Smartphone Addiction Scale Scores Research Articles

Comparison of Smartphone and internet addiction and Optical Coherence Tomography findings among University students

IntroductionInternet and smartphone use that reaches the level of addiction often leads to deterioration in the quality of life and functionality of individuals.ObjectivesIn our study, we aimed to investigate possible differences in retinal nerve fiber layer (RNFL) thickness and central macular thickness obtained by optical coherence tomography in internet and smartphone addiction.MethodsA total of 212 volunteer university students participated in our study. All participants were administered the Sociodemographic Information Form, Chen Internet Addiction Scale, Smartphone Addiction Scale-Short Form. Participants who completed the scales underwent routine eye examinations by experienced physicians in the ophthalmology outpatient clinic. Retinal nerve fiber layer (RNFL) thickness and central macular thickness were measured by optical coherence tomography (OCT).ResultsIn our study, internet addiction rate was 17% and smartphone addiction rate was 38.2%. RNFL thickness was found to be statistically significantly increased in the temporal superior and temporal inferior quadrants in those with internet addiction compared to healthy subjects (p<0.05). In smartphone addiction, RNFL thickness was found to be statistically significantly increased in the temporal inferior quadrant compared to healthy subjects (p<0.05). In the analyses comparing OCT measurements according to sex, it was found that nasal inferior (p<0.01) and global (p<0.05) quadrants in women and central macular thickness (p<0.01) in men were statistically significantly increased.The correlation analyses in our study revealed statistically significant positive correlations between internet addiction scale scores (p<0.01) and smartphone addiction scale scores (p<0.01), RNFL temporal superior quadrant thickness (p<0.01); smartphone addiction scale scores and RNFL temporal superior quadrant thickness (p<0.05).ConclusionsInternet and smartphone addiction are seen considerable rates among university students. In OCT measurements, RNFL thickness was found to be increased in various quadrants in patients with addiction. In addition, RNFL thickness was found to be increased in all quadrants in female gender and central macular thickness was found to be increased in male gender. Correlation analysis revealed that internet addiction scale scores, smartphone addiction scale scores, and RNFL temporal superior quadrant thickness were positively correlated. In addition, there was a positive correlation between smartphone addiction scale scores, and RNFL temporal superior quadrant thicknessDisclosure of InterestNone Declared

Extent of Smartphone Addiction and its Association with Physical Activity Level, Anthropometric Indices, and Quality of Sleep in Young Adults: A Cross-Sectional Study.

Smartphone addiction among young adults is a growing concern that is often underestimated despite its significant health hazards. The objective of this study was to assess the extent of smartphone addiction and its association with physical activity level, anthropometric indices, and quality of sleep in young adults. This cross-sectional study was conducted among 138 allied health sciences undergraduates of a tertiary care medical school in Puducherry, South India. The participants' extent of smartphone addiction, physical activity, and sleep quality were assessed using the Smartphone Addiction Scale (SAS), International Physical Activity Questionnaire, and Pittsburgh Sleep Quality Index (PSQI), respectively. Anthropometric indices (body mass index [BMI], waist-to-hip [W: H] ratio, waist-to-height [W: Ht] ratio, Conicity Index, and A Body Shape Index [ABSI]) were also measured following standardized procedures. Correlations between smartphone addiction, physical activity, anthropometric indices, and sleep quality were evaluated using Pearson's/Spearman's rank correlation coefficient. P <0.05 was considered statistically significant. Over 50% of participants showed smartphone addiction and poor sleep quality. Although a significant negative correlation was observed between SAS scores and physical activity levels, significant positive correlations were noted between SAS scores and BMI and SAS and PSQI scores. Smartphone addiction is associated with decreased physical activity, increased BMI, and poor sleep quality in young adults.

Akıllı Telefon Bağımlılığına Bağlı Boyun Ağrısında Kı̇nesyolojik Bandın Ağrı ve Trapez Kasının Mekanı̇k Özellı̇klerı̇ Üzerı̇ne Akut Etkı̇sı̇

This study aimed to assess the short-term effects of inhibitory kinesiologic tape (KT) and sham tape on neck pain and muscle tone in individuals with smartphone addiction. Forty-two healthy participants were randomized to receive either KT or sham tape application to their upper trapezius muscle (UTM). Smartphone addiction was assessed using the Smartphone Addiction Scale (SAS), and pain severity was measured using the Visual Analogue Scale (VAS). Muscle tone and stiffness were evaluated using a myotonometer. No significant differences were found in pain or muscle mechanical properties between the groups after treatment. However, KT recipients had significantly higher tone and stiffness in their left trapezius muscle compared to their right trapezius muscle (p &lt; 0.05). Additionally, right trapezius stiffness was significantly higher in participants with SAS scores of 100 points or more compared to those with SAS scores below 100 points (p &lt; 0.05). Finally, left trapezius elasticity increased significantly in participants with SAS scores of 100 points or more (p &lt; 0.05). Overall, the study findings suggest that short-term application of KT to the UTM may reduce neck pain in individuals with smartphone addiction.

Online Behavioral Addictions Among Adolescents Before and After the COVID-19 Pandemic.

Background The COVID-19 pandemic changed people's lives and created a "new normal." It threatened individuals' mental health owing to reduced physical activity and social interaction, excessive indoor time, financial hardship, and insecurity. Moreover, the risk of online behavioral addiction increased in the general population, particularly among adolescents. The present study examined the differences between the pre-and post-pandemic periods regarding online behavioral addictions in adolescents. Methods The pre-pandemic data were obtained from 175 adolescents (August 2019 to February 2020) (T1). An online survey was sent to these participants to obtain the post-pandemic data (March to September 2022) (T2). Seventy participants completed the online survey (response rate: 40%). The participants completed the Smartphone Addiction Scale (SAS), the Internet Gaming Disorder Scale 9-Short Form (IGDS9-SF), and the Social Media Disorder Scale-Short Form (SMDS-SF) both before and after the pandemic. Results Before the pandemic, females had significantly higher SMDS-SF scores compared to males (p = 0.005). On the other hand, males had higher IGDS9-SF scores than females before the pandemic (p<.001). Individuals with attention deficit hyperactivity disorder (ADHD) had higher IGDS9-SF scores before the pandemic than those with depressive disorders or other diagnoses (p = 0.004). However, the primary diagnosis was not related to pre-pandemic SAS and SMDS-SF scores. Lastly, there was no significant difference in IGDS9-SF (p = 0.151), SMDS-SF (p = 0.200), or SAS scores (p = 0.413) between pre-pandemic and post-pandemic scores. Conclusion Although the current study did not support this view, in emotionally challenging times, people may spend more time on online activities, which can lead to behavioral addiction. It is important for parents to monitor their children's online activities and provide guidance. More research is needed to compare online behavioral addictions before and after the pandemic.

Digital Danger in Our Pockets: Effect of Smartphone Overuse on Mental Fatigue and Cognitive Flexibility.

Smartphones have become essential to life in Western society. This situation reached its peak, especially during the pandemic period. The possibility that smartphone use has negative impact on brain activity has attracted increasing interest among researchers. Medical faculty students were contacted via e-mail and invited to participate in the study. Participants completed the Smartphone Addiction Scale (SAS), the Mental Fatigue Scale (MFS), and the Cognitive Flexibility Inventory (CFI). The total SAS scores of participants with mental fatigue were significantly higher than those of participants without mental fatigue ( p < 0.001). In the smartphone addiction tendency group, the total MFS scores were significantly higher, and the total CFI scores were significantly lower than the scores of the control group ( p < 0.001 for both). In the logistic regression analysis, the total SAS score was associated with the development of mental fatigue ( B coefficient, 0.031, p < 0.001). This study identified potential relationships between smartphone addiction, mental fatigue, and cognitive flexibility. We suggest that smartphone overuse may be an independent risk factor for the development of mental fatigue.

The Evaluation of the Relation Between Smartphone Addiction and Eating Attitudes among Medical Students

Objective: This study aims to evaluate the relationship between smartphone addiction and demographical characteristics, eating attitudes, and body mass index of medical students. Methods: The data was obtained through personal questionnaires, the scales regarding eating attitudes and smartphone addiction. The questionnaire and the scales were carried out online among current medical students of Zonguldak Bülent Ecevit University. Results: Of the participants (n=347), 59.9% were female,40.1% were male. Participants were found to use internet mostly through their smartphones and they were also found to use their smartphones mostly for messaging and monitoring social media. The scores for the Smartphone Addiction Scale were found to be significantly higher in students who were using their smartphones for social media than those who were not. A weak, positive, and significant relationship was found between the scores of the Smartphone Addiction Scale and Eating Attitudes Test of the students (r=0.135,p=0.12). As a result of the correlation analysis, a very weak, positive and significant relationship was found between Body Mass Index and the age of owning the first smartphone (r=0.113, p=0.036). Conclusion: We reported a relation between the intentions of using a smartphone and smartphone addiction and another relation between one’s age of owning the first smartphone and these individuals’ body mass indexes. A difference was observed between those who have abnormal eating attitudes and who have not regarding smartphone addiction scale scores.

Effects of elongation longitudinaux avec decoaption osteo-articulaire and post-facilitation stretching technique on pain and functional disability in mobile users with text neck syndrome during COVID-19 pandemic: A randomized controlled trial.

Adolescent smartphone users have developed a variety of musculoskeletal conditions due to their use of smartphones. ELDOA (Elongation longitudinaux avec decoaption osteo-articulaire) is based on maximizing facial and spinal stretching by assuming specific posture for 1 minute and post facilitation stretching is also a type of muscle energy technique that is used for chronically shortened or tight muscles. This aimed to compare the effects of post-facilitation stretching technique and ELDOA on neck pain and functional disability in mobile users experiencing Text neck syndrome during COVID-19. This single-blinded randomized control trial with a parallel group design was conducted at the Department of Physiotherapy Safi Hospital (Faisalabad, Pakistan) from September 2021 to April 2022. Forty smartphone users between the ages of 18 and 35 who had a Neck Disability Index score of >10 due to neck pain without unilateral arm symptoms participated in the study. Of the 40 participants, twenty were randomly assigned to the ELDOA group and twenty were assigned to the post facilitation stretching group, and each group received 3 weekly sessions of treatment for 6 weeks. The Numeric Pain Rating Scale (NPRS), Neck Disability Index (NDI), and Smartphone Addiction Scale (SAS) were used to measure pain intensity, functional disability, and smartphone addiction at baseline and after 18 sessions of treatment. SPSS version 22 was used to enter and analyze the data. To find comparisons between groups an independent sample t test was used, and a paired sample t test was used to find the difference within each group. Post-treatment values showed statistically significant difference between groups. ELDOA group showed greater improvement in pain (P < .03) with 95% CI [-1.33, -0.068] and functional disability (P < .05) with 95% CI [-4.44, 0.143] at 6th week. There was no statistically significant difference (P = .35) with 95% CI [-28.6, 10.4] between the two groups regarding smartphone addiction. The NPRS, NDI, SAS scores were significantly different within each group with P < .05. The study concluded that ELDOA method and post-facilitation stretching both were effective in treating neck pain and functional disability. However, ELDOA method was superior to post-facilitation stretching effects on neck pain and functional disability among patients with text neck syndrome.

Excessive smartphone use and its correlations with social anxiety and quality of life among medical students in a public university in Malaysia: A cross-sectional study.

Smartphone usage has significantly increased in the last decade among young adults has significantly increased in the last decade. While its benefits are undeniable, its negative implications are increasingly emerging. Studies are needed to investigate the effects of excessive smartphone use on a young person's life. This study aimed to determine the prevalence of excessive smartphone use among medical students and its relations with social anxiety, self-esteem, and quality of life. A cross-sectional study was conducted among medical students from Universiti Kebangsaan Malaysia (UKM) in UKM Medical Center. A total of 273 students have consented to participate and completed self-reported questionnaires encompassing sociodemographic information, the Short Version Smartphone Addiction Scale (SAS-SV), the Social Interaction Anxiety Scale (SIAS), the World Health Organization Quality of Life (WHOQOL-BREF) and the Rosenberg Self-esteem Scale (RSES). Sociodemographic data, SIAS score, WHOQOL-BREF score and the Rosenberg Self-esteem Scale score were treated as independent variables. Smartphone addiction Scale score was treated as the dependent variable. Bivariate analysis was used to explore the relationship between independent and dependent variables using the Fisher exact test, Pearson Chi-Square and Pearson correlation coefficient. Multiple linear regression analysis was used to analyze the variables with a p-value of < 0.05 from the Pearson correlation coefficient test. The percentage of excessive smarphone use among UKM medical students is 48%. The bivariate analysis showed that excessive smartphone use has a small but significant positive correlation with social anxiety (r = 0.173, p = 0.004) and negative correlations with physical health (r = -0.133, p = 0.028), psychological wellbeing (r =-0.135, p = 0.026), social relationships (r = -0.232, p = 0.001), environment (r = -0.260, p = 0.001) and self-esteem (r = -0.128, p = 0.035). In the multiple regression analysis, a better environment predicted a reduced risk for smartphone addiction (β = -0.233, p = 0.013). Almost half of the students were found to have smartphone overdependence. Excessive smartphone use has shown a significant relationship with an increased risk for social anxiety, reduction in self-esteem, and quality of life among medical students. A closer look into the possible intervention is needed in the future to curb the negative effects arising from excessive smartphone use.

Addictive smartphone use in the elderly: relationship with depression, anxiety and sleep quality.

The aim of the present study was to explore the relationship between addictive smartphone use (ASU) and depressive symptoms, anxiety and sleep quality in elderly adults. The study sample included smartphone users over the age of 65 years. The research data were obtained from social networking sites via a Google survey link. In addition to filling out a sociodemographic data form, the participants were also assessed with Smartphone Addiction Scale (SAS), Geriatric Depression Scale, Beck Anxiety Inventory and Pittsburgh Sleep Quality Index tools. The correlation analysis revealed the SAS score to be positively correlated with depression and anxiety, and negatively correlated with sleep quality. In the regression analysis, depressive symptoms, anxiety level and sleep quality were all found to have an effect on the SAS total score. Furthermore, the SAS score was found to have an effect on depressive symptoms, anxiety and sleep quality. Our findings reveal a bidirectional relationship between ASU and depressive, anxiety symptoms and impaired sleep quality in elderly adults. It is important to question smartphone use patterns in people with sleep problems, symptoms of depression or anxiety.

Associated Factors of Smartphone Addiction in the Students of the Faculty of Health Sciences

Aim: This study aimed to investigate associated factors of smartphone addiction in the students of a faculty of health sciences. Material and Methods: This is a cross-sectional study. A total of 839 health sciences students participated in our study. The data was collected by Personal Information Form, the Smartphone Addiction Scale-Short Form, the Pittsburg Sleep Quality Index, and the Piper Fatigue Scale. Results: As the smartphone addiction score increases by 0.59 units, the sleep quality total score increases by one unit. The smartphone addiction score decreases by 0.12 units when the fatigue score increases by one unit. It was determined that female students' smartphone addiction scale scores were higher than male students. Conclusion: The female students’ Smartphone Addiction Scale scores average were higher than males. In addition, fatigue and sleep problems were more significant in individuals with smartphone addiction. Appropriate Technology Use courses should be added to the curriculum to prevent telephone addiction.

Relationship of Sleep Quality and Oxidative Stress Level in Smartphone Users; Study in Faculty of Medicine Student, Universitas Baiturrahmah

BACKGROUND: Excessive smartphones can affect sleep quality, reducing sleep duration. This lack of sleep will impact various health and increase levels of free radicals in the body, affecting various cell functions. AIM: The aim of the study was to measure the relationship between sleep quality due to smartphone use and serum malondialdehyde (MDA) levels. MATERIALS AND METHODS: This was a quasi-experimental with pre- and post-test group study. Sleep quality was assessed with Pittsburgh Sleep Quality Index (PSQI), and the smartphone addiction was assessed based on Smartphone Addiction Scale (SAS) score. The subjects were divided into four groups (n = 6) based on their PSQI and SAS score. The first group was the subjects with normal sleep and non-smartphone addict. The second group was the subjects with normal sleep but smartphone addict. The third group was the subjects with abnormal sleep and non-smartphone addict. Furthermore, fourth was the subjects with abnormal sleep and smartphone addiction. All the subjects were asked to sleep usually and used the smartphone as necessary a day before the study started. Blood plasma was collected from the subject before and after the study for MDA measurement. Plasma MDA was determined using the thiobarbituric acid test. RESULTS: Smartphone use can reduce sleep quality and duration, resulting in sleep deprivation. There was no increase in MDA concentration (p &gt; 0.05) in the ordinary and non-addictive or smartphone-addicted sleep group. Meanwhile, the group that stayed up late and was neither addictive nor addictive showed an increase in MDA levels and was statistically significant (p &lt; 0.05). CONCLUSION: Adequate sleep can reduce blood serum MDA levels and smartphone use does not affect MDA levels.

Smartphone addiction and depression, anxiety: The role of bedtime procrastination and self-control

Owing to the widespread use of smartphones, researchers have an increasing interest in smartphone addiction. The purpose of this study is to look into the outcomes of smartphone addiction while answering when and how smartphone addiction may predict university students' depression and anxiety. Primary data were collected from 355 students studying in different universities in China. Participants completed Smartphone Addiction Scale-Short Version (SAS-SV), Bedtime Procrastination Scale (BPS), Self-control Scale (SCS) and Depression-Anxiety-Stress Scale (DASS). PROCESS macros in SPSS24.0 were used to examine the moderated mediating effects. Smartphone addiction Scale scores were positively correlated with depression, anxiety among university students through bedtime procrastination. Self-control was found to play the moderating role such that the mediated relationships were weak for students with high self-control. This study is a cross sectional study, so we cannot make causal inferences. Individuals with smartphone addiction are inclined to postpone their bedtime and further experience more depression and anxiety. Self-control serves as a protective factor for bedtime procrastination, depression and anxiety.

Is there a relationship between nursing students’ smartphone use, their fear of missing out and their care-related behaviour?

AimThis study aims to determine the relationship between smartphone use among intern nursing students, fear of missing out and their care-related behaviour. BackgroundToday, smartphone use is common and fear of missing out is a prominent issue in our society; this behaviour and issue are linked and may lead to adverse consequences. Design: The study is conducted as a cross-sectional design. MethodsThe target population in this research consists of senior nursing students (n = 101). There was no sampling selection and the study was completed with 97 students who agreed to participate in the research. Data were collected with introductory questionnaire forms, the Smartphone Addiction Scale (SAS), the Fear of Missing Out Scale (FoMOs) and the Caring Assessment Questionnaire (Care-Q). The valid and reliable SAS, FoMOs and Care-Q were transformed into online forms and the link was distributed to the participants via online Google forms. ResultsBased on the results of the study, the mean scores were 83.30 ± 2.21 of SAS, 21.56 ± 6.42 for FOMOs and 5.54 ± 0.87 for Care-Q scale. There were statistically significant differences between gender and Care-Q score and between the duration of smartphone use, daily smartphone usage time and SAS score (p < 0.05). There was a weak positive relationship between SAS and FoMOs scale scores, along with a weak negative relationship between the accessibility and comfort sub-dimensions of the SAS and Care-Q scale. Also, the SAS score explains 0.4% of the comfort subscale of the Care-Q scale (p < 0.05). ConclusionsFurther research focusing on nursing students is recommended to identify potential adverse effects created by the use of smartphones and the fear of missing out. In addition, educators are recommended to develop guidelines and prepare new policies for smartphone use to minimise the potential negative effects of smartphone use and fear of missing out on student care behaviour.

English

BACKGROUND Smartphone use has gone tremendously up throughout the world during the past decade and addiction potential is well documented among the users. Smartphone addiction among adolescents and young adults should be seen with caution as they lay the foundations not only for their healthy lives but also of the country’s future and its economy, they live in. METHODS Both school and college students aged between 15 - 24 years were enrolled in this cross sectional study, using systematic random sampling technique to determine smartphone addiction potential through Smartphone Addiction Scale (SAS). The possible factors contributing to SAS scores were also determined through statistical tests (Chi-square test, Kruskal-Wallis H test and Mann Whitney U test). RESULTS Among a total of 239 respondents of age 15 to 24 years (mean age 18.5 ± 2.35 years), there were 124 (51.9 %) females and 231 (96.7 %) belonged to the urban area. Most of the participants were studying in high school (40.6 %) followed by medical field (40.2 %). A total of 110 (46 %), students had used their smartphone commonly for social media purpose, followed by entertainment purpose (29.3 %), education (36 %), web surfing (20 %). The median daily usage of smartphone was found to be 4 hours among participants. The mean score of SAS was found to be 103.59 ± 25.08. There was statistically significant difference in SAS score according to age group, according to their smartphone usage per day, according to common purpose of usage of smartphone and also according to their streams, and monthly expenses the student spends to recharge their smartphone. CONCLUSIONS Smartphone addiction potential was determined according to various social and demographic characteristics. Factors causing smartphone addiction should be tackled by multiple stakeholders to reduce the chances of addiction due to smartphone and to protect them from future health consequences. KEYWORDS Adolescent, Smartphone Addiction, Smartphone Addiction Scale, Young Adults

The association of smartphone addiction, sleep quality and perceived stress amongst Jordanian dental students.

Smartphone addiction has been associated with sleeping problems and psychological anxiety. However, little is known about the association of smartphone addiction, sleep quality and perceived stress amongst university students particularly in dentistry. This study investigated the association of these variables amongst dental students in a Jordanian dental faculty. A sample of 420 undergraduate dental students enrolled for academic year 2017/2018 at the Jordan University of Science and Technology participated in this study. They were given a set of self-administered questionnaires which consisted of questions regarding demographic information, characteristics of smartphone use, assessment of smartphone addiction, sleep quality as well as perceived stress. The mean Smartphone Addiction Scale (SAS) score amongst Jordanian dental students was high (109.9±23.8). The correlation between SAS and Global Pittsburg Sleep Quality Index (PSQI) score was significant (r=0.137, P=0.005) indicating that the higher the smartphone addiction level, the poorer the quality of sleep. Similarly, a significant correlation was found between Perceived Stress Scale-10 (PSS-10) score and Global PSQI score (r=0.348, P<0.001) which implied that students who have higher perceived stress experienced poorer sleep quality. Year 3 students (clinical year), smartphone addiction and perceived stress were determinants of sleep quality. Dental students who have high levels of smartphone addiction or high perceived stress levels experienced poor sleep quality. Identifying smartphone addicts amongst students as well as stressors are imperative measures to allow timely assistance and support in the form of educational campaigns, counselling, psychotherapy and stress management.

The relationship between smartphone addiction and musculoskeletal pain prevalence among young population: a cross-sectional study.

BackgroundIn the literature, there have been debates as to whether smartphone use has negative effects on physical and mental health. The present study investigated the extent to which smartphone addiction impacts on musculoskeletal pain prevalence among university students.MethodsThe questionnaire consisted of three sections demographic information, the Smartphone Addiction Scale (SAS), and the modified Nordic Musculoskeletal Questionnaire.ResultsA total of 249 participants were included in this cross-sectional study. The body parts that were reported with highest prevalence of musculoskeletal pain were the upper back (70.3%), neck (65.9%), and wrists/hands (68.7%). The SAS scores were correlated with duration of smartphone use on a typical day (P = 0.001), duration of owning a smartphone (P = 0.027), and musculoskeletal pain prevalence in the neck (P = 0.001), wrists/hands (P = 0.001), shoulders (P = 0.025), and upper back (P = 0.023). The SAS score was significantly associated with prevalence of musculoskeletal pain in the neck (odd ratio [OR], 1.08; 95% confidence interval [CI], 0.98-1.10; P = 0.002), wrists/hands (OR, 1.07; 95% CI, 0.97-1.09; P = 0.001), and upper back (OR, 1.10; 95% CI, 0.98-1.11; P = 0.033).ConclusionsThe findings indicated that the upper back, neck, and wrists/hands have a higher prevalence of musculoskeletal pain among smartphone users, particularly those with a smartphone addiction. Smartphone addiction scores were correlated with duration of smartphone use on a typical day, duration of owning smartphone, and musculoskeletal pain prevalence in the neck, wrists/hands, shoulders, and upper back.

The Effect of Social Media/Smartphone Addiction and Sleep Quality on Academic Success in Medical Faculty Students

Objective: The aim of this study was to evaluate the effects of smartphone / social media use and sleep related variables on academic achievement in pre-clinical medical students. Material and Methods: 226 students receiving pre-clinical medical education at the same institution were included in the study. All participants completed the Pittsburg Sleep Quality Index (PSQI), Social Media Addiction Scale - Adult Form (SMAS-AF), Smartphone Addiction Scale (SAS) and Personal Information Form prepared for the study. All participants were questioned Average Academic Marks (AAM). The participants were categorized as low / medium / high risk according to the scores of smartphone addiction scale. Results: According to the risk of smartphone addiction, the mean score of AAM of the group with low dependency risk was 71.8 ± 8, the mean score of AAM of the group with the middle risk dependency risk was 68.4 ± 8.77, and the mean score of AAM of the group with the high dependency risk was 67.4 ± 9.75. The mean scores of AAM of the low-risk group in terms of smartphone addiction were significantly higher than the high-risk group (p = 0.035). Similarly, when the total scores of the participants' social media addiction are examined; the average of the low-risk group was 40.4 ± 11.17, the average of the medium-risk group was 51.7 ± 9.2, and the average of the high-risk group was 50.8 ± 12.9. Social media addiction total scores of the high risk group in terms of smartphone addiction were significantly higher than the medium and low risk group (p <0.001). There was no significant difference between sleep times and PDQI total scores between groups. When the factors associated with AAM were analyzed by Pearson correlation analysis, a positive statistically significant relationship was detected with sleep efficiency (r = 0.19, p <0.001), while a significant negative correlation was observed with total SAS score and hours spent in bed (r = -23, p <0.001; r = -27, p <0.05, respectively). A multivariate hierarchical regression analysis model suggested that gender, sleep efficiency, SAS and SMASAF scores affected AAM. Conclusion: Use of social media / smartphone by medical students can affect sleep habits and academic success during the period of pre-clinical education. Inclusion of training on conscious use of social media and smartphone and emphasizing the importance of sleep habits in medical education have the potential to contribute to the development of better-equipped physicians.

Effect of Aerobic Training and Mindfulness Therapy on Smartphone Addicted Young Individuals

A comparative study on effect of Aerobic training and Mindfulness therapy on Smartphone addicted young individuals. Objective: To compare effect of aerobic training and mindfulness therapy on smartphone addicted young individuals according to Smartphone Addiction Scale (SAS). Methodology: A simple random sampling of 30 smartphone addicted young individuals between age group of 18-25 years and were screened according to inclusion and exclusion criteria. They were divided into two groups: Group A (Aerobic training) and Group B (Mindfulness therapy). The rate of addiction in young individuals was assessed using the Smartphone Addiction Scale. the entire study was explained to the individuals and written informed consent was obtained. Aerobic training was performed 4days/week for 6 weeks and mindfulness therapy was performed 2days/week for 6 weeks. The Smartphone Addiction Scale score was taken before and after the intervention and results were analyzed. Outcome measure: Smartphone Addiction Scale (SAS) was used for the purpose of measuring the rate of addiction. Result: The comparison of pre to post intervention score of group A was statistically not significant (p=1.05) and also the comparison of group B was statistically not significant (p=3.86) Conclusion: The present study compares the effects of both aerobic training and mindfulness therapy on rate of addiction and it concludes that both these interventions are similarly effective in reducing the addiction to smartphone when assessed in terms of Smartphone Addiction Scale (SAS). There is no significant difference in improvement with respect to aerobic training and mindfulness therapy according to the Smartphone Addiction Scale (SAS).

Problematic smartphone use and related factors in young patients with schizophrenia.

The present study aimed to examine smartphone use in young patients with schizophrenia and to explore factors that may affect the severity of problematic smartphone use. A total of 148 schizophrenia patients aged 18 to 35years completed self-administered questionnaires exploring sociodemographic characteristics; Smartphone Addiction Scale (SAS), the Big Five Inventory-10 (BFI-10), the Hospital Anxiety and Depression Scale (HADS), the Perceived Stress Scale (PSS), and the Rosenberg Self-Esteem Scale (RSES). All were also assessed using the Clinician-Rated Dimensions of Psychosis Symptom Severity (CRDPSS) Scale and the Personal and Social Performance (PSP) Scale. The mean subject age was 27.5±4.5years. No significant differences in the SAS scores occurred between gender, jobs, and level of education. The Pearson r-correlation test showed that the SAS scores were significantly positively correlated with HADS anxiety, PSS, and BFI-10 neuroticism scores; it was negatively correlated with RSES, BFI-10 agreeableness, and conscientiousness scores. In the stepwise linear regression analysis, the severity of PSU was significantly associated with both high anxiety and low agreeableness. Our results suggest that specific groups of patients with schizophrenia may require special care to prevent problematic smartphone use.

The relationship between smartphone addiction and symptoms of depression, anxiety, and attention-deficit/hyperactivity in South Korean adolescents

BackgroundExcessive smartphone use has been associated with numerous psychiatric disorders. This study aimed to investigate the prevalence of smartphone addiction and its association with depression, anxiety, and attention-deficit hyperactivity disorder (ADHD) symptoms in a large sample of Korean adolescents.MethodsA total of 4512 (2034 males and 2478 females) middle- and high-school students in South Korea were included in this study. Subjects were asked to complete a self-reported questionnaire, including measures of the Korean Smartphone Addiction Scale (SAS), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), and Conners-Wells’ Adolescent Self-Report Scale (CASS). Smartphone addiction and non-addiction groups were defined using SAS score of 42 as a cut-off. The data were analyzed using multivariate logistic regression analyses.Results338 subjects (7.5%) were categorized to the addiction group. Total SAS score was positively correlated with total CASS score, BDI score, BAI score, female sex, smoking, and alcohol use. Using multivariate logistic regression analyses, the odds ratio of ADHD group compared to the non-ADHD group for smartphone addiction was 6.43, the highest among all variables (95% CI 4.60–9.00).ConclusionsOur findings indicate that ADHD may be a significant risk factor for developing smartphone addiction. The neurobiological substrates subserving smartphone addiction may provide insights on to both shared and discrete mechanisms with other brain-based disorders.