From Necessity to Excess: Temporal Differences in Smartphone App Usage–PSU Links During COVID-19

Abstract

With the growing prevalence of digital media use, increasing attention has been directed toward the impact of smartphone usage patterns on mental health. In particular, the COVID-19 pandemic fundamentally altered daily life, accelerating the integration of smartphones into social and economic activities. This study utilized four years of cross-sectional data (N = 75,450) to examine how different types of smartphone application usages—specifically gaming, social networking services (SNS), and online shopping—are associated with problematic smartphone use (PSU), comparing patterns during and after the pandemic. The findings indicate that excessive gaming had a consistently notable association with PSU across both periods. However, the relationship between SNS and shopping app usage on PSU was significantly stronger after the pandemic. This shift suggests that while such applications served essential roles during the pandemic, their continued and excessive use after the return to face-to-face interaction may potentially contribute to problematic use. These results highlight the evolving nature of smartphone application use and its psychological consequences, underscoring the importance of developing tailored intervention strategies that reflect post-pandemic digital behaviors.

1. Introduction

With the widespread integration of smartphones into daily life, academic interest in their psychological and behavioral consequences has grown markedly. In particular, problematic smartphone use (PSU) has emerged as a major public health concern, linked to depression, anxiety, loneliness, and reduced well-being [1,2,3]. Beyond documenting associations, recent theoretical frameworks such as the Interaction of Person-Affect-Cognition-Execution (I-PACE) model provide a useful lens for conceptualizing PSU [4,5]. This model emphasizes that PSU results from dynamic interactions among individual predispositions, affective and cognitive responses, and executive functioning, suggesting that both personal vulnerabilities and situational contexts jointly shape problematic digital behaviors. Researchers have increasingly sought to understand how specific smartphone application categories—such as gaming, social networking services (SNS), and online shopping—contribute differently to PSU [6,7]. For instance, gaming apps are often associated with compulsive engagement due to their reward systems and immersive design [8,9], while SNS use has been linked to fear of missing out (FOMO), social comparison, and emotional dysregulation [10]. Moreover, research suggests that experiences of FOMO may vary across cultural contexts, with collectivist and individualist societies emphasizing different social expectations and relational dynamics [11]. Online shopping apps, in turn, have been found to encourage impulsivity and gratification-seeking behavior, especially among individuals with lower self-regulation [12]. These studies highlight that not all smartphone use is equally problematic—rather, PSU risk varies depending on the type and purpose of usage.

However, contextual factors such as societal norms, external stressors, and technological dependency can significantly moderate the relationship between application use and PSU. The COVID-19 pandemic, in particular, created a unique digital environment wherein smartphones became essential tools for maintaining social connections, conducting remote work, and accessing goods and services [13]. During this period, the line between necessary and excessive smartphone use became blurred, potentially masking the severity of PSU among users. Recent longitudinal evidence further demonstrates that these changes were not only immediate but also persistent. Poulain et al. [14], tracking German children and adolescents from 2018 to 2024, found that PSU symptoms and daily smartphone use above three hours increased following the onset of COVID-19, accompanied by declines in quality of life. Similarly, Zhang et al. reported longitudinal changes in PSU and sleep disturbances during the pandemic, indicating that the pandemic context altered both the level and correlates of PSU [15]. These findings underscore the importance of examining not just short-term spikes but also longer-term shifts in digital engagement. While prior research has explored increased screen time and PSU during the pandemic [16,17], few studies have addressed how the cessation of pandemic-related restrictions may have altered these dynamics. As offline activities and face-to-face interactions resumed post-COVID-19, the functional necessity of digital tools may have decreased, potentially transforming once-justified usage patterns into sources of overuse and psychological strain.

To address this research gap, the present study investigates whether and how the relationship between smartphone application use—specifically gaming, SNS, and online shopping—and PSU has shifted from the COVID-19 period to the post-COVID-19 context (see Figure 1). Prior research has established that smartphone use and PSU increased during the pandemic, with studies documenting heightened screen time, elevated dependence, and associations with negative psychological outcomes [16,17]. However, much of this literature has treated smartphone use as a general construct, without distinguishing between specific application types or considering how their impacts may have evolved once pandemic restrictions eased. Drawing on a large cross-sectional dataset of 75,450 individuals collected between 2020 and 2024 (two years for the pandemic, two years for the endemic, respectively), this study examines temporal associations between smartphone application use and PSU across pandemic and post-pandemic periods. Given South Korea’s uniquely high smartphone penetration rate (over 95%) and its strong digital culture characterized by ubiquitous mobile services and rapid adoption of online platforms [18], this context provides a particularly valuable setting for investigating how shifts in app use intersect with PSU. By moving beyond documenting pandemic-era increases in screen time, this research clarifies whether application-specific risks persisted or evolved as social conditions changed. In doing so, it contributes to the literature by diagnosing how contextual shifts reshape the mechanisms of PSU, thereby extending app-based PSU research into a post-pandemic framework. Understanding these temporal shifts is critical for developing nuanced interventions and public health strategies tailored to specific usage patterns and social contexts. As digital behaviors continue to evolve, it is essential to distinguish between necessary engagement and problematic dependence.

2. Literature Review

Problematic smartphone use (PSU) has been extensively studied with particular attention to various smartphone application types that may contribute to excessive or harmful usage patterns. PSU is conceptualized as an impaired capacity for self-regulation in smartphone use, often manifesting in symptoms like tolerance loss, withdrawal, avoidance behaviors, and compulsive urges [19]. Recent theoretical developments further highlight that PSU arises from complex interactions among individual predispositions, affective and cognitive responses, and executive functioning. For instance, the Interaction of Person-Affect-Cognition-Execution (I-PACE) model provides a comprehensive framework suggesting that addictive behaviors, including PSU, are shaped not only by usage frequency but also by underlying psychological and neurobiological processes [4,5]. This perspective underscores the need to examine PSU as a multidimensional phenomenon that extends beyond time-based metrics of use.

2.1. Gaming and PSU

A substantial body of research identifies gaming as a prominent risk factor for PSU, especially among younger populations. For example, Fischer-Grote et al. conducted a comprehensive literature review analyzing 38 empirical studies focused on children and adolescents, highlighting that smartphone gaming is associated with increased risk for problematic use within this age group [20]. Their synthesis suggested that gaming, along with social networking, could exacerbate PSU risks, particularly in female adolescents and those exhibiting low self-control or self-esteem. Lopez-Fernandez et al. administered the Problematic Mobile Phone Use Questionnaire-Short Version (PMPUQ-SV) to 899 participants across Belgium and Finland, aiming to validate the scale and examine predictors of PSU [21]. Although mobile games were commonly used by about one-third of respondents, the study emphasized that gaming’s addictive potential aligns with increased PSU risk in these populations. More recently, Akbari et al. employed a cross-sectional survey involving 642 adults, comparing problematic and non-problematic smartphone user groups based on self-reported use and psychological distress [22]. Their findings showed that problematic users spent significantly more time on gaming, social networking, information seeking, and short-video viewing, thereby reinforcing the notion that gaming is a key contributor to problematic smartphone behaviors.

Nevertheless, the role of gaming as a causal factor for PSU remains inconclusive, with several studies challenging this assumption. Lopez-Fernandez et al. [21], despite acknowledging gaming prevalence, reported no significant predictive relationship between mobile gaming and PSU symptoms in their cross-national analysis, suggesting other factors may play larger roles. Sanchez-Fernandez and Borda-Mas conducted a systematic review of 117 studies involving university students, categorizing problematic online behaviors into smartphone use, social media, gaming disorder, and pornography use [23]. Their meta-analysis identified only one significant predictor related to problematic gaming, compared to multiple predictors for social media use and generalized smartphone addiction, which indicates that gaming may have a comparatively limited influence within young adult populations. Furthermore, Chan et al. reviewed 42 studies comprising over 139,000 adolescents to investigate how different smartphone use types relate to PSU [24]. Using a categorization into social use (e.g., SNS, chatting) and process use (e.g., gaming, video watching), their review highlighted inconsistent and mixed findings concerning gaming. Some studies found positive correlations, others negative or null results, indicating that gaming’s association with PSU is highly context-dependent and influenced by demographic, cultural, and methodological variations across studies.

2.2. SNS and PSU

Similarly, PSU has been widely associated with social networking service (SNS) usage, which is considered a key predictor of excessive smartphone behaviors. Kwak and Kim surveyed 433 smartphone-based SNS users aged 20 to 40, using binary logistic regression to identify psychological and motivational factors influencing PSU [25]. They found reward responsiveness, lack of self-control, and anxiety significantly increased PSU risk, with reward responsiveness as the strongest predictor. Marino et al. systematically reviewed 13 studies and reported medium to large effect sizes linking PSU and problematic social media use (PSMU), especially involving instant messaging apps like WhatsApp [26]. Fuzeiro et al. analyzed data from over 1100 Portuguese adults and revealed problematic SNS use correlated with sexual dysfunction, highlighting wider psychosocial impacts [27]. Tugtekin et al. adapted an SNS fatigue scale for Turkish university students and found that fear of missing out (FoMO) and SNS fatigue significantly predicted PSU, with females showing higher vulnerability [28]. Hussain et al. surveyed 640 participants and demonstrated that personality traits and anxiety influenced PSU risk [29]. Elhai et al. differentiated between process (content consumption) and social use in 309 participants, finding PSU more strongly associated with process use but also linked to social use, emphasizing the complex nature of smartphone behaviors [1].

Despite substantial evidence linking SNS usage to PSU, findings indicate that the influence of SNS on problematic smartphone behaviors varies considerably depending on contextual and individual factors. The inconsistency in results across studies suggests that the relationship is not universal but moderated by psychological, social, and cultural contexts. For instance, Marino et al. pointed out that PSU should be examined with a focus on specific app usage rather than as a generalized phenomenon, implying that different SNS platforms or usage patterns may yield distinct effects [26]. Tugtekin et al. highlighted gender differences in SNS fatigue and PSU, underscoring demographic moderators in this relationship [28]. Moreover, Hussain et al. showed that personality traits such as conscientiousness and emotional stability could buffer or exacerbate PSU risk associated with SNS use [29]. Elhai et al. suggested that content consumption versus social interaction on smartphones differentially predicts PSU, indicating that the type of engagement within SNS platforms is a critical contextual factor [1]. Taken together, these studies emphasize the necessity of considering the multifactorial context in which SNS use occurs, including individual psychological profiles and sociocultural environment, to fully understand its impact on problematic smartphone use. This recognition of context-dependent effects aligns with the rationale of the present study, which posits that certain contexts may serve as a crucial moderating factor shaping the dynamic relationship between SNS usage and PSU.

2.3. Shopping and PSU

Prior research has increasingly highlighted the association between shopping application usage and PSU. Park et al. distinguished between habitual and addictive smartphone behaviors and found that shopping apps use significantly predicted addictive smartphone behavior [30]. Similarly, Nyrhinen et al. emphasized that self-regulation failure mediates the pathway from smartphone use to online shopping addiction, underscoring the psychological vulnerability underlying compulsive mobile shopping [31]. These findings are supported by Müller et al. [32], who proposed that problematic online shopping may warrant recognition as either a subtype of compulsive buying-shopping disorder (CBSD) or as an independent specific internet-use disorder. Collectively, these studies suggest that shopping-related behaviors on smartphones—especially under conditions of reduced self-control or increased psychological distress—can escalate into compulsive patterns that overlap with PSU.

However, the relationship between shopping behaviors and PSU is not necessarily uniform across populations or contexts. For example, Greenberg et al. found that problematic shopping in adolescents was not only associated with impulsivity and sensation-seeking but also with self-injurious behaviors, suggesting a deeper link between mobile shopping and psychological maladjustment [33]. Rozgonjuk et al. [7], analyzing a global sample of gamers, reported that while problematic online shopping co-occurred with other problematic online behaviors such as gaming and social networking, it remained a distinct construct within a broader network of internet-based behavioral addictions. These findings imply that the connection between shopping and PSU may depend on user characteristics (e.g., age, psychological traits), digital environments (e.g., gaming platforms), and the motivational underpinnings of app usage. Thus, mobile shopping should be considered not merely as passive behavior but as one embedded in broader patterns of digital consumption that may contribute uniquely to PSU.

3. Hypotheses Development

3.1. COVID-19 Context

The COVID-19 pandemic has fundamentally and rapidly transformed global lifestyles, with governments worldwide enforcing lockdowns, social distancing, and quarantine measures to curb virus transmission [13,34]. Such measures led to prolonged home confinement, reducing face-to-face social interactions and increasing uncertainty about health and economic stability. For example, Elhai et al. surveyed 908 Chinese adults between February and March 2020 during early pandemic stages, revealing elevated levels of COVID-19-specific anxiety, general anxiety, and depression [13]. Their structural equation modeling showed that anxiety related to COVID-19 significantly correlated with increased severity of PSU, suggesting psychological distress as a key driver. Similarly, Islam et al. conducted a large cross-sectional study of 5511 Bangladeshi college and university students during the July 2020 social distancing period [35]. They found high prevalence of anxiety and depression linked to PSU and problematic social media use (PSMU), further corroborating the mental health burden posed by the pandemic. These psychological stressors—such as increased worry, fear of infection, and social isolation—have been consistently documented as pervasive during COVID-19, with wide-ranging effects on well-being and behavior.

In parallel, the pandemic accelerated reliance on digital technologies, particularly smartphones, to maintain social, educational, and occupational functioning amid physical distancing [36,37,38]. For example, Wright et al. compared U.S. college students’ media use and health indicators before and during the pandemic and found notable increases in social media and overall screen time (14% and 30% higher, respectively), accompanied by mixed health outcomes [36]. Their findings underscore that the pandemic did not simply increase digital engagement but also altered the relationship between media use and health, suggesting a fundamental shift in usage patterns. Also, Hosen et al. surveyed 601 Bangladeshi students using an online questionnaire during late 2020 and reported an alarmingly high prevalence of PSU (approximately 87%), attributing this surge to increased online engagement and the need for virtual communication during lockdowns [37]. Similarly, Mengistu et al. studied 1232 Ethiopian university students and identified factors such as poor sleep quality and depression as significantly associated with both PSU and PSMU during the pandemic, indicating broader psychosocial impacts [38]. Moreover, Zhang et al. conducted a telephone survey of 616 adult smartphone users in Macao, China, and found that pandemic-related self-efficacy and beliefs about government effectiveness were negatively correlated with PSU, while social cynicism positively predicted it, emphasizing the role of cognitive and social perceptions [39]. Taskin and Ok further demonstrated through hierarchical regression analyses of nationwide South Korean data (n = 41,883) that digital literacy’s positive influence on life satisfaction increased post-pandemic, while the negative impact of PSU intensified, highlighting the complex and heightened role smartphones have taken in supporting and simultaneously challenging psychological well-being during COVID-19 [40]. Collectively, these findings illustrate how smartphones have become indispensable digital lifelines yet simultaneously introduce risks when used excessively under pandemic-induced stress.

3.2. Moderating Role of COVID-19

The surge in smartphone use during the COVID-19 pandemic was largely driven by necessity. As in-person activities were restricted due to lockdowns, individuals turned to digital technologies to fulfill social, emotional, and practical needs [41,42]. While this increase in smartphone use contributed to greater exposure to potentially problematic applications, such as gaming, social media, and online shopping, it did not always result in heightened PSU. This is because the context of the pandemic normalized excessive digital engagement and reduced the negative social or functional consequences that typically characterize PSU [13]. For many, smartphone use during this period functioned as a coping mechanism and an adaptive response to environmental stressors rather than dysfunctional behavior [43].

However, in the post-pandemic context, as offline routines and social activities have resumed, continued excessive use of certain smartphone applications may increasingly be perceived as maladaptive or disruptive. What was once seen as necessary behavior during COVID-19 may now be viewed as compulsive or avoidant in nature [6]. The same levels of gaming, SNS engagement, or online shopping that were normalized during the pandemic may now more clearly contribute to PSU by interfering with work, study, and social life in the “new normal”. Moreover, pandemic-related habits may persist even in the absence of situational constraints, making users more vulnerable to problematic patterns of use in the post-COVID-19 period [34,40,44].

These shifts can be better understood through established theoretical perspectives. According to Compensatory Internet Use Theory [45], individuals often engage in excessive digital behaviors to alleviate stress or negative emotions. During the pandemic, such compensatory use was situationally adaptive, but as offline opportunities have returned, the same behaviors may appear excessive or maladaptive. Similarly, the I-PACE model conceptualizes problematic technology use as the outcome of interactions between individual predispositions, affective and cognitive responses, and executive functioning [4,5]. This framework suggests that when contextual demands (e.g., pandemic restrictions) are removed, persistent high engagement with certain apps may be more strongly driven by maladaptive cognitive-emotional mechanisms, thereby increasing the risk of PSU.

Based on this perspective, we posit that the COVID-19 pandemic served as a temporary contextual buffer that mitigated the negative consequences of excessive smartphone use. During the pandemic, increased usage of applications such as gaming, SNS, and shopping was often perceived as justifiable and situationally appropriate. However, in the post-pandemic period, when face-to-face interactions and offline activities have largely returned to normal, the same levels of application usage may be more likely to lead to PSU. As users continue behaviors established during the pandemic without the previous environmental constraints, these behaviors may become more excessive or compulsive in nature, increasing the risk of PSU. Thus, we propose that the relationship between specific types of application use and PSU will be stronger after COVID-19 than during the pandemic. Accordingly, we hypothesize:

Hypothesis 1. 

COVID-19 will moderate the relationship between gaming and PSU such that the positive relationship between gaming and PSU will be stronger after COVID-19 rather than during COVID-19.

Hypothesis 2. 

COVID-19 will moderate the relationship between SNS and PSU such that the positive relationship between SNS and PSU will be stronger after COVID-19 rather than during COVID-19.

Hypothesis 3. 

COVID-19 will moderate the relationship between online shopping and PSU such that the positive relationship between shopping and PSU will be stronger after COVID-19 rather than during COVID-19.

4. Methodology

4.1. Participant

This study utilized data from the nationwide surveys on smartphone over-dependence annually conducted in South Korea by the Ministry of Science and ICT and the National Information Society Agency [46]. These annual surveys are designed to gather essential data for developing policies that promote responsible smartphone use. Participants were recruited through stratified sampling based on regional population distributions, with approximately 10,000 households surveyed each year. All respondents participated voluntarily with informed consent, and those who agreed received a small non-monetary gift (equivalent to approximately USD 3). If a selected individual declined or withdrew during the survey, a replacement sample was used. Although it is possible that some individuals may have participated across different years, the large sampling frame and anonymization of personal information mean that each year’s survey can be reasonably treated as independent. No formal exclusion criteria were applied beyond refusal or discontinuation. The survey questions were originally developed and validated in Korean by local researchers with reference to relevant academic sources for each item, ensuring linguistic and conceptual validity without requiring forward–backward translation. On average, the survey required approximately 20 min per individual; when all household members participated, the process typically required about one hour. For the purposes of the survey, smartphone users were defined as individuals who accessed the internet on their smartphones at least once per month. The survey targeted individuals aged 3 to 69 living in households across the country as of September of each year. Data for this study were drawn from four waves: 2021, 2022, 2023, and 2024. The final analytic sample sizes were 21,713 in 2021, 20,870 in 2022, 19,438 in 2023, and 20,967 in 2024, totaling 75,450 participants after excluding children under 10. While the numbers varied slightly by year, the samples were relatively balanced, reflecting the consistent nationwide survey design.

Among the 75,450 participants, 36,963 (49.0%) were male and 38,487 (51.0%) were female. The average age was 41.1 years (SD = 15.1), ranging from 10 to 69 years. A total of 7189 participants (9.5%) were under the age of 20. The average daily smartphone use was 3.1 h. The average monthly household income was approximately KRW 3.12 million (≈USD 2300), and the average education level was 14 years (equivalent to junior college completion). Because the survey employed a stratified sampling strategy aligned with national census distributions, the dataset is considered highly representative of the South Korean population, thereby strengthening confidence in the generalizability of the findings.

4.2. Measurement

Problematic smartphone use was assessed using the Smartphone Overdependence Scale (S-scale), which includes three key dimensions: self-control failure, salience, and serious consequences [47]. The “self-control failure” subscale reflects difficulty in regulating smartphone usage in accordance with one’s own goals. “Salience” indicates the extent to which smartphone use dominates a person’s attention and daily priorities. “Serious consequences” captures the adverse effects—physical, mental, and social—associated with excessive smartphone use. Based on these three components, participants responded to 10 self-report items. Each item was rated on a four-point Likert scale (1 = strongly disagree to 4 = strongly agree), with higher scores reflecting more problematic use. The internal consistency of the scale, as measured by Cronbach’s alpha, was 0.884.

The key independent variables—gaming, SNS, and online shopping—were assessed based on participants’ self-reported perceptions of their smartphone usage. Each type of application usage was measured using a single-item question, asking respondents to indicate how frequently they use the respective application on their smartphone. Responses were measured by a seven-point Likert scale ranging from 1 (not at all) to 7 (very frequently). Given the challenges of administering lengthy, multi-item scales in nationwide surveys of this size, single-item frequency measures provide practical indicators of app usage patterns. This approach is consistent with prior large-scale studies where brevity and feasibility are prioritized, although we acknowledge that such measures may not capture the full dimensionality of app use behavior.

Post-COVID-19, a moderator variable in this study, was dummy-coded. Specifically, among the four waves of annual survey data used in the analysis, two waves collected during the COVID-19 pandemic were coded as 0 (“during COVID-19”), while the two waves collected after the pandemic was officially declared over were coded as 1 (“after COVID-19”). In South Korea, where the survey was conducted, the government formally declared the end of the COVID-19 pandemic and the beginning of the endemic phase in May 2023. Accordingly, data collected in 2023 (after the declaration) and 2024 were coded as 1, while data from the pandemic years were coded as 0. This clear governmental timeline provides a quasi-natural experiment context, strengthening the methodological validity of our temporal comparisons.

We controlled several individual-level demographic variables known to influence PSU, including gender, age, income, and education. Gender was dummy-coded (1 = male, 0 = female), age was recorded as the respondent’s age in the survey year, and income was measured based on average monthly earnings coded on a six-point scale ranging from under one million won (approximately 750 EUR) to over five million won (approximately 3800 EUR). Education was controlled by the total years of schooling corresponding to each respondent’s highest educational attainment. Additionally, digital literacy was included as a control variable, measured with a concise six-item questionnaire assessing abilities such as internet searching, content evaluation, communication, and digital content creation. Responses were rated on a 4-point Likert scale, with acceptable internal consistency (Cronbach’s alpha = 0.805).

4.3. Procedure and Data Analysis

Since the dependent variable in this study—problematic smartphone use—was measured on a 1–4 Likert-type scale, it does not represent a truly continuous variable. However, following common practice in large-scale behavioral research, we treated it as continuous for analytic purposes. Accordingly, ordinary least squares (OLS) regression analysis was utilized to examine the relationships among the variables. This approach provides interpretable results, though the restricted range should be considered a statistical limitation. To investigate the moderating role of COVID-19 in the associations between smartphone application use and PSU, a hierarchical regression analysis was conducted in four steps. Model 1 included control variables such as gender, age, income, education level, and digital literacy to establish a baseline. Model 2 added the independent variables—gaming, SNS, and online shopping—to assess their direct relationship with PSU. Model 3 introduced the moderating variable, COVID-19, and Model 4 incorporated the interaction terms between each independent variable and COVID-19 to test potential moderation relationships. To reduce multicollinearity, all interaction terms were mean-centered prior to analysis. All statistical analyses were performed using STATA version 17.0.

5. Analyses and Results

5.1. Descriptive and Correlation Analysis

Table 1. Descriptive statistics and correlation matrix.

Variables	1	2	3	4	5	6	7	8	9	10
1. PSU	(0.80)
2. Gaming	0.22 *	1.00
3. SNS	0.20 *	0.38 *	1.00
4. Online Shopping	0.13 *	0.26 *	0.51 *	1.00
5. Post-COVID-19	−0.06 *	−0.25 *	−0.28 *	−0.18 *	1.00
6. Gender	0.03 *	0.12 *	−0.02 *	−0.07 *	−0.02 *	1.00
7. Age	−0.24 *	−0.33 *	−0.34 *	−0.15 *	0.17 *	0.02 *	1.00
8. Income	0.06 *	0.07 *	0.12 *	0.11 *	0.05 *	0.02 *	−0.15 *	1.00
9. Education Level	0.04 *	0.04 *	0.24 *	0.36 *	−0.10 *	0.09 *	−0.09 *	0.14 *	1.00
10. DL	0.18 *	0.26 *	0.36 *	0.33 *	−0.05 *	0.06 *	−0.33 *	0.17 *	0.30 *	(0.81)
Mean	1.95	3.63	4.12	4.08	0.59	0.49	41.12	3.12	14.00	2.77
S.D.	0.53	2.04	2.02	1.90	0.49	0.50	15.20	0.98	2.61	0.60

Note 1. PSU = problematic smartphone use; DL = digital literacy. Note 2. The number in parentheses means the Cronbach alpha value. * p < 0.001.

presents the descriptive statistics and correlation matrix for the study variables. The mean score for problematic smartphone use (PSU) was 1.951 (SD = 0.530). All three types of application use—gaming, SNS, and online shopping—showed positive and significant correlations with PSU (r = 0.224, 0.204, and 0.131, respectively, all p < 0.001). Although statistically significant due to the large sample size, these coefficients represent small to moderate associations, suggesting that while the relationships are consistent, their magnitude is modest. In line with Cohen’s conventional benchmarks (small ~0.10, medium ~0.30, large ~0.50), the associations observed here can be understood as meaningful but not strong [48]. Demographic variables such as gender (coded as 1 = male) showed a very small positive correlation with PSU (r = 0.028, p < 0.001), while age was negatively correlated with PSU (r = −0.243, p < 0.001), the latter approaching a moderate effect size. Income, education level, and digital literacy also showed positive but modest correlations with PSU. The internal consistency of PSU and digital literacy measures was acceptable, with Cronbach’s alpha values of 0.796 and 0.805, respectively. Taken together, these preliminary results indicate meaningful yet relatively modest associations, warranting further analysis of how application use and demographic factors relate to PSU in the context of COVID-19.

5.2. Hierarchical Regression Analysis

The results of the hierarchical regression analyses are presented in

Table 2. The results of hierarchical regression analyses predicting PSU.

Variables	Model 1			Model 2			Model 3			Model 4
	b	β	s.e.	b	β	s.e.	b	β	s.e.	b	β	s.e.
Constant	1.981 *	N/A	0.014	1.837 *	N/A	0.015	1.814 *	N/A	0.015	1.899 *	N/A	0.015
Gender	0.028 *	0.027	0.003	0.019 *	0.018	0.003	0.019 *	0.018	0.003	0.018 *	0.017	0.003
Age	−0.007 *	−0.207	0.0001	−0.005 *	−0.154	0.0001	−0.005 *	−0.156	0.0001	−0.005 *	−0.150	0.0001
Income	0.005	0.009	0.001	0.003	0.006	0.001	0.001	0.003	−0.001	0.0003	0.000	0.001
Education Level	−0.002 *	−0.012	0.0007	−0.005 *	−0.025	0.0007	−0.004 *	−0.023	0.0007	−0.006 *	−0.031	0.0007
DL	0.098 *	0.112	0.003	0.059 *	0.068	0.003	0.056 *	0.064	0.003	0.056 *	0.064	0.003
Gaming				0.030 *	0.118	0.001	0.032 *	0.123	0.001	0.032 *	0.126	0.001
SNS				0.019 *	0.074	0.001	0.021 *	0.081	0.001	0.016 *	0.064	0.001
Online Shopping				0.007 *	0.025	0.001	0.007 *	0.027	0.001	0.002	0.008	0.001
Post-COVID-19							0.034 *	0.031	0.003	0.013 *	0.012	0.004
Post-COVID-19 * Gaming										0.003	0.006	0.002
Post-COVID-19 * SNS										0.029 *	0.049	0.002
Post-COVID-19 * Online Shopping										0.032 *	0.052	0.002
F-Value	1160.48 *			988.16 *			887.40 *			718.48 *
R-Squared	0.0714			0.0948			0.0957			0.1026
Δ R-Squared	0.0714			0.0234			0.0009			0.0069
Adj. R-Squared	0.0714			0.0948			0.0956			0.1024

Note. DL = digital literacy; PSU = problematic smartphone use. * p < 0.001.

. Both unstandardized (b) and standardized (β) coefficients are reported for clarity. Although statistically significant, the effect sizes are small in magnitude, suggesting nuanced rather than large associations. This indicates that while the explanatory power of individual predictors is limited, even small effects can accumulate to produce meaningful consequences at the population level, particularly given the scale of smartphone use in daily life. These results should therefore be interpreted cautiously, while recognizing that even modest associations can have meaningful implications in large populations. Model 1 included only control variables. Model 2 introduced the independent variables—gaming, SNS, and online shopping—which were all positively associated with PSU. Gaming showed the strongest relationship (b = 0.030, p < 0.001), followed by SNS (b = 0.019, p < 0.001), and online shopping (b = 0.007, p < 0.001). Model 3 added the COVID-19 variable to examine whether PSU levels differed between the pandemic and post-pandemic periods. The positive and significant coefficient for COVID-19 (b = 0.034, p < 0.001) indicates that PSU was higher after COVID-19 than during the pandemic. Model 4 tested the moderating relationship of COVID-19 by including interaction terms between COVID-19 and the three app usage variables. The interaction between COVID-19 and gaming was not significant (b = 0.003, n.s.), indicating that the strength of the relationship between gaming and PSU did not differ significantly between the two time periods. Thus, Hypothesis 1 was not supported. However, the interaction between COVID-19 and SNS use was significant and positive (b = 0.029, p < 0.001), suggesting that the positive association between SNS use and PSU was stronger after COVID-19 than during it, supporting Hypothesis 2. Similarly, the interaction between COVID-19 and online shopping was significant (b = 0.032, p < 0.001), indicating that the relationship between online shopping and PSU also intensified after COVID-19, thereby supporting Hypothesis 3. Although the overall R² values are modest, this highlights an opportunity for future research to incorporate additional explanatory variables that may better capture the multifaceted nature of PSU.

Figure 2 illustrates the moderating role of SNS app use and COVID-19 period on problematic smartphone use. The solid line represents the relationship during the COVID-19 period, while the dashed line represents the relationship after COVID-19. The slope of the line for the during COVID-19 period is flat and non-significant (t = −0.232, p = 0.817), indicating that SNS use had little impact on PSU when face-to-face interactions were restricted. In contrast, after COVID-19, there is a significant positive slope (t = 21.367, p < 0.001), demonstrating that higher SNS use is strongly associated with greater PSU when social restrictions have been lifted. This pattern suggests that the influence of SNS app use on problematic smartphone behavior is substantially more pronounced after the pandemic compared to during it, supporting the hypothesized moderating role of COVID-19 on this relationship.

Figure 3 presents the moderating role of online shopping app use and the COVID-19 period on problematic smartphone use. During the COVID-19 pandemic, individuals with high use of online shopping apps exhibited slightly lower levels of problematic smartphone use compared to those with low use (t = −7.534, p < 0.001). However, this pattern reversed after the pandemic; problematic smartphone use was significantly higher among individuals with greater online shopping app use (t = 11.402, p < 0.001). These results indicate that the relationship between shopping app use and problematic smartphone use was moderated by the COVID-19 context, suggesting that the psychological or behavioral drivers behind app use may have shifted between the pandemic and post-pandemic periods.

6. Discussion

6.1. Research Summary

This study examined how different patterns of application use—namely gaming, social networking, and online shopping—relate to problematic smartphone use, and whether these relationships changed during and after the COVID-19 pandemic. The results revealed that all three types of app use were positively associated with PSU, with gaming having the strongest impact. Additionally, the level of PSU was significantly higher in the post-COVID-19 period compared to during the pandemic, suggesting the persistence or escalation of problematic smartphone behaviors beyond the initial crisis. Importantly, the positive associations between SNS and shopping app use with PSU were stronger after COVID-19, highlighting the pandemic’s long-term impact on digital habits. In contrast, the relationship between gaming and PSU remained stable across time, suggesting different mechanisms may underlie gaming-related smartphone use compared to other app categories.

6.2. Theoretical and Practical Implications

This study reaffirms prior findings that gaming app use is a robust predictor of PSU, regardless of the broader social context. Previous research has consistently shown that mobile gaming can promote compulsive behaviors due to its immersive nature, immediate rewards, and social competitiveness [6,7,49]. The lack of a significant moderating role of COVID-19 in this study suggests that gaming-induced PSU is less sensitive to external environmental changes such as the pandemic. This stability may reflect the self-reinforcing design of gaming apps, which maintain user engagement independent of situational stressors or social isolation. The finding supports the theoretical notion that certain app types have intrinsic addictive potentials that are consistent across contexts [9,50].

Unlike gaming, the relationship between SNS use and PSU was significantly stronger after COVID-19 than during the pandemic. This aligns with earlier studies suggesting that SNS platforms fulfill evolving emotional and social needs, particularly in times of uncertainty or distress [13,25]. The intensification of this post-pandemic may be attributed to lasting psychological effects, such as increased dependence on digital social interaction and reduced tolerance for offline social discomfort. From a theoretical perspective, this finding highlights the contextual sensitivity of PSU driven by social connectivity motives. It underscores the importance of examining how prolonged reliance on SNS for emotional regulation may alter digital consumption patterns even after the triggering events have subsided.

The study also finds that the association between online shopping app use and PSU became stronger after COVID-19, offering new insights into how digital consumption behaviors evolve over time. Prior studies have shown that individuals may turn to mobile shopping for mood regulation, escapism, or convenience [39,51]. During the pandemic, when physical retail access was limited, shopping apps likely gained traction as substitutes. However, the sustained or even escalated problematic use in the post-pandemic period suggests the development of habitual or compulsive consumption patterns [44]. This finding contributes to the theoretical understanding of digital consumption by emphasizing how contextually driven adaptations—such as increased e-commerce use during lockdowns—can persist and morph into problematic behaviors.

The findings of this study offer important implications for both intervention development and policymaking to address the growing concern of PSU. At the short-term level, interventions should focus on raising awareness and equipping vulnerable groups with immediate coping strategies. Educational institutions can provide training on self-regulation and digital literacy, with special attention to younger populations and transitional groups (e.g., first-semester university students) who may be particularly susceptible to problematic use in the wake of remote learning and social adaptation challenges [52]. Public health campaigns could also encourage healthy app engagement by promoting built-in features such as screen time reminders, activity dashboards, or default usage limits, especially on platforms like SNS and online shopping apps that showed stronger associations with PSU post-pandemic.

At the long-term level, policies should aim to promote sustained digital well-being by addressing structural and contextual factors. Governments may consider supporting the institutionalization of digital literacy programs that integrate emotional regulation strategies, recognizing that PSU often arises from attempts to cope with stress, boredom, or loneliness. Additionally, app-specific regulation could be implemented to ensure that platform designs align with public health objectives rather than reinforcing compulsive behaviors. Finally, recognizing that habitual patterns of smartphone overuse became normalized during the pandemic, public health strategies should shift from short-term mitigation to the cultivation of long-term resilience and balanced digital lifestyles [53]. These recommendations underscore how differentiated app effects observed in this study can inform both the design of tailored interventions and the development of sustainable policy frameworks.

6.3. Limitations and Future Research

Despite its contributions, this study is subject to several limitations that warrant consideration. First, all data were collected through self-report surveys, which may be subject to response biases, particularly in estimating smartphone usage and levels of PSU. Social desirability bias could have led participants to underreport behaviors perceived as excessive or unhealthy. Future research should consider incorporating more objective measures of smartphone use—such as digital trace data or usage logs—to enhance the validity of the findings. Second, although this study utilized annual cross-sectional data over a four-year period, it did not employ panel design. As such, the same individuals were not surveyed repeatedly across years, limiting the ability to observe intra-individual changes over time. A longitudinal panel study would be better suited to track the evolution of smartphone use patterns and to draw stronger conclusions about causal relationships. In particular, collecting independent and dependent variables at separate time points would improve the temporal ordering needed for causal inference. Moreover, because our dataset began in 2020, we were unable to include a true pre-pandemic baseline, which limits the ability to definitively distinguish pandemic-related changes from pre-existing trends. Nevertheless, the use of one of the largest nationwide datasets (N = 75,450) across four consecutive years represents a unique strength, allowing for robust comparisons of temporal differences in application-specific PSU patterns during and after the pandemic. This large-scale perspective provides insights into the idea that smaller-scale longitudinal studies may not readily capture, thereby offering complementary value to literature. Third, the study focused exclusively on three application types—gaming, SNS, and online shopping—as potential predictors of PSU. However, other app categories, such as video streaming or short-form content platforms, have also become prominent in recent years. Future research should expand the range of app types considered in order to capture a more comprehensive picture of app-based influences on PSU. Another limitation of this study is that it did not account for the motivations underlying SNS use, which may significantly influence users’ well-being profiles [54]. Future research should therefore explore how diverse motives for app use—such as entertainment, boredom relief, or information seeking—affect problematic smartphone use, particularly in the shifting digital environment after the pandemic. Last, the generalizability of the findings is limited by the focus on South Korea as the sole research context. While South Korea is one of the most digitally connected countries, with among the highest smartphone penetration rates globally, caution is needed when applying the results to other cultural or technological environments. Comparative studies across multiple countries are necessary to test the cross-cultural robustness of the observed relationships. Also, future studies should further examine how app usage and PSU vary across specific social contexts, such as workplaces, schools, or diverse household structures. Such subgroup analyses could provide more nuanced insights that complement the nationally representative perspective of the present study. Looking forward, future research should build on these findings to develop integrative models that account for both technological design and contextual factors, ultimately advancing evidence-based interventions and policies to promote healthier digital ecosystems.

7. Conclusions

This study offers important implications for understanding how specific app types contribute differently to PSU in a post-pandemic context. The strengthened links between SNS and shopping app usage with PSU after COVID-19 suggest that emotional coping and habitual behaviors formed during the pandemic may persist and evolve into long-term digital dependencies. In contrast, the consistent impact of gaming apps implies that certain applications have stable, intrinsic addictive potentials, regardless of external stressors. These findings call for more nuanced, app-specific approaches to digital well-being—emphasizing the need for emotional regulation strategies in SNS and shopping contexts, while recognizing the structural engagement mechanisms of gaming apps. Looking ahead, future research should further investigate how evolving digital environments, technological innovations, and shifting cultural practices continue to shape app-specific risks of PSU. Longitudinal studies and cross-cultural comparisons, in particular, could provide deeper insight into the persistence or transformation of these patterns over time. Such efforts would help build a more comprehensive framework for understanding and addressing problematic smartphone use in the years to come.