REVIEW OF LITERATURE:
1) Backman et al. (2016) conducted a study on Finnish adolescents on the topic of severe sleep problems and psychopathic features. This study includes a population-based sample of 4855 Finnish adolescents. Antisocial Process Screening Device-Self Report (APSD-SR) was used to measure psychopathic feature and sleep problems were evaluated by asking questions regarding frequency and persistence of sleep problems and the amount of sleep on school and weekend nights. 5% of the adolescents reported frequent and persistent sleep problems & 3.2% of adolescents reported continuous short sleep. Both sleep problems and short sleep were associated with higher scores on APSD-SR. Results were concluded that vulnerability to lack of behavioral control and prosocial behavior may be associated with severe problems of sleep quality and quantity among adolescents.
2) Demi?rci?, Akgönül, & Akpinar (2015) conducted a research on relationship of Smartphone Use Severity with Sleep Quality, Depression, and Anxiety in 319 male & female University Students and those samples were divided into three groups as a smartphone non-user group, a low smartphone use group and a high smartphone use group. The Pittsburgh Sleep Quality Index, Beck Depression Inventory, Beck Anxiety Inventory and the Smartphone Addiction Scale were administered on the participants. Findings of the study indicated that smartphone overuse may be associated with depression, anxiety, and sleep quality.
3) Kim et.al (2018) conducted a research study on lack of sleep is associated with internet use for leisure. Data was collected from 57,425 middle school students in 2014 and 2015 using population-based, cross-sectional study group from the Korea Youth Risk Behavior Web-based Survey (KYRBWS). The multinomial logistic regression with complex sampling was used to analyze the relationships between sleep time and internet use time for leisure. Sleep satisfaction and the associations of sleep time with internet use for leisure were analyzed using the same methods. Results indicated that long-term use of the internet for leisure was significantly related to less sleep, whereas this association was not definite for internet use for study. Furthermore, it was found that the relationship between less sleep time and internet use for leisure potentiated to poor sleep quality.
4) Jonason, Peter, & Laura Krause (2013) conducted a study on emotional deficits associated with the Dark Triad traits: Cognitive empathy, affective empathy, and alexithymia. Data was collected from 322 volunteers through online survey and it revealed the complex correlational patterns between the Dark Triad traits and two forms of “emotional deficiencies” (i.e., limited empathy and alexithymia) overall and in each sex. A unique pattern of emotional deficits was associated with each Dark Triad trait. Limited overall empathy, difficulty describing feelings, and externally oriented thinking was correlated with Psychopathy. Limited affective empathy and difficulty identifying feelings was associated with Narcissism, whereas externally oriented thinking was associated with Machiavellianism. The Dark Triad mediated sex differences in empathy and externally oriented thinking. The differential facets of alexithymia predict different forms of limited empathy that in turn predict specific Dark Triad traits was suggested by Structural Equation Modeling. They used evolutionary paradigm to discuss the results.
5) Tamura et.al (2017) investigated the relationship between mobile phone use and insomnia and depression in adolescents. Data was collected from 295 high school students aged 15-19 in Japan. Athene Insomnia Scales (AIS) and the Center for Epidemiologic Studies Depression Scale (CES-D), were used to assess Insomnia and depression. Results indicated that 98.6% of students had own mobile phones; 58.6% used mobile phones for over 2 hours per day and 10.5% used them for over 5 hours per day. Shorter sleep duration and insomnia was associated with overall mobile phone use of over 5 hours per day but not with depression. Higher risk of depression was associated with mobile phone use of 2 hours or more per day for social network services and online chats, respectively. Unhealthy sleep habits and insomnia were linked with Mobile phone overuse. They also found that mobile phone overuse for social network services and online chats may contribute more to depression than the use for internet searching, playing games or viewing videos.
6) Sehar, & Fatima (2016) conducted a study on Dark Triad Personality Traits as predictors of bullying and victimization in adolescents. This study consists of 479 students including both boys and girls. Bullying and victimization was assessed using The Adolescent Peer Relations Instruments” was used and to measure three dark triads “The Short D3” was used. Results indicated that narcissism was positively associated with victimization and Machiavellianism & psychopathy was positively related to bullying. The role of dark triads in bullying, controlling for victimization; and role of dark triads in victimization, controlling for bullying, in boys and girls was assessed by conducting Hierarchical regression analysis. The results indicated that among dark triads only psychopathy was positively predicted bullying but none of the dark triads predicted victimization in adolescents.
7) Randler et.al (2016) conducted a study on Smartphone addiction proneness in relation to sleep and morningness–eveningness in German adolescents. Two studies were conducted on two different measures of smartphone addiction. To 342 younger adolescents “The Smartphone Addiction Proneness Scale (SAPS)” was applied in study 1 and to 208 older adolescents the Smartphone Addiction Scale was applied in study 2. All the samples are from southwest Germany. In addition, a demographic questionnaire and the Composite Scale of Morningness (CSM) and sleep measures were implemented. Result indicated that morningness–eveningness (as measured by CSM scores) was an important predictor for smartphone addiction and Evening types and girls are more prone to become smartphone addicted.
8) Li et.al (2017) aimed to study the mediating effects of insomnia on the associations between online social networking addiction (OSNA), depression among adolescents and problematic Internet use, including Internet addiction (IA). Participants of the study are from Guangzhou in China and the data was collected from 1,015 secondary school students. Depression Scale, Pittsburgh Sleep Quality Index, Young’s Diagnostic Questionnaire, and Online Social Networking Addiction Scale were used in the study to assess depression, insomnia, internet addiction and OSNA. Logistic regression models ; Baron and Kenny’s strategy were used to assess the association between the variables and the mediation effect of insomnia in the study. Results indicated that increased risk of developing depression among adolescents was associated with both through direct and indirect effects (via insomnia) and with the high prevalence of IA and OSNA.
9) Lee et.al (2017) conducted a study by aiming to find the relationship between mobile phone addiction and the incidence of poor sleep quality and short sleep duration among adolescents. They used longitudinal data from the Korean Children & Youth Panel Survey conducted by the National Youth Policy Institute in Korea (2011–2013). The data was collected from 1,125 students and the students those who already had poor sleep quality or short sleep duration in the previous year were excluded from the study. To analyze the data a generalized estimating equation was used. Results indicated that high mobile phone addiction increased the risk of poor sleep quality but not short sleep duration.
10) Hussain, Griffiths, & Sheffield (2017) conducted a study to examine the relationship between the psychological aspects of smartphone use particularly in relation to problematic use, narcissism, anxiety, and personality factors. Samples include 640 smartphone users ranging from 13 to 69 years of age. The problematic smartphone use, anxiety, narcissism and personality factors were assessed through online survey including modified DSM-5 criteria of Internet Gaming Disorder to assess problematic smartphone use, the Spielberger State-Trait Anxiety Inventory, the Narcissistic Personality Inventory, and the Ten-Item Personality Inventory was used. Data was collected through online survey. Findings of the study indicated that problematic smartphone use is associated with various personality factors and also it contributes to further understanding the psychology of smartphone behavior and associations with excessive use of smartphones.
11) De Vries et.al (2018) conducted a study on the topic of problematic internet use and psychiatric co-morbidity in a population of Japanese adult psychiatric patients. Samples included 231 adults. A combination of Young’s Internet Addiction Test (IAT) and the Compulsive Internet Use Scale (CIUS) was used to divide the participants into normal internet users and problematic internet users. Athens Insomnia Scale, Beck Depression Inventory, State-trait Anxiety Inventory, Adult ADHD Self-report Scale, Autism Spectrum Quotient, Obsessive-Compulsive Inventory, Liebowitz Social Anxiety Scale and Barratt Impulsive Scale was used to assess insomnia, depression, anxiety, ADHD, autism, OCD, social anxiety disorder and impulsivity. Results indicated that among adult psychiatric patients prevalence of Problematic internet use was relatively high.
12) Tao et.al (2017) conducted a study regarding the relationship between problematic mobile phone use, sleep quality and mental health symptoms among Chinese College Students. They collected data from 4747 college students using standardized questionnaires which assess participants’ problematic mobile phone use, sleep quality, and mental health. To assess independent effects and interactions of problematic mobile phone use and sleep quality with mental health Multivariate logistic regression analysis was used. They found that problematic mobile phone use and poor sleep quality were observed in 28.2% and 9.8% of participants, respectively but adjusted logistic regression models suggested independent associations of problematic mobile phone use and sleep quality with mental health. A significant interaction between these measures was found through further regression analyses. Findings of the study highlights that poor sleep quality may play a more significant role in increasing the risk of mental health problems in students with problematic mobile phone use than in those without problematic mobile phone use.
13) Higuchi et.al (2005) conducted a study on the effects of playing a computer game using a bright display on pre-sleep physiological variables, sleep latency, slow wave sleep and REM sleep. The study was conducted in a laboratory setting and the examined the nocturnal sleep. Seven male adults was selected for the study and they were asked to play an exciting computer games with a bright display (game-BD) and a dark display (game-DD) .After that they performed a simple tasks between 23:00 and 1:45 hours in randomized order with low mental load as a control condition in front of a BD (control-BD) and DD (control-DD). They then went to bed at 2:00 hours and slept until 8:00 hours. Before sleep rectal temperature, EEG, heart rate and subjective sleepiness were recorded and a polysomnogram was recorded during sleep. Findings of the study indicated that after playing games heart rate was significantly higher than after the control conditions, and it was also significantly higher after using the BD than after using the DD. Subjective sleepiness and relative theta power of EEG were significantly lower after playing games than after the control conditions. And sleep latency was significantly longer after playing games than after the control conditions & REM sleep was significantly shorter after the playing games than after the control conditions. The researchers also found that no significant effects of either computer games or BD were found on slow-wave sleep. These results suggest that playing an exciting computer game affects sleep latency and REM sleep but that a bright display does not affect sleep variables.
14) Younes et al (2016) conducted a study on Internet Addiction and Relationships with Insomnia, Anxiety, Depression, Stress and Self-Esteem in University Students. They investigated 1) Potential IA in university medical students; 2) The Relationships between potential IA, insomnia, depression, anxiety, stress and self-esteem. Data was collected from 600 students. They used four validated and reliable questionnaires including the Young Internet Addiction Test, the Insomnia Severity Index, the Depression Anxiety Stress Scales (DASS 21), and the Rosenberg Self Esteem Scale (RSES). Result indicated that the potential IA prevalence rate was 16.8% and it was significantly different between males and females, with a higher prevalence in males (23.6% versus 13.9%). Significant correlations were found between potential IA and insomnia, stress, anxiety, depression and self-esteem; ISI and DASS sub-scores were higher and self-esteem lower in students with potential IA.
15) Baughman & Holly (2015) aimed to study the relationships between the Dark Triad and Delayed Gratification: An Evolutionary Perspective. The data for the study was collected from 364 undergraduate students which included both the genders. They found that psychopathy was the most strongly linked to an inability to delay gratification, followed by Machiavellianism and narcissism. Findings also indicated that sex also moderately played a role in these relationships, such that women who scored high on Machiavellianism were less likely to delay gratification than men; however, the researchers said that these associations were no longer significant when a more conservative Bonferroni correction was applied.
16) Pugh & Sinead (2017) aimed to study the relationship between smartphone addiction, self-esteem, social anxiety, gender and age. The sample for the study consisted of 126 participants including both male and female between the ages of 18-52 years old. Data was collected from the participants through online survey which comprised of three different questionnaires: The Smartphone Addiction Scale, Rosenberg’s Self-Esteem Scale, and the Interaction Anxiousness Scale. To examine the relationship between the variables a Spearman’s Rho test was used, and to examine the differences between the variables a Mann Whitney U test was used. The findings of the study indicated that no significant relationship between the variables was found, however there were age and gender differences found between the variables examined.
17) Punamäki et.al (2007) investigated on gender and age differences in the intensity of usage of information and communication technology. Second, they aimed to study the possible mediating role of sleeping habits and waking-time tiredness in the association between ICT usage and perceived health. The data was collected from the participants of 7292 Finns aged 12, 14, 16 and 18 years. The findings of the study showed that boys played digital games and used Internet more often than girls, and their mobile phone usage was also found to be more intensive. Structural equation model analyses showed supporting results to the mediating hypothesis. They also found that Intensive computer usage forms a risk for boys’, and intensive mobile phone usage for girls’ perceived health through the mediating links. Findings indicated that girls were vulnerable to the negative consequences of intensive mobile phone usage, as it associated with perceived health complaints both directly and through deteriorated sleep and increased waking-time tiredness.
18) Bergkvist & Una (2016) conducted a study to investigate whether Dark Triad personalities such as Machiavellianism, narcissism and psychopathy predicted selfie-sharing behaviour. The data was collected through online survey from 142 participants (age 18-67). Researchers used Multiple Regressions, a linear regression, Pearson’s correlations, a one-way ANOVA and T-tests to find the relationships and correlations between age, sex, Dark Triad, selfie-sharing, negative attitudes and self-enhancement. Findings of the study indicated that the results showed no significant relationship between Dark Triad and selfie sharing. They also found that narcissism predicted self-enhancement as well as several variables were found to correlate with negative attitudes toward selfies.
19) Sabouri, et al. (2016) examined Dark Triad traits in relation to sleep disturbances, anxiety sensitivity and intolerance of uncertainty in 341 young adults. The participants answered the questionnaire that assess DT traits, sleep disturbances, anxiety sensitivity, and intolerance of uncertainty. Findings of the study indicate that specific Dark Triad traits, which include Machiavellianism and psychopathy, are associated with sleep disturbances, anxiety sensitivity and intolerance of uncertainty in young adults.
20) Cha ; Seo (2018) investigated the relationship between smartphone use and smartphone addiction in middle school students in Korea with regard to its Prevalence, social networking service, and game use. Using Smartphone Addiction Proneness Scale scores, 30.9% were classified as a risk group for smartphone addiction and 69.1% were identified as a normal user group. The results also indicated that adolescents used mobile messengers for the longest, followed by Internet surfing, gaming, and social networking service use. The findings of the two groups showed significant differences in smartphone use duration, awareness of game overuse, and purposes of playing games. The result findings also indicated the predictive factors of smartphone addiction were daily smartphone and social networking service use duration, and the awareness of game overuse.
21) Thomée, Härensta, ; Hagberg (2011) conducted a study to examine the relationship between Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults. Data was collected from 4156 adults aged between 20 – 24, who responded to a questionnaire at baseline and 1-year follow-up. Results indicated that at 1-year follow-up among the young adults it was found that high frequency of mobile phone use at baseline was a risk factor for mental health outcomes. It was also indicated that the risk for reporting mental health symptoms at follow-up was greatest among those who had perceived accessibility via mobile phones to be stressful.
22) Sahin et.al (2013) aimed to evaluation of mobile phone addiction level and sleep quality in university students. The data for the study was collected from the 576 students of the Sakarya University. The Problematic Mobile Phone Use Scale and the Pittsburgh Sleep Quality Index was used for evaluating the mobile phone addiction level and sleep quality. For analyxing the data Mann-Whitney U test, Kruskal-Wallis test and Spearman’s Correlation Analysis were used. The result findings indicated that sleep quality worsens with increasing addiction level.
23) Munezawa et.al (2011) conducted a study to examine the association between the use of mobile phones after lights out and sleep disturbances among Japanese adolescents. Across-sectional survey was conducted. Total of 95,680 adolescents responded but 94,777 questionnaires were subjected to analysis. Using Multiple logistic regression analysis it was found that mobile phone use for calling and for sending text messages after lights out was associated with sleep disturbances such as short sleep duration, insomnia symptoms etc.,
24) Christensen et al. (2016) aimed to find the determining factors associated with increased Smartphone Screen-Time and its Relationships with Demographics and Sleep. Data was collected from 653 participants. Using smartphone application Smartphone screen-time was measured, Demographics, and sleep habits (Pittsburgh Sleep Quality Index–PSQI) were obtained by survey. Linear regression was used to obtain effect estimates. Findings of the study indicated that adults spend a substantial amount of time using their smartphones. It was also found that Screen-time differs across age and race and it was found to be associated with poor sleep. Researchers also said that poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.
25) Khan, Nock, & Gooneratne (2015) conducted a study on relationship between Mobile Devices and Insomnia by understanding Risks and Benefits. Study findings indicated that around 35 to 49 % of the US adult populations have problems falling asleep, daytime sleepiness and often they are left under-recognized, low rates of medical awareness resulting in underreporting of insomnia symptoms, and limited primary care physician training in insomnia recognition. They also found that mobile devices have the potential to be the cause for these problems and can also lead to sleep difficulties when used inappropriately.
26) Fossum et.al (2014) conducted a study on investigated the association between the use of a T.V, computer, gaming console, tablet, mobile phone, or audio player in bed before going to sleep with insomnia, daytime sleepiness, morningness, or chronotype. Sample for the study included 532 students aged 18-39. Participants reported the frequency and average duration of their in-bed media use, as well as insomnia symptoms, daytime sleepiness, morningness-eveningness preference and bedtime/rise time on days off. The findings of the research indicated that computer usage and mobile phone usage was positively associated with insomnia, and negatively associated with morningness. They also found that none of the other media devices were related to either of these variables, and no type of media use was related to daytime sleepiness.
27) Adams, Daly, & Williford (2013) aimed to review regarding the adolescent sleep patterns and the effects of sleep deprivation on adolescent physical and mental health. They also reviewed on current trends in technology use among adolescents, making associations to how technology impacts sleep. Lastly, they investigated on some of the methodological barriers of conducting sleep and technology research with adolescents and young adults and offer suggestions for overcoming those barriers. Researchers concluded that poor and disrupted sleep can cause problems with physical and psychological functioning, adolescents with depressive symptoms or increased illness presentation that are exacerbated by poor sleep were increasing.
28) Roane & Taylor (2008) conducted a study to find the relationship between adolescent Insomnia and mental health during adolescence and young adulthood. Sample for the study consists of 4494 adolescents and 3582 young adults. Data was collected from the sample at 6-7 year follow up. Self-report measures of mental health were used in the study. Findings of the study indicated that insomnia symptoms were reported by 9.4% of the adolescents and it also found to be associated with use of alcohol, cannabis, and drugs other than cannabis; depression; suicide ideation; and suicide attempts.
29) Chen et.al (2017) conducted a study to examine the prevalence of smartphone addiction and the associated factors in male and female undergraduates. Data was collected from 1441 undergraduate students. To assess smartphone addiction among the students Smartphone Addiction Scale short version (SAS-SV) was used. To find associations between smartphone addiction and independent variables among the males and females Multivariate logistic regression models were used. Findings of the study indicated that the prevalence of smartphone addiction among participants was 29.8%. Use of game apps, anxiety, and poor sleep quality was associated with the smartphone addiction in male students and use of multimedia applications, use of social networking services, depression, anxiety, and poor sleep quality were found to be significant factors for female undergraduates.
30) Amra, et.al (2017) conducted a study to investigate the relationship of late-night cell phone use with sleep duration and quality in a sample of Iranian adolescents. 2400 samples were chosen for the study aged between 12-18 years. Data regarding age, body mass index, sleep duration, cell phone use after 9p.m., and physical activity were documented and sleep was assed using the Pittsburgh Sleep Quality Index questionnaire. Result of the study indicated that 1270 participants reported to use cell phone after 9p.m and 56.1% of girls and 38.9% of boys reported poor quality sleep. Findings of the study indicated that late-night cell phone use by adolescents was associated with poorer sleep quality and Participants who were physically active had better sleep quality and quantity.
31) Muris, Meesters, & Timmermans (2013) conducted a study a cross sectional study to investigate the Dark Triad personality traits and their correlates in non-clinical youths. Samples for includes 117 participants aged 12-18 years. Data was collected regarding on Machiavellianism, narcissism, and psychopathy as well as on Big Five personality factors and symptoms of aggression and delinquency from child and parent. Findings of the study indicated that especially Machiavellianism and psychopathy were associated with lower levels of agreeableness, conscientiousness, and openness/intellect, and higher levels of emotional instability. It was also found that significant and unique correlates of symptoms of aggression and delinquency are associated with Machiavellianism and psychopathy, which further underlines the importance of these Dark Triad traits in the pathogenesis of disruptive behavior problems in youths.
32) Geel et.al (2017) conducted a study to examine whether the Big Five, Dark Triad and sadism predict traditional bullying and cyberbullying. Data was collected from1568 aged 16-21 years. Results indicated that agreeableness, Machiavellianism, psychopathy and sadism were significantly related to traditional bullying, and agreeableness and sadism were related to cyberbullying and it was found using hierarchical linear regression analyses, controlling for age and gender. This indicated that sadism could be a predictor of antisocial behaviors, by establishing its relations with bullying and cyberbullying.
33) Pearson & Hussain (2016) conducted a study to investigate the association between smartphone use, narcissistic tendencies, and personality as predictors of smartphone addiction. Data was collected from 256 smartphone users through online survey. Fin dings of the study indicated that 13.3% of the sample was classified as smartphone addicted. Smartphone addiction was linked with narcissism, openness, neuroticism, and age which were found using regression analysis. This shows that smartphones encourage narcissism, even in non-narcissistic users.
34) Brambilla et.al (2017) studied about the relationship between sleep habits& pattern in 1-14 years old children and video devices use and evening and night child activities. Through structured interview data was collected from 2030 healthy children. Using National Sleep Foundation Recommendations as reference they calculated the total sleep duration and they considered an optimal sleepers as children sleeping in own bed all night without awakenings. Multivariable median regression and multivariable logistic regression was used to depict the predictors of sleep duration and optimal sleep. Results of the study indicated that total sleep duration and numbers of awakenings decreased with age and they also found that video devices use was negative predictor of sleep duration.
35) Calverley & Grieve (2017) conducted a research to investigate the mechanisms by which dark personality traits and perceived ability to deceive are associated with cyberloafing (use of the Internet for non-work related purposes). Data was collected from 273 employees. Path analysis was used in the study to find the relationship. It was found that perceived ability to deceive mediated the relationships between the Dark Triad and cyberloafing, they also found that psychopathy also directly related to cyberloafing. Researchers concluded that perceived ability to deceive plays a vital role in determining the way in which individuals possessing dark personality characteristics engage in technology-based counterproductive work behaviours.
36) Barlett (2016) conducted a study to investigate the precursors to the Dark Triad traits, as well as the role on predicting aggression. Data was collected from 599 participants, aged 18–83 years. Participants completed the measures of the Dark Triad traits, emerging adulthood facets, and reactive and proactive aggression. Results indicated that (a) participant’s age was related to all emerging adulthood facets except other and self-focused, (b) aggression was predicted by all the Dark Triad traits, and (c) several emerging adult facets predicted various Dark Triad traits. Further they found that the Dark Triad traits are an important precursor to aggressive behavior, but also likely develop as a function of adult developmentally relevant predictors.
37) Sanecka (2017) conducted a study to examine the relationship between the Dark Triad personality traits, self-disclosure online and selfie related behaviours. Findings of the study indicated that indicated posting and editing selfies on social networking sites were positively correlated with all three Dark Triad components. But only narcissism predicted selfie-related behaviours when they used multiple regression analysis. The amount of personal information disclosed online and the tendency to intentionally self-disclose in a computer-mediated communication were positively related to Narcissism and Machiavellianism. They also found no significant correlations between the perceived controllability of Internet communication and two types of self-promotion in the Internet.
38) Vander et.al (2018) aimed to investigate observer accuracy for the Dark Triad (DT) traits – narcissism, psychopathy, and Machiavellianism – based on Facebook profiles. Researchers used a round-robin design, 145 individuals were selected and divided into 34 groups provided with DT self-ratings and rated their group members on these traits based on Facebook profiles. Significant observer accuracy for narcissism, but not for psychopathy or Machiavellianism was revealed by Social Relations Model analyses. Variance component estimates suggested that unique perceiver-target relationships account for a majority of variance in ratings of the DT. They found that narcissism and psychopathy moderate association between the cues observers utilize in making judgments of the DT traits and the cues that correspond to targets’ personality using Brunswik lens model analyses.
39) Jonason & Davis (2018) conducted a study to examine how the Dark Triad traits were correlated with individual differences in gender roles and whether gender roles can account for sex differences in the Dark Triad traits. Data was collected from 305 Australia and 207 Alabama college-students. Results indicated that the Dark Triad traits were associated with less femininity and more masculinity and sex differences in the traits were mediated by femininity only in study 1. Psychopathy and Machiavellianism were associated with less femininity and narcissism & psychopathy were associated with more masculinity and we replicated the mediation for psychopathy and Machiavellianism in study 2.
40) Annen et.al (2017) conducted a study to investigate the associations between Dark Triad traits and vulnerable narcissism, mental toughness, sleep quality, and stress perception in 720 samples aged between 18 to 28 years. Participants completed self-rating questionnaires on Dark Triad traits, mental toughness, vulnerable narcissism, sleep quality, and perceived stress. Results indicated that participants who scored high on vulnerable narcissism also reported higher Dark Triad traits, lower mental toughness, poor sleep quality, and higher scores on perceived stress. This indicated that vulnerable narcissism seems to be key for more unfavourable behavior.
41) Soni, Upadhyay & Jain (2017) conducted a study to investigate the magnitude of smart phone addiction and to evaluate the impact of smart phone addiction on their mental health and sleep quality. Data was collected from 587 repudiated school students. Samples completed the Smart phone addiction scale (SAS), DASS-21, and Pittsburgh sleep quality inventory questionnaire. Results indicated that samples those who used smart phone excessively had high PSQI scores and DASS-21scores in terms of depression, anxiety and stress.
42) Sabouri et.al (2016) conducted a study to investigate the association between dark triad trait, mental toughness & physical activity, and to compare the scores of men and women. Data was collected from 341 adults male and female aged between 18–37 years. Samples completed a series of questionnaires assessing dark trait, mental toughness & physical activity. Results indicated that Machiavellianism, narcissism, and psychopathy were all significantly associated with higher mental toughness scores. Dark triad traits and mental toughness were associated with more vigorous physical activity. They also found that women participants had lower scores for Dark triad traits than men while no differences were found for MT or PA in both sexes. Findings indicated that DT traits, high MT, and vigorous PA are interrelated.
43) Lopes & Yu (2017) conducted a study to examine the influence of Dark Personalities in trolling behavior towards popular and less popular Facebook profiles. Data was collected from 135 samples through Short Dark Personality Questionnaire. The samples were shown two fake Facebook profiles and they were asked to rate how much they would agree with some trolling comments to each profile, as well as how they perceived themselves in comparison to each profile in terms of social acceptance and rank. Findings of the study indicated that trolling behaviors was positively associated with psychopathy and tendency to see oneself superior to others was associated with narcissism. Findings of the study show that Dark triad personality plays an important role in influencing the behavior of an individual.
44) March & McBean (2018) conducted a research study regarding the relationship between narcissism and self-esteem in predicting posting selfies. Data was collected from 257 samples through online survey. To assess narcissism and self-esteem, the Narcissistic Personality Inventory (NPI-40) and the Rosenberg Self-Esteem Scale was used, and they also measured the selfie posting frequency. Results indicated that participants with low self-esteem posted more selfies and those participants with higher levels of grandiose-exhibitionism narcissism. It was also found that self-esteem moderate the relationship between narcissism and posting selfies.
45) Lee (2017) conducted a study to examine the association and whether attachment instability mediated between implicit narcissism and social networking services addiction. Data was collected from 185 cyber university students including both male and female students. Covert Narcissism Scale, Experience of Close Relationship, and Social Networking Service Addiction Tendency were used to assess narcissism, relationship and social network addiction. Through Simple correlation it was found that implicit narcissism and attachment instability were highly correlated with SNS addiction. Findings of the study also indicated that attachment anxiety mediated between implicit narcissism and SNS addiction which was found using the regression analysis.
46) Elhai et.al (2017) conducted a study to find the relationship between the types of smartphone use and problematic smartphone behaviors along with the role of content consumption vs. social smartphone use. Data was collected from 309 community samples through online. Findings of the study indicated that mostly significant relationships between problematic smartphone behaviors and both process and social usage was found through Bivariate correlations. They also found stronger correlations for process usage. Using Regression analyses, it was found that problematic smartphone-related overuse was significantly associated with process smartphone usage but lesser for social usage. The results indicated that daily life problems because of smartphone were inversely related to process and social usage.
47) Exelmans & Bulck (2016) conducted a study to investigate the relationship between bedtime mobile phone use and sleep among adults. Data was collected from 844 Flemish adults regarding electronic media use and sleep habits. The Pittsburgh Sleep Quality Index (PSQI), the Fatigue Assessment Scale (FAS) and the Bergen Insomnia Scale (BIS) were used to assess sleep quality, daytime fatigue and insomnia. Hierarchical and Multinomial regression analyses were used to analyze data. Results indicated that six out of ten participants took their mobile phone with them to the bedroom. Participants who used mobile phone after lights out significantly scored on the PSQI. They also found that later rise time, higher insomnia score and increased fatigue were associated with bedtime mobile phone use. Findings of the study indicated that bedtime mobile phone use was negatively related to sleep outcomes in adults. They also found that age significantly moderated the relationship between bedtime mobile phone use and fatigue, rise time, and sleep duration.
48) Polos et.al (2015) conducted a study to examine the effect and impact of mobile device-based Sleep Time-Related Information and Communication Technology (STRICT). Data was collected from American adolescents. Results indicated that 62% used STRICT after bedtime, 56.7% used to message and text in bed, and 20.8% awoke to texts. They also found that STRICT use was associated with insomnia, daytime sleepiness, shorter sleep duration etc., It also indicated that insomnia and daytime sleepiness partially mediated the relationship between STRICT use and academic underperformance.
49) Akram et.al (2018) conducted a study to investigate the association between the dark triad personality traits and insomnia symptoms amongst a sample of the general-population. Data was collected from 475 participants through online survey. They completed dark triad personality traits (SD3) and insomnia severity questionnaires. Results indicated that Machiavellianism and psychopathy are independent on insomnia symptoms, but not narcissism in univariate analyses. They used linear regression analysis and found that insomnia symptoms are predicted by psychopathy and sex, but not Machiavellianism. So the researcher explained that because psychopaths have deficits emotion they have disturbed sleep.
50) Paulhus and Williams (2002) conducted a study to investigate on dark triad. They collected data from the 245 samples using standardized questionnaires to assess. They found the correlation between self-reports and lab tests. Results indicated that there is a moderate correlation between them. They also found that disagreeableness correlates between self-reports and lab tests. It indicated that low neuroticism differentiated subclinical psychopaths; conscientiousness was low in both Machiavellians, and psychopaths; cognitive ability was positively correlated with narcissism to a small extent. Narcissists and, to a lesser extent, psychopaths exhibited self-enhancement on two objectively scored indexes. Researchers concluded that these personality traits overlaps on each other.
51) Lee et.al (2014) conducted a study to examine the possible relationship between smartphone addiction proneness and certain psychopathological features. They collected data from 755 adults. Korean Smartphone Addiction Proneness Scale (SAPS), the Beck Depression Inventory (BDI), the Beck Anxiety Inventory (BAI), the Obsessive-Compulsive Inventory-Revised (OCI-R), and the Barratt Impulsivity Scale-11 (BIS-11) were used to assess the samples. Based on the samples score on SAPS, they were divided into two groups as the addiction proneness group and the normal-user group. Findings of the study indicated that addiction proneness group scored high in the Beck Depression Inventory, Beck Anxiety Inventory, Obsessive-Compulsive Inventory-Revised and Barratt Impulsivity Scale-11 than normal user group. Researchers also found that smartphone addiction proneness showed a significant association with BIS-11 when they used logistic regression analysis. They concluded that impulsivity could be a vulnerability marker for smartphone addiction proneness.
52) Taherifard, Abolghasemi & Hajloo (2015) conducted a study to find the relationship between positive and negative urgency as well as sleep quality. The data was collected from 50 patients diagnosed with Anti-Social Personality Disorder and 50 patients with Borderline Personality Disorder. Then from 50 healthy individuals data was collected and this group was control group. Using Lynam et al.’s Impulse Control Scale and Pittsburgh’s Sleep Quality Index the data was collected. Findings indicated that Borderline Personality Disorder patients’ mean of negative urgency was high than Anti-Social Personality Disorder patients, It was also found Anti-Social Personality Disorder patients scored high in positive urgency than Borderline Personality Disorder patients. Sleep quality was higher in healthy controls than in Borderline Personality Disorder and Anti-Social Personality Disorder patients. Results of the study indicated that there is a significant difference between in Borderline Personality Disorder, Anti-Social Personality Disorder patients and healthy controls in the positive and negative urgency and sleep quality because the sample with disorders engage in reckless behavior when they experience both positive and negative emotions with low sleep quality.
53) Chung et.al (2018) conducted a study to investigate the smartphone overuse with daytime sleepiness. The data was collected from 1796 adolescents using smartphones which includes both male and female. Daytime sleepiness and smartphone addiction was assessed using The Pediatric Daytime Sleepiness Scale and the Korean Smartphone Addiction Proneness Scale index. Findings indicated that 15.1% of boys and 23.9% of girls were classified as at-risk smartphone users. Researchers used multivariate analyses and they found that the at-risk smartphone user group was independently associated with the upper quartile Pediatric Daytime Sleepiness Scale score than students with the following factors: Female gender, alcohol consumption, poor self-perceived health level, initiating sleep after 12 am, longer time taken to fall asleep and duration of night sleep less than 6 h.
54) Dongwon (2015) conducted a study to examine the relationship between physical activity level, sleep quality, attention control, and self-regulated learning along with smartphone addiction level among college students. 269 college students were selected as a sample to collect data. The data was collected using structured questionnaire and they were analyzed using SPSS 18.0. Results indicated that there is a significant relationship between smartphone addiction level and physical activity level, sleep quality, attention control, and self-regulated learning.
55) Min et.al (2017) conducted a study to investigate the association between smartphone addiction proneness and sleep problems among Korean university students. Data was collected from 608 samples through online-survey. The Korean smartphone addiction scale (K-SAS) ; the Pittsburgh Sleep Quality Index (PSQI) were used to assess smartphone addiction and sleep quality and personal characteristics was also collected. Samples were classified into two groups as the addiction proneness group and the normal-user group based on Korean smartphone addiction scale score. Findings of the study indicated that the addiction proneness groups scored high in PSQI score than the normal-user group which shows that the risk of getting sleep problems was more in the addiction proneness groups. Result shows that smartphone addiction leads to sleep problems.
56) Chang ; Choi (2016) conducted a study to find the influencing factors of gender differences in sleep quality between men and women. The data was collected from convenience sample of 300 young adults aged between 20–40 years who used smartphones and took no sleep medication. Data regarding sleep quality, exercise, stress, depression, and smart phone addiction was collected. Results indicated that in men sleep quality was predicted by coffee consumption, napping, depression, failure to engage in light exercise, being overweight, being smart phone addicted, and being employed, which explained 30.2% of the variance where as in women it were education, smoking, and stress, which explained 30.5% of the variance.
57) Meldrum, Barnes and Hay (2015) conducted a study on Sleep Deprivation, Low Self-Control, and Delinquency. Researchers used Baumeister and colleagues’ strength model of self-control to give an explanation for the association between sleep deprivation and delinquency. Data was collected from 825 adolescents and it was a longitudinal multi-city cohort study. Using regression models it was found that low self-control was positively associated with sleep deprivation and delinquency. It also showed that sleep deprivation and delinquency was indirect associated through low self-control. They also found that these relationships emerged when there is a presence of spuriousness, depressive symptoms, parenting practices, unstructured socializing with peers, and prior delinquency.
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