidents.

Methods

Study design

This study is a nationwide, population-based cross-sectional study conducted in Iran in 2024. The study followed ethical guidelines and adhered to methodological best practices to enhance the accuracy and generalizability of the findings¹⁴.

Participants and sampling

We collected data from a total of 983 individuals aged 18 to 65 who resided in Iran, were literate, and had access to both the Internet and a smartphone. These participants completed and returned the survey questionnaires, which were distributed to approximately 5,000 potential respondents. Data collection was conducted over an eight-month period, from October 1, 2023, to late May 2024. Given the use of online and social media platforms for survey distribution, we employed a convenience sampling method. To enhance geographic diversity and improve the representativeness of the sample, we disseminated the survey links across all provinces using multiple distribution channels.

Data collection tools and procedures

We used the Smartphone Addiction Scale (SAS) as a validated self-report instrument to measure smartphone addiction. Participants rated each item on a 6-point Likert scale ranging from 1 (Strongly Disagree) to 6 (Strongly Agree), with total scores ranging from 33 to 198. The SAS comprises 33 items across six subscales: daily-life disturbance (5 items, score range: 5–30), positive anticipation (6 items, range: 6–36), withdrawal (6 items, range: 6–36), cyberspace-oriented relationship (3 items, range: 3–18), overuse (8 items, range: 8–48), and tolerance (5 items, range: 5–30). Each subscale represents a specific dimension of smartphone addiction, and higher scores on the subscale indicate more severe symptoms in that domain.

The internal consistency and concurrent validity of SAS were verified (Cronbach’s alpha = 0.967)¹⁴.The Persian version of this questionnaire has been psychometrical by Haj Hosseini and colleagues (2022). The cultural adaptation involved standard forward-backward translation and expert review to ensure linguistic and contextual appropriateness. The correlation coefficient in the retest is (r = 0.93) and Cronbach’s alpha coefficient for internal consistency is reported (α = 0.92)³⁰.

We assessed participants’ experiences with accidents using self-reported measures included in the questionnaire. Through a two-choice question, respondents indicated the experience of each of the following eight types of accidents while using a smartphone:

1. The experience of slipping.

2. Falling (from stairs, heights, etc.)

3. Traffic accident (as a driver).

4. Traffic accident (as a pedestrian).

5. Near-miss traffic accident while driving.

6. Near-miss traffic accident as pedestrian.

7. Burns.

8. Electric shock/ electrocution.

Responses were coded as dichotomous variables (Yes/No). We collected the required data anonymously through online platforms, using emails, social media networks, and direct link distribution. The survey link was generated via the website www.questionpro.com. To improve response rates, we conducted follow-ups with non-respondents. The first follow-up occurred four weeks after the initial distribution, and the second was conducted eight weeks after the initial contact. The questionnaire consisted of three sections:

1. Demographic information (9 items), including age, sex, marital status, education level, city name, place of residence, and occupation.

2. Smartphone addiction (33 items), based on the validated Smartphone Addiction Scale (SAS).

3. Accident experiences (8 items), assessing the occurrence of accidents while using a smartphone.

We did not offer any form of compensation; financial or otherwise; to participants for completing the survey.

Data analysis

We conducted a descriptive analysis to summarize the overall Smartphone Addiction Scale (SAS) scores and subscale scores. For each subscale, we calculated the mean of the items associated with that specific domain. Response options ranged from 1 to 6, with higher scores indicating more severe smartphone addiction-related behaviors.

To analyze differences in SAS scores across socio-demographic characteristics, we applied non-parametric tests. We used the Mann–Whitney U test to compare two independent groups (e.g., sex and work-related smartphone use) and the Kruskal–Wallis H test for comparisons involving more than two groups (e.g., daily smartphone use time, monthly internet spending).

We performed a series of multivariate logistic regression analyses to examine the association between smartphone addiction and various types of accident experiences, while adjusting for potential confounders. We included the SAS score as the primary independent variable. Our models controlled for age, sex, marital status, education level, daily smartphone use, work-related smartphone use, monthly internet spending, social media usage, and daily time spent on social media. We treated each type of accident experience; slipping, falling, traffic accidents (as a driver and as a pedestrian), near-miss traffic accidents (while driving and as a pedestrian), burn injuries, and electric shocks; as a binary dependent variable (Yes/No).

We carried out all analyses using SPSS software, version 24. For each regression model, we reported the adjusted odds ratio (AOR), 95% confidence interval (CI), and Wald statistic. We evaluated model fit and explanatory power using the Hosmer–Lemeshow goodness-of-fit test and the Nagelkerke R² statistic, respectively.

Results

A total of 983 of Iranian people participated in the study, with a mean age of 33.59 years. Among them, 676 (68.8%) were female, 537 (54.6%) were married, 592 (60.3%) had high-school education. Participants reported varying daily smartphone usage times. The distribution was as follows: 318 individuals (32.3%) reported using their smartphones for 0–2 h. The mean SAS score increased significantly with greater daily smartphone usage (p < 0.001). Work-related smartphone use was reported by 241 participants (24.5%). Monthly internet spending also varied among participants: 70 (7.2%) spent between 0 and 1, 319 (32.8%) spent 1–5, 337 (34.7%) spent 5–10, and 246 (25.3%) spent over 10 (10000 IRR). There was a significant difference in SAS scores based on monthly internet spending (p < 0.001). Social media usage was prevalent, with 969 participants (98.6%) indicating active engagement. The daily time spent on social media varied, with 261 (26.6%) using it for 0–2 h, 242 (24.6%) for 2–4 h, 230 (23.4%) for 4–6 h, 149 (15.2%) for 6–8 h, 60 (6.1%) for 8–10 h, and 41 (4.2%) for over 10 h. A significant association was observed between daily social media use and SAS scores (p < 0.001). Table 1 summarizes the socio-demographic characteristics of participants and their corresponding SAS.

Table 1.

Socio-demographic characteristics and smartphone addiction scores of Iranian adults.

Variables		N (%)	SAS Mean ± SD	P
Age (Years)	18–25	239 (24.5)	122.4 ± 30.7	0.44b
	26–35	317 (32.5)	124.7 ± 32.5
	36–50	366 (37.6)	121.9 ± 32
	51–65	52 (5.3)	126.7 ± 29.5
Sex	Male	307 (31.2)	123.8 ± 32.2	0.57a
	Female	676 (68.8)	123.1 ± 31.4
Marriage	Single	442 (45.1)	123.5 ± 31.8	0.69a
	married	537 (54.9)	123.1 ± 31.7
Education	High school	592 (60.3)	124.2 ± 31.1	0.28a
	Less than high school	390 (39.7)	121.8 ± 32.6
Daily time of smartphone use (Hours)	0–2	318 (32.3)	121 ± 31.5	< .001b
	2–6	419 (42.7)	117.6 ± 31.5
	> 6	245 (24.9)	136 ± 28.7
Work-related smartphone use	Yes	241 (24.5)	121.9 ± 29.9	0.21a
	No	742 (75.5)	123.8 ± 32.3
Monthly internet spending (10000 IRR)	< 5	389 (40.1)	107 ± 31.9	< .001b
	5–10	337 (34.7)	126.9 ± 28.4
	> 10	246 (25.3)	143.5 ± 20.8
Social media usage	Yes	969 (98.6)	124 ± 31.3	< .001a
	No	14 (1.4)	77.9 ± 26.7
Daily time spent on social media (Hours)	0–2	261 (26.6)	117.9 ± 31.2	< .001b
	2–4	242 (24.6)	109.8 ± 30.9
	4–6	230 (23.4)	126.3 ± 29.6
	> 6	249 (25.4)	139.1 ± 27.3

a: Mann-Whitney U, b: Kruskal Wallis H.

The mean score of the SAS among Iranian adults was estimated to be 123.27 (31.7). The subscale scores of the SAS among Iranian participants revealed varying levels of addiction-related behaviors. The highest score was observed in the Tolerance subscale (Mean (SD) = 4.1 (1.4)). Conversely, the Cyberspace-oriented Relationship subscale received the lowest score (Mean (SD) = 3.1 (1.1)) (Fig. 1).

Fig. 1.

Subscale scores (Mean) of the Smartphone Addiction Scale among Iranian adults.

The most prevalent accident reported is slipping experience (56.26%), followed by falling experience (47.61%) and burn experience (30.11%). Additionally, less common accidents include traffic accidents as pedestrians (15.56%) and as drivers (11.09%). (Fig. 2).

Fig. 2.

Distribution of accidents experienced by Iranian adults.

The SAS scores were significantly higher among individuals who had experienced accidents compared to those who had not, across all accident categories (Table 2). Higher SAS scores were associated with increased odds of experiencing slipping (AOR = 1.031; 95% CI: 1.025–1.038; p < 0.001) and falling accidents (AOR = 1.030; 95% CI: 1.024–1.037; p < 0.001), with corresponding R² values of 0.489 and 0.47, respectively. Regarding traffic-related incidents, smartphone addiction significantly predicted accident occurrence both as a driver (AOR = 1.016; 95% CI: 1.007–1.025; p < 0.001) and as a pedestrian (AOR = 1.024; 95% CI: 1.015–1.032; p < 0.001), as well as near-miss experiences while driving (AOR = 1.017; 95% CI: 1.011–1.024; p < 0.001) and as a pedestrian (AOR = 1.014; 95% CI: 1.007–1.021; p < 0.001).

Table 2.

Summary of multivariate logistic regression results for smartphone addiction Scale.

Variable	Accident Experience		Type of Accident Experiences
	Yes	No
	SAS Mean (SD)		R 2	Wald	AOR*	95% CI for AOR	Sig.
Smartphone Addiction scale		Slipping experience
	136.13(28.66)	106.84(27.6)	0.489	164.4	1.031	1.025–1.038	< 0.001
			Falling experience
	138.58(26.66)	109.45(29.52)	0.47	165.01	1.03	1.024–1.037	< 0.001
		Traffic accident as driver
	132.61(28.65)	122.16(31.9)	0.138	11.7	1.016	1.007–1.025	< 0.001
		Traffic accident as pedestrian
	140.4(27.34)	120.17(31.47)	0.211	45.01	1.024	1.015–1.032	< 0.001
		Near-miss traffic accident while driving
	134.42(27.79)	119.71(32.1)	0.164	38.9	1.017	1.011–1.024	< 0.001
		Near-miss traffic accident as pedestrian
	132.3(32.2)	121.57(31.54)	0.105	17.18	1.014	1.007–1.021	< 0.001
		Burn experience
	143.36(23.28)	114.68(30.96)	0.479	131.78	1.039	1.031–1.047	< 0.001
		Electric shock
	143.62(19.16)	117.05(32.2)	0.456	97.04	1.028	1.020–1.037	< 0.001

*AOR: adjusted odds ratio

Among non-traffic-related accidents, participants who had experienced burns and electric shocks showed the highest mean SAS scores (143.36 ± 23.28 and 143.62 ± 19.16, respectively), with both accident types significantly associated with higher odds of smartphone addiction (burn: AOR = 1.039; 95% CI: 1.031–1.047; p < 0.001; electric shock: AOR = 1.028; 95% CI: 1.020–1.037; p < 0.001).

These findings highlight the significant association between smartphone addiction and the likelihood of experiencing various types of accidents. The regression models were adjusted for potential confounding variables, including age, sex, marital status, education, daily smartphone use, work-related smartphone use, monthly internet spending, social media usage, and time spent on social media per day. These adjustments ensured that the observed relationships reflect the independent effect of smartphone addiction on accident occurrence.

The Hosmer–Lemeshow goodness-of-fit test was non-significant for most models, indicating an adequate model fit. However, the models for burn experience, electric shock, near-miss traffic accidents as a pedestrian, and falling experience yielded statistically significant Hosmer–Lemeshow test results, suggesting potential limitations in model fit for these specific outcomes.

Discussion

Main findings and interpretation

This study aimed to investigate and interpret the association between smartphone addiction and the occurrence of various types of accidents among Iranian adults. The findings revealed a high score of smartphone addiction, which was significantly associated with multiple types of accidents. Traffic-related accidents were the most prevalent, accounting for approximately 70% of reported cases. Additionally, around 56% of participants reported experiencing slips while using a smartphone. The findings indicate a high score of smartphone addiction among Iranian adults, highlighting its significant role as a predictor of various types of accidents.

The study findings revealed statistically significant associations between smartphone addiction scores and accident-related experiences. Specifically, higher scores of the SAS were associated with increased odds of slipping, falling, traffic accidents (as both driver and pedestrian), near-miss incidents, burns, and electric shocks. These associations remained significant after adjusting for potential confounders such as age, gender, marital status, and education level, indicating an independent predictive role of smartphone addiction in the likelihood of such adverse events. Although the adjusted odds ratios (AORs) per unit increase in SAS ranged from 1.012 to 1.039 and may appear modest, their clinical and practical significance becomes evident when considering the continuous nature of the SAS and its broad range (33 to 198). A 20-point increase in the SAS score; a plausible variation in behavioral addiction severity; corresponds to nearly a twofold increase in the odds of certain accidents. This indicates that even moderate increases in smartphone addiction may substantially elevate the risk of unintentional injuries, supporting the clinical meaningfulness of these effect sizes.

Distraction while driving

The study reveals high smartphone addiction among adults, a trend that has become a major social and health concern in various societies due to the increasing use of smartphones and their entertainment programs^10,31–36. While much of the existing literature has focused on the psychological and social impacts of excessive smartphone use, there is growing concern about its contribution to unintentional injuries and accidents²². One of the key issues is cognitive distraction, which significantly increases the risk of accidents; especially while driving³⁷. Using a smartphone while driving interferes with both visual and mental attention, making it harder for drivers to stay focused and increasing the chances of collisions. The purpose of smartphone use also plays a critical role in driver behavior, with activities like texting or browsing often associated with more frequent traffic violations and a higher likelihood of accidents compared to phone conversations³⁸. One well-known phenomenon is “inattention blindness,” where drivers look at the road but fail to notice critical cues; something that has been observed in phone-using drivers³⁷. Risk levels can also vary based on the driver’s age. Younger drivers, particularly those under 24, are more likely to use their phones while driving and also experience more accidents as a result³⁹. Although a small number of people; sometimes called “supertaskers”; can handle multitasking with little loss in performance, most drivers show a clear decline in their ability to drive safely when distracted³⁷. Environmental factors, like complicated road layouts or heavy traffic, can make things even worse. Research shows that smartphone use in such situations is linked to unsafe behaviors, such as improper lane changes⁴⁰.

Addressing smartphone-related distractions while driving requires a comprehensive strategy. The use of emerging technologies such as augmented reality (AR) headsets can block access to phones and reduce psychological stress⁴¹. Reducing speed or increasing the distance between vehicles can also help manage risk⁴². While completely eliminating smartphone use in cars may not be realistic, a combination of technology, public awareness, and policy can help reduce the likelihood of accidents^43,44.

The role of social networks in smartphone addiction

The results of the study showed that the use of virtual networks and the duration of their use have a significant relationship with smartphone addiction. Previous studies have also shown that with the rapid growth and appeal of social networks, user engagement and screen time have markedly increased, contributing to higher levels of smartphone addiction^45–47. Although social networks can offer various benefits when used effectively^48–50concerns remain about their negative consequences; especially in the context of low- and middle-income countries (LMICs) like Iran. In Iran, insufficient public awareness campaigns, and infrastructural challenges such as inadequate pedestrian pathways may amplify the risks associated with distracted smartphone use^51,52. These conditions underscore the need for targeted interventions by policymakers and families to promote the responsible use of social networks and mitigate their contribution to smartphone addiction and its related safety risks.

Accident types and public health significance

As mentioned, the results showed that smartphone addiction was significantly associated with the occurrence of all eight types of harm studied, and the average smartphone addiction score was significantly higher among respondents who experienced these types of harm. The results showed that slipping was the most common type of accident reported by about 56% of participants while using smartphones. While in the study by Kim HJ et al. (2017) who examined the association between the occurrence of accidents and smartphone addiction among 608 college students in South Korea, the results showed that the most reported accidents were bumps and collisions with about 23% and slipping was reported by about 10% of respondents⁵³. An important point to note is that these types of accidents, although important and vital, due to their nature may not pose a serious threat of death or severe injury to individuals. But traffic related accidents are very concerning and pose a serious threat. In this study, four types of traffic related accidents were examined separately, and the results showed that, overall, about 70% of participants experienced at least one of these types of accidents while using a smartphone, which is a very high and worrying statistic. In recent years, the results of a large number of studies have shown that using a smartphone (talking, texting, listening to music, or any other use) significantly increases the risk of traffic accidents^21,54–56. Based on the Global Burden of Disease Study, it is estimated that injuries account for about 10% of deaths in the world⁵⁷. And about 90% of these deaths occur in low- or middle-income countries⁵⁸. Injuries and violence are threats to health in every country in the world. Every year, more than five million people around the world die due to some form of injuries, and many more disabilities remain in their lives^59–61. According to the current trend, the global burden of injuries is expected to increase significantly during the coming decades, especially in low-income and middle-income countries⁶². On the other hand, as mentioned, there is a lot of evidence that distraction due to the use of smartphones is a very important factor in the occurrence of accidents. However, very few studies have been conducted regarding the relationship between smartphone addiction and the occurrence of various accidents. Therefore, it is necessary to design and conduct more similar studies. It is suggested that future studies specifically focus on each accident and, in addition to correlation, also pay attention to their consequences and prevention.

Study limitations and future research directions

However, based on the best knowledge of the researchers and the preliminary results of the literature search, the present study for the first time in the entire country examines the relationship between smartphone addiction and the occurrence of various types of accidents among adults (unlike most previous studies in other countries, which are among students and special stratum) and tried to provide accurate and practical information for readers and decision makers, however, the present study was faced with several limitations. In order to be able to get a high variety of participants from different regions of the country, the researchers used internet and self-reported methods. In this situation, people may not provide accurate and correct information to the researchers for various reasons. Also, due to the retrospective nature of data collection, people may not remember the occurrence of some accidents.

The use of convenience sampling via internet-based platforms can lead to sampling bias, and individuals without consistent access to the internet or smartphones; particularly those in rural or low-income areas; are likely to be underrepresented in the sample. As a result, findings may not fully reflect the perspectives of these populations, potentially limiting the generalizability of results. To increase comprehensiveness in future research, alternative approaches such as paper-based surveys or face-to-face data collection should be considered to achieve a more diverse pool of participants.

An important consideration in interpreting our findings is the gender distribution of the study population, as 68.8% of the participants were female. While such an imbalance may raise concerns regarding potential bias, our data showed no significant difference in smartphone addiction scores between males and females (Male: 123.8 ± 32.2, Female: 123.1 ± 31.4; p = 0.57). Additionally, gender was not a significant predictor of accident involvement in our secondary analysis. These findings suggest that gender did not confound the relationship between smartphone addiction and accidents, and therefore, the overrepresentation of female participants is unlikely to have affected the main outcomes of the study.

Despite these limitations, the study offers valuable insights into the overlooked dimension of injury-related consequences of smartphone addiction, and provides a foundation for more targeted and inclusive research in the future.

Conclusion

The study demonstrated a high score of smartphone addiction among Iranian adults and identified it as a significant predictor of various types of accidents, particularly traffic related accidents. Due to the considerable public health burden; including the potential for severe injuries and fatalities; there is an urgent need for the implementation of evidence-based preventive strategies and regulatory frameworks. Policymakers are encouraged to adopt specific measures, including the development of nationwide public education campaigns addressing the risks of distracted walking and driving, as well as the rigorous enforcement of traffic regulations pertaining to mobile phone use.

In the context of a LMICs like Iran, the findings provide valuable insights for efficient and optimal resource allocation. Urban areas may need more advanced law enforcement mechanisms and media-based educational interventions due to higher traffic density, while rural areas may benefit from community-based awareness programs and better access to digital literacy initiatives. To better understand how smartphone addiction is associated with crash risk, it is important to conduct longitudinal studies. These studies can provide deeper insights into how this relationship unfolds. Also, using mixed approaches allows researchers to examine broader environmental and behavioral factors involved.

Author contributions

Conceptualization: AA.S, R.R, A.F, N.D; Data curation: AA.S, R.R, A.F. N.D; Formal analysis: R.R, Writing—original draft: AA.S, R.R, MA. H; Writing—review and editing: AA.S, R.R, A.F; Supervision: AA.S.; Funding acquisition: AA.S. All authors approved the final version of the manuscript.

Funding

The research protocol was approved & supported by Tabriz Health Services Management Research Center, Tabriz University of Medical Sciences, Tabriz, Iran: [Grant number: 73460]

Data availability

The data that support the findings of this study are available from the corresponding author, [R.R], upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study is based on a database from a B.Sc. thesis registered at Tabriz University of Medical Sciences (Approval Number: IR.TBZMED.REC.1403.149). The study protocol was reviewed and approved by the Ethics Committee of Tabriz University of Medical Sciences. Informed consent was obtained from all participants, who were assured of data confidentiality and their right to voluntary participation. All procedures complied with the Declaration of Helsinki and the national ethical guidelines of the Iranian Ministry of Health and Medical Education.

Consent for publication

All participants were required to express their informed consent for publication prior to data collection.