Jeong Soon Yu; Myung Soon Kwon

doi:10.4040/jkan.24092

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Research Paper
Factors influencing smartphone overdependence in university students: an ecological model: a descriptive study: Jeong Soon Yu¹, Myung Soon Kwon²; Journal of Korean Academy of Nursing 2025;55(1):64-80.
DOI: https://doi.org/10.4040/jkan.24092
Published online: February 20, 2025

¹Department of Education & Research, Global Korean Nursing Foundation, Seoul, Korea

²Research Institute of Nursing Science, School of Nursing, Hallym University, Chuncheon, Korea

Corresponding author: Myung Soon Kwon Research Institute of Nursing Science, School of Nursing, Hallym University, 1 Hallimdaehak-gil, Chuncheon 24252, Korea E-mail: kwon1314@hallym.ac.kr

• Received: August 2, 2024 • Revised: October 2, 2024 • Accepted: December 17, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License (http://creativecommons.org/licenses/by-nd/4.0) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

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Abstract
Introduction
Methods
Results
Discussion
Conclusion
Article Information
References

Abstract

Purpose
This study investigated the factors influencing smartphone overdependence in university students using an ecological model and descriptive research.
Methods
Data were collected from 482 students at 13 universities in the six regions in South Korea from October 20, 2020, to March 25, 2021. Data analysis involved descriptive statistics, the chi-square test, the independent samples t-test, analysis of variance, and hierarchical multiple regression.
Results
The significant ecological factors influencing smartphone overdependence included self-awareness of smartphone overdependence (β=.33, p<.001), autonomy (β=–.25, p<.001), average daily smartphone usage time (β=.18, p<.001), gender (β=.15, p=.001), college year (β=.15, p=.020), forming relationships with others as a motivation for smartphone use (β=–.15, p=.008), friend support (β=.14, p=.006), and age (β=–.12, p=.047). The model explained 34.9% of the variance.
Conclusion
The study emphasized the role of personal and interpersonal factors, in smartphone overdependence among university students. Tailored intervention strategies are necessary to address smartphone overdependence, considering the unique characteristics of students’ environments. A significant aspect of this study is that it provides an explanation of the multidimensional factors contributing to smartphone overdependence among university students, including intrapersonal, interpersonal, and environmental influences.
Key words: Addictive behavior; Environment; Personal autonomy; Smartphone; Universities

Introduction

Smartphones, which offer convenient access to the Internet and a wide variety of applications, can be a very useful tool for maintaining social connectivity while overcoming physical distance limitations in situations such as the coronavirus disease 2019 (COVID-19) pandemic [1]. Smartphone functions also provide many advantages in daily life that can contribute to improved productivity, social interactions, rest and relaxation, and entertainment [2,3]. However, this convenience can also lead to excessive usage, which can have negative effects on social relationships, productivity, and academic performance [4,5] and contribute to mental health problems such as physical health problems [6], depression, and anxiety [7,8]. Smartphone overdependence refers to a state of excessive smartphone use or the inability to control such usage. It can lead to negative psychological, social, and interpersonal consequences, potentially disrupting one’s daily life, including work, school, and social activities [9]. It is also known as smartphone addiction [5], excessive smartphone use [10], and problematic smartphone use [2]. While smartphone addiction is commonly used in the literature [11], in South Korea, smartphone overdependence has been adopted to mitigate the stigma associated with the word “addiction” [12,13].

In Korea, 35.4% of university students, which is an increasing proportion, are at risk of becoming overdependent on smartphones [14]. Smartphones also serve as a shelter for university students when they need to adapt to a new environment, and the educational environment in which the use of digital devices is inevitable represents an obstacle to controlling smartphone use [15]. Furthermore, the unprecedented COVID-19 pandemic, which affected the entire world, led to the risk of severe addiction among university students due to the excessive use of digital devices [16]. Therefore, a comprehensive evaluation of the factors affecting smartphone overdependence must consider the effect of environmental factors surrounding university students.

The ecological approach, which considers the interaction between various environmental factors surrounding individuals to understand human behavior in a multidimensional context that includes social phenomena, is a suitable model for examining the environmental factors that can affect university students’ overdependence on smartphones [17]. According to McLeroy et al. [17], individual behavior is influenced by and interconnected with intrapersonal, interpersonal, institutional, community, and public policy factors. Therefore, an ecological model can propose comprehensive and systematic intervention strategies that consider components at each level to promote positive changes in health behaviors.

Previous studies on smartphone overdependence among university students analyzed the key variables in an ecological model and found that woman students exhibited higher levels of smartphone overdependence than man students [18-20]. Men had higher frequencies of using text messaging, voice calls, and gaming apps [18], while women primarily used smartphones for social interactions, showing greater engagement in social networking services [21]. Furthermore, younger individuals [22] and those who spend more time on their smartphones exhibit a sharply increased risk of smartphone overdependence [20,23]. These findings suggest that variations in smartphone overdependence levels are influenced by individual characteristics and usage behaviors. Self-determination focuses on self-regulation in the context of health behavior change and emphasizes that motivation comes from within. Thus, when autonomy, competence, and relatedness, the three basic psychological needs, are satisfied, individuals are motivated to engage in self-directed behavior and make their own decisions [24]. Self-determination can be particularly beneficial for university students in reflecting on problematic behaviors, such as smartphone overdependence, and fostering healthier habits [12,25]. Additionally, various intrapersonal factors, including psychological elements, such as depression [7], anxiety [7,8], loneliness [10], and stress [5], have been identified as factors influencing smartphone overdependence among university students. As a key variable of interpersonal factors, social support such as family, friends, and specific other support, can serve as a positive resource for individuals [26] that mitigates negative Internet usage [27]. Additionally, intimate interactions among individuals are important stimuli for regulating smartphone use among university students, highlighting the need for social resources connected to an individual [28]. Thus, social support is a significant factor that can positively influence smartphone overdependence. In terms of institutional and community factors, the university environment plays a crucial role in influencing overall life satisfaction during college years. Lower satisfaction with university life is associated with higher levels of smartphone overdependence, potentially reflecting difficulties in adjusting to college life [29]. Regional characteristics, such as urban scale and high population density, are also linked to increased Internet addiction [30,31]. These findings indicate that individuals are also influenced by their interactions with external environmental factors [17].

As previously discussed, both internal and external environmental factors can closely affect smartphone overdependence among university students. However, the studies investigating the relationship or influence of smartphone overdependence have been fragmented, as they have focused on variables limited to the personal characteristics or social psychological factors of university students. Research that considers external environments (such as universities and communities) and regional characteristics is lacking. Therefore, this study aimed to provide basic data for the prevention and management of smartphone overdependence by identifying the intrapersonal, interpersonal, institutional, and community factors that affect university students’ smartphone overdependence using an ecological model.

Methods

1. Study design

This was a descriptive research study conducted to identify the factors influencing university students’ smartphone overdependence based on an ecological model [17] (Figure 1).

2. Participants

The subjects considered in this study were selected from 13 universities from a total of six regions in Korea: Seoul/Gyeonggi, Gangwon, Gyeongsang, Jeolla, Chungcheong, and Jeju to reflect the local environment for the ecological model. The subjects were university students who were provided with a description of the purpose of the study and voluntarily agreed to participate after seeing a recruitment notice that included the purpose of the study and the online survey uniform resource locator (URL), which was posted on each university’s online community (Everytime website).

Based on the 1:10 minimum ratio of independent variables and samples [32], a sample size of 1:15 to 1:20 per independent variable was applied, considering the vulnerabilities of online surveys such as regional distribution, response rate, and sampling bias, which are important indicators of environmental factors in this study. Therefore, based on the 26 predictors of this study, the number of samples that should be collected was calculated to range from a minimum of 360 to a maximum of 520. Meanwhile, the sample size of this study was determined based on the minimum number of subjects required for multiple regression analysis, calculated using the G*Power 3.1.9.2 program (Heinrich-Heine-Universität Düsseldorf). A two-tailed significance level (⍺) of .05, a power (1-β) of 80%, and an effect size (f²) of .15 (medium) were set, with 26 predictor variables included, the minimum required sample size was calculated to be 175 participants.

A total of 498 questionnaires were collected for the online survey, with 67 to 93 subjects per region. The final analysis was conducted using 428 responses, excluding 16 cases of missing data and duplicate responses (identified by duplicate IP addresses and phone numbers).

3. Ethical consideration

This study was conducted after obtaining approval from the institutional review board of Hallym University (HIRB-2020-063-1-R). Information on the research purposes, research procedures, and data protection was explained to the study subjects, and they were notified that they could withdraw their participation at any time, even during the study. It was explained that the study results and anonymity of the survey responses of the data collected through the survey would be used only for research purposes, retained on a private computer, not disclosed and that they would be permanently deleted after the study was completed.

4. Measurements

1) Smartphone overdependence

Smartphone overdependence was measured using the smartphone addition scale-short version (sas-sv) scale developed by Kwon et al. [33]. This tool consists of a total of 10 questions, including daily-life disturbance, positive expectation, withdrawal, cyberspace-oriented relationship, overuse, and tolerance, rated on a 6-point Likert scale (1=strongly disagree, 6=strongly agree). The smartphone-dependent high-risk group included men who scored 31 points or higher and women who scored 33 points or higher [33]. At the time of tool development, reliability rated by Cronbach’s α was .97, and in this study, Cronbach’s α was .85.

2) Intrapersonal factors

Intrapersonal factors are individual characteristics, such as knowledge, attitudes, self-concept, behavior, and skills [17]. This study included demographic characteristics, smartphone usage behavior, and self-determination.

(1) Demographic and sociological characteristics

Demographic characteristics such as gender, age, university major, type of residence, and whether or not someone lived alone were collected. The type of residence was divided into residence in a family home and commuting to school with movement between regions, living alone/dormitory, and living in a school dormitory.

(2) Smartphone usage behavior

Smartphone usage behavior included the first age of smartphone use, the motivation for smartphone use, the main smartphone use, the average daily smartphone usage time, self-awareness of smartphone overdependence, satisfaction of smartphone usage for the purpose, problematic smartphone use, health status related to smartphone use, and smartphone usage time after the COVID-19.

(3) Self-determination

Self-determination was measured using the Korean version of the basic psychological needs scale developed and validated by Lee and Kim [34] based on the basic psychological needs scale of the self-determination theory developed by Deci and Ryan [24]. This consists of 18 items, including autonomy (six items), competence (six items), and relatedness (six items). Each item is rated on a 6-point Likert scale (1=not at all, 6=always), the higher scores indicate higher the basic psychological needs. In a study by Lee and Kim [34], the overall reliability was indicated by a Cronbach’s α of .87, the score for autonomy was .70, that for competency was .75, and that for relatedness was .79. In the present work, the overall reliability had a Cronbach’s α of .87, that for autonomy was .87, that for competence was .82, and that for relatedness was .82.

3) Interpersonal factors

Interpersonal factors included social support and networks, such as family, friends, and workplace, which could change or influence an individual’s behavior [17]. Social support and family life satisfaction were included in this study.

(1) Social support

Social support was measured using the multidimensional Scale of Perceived Social Support developed by Zimet et al. [35] and adapted by Shin and Lee [36]. This scale consisted of 12 items, including family support (four items), friend support (four items), and significant other support (four items). In this study, significant others (including professors) included support by meaningful others, excluding family and friends. Each item was rated on a 5-point Likert scale (1=strongly disagree, 5=strongly agree). At the time of the tool’s development, Cronbach’s ⍺ was .85 for total reliability for social support, .85 for family support, .75 for friend support, and .72 for significant other support. In the present study, Cronbach’s ⍺ was .91 for overall reliability, .87 for family support, .87 for friend support, and .83 for significant other support.

(2) Family life satisfaction

Family life satisfaction was measured using a question about the degree of satisfaction with relationships with family members, which was rated on a 5-point Likert scale (1=not satisfying at all, 5=very satisfying).

4) Institutional and community factors

Institutional factors refer to formal and informal rules and the characteristics of an organization that support behavioral changes among its members. Community factors are relationships between organizations, institutions, and networks in a community [17]. In this study, the perceived university environment, experience in receiving education on smartphone overdependence prevention, college life satisfaction, city size of residence, and the COVID-19 incidence rate were included as institutional and community factors.

(1) Perceived university environment

The perceived university environment was measured by modifying and supplementing the perceived school environment tool developed by Heo [37] based on the environmental factors proposed by Kamphuis et al. [38]. The content of the modified tool was validated by two advisory committees (consisting of six professors in social welfare, psychology, psychiatry, psychiatric nursing, and community nursing) and through three rounds of expert group meetings (including one counseling psychologist and two nursing professors). In this study, a total of five items consisting of two accessibility items, two availability items, and one program provision item for performing activities related to overdependence on smartphones were used. Each item was rated on a 5-point Likert scale (1=strongly disagree, 6=strongly agree). The reliability of the tool used in the study of Heo [37] had a Cronbach’s ⍺ of .65. In the present study, it was .84.

(2) Experience in receiving education on smartphone overdependence prevention

Receiving education on smartphone overdependence prevention in universities was classified as “yes,” while not receiving such education was classified as “no.”

(3) College life satisfaction

College life satisfaction was surveyed by a question about the “overall degree of satisfaction with the social and physical environment in your college life.” Responses were rated on a 5-point scale (1=not satisfying at all, 5=very satisfying).

(4) City size of residence

Based on the criteria for classifying cities by population distribution [39], cities with a population of more than 1 million can be classified as large cities, those with a population of 500,000 to less than 1 million can be classified as medium cities, and those with a population from 100,000 to less than 500,000 can be classified as small cities. In this study, megalopolises and large cities were combined and classified as large cities with populations of more than 100 million, and medium-sized and small cities were combined and classified as small cities with populations of less than 500,000.

(5) COVID-19 incidence rate

The COVID-19 incidence rate was calculated on October 1, 2020 [40], which was about 2 weeks prior to the administration of the survey (considering the guidelines for applying the quarantine stage based on the average number of confirmed cases in the previous week), and the cumulative number of confirmed cases by region [41]. For this study, participants’ regions of residence were identified down to the city level of their actual residence because it could be directly affected by COVID-19.

5. Data collection

Data were collected from students who attended 13 universities in six regions from October 20, 2020, to March 25, 2021, by posting a recruitment notice that described the research purpose and providing a URL to respond to the online survey on the bulletin board of the online community site (Everytime website) of each university. The online survey utilized the Naver office questionnaire (Naver Corp.), which included a description of the study subjects and a consent form. The questionnaire could be completed in about 15 minutes through a preliminary survey. It could only be completed if the participants read and checked the explanation about the study purpose and consent form after accessing the URL. Those who did not agree were considered unwilling to participate in this study. Thus, the survey was not conducted. Duplicate participation and false entries were addressed by limiting participation to one time per identification (ID). However, in the case of Naver, since each individual can create up to three IDs, participants with the same phone number and Internet Protocol address were also excluded to minimize duplicate participation. After the survey was completed, the participant’s contact information was entered, and a mobile beverage exchange ticket was provided as a token of gratitude to those who provided their personal information.

6. Data analysis

The collected data were analyzed using the IBM SPSS ver. 25.0 program (IBM Corp.). Descriptive statistics were used for the distribution of smartphone overdependence and the ecological factors of the university students. The χ² test, independent t-test, and analysis of variance analysis of variance were performed to determine the difference between smartphone overdependence and the ecological factors. The correlation between variables was analyzed using Pearson’s correlation coefficients, and hierarchical multiple regression analysis was used to verify the ecological factors affecting smartphone overdependence.

Results

1. Ecological characteristics of the participants

1) Intrapersonal factors

(1) Demographic characteristics and smartphone usage behavior

A total of 482 participants were included in this study, with 295 woman students (61.2%) and 187 man students (38.8%), and an average age of 21.3±1.96 years. By college year, first-year students were the most common, with 150 students (31.1%), followed by 130 students (27.0%) in the second year, 111 students (23.0%) in the third year, and 91 students (18.9%) in the fourth year. There were 122 engineering and technology majors (25.3%), 85 social sciences majors (17.6%), and 69 students (14.3%) each in humanities and nursing/health science. As for the residence type of the subjects, 196 students (40.7%) lived in a family home and 60 students (12.4%) commuted to college from their family home in other areas, while 121 students (25.1%) lived outside of the home, and 105 students (21.8%) lived in a school dormitory. Two hundred and fifty-six students were living with their parents (53.1%), 123 students were living alone (25.5%), and 103 students were living with friends or colleagues (21.4%) (Table 1).

Examination of the smartphone usage behaviors of the subjects in this study found that the average level of smartphone overdependence was 37.63±8.62 points out of 60 points in total, of which 376 (77.8%) were at risk of smartphone overdependence. In terms of the first age of smartphone use, 229 students (47.5%) were in middle school, and 158 students (32.8%) were in elementary school. As for the motivation for smartphone use, 301 students (62.4%) used them to form relationships with others, followed by 18.7% for information searching and 13.7% for the latest trends. For the main use of smartphones, the percentage used for social network service (SNS; 236 students, 49.0%) and watching videos (118 students, 24.5%) was high. The average daily smartphone usage time was 5.60±2.69 hours, with 147 students (30.5%) increasing their smartphone usage time by less than an hour after the COVID-19 outbreak, followed by 24.7% with a 1-hour or less increase, 23.6% with a 3-hour increase, and 21.2% with a 4-hour or more increase (Table 1).

(2) Self-determination

The participant’s self-determination scored an average of 82.61±10.85 points out of 108 points in total, and among the sub-factors, autonomy scored 27.42±4.99 points, competence scored 26.05±4.73 points, and relatedness scored 29.14±4.13 points (Table 1).

2) Interpersonal factors

The subjects’ social support scored 48.70±7.64 points out of a total of 60 points, and among the sub-factors, family support scored 16.20±3.16 points, friend support scored 16.22±2.93 points, and significant other support scored 16.29±2.96 points. For family life satisfaction, the results scored 4.09±0.86 points out of 5, and 376 students (78.0%) responded that they were satisfied with their family life (Table 1).

3) Institutional and community factors

The perceived university environment averaged 2.62±0.78 points out of 5 points, and satisfaction with college life averaged 3.54±0.88 points out of a total of 5 points, while 264 students (54.8%) said they were satisfied with college life. Meanwhile, 115 students (23.9%) responded that they had education related to overdependence on smartphones in universities. As for the size of cities in the residential area, 347 students (72.0%) resided in large cities and 135 students (28.0%) in small and medium-sized cities. The average COVID-19 incidence was 109.19±73.36 per 100,000 population, which was slightly lower than the domestic incidence rate of 139.13 per 100,000 population (based on October 1, 2020–March 25, 2021) (Table 1).

2. Degree of smartphone overdependence according to ecological factors

On the intrapersonal factor of gender, smartphone overdependence was rated 39.64±7.87 points for woman students, significantly (t=–6.70, p<.001) higher than 34.47±8.83 points for man students. The degree of smartphone overdependence according to the smartphone usage behaviors of the study participants was 41.92±7.08 points for smartphone overdependence self-awareness, which was statistically significantly higher than 33.91±8.11 points when not perceived as smartphone overdependence (t=––11.47, p<.001). In terms of the degree of increase in smartphone usage time after COVID-19, more than 4 hours was the highest, with 41.64±7.60 points, and this difference was statistically significant (F=11.39, p<.001) (Table 2).

The analysis of participants’ smartphone overdependence according to interpersonal factors showed a score of 39.28±8.82 points for family life dissatisfaction, which was statistically significantly higher than 37.16±8.52 points for family life satisfaction (t=2.24, p=.025). The participants’ smartphone overdependence according to institutional factors showed a score of 38.67±8.54 points for college life dissatisfaction, which was statistically significantly higher than 36.77±8.61 points (t=2.43, p=.016). The participant’s smartphone overdependence according to community factors scored 37.84±8.56 points for living in large cities, which was higher than that for living in small and medium-sized cities, but the difference was not statistically significant (Table 2).

3. Correlation between ecological factors and smartphone overdependence

The participants’ degree of smartphone overdependence was measured by the average daily smartphone usage time (r=.34, p<.001) and showed a negative correlation with age (r=–.09, p=.049). Autonomy (r=–.27, p<.001) and competence (r=–.13, p=.003) which are sub-factors of self-determination, showed a negative correlation. Thus, the higher the smartphone usage time, the higher the degree of overdependence, and the higher the age, the higher the level of autonomy and competence and the lower the degree of smartphone overdependence (Table 3).

4. Ecological factors influencing university students’ smartphone overdependence

In this study, hierarchical multiple regression analysis, an analysis method that could control the input order of the independent variables, was used to verify the ecological factors affecting smartphone overdependence. The independent variables used in the analysis included variables that showed statistically significant differences in the analysis of smartphone overdependence and variables of interest that were considered to be important in this study. These independent variables of intrapersonal factors were age (r=–.09, p=.049), gender (t=–6.70, p<.001), average daily smartphone usage time (r=.34, p<.001), self-awareness of smartphone overdependence (t=11.47, p<.001), autonomy (r=–.27, p<.001), and competence (r=–.13, p=.003) of self-determination. There was no significant difference in the univariate analysis results, but college year (F=1.93, p=.124) and motivation for smartphone use (t=2.43, p=.064), which showed differences in the level of smartphone overdependence according to the category of each variable, were included as independent variables. Interpersonal factors included the degree of family life satisfaction (t=2.24, p=.025), as well as family support (r=–.06, p=.179), friend support (r=.04, p=.441), and significant other support (r=–.03, p=.507). These are sub-factors of social support and important variables of interest in this study. The institutional and community factors included the perceived university environment (r=–.05, p=.286), the degree of college life satisfaction (t=2.43, p=.016), the city size of residence (t=0.86, p=.390), and the COVID-19 incidence rate (r=.03, p=.477). Smartphone usage time after COVID-19 was found to be statistically significant; however, this factor was excluded from hierarchical multiple regression analysis because it was judged to overlap with the usage time variable among the independent variables (Table 2, 3).

Testing the assumption of regression analysis showed that the Durbin-Watson statistic was 2.002, which was close to the reference value of ±2, thus satisfying the independence of the error term. The uniform variance and normality of the residuals were confirmed through a scatter plot and a P-P diagram of the standardized residuals. The correlation coefficient between independent variables was an absolute value of .00 to .69, and all variables were independent. Tolerance for testing multicollinearity was .35 to .89, which was higher than 0.1. The variance inflation factor was 1.12–2.81, which was lower than the reference level of 10. Thus, there was no multicollinearity problem.

The analysis showed that the fit of the regression of model I with intrapersonal factors was suitable (F=22.07, p<.001). Self-awareness of smartphone overdependence (β=.33, p<.001), autonomy (β=–.23, p<.001), average daily smartphone usage time (β=.20, p<.001), forming relationships with others as a motivation for smartphone use (β=–.16, p=.004), being a fourth-year student (β=.16, p=.011), being a woman student (β=.15, p<.001), and age (β=–.14, p=.024) were found to be variables that significantly affected smartphone overdependence. The explanatory power of the model was 34.5% (Table 4).

Model II additionally introduced interpersonal factors. The fit of the regression model was found to be suitable (F=17.38, p<.001). In model II, self-awareness of smartphone overdependence (β=.33, p<.001), autonomy (β=–.25, p<.001), average daily smartphone usage time (β=.18, p<.001), being a woman student (β=.15, p<.001), forming relationships with others as a motivation for smartphone use (β=–.15, p=.010), being a fourth-year student (β=.15, p=.017), friend support (β=.14, p=.006), and age (β=–.13, p=.033) were found to be variables that statistically significantly influenced smartphone overdependence. The explanatory power of model II was 0.8% higher than that of model I, explaining 35.3% (Table 4).

Model III additionally introduced institutional and community factors. The fit of the regression model was found to be suitable (F=13.92, p<.001). In model III, self-awareness of smartphone overdependence (β=.33, p<.001), autonomy (β=–.25, p<.001), average daily smartphone usage time (β=.18, p<.001), being a woman student (β=.15, p=.001), being a fourth-year student (β=.15, p=.020), forming relationships with others as a motivation for smartphone use (β=–.15, p=.008), friend support (β=.14, p=.006), and age (β=–.12, p=.047) were found to be variables that statistically significantly influenced smartphone overdependence. The explanatory power of the model was 34.9%. Institutional and community factors reduced the explanatory power by 0.4% (Table 4).

Discussion

This study attempted to identify the factors influencing university students’ smartphone overdependence based on an ecological model. The study findings provide basic data for preparing an intervention plan for the prevention and management of smartphone overdependence.

In this study, the level of smartphone overdependence of university students was found to be 37.63 points out of 60 points, with 77.8% classified as high-risk users, which was a very high rate. This is higher than the average score of 35.29 obtained in a previous study [2], and the proportion of high-risers in this study is very high compared to 67.0% reported by Olson et al. [2]. According to the ecological approach, the intrapersonal factors influencing smartphone overdependence were identified in the following order: self-awareness of smartphone overdependence, autonomy, average daily smartphone usage time, gender, forming relationships with others as a motivation for smartphone use, college year, and age. Additionally, the interpersonal factor of “friend support” was identified.

Firstly, when the intrapersonal factors influencing smartphone overdependence were examined, the self-awareness of smartphone overdependence was found to have the greatest impact. In this study, 46.5% of the respondents perceived themselves as having smartphone overdependence, and the actual degree of smartphone overdependence was significantly higher among those who perceived themselves as overdependent. This can be seen in a context similar to a study by Carbonell et al. [42], which reported that the degree of smartphone overdependence increases as one’s awareness of the use of smartphones increases. Applying this to the KAP model (knowledge, attitude, practice model) that explains health behavior can have a positive effect on actual health behavior due to increased awareness or attitude [43]. Therefore, above all, accurately recognizing one’s smartphone use status can be an internal motivation for controlling smartphone use.

In this study, high autonomy was found to reduce the degree of smartphone overdependence among university students. Autonomy of basic psychological needs is the ability to establish objectives and regulate the behavior of one’s own free will [24]. High autonomy can help control smartphone immersion and it is considered a very important positive factor in managing smartphone use [29]. Therefore, improving the autonomy of university students can be an important strategy that can help them control their smartphone use on their own.

Smartphone usage time was also identified as a factor influencing smartphone overdependence. In this study, the average daily smartphone usage time was 5.60 hours, and the higher the usage time, the higher the risk of smartphone overdependence. This was consistent with the results of a study by Choo and Bae [20], reporting a quantitative relationship between smartphone overdependence and smartphone usage time. Moreover, the present study found that smartphone usage increased by an average of up to 4 hours a day after COVID-19. Thus, face-to-face contact restrictions, such as social distancing, in the early stages of the COVID-19 pandemic are believed to have affected the excessive use of smartphones.

Gender was identified as a factor influencing smartphone overdependence. The results that woman students had a higher degree of smartphone overdependence than man students are consistent with several previous studies [19,20]. Different genders have different patterns of use or addiction [33]; in particular, women tend to show an increased tendency to focus on social interactions than men [18]. Therefore, an intervention approach that considers gender differences in personal propensity or motivation for smartphone use is necessary.

Smartphone overdependence was found to decrease among university students when the motivation for smartphone use was to “form relationships with others” rather than to follow “the latest trends.” Interpersonal relationship formation can be seen as a purposeful act that requires positive achievement expectations at the psychological development stage for university students [44]. Using smartphones for a purpose has a positive effect on smartphone overdependence, and once the purpose for using it is achieved, its necessity decreases and does not lead to addictive use [45]. However, the influence of COVID-19 has caused the space for interaction to be concentrated online, and students have spent a lot of time on their smartphones to form and maintain relationships. Therefore, the method of online-dependent interaction must change.

Age was found to affect smartphone overdependence, and the younger the age, the higher the level of smartphone overdependence. In the case of university students, new students, who are relatively young, can be confused by rapid environmental changes upon admission, and their adaptation can affect smartphone overdependence [25]. Therefore, intervention in the proper use of smartphones is required from the beginning of admission.

In contrast, fourth-year students were found to have a higher degree of smartphone overdependence than first-year students. The university period is a time of preparing for a future career path and performing developmental tasks for self-identity and career choice [46]. In particular, fourth-year students experience stress due to crises, tension, and anxiety, along with psychological difficulties during college life related to employment. Such stress among university students acts as a factor that increases smartphone overdependence by leading them to temporarily immerse themselves in smartphone use to avoid their worries or psychological conflicts [5]. However, these findings contradict the results that showed higher smartphone overdependence among younger students. Thus, further investigation is needed to determine why fourth-year students, who are expected to be relatively older, showed higher smartphone overdependence compared to first-year students.

Second, the interpersonal factor that influenced smartphone overdependence was friend support; higher friend support was associated with a higher degree of smartphone overdependence. This is similar to the findings of Jin et al. [47] which showed that higher social support was associated with excessive smartphone use. This suggests that although new human relationships were formed and intimate interpersonal relationships were strengthened through the active use of smartphone functions such as SNS [48], it could also act as a risk factor for excessive smartphone use. Given the temporary environmental changes caused by the COVID-19 pandemic, it is likely that constant online interaction with friends and increased social media engagement contributed to the increase in smartphone overdependence [47].

Third, institutional and community factors were not identified as factors influencing smartphone overdependence. No studies have examined how the physical environment affects smartphone overdependence, so it is difficult to make direct comparisons. However, it is believed that the pandemic blocked access to the college environment due to the suspension of college attendance and conversion to non-face-to-face classes. As a result, the failure to consider a situation in which access to the physical university environment was difficult due to changes in the external environment was a limitation in deriving the results. Further, the incidence of COVID-19 by region was not significant. This suggests that differences in sample size across regions in this study may serve as a limitation in identifying community and environmental factors. Therefore, future research should consider sampling methods that account for community characteristics, such as population and city size. However, the study showed that the time spent using smartphones has increased since COVID-19, and the level of overdependence on smartphones has increased accordingly. Thus, the external community environment was judged to have an insufficient impact. The pandemic situation is a social phenomenon that can affect anyone, and since previous studies have already expressed concerns about digital addiction [49], measures to improve self-regulation capabilities for smartphone control should be established.

In this study, due to the lack of existing research on the relationship between university students’ smartphone overdependence and environmental factors, there was a limit to selecting variables that could explain each level well in terms of the institutional and community environments of university students. Future studies should use objective indicators that can grasp environmental effects by reflecting the ecological characteristics of university students.

Conclusion

This study was conducted to identify the factors that affect university students’ overdependence on smartphones by applying them to ecological models and dividing them into intrapersonal factors, interpersonal factors, and institutional and community factors. Based on the results of the above study, the characteristics of individuals in understanding the factors influencing university students’ smartphone overdependence were identified as effective influential factors, consistent with previous studies. Intrapersonal characteristics are essential to explaining smartphone overdependence and should be used as basic information for prevention and management. In particular, the characteristics of smartphone use differ according to the degree of smartphone overdependence and gender, so a differentiated intervention strategy is needed. The results of this study using an ecological approach to reflect on recent social and environmental phenomena in which the use of smartphones has become vital to forming and maintaining positive interpersonal relationships are significant. Moreover, although there were limitations in deriving significant influencing factors, it is meaningful to attempt to comprehensively approach the environment to which an individual belongs by including variables for community external environmental factors that were rarely used in previous studies. Furthermore, the findings provide scientific evidence for the development of intervention programs and training that incorporate multiple perspectives to prevent, manage, and intervene in excessive smartphone dependence among college students.

Article Information

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Acknowledgements

None.

Funding

This study was supported by the Hallym University Research Fund, 2022 (HRF-202203-002).

Data Sharing Statement

Please contact the corresponding author for data availability.

Author Contributions

Conceptualization or/and Methodology: JSY, MSK. Data curation or/and Analysis: JSY. Funding acquisition: MSK. Investigation: JSY. Project administration or/and Supervision: MSK. Resources or/and Software: JSY. Validation: JSY, MSK. Visualization: JSY. Writing: original draft or/and review & editing: JSY, MSK. Final approval of the manuscript: JSY, MSK.

Figure 1.

Study framework. COVID-19, coronavirus disease 2019.

Table 1.

Ecological characteristics of participants (n=482)

Factor	Variable	Value
Intrapersonal	Demographic characteristics
	Gender
	Men	187 (38.8)
	Women	295 (61.2)
	Age (yr)	21.3±1.96 (18–29)
	College year
	First year	150 (31.1)
	Second year	130 (27.0)
	Third year	111 (23.0)
	Fourth year	91 (18.9)
	Major department
	Humanities	69 (14.3)
	Social sciences	85 (17.6)
	Education	53 (11.0)
	Engineering & technology	122 (25.3)
	Natural sciences	40 (8.3)
	Nursing/health science	69 (14.3)
	Art & physical	44 (9.2)
	Residence type
	Family home	196 (40.7)
	Commute to college from other areas	60 (12.4)
	Living outside of home	121 (25.1)
	School dormitory	105 (21.8)
	Cohabitation status
	Parents (include family)	256 (53.1)
	Friend or colleague	103 (21.4)
	Alone	123 (25.5)
	Smartphone usage behaviors
	Smartphone overdependence^a)	37.63±8.62 (10–60)
	High risk user	376 (77.8)
	General user	107 (22.2)
	Age at first smartphone use
	Elementary school	158 (32.8)
	Middle school	229 (47.5)
	High school	63 (13.1)
	University	32 (6.6)
	Motivation for smartphone use
	Latest trends	66 (13.7)
	Information searching	90 (18.7)
	Studying purposes	25 (5.2)
	Forming relationships with others	301 (62.4)
	Main usage of smartphone
	Phone function (voice call, message, etc.)	14 (2.9)
	Entertainment function^b)	67 (13.9)
	Information searching	47 (9.7)
	SNS (KakaoTalk, Line, etc.)	236 (49.0)
	Watching videos	118 (24.5)
	Average smartphone usage time per day (hr)	5.60±2.69 (0.75–16.00)
	During week average usage time (hr)	5.22±2.75 (0.65–17.00)
	Weekend average usage time (hr)	5.98±2.94 (0.50–16.00)
	Self-awareness of smartphone overdependence
	Yes	224 (46.5)
	No	258 (53.5)
	Smartphone usage time after the COVID-19 (hr)
	≤1	147 (30.5)
	2	114 (23.6)
	3	119 (24.7)
	≥4	102 (21.2)
	Satisfaction of smartphone usage for specific purposes^c)
	Information searching	371 (77.0)
	Networking through SNS	250 (51.9)
	Entertainment function^b)	161 (33.4)
	Financial benefits	42 (8.7)
	Use for learning	36 (7.5)
	Problematic smartphone use^c)
	Conflict with parents/family	56 (11.6)
	Difficulties in interpersonal relationships	58 (12.0)
	Excessive usage charges	65 (13.5)
	Decreased health status	196 (40.7)
	Decreased academic and work efficiency	303 (62.9)
	Others	28 (5.8)
	Health status related to smartphone use^c)
	Lack of sleep	155 (32.2)
	Impaired vision and dry eye	224 (46.5)
	Headache	50 (10.4)
	Decreased concentration	191 (39.6)
	Decreased physical strength	35 (7.3)
	Musculoskeletal pain (neck, back, wrist, etc.)	121 (25.1)
	Self-determination	82.61±10.85 (39–108)
	Autonomy	27.42±4.99 (10–36)
	Competence	26.05±4.73 (10–36)
	Relatedness	29.14±4.13 (16–36)
Interpersonal	Social support	48.70±7.64 (22–60)
	Family support	16.20±3.16 (4–20)
	Friend support	16.22±2.93 (4–20)
	Significant other support	16.29±2.96 (4–20)
	Family life satisfaction	4.09±0.86 (1–5)
	Satisfied	376 (78.0)
	Unsatisfied	106 (22.0)
Institutional & community	Perceived university environment	2.62±0.78 (1–5)
	Experience of receiving education on smartphone overdependence prevention
	Yes	115 (23.9)
	No	367 (76.1)
	College life satisfaction	3.54±0.88 (1–5)
	Satisfied	264 (54.8)
	Unsatisfied	218 (45.2)
	City size of residence^d)
	Metropolitan city	347 (72.0)
	Small-medium size city	135 (28.0)
	COVID-19 incidence rate^e)	109.19±73.36 (9.67–271.91)

Values are presented as number (%) or mean±standard deviation (range).

COVID-19, coronavirus disease 2019; SNS, social network service.

^a)Smartphone overdependence criteria: Men: ≥31, Women: ≥33. ^b)Game, hobby, music, movie, etc. ^c)Multiple response. ^d)City size classification criteria: large metropolitan area, 1 million or more; metropolitan area, 500,000 or more and less than 1 million; small-medium size area, less than 500,000 according to population data from the National Environment Information Network System (https://www.neins.go.kr/mid=11010200). ^e)Based on COVID-19 incidence rate: 139.13/100,000 population on period from October 1, 2020 to March 25, 2021; http://ncov.mohw.go.kr/; Population Source by Region-Ministry of the Interior and Safety, Resident Registered Population Status (based on January 2020).

Table 2.

Degree of smartphone overdependence according to ecological factors (n=482)

Factor	Variable	Smartphone overdependence
Factor	Variable	Mean±SD	t or F (p)
Intrapersonal	Demographic characteristics
	Gender		–6.70 (<.001)
	Men	34.47±8.83
	Women	39.64±7.87
	College year		1.93 (.124)
	First year	37.29±8.23
	Second year	36.69±8.30
	Third year	37.72±9.01
	Fourth year	39.43±9.09
	Major department		1.01 (.416)
	Humanities	39.01±9.13
	Social sciences	37.52±8.06
	Education	38.30±8.55
	Engineering & technology	36.24±9.06
	Natural sciences	37.13±8.05
	Nursing/health science	38.42±8.38
	Art & physical	37.95±8.63
	Residence type		0.60 (.615)
	Family home	37.96±9.19
	Commute to college in other areas	37.65±8.31
	Living outside of home	37.94±7.83
	School dormitory	36.65±8.63
	Cohabitation status		0.41 (.667)
	Parents (include family)	37.89±8.97
	Friend or colleague	36.98±8.92
	Alone	37.64±7.60
	Smartphone usage behaviors
	Age at first smartphone use		0.61 (.611)
	Elementary school	37.81±7.99
	Middle school	37.83±8.97
	High school	36.27±8.93
	University	38.03±8.63
	Motivation for smartphone use		2.43 (.064)
	Latest trends	39.71±9.48
	Information searching	37.97±8.33
	Studying purposes	34.72±7.69
	Forming relationships with others	37.32±8.53
	Main usage of smartphone		1.00 (.406)
	Phone function (voice call, message, etc.)	35.93±8.49
	Entertainment function^a)	36.21±9.39
	Information searching	36.98±8.77
	SNS (KakaoTalk, Line, etc.)	38.29±8.41
	Watching video	37.58±8.56
	Self-awareness of smartphone overdependence		–11.47 (<.001)
	Yes	41.92±7.08
	No	33.91±8.11
	Smartphone usage time after the COVID-19 (hr)		11.39 (<.001) ^a,b,c<d
	≤1^a	35.91±9.30
	2^b	35.96±8.40
	3^c	38.05±7.69
	≥4^d	41.64±7.60
Interpersonal	Family life satisfaction		2.24 (.025)
	Satisfied	37.16±8.52
	Unsatisfied	39.28±8.82
Institutional & community	Experience of receiving education on smartphone overdependence prevention		–0.15 (.877)
	Yes	37.74±8.45
	No	37.60±8.68
	College life satisfaction		2.43 (.016)
	Satisfied	36.77±8.61
	Unsatisfied	38.67±8.54
	City size of residence^b)		0.86 (.390)
	Metropolitan city	37.84±8.56
	Small-medium size city	37.09±8.80

COVID-19, coronavirus disease 2019; SD, standard deviation; SNS, social network service.

^a)Game, hobby, music, movie, etc. ^b)City size classification criteria: large metropolitan area, 1 million or more; metropolitan area, 500,000 or more and less than 1 million; small-medium size area, less than 500,000 according to population data from the National Environment Information Network System (https://www.neins.go.kr/mid=11010200).

Table 3.

Correlation between smartphone overdependence and ecological factors (n=482)

Factors	Variable	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10	x11
Smartphone overdependence	x1	1
Intrapersonal	x2	-.09 (.049)	1
	x3	.34 (<.001)	-.17 (<.001)	1
	x4	-.27 (<.001)	-.05 (.305)	-.05 (.236)	1
	x5	-.13 (.003)	.02 (.614)	-.10 (.035)	.44 (<.001)	1
	x6	-.03 (.472)	-.14 (.002)	.06 (.223)	.42 (<.001)	.39 (<.001)	1
Interpersonal	x7	-.06 (.179)	-.08 (.073)	.02 (.715)	.29 (<.001)	.32 (<.001)	.51 (<.001)	1
	x8	.04 (.441)	-.14 (.003)	.09 (.056)	.31 (<.001)	.30 (<.001)	.69 (<.001)	.49 (<.001)	1
	x9	-.03 (.507)	-.08 (.092)	.03 (.460)	.22 (<.001)	.31 (<.001)	.56 (<.001)	.57 (<.001)	.64 (<.001)	1
Institutional & community	x10	-.05 (.286)	-.14 (.002)	.02 (.650)	-.04 (.365)	.04 (.330)	.07 (.109)	.12 (.011)	.15 (.001)	.14 (.002)	1
	x11	.03 (.477)	-.08 (.090)	.01 (.818)	.01 (.817)	.07 (.139)	.04 (.438)	-.07 (.117)	-.00 (.993)	.05 (.244)	.01 (.817)	1

x1, smartphone overdependence; x2, age; x3, average smartphone usage time per day (hr); x4, autonomy; x5, competence; x6, relatedness; x7, family support; x8, friend support; x9, significant other support; x10, perceived university environment; x11, COVID-19 incidence rate (from October 1, 2020 to March 25, 2021).

COVID-19, coronavirus disease 2019.

Table 4.

Factor influencing smartphone overdependence of university students (n=482)

Level	Variable	Model I			Model II			Model III
Level	Variable	β	SE	t (p)	β	SE	t (p)	β	SE	t (p)
Intrapersonal	(constants)	-	6.09	8.72 (<.001)	-	6.36	7.92 (<.001)	-	7.00	6.87 (<.001)
	Gender^a)
	Women	.15	0.77	3.35 (<.001)	.15	0.77	3.55 (<.001)	.15	0.78	3.43 (.001)
	Age	–.14	0.26	–2.27 (.024)	–.13	0.26	–2.14 (.033)	–.12	0.27	–1.99 (.047)
	College year^a)
	Second year	.01	0.89	0.27 (.790)	.00	0.89	0.09 (.931)	.01	0.90	0.13 (.898)
	Third year	.10	1.12	1.83 (.067)	.10	1.19	1.77 (.077)	.10	1.12	1.78 (.076)
	Fourth year	.16	1.36	2.54 (.011)	.15	1.36	2.39 (.017)	.15	1.37	2.33 (.020)
	Average smartphone usage time per day	.20	0.13	4.96 (<.001)	.18	0.13	4.65 (<.001)	.18	0.13	4.58 (<.001)
	Motivation for smartphone use^a)
	Information searching	–.10	1.14	–1.97 (.049)	–.08	1.15	–1.57 (.118)	–.08	1.16	–1.46 (.144)
	Studying purposes	–.09	1.66	–2.10 (.036)	–.08	1.66	–1.90 (.059)	–.08	1.67	–1.91 (.057)
	Forming relationships with others	–.16	0.97	–2.93 (.004)	–.15	0.97	–2.77 (.006)	–.15	0.97	–2.66 (.008)
	Self-awareness of smartphone overdependence: yes^a)	.33	0.70	8.18 (<.001)	.33	0.70	8.11 (<.001)	.33	0.71	8.06 (<.001)
	Self-determination
	Autonomy	–.23	0.07	–5.57 (<.001)	–.25	0.07	–5.82 (<.001)	–.25	0.08	–5.79 (<.001)
	Competence	.03	0.08	0.80 (.425)	.02	0.08	0.48 (.630)	.02	0.08	0.41 (.681)
Interpersonal	Social support
	Family support				.03	0.14	0.47 (.640)	.03	0.15	0.57 (.569)
	Friend support				.14	0.15	2.78 (.006)	.14	0.15	2.78 (.006)
	Special person support				–.07	0.15	–1.27 (.203)	–.07	0.16	–1.37 (.173)
	Satisfaction with family life: satisfied^a)				–.07	0.93	–1.49 (.138)	–.07	0.95	–1.53 (.127)
Institutional & community	Perceived university environment							–.00	0.44	–0.09 (.925)
	Satisfaction of college life: satisfied^a)							.02	0.71	0.36 (.718)
	City size of residence: small-medium size city^a,b)							–.02	-0.75	–0.42 (.677)
	COVID-19 incidence rate^c)							.04	0.46	1.03 (.302)
R² (adjusted R²)		.36 (.345)			.37 (.353)			.38 (.349)
△R²		.36			.01			.00
F (p)		22.07 (<.001)			17.38 (<.001)			13.92 (<.001)
△F (p)		22.07 (<.001)			2.47 (.044)			0.42 (.796)

COVID-19, coronavirus disease 2019; SE, standard error.

^a)Reference of dummy variables: gender, Men; college year, first year; motivation for smartphone use, latest trend; self-awareness of smartphone overdependence, no; satisfaction with family life, unsatisfied; satisfaction of college life, unsatisfied; city size of residence, metropolitan city. ^b)City size classification criteria: large metropolitan area, 1 million or more; metropolitan area, 500,000 or more and less than 1 million; small-medium size area, less than 500,000 according to population data from the National Environment Information Network System (https://www.neins.go.kr/mid=11010200). ^c)Based on COVID-19 incidence rate: 139.13/100,000 population on period from 2020 October 1 to 2021 March 25; http://ncov.mohw.go.kr/; Population Source by Region-Ministry of the Interior and Safety, Resident Registered Population Status (based on January 2020).

References

1. David ME, Roberts JA. Smartphone use during the COVID-19 pandemic: social versus physical distancing. Int J Environ Res Public Health. 2021;18(3):1034. https://doi.org/10.3390/ijerph18031034Article PubMed PMC
2. Olson JA, Sandra DA, Chmoulevitch D, Raz A, Veissière SP. A nudge-based intervention to reduce problematic smartphone use: randomised controlled trial. Int J Ment Health Addict. 2023;21:3842-3864. https://doi.org/10.1007/s11469-022-00826-wArticle
3. Van Deursen AJ, Bolle CL, Hegner SM, Kommers PA. Modeling habitual and addictive smartphone behavior: the role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Comput Hum Behav. 2015;45:411-420. https://doi.org/10.1016/j.chb.2014.12.039Article
4. Duke É, Montag C. Smartphone addiction, daily interruptions and self-reported productivity. Addict Behav Rep. 2017;6:90-95. https://doi.org/10.1016/j.abrep.2017.07.002Article PubMed PMC
5. Samaha M, Hawi NS. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Comput Hum Behav. 2016;57:321-325. https://doi.org/10.1016/j.chb.2015.12.045Article
6. Mustafaoglu R, Yasaci Z, Zirek E, Griffiths MD, Ozdincler AR. The relationship between smartphone addiction and musculoskeletal pain prevalence among young population: a cross-sectional study. Korean J Pain. 2021;34(1):72-81. https://doi.org/10.3344/kjp.2021.34.1.72Article PubMed PMC
7. Elhai JD, Tiamiyu M, Weeks J. Depression and social anxiety in relation to problematic smartphone use: the prominent role of rumination. Internet Res. 2018;28(2):315-332. https://doi.org/10.1108/IntR-01-2017-0019Article
8. Kim E, Koh E. Avoidant attachment and smartphone addiction in college students: the mediating effects of anxiety and self-esteem. Comput Hum Behav. 2018;84:264-271. https://doi.org/10.1016/j.chb.2018.02.037Article
9. National Information Society Agency. 2016 Digital culture forum policy research report. National Information Society Agency; 2016. Report No.: NIA V-RER-B-16007.
10. Shen X, Wang JL. Loneliness and excessive smartphone use among Chinese college students: moderated mediation effect of perceived stressed and motivation. Comput Hum Behav. 2019;95:31-36. https://doi.org/10.1016/j.chb.2019.01.012Article
11. Ching SM, Yee A, Ramachandran V, Sazlly Lim SM, Wan Sulaiman WA, Foo YL, et al. Validation of a Malay version of the Smartphone Addiction Scale among medical students in Malaysia. PLoS One. 2015;10(10):e0139337. https://doi.org/10.1371/journal.pone.0139337Article PubMed PMC
12. National Information Society Agency. The 2015 survey on internet overdependence [Internet]. National Information Society Agency; 2016 [cited 2023 Jul 20]. Available from: https://www.nia.or.kr/site/nia_kor/ex/bbs/View.do?cbIdx=65914&bcIdx=17132&parentSeq=17132
13. Shin SM, Koh YS, Song YS, Oh JS, Park MJ. Delphi analysis on the use of the term ‘Internet addiction’ and development of an alternate term. Korean J Addict Psychol. 2017;2(2):15-29. https://doi.org/10.23147/ADDICTPSY.PUB.2.2.15Article
14. National Information Society Agency. The 2023 survey on smart phone overdependence [Internet]. National Information Society Agency; 2023 [cited 2023 Jul 20]. Available from: https://www.nia.or.kr/site/nia_kor/ex/bbs/View.do?cbIdx=65914&bcIdx=26508&parentSeq=26508
15. Kwon MS, Jin J. Exploring the basic psychological needs necessary for the internalized motivation of university students with smartphone overdependence: applying a self-determination theory. J Korean Acad Psychiatr Ment Health Nurs. 2019;28(1):26-36. https://doi.org/10.12934/jkpmhn.2019.28.1.26Article
16. Elhai JD, Yang H, McKay D, Asmundson GJ. COVID-19 anxiety symptoms associated with problematic smartphone use severity in Chinese adults. J Affect Disord. 2020;274:576-582. https://doi.org/10.1016/j.jad.2020.05.080Article PubMed PMC
17. McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988;15(4):351-377. https://doi.org/10.1177/109019818801500401Article PubMed
18. Billieux J, Van der Linden M, d’Acremont M, Ceschi G, Zermatten A. Does impulsivity relate to perceived dependence on and actual use of the mobile phone? Appl Cogn Psychol. 2007;21(4):527-537. https://doi.org/10.1002/acp.1289Article
19. Celikkalp U, Bilgic S, Temel M, Varol G. The smartphone addiction levels and the association with communication skills in nursing and medical school students. J Nurs Res. 2020;28(3):e93. https://doi.org/10.1097/jnr.0000000000000370Article PubMed
20. Choo YK, Bae WS. Comparative impact analysis of attention control and interpersonal support according to the degree of smartphone addiction of health-related college students. J Korean Soc Integr Med. 2021;9(2):183-192. https://doi.org/10.15268/ksim.2021.9.2.183Article
21. Roberts JA, Yaya LH, Manolis C. The invisible addiction: cell-phone activities and addiction among male and female college students. J Behav Addict. 2014;3(4):254-265. https://doi.org/10.1556/JBA.3.2014.015Article PubMed PMC
22. De-Sola Gutiérrez J, Rodríguez de Fonseca F, Rubio G. Cell-phone addiction: a review. Front Psychiatry. 2016;7:175. https://doi.org/10.3389/fpsyt.2016.00175Article PubMed PMC
23. Robayo-Pinzon O, Foxall GR, Montoya-Restrepo LA, Rojas-Berrio S. Does excessive use of smartphones and apps make us more impulsive?: an approach from behavioural economics. Heliyon. 2021;7(2):e06104. https://doi.org/10.1016/j.heliyon.2021.e06104Article PubMed PMC
24. Deci EL, Ryan RM. The “what” and “why” of goal pursuits: human needs and the self-determination of behavior. Psychol Inq. 2000;11(4):227-268. https://doi.org/10.1207/S15327965PLI1104_01Article
25. Kwon MS, Yu JS. Development and effect of a smartphone overdependence prevention program for university students based on self-determination theory. J Korean Acad Nurs. 2020;50(1):116-131. https://doi.org/10.4040/jkan.2020.50.1.116Article PubMed
26. Cohen S, Hoberman HM. Positive events and social supports as buffers of life change stress. J Appl Soc Psychol. 1983;13(2):99-125. https://doi.org/10.1111/j.1559-1816.1983.tb02325.xArticle
27. Mazzoni E, Baiocco L, Cannata D, Dimas I. Is internet the cherry on top or a crutch?: offline social support as moderator of the outcomes of online social support on problematic internet use. Comput Hum Behav. 2016;56:369-374. https://doi.org/10.1016/j.chb.2015.11.032Article
28. Ko M, Yang S, Lee J, Heizmann C, Jeong J, Lee U, et al. NUGU: a group-based intervention app for improving self-regulation of limiting smartphone use. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing; 2015 Mar 14-18; Vancouver, Canada. Association for Computing Machinery; 2015. p. 1235-1245. https://doi.org/10.1145/2675133.2675244Article
29. Kwon MS, Lee BY. Relationship among basic psychological needs, smartphone addiction and adaptation to school life in university students. J Korean Public Health Nurs. 2017;31(3):540-553. https://doi.org/10.5932/JKPHN.2017.31.3.540Article
30. National Information Society Agency. The 2012 survey on internet addiction [Internet]. National Information Society Agency; 2012 [cited 2023 Jul 20]. Available from: https://www.nia.or.kr/site/nia_kor/ex/bbs/View.do?cbIdx=65914&bcIdx=11067&parentSeq=11067
31. Yu JD, Lee IS, Yang JH. A study on regional smart-phone addiction with demographic and sociodemographic factors. J Soc Sci. 2014;40(3):51-75. https://doi.org/10.15820/khjss.2014.40.3.003Article
32. Halinski RS, Feldt LS. The selection of variables in multiple regression analysis. J Educ Meas. 1970;7(3):151-157. https://doi.org/10.1111/j.1745-3984.1970.tb00709.xArticle
33. Kwon M, Kim DJ, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One. 2013;8(12):e83558. https://doi.org/10.1371/journal.pone.0083558Article PubMed PMC
34. Lee MH, Kim A. Development and construct validation of the basic psychological needs scale for Korean adolescents：based on the self-determination theory. Korean J Soc Personal Psychol. 2008;22(4):157-174. https://doi.org/10.21193/kjspp.2008.22.4.010Article
35. Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. J Pers Assess. 1988;52(1):30-41. https://doi.org/10.1207/s15327752jpa5201_2Article
36. Shin JS, Lee YB. The effects of social supports on psychosocial well-being of the unemployed. Korean J Soc Welf [Internet]. 1999 [cited 2024 Dec 10];37:241-269. Available from: https://koreascience.kr/article/JAKO199903749521496.pdf
37. Heo MJ. Physical activity of college students based on ecological model [master’s thesis]. Seoul: Ewha Womens University; 2014 [cited 2024 Jun 10]. Available from: https://dspace.ewha.ac.kr/handle/2015.oak/211645
38. Kamphuis CB, van Lenthe FJ, Giskes K, Brug J, Mackenbach JP. Perceived environmental determinants of physical activity and fruit and vegetable consumption among high and low socioeconomic groups in the Netherlands. Health Place. 2007;13(2):493-503. https://doi.org/10.1016/j.healthplace.2006.05.008Article PubMed
39. National Environment Information Network System. Understanding the national environment: cities in Korea [Internet]. National Environment Information Network System; 2021 [cited 2024 Jun 10]. Available from: https://www.neins.go.kr/mid=11010200
40. Ministry of Health and Welfare. Current status of COVID-19 domestic outbreaks in Korea (as of October 1, 2020) (No. 17337) [Internet]. Ministry of Health and Welfare; 2021 [cited 2024 Jun 10]. Available from: https://www.mohw.go.kr/board.es?mid=a10503010100&bid=0027&cg_code=
41. Ministry of Health and Welfare. COVID-19 outbreak and vaccination status in Korea (as of March 25, 2021) (No. 18192) [Internet]. Ministry of Health and Welfare; 2021 [cited 2024 Jun 10]. Available from: https://www.mohw.go.kr/board.es?mid=a10503010100&bid=0027&cg_code=
42. Carbonell X, Chamarro A, Oberst U, Rodrigo B, Prades M. Problematic use of the Internet and smartphones in university students: 2006-2017. Int J Environ Res Public Health. 2018;15(3):475. https://doi.org/10.3390/ijerph15030475Article PubMed PMC
43. Launiala A. How much can a KAP survey tell us about people’s knowledge, attitudes and practices?: some observations from medical anthropology research on malaria in pregnancy in Malawi. Anthropol Matters. 2009;11(1):1-13. https://doi.org/10.22582/am.v11i1.31Article
44. Hong K, Jeon H. The relationship between college students’ SNS addiction tendency and their interpersonal problems: focused on the moderating effect of social support. Health Soc Welf Rev. 2017;37(1):34-67. https://doi.org/10.15709/hswr.2017.37.1.34Article
45. Lee SJ, Rhee MK. Effect of personalities and use motivations of smartphone users on smartphone addiction. Korean J Health Psychol. 2016;21(2):357-372. https://doi.org/10.17315/KJHP.2016.21.2.004Article
46. Felsman DE, Blustein DL. The role of peer relatedness in late adolescent career development. J Vocat Behav. 1999;54(2):279-295. https://doi.org/10.1006/jvbe.1998.1664Article
47. Jin L, Hao Z, Huang J, Akram HR, Saeed MF, Ma H. Depression and anxiety symptoms are associated with problematic smartphone use under the COVID-19 epidemic: the mediation models. Child Youth Serv Rev. 2021;121:105875. https://doi.org/10.1016/j.childyouth.2020.105875Article PubMed PMC
48. Seo M, Kim JH, David P. Always connected or always distracted? ADHD symptoms and social assurance explain problematic use of mobile phone and multicommunicating. J Comput Mediat Commun. 2015;20(6):667-681. https://doi.org/10.1111/jcc4.12140Article
49. Son YJ, Heo MS. A study on social media usage, helplessness, and loneliness experienced by college students since the COVID-19 pandemic. J Digit Contents Soc. 2020;21(11):1957-1971. https://doi.org/10.9728/dcs.2020.21.11.1957Article

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Factors influencing smartphone overdependence in university students: an ecological model: a descriptive study

J Korean Acad Nurs. 2025;55(1):64-80. Published online February 20, 2025

DOI: https://doi.org/10.4040/jkan.24092

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Factors influencing smartphone overdependence in university students: an ecological model: a descriptive study

Figure 1. Study framework. COVID-19, coronavirus disease 2019.

Figure 1.

Factors influencing smartphone overdependence in university students: an ecological model: a descriptive study

Factor	Variable	Value
Intrapersonal	Demographic characteristics
	Gender
	Men	187 (38.8)
	Women	295 (61.2)
	Age (yr)	21.3±1.96 (18–29)
	College year
	First year	150 (31.1)
	Second year	130 (27.0)
	Third year	111 (23.0)
	Fourth year	91 (18.9)
	Major department
	Humanities	69 (14.3)
	Social sciences	85 (17.6)
	Education	53 (11.0)
	Engineering & technology	122 (25.3)
	Natural sciences	40 (8.3)
	Nursing/health science	69 (14.3)
	Art & physical	44 (9.2)
	Residence type
	Family home	196 (40.7)
	Commute to college from other areas	60 (12.4)
	Living outside of home	121 (25.1)
	School dormitory	105 (21.8)
	Cohabitation status
	Parents (include family)	256 (53.1)
	Friend or colleague	103 (21.4)
	Alone	123 (25.5)
	Smartphone usage behaviors
	Smartphone overdependence^a)	37.63±8.62 (10–60)
	High risk user	376 (77.8)
	General user	107 (22.2)
	Age at first smartphone use
	Elementary school	158 (32.8)
	Middle school	229 (47.5)
	High school	63 (13.1)
	University	32 (6.6)
	Motivation for smartphone use
	Latest trends	66 (13.7)
	Information searching	90 (18.7)
	Studying purposes	25 (5.2)
	Forming relationships with others	301 (62.4)
	Main usage of smartphone
	Phone function (voice call, message, etc.)	14 (2.9)
	Entertainment function^b)	67 (13.9)
	Information searching	47 (9.7)
	SNS (KakaoTalk, Line, etc.)	236 (49.0)
	Watching videos	118 (24.5)
	Average smartphone usage time per day (hr)	5.60±2.69 (0.75–16.00)
	During week average usage time (hr)	5.22±2.75 (0.65–17.00)
	Weekend average usage time (hr)	5.98±2.94 (0.50–16.00)
	Self-awareness of smartphone overdependence
	Yes	224 (46.5)
	No	258 (53.5)
	Smartphone usage time after the COVID-19 (hr)
	≤1	147 (30.5)
	2	114 (23.6)
	3	119 (24.7)
	≥4	102 (21.2)
	Satisfaction of smartphone usage for specific purposes^c)
	Information searching	371 (77.0)
	Networking through SNS	250 (51.9)
	Entertainment function^b)	161 (33.4)
	Financial benefits	42 (8.7)
	Use for learning	36 (7.5)
	Problematic smartphone use^c)
	Conflict with parents/family	56 (11.6)
	Difficulties in interpersonal relationships	58 (12.0)
	Excessive usage charges	65 (13.5)
	Decreased health status	196 (40.7)
	Decreased academic and work efficiency	303 (62.9)
	Others	28 (5.8)
	Health status related to smartphone use^c)
	Lack of sleep	155 (32.2)
	Impaired vision and dry eye	224 (46.5)
	Headache	50 (10.4)
	Decreased concentration	191 (39.6)
	Decreased physical strength	35 (7.3)
	Musculoskeletal pain (neck, back, wrist, etc.)	121 (25.1)
	Self-determination	82.61±10.85 (39–108)
	Autonomy	27.42±4.99 (10–36)
	Competence	26.05±4.73 (10–36)
	Relatedness	29.14±4.13 (16–36)
Interpersonal	Social support	48.70±7.64 (22–60)
	Family support	16.20±3.16 (4–20)
	Friend support	16.22±2.93 (4–20)
	Significant other support	16.29±2.96 (4–20)
	Family life satisfaction	4.09±0.86 (1–5)
	Satisfied	376 (78.0)
	Unsatisfied	106 (22.0)
Institutional & community	Perceived university environment	2.62±0.78 (1–5)
	Experience of receiving education on smartphone overdependence prevention
	Yes	115 (23.9)
	No	367 (76.1)
	College life satisfaction	3.54±0.88 (1–5)
	Satisfied	264 (54.8)
	Unsatisfied	218 (45.2)
	City size of residence^d)
	Metropolitan city	347 (72.0)
	Small-medium size city	135 (28.0)
	COVID-19 incidence rate^e)	109.19±73.36 (9.67–271.91)

Factor	Variable	Smartphone overdependence
Factor	Variable	Mean±SD	t or F (p)
Intrapersonal	Demographic characteristics
	Gender		–6.70 (<.001)
	Men	34.47±8.83
	Women	39.64±7.87
	College year		1.93 (.124)
	First year	37.29±8.23
	Second year	36.69±8.30
	Third year	37.72±9.01
	Fourth year	39.43±9.09
	Major department		1.01 (.416)
	Humanities	39.01±9.13
	Social sciences	37.52±8.06
	Education	38.30±8.55
	Engineering & technology	36.24±9.06
	Natural sciences	37.13±8.05
	Nursing/health science	38.42±8.38
	Art & physical	37.95±8.63
	Residence type		0.60 (.615)
	Family home	37.96±9.19
	Commute to college in other areas	37.65±8.31
	Living outside of home	37.94±7.83
	School dormitory	36.65±8.63
	Cohabitation status		0.41 (.667)
	Parents (include family)	37.89±8.97
	Friend or colleague	36.98±8.92
	Alone	37.64±7.60
	Smartphone usage behaviors
	Age at first smartphone use		0.61 (.611)
	Elementary school	37.81±7.99
	Middle school	37.83±8.97
	High school	36.27±8.93
	University	38.03±8.63
	Motivation for smartphone use		2.43 (.064)
	Latest trends	39.71±9.48
	Information searching	37.97±8.33
	Studying purposes	34.72±7.69
	Forming relationships with others	37.32±8.53
	Main usage of smartphone		1.00 (.406)
	Phone function (voice call, message, etc.)	35.93±8.49
	Entertainment function^a)	36.21±9.39
	Information searching	36.98±8.77
	SNS (KakaoTalk, Line, etc.)	38.29±8.41
	Watching video	37.58±8.56
	Self-awareness of smartphone overdependence		–11.47 (<.001)
	Yes	41.92±7.08
	No	33.91±8.11
	Smartphone usage time after the COVID-19 (hr)		11.39 (<.001) ^a,b,c<d
	≤1^a	35.91±9.30
	2^b	35.96±8.40
	3^c	38.05±7.69
	≥4^d	41.64±7.60
Interpersonal	Family life satisfaction		2.24 (.025)
	Satisfied	37.16±8.52
	Unsatisfied	39.28±8.82
Institutional & community	Experience of receiving education on smartphone overdependence prevention		–0.15 (.877)
	Yes	37.74±8.45
	No	37.60±8.68
	College life satisfaction		2.43 (.016)
	Satisfied	36.77±8.61
	Unsatisfied	38.67±8.54
	City size of residence^b)		0.86 (.390)
	Metropolitan city	37.84±8.56
	Small-medium size city	37.09±8.80

Factors	Variable	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10	x11
Smartphone overdependence	x1	1
Intrapersonal	x2	-.09 (.049)	1
	x3	.34 (<.001)	-.17 (<.001)	1
	x4	-.27 (<.001)	-.05 (.305)	-.05 (.236)	1
	x5	-.13 (.003)	.02 (.614)	-.10 (.035)	.44 (<.001)	1
	x6	-.03 (.472)	-.14 (.002)	.06 (.223)	.42 (<.001)	.39 (<.001)	1
Interpersonal	x7	-.06 (.179)	-.08 (.073)	.02 (.715)	.29 (<.001)	.32 (<.001)	.51 (<.001)	1
	x8	.04 (.441)	-.14 (.003)	.09 (.056)	.31 (<.001)	.30 (<.001)	.69 (<.001)	.49 (<.001)	1
	x9	-.03 (.507)	-.08 (.092)	.03 (.460)	.22 (<.001)	.31 (<.001)	.56 (<.001)	.57 (<.001)	.64 (<.001)	1
Institutional & community	x10	-.05 (.286)	-.14 (.002)	.02 (.650)	-.04 (.365)	.04 (.330)	.07 (.109)	.12 (.011)	.15 (.001)	.14 (.002)	1
	x11	.03 (.477)	-.08 (.090)	.01 (.818)	.01 (.817)	.07 (.139)	.04 (.438)	-.07 (.117)	-.00 (.993)	.05 (.244)	.01 (.817)	1

Level	Variable	Model I			Model II			Model III
Level	Variable	β	SE	t (p)	β	SE	t (p)	β	SE	t (p)
Intrapersonal	(constants)	-	6.09	8.72 (<.001)	-	6.36	7.92 (<.001)	-	7.00	6.87 (<.001)
	Gender^a)
	Women	.15	0.77	3.35 (<.001)	.15	0.77	3.55 (<.001)	.15	0.78	3.43 (.001)
	Age	–.14	0.26	–2.27 (.024)	–.13	0.26	–2.14 (.033)	–.12	0.27	–1.99 (.047)
	College year^a)
	Second year	.01	0.89	0.27 (.790)	.00	0.89	0.09 (.931)	.01	0.90	0.13 (.898)
	Third year	.10	1.12	1.83 (.067)	.10	1.19	1.77 (.077)	.10	1.12	1.78 (.076)
	Fourth year	.16	1.36	2.54 (.011)	.15	1.36	2.39 (.017)	.15	1.37	2.33 (.020)
	Average smartphone usage time per day	.20	0.13	4.96 (<.001)	.18	0.13	4.65 (<.001)	.18	0.13	4.58 (<.001)
	Motivation for smartphone use^a)
	Information searching	–.10	1.14	–1.97 (.049)	–.08	1.15	–1.57 (.118)	–.08	1.16	–1.46 (.144)
	Studying purposes	–.09	1.66	–2.10 (.036)	–.08	1.66	–1.90 (.059)	–.08	1.67	–1.91 (.057)
	Forming relationships with others	–.16	0.97	–2.93 (.004)	–.15	0.97	–2.77 (.006)	–.15	0.97	–2.66 (.008)
	Self-awareness of smartphone overdependence: yes^a)	.33	0.70	8.18 (<.001)	.33	0.70	8.11 (<.001)	.33	0.71	8.06 (<.001)
	Self-determination
	Autonomy	–.23	0.07	–5.57 (<.001)	–.25	0.07	–5.82 (<.001)	–.25	0.08	–5.79 (<.001)
	Competence	.03	0.08	0.80 (.425)	.02	0.08	0.48 (.630)	.02	0.08	0.41 (.681)
Interpersonal	Social support
	Family support				.03	0.14	0.47 (.640)	.03	0.15	0.57 (.569)
	Friend support				.14	0.15	2.78 (.006)	.14	0.15	2.78 (.006)
	Special person support				–.07	0.15	–1.27 (.203)	–.07	0.16	–1.37 (.173)
	Satisfaction with family life: satisfied^a)				–.07	0.93	–1.49 (.138)	–.07	0.95	–1.53 (.127)
Institutional & community	Perceived university environment							–.00	0.44	–0.09 (.925)
	Satisfaction of college life: satisfied^a)							.02	0.71	0.36 (.718)
	City size of residence: small-medium size city^a,b)							–.02	-0.75	–0.42 (.677)
	COVID-19 incidence rate^c)							.04	0.46	1.03 (.302)
R² (adjusted R²)		.36 (.345)			.37 (.353)			.38 (.349)
△R²		.36			.01			.00
F (p)		22.07 (<.001)			17.38 (<.001)			13.92 (<.001)
△F (p)		22.07 (<.001)			2.47 (.044)			0.42 (.796)

Table 1. Ecological characteristics of participants (n=482)

Values are presented as number (%) or mean±standard deviation (range).

COVID-19, coronavirus disease 2019; SNS, social network service.

Table 2. Degree of smartphone overdependence according to ecological factors (n=482)

COVID-19, coronavirus disease 2019; SD, standard deviation; SNS, social network service.

Table 3. Correlation between smartphone overdependence and ecological factors (n=482)

COVID-19, coronavirus disease 2019.

Table 4. Factor influencing smartphone overdependence of university students (n=482)