Studies evaluating link between smartphone use and depression (N = 19).

Table 3: Studies evaluating link between smartphone use and depression (N = 19).

Author (s)	Smartphone use scale	Depression scale	Findings
Boumosleh, et al. [15]	Smartphone addiction inventory (SPAI) [45]	Patient health questionnaire-9 (PHQ-9) [68]	Depression was an independent positive predictive factor of smartphone addiction after adjustment for potential confounders. A multiple linear regression with adjustment for age, personality type, year in program, age at first smartphone use, duration of smartphone use, and use a smartphone for calling family members, entertainment, or other purposes showed the total variance explained by the model was 23% for depression (Std. B = 0.201, p < 0.01).
Chen, et al. [17]	Mobile phone addiction scale-xiong (MPAS-X) [49]	Center for epidemiologic studies depression scale (CES-D) [69]	In the depression regression model, 45% of the variance in depression is accounted for by the mobile phone addiction (MPA) level (p < 0.01). MPA addiction level was a significant predictor of depression (B = 0.43, t = 3.15, p < 0.01). 33% of the variance in interpersonal problems could be accounted for by the MPA level, and the MPA level significantly predicted interpersonal problems (B = 0.49, t = 3.30, p < 0.01).
Chen, et al. [18]	Smartphone addiction scale-short version (SAS-SV) [51]	Center for epidemiologic studies depression scale (CES-D) [69]	Overall smartphone addiction rate was 29.8% (30.3% in males, 29.3% in females). In a multivariate logistic regression analysis, male students with depression symptoms were not significantly more likely to have smartphone addiction, while female students with depression symptoms were more likely to have smartphone addiction (OR = 1.84, 95% CI 1.21-2.79, p < 0.01).
Choi, et al. [19]	Smartphone addiction scale (SAS) [53]	Beck depression inventory (BDI) [70]	In a stepwise multiple regression analysis, higher depression scores were found to be protective against smartphone addiction, meaning lower smartphone addiction scores (Std. B = -0.215, t = -3.598, p < 0.01).
Demirci, et al. [21]	Smartphone addiction scale (SAS) [53]	Beck depression inventory (BDI) [70]	Smartphone use severity was positively correlated with depression scores (r = 0.276, p < 0.001). Significantly more high smartphone users had depression than low smartphone users (p = 0.005). In a stepwise linear regression model, depression was significantly associated with smartphone addiction severity (Std. B = 0.067, t = 2.069, p = 0.040).
Elhai, et al. [22]	Smartphone addiction scale-short version (SAS-SV) [51]	Patient health questionnaire-9 (PHQ-9) [68]	Smartphone use minutes were significantly correlated with problematic smartphone use (p = 0.01). Depression severity was not related to objective smartphone use (B = 36.53, p = 0.43) and the slope in the growth curve model showed and inverse relationship between objective smartphone use and initial depression. Therefore, higher baseline depression severity was associated with decreased use over the week.
Eyvazlou, et al. [23]	Internet over-use scale and cell-phone over-use scale (IOS/COS) [55]	General health questionnaire 28-severe depression subscale (GHQ-28) [56]	Lowest scores of general health status were from females (16.83 ± 12.45) and fourth-year occupational health students (9.82 ± 7.86) smartphone overuse had a significant effect on depression subscales (p < 0.01).
Ezoe, et al. [24]	Mobile phone dependence questionnaire (MPDQ) [71]	Self-rating depression scale (SDS) [72]	There was a positive small but not significant correlation between mobile phone dependence and depression scores (r = 0.12, p > 0.05).
Gao, et al. [25]	Mobile phone addiction index (MPAI) [57]	Depression anxiety and stress scale (DASS-21) [58]	Alexithymia affected depression (B = 0.503, t = 19.311, p < 0.01) and the mediating effect was 62%. In the regression model, depression significantly affected mobile phone addiction (B = 0.407, t = 14.799, p < 0.001). Inclusion of anxiety in the model increased the explanation of mobile phone addiction by 7.8%. The regression showed that depression was a predictor of mobile phone addiction due to inability to control craving (p < 0.01), feeling anxious and lost (p < 0.01), withdrawal or escape (p < 0.01), and productivity loss (p < 0.01).
Gao, et al. [26]	Mobile phone addiction scale-hong (MPAS-H) [73]	Beck depression inventory-II (BDI-II) [74]	Smartphone addiction and depression were positively correlated (p < 0.05) among participants. In a structural equation model, there were indirect relationships of neuroticism on quality of life through smartphone addiction. The total effect of smartphone addiction and depression was -2.171 based on direct effect (25%), sole mediation of depression (39%), sole mediation of smartphone addiction (29%), and the continuous path of smartphone addiction and depression (41%).
Kim, et al. [28]	Smartphone addiction proneness scale (SAPS) [59]	Center for epidemiologic studies depression scale (CES-D) [69]	The paths from loneliness to depression (B = 0.54, t = 2.46) and from depression to smartphone addiction (B = 0.34, t = 3.06) were valid paths of significance in the effect analysis. Depression’s mediating effect with attachment avoidance (p < 0.05) and attachment anxiety (p < 0.05) were significant. High attachment anxiety, loneliness, and depression predicted smartphone addiction.
Kim, et al. [29]	Smartphone addiction proneness scale (SAPS) [59]	Investigator-developed	Perceived psychological health, such as stress and depression, closely related to smartphone overuse (p < 0.05). Participants that had symptoms of depression were 1.91 times more likely to overuse smartphones (95% CI 1.27-2.86, p = 0.0018).
Long, et al. [33]	Problematic cellular phone use questionnaire (PCPUQ) [62]	Self-rating depression scale (SDS) [72]	Problematic cell phone use was associated with hours of use (p < 0.01), frequency of mobile phone change (p < 0.01), and monthly smartphone bill (p < 0.01). There was no difference in usage preference between problematic versus non problematic users (p = 0.18). The risk factors for problematic smartphone use included depression as measured by the SDS (p < 0.01).
Mok, et al. [34]	Smartphone addiction scale (SAS) [53]	Beck depression inventory (BDI) [70]	Mean depression scores were significantly higher in female respondents (19.58 ± 13.40) vs. male respondents (15.58 ± 10.75), which was significant (p < 0.01). Smartphone addiction scores were also higher in female respondents (74.67 ± 25.50) vs. male respondents (59.65 ± 21.08), which was significant (p < 0.01). In latent class analysis, smartphone addiction was not a significant factor in differentiating levels of depressive feelings (F = 0.275, p = 0.76).
Panova, et al. [35]	Questionnaire about experiences related to the internet (CERI) [75]	Mood and anxiety symptom questionnaire-anhedonic depression subscale (MASQ) [64]	A significant correlation was found between maladaptive mobile phone use and depression (r = 0.194, p < 0.01).
Park, et al. [36]	Investigator-developed	Center for epidemiologic studies depression scale (CES-D) [69]	Respondents who used smartphones for bonding and bridging ties as well as for sharing a sense of support were likely to have lower levels of depression. Bonding social ties were positively related to perceived social support (r = 0.44, p < 0.001).
Saadat, et al. [38]	Investigator-developed	Beck depression inventory (BDI) [70]	A significant correlation was found between mobile phone dependency and depression scores (r = 0.295, p = 0.000).
Tao, et al. [43]	Self-rating questionnaire for adolescent problematic mobile phone use (SQAPMPU) [67]	Self-rating depression scale (SDS) [72]	Prevalence of problematic mobile phone use was 27.9%, while prevalence of depressive symptoms was 18.9% and alcohol use was 37.5%. There was no significant increase in alcohol use in depressed students (OR 1.183, 95% CI 0.931-1.502). Problematic mobile phone use was independently associated with alcohol use (OR 1.295, 95% CI 1.040-1.611). In a multivariate regression analysis, there was an additive interaction between problematic use and depressive symptoms with alcohol use (OR 1.456, 95 % CI 1.044-2.030, p = 0.027).
Tao, et al. [42]	Self-rating questionnaire for adolescent problematic mobile phone use (SQAPMPU) [67]	Center for epidemiologic studies depression scale (CES-D) [69]	Problematic mobile phone use and poor sleep quality were observed in 28.1% and 9.8% of participants, respectively. Depression symptoms were significantly higher in students with problematic mobile phone use (p < 0.01).