Longitudinal Cross-Lagged Analysis Between Mobile Phone Dependence,Friendships,and Depressive Symptoms Among Korean Adolescents

Abstract

This study examined reciprocal longitudinal effects between mobile phone dependence, friendships, and depressive symptoms using 3-year longitudinal data from the Korean Children and Youth Panel Survey. An autoregressive cross-lagged model was applied to a sample of 1,737 adolescents. Results suggested that unidirectional relationships exist between the three variables: friendships reduce mobile phone dependence (B = −0.068, p = 0.058; B = −0.118, p < 0.001) and depressive symptoms increase mobile phone dependence (B = 0.082, p = 0.001; B = 0.128, p < 0.001); however, mobile phone dependence does not affect friendships and depressive symptoms. In addition, this study provided evidence of the negative bidirectional relationship between friendships and depressive symptoms. We suggest that, to prevent or treat adolescents’ mobile phone dependence, a practical approach regarding friendships and depressive symptoms is needed.

Introduction

Mobile phones have become essential devices in daily life, and people have been increasingly involved in online activities through them. Mobile phone use is increasing rapidly, especially among youths.¹ They contain a variety of functions to attract adolescents, including easy and fast communication and Internet access, ability to shoot photos and videos, various mobile applications, and virtual games; these lead youths to become heavy mobile phone users.²

Although mobile phones are useful tools for social and personal functions, a maladaptive and intensive use may have detrimental effects on adolescents’ other everyday activities, interpersonal relationships, health, and well-being.^3,4 Problematic use of mobile phones indicates (1) an inability to control one's use of the mobile phone, which eventually entails negative consequences in daily life, and (2) mobile phone use in dangerous situations or prohibited contexts.^5,6 Excessive use of mobile phones, which includes using mobile phone more than necessary, and having high frequency in unproductive smartphone use (e.g., spending too much time in gaming and social media use), can be considered problematic.⁷

Mobile phone addiction shares some common features with substance use disorder and gambling disorder as described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), notably: tolerance, withdrawal, loss of control, preoccupation, and associated life dysfunction.⁸ It is characterized as compulsive use (irresistible impulse to use the mobile phone despite wanting to stop); dependence such as tolerance (increasing mobile phone use to achieve satisfaction) and withdrawal (negative symptoms that occur after reducing or discontinuing mobile phone use); and functional impairment in individual and social life.^9,10 To some extent, dependence manifests among problematic mobile phone users and mobile phone addicts.^10,11

Mobile phone dependence is characterized by users feeling unable to cope with not having their mobile phone with them, feeling uneasy when unable to use it, and needing to use it more to achieve satisfaction and relaxation.⁴ It is notable that high rates of mobile phone dependence have been reported among adolescents.^2,12 According to Korean national survey, 30.2 percent of teenagers were potential addicts of mobile phones, compared with children under 10 (22.9 percent), adults aged 20–59 years (18.8 percent), and those in their 60s (14.9 percent).¹³ Mobile phone dependence is associated with negative mental health (such as depression, social anxiety, and stress), and damaged relationships (e.g., withdrawal from society, avoidance of face-to-face interactions, and shallow and compulsive relationships).^2,5,14

Attachment to friends and depressive symptoms may be particularly associated with mobile phone dependence among adolescents, as they tend to increase during this period.^15,16 Previous studies found that the better the friendships, the lower the risk of mobile phone dependence among teenagers.^17,18 Moreover, adolescents who fear face-to-face interactions or have social anxiety tend to be more immersed in mobile phones to fulfill their social needs through Internet- or text-based communication, online communities, and entertainment.^19,20

Several studies highlight that excessive screen time causes withdrawal from interpersonal relationships.²¹ Spending time online displaces time spent on social activities, which in turn may lead individuals to be socially isolated.²² Mobile phone dependence is mainly derived from use of smartphones and given that addicts are heavily immersed in screen and online activities,²³ this could have a negative effect on friendships. These results imply that the relationship between mobile phone dependence and friendships can be reciprocal. In other words, the more severe the dependence, the worse the relationships with friends; youths with poor relationships with friends are more likely to become dependent on mobile phones. However, because previous studies did not employ longitudinal designs, they were limited in identifying the temporal direction of these relations and transactional associations.

Depression is one of the most serious psychological problems among adolescents and can lead to substance abuse,²⁴ failure in school,²⁵ and even suicide,²⁶ and mobile phone dependence has been reported to increase depressive symptoms.^27,28 Moreover, excessive mobile phone use may lead to reduced personal contact in real life, conflicts with family or friends, negative comparisons of self with others, lack of engagement in physical activity, and disturbed sleep, which in turn may cause depressive symptoms.^21,22

However, the cause-and-effect relationship between mobile phone dependence and depressive symptoms remains unclear. Some studies reveal that adolescents with depressive symptoms are more prone to mobile phone dependence than those without such symptoms.^29,30 However, previous studies that analyzed cross-sectional data were unable to fully identify a causal relationship. We believe that this association needs to be clarified by examining the longitudinal and reciprocal effects among the relationships.

Several studies have reported that depressive symptoms are associated with friendships during adolescence. Depressed adolescents elicit negative interactions with peers, such as conflict, indifference, and a lack of collaboration and mutuality, all of which lead to a deterioration in the quality of relationships.^31,32 Conversely, friendships provide a sense of security for adolescents and can help to relieve stress. Those young people with close and supportive peer relationships were revealed to have a lower risk of depression. Thus, the relationship between depressive symptoms and friendships is likely to be bidirectional, indicating that their longitudinal and reciprocal effects need to be investigated to minimize the possibility of reverse causation bias.

Although there is a concurrent link between mobile phone dependence, friendships, and depressive symptoms, few longitudinal studies have explored their potential bidirectional and transactional associations.^2,33 Thus, the purpose of this study was to examine the cross-lagged paths between these three variables, using three-wave longitudinal data on Korean adolescents. We investigated whether there was a reciprocal longitudinal relationship between mobile phone dependence, friendships, and depressive symptoms, based on the following hypotheses:

H1: Mobile phone dependence, friendships, and depressive symptoms in waves 5 and 6 will positively affect the same variables in waves 6 and 7.

H2: Mobile phone dependence and friendships will have a negative effect on each other.

H3: Mobile phone dependence and depressive symptoms will have a positive effect on each other.

H4: Friendships and depressive symptoms will have a negative effect on each other.

Methods

Data and participants

We used 3-year longitudinal data from the Korean Children and Youth Panel Survey—(KCYPS) the 4th grader panel. The National Youth Policy Institute in Korea collected the data. The KCYPS was conducted annually from 2010 to 2016 to collect information about young peoples' school and family life, their use of mass media, and their mental health.

The sample was extracted using multi-stage stratified cluster sampling: (1) The number of individuals to be surveyed by region was allocated in proportion to the number of fourth-grade elementary school students across 16 cities and provinces (administrative units in Korea) in 2010; (2) the schools for survey were extracted based on probability proportional to size sampling; (3) the survey class was randomly selected after confirming the characteristics and location of the school, obtaining information on the number of classes, and the number of students per class; and (4) 2,378 students enrolled in fourth-grade elementary school in 2010 (wave 1) were selected as the original sample.

We used the data from wave 5 (8th graders in 2014) to wave 7 (10th graders in 2016) for analysis, because they were drawn from the latest data sets and reflect mobile phone use in mid adolescence. The final sample included 1,737 adolescents who had responded to the survey questions related to mobile phone dependence, friendships, and depressive symptoms during the three consecutive years of our study period. The sample consisted of 896 (51.6 percent) male and 841 (48.4 percent) female students. Mean age of the sample was 14 (SD = 0.2), 15 (SD = 0.2), and 16 (SD = 0.2) years for wave 5, wave 6, and wave 7, respectively.

Their school regions were Seoul (11.2 percent), Busan (6.4 percent), Daegu (6.6 percent), Incheon (3.9 percent), Gwangju (5.1 percent), Daejeon (5 percent), Ulsan (4.1 percent), Gyeonggi (15.9 percent), Gangwon (4.8 percent), Chungbuk (6.4 percent), Chungnam (6.2 percent), Jeonbuk (5.6 percent), Jeonnam (4 percent), Gyeongbuk (5.5 percent), Gyeongnam (6.2 percent), and Jeju (3.1 percent). Their average household annual incomes were 46 million won.* The data were collected ensuring anonymity and privacy of participants in compliance with the Declaration of Helsinki. Because we used secondary data, the approval of the Research Ethics Committee was exempted.

Measures

Mobile phone dependence

Mobile phone dependence was assessed with seven items (e.g., “I am anxious if I do not carry my mobile phone”) based on the Mobile Phone Dependence Scale.^34,35 The participants were asked to rate their level of mobile phone dependence on a 4-point scale ranging from 1 (strongly disagree) to 4 (strongly agree) for each question. The Cronbach's alpha coefficients were 0.884 for wave 5, 0.873 for wave 6, and 0.867 for wave 7. For our analysis, we used the mean score of the seven items. A higher value indicated higher mobile phone dependence.

Friendships

Friendships were assessed with five items (e.g., “I get along well with my classmates”) from the School Life Adaptation Scale—Peer Relationships.^36,37 The participants were asked to rate their level of friendships on a 4-point scale ranging from 1 (strongly disagree) to 4 (strongly agree) for each question. The Cronbach's alpha coefficients were 0.633 for wave 5, 0.657 for wave 6, and 0.643 for wave 7. For our analysis, we used the mean score of the five items. A higher value indicated better friendships.

Depressive symptoms

Depressive symptoms were measured with 10 items (e.g., “I am not interested in anything”) from the Depression Scales of the Korean Mental Diagnosis Test.^38,39 The participants were asked to rate their level of depressive symptoms on a 4-point scale ranging from 1 (strongly disagree) to 4 (strongly agree) for each question. The Cronbach's alpha coefficients were 0.907 for wave 5, 0.894 for wave 6, and 0.894 for wave 7. For our analysis, we used the mean score of the 10 items. A higher value indicated more severe depressive symptoms.

Data analysis

In this study, to investigate the reciprocal longitudinal relationships between mobile phone dependence, friendships, and depressive symptoms, we adopted an autoregressive cross-lagged (ARCL) model (Fig. 1). In the ARCL model, the autoregressive effect is represented with coefficient values obtained by regressing the measures at a time point (t) onto the same measure at a previous time point (t–1). This model also estimates the cross-lagged effect; that is, the reciprocal influence of one variable at a previous time (t–1) on another variable at a later time (t), controlled for the autoregressive effect.

FIG. 1.

ARCL research model. e1, e2, e3, e4, e5, and e6 are error terms for each variable. ARCL, autoregressive cross-lagged; W5, wave 5; W6, wave 6; W7, wave 7.

We used the goodness-of-fit index (GFI) and the root mean residual (RMR) as model-fit indices. A GFI ≥0.9 is considered acceptable together with an RMR ≤0.05.^40,41 When the p value is <0.1, the estimate is considered statistically significant. To conduct the ARCL analysis, we used statistical software, AMOS version 23.

Results

Descriptive statistics and correlations

The descriptive statistics and correlations of mobile phone dependence, friendships, and depressive symptoms for waves 5, 6, and 7 are presented in Table 1; the means of the variables were 2.231, 2.253, and 2.236 (mobile phone dependence); 3.140, 3.149, and 3.198 (friendships); and 1.771, 1.778, and 1.786 (depressive symptoms), respectively. Their distributions were found to be normal with skewness values under 2 and kurtosis values under 7,⁴² which enabled us to proceed with the ARCL model. The correlation coefficients for the nine variables demonstrated that mobile phone dependence for waves 5, 6, and 7 was negatively related to friendships for the same; however, it was positively related to depressive symptoms for waves 5, 6, and 7. Friendships and depressive symptoms for the waves demonstrated negative correlations.

Table 1.

Descriptive Statistics and Correlations Between the Time Series Variables: Mobile Phone Dependence, Friendships, and Depressive Symptoms

Variables	1	2	3	4	5	6	7	8	9
1. MPD W5	1
2. MPD W6	0.524^**	1
3. MPD W7	0.407^**	0.487^**	1
4. FRD W5	−0.190^**	−0.165^**	−0.182^**	1
5. FRD W6	−0.150^**	−0.209^**	−0.212^**	0.521^**	1
6. FRD W7	−0.112^**	−0.142^**	−0.271^**	0.434^**	0.455^**	1
7. DS W5	0.337^**	0.257^**	0.236^**	−0.403^**	−0.325^**	−0.233^**	1
8. DS W6	0.214^**	0.294^**	0.271^**	−0.317^**	−0.419^**	−0.274^**	0.562^**	1
9. DS W7	0.220^**	0.172^**	0.320^**	−0.291^**	−0.294^**	−0.392^**	0.468^**	0.546^**	1
Mean	2.231	2.253	2.236	3.140	3.149	3.198	1.771	1.778	1.786
SD	0.669	0.640	0.638	0.399	0.397	0.389	0.579	0.553	0.551
Range	1–4	1–4	1–4	1–4	1–4	1–4	1–4	1–4	1–4
Skewness	0.299	0.277	0.171	0.286	0.203	0.272	0.491	0.490	0.406
Kurtosis	0.008	0.107	−0.066	−0.016	0.324	−0.172	−0.097	0.265	−0.111

Note: N = 1,737.

p < 0.01.

DS, depressive symptoms; FRD, friendships; MPD, mobile phone dependence; W5, wave 5; W6, wave 6; W7, wave 7.

Estimation results from the ARCL model

We tested configural, metric, scalar, and residual invariance to confirm the longitudinal invariance of the measures. As a result of testing, we found that configural invariance (root-mean-squared error of approximation [RMSEA] = 0.053), metric invariance (RMSEA = 0.053), scalar invariance (RMSEA = 0.053), and residual invariance (RMSEA = 0.052) were satisfied.

Table 2 and Figure 2 display the coefficient estimates of the ARLC model. Autoregressive effects of the variables were statistically significant across the 3 years. Specifically, mobile phone dependence, friendships, and depressive symptoms in waves 5 and 6 positively affected the same variable in wave 6 and 7, respectively, confirming H1. These results imply that those who had high values of each variable in the past may have high values for each of them subsequently.

FIG. 2.

Path estimation results from the ARCL model. N = 1,737. Unstandardized beta coefficients reported. Model fit: 758.809 (p = 0.000); goodness-of-fit index = 0.904; root mean residual = 0.024. ⁺p < 0.1; **p < 0.01; ***p < 0.001. ns, not significant.

Table 2.

Estimation Results from the Autoregressive Cross-Lagged Model

Path	B	SE	CR	p
Mobile phone dependence W5 → Mobile phone dependence W6	0.469	0.021	22.718	0.000
Mobile phone dependence W6 → Mobile phone dependence W7	0.438	0.021	20.871	0.000
Friendships W5 → Friendships W6	0.461	0.022	20.924	0.000
Friendships W6 → Friendships W7	0.401	0.021	18.689	0.000
Depressive symptoms W5 → Depressive symptoms W6	0.489	0.021	22.778	0.000
Depressive symptoms W6 → Depressive symptoms W7	0.51	0.021	24.673	0.000
Mobile phone dependence W5 → Friendships W6	−0.01	0.013	−0.785	0.433
Mobile phone dependence W6 → Friendships W7	−0.017	0.013	−1.299	0.194
Mobile phone dependence W5 → Depressive symptoms W6	0.018	0.017	1.026	0.305
Mobile phone dependence W6 → Depressive symptoms W7	0.004	0.018	0.252	0.801
Friendships W5 → Mobile phone dependence W6	−0.068	0.036	−1.897	0.058
Friendships W6 → Mobile phone dependence W7	−0.118	0.034	−3.445	0.000
Friendships W5 → Depressive symptoms W6	−0.148	0.03	−4.951	0.000
Friendships W6 → Depressive symptoms W7	−0.109	0.029	−3.804	0.000
Depressive symptoms W5 → Mobile phone dependence W6	0.082	0.026	3.2	0.001
Depressive symptoms W6 → Mobile phone dependence W7	0.128	0.025	5.194	0.000
Depressive symptoms W5 → Friendships W6	−0.091	0.016	−5.732	0.000
Depressive symptoms W6 → Friendships W7	−0.066	0.015	−4.287	0.000
Mobile phone dependence W5 ↔ Friendships W5	−0.051	0.007	−7.762	0.000
Mobile phone dependence W5 ↔ Depressive symptoms W5	0.13	0.01	13.294	0.000
Friendships W5 ↔ Depressive symptoms W5	−0.093	0.006	−15.558	0.000

Note: N = 1,737. Model fit: $χ^{2} (15)$ = 758.809 (p = 0.000); goodness-of-fit index = 0.904; root mean residual = 0.024.

B, unstandardized beta; CR, critical ratio; SE, standard error.

As a result of cross-lagged analysis, it was found that although mobile phone dependence did not affect friendships and depressive symptoms, friendships and depressive symptoms significantly affected mobile phone dependence, which did not confirm H2 and H3. Statistically significant coefficients were found on the paths: (1) from friendships in waves 5 and 6 to mobile phone dependence in waves 6 and 7 (B = −0.068, p < 0.1; B = −0.118, p < 0.001), and (2) from depressive symptoms in waves 5 and 6 to mobile phone dependence in waves 6 and 7 (B = 0.082, p < 0.01; B = 0.128, p < 0.001). Our findings indicate that adolescents with better friendships are less likely to be dependent on mobile phones, and those with more depressive symptoms are more likely to have mobile phone dependence.

The relationships between friendships and depressive symptoms across time were significant in both directions. We observed statistically significant coefficients on the paths: (1) from friendships in waves 5 and 6 to depressive symptoms in waves 6 and 7 (B = −0.148, p < 0.001; B = −0.109, p < 0.001), and (2) from depressive symptoms in waves 5 and 6 to friendships in waves 6 and 7 (B = −0.091, p < 0.001; B = −0.066, p < 0.001), which confirmed H4. This implies that adolescents with better friendships are less likely to have depressive symptoms, and vice versa. The results demonstrate a bidirectional relationship between friendships and depressive symptoms over the 3 years.

Discussion

The purpose of this study was to examine reciprocal relationships between mobile phone dependence, friendships, and depressive symptoms using nationally representative data on Korean adolescents. First, our findings demonstrated positive autoregressive longitudinal effects among the three variables, which is congruent with past studies.^2,33,43 This suggests that the previous level of mobile phone dependence, friendships, and depressive symptoms may remain constant in the future. That is, adolescents who demonstrate high levels of the variables previously will continue to show a high level in the future, whereas those with low values for each variable will show a low level as time goes by.

Second, in this study, mobile phone dependence did not predict future friendships and depressive symptoms among adolescents. This key finding challenges previous research, which suggested that mobile phone dependence influences friendships^21,22 and depressive symptoms.^28,29 In addition, the finding that friendships and depressive symptoms significantly influence mobile phone dependence shed lights on the unidirectional relationship between the variables. In other words, friendships and depressive symptoms are the impact in mobile phone dependence, but not vice versa.

As in previous studies, good friendships were found to reduce mobile phone dependence, suggesting that adolescents with poor friendships may rely more on mobile phones.^17,18 For instance, when adolescents are unable to form good friendships because of shyness in face-to-face interactions with friends or communication difficulties, they are more likely to participate in online activities through mobile phones (e.g., online communities, chat rooms, instant messages, online games, and watching videos) to satisfy their social needs, which could lead to mobile phone dependence.^19,20 Consistent with previous studies, the more depressed the adolescents were, the greater their mobile phone dependence.^29,30 Therefore, mobile phone dependence may stem from, or simply reflect, adolescents’ pre-existing friendship problems and depressive symptoms.

Finally, we observed that the relationships between friendships and depressive symptoms were reciprocal throughout the time period. On the one hand, friendships decreased depressive symptoms, as suggested by previous studies^44,45; on the other hand, depressive symptoms also affected friendships negatively, which is also in line with previous studies^31,32 These results indicate that adolescents with poor friendships become depressed, which further worsens their friendships, which in turn increases depressive symptoms, and vice versa. Therefore, we believe that friendship and depressive symptoms intertwine and affect each other sequentially.

Practical implications

This study has several practical implications. First, the autoregressive propensity of the three variables suggests that once adolescents’ mobile phone dependence, friendships, and depressive symptoms are formed, the pattern can continue rather than improve over time. Therefore, when adolescents face these problems, parents and practitioners (such as teachers, social workers, and counselors) should not overlook the phenomenon, presuming that it is temporary, and their symptoms will improve shortly. As shown in this study, once these problems occur, their existing state may continue or even worsen. Therefore, it is imperative to intervene early to solve the problems, as well as maintaining continuous attention on and management of youths who exhibit them.

Second, the results of this study suggest that it is necessary to manage adolescents’ friendships and depressive symptoms, thereby reducing mobile phone dependence. For example, in the case of adolescents who have difficulty in forming friendships through face-to-face contact, it is necessary to investigate the cause of this phenomenon (e.g., shyness, fear, anxiety, and communication problems) through consultation with experts such as psychologists and social workers. Based on the investigation of what causes the problem, appropriate intervention methods (such as participation in relational programs and activities, group therapy, cognitive behavioral therapy, and communication training) can be selected and applied so that adolescents learn how to interact and communicate with others, which will help them build friendships.

To reduce mobile phone dependence, it is also important to manage adolescent depressive symptoms, the causes of which are diverse, including stress, negative thinking, hormonal changes, and both large and small life events.⁴⁶ In the case of Korean adolescents, the burden and stress caused by studies is high as the competition for entrance examinations is fierce, and this is reported to lead to an increase in depressive symptoms.⁴⁷ In addition, lack of sleep and physical activity due to an excessive amount of studying will have the same effect.^48,49

To reduce depressive symptoms, rather than overemphasizing the importance of studying, young people should be encouraged to balance this with other areas of life. Thus, they should get enough sleep and physical activity, along with studying. More specifically, classes related to physical activities need to be added to the school curriculum, and a wider variety of sports need to be provided in a greater capacity in after-school programs.

Limitations and future research

This study has several limitations. First, mobile phone dependence, friendships, and depressive symptoms were measured through self-reported items, which could inflate their associations. In future research, the inclusion of objective measures of the variables might improve the reliability and validity of the data. For example, using a smartphone application that can track time and contents of phone usage, gathering opinions of teachers and friends about friendships, and views of parents and teachers about adolescents’ depressive symptoms could be used alongside self-reported measures.

Second, the Cronbach's alpha for friendships (0.633 for wave 5, 0.657 for wave 6, and 0.643 for wave 7) was relatively lower than the other two variables (>0.8). However, we assumed it to be acceptable based on the general rule of thumb: good reliability: Cronbach's alpha of 0.7 or higher, and acceptable reliability: 0.6 or higher.⁵⁰ Nevertheless, we recommend that future research revisit and redesign items to assess friendships to improve their internal consistency reliability.

Finally, since 2014, the penetration rate of smartphone use among Korean youth has already exceeded 90 percent and most cases of mobile phone dependence occur due to smartphones.⁵¹ Therefore, it is assumed in this study that mobile phone dependence is equivalent to smartphone dependence. However, in the questionnaire used in this study, the term “mobile phone dependence” was used rather than “smartphone dependence.” Given that the penetration rate of smartphones among adolescents exceeded 95 percent in 2018, it is necessary to further refine the measurement parameter by using the term “smartphone dependence” in future panel data.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

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