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Effects Of Smartphone Addiction Level On Social And Educational Life In Health Sciences Students
Sağlık Bilimleri Fakültesi Öğrencilerinin Akıllı Telefon Bağımlılık Düzeylerinin Sosyal Ve Eğitim Hayatına Etkisi
Hatice Kahyaoglu Sut, Seda Kurt, Ozge Uzal, Saadet Ozdilek

 

How to cite / Atıf için: Kahyaoglu Sut, Kurt S, Uzal O, Ozdilek S. Effects Of Smartphone Addiction Level On Social And Educational Life In Health Sciences Students. Euras J Fam Med 2016;5(1):13-9

 

Original Research / Orijinal Araştırma


ABSTRACT

Aim: This study investigated the effect of smartphone addiction levels on social and educational life in health sciences students. 

Methods: In a cross-sectional design, 785 students filled out a questionnaire that included demographic characteristics and smartphone use behavior. The smartphone users then completed the Smartphone Addiction Scale. 

Results: Of the 785 students, 91.7% were smartphone users. Total, daily-life disturbance, and cyber-oriented relationship scores of the Smartphone Addiction Scale in the ≤20 age group were found to be significantly higher than in the >20 age group (p<0.05). All subscale and total scores of students who thought smartphone use made education more difficult were significantly higher (p<0.05). Positive anticipation and cyberspace-oriented relationship subscale scores of students who thought smartphone use affected their social life was significantly higher (p<0.05). Daily-life disturbance and tolerance subscale scores in students who thought that smartphone use negatively affected their verbal communication was significantly higher (p<0.05); however, their positive anticipation score was significantly lower (p<0.001). 

Conclusion: The prevalence of smartphone use among students was quite high. The smartphone addiction level was higher in students who were 20 or less years old. The higher addiction scores negatively affected social life, verbal communication, and presented difficulties to education. 

Keywords: mobile phone, addiction, students, health occupations, nursing, health sciences

ÖZET

Amaç: Sağlık Bilimleri Fakültesi öğrencilerinin akıllı telefon kullanma sıklığı ve akıllı telefon bağımlılık düzeylerinin incelenmesi amaçlanmaktadır.

Yöntem: Kesitsel ve tanımlayıcı özellikte olan çalışmada 785 öğrenci sosyodemografik özellikleri ve akıllı telefon kullanımına ilişkin bazı soruları içeren anket formunu doldurmuşlardır. Akıllı telefon kullanan öğrenciler ayrıca Akıllı Telefon Bağımlılık Ölçeğini doldurmuşlardır.

Bulgular: Çalışmaya katılan 785 öğrenciden %91,7’sinin akıllı telefon kullandığı bulundu. ≤20 yaş grubu öğrencilerde Akıllı telefon kullanımı ölçeğinin toplam, günlük yaşamda rahatsızlık ve siber-odaklı ilişki alt boyut puanlarının >20 yaş grubundan anlamlı olarak yüksek olduğu bulundu (p<0.05). Akıllı telefon kullanımının eğitimi güçleştirdiğini ifade edenlerin toplam ve tüm alt boyut puanları anlamlı olarak yüksek bulundu (P<0.05). Akıllı telefon kullanımının sosyal yaşamlarını olumsuz etkilediğini ifade edenlerin pozitif bekleyiş ve siber-odaklı ilişki altboyut skorları anlamlı olarak yüksek bulundu (p<0.05). Akıllı telefon kullanımının sözel iletişimlerini olumsuz etkilediğini düşünen öğrencilerin günlük yaşamda rahatsızlık ve tolerans alt boyut skorları anlamlı olarak yüksek bulundu (p<0.05).

Sonuç: Sağlık bilimleri fakültesi öğrencileri arasında akıllı telefon kullanım sıklığı oldukça yüksekti. 20 yaş ve altı öğrencilerin bağımlılık düzeyleri daha yüksekti. Öğrencilerin ölçek skorlarının yükselmesi (bağımlılık düzeylerinin artması) sözel iletişimlerini ve sosyal yaşamlarını negatif yönde etkilemekte, eğitimlerini zorlaştırmaktadır.

Anahtar kelimeler: cep telefonu, bağımlılık, sağlık meslek okulu öğrencileri, hemşirelik, sağlık bilimleri


Introduction

Smartphone use is becoming more common every day and is seen as an indispensable part of daily life. Smartphones are more common than portable computers because they are small and easy to use. They are used to access social media (Facebook, Twitter, Instagram, and other programs), surfing the web, e-mail communication, and messaging. In addition, they are used to play games, access the Internet, and in navigation (1). 

Smartphones are very useful when they are not used excessively. Because smartphone use among students is more common than in older individuals, students are more exposed to the negative effects (2). It has been reported that smartphone use has adversely affected learning in the classroom, has endangered driving safety, and has negatively affected work performance (1).

Smartphone addiction is defined as excessive use of smartphones that affects the daily lives of users and has various clinical features, including salience, tolerance, loss of control, mood modification, withdrawal symptoms, and craving (3). Kwon et al. (4) in 2013 developed the Smartphone Addiction Scale (SAS) in order to measure the addiction levels of smartphone users. The prevalence of smartphone addiction among Indian adolescents was reported to range from 39% to 44% (5). The overall prevalence of smartphone ownership among Asian adolescents from China, Hong Kong, Japan, South Korea, Malaysia, and the Philippines was reported at 62% (6). Smartphone addiction is considered to be similar to Internet addiction. “Low Internet self-efficacy,” “Favorable outcome expectancies,” and “High impulsivity traits” were reported as psychological risk factors for addiction to social networking sites among Chinese smartphone users (7). Excessive use of smartphones is related to mental health problems (2). Psychological and emotional factors, such as anxiety and depression, are linked to smartphone and Internet addiction. Internet addiction can lead to social isolation, loneliness, family problems, academic failure, and a decrease in work performance (8).

When used correctly, there are several benefits of smartphone applications. For instance, smartphone applications may be used as a support tool for patients with alcohol and drug dependence (9). They are also useful for HIV risk reduction by means of video-based education applications (10). 

Patient safety may be compromised due to excessive smartphone use by health care workers. Therefore, smartphone addiction among health care workers is an important issue that must be addressed. It is important for the Health Sciences Faculty students, who will become health care workers, to know that smartphone use may lead to addiction and may jeopardize patient safety. Since smartphone addiction can be seen as a new behavior addiction, in this study we aimed to investigate the effect of smartphone addiction levels on social and educational life in health sciences students.

Methods 

Participants: 

This study was carried out using a cross-sectional design on students of Trakya University, Health Sciences Faculty between January and April 2015. Of the 1,022 Health Sciences Faculty students, 785 (76.8%) volunteered to participate in the study. 

Data collection instruments:

The students who agreed to participate in the study completed a questionnaire that asked for socio-demographic characteristics and the attitudes of smartphone use developed by the researchers. The smartphone users then completed the SAS. The SAS was developed by Kwon et al. (4) in 2013 in order to measure the addiction levels of smartphone users. It consists of a six-point Likert-type scale with 33 items and six subscales (daily-life disturbance, positive anticipation, withdrawal, cyberspace-oriented relationship, overuse, and tolerance). Kwon et al. described the SAS subscales as follows: “Daily-life disturbance” describes the difficulty of concentrating in class, missing planned work, and suffering from disturbances such as lightheadedness, sleep, or neck pain. “Positive anticipation” describes reducing stress by using the smartphone and feeling empty when there is no smartphone. “Withdrawal” describes the impatient, fretful, and intolerable feelings when there is no smartphone. “Cyberspace-oriented relationship” describes closer relationships with friends on social networking services than in real life. “Overuse” describes uncontrollable smartphone use. “Tolerance” can be described as always trying to control the use of the smartphone, but not being successful (4).

Higher SAS scores indicate a higher smartphone addiction level. As well as the six subscales, a total SAS score varying from 33 to 198 can be calculated. Internal consistency (Cronbach alpha) of the SAS was reported as 0.96 by Kwon et al. (4). The Turkish version of the SAS was translated by Demirci et al. (11).

Ethical procedures:

The aims of the study were explained to the students at the beginning of the study. Then, it was explained to the students who are free to participate in the study, could stop at any time complete the questionnaire in the case of disturbing questions in the survey, data will be kept confidential and only used for scientific research. An informal verbal consent was obtained from all volunteer participants. Written permission to conduct the study was obtained from the Trakya University Health Sciences Faculty.

Data analysis:

Internal consistency of the SAS was assessed with Cronbach’s alpha coefficient. Subscale and total scores of the SAS were compared by using the Mann-Whitney U test for categorical variables consisting of two categories or by using the Kruskal-Wallis test for categorical variables consisting of three or higher categories. Statistical analysis was done by SPSS 20.0 statistical software (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.), and the p value

Results 

Cronbach’s alpha coefficient of SAS was 93.2%. Of the 785 students, 720 (91.7%) were smartphone users, 79.3% were female, and 20.7% were male. The average age of smartphone users was 20.4±1.6 and ages ranged from 17 to 32.

The major component of daily smartphone usage time was 4-6 hours (40.1%) per day. Of all of the students, 8.2% reported that message control was less than 5 minutes. The primary purpose of smartphone use was to access social networking services (56.8%), such as Facebook, Twitter, WhatsApp, and Instagram. (Table 1).

 

Table 1. Attitudes related with smartphone of students (n=720)

 

Daily smartphone usage time was positively correlated with the SAS total score (r=0.374 ; p<0.001) and all the subscale scores (a range from 0.242 to 0.375 ; p

 

Table 2. Spearman correlation coefficients of SAS subscale scores with daily smartphone usage time and smartphone message control period (n=720)

 

 

The mean total score of the SAS was 84.0±24.7. Total, daily-life disturbance, and cyber-oriented relationships subscale scores of the SAS in the ≤20 age group were found to be significantly higher than in the >20 age group (p=0.018, p=0.013, and p=0.015, respectively). There was no statistical significance in the SAS scores among gender, academic department, mother’s education, father’s education, income, smoking, or alcohol use (p>0.05). (Table 3).

 

Table 3. Subscale scores and total score of SAS by socio-demographic characteristics (n=720)

 

The daily-life disturbance and tolerance subscale scores of the SAS for students who thought that smartphone use negatively affected their verbal communication was significantly higher (p=0.007, and p=0.032, respectively), however their positive anticipation score was significantly lower (p<0.001). The positive anticipation and cyberspace-oriented relationship subscale scores of the SAS in students who thought smartphone use affected their social life was significantly lower (p

 

Table 4. Subscale scores and total score of SAS by several factors related with smartphone use (n=720)

 

Discussion 

Kwon et al. developed the Korean version of the SAS consisting of a six-point Likert-type scale with 33 items and six subscales in 2013 in order to measure the addiction levels of smartphone users.(4) The SAS was then translated into Turkish by Demirci et al. (11). We investigated smartphone use and the addiction level among students by using this instrument. We showed that the majority (91.7%) of the students who have smartphones had a mean total score of 84 on the SAS. Kwon et al. reported the mean total score on the SAS as 110 for the Korean population (4). When we analyzed the SAS total score by gender, we found that the average total score among females (84.3) was higher than among males (82.7). The average total SAS score was 78.7 for females and 72.2 for males in the work of Demirci et al. (11) and 112.7 for females and 104.5 for males in the work of Kwon et al. (12). Our results are closer to the results of Demirci el al. and showed us that Turkish students have a lower smartphone addiction score as compared to the results of Kwon et al.

The main purpose of smartphone use has been reported as a phone at higher rates (4, 11). Differing from the results of these studies, in our study, the main purpose of smartphone use was to access social networking services at higher rate. Our results showed that a major component of daily smartphone usage time among students was 4-6 hours (40.1%) per day. In other studies, it was reported that 71.4% of students used the smartphone less than four hours per day (11). Close to half the students use their smartphones more than four hours per day, and it can said that the daily smartphone usage time is quite long. Almost one in ten students checks message control on an average of less than 5 minutes. According to the results of Table 3, positive Spearman correlation coefficients of SAS scores with daily smartphone usage time and negative Spearman correlation coefficients of SAS scores with smartphone message control period showed that the smartphone addiction level increases when the usage time increases and message control period decreases.

In a recently published meta-analysis, the prevalence of smartphone addiction among Indian adolescents ranged from 39% to 44% and the study concluded that this situation may lead to psychologically harmful effects and adverse health outcomes (5). Life stresses among university students influenced smartphone addiction (13). Choi et al. reported that smartphone addiction had negative effects on mental health, campus life, and personal relations (14). According to the results of Table 3, higher SAS scores were observed in students of 20 years or less. Consistent with our results, it was reported that smartphone addiction among students of 10-20 years of age is more prevalent than among students of 20-30 years of age (11, 15). Since this situation shows us that smartphone addiction level is higher in the 20 or lower age range, proactive measures should be taken to prevent smartphone addiction and rehabilitation programs could be used to treat smartphone addiction (8).

According to the results of Table 4, students who thought that smartphone use negatively affected their verbal communication had higher daily-life disturbance and tolerance subscale scores on the SAS, although their positive anticipation subscale score was lower. These results show us that difficulty in concentrating in class and always trying to control the use of smartphone are more common, although feeling empty when there is no smartphone is less common among students who thought smartphone use negatively affected their verbal communication. Students who thought smartphone use negatively affected their social life had higher positive anticipation and cyberspace-oriented relationship subscale scores. They felt empty when there was no smartphone, and felt they had closer relationships with friends through social networking than in real life. Students who thought smartphone use complicated their education had higher scores on the SAS and on all subscales. From this we can infer that students believe their education became more difficult when the smartphone addiction level increased. Students who thought smartphone use eased their access to educational information had a lower cyberspace-oriented relationship subscale score. This result shows that these students had closer relationships with friends through social networking than in real life. 

Today, the development and easily availability of smartphone technology offers assistance and several smartphone applications are specific to healthcare. Chih et al. developed a novel Bayesian network model to predict alcohol relapse events based on a smartphone application (16). This and similar smartphone applications may help patients who are undergoing addiction treatment. A brief instrument was developed for smartphone addiction among nursing students from the work of Cho et al. They suggested that practical guidelines and policies should be created in order to correct smartphone use in clinical practices (1). Smartphones have many advantages when they are used correctly, but they may lead to addiction when they are used excessively. 

Conclusion

In conclusion, the prevalence of smartphone use among students is quite high. Since the smartphone addiction level is higher for those who are 20 years or less, proactive measures should be taken to prevent smartphone addiction. An increase in the scores on the SAS negatively affects verbal communication and social life, contributes to difficulties in education, and adversely affects access to the students’ educational information.

 

References

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