Association between Smartphones Addiction and Neck Disability among Private Universities Students in Riyadh

Introduction

In the last few years, smartphones and electronic devices (e-devices; smartphones, computers, and others) have been become widely spread among many communities and across all ages. Modern people use a smartphone in most areas of their lives, including occupational and leisure activities, which means their duration of smartphone usage is also increasing [1]. Recent estimates showed that at least 77% of the world’s population has their own mobile phone [2]. The excessive use of smartphones to a level where it interferes with the daily lives of users is thus considered to be smartphone addiction [3].

Regarding musculoskeletal problems, many studies found a relationship between neck disabilities and smart devices usage. For instance, in a study carried out in Korea in 2016, they found a significant correlation between neck flexion angle and the posture while using the smartphone[4].Moreover , a study from India conducted on university students in 2018 shows the repetitive and continuous neck flexion posture considered as high risk of chronic neck pain which explains the strong association between smartphone addiction and neck disability[5]. In another study in Saudi Arabia, there was a positive association between smartphone addiction and neck problems [6]. This pain is most probably explained by the inappropriate posture while using the smart device as the person bends his or her neck more in purpose to look to a small screen [7]. In addition, as a normal response to keep the neck in balance the extensor muscle activated more which results in increase the load on the cervical erector spine and trapezius muscle [8]. Another study in Korea found out that smartphone usage produces chronic neck pain and severe upper back muscles spasm by reducing cervical lordosis and the pain threshold of the neck muscles. Also it causes shoulder pain and tightness, resulting in painful shoulder muscle spasm [9]. Although this correlation remains controversial, as another study found that there is no correlation between neck pain and the time spends on smartphone usage [10]. On the other hand, there is a study in Saudi Arabia conducted in 2018 found that there is a positive correlation between long-time smart devices usage and neck pain, specifically for those who spends 5-8 hours a day on smart devices [11]. According to a study in Korea in 2016, when they evaluated 34 respondents of adult individuals and divided them into 3 groups with a different duration of a smartphone usage as: group 1 for 10 minutes, group 2 for 20 minutes and group 3 for 30 minutes; showing a direct proportion between neck pain and duration, so the neck pain increases when the duration of the smartphone usage gets longer [12]. Another study conducted in Saudi Arabia finds out that nearly one-third of the students who complained of neck pain symptoms showed significant associations with female gender 44% in comparison with only 29% of male whose use smartphone either for study or leisure purposes [13]. Another study in Shanghai showed high prevalence neck/shoulder Pain Significantly more in female 44.5% compared with male 36.8% this is closely related to multiple factors including digital products, physical activity, and psychological status [14].

The aim of this study is to measure the usage of smartphones among students studying in private universities in Riyadh and identify the negative effects and consequences in their life particularly neck disabilities.

Methodology

This was a cross-sectional study by design and was carried out on a representative sample of Riyadh private universities students including medical and non-medical colleges from the period of January to August 2019. We targeted females and males from private universities students in Riyadh city, Kingdom of Saudi Arabia. We expected a high response rate; however, we eliminated all invalid, incomplete responses or any responses that did not match the inclusion criteria with a total of 270 participants. Our sample size included 500 university students including both females and males. The sample size derived based on computing the minimum sample size required for accuracy in estimating proportions by considering the standard normal deviation set at 95% confidence level (1.96), percentage picking a choice or response (50% = 0.5) and the confidence interval (0.05 = ±5) [15] . The formula is:

n = z 2 (p)(1-p)

________________

c 2

Where:

z = standard normal deviation set at 95% confidence level

p = percentage picking a choice or response

c = confidence interval

Putting the values in above formula yielded a minimum sample size of 384. However, for more precise results we increased the sample size to (500) including those who match the eligibility criteria which was 1) must be a university student 2) aged 18 years and above 3) regularly using the smart phones 4) no blindness or vision loss 5) no mental disability 6) No paralysis or physical disabilities.

Validated questionnaires were used to collect the data from study subjects; the data was collected by trained students using online and direct methods. The first part of the questionnaire contained socio-demographic data, whereas, the second part contains 1) the Arabic version of the problematic use of mobile phones (PUMP) scale. The PUMP scale is a 20-item questionnaire that assesses mobile phone use based on the DSM-5 criteria for substance use disorder [16, 17]. The original English language PUMP scale score was compared to other scores of smartphone addiction and showed a convergent validity and excellent internal consistency. The PUMP scale score was positively correlated with excessive smartphone usage and the self- report feelings of addiction. All items were rated on a 5-point scale ranging from 1 = “strongly disagree” to 5 = “strongly agree” [17]. The Arabic version of PUMP scale score was previously used in a study conducted in Saudi Arabia among university students. The Arabic version was validated by experts in psychiatry and addiction and reliability tested as well. [16]. (2) the neck disability index (NDI), which is a modification of the Oswestry Low Back Pain Disability Index [18]. Validity was ensured through peer-review and patient feedback sessions. Test-retest reliability was determined by using correlation coefficient and Pearson's correlation analysis. For the determination of concurrent validity concurrent validity was assessed in two ways. First, on a smaller subset of 10 patients who completed a course of conservative care, the percentage of change on NDI scores before and after treatment was compared to visual analogue scale scores of present of perceived improvement in activity levels. These scores correlated at 0.60. Secondly, in a larger subset of 30 subjects, NDI scores were compared to scores on the McGill Pain Questionnaire, with similar moderately high correlations (0.69-0.70). While the sample size of some of the analyses is somewhat small, this study demonstrated that the NDI achieved a high degree of reliability and internal consistency.[19].

Ethical Considerations:

The study was reviewed and approved from the ethical committee in Dar Al Uloom University, Riyadh, Saudi Arabia. The participants informed that all the collected information for statistical analysis only and their consents have obtained.

Data Analysis:

The collected data was entered and analyzed using the Statistical Package for the Social Science (SPSS Inc. Chicago, IL, USA) version 23. Descriptive statistics were calculated. Percentages are reported for qualitative variables. Fisher Exact test was applied to observe associations between qualitative variables. One-Way-Analysis was applied to compare the PUMP score with Neck Disability Syndrome. Post-Hoc Tukey test was also applied to observe significant multiple comparison differences. A p-value of < 0.05 was considered as statistically significant.

Results

Majority of the participants n=465 (88.2%) belonged to the age group of 18-25 years, followed by n=43 (8.2%) who belonged to the age group of 26-33 years. A few numbers of respondents n=19 (3.7%) were from the age group of 34-55 years. The major chunk of the participants was females n=427 (81%) as compared to males n=100 (19%). Most of the participants were single n=449 (85.2%), around 14% were married and a small percentage were divorced and widowed.

PUMP: problematic use of mobile phones , NDI : Neck disability index, DAU: Dar Al Uloom University, AMU: AlMaarefa University, PSU: Prince Sultan University , IMC: Inaya Medical College, AU: Alfaisal University, REU: Riyadh Elm University, FC: Al-Farabi College, YU: Al Yamamah University, GC: Al Ghad Colleges, AOU: Arab Open University.

Results presented in Table 1 showed that majority of the participants were studying in DAU n=240 (45.5%), followed by n=101 (19.2%) who were studying in AMU, n=60 (11.4%) in PSU, nearly 6% were studying in REU, AU, and IMC respectively. A few numbers of participants were studying in FC, YU, GC AOU and others. The academic performance categorized into, excellent, very good, good and poor based on the university grade point average range from 0.00 to 4.00. Excellent performance range from 4.00 to 3.5, very good from 3.49 to 2.75, good from 2.74 to 1.75 and from 1.74 to 0.00 considered poor. Most of the participants was very good n=261 (49.5%), around 28% has excellent performance, one-fifth had good academic performance and only n=15 (2.8%) had poor academic performance. Major chunk of the participants was using smart devices for 7 hours and above, followed by n=217 (41.2%) who were using it between 4-6 hours and only 14% of the participants were using them between 1-3 hours. The distribution of participants studying in 1st-2nd and 3rd-4th was almost equal (18%) respectively. Around 15% were studying in 13th-14th year, followed by 7th-8th (13.3%), 9th-10th (12.5%), 9% were studying in 11th-12th year, around 2% were doing master’s and a small number of participants were enrolled in Ph.D. Mobile phone was the most used smart device by the participants n=485 (92%), followed by laptop n=25 (4.7%), iPad n=9 (1.7%) and less than 1% were using desktop computer and video games. Almost two-quarter of the participants belonged to medical specialty and one-quarter were enrolled in non-medical specialty.

Table 1: Educational characteristics and smart devise usage of participants (n=527)
	n (%)		n (%)
UniversityDAUAMUPSUIMCAUREUFCOthersYUGCAOU	240 (45.5)101 (19.2)60 (11.4)34 (6.5)33 (6.3)31 (5.9)12 (2.3)5 (0.9)4 (0.8)4 (0.8)3 (0.6)	Academic Level1st – 2nd3rd – 4th5th – 6th7th – 8th9th – 10th11th – 12th13th – 14thMasterPh.D.	97 (18.4)96 (18.2)62 (11.8)70 (13.3)66 (12.5)47 (8.9)76 (14.4)10 (1.9)3 (0.6)
Academic PerformanceExcellentVery GoodGoodPoor	146 (27.7)261 (49.5)105 (19.9)15 (2.8)	Smart DeviceMobile phoneLaptopiPadVideo gameDesktop computer	485 (92.0)25 (4.7)9 (1.7)5 (0.9)3 (0.6)
Hours Spending on using smart devises (hours)1-3 4-67 and above	73 (13.9)217 (41.2)237 (45.0)	SpecialtyNon-medicalMedical	189 (35.9)338 (64.1)

Neck disability was significantly more in females as compared to males (p=0.005). Likewise, neck disability was significantly more in participants who were using the screen for 7 hours and more (p=0.015). No significant association was observed between neck disability and age (p=0.197), marital status (p=0.076) and screen angle (p=0.326). Results are presented in Table 2.

Table 2: Association between Neck Disability Syndrome, sociodemographic data and device usage
PARAMETER	NECK DISABILITY SYNDROME		P value(Fischer Exact Test)
Non (%)	Mildn (%)	Moderaten (%)	Severen (%)	Completen (%)	Total
Age18-2526-3334-4142-4950-55	148 (87.1)14 (8.2)7 (4.1)0 (0.0)1 (0.6)	244 (89.4)21 (7.7)4 (1.5)3 (1.1)1 (0.4)	61 (89.7)6 (8.8)0 (0.0)0 (0.0)1 (1.5)	9 (75.0)1 (8.3)2 (16.7)0 (0.0)0 (0.0)	3 (75.0)1 (25.0)0 (0.0)0 (0.0)0 (0.0)	465431333	0.197
Total (n)	170	273	68	12	4	527
GenderMaleFemale	45 (26.5)125 (73.5)	49 (17.9)224 (82.1)	4 (5.9)64 (94.1)	2 (16.7)10 (83.3)	0 (0.0)4 (100)	100427	0.005*
Total (n)	170	273	68	12	4	527
Marital StatusSingleMarriedDivorcedWidow / Widowed	145 (53.7)25 (19.0)0 (0.0)0 (0.0)	232 (85.0)39 (14.3)2 (0.7)0 (0.0)	60 (88.2)5 (7.4)1 (1.5)2 (2.9)	9 (75.0)3 (25.0)0 (0.0)0 (0.0)	3 (75.0)1 (25.0)0 (0.0)0 (0.0)	4497332	0.076
Total (n)	170	273	68	12	4	527
Center ScreenAboveLowerSame Level	9 (5.3)100 (58.8)61 (35.9)	17 (6.2)153 (56.0)103 (37.7)	5 (7.4)43 (63.2)20 (29.4)	1 (8.3)11 (91.7)0 (0.0)	0 (0.0)3 (75.0)1 (25.0)	32310185	0.326
Total (n)	170	273	68	12	4	527
Hours Spending1-34-67 and above	27 (15.9)86 (50.6)57 (33.5)	38 (13.9)108 (39.6)127 (46.6)	5 (7.4)18 (26.5)45 (66.2)	2 (16.7)4 (33.3)6 (50.0)	0 (0.0)0 (0.0)4 (100.0)	72216237	0.015*
Total (n)	170	273	68	12	4	527
*-Statistically significant (P<0.05), n and % – Number and percent of subjects respectively

PUMP: problematic use of mobile phones , NDI : Neck disability index, DAU: Dar Al Uloom University, AMU: AlMaarefa University, PSU: Prince Sultan University , IMC: Inaya Medical College, AU: Alfaisal University, REU: Riyadh Elm University, FC: Al-Farabi College, YU: Al Yamamah University, GC: Al Ghad Colleges, AOU: Arab Open University.

Results presented in Table 3 showed that most of the participants studying in DAU had significantly more neck disability as compared to participants studying in other institutions (p=0.001). Moreover, participants studying in non-medical disciplines had significantly more neck disability (p=0.044). However, neck disability was not significantly associated with the year of study (p=0.238) and academic performance (p=0.076).

Table 3: Association between Neck Disability Syndrome and Academic data
	NECK DISABILITY SYNDROME
PARAMETER	Non (%)	Mildn (%)	Moderaten (%)	Severen (%)	Completen (%)	Total	P value(Fischer Exact Test)
UniversityDAUAMUREUPSUAUIMCYUGCAOUFCOthers	79 (46.5)46 (27.1)8 (4.7)14 (8.2)11 (6.5)5 (2.9)0 (0.0)0 (0.6)1 (0.6)5 (2.9)1 (0.6)	122 (44.7)50 (18.3)18 (6.6)34 (12.5)14 (5.1)20 (7.3)3 (1.1)3 (1.1)2 (0.7)4 (1.5)3 (1.1)	34 (50.0)5 (7.4)3 (4.4)8 (11.8)7 (10.3)7 (10.3)1 (1.5)0 (0.0)0 (0.0)2 (2.9)1 (1.5)	4 (33.3)0 (0.0)2 (16.7)2 (16.7)1 (8.3)2 (16.7)0 (0.0)0 (0.0)0 (0.0)1 (8.3)0 (0.0)	1 (25.0)0 (0.0)0 (0.0)2 (50.0)0 (0.0)0 (0.0)0 (0.0)1 (25)0 (0.0)0 (0.0)0 (0.0)	24010131603334443125	0.001*
Total	170	273	68	12	4	527
Year1st – 2nd3rd – 4th5th – 6th7th – 8th9th – 10th11th – 12th13th – 14thMasterPh.D.	28 (16.5)30 (17.6)16 (9.4)16 (9.4)19 (11.2)16 (9.4)38 (22.4)4 (2.4)3 (1.8)	52 (19.0)48 (17.6)37 (13.6)41 (15.0)38 (13.9)23 (8.4)31 (11.4)3 (1.1)0 (0.0)	14 (20.6)13 (19.1)7 (10.3)10 (14.7)9 (13.2)7 (10.3)6 (8.8)2 (2.9)0 (0.0)	1 (8.3)4 (33.3)1 (8.3)3 (25.0)0 (0.0)1 (8.3)1 (8.3)1 (8.3)0 (0.0)	2 (50.0)1 (25.0)1 (25.0)0 (0.0)0 (0.0)0 (0.0)0 (0.0)0 (0.0)0 (0.0)	97966270664776103	0.238
Total	170	273	68	12	4	527
Academic PerformancePoorGoodVery GoodExcellent	4 (2.4)20 (11.8)90 (52.9)56 (32.9)	9 (3.3)58 (21.2)138 (50.5)68 (24.9)	1 (1.5)22 (32.4)27 (39.7)18 (26.5)	1 (8.3)3 (25.0)4 (33.3)4 (33.3)	0 (0.0)2 (50.0)2 (50.0)0 (0.0)	15105261146	0.076
Total	170	273	68	12	4	527
SpecialtyMedicalNon-Medical	53 (31.2)117 (68.8)	101 (37.0)172 (63.0)	27 (39.7)41 (60.3)	6 (50.0)6 (50.0)	0 (0.0)0 (0.0)	189338	0.044*
Total	170	273	68	12	0	527

*statistically significant at 5% level of significance

**Tukey’s test significant multiple comparisons

A significant difference was observed between PUMP score and Neck Disability (p<0.001). Post-Hoc analysis showed that PUMP score significantly varies between participants having mild and moderate (p=0.014) neck disability. However, Post-HOC analysis was not statistically significant for PUMP score and patients having no (p=0.654), severe (p=0.056) or complete neck disability (p=0.308). Results are presented in Table 4.

Table 4: Comparison of PUMP score with Neck Disability Syndrome
Neck Disability Syndrome	n	PUMP Score(Mean + S.D)	p-value
NoMildModerateSevereComplete	17027368124	53.28 + 15.0859.36 + 14.58**65.76 + 15.23**65.25 + 14.3673.75 + 20.83	<0.001*

Discussion

This study found that major of the participants (45.5%) of 527 students were using smart devices for 7 hours and above, followed by (41.2%) who were using it between 4-6 hours and only 14% of the participants were using them between 1-3 hours. Similar to these results, smartphone addiction is a worldwide phenomenon in a range of 9.3% to 48% of the population [20, 21]. In contrast to our results, in Kingdom of Saudi Arabia, another study was conducted among 2367 subjects; the majority (61%) of the study participants reported that they spend at least 5 hours per day using their smartphones, whereas 27.2% spend more than 8 hours per day [16]. Another cross-sectional study was conducted in Saudi Arabia using an online google survey form included 2435participants, most of participants who use smart device, allocate 5-8 hours per day and represent (31.2%), while who use it for 2-4 hours a day represent (27.1%) [22]. In Jeddah, a cross-sectional study was conducted on 203 sixth-year medical students at the Faculty of Medicine, King Abdul-Aziz University which reported that the overall prevalence of smartphone addiction was 66 (36.5%) [23]. Another study carried out in a group of King Saud University students to investigated smartphone addiction revealed that addiction percentage among participants was 48% [21]. A mixed-method study that included systematic-review and meta-analysis found that the smartphone addiction magnitude in India ranged from 39-44% [24]. Another study was conducted on sample comprising of 587 students of repudiated school. Students were assessed with a specially designed preform and Smartphone addiction scale (SAS) reported that 53.62% were low users and 33.3% were high users of Smartphone as estimated by Smart Phone addiction scale [25].

Regarding the association between smartphones addiction and neck disability, our study found that neck disability was significantly more in participants who were using the screen for 7 hours and more (p=0.015). Similarly, another study was conducted among 100 participants showed that the degree of smartphone influence was significantly correlated with musculoskeletal discomfort in the participants. They found a moderate positive correlation between both Smartphone Addiction Scale (SAS) and Neck Disability Index (NDI) (p<0.001). Moreover, SAS showed a higher score- indicating addiction to smartphone use, along with it the scores of NDI showing moderate disability (30-48%-moderate disability) [5]. In Riyadh, The result of another study showed a clear association between addiction to smartphone use and various degrees of neck problems among the participants [6]. In 2016, Sang in Jung conducted a study in Daegu University, South Korea taken from fifty volunteers using the electronic devices daily. It showed that using a smartphone for a prolonged duration could negatively affect posture; therefore, predisposing the users to vertebral diseases, and neck pain [26]. Furthermore, Kim et al. (2015) reported that a longer duration of smartphone usage caused a higher degree of neck pain [27]. Other studies have found that smartphone use could be related to musculoskeletal symptoms, including muscular fatigue and tenderness, as well as a decreased cervical range of motion [28]. In general, smartphone addiction could cause significant neck disability because of the bad posture associated with their use [28].

Conclusion:

The study concluded that the majority of cases using smartphones for 7 hours and above and there were significant correlations between smartphones usage and neck disability. This finding highlighted the need for increase the awareness of the negative impacts of prolonged use of smartphone on our health.

Acknowledgement:

We would like to express our thanks to Dar Al Uloom University for their great help and support in completing our research.

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