Introduction
Methods
Search strategy
Study selection process
Results
Sample of included studies
Study | Sample size | Age | Gender | Country | Measure | Main results |
---|---|---|---|---|---|---|
Ayar et al. (2017) [34] | N = 609 | M = 12.3 SD = 0.9 | Female = 47.7% Male = 52.3% | Turkey | SAS V1 | No effect of sociodemographic variables (age, parents’ educational level, monthly income levels) on smartphone addiction was found |
Bae (2015) [35] | N = 2376 N = 2264 N = 2218 | Primary school students (4th grade) | 1. Female = 47.8% Male = 52.2% 2. Female = 47.9% Male = 52.1% 3. Female = 47.4% Male = 52.6% | South Korea | AUSS | More democratic parenting style was associated with less addictive smartphone use |
Increase in academic motivation was related to decrease in addictive smartphone use | ||||||
Increase in friendship satisfaction was related to decrease in addictive smartphone use | ||||||
Bae (2017) [13] | N = 2212 | 13–18 years | Female = 48.6% Male = 51.4% | South Korea | S Scale | Frequency of smartphone use on weekdays and weekends was related to dependence |
Duration of use for information seeking, entertainment seeking, and gaming was related to dependence | ||||||
Duration of use for SNS and instant messenger was not related to dependence | ||||||
Cha and Seo (2018) [9] | N = 1824 | M = 15.6 SD = 0.78 | Female = 49.0% Male = 51.0% | South Korea | SAPS | 30.9% of participants were classified as a risk group for smartphone addiction |
Significant differences were found between addiction risk group and normal users regarding smartphone use duration, awareness of game overuse, and purposes of game playing | ||||||
Predictive factors: daily smartphone and SNS use duration, awareness of game overuse | ||||||
Chóliz (2012) [36] | N = 2486 | 12–18 years | Female = 51.4% Male = 48.6% | Spain | TMD | Girls relied to a higher extent on the mobile phone; there were more negative consequences for girls |
Associations were found between TMD and use patterns | ||||||
Cocoradă et al. (2018) [27] | N = 717 | M = 19.8 (40% high school students) | Female = 65.0% Male = 35.0% | Romania | SAS–SV | High school students showed higher levels of addiction |
Girls showed higher levels of addiction | ||||||
Boys used more technology and for different activities | ||||||
High school students used smartphones more often and more for video gaming, phone calls, and TV viewing | ||||||
Correlations between personality traits, attitudes, and addiction were found | ||||||
Negative correlations existed between addiction and neuroticism, conscientiousness, and openness | ||||||
De Pasquale et al. (2015) [28] | N = 200 | 14–19 years | Female = 42.0% Male = 58.0% | Italy | SAS–SV | Smartphone addiction was found only in boys, not in girls |
Emirtekin et al. (2019) [37] | N = 443 | M = 16.0 SD = 1.1 | Female = 60.0% Male = 40.0% | Turkey | SAS–SV | Significantly higher score of problematic use was found in girls |
Emotionally traumatic experiences were associated with problematic use, partially mediated by psychosocial risk factors | ||||||
Firat and Gül (2018) [38] | N = 150 | M = 15.3 SD = 1.7 | Female = 58.7% Male = 41.3% | Turkey | PMPUS | Higher level of problematic use was found in older adolescents |
Somatization, interpersonal sensitivity, and hostility predicted the risk of problematic smartphone use | ||||||
Foerster et al. (2015) [16] | N = 412 | 12–17 years | Female = 61.4% Male = 38.6% | Switzerland | MPPUS-10 | A higher score correlated with more time spent online and more online data traffic |
Gallimberti et al. (2016) [39] | N = 1156 | M = 12.0 SD = 1.0 | Female = 46.5% Male = 53.5% | Italy | SMS–PUDQ | A positive association between problematic cellular phone use and having a larger circle of friends was found |
Güzeller and Cosguner (2012) [40] | N = 950 | 1. M = 16.1 SD = 0.9 2. M = 16.0 SD = 0.9 | 1. Female = 56.0% Male = 44.0% 2. Female = 60.1% Male = 39.9% | Turkey | PMPUS | A correlation between problematic use and loneliness was found |
Ha et al. (2008) [41] | N = 595 | M = 15.9 SD = 0.8 | Female = 7.2% Male = 92.8% | South Korea | ECPUS | Lower self-esteem was related to excessive mobile phone use |
Haug et al. (2015) [42] | N = 1519 | M = 18.2 SD = 3.6 | Female = 51.8% Male = 48.2% | Switzerland | SAS–SV | Addiction was more prevalent in younger (15–16 years) than in older (>19 years) adolescents |
Ihm (2018) [26] | N = 2000 | M = 12.3 SD = 2.6 | Female = 50.5% Male = 49.5% | South Korea | Adapted version of GPIUS 2 | Social network variables were negatively related to smartphone addiction |
Higher level of addiction was associated with less social engagement | ||||||
Jeong et al. (2016) [43] | N = 944 | Sixth grade | Female = 49.0% Male = 51.0% | South Korea | Modified version of IAT | Children with lower self-control were more likely to be addicted to smartphones |
Those who used smartphones for SNS, games, and entertainment were more likely to be addicted | ||||||
Those who used smartphones for study-related purposes were not addicted | ||||||
SNS was a stronger predictor of smartphone addiction than gaming | ||||||
Sensation seeking and loneliness were not significant predictors | ||||||
Kim et al. (2018) [44] | N = 3380 | 10–19 years | Female = 48.7% Male = 51.3% | South Korea | SAPS | Family dysfunction (domestic violence, parental addiction) was significantly associated with smartphone addiction |
Self-control and friendship quality were protective factors | ||||||
Kwak et al. (2018) [45] | N = 1170 | Middle school students | Female = 58.4% Male = 41.6% | South Korea | Modified version of IAT | Parental neglect was significantly associated with smartphone addiction |
Relational maladjustment with peers negatively influenced smartphone addiction | ||||||
Relational maladjustment with teachers had a partial mediating effect between parental neglect and smartphone addiction | ||||||
Kwon et al. (2013) [10] | N = 540 | M = 14.5 SD = 0.5 | Female = 36.5% Male = 63.5% | South Korea | SAS–SV | Significantly higher scores existed in girls |
Lee et al. (2016) [46] | N = 3000 | 13–18 years | Female = 47.3% Male = 52.7% | South Korea | SAPS | Frequent use of social networking site applications (apps), game apps, and video apps tended to exacerbate addiction to smartphones |
Active parental mediation was effective in young adolescent girls, technical restrictions were effective in young adolescent boys, and limited service plans were effective for both | ||||||
Parental restriction tended to increase likelihood of addiction | ||||||
Lee and Lee (2017) [47] | N = 3000 | Grades 7–12 | Female = 47.3% Male = 52.7% | South Korea | SAPS | 35.6% classified as addicts |
Students with high academic performance showed lower addiction rates | ||||||
Higher proportion of addicted females | ||||||
Attachment to parents and satisfaction with school life might serve as protective factors | ||||||
Motive for smartphone to gain peer acceptance was the most significant factor related to smartphone addiction | ||||||
Lee et al. (2017) [21] | N = 370 | 1. M = 13.1 SD = 0.8 2. M = 13.3 SD = 0.9 | Female = 50.8% Male = 49.2% | South Korea | SAPS | Addiction group showed significantly higher scores in online chat |
Purpose of use: addiction group showed higher levels of use for habitual use, pleasure, communication, games, stress relief, ubiquitous trait, and desire not to be left out | ||||||
Females: use for learning, use for ubiquitous trait, preoccupation, and conflict were significantly correlated with smartphone addiction | ||||||
Females: use for ubiquitous trait, preoccupation, and conflict were predictors | ||||||
Use for learning was a protective factor | ||||||
Lee and Ogbolu (2018) [48] | N = 208 | 10–12 years | Female = 52.4% Male = 47.6% | South Korea | SAPS | Gender: no predictor of addiction |
Age, depression, and parental control positively predicted smartphone addiction | ||||||
Lee et al. (2016) [5] | N = 289 | M = 13.1 SD = 0.8 | Female = 50.9% Male = 49.1% | South Korea | SAPS | Significantly more females were in the high-risk group |
Use per day was significantly higher in the high-risk group | ||||||
Lee (2016) [49] | N = 490 | M = 14.0 SD = 0.9 | Female = 0% Male = 100% | South Korea | SAS–SV | High-risk group showed significantly lower self-esteem and poorer quality of communication with parents |
Severity of smartphone addiction was negatively associated with self-esteem | ||||||
Liu et al. (2016) [50] | N = 689 | M = 18.2 SD = 3.6 | Female = 6.2% Male = 93.8% | Taiwan | SPAI–SF | Smartphone gaming and frequent use were associated with addiction |
Lopez-Fernandez et al. (2014) [51] | N = 1026 | M = 13.5 SD = 1.5 | Female = 45.0% Male = 55.0% | UK | MPPUSA | Prevalence of problematic use: 10% |
Typical problematic user: 10–14 years, studying at a public school, considered themselves to be experts in this technology | ||||||
Lopez-Fernandez et al. (2015) [52] | N = 2228 MPPUSA–sample: N = 1438 | MPPUSA–sample: M = 14.2 SD = 1.7 | Female = 48.2% Male = 53.8% | Spain UK | MPPUSA | Estimated risk showed stronger relationships with gender, age, type of school, parents’ education |
Being a girl, being older, going to private school, having a parent with a university degree were possible predictors of excessive mobile phone use | ||||||
Lopez-Fernandez (2015) [17] | N = 2356 | M = 14.1 SD = 1.7 | Female = 39.1% Male = 60.9% | UK (52%) Spain (48%) | MPPUSA | Prevalence of problematic use: 14.9% in Spain and 5.1% in UK |
Patterns of usage were similar between British and Spanish students | ||||||
No gender differences were found | ||||||
Randler et al. (2016) [31] | 1. N = 342 2. N = 208 | 1. M = 13.4 SD = 1.8 2. M = 17.1 SD = 4.3 | 1. Female = 48.5% Male = 51.5% 2. Female = 70.2% Male = 29.8% | Germany | 1. SAPS 2. SAS–SV | Girls were more prone to become addicted |
Age did not predict addiction | ||||||
Sánchez-Martínez and Otero (2009) [18] | N = 1328 | 13–20 years | Female = 53.7% Male = 46.3% | Spain | Questionnaire designed for this study | 41.7% were extensive cell phone users |
Significant associations of extensive phone use were found with age, sex, cell phone dependence, demographic factors | ||||||
Seo et al. (2016) [53] | N = 2159 | Middle and high school students | Female = 50.3% Male = 49.8% | South Korea | Items selected from KCYPS | Mobile phone dependency increased relationships with friends in girls |
Soni et al. (2017) [19] | N = 587 | M = 16.2–16.8 | Female = 42.1% Male = 57.9% | India | SAS | Addiction scores were higher in males than in females |
Sun et al. (2019) [54] | N = 1041 | M = 12.4 SD = 0.7 | Female = 44.5% Male = 55.5% | China | SAS V2 | Child neglect, psychological abuse, and emotion-focused coping were risk factors for smartphone addiction |
Emotional intelligence and coping style mediated the relationship between neglect/abuse and addiction | ||||||
Wang et al. (2017) [55] | N = 768 | M = 16.8 SD = 0.7 | Female = 56.0% Male = 44.0% | China | SAS–SV | Students with better student–student relationships were less likely to be addicted |
Students with higher self-esteem were less likely to be addicted | ||||||
Self-esteem was a mediator between student–student relationships and smartphone addiction | ||||||
This was moderated by the need to belong | ||||||
Warzecha and Pawlak (2017) [56] | N = 470 | 16–20 years | Female = 61.1% Male = 39.9% | Poland | KBUTK | Around 35% at risk for smartphone addiction; around 4% showed smartphone addiction |
Higher amount of smartphone addiction and risk for smartphone addiction in girls than in boys | ||||||
Yang et al. (2010) [57] | N = 11,111 | M = 14.6 SD = 1.7 | Female = 50.3% Male = 49.7% | Taiwan | PCPU–Q | 16.4% had problematic cell phone use, girls more likely than boys |
<15 years were more likely to show problematic phone use | ||||||
Yildiz (2017) [58] | N = 262 | M = 16.6 SD = 1.1 | Female = 50.4% Male = 49.6% | Turkey | SAS–SV | External-dysfunctional emotion regulation, internal-dysfunctional emotion regulation, and internal-functional emotion regulation significantly predicted Internet and smartphone addiction |
Emotion-regulation strategies explained 19% of variance in smartphone addiction |