Introduction

The role of sleep on cognitive, physical and wellbeing of individuals is well established.¹ Good sleep quality might be related and linked to several factors, including social life aspects, general health status, environmental factors, and stress. Moreover, it is essential for health and life quality in all people. It has been shown, that poor sleep quality can lead to multiple health related issues that might lead to physical or mental disorders.²Thus, studying and identifying possible contributed factors for poor sleep quality is essential.

Over-use of smartphones is one of the environmental factors that might be considered a possible contributor for poor quality of sleeping. The mobile phone has evolved in recent decades to become a smartphone and caused a dramatic change in societies worldwide, as well as transforming communication. These tools now provide communication, entertainment, and education functions. ³The term of smartphone in English is defined as a”mobile phone that performs many of the functions of a computer, typically having a touchscreen interface, Internet access, and an operating system capable of running downloaded applications”.⁴

It was in 1980s, were phones were started to be enhanced using computer science and technology. Since then, terms as with mobile phones, cell phones, and smart phones were started to be used gradually and interchangeably.^{5, 6}Usage of smartphones has substantially increased in recent years in all communities. Kingdom of Saudi Arabia ranked third in the worldin the number of smartphone users. According to recent reports, they make up 72.8% of the population. ³

A growing attention in medical and academic fields was observed for health hazards related to sleep deprivation and its relationship with over-use of smartphones.^{1, 7, 8}A cross-sectional study conducted in Japan among adolescents in 2011 showed that there was a strong correlation between smartphone usage after lights tuned off and sleep related issues, as with poor sleep quality, sleep duration, daytime sleepiness, and insomnia among the participants. ⁹ Some other studies reported other sleep related issues as with poor sleep efficiency, rapid eye movement sleep, and sleep latency among participants.^{1, 10-13} 

There have been few studies performed in the Middle East. For example, in 2018, a regional study among medical students at King Abdul-Aziz University (KAU), showed a high prevalence for frequent smartphones usage accounting for 73% of the participants for a duration that exceeds 5 hours per day. Similarly, most of the participants have reported poor sleep quality. The study has concluded that smartphone dependency had been associated with poor sleep quality and latency.¹⁴

However, the relationship between over-use of smartphones and its effect on sleeping is a matter of debate with no concrete evidence. Therefore, this study was conducted to investigate the relationship between the excessive use of smartphones and quality of sleep in Primary Health Care Centers at the Ministry of National Guard, Health Affairs in Riyadh, Saudi Arabia. Our secondary objective was determining the factors associated with poor sleep quality from demographic data.

Material and Methods

Study design and settings

This study was a descriptive cross-sectional study. It was conducted at four primary health care centers at Ministry of National Guard, Health Affairs in Riyadh, Kingdom of Saudi Arabia: National Guard Comprehensive Specialized Clinic (NGCSC), Health Care Specialty Center (HCSC), King Abdulaziz City Housing (Iskan-Yarmouk) and King Saud City Housing (Dirab).

Study population

This study targeted all adults 17 years old or older,including both genders, who were followed in four previous NGHA centers in Riyadh. A patient diagnosed with a preexisting sleeping disorder, mental disorder (e.g.,depression orinsomnia), non-smart phone users, children and pregnant females were excluded from this study.

Sampling technique

A sample size frame was unavailable for the four targeted centers; thus, a convenience sampling technique was used to distribute the self-administered questionnaire among the participants. As we used such a technique, the sample in our study is unlikely to represent the overall population, and sampling size calculations were not needed.

Data collection methods, instruments, and measurements

The participants completed a self-administered questionnaire, which includes questions that were taken from the Pittsburgh Sleep Quality Index (PSQI) and Smartphone Addiction Scale – Short Version (SAS-SV).

In addition, questions about demographic and personal/professional characteristics were added as in gender, age, marital status, working time, usage hours for smartphones,and four other questions to meet the excluding criteria as with pregnancy, being diagnosed with mental or sleeping disorders and type of phone being used.The questionnaire was initially developed in English then was translated to Arabic¹⁵. The questionnaire was adapted after the comprehensive literature search, and the content was validated by Kwon et al. in 2013 ¹⁶ and Buysse et al. (1988) ¹⁷. The validity of translation was ensured by forward-backward validation methods. The questionnaire pre-testing was conducted on ten subjects from our target population to assure clarity and accuracy of the tool. The approximate time to complete the questionnaire was 4 to 5 min. The targeted subjects were asked to completethe questionnaire after obtaining their consent.

Two scales were adopted in our study, PSQI and SAS-SV. PSQI is a ten-item scale that aims to measure seven components that affect sleep quality. It was developed by Buysse et al. (1988)¹⁷. and was validated with high internal consistency and a Cronbach’s alpha of 0.83^{18, 19}. Each of the seven components will have a grade between 0 (which indicates no sleep difficulties) and3 (which indicates severe difficulty within the component). The higher the overall score for all seven components, the higher the sleep issues for the participants, with an overall score ranging between 0 to 21. A cut-off number of 5 was selected where an overall mean response for a participant of 5 or more will indicate that he most likely has a sleep difficulty^{17, 20}.

The SAS-SV is a ten-item score that was developed to measure the addiction of smartphone usage. It was developed by Kwon et al. in 2013 ¹⁶. The SAS-SVhas a Cronbach’s alpha correlation coefficient of 0.91, which is considered withinthedesired range²¹. For each item or phrase, the respondent will choose amongsix answersof aLikert scale ranging from strongly disagree to strongly agree, with an overall score ranging from 10 to 60, where higher scores indicate higher smartphone addiction. A cut-off mean score of 31 for males and 33 for females was selected to indicate smartphone addictions based on the Kwon et al. validation study ¹⁶.

Data management and analysis plan

Data were initially managed and coded using Excel, in which exclusion criteria were performed. Then,datawere entered and analyzed usingastatistical package for the social sciences version 20 (SPSS, 20) software.Categorical variableswerereported as frequencies and percentages, while numerical variables werereported as mean and standard deviation.Further analysis was carried out using inferential statistics, using the chi-square test to assess differences between categorical variables andindependent studentt-test, and ANOVA for numerical variables. Statistical significance was set to be <0.05. Scatterplot graphs were used to illustrate the association between study scales, and boxplotswere used to illustrate categorical-numerical variables relationships.

Ethical Considerations

The study was conducted after getting approval from the Institutional Research Board (IRB) of King Abdullah International Medical Research Center KAIMRC. All data, both hard and soft copies,werestored within MNGHA premises and access by the research team only. All data were anonymous,keptconfidential, and used for research purposes only. A consent form was obtained from each participant before they completed the questionnaire.

Results

A total of 205 had participated in our study, of which 99 were female (51%) with a response rate of 84%. For social status, small numbers were noticed for the divorced category (five,2%); thus, they were merged into the “Ever married” category, which accounts for most of the participants (123,63%). Smartphone usage was diverse, with a comparable number for participants who were using their smartphones frequently, moderately, or infrequently: 53 (27%), 70 (36%), and 72 (37%). Most of our participants were doing their jobs in the morning (100, 51%), while only 26 (13%) had alsooccasionally beenworking at night. Table 1 gives the baseline characteristics for our participants and their mean PSQI score for sleep quality. Mean PSQI scores were higher among females, single, frequent smartphone users, and unemployed sub-categories; thus, they indicated poorer sleep quality. Figure 1 further illustrates this by comparing different variables and their sub-categories with mean global PSQI scores for sleep quality.

Table 1: Baseline characteristics and their mean PSQI score for sleep quality.

Figure 1: Boxplots illustrating and comparing gender, social status, usage hours for smartphones, and work time by their sub-categories with mean globalPSQI scores among the participants. Click here to view figure

Further analysis of respondent’s answers for PSQI scores can be found in Table 2. For subjective sleep quality, most respondents had fairly good to very good subjectivesleep quality (80%). Similarly, most of the respondents had a good sleep duration >6 h (70%) and sleep efficiency (69%). However, most of our respondents reported minimal to fair disturbances during their sleep (75%, 18%), respectively. More than 90% of the participants didnot use any sleeping medication during the past month, and only 14% have reported that they have fair or severe daytime dysfunction.

Table 2: PSQI component scores results

The student’s t-test was used to determine the association between mean global PSQI scores with age and SAS-SV scores, whiletheChi-square test was used to compare between categorical global PSQI scores using a cut-off point of 5 out of 21 with categorical variables such as gender and social status.

As shown in Table 3, there was a significant negative association between age and PSQI scores t(193)= -2.45, p=0.015. This means younger participants have significantly poorer sleep quality and higher PSQI scores. Gender and work time period subcategories did not have a statistical significant difference, (Χ²(1) = 0.58, p = 0.45), (Χ²(2) = 1.6, p = 0.45), respectively. However, social status and smartphone usagehours of were significantly associated with PSQI, (Χ²(1) = 4.8, p = 0.03), (Χ²(2) = 7.9, p = 0.02), respectively. The single sub-category and frequent smartphone users had higher numbers with abnormal sleep scores (Table 3), indicating significantly poorer sleep quality among single and frequent smartphone users. Finally, there was a significant positive association between mean SAS-SV score and categorical global PSQI scores using cut off point of 5 out of 21 t(193)= 3.4, p=0.001. This means higher SAS-SV scores, which indicates smartphone addiction users had higher PSQI scores indicated poorer sleep quality. The association between PSQI scores with age and SAS-SV scores were further illustrated in Figure 2.

Table 3: Association between poor sleep quality, measured by PSQI score and demographic variables.

Figure 2: The linear relationships between PSQI scores andage and SAS-SV scores. The dots were scattered moderately indicating intermediate correlation. Click here to view figure

Discussion

In our study, 63% of participants hadanabove cut-off score for SAS-SV,which indicated that they have problematic smartphone usage, which can be a risk for addiction. This result was much higher than the reported percentage of smartphone addiction in the literature, where it was ranged between 27.2% and 36.5%.^{3, 5}

This can be explained by the variation in the cut-off number being used in some studies and the current global increase in phone usage due to the COVID-19 pandemic and quarantine in Saudi Arabia.

For sleep difficulties, most of the participants (68%) in our study had sleep difficulties with a mean PSQI score of 6±3. The extent of sleep difficulties among our participants seems to be higher than reported in previous studies. In a study conducted by Amra et al. (2017)¹,  56.1% of girls and 38.9% of boys reported poor quality sleep, and late-night cell phone use was associated with poorer sleep quality, which might be comparable to our findings for poorer sleep quality among heavy smartphones users >6 h per day. In another study conducted by Mohammad beigiet al. (2016), using the Cell-Phone Over-Use Scale (COS) and (PSQI) sleep quality score, the prevalence of poor sleep quality was 61.7% (CI 0.95; 57.1%, 66.3%).²

We also found a significant association between poor sleep quality and being younger, single, a heavy phone user, and a high SAS-SV score. This was not reported in previous local studies and can be considered an important factor if a larger random study to be conducted or to be used in linear regression. Despite the promising results we showed, our research has several limitations. We had concerns about our sampling technique and low reported numbers for divorced population or infrequent smartphone users (less than 2 h per day), which we tried to overcome by increasing the number of participants and by merging divorced and infrequent smartphone users’ groups. The COVID pandemic year might be a cause of the relatively low response rate in our study.

Overall, our findings will likely be helpful for initiating a clinical trial to compare sleep quality and its relationship with multiple aspects of smartphone usage. We encourage further studies,given that the number of smartphone users continues to increase.

Limitations

We had concerns about our sampling technique and the low reported numbers for divorced population or infrequent smartphone users (less than 2 h per day), which we tried to overcome by increasing the number of participants and by merging divorced and infrequent smartphone users’ groups. The COVID pandemic might account for therelatively low response rate in our study.

Conclusion

Our study showed a high prevalence of smartphone addiction and poor sleep quality for the participants. Younger age, being single, and heavy usage hours of smartphones seem to be indicators for poorer sleep quality. A high SAS-SV score was also found to be a significant indicator of poorer sleep quality.

Acknowledgement

The author would like to thank department of Family Medicine at King AbdulAziz Medical City – National Guard Health Affairs, is highly appreciated for cooperative the PHC centers.The author is also profoundly grateful to the King Abdullah International Medical Research Center (KAIMRC).

Conflict of interest

We have no Conflict of interest for our article.

Funding Sources

This research received no funds.

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