Association Between Digital Addiction and Physical Activity in Korean Adults Across Age Groups: An Analysis of Community Health Survey Data

Article information

J Prev Med Public Health. 2025;58(3):289-297

Publication date (electronic) : 2025 January 24

doi : https://doi.org/10.3961/jpmph.24.683

Kyungsin Lee ¹

, Inmyung Song ²^,

¹Department of Building a Digital Health, Korea Health Industry Development Institute, Cheongju, Korea

²Department of Health Administration, Kongju National University College of Nursing and Health, Gongju, Korea

Corresponding author: Inmyung Song, Department of Health Administration, Kongju National University College of Nursing and Health, 56 Gongjudaehak-ro, Gongju 32588, Korea E-mail: inmyungs@gmail.com

Received 2024 November 11; Revised 2025 January 5; Accepted 2025 January 7.

Abstract

Objectives:

Extensive research has been conducted on digital addiction, particularly concerning internet, gaming, and smartphone use among adolescents and young adults. However, there is limited information about digital addiction in adults, especially in relation to physical activity (PA). This study investigated the prevalence of digital addiction among Korean adults and explored its association with PA across various age groups.

Methods:

Using data from the 2023 Community Health Survey (n=231 752), this study estimated the prevalence of digital addiction, defined as experiencing impaired daily functioning due to excessive internet, gaming, or smartphone use. PA was defined as exercise and regular walking. Multiple logistic regression was conducted to examine the association between PA and digital addiction in the full sample and by age group (<40, 40-59, and ≥60 years).

Results:

In 2023, the prevalence of digital addiction among adults was estimated to be 12.0%. Significantly higher odds of digital addiction were observed in younger age groups. Individuals who reported perceived stress or depression were at an increased risk for digital addiction. Conversely, PA was linked to a reduced risk of digital addiction, with odds ratios of 0.96 (p<0.05) for exercise and 0.93 (p<0.001) for regular walking. Notably, regular walking was associated with a reduced risk of digital addiction only in the 40-59 age group.

Conclusions:

PA, particularly regular walking, is linked to a lower risk of digital addiction in Korean adults. Encouraging regular walking may help reduce digital addiction among middle-aged adults.

Keywords: Technology addiction; Digital technology; Internet; Exercise; Smartphone; Walking

INTRODUCTION

The internet and smartphones have become integral to modern life, serving as tools for communication, information searching, and entertainment [1]. However, concerns about the excessive use of these digital technologies have led to a growing body of research focused on various behaviors associated with digital device usage. These behaviors are often referred to using terms such as “problematic or excessive use” and “addiction disorder” in relation to internet and smartphone use [2-4]. Additionally, the term “digital addiction” is widely used to describe behavioral addictions linked to smartphones, the internet, social media, and gaming [5,6]. Digital addiction is characterized by several common features, including impulsive and compulsive behaviors, a lack of control, negative emotional states, and impairments in work performance [7].

Digital addiction is a global issue, with prevalence rates differing by subtype: estimates suggest 6.0% for gaming addiction, 14.2% for internet addiction, and 26.0% for smartphone addiction [5]. The prevalence of digital addiction also varies across regions [8]. Furthermore, the global prevalence of problematic smartphone use has risen over the past decade, a trend that the coronavirus disease 2019 pandemic and subsequent lockdowns have exacerbated [3].

Digital addiction can significantly affect various aspects of daily life. For instance, smartphone addiction is characterized by several problematic behaviors, including frequent checking, disruptions to family and work life, sensations of phantom phone signals, and withdrawal symptoms [9,10]. Both internet gaming and smartphone addiction are associated with physical inactivity, poor sleep quality, and a reduced quality of life, especially among younger adults [11,12]. Excessive use of the internet, gaming, and smartphones has been linked to impaired daily activities and a lower health-related quality of life among Korean adults [13]. Additionally, smartphone and internet addictions have been correlated with mental health issues such as depression and anxiety [14]. The severity of depression and stress has also been connected to problematic smartphone use [10]. Some individuals with depression may excessively use smartphones as a coping mechanism to mitigate negative moods [15]. Older Koreans who use smartphones primarily for entertainment and online shopping, rather than for communication, face a higher risk of smartphone addiction [1].

The problematic use of the internet and smartphones has been identified as a public health concern, prompting the creation of various applications designed to track and monitor usage [16]. Furthermore, numerous studies have explored the role of physical activity (PA) in reducing digital addiction [17,18]. Research indicates that physical inactivity mediates the relationship between internet addiction and depressive symptoms [19]. Among Korean adolescents, those who engage in regular PA are less prone to problematic internet use [20]. Efforts have also been made to promote PA in school settings and to assess the impact of lifestyle interventions on internet usage among young people [17].

Although there is extensive research on digital addiction among adolescents and young adults [21,22], studies that focus on digital addiction among adults are either relatively limited [1] or based on small sample sizes [9,23]. The relationship between PA and the risk of digital addiction may vary by age; however, the role of PA in mitigating digital addiction across different age groups remains poorly understood. Therefore, this study aimed to estimate the prevalence of digital addiction and examine its association with PA across age groups in the Korean adult population.

METHODS

Data

This population-based study utilized data from the 2023 Community Health Survey (CHS), an annual survey designed to support health policy development in Korea [24]. The 2023 CHS collected data from a representative sample of adults aged 19 years and older, totaling 231 752 respondents. The survey employed a 2-stage stratified sampling method. Initially, small administrative districts were categorized based on the number of households and housing type (house vs. apartment). Subsequently, households within these categories were systematically selected for sampling. The analysis included all respondents who answered questions related to the excessive use of the internet, gaming, and smartphones, amounting to 231 727 individuals.

Digital Addiction

In this study, digital addiction was defined as experiencing impaired daily functioning due to excessive use of the internet, gaming, or smartphones. The 2023 CHS assessed digital addiction with a single-item question: “Have you experienced impaired activities of daily living due to excessive use of the internet, gaming, or smartphone in the past year?” Respondents were given the following response options: “nearly every day,” “about once a week,” “about once a month,” “less than once a month,” “never,” or “never used the internet, gaming, or smartphone.” The first 4 categories were classified as indicative of digital addiction, while the latter responses were classified as non-addiction. This classification was based on a similar cut-off used in a previous study [13].

Physical Activity

In this study, PA was defined as both exercise and regular walking per week. The 2023 CHS evaluated participants’ levels of PA, which included vigorous exercise, moderate exercise, and daily walks. Participants self-reported the number of days in the previous week they had engaged in vigorous activities such as running or hiking, along with the duration of each session. They also provided information on moderate exercises like slow swimming or playing badminton. A new summary variable, ‘exercise,’ was introduced to determine whether a participant engaged in vigorous exercise for at least 20 minutes per day on 3 or more days, or in moderate exercise for at least 30 minutes per day on 5 or more days in the past week (1=yes; 0=no). Additionally, participants were queried about their walking habits through 2 questions: “How many days did you walk for at least 10 minutes per day in the past week?” and “On those days, how long did you walk on average per day?” Regular walking was defined as walking for 30 minutes or more per day on 5 or more days in the past week.

Covariates

Covariates in the study included socio-demographic variables, perceived stress, and depression. The socio-demographic variables consisted of sex, age group (19-29, 30-39, 40-49, 50-59, 60-69, ≥70 years), education level (no education or primary, middle, high school, and college or higher), marital status (married, divorced/separated/widowed, and single), and employment status (self-employed, salaried, and economically inactive).

In the 2023 CHS, perceived stress was evaluated using the question, “How stressed are you in daily life?” Participants selected from 4 possible responses: “extremely stressed,” “very stressed,” “slightly stressed,” and “rarely stressed.” The responses “extremely stressed” and “very stressed” were grouped under the category “stressed,” whereas “slightly stressed” and “rarely stressed” were classified as “not stressed.”

Depression was measured using the Korean version of the Patient Health Questionnaire-9. Participants evaluated the frequency of 9 depressive symptoms they experienced over the previous 2 weeks using a 4-point scale: 0 (not at all), 1 (several days), 2 (more than half the days), and 3 (nearly every day). The scores from all 9 items were totaled to yield a composite score ranging from 0 to 27, where higher scores indicate more severe depression. This total score was then categorized into 2 groups: ‘not depressed’ (0-9) and ‘depressed’ (10 or greater).

Statistical Analysis

The socio-demographic characteristics of the study participants were described using frequency and percentage. Participants were also characterized based on variables related to perceived stress, depression, and PA. This study accounted for the complex sampling design of the CHS by utilizing the SURVEYFREQ procedure in SAS, which applies sampling weights from the CHS data to provide population estimates. The prevalence of digital addiction was estimated, and the Rao-Scott chi-square test was conducted to determine if there were differences in digital addiction across various categories of variables.

Multiple logistic regression analysis was employed to evaluate the relationship between digital addiction and PA. Three distinct models were utilized: Model 1 incorporated all socio-demographic factors, perceived stress, depression, and exercise. Model 2 replaced exercise with regular walking. Model 3 included variables for both exercise and regular walking. To further explore how PA and digital addiction vary across different age groups, participants were categorized into 3 age brackets: <40, 40-59, and ≥60 years. Logistic regression analyses were then conducted for each group. Odds ratios (ORs) and 95% confidence intervals (CIs) were computed. The SURVEYLOGISTIC procedure in SAS was used to adjust for the complex survey design. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).

Ethics Statement

The Institutional Review Board of Kongju National University approved the study protocol and granted a waiver for the requirement of informed consent (ref. No.: KNU_IRB_2024-103).

RESULTS

In total, 54.4% of the study participants (n=231 727) were women, and 24.8% were aged 70 years or older (Table 1). Additionally, 34.6% had attained a college-level education, and 63.1% were currently married. Among the participants, 42.2% were salaried workers, 22.5% reported feeling stressed, and 3.8% were estimated to have depression. Furthermore, 22.5% of the participants engaged in exercise, while 47.3% took regular walks. In 2023, it was estimated that 12.0% of adult Koreans aged 19 years and older had a digital addiction. There was a significant difference in the prevalence of digital addiction across all socio-demographic categories, as well as among those reporting perceived stress, depression, and varying levels of PA (p<0.001).

Table 1.

Descriptive statistics of digital addiction among Korean adults (n=231 727)

The results of multiple logistic regression analyses indicate that men were more likely to report digital addiction than women (p<0.001 in all models, Table 2). Younger age groups had significantly higher odds of digital addiction compared to those aged 70 years or older (OR, 16.97; 95% CI, 14.65 to 19.67; p<0.001 in model 1). Individuals who perceive stress are at an increased risk of digital addiction (OR, 1.34; 95% CI, 1.29 to 1.40; p<0.001 in all models). Similarly, individuals with depression faced a higher risk of digital addiction (OR, 2.04; 95% CI, 1.88 to 2.21; p<0.001 in model 1). Conversely, individuals who engaged in exercise had a decreased risk of digital addiction (OR, 0.96; 95% CI, 0.92 to 1.00; p=0.045 in model 1). Regular walking was also associated with a reduced risk of digital addiction (OR, 0.93; 95% CI, 0.90 to 0.97; p<0.001 in model 2). When both PA variables (exercise and regular walking) were included in model 3, only regular walking remained a significant protective factor against digital addiction (OR, 0.94; 95% CI, 0.90 to 0.97; p=0.001).

Table 2.

Multiple logistic regression analyses on digital addiction for the entire sample

The positive association between digital addiction and both perceived stress and depression remained significant across all age groups (Table 3). However, the inverse relationship between digital addiction and PA did not persist across all age groups; it was only in the 40-59 age group that regular weekly walking was associated with a lower risk of digital addiction (OR, 0.89; 95% CI, 0.84 to 0.95; p<0.001). Exercise, however, was not associated with a reduced risk of digital addiction in any age group.

Table 3.

Multiple logistic regression analyses on digital addiction by age group

DISCUSSION

Using a nationally representative sample of Korean adults aged 19 and older, this study estimated the prevalence of digital addiction to be 12.0% in 2023, a slight increase from the previous estimate of 11.2% in 2017 [13]. This study also showed that PA was associated with a reduced risk of digital addiction in the general adult population, which is consistent with the findings of previous studies conducted among youth [25,26].

The literature identifies several potential mechanisms underlying the association between PA and digital addiction [18,27,28]. Among college students, the negative correlation between PA and internet addiction has been found to be mediated by increased self-esteem [27], improved self-control [28], and adaptive coping styles [25]. The mitigating effects of exercise on digital addiction may be related to its role in modulating the neurobiology of the central and autonomic nervous systems, as well as managing reward impulses [18]. Additionally, the positive impact of PA on digital addiction could be facilitated by the mental health benefits of exercise, as demonstrated in an experimental study where daily walking led to improvements in psychological health, including reductions in anxiety [29]. In contrast, excessive use of the internet, gaming, and smartphones can negatively affect daily functioning [13] and contribute to decreased PA [12]. Individuals with severe symptoms of digital addiction are at a particularly higher risk of obesity, eating disorders, and other health complications due to reduced PA [30]. Therefore, understanding the relationship between PA and digital addiction is crucial for developing effective interventions to mitigate these risks.

Our research demonstrated that vigorous exercise, including activities like running and hiking, is linked to a lower risk of digital addiction when regular walking is not a factor (model 1). However, in model 3, which considered both types of PA (exercise and regular walking), only regular walking was independently associated with a decreased risk of digital addiction. These results indicate that while vigorous exercise can help reduce the risk of digital addiction, as supported by previous findings [18]. Engaging in mild PAs such as regular walking might alone provide a protective effect, irrespective of vigorous activity. When vigorous exercise is combined with regular walking, its benefits appear to be less pronounced, suggesting that vigorous exercise might be unnecessary if consistent mild exercise is already part of one’s routine.

Our findings further reveal that the relationship between PA and the likelihood of digital addiction varies across different age groups. Specifically, when analyzed by age group (<40, 40-59, and ≥60 years), regular walking was found to be negatively associated with digital addiction only in the 40-59 age group. Generally, walking is recognized for its numerous benefits in promoting healthy aging [31]. A study among middle-aged Finnish women demonstrated that regular walking is significantly linked to higher life satisfaction and positive affectivity [32]. These positive mental health outcomes from regular walking may help lower the risk of digital addiction among middle-aged adults.

In our study, we found no significant association between regular walking and reduced digital addiction in individuals under the age of 40. The onset of middle age seems to be a pivotal factor in altering smartphone usage patterns among Korean adults [33]. This indicates that lifestyle interventions like regular walking might not be effective in lowering the risk of digital addiction in young adults, who typically are the most frequent users of digital technologies. Additionally, the lack of a significant relationship in individuals aged 60 and older may stem from the limited number of participants with digital addiction in this demographic.

Our study corroborates earlier research on the relationship between socio-demographic characteristics and digital addiction [5,34]. Consistent with the earlier finding [34,35], we showed that younger adults faced a higher risk of digital addiction than older adults. Additionally, we identified male gender, unemployment, and higher education levels as risk factors for digital addiction. In line with global trends, the prevalence of internet and gaming addiction was also found to be higher among males than females [5]. In Norway, among adults aged 16 to 74, having a college education and experiencing financial hardship are linked to an increased risk of problematic internet use [35]. A survey of Korean adults in their 50s and 60s indicated that those with lower educational levels are more prone to smartphone overdependence, though the risk is lower among those who are employed compared to their unemployed counterparts [36]. Furthermore, our research explored the mental health correlates of digital addiction, finding that perceived stress and depression are associated with an increased risk, aligning with the conclusions of prior studies [6,37].

Previous research has primarily focused on the association between PA and a lower risk of digital addiction in adolescents and young adults, with less attention given to this relationship in older adults. This study utilized a large, representative sample of Korean adults to explore how the relationship between PA and digital addiction differs across age groups. The findings reveal that regular walking significantly protects against digital addiction, particularly in middle-aged adults. This indicates that lifestyle-promotion programs, which include regular walking, may be highly effective in mitigating digital addiction among this demographic. Consequently, interventions tailored to specific age groups could be crucial in reducing digital addiction.

These findings should be interpreted with caution due to several limitations. First, the cross-sectional design of this study limits our ability to draw causal conclusions between PA and digital addiction. Second, the reliance on self-reported data may introduce recall bias; wearable devices could provide a more accurate measurement of PA [29]. Third, digital addiction in this study was assessed using a single question from the 2023 CHS, whereas smartphone and internet addiction have typically been measured with multi-item scales that capture various dimensions of digital addiction [38]. Fourth, this study did not analyze associations with specific subtypes of digital addiction. Previous research among college students in Korea indicates that males are at a higher risk of internet addiction, while females are more prone to smartphone addiction [39]. Future research should consider additional factors, such as social engagement, that may help protect against digital addiction.

In conclusion, this study conducted with a large, representative sample of Korean adults, indicates that PA, especially regular walking, may protect against digital addiction. The protective link between regular walking and digital addiction is significant only in adults aged 40 to 50; it does not appear in other age groups. These results suggest that promoting PA, particularly regular walking, could help alleviate digital addiction that hinders daily activities in middle-aged adults. Future studies employing experimental designs could yield more definitive evidence regarding the role of PA and produce clinically relevant findings for the reduction of digital addiction.

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

None.

Author Contributions

Both authors contributed equally to conceiving the study, analyzing the data, and writing this paper.

References

1. Jeong JH, Bae SM. The relationship between types of smartphone use, digital literacy, and smartphone addiction in the elderly. Psychiatry Investig 2022;19(10):832–839. https://doi.org/10.30773/pi.2021.0400.

2. Cash H, Rae CD, Steel AH, Winkler A. Internet addiction: a brief summary of research and practice. Curr Psychiatry Rev 2012;8(4):292–298. https://doi.org/10.2174/157340012803520513.

3. Olson JA, Sandra DA, Colucci ÉS, Al Bikaii A, Chmoulevitch D, Nahas J, et al. Smartphone addiction is increasing across the world: a meta-analysis of 24 countries. Comput Hum Behav 2022;129:107138. https://doi.org/10.1016/j.chb.2021.107138.

4. Panova T, Carbonell X. Is smartphone addiction really an addiction? J Behav Addict 2018;7(2):252–259. https://doi.org/10.1556/2006.7.2018.49.

5. Meng SQ, Cheng JL, Li YY, Yang XQ, Zheng JW, Chang XW, et al. Global prevalence of digital addiction in general population: a systematic review and meta-analysis. Clin Psychol Rev 2022;92:102128. https://doi.org/10.1016/j.cpr.2022.102128.

6. Cemiloglu D, Almourad MB, McAlaney J, Ali R. Combatting digital addiction: current approaches and future directions. Technol Soc 2022;68:101832. https://doi.org/10.1016/j.techsoc.2021.101832.

7. Almourad MB, McAlaney J, Skinner T, Pleya M, Ali R. Defining digital addiction: key features from the literature. Psihologija 2020;53(3):237–253. https://doi.org/10.2298/PSI191029017A.

8. Weinstein A, Lejoyeux M. Internet addiction or excessive internet use. Am J Drug Alcohol Abuse 2010;36(5):277–283. https://doi.org/10.3109/00952990.2010.491880.

9. Li L, Lin TT. Over-connected? A qualitative exploration of smartphone addiction among working adults in China. BMC Psychiatry 2019;19(1):186. https://doi.org/10.1186/s12888-019-2170-z.

10. Elhai JD, Dvorak RD, Levine JC, Hall BJ. Problematic smartphone use: a conceptual overview and systematic review of relations with anxiety and depression psychopathology. J Affect Disord 2017;207:251–259. https://doi.org/10.1016/j.jad.2016.08.030.

11. Kumar VA, Chandrasekaran V, Brahadeeswari H. Prevalence of smartphone addiction and its effects on sleep quality: a cross-sectional study among medical students. Ind Psychiatry J 2019;28(1):82–85. https://doi.org/10.4103/ipj.ipj_56_19.

12. Kwok C, Leung PY, Poon KY, Fung XC. The effects of internet gaming and social media use on physical activity, sleep, quality of life, and academic performance among university students in Hong Kong: a preliminary study. Asian J Soc Health Behav 2021;4(1):36–44. https://doi.org/10.4103/shb.shb_81_20.

13. Jeong YW, Han YR, Kim SK, Jeong HS. The frequency of impairments in everyday activities due to the overuse of the internet, gaming, or smartphone, and its relationship to health-related quality of life in Korea. BMC Public Health 2020;20(1):954. https://doi.org/10.1186/s12889-020-08922-z.

14. Alhassan AA, Alqadhib EM, Taha NW, Alahmari RA, Salam M, Almutairi AF. The relationship between addiction to smartphone usage and depression among adults: a cross sectional study. BMC Psychiatry 2018;18(1):148. https://doi.org/10.1186/s12888-018-1745-4.

15. Kim JH, Seo M, David P. Alleviating depression only to become problematic mobile phone users: can face-to-face communication be the antidote? Comput Hum Behav 2015;51:440–447. https://doi.org/10.1016/j.chb.2015.05.030.

16. van Velthoven MH, Powell J, Powell G. Problematic smartphone use: digital approaches to an emerging public health problem. Digit Health 2018;4:2055207618759167. https://doi.org/10.1177/2055207618759167.

17. Greco F, Quinzi F, Papaianni MC, Cosco LF, Segura-Garcia C, Emerenziani GP. Effects of school-based physical activity on volition in exercise, sleep quality and internet addiction in Italian adolescents. Heliyon 2024;10(11)e32129. https://doi.org/10.1016/j.heliyon.2024.e32129.

18. Li S, Wu Q, Tang C, Chen Z, Liu L. Exercise-based interventions for internet addiction: neurobiological and neuropsychological evidence. Front Psychol 2020;11:1296. https://doi.org/10.3389/fpsyg.2020.01296.

19. Demenech LM, Domingues MR, Muller RM, Levien VR, Dumith SC. Internet addiction and depressive symptoms: a dose-response effect mediated by levels of physical activity. Trends Psychiatry Psychother 2023;45e20210279. https://doi.org/10.47626/2237-6089-2021-0279.

20. Park S. Associations of physical activity with sleep satisfaction, perceived stress, and problematic Internet use in Korean adolescents. BMC Public Health 2014;14:1143. https://doi.org/10.1186/1471-2458-14-1143.

21. Saikia AM, Das J, Barman P, Bharali MD. Internet addiction and its relationships with depression, anxiety, and stress in urban adolescents of Kamrup District, Assam. J Family Community Med 2019;26(2):108–112. https://doi.org/10.4103/jfcm.JFCM_93_18.

22. Haug S, Castro RP, Kwon M, Filler A, Kowatsch T, Schaub MP. Smartphone use and smartphone addiction among young people in Switzerland. J Behav Addict 2015;4(4):299–307. https://doi.org/10.1556/2006.4.2015.037.

23. Lim PK, Amer Nordin AS, Yee A, Tan SB. Prevalence of smartphone addiction in patients with depression and its association with depression severity: a cross-sectional study. Int J Ment Health Addict 2021;19:919–933. https://doi.org/10.1007/s11469-019-00203-0.

24. Korea Disease Control and Prevention Agency. Community Health Survey 2023 user guide; 2023 [cited 2024 Nov 3]. Available from: https://chs.kdca.go.kr/chs/mnl/mnlBoardMain.do (Korean).

25. Cheng M, Wang S, Wang Y, Zhang R, Qin L. Physical activity reduces internet addiction among “post-00” college students: the mediating role of coping styles. Front Psychol 2023;13:1052510. https://doi.org/10.3389/fpsyg.2022.1052510.

26. Dang AK, Nathan N, Le QN, Nguyen LH, Nguyen HL, Nguyen CT, et al. Associations between internet addiction and physical activity among Vietnamese youths and adolescents. Child Youth Serv Rev 2018;93:36–40. https://doi.org/10.1016/j.childyouth.2018.06.030.

27. Zhihao D, Tao W, Yingjie S, Feng Z. The influence of physical activity on internet addiction among Chinese college students: the mediating role of self-esteem and the moderating role of gender. BMC Public Health 2024;24(1):935. https://doi.org/10.1186/s12889-024-18474-1.

28. Xu J, Tang L. The relationship between physical exercise and problematic internet use in college students: the chain-mediated role of self-control and loneliness. BMC Public Health 2024;24(1):1719. https://doi.org/10.1186/s12889-024-19226-x.

29. Murphy M, Nevill A, Neville C, Biddle S, Hardman A. Accumulating brisk walking for fitness, cardiovascular risk, and psychological health. Med Sci Sports Exerc 2002;34(9):1468–1474. https://doi.org/10.1097/00005768-200209000-00011.

30. Choi D. Physical activity level, sleep quality, attention control and self-regulated learning along to smartphone addiction among college students. J Korea Acad Ind Coop Soc 2015;16(1):429–437. (Korean). https://doi.org/10.5762/KAIS.2015.16.1.429.

31. Ungvari Z, Fazekas-Pongor V, Csiszar A, Kunutsor SK. The multifaceted benefits of walking for healthy aging: from Blue Zones to molecular mechanisms. Geroscience 2023;45(6):3211–3239. https://doi.org/10.1007/s11357-023-00873-8.

32. Bondarev D, Sipilä S, Finni T, Kujala UM, Aukee P, Kovanen V, et al. Associations of physical performance and physical activity with mental well-being in middle-aged women. BMC Public Health 2021;21(1):1448. https://doi.org/10.1186/s12889-021-11485-2.

33. Chung MS. The effects of age and need for cognition on smart phone using behavior: a study of smart phone dependency, mobile application, privacy concern, and mobile ad avoidance. Korean J Advert 2014;25(6):105–133. (Korean). https://doi.org/10.14377/KJA.2014.8.31.105.

34. Csibi S, Griffiths MD, Demetrovics Z, Szabo A. Analysis of problematic smartphone use across different age groups within the ‘components model of addiction’. Int J Ment Health Addict 2021;19:616–631. https://doi.org/10.1007/s11469-019-00095-0.

35. Bakken IJ, Wenzel HG, Götestam KG, Johansson A, Oren A. Internet addiction among Norwegian adults: a stratified probability sample study. Scand J Psychol 2009;50(2):121–127. https://doi.org/10.1111/j.1467-9450.2008.00685.x.

36. Kim SH, Kim YH, Lee CH, Lee Y. Smartphone usage and overdependence risk among middle-aged and older adults: a cross-sectional study. BMC Public Health 2024;24(1):413. https://doi.org/10.1186/s12889-024-17873-8.

37. Choi T, Park JW, Kim DJ. The effect of stress on internet game addiction trends in adults: mindfulness and conscientiousness as mediators. Psychiatry Investig 2021;18(8):779–788. https://doi.org/10.30773/pi.2020.0034.

38. Kwon M, Lee JY, Won WY, Park JW, Min JA, Hahn C, et al. Development and validation of a smartphone addiction scale (SAS). PLoS One 2013;8(2)e56936. https://doi.org/10.1371/journal.pone.0056936.

39. Choi SW, Kim DJ, Choi JS, Ahn H, Choi EJ, Song WY, et al. Comparison of risk and protective factors associated with smartphone addiction and Internet addiction. J Behav Addict 2015;4(4):308–314. https://doi.org/10.1556/2006.4.2015.043.

Article information Continued

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Variables	Category	Total, n (%)	% (weighted)¹	Addicted, n (%)	Rao-Scott χ²
Digital addiction	No	212 239 (91.0)	88.0
Digital addiction	Yes	19 488 (8.4)	12.0
Sex	Male	105 753 (45.6)	49.5	9721 (9.2)	49.6^***
Sex	Female	125 976 (54.4)	50.5	9767 (7.8)
Age (y)	19-29	21 540 (9.3)	15.6	5901 (27.4)	3432.6^***
	30-39	24 339 (10.5)	15.1	4738 (19.5)
	40-49	33 815 (14.6)	18.3	4253 (12.6)
	50-59	42 752 (18.4)	19.6	2579 (6.0)
	60-69	51 852 (22.4)	16.5	1522 (2.9)
	≥70	57 429 (24.8)	14.8	495 (0.9)
Education level	No or primary school	50 036 (21.6)	11.2	428 (0.9)	286.2^***
	Middle school	24 720 (10.7)	7.5	656 (2.7)
	High school	76 754 (33.1)	35.4	7207 (9.4)
	College or higher	80 115 (34.6)	45.9	11 196 (14.0)
Marital status	Married	146 135 (63.1)	60.7	9254 (6.3)	6906.4^***
	Divorced/separated/widowed	47 261 (20.4)	14.8	1510 (3.2)
	Single	38 289 (16.5)	24.5	8720 (22.8)
Employment status	Self-employed	41 781 (18.0)	14.7	2578 (6.2)	286.2^***
	Salaried	97 671 (42.2)	49.1	10 442 (10.7)
	Economically inactive	92 251 (39.8)	36.2	6467 (7.0)
Perceived stress	Stressed	52 094 (22.5)	24.6	6399 (12.3)	668.1^***
Perceived stress	Not stressed	179 592 (77.5)	75.4	13 089 (7.3)
Depression	No	222 324 (96.2)	96.0	18 114 (8.1)	522.2^***
Depression	Yes	8801 (3.8)	4.0	1346 (15.3)
Exercise	No	179 582 (77.5)	76.8	14 393 (8.0)	80.2^***
Exercise	Yes	52 121 (22.5)	23.2	5093 (9.8)
Regular walking	No	122 056 (52.7)	48.3	9913 (8.1)	0.5^***
Regular walking	Yes	109 653 (47.3)	51.7	9574 (8.7)

Variables	Category	Model 1	p-value	Model 2	p-value	Model 3	p-value
Sex	Male	1.09 (1.04, 1.13)	<0.001	1.08 (1.04, 1.12)	<0.001	1.09 (1.04, 1.13)	<0.001
Sex	Female	1.00 (reference)		1.00 (reference)		1.00 (reference)
Age (y)	19-29	16.97 (14.65, 19.67)	<0.001	16.87 (14.56, 19.55)	<0.001	16.94 (14.62, 19.64)	<0.001
	30-39	12.82 (11.11, 14.79)	<0.001	12.73 (11.03, 14.68)	<0.001	12.77 (11.07, 14.74)	<0.001
	40-49	8.72 (7.57, 10.04)	<0.001	8.66 (7.52, 9.97)	<0.001	8.68 (7.53, 10.00)	<0.001
	50-59	4.55 (3.95, 5.25)	<0.001	4.53 (3.93, 5.22)	<0.001	4.54 (3.94, 5.23)	<0.001
	60-69	2.50 (2.18, 2.87)	<0.001	2.50 (2.17, 2.87)	<0.001	2.50 (2.18, 2.87)	<0.001
	≥70	1.00 (reference)		1.00 (reference)		1.00 (reference)
Education level	No or primary school	1.00 (reference)		1.00 (reference)		1.00 (reference)
	Middle school	1.66 (1.40, 1.98)	<0.001	1.67 (1.40, 1.99)	<0.001	1.67 (1.40, 1.99)	<0.001
	High school	2.27 (1.95, 2.65)	<0.001	2.28 (1.95, 2.66)	<0.001	2.28 (1.96, 2.67)	<0.001
	College or higher	2.76 (2.36, 3.22)	<0.001	2.77 (2.37, 3.24)	<0.001	2.77 (2.37, 3.24)	<0.001
Marital status	Married	1.00 (reference)		1.00 (reference)		1.00 (reference)
	Divorced/separated/widowed	0.97 (0.90, 1.05)	0.438	0.97 (0.90, 1.05)	0.438	0.97 (0.90, 1.05)	0.441
	Single	1.42 (1.35, 1.50)	<0.001	1.42 (1.35, 1.50)	<0.001	1.42 (1.35, 1.50)	<0.001
Employment status	Self-employed	1.06 (1.00, 1.12)	0.050	1.05 (0.99, 1.12)	0.078	1.05 (1.00, 1.12)	0.074
	Salaried	1.00 (reference)		1.00 (reference)		1.00 (reference)
	Economically inactive	1.34 (1.28, 1.40)	<0.001	1.34 (1.28, 1.40)	<0.001	1.34 (1.28, 1.40)	<0.001
Perceived stress	Stressed	1.34 (1.29, 1.40)	<0.001	1.34 (1.29, 1.40)	<0.001	1.34 (1.29, 1.40)	<0.001
Perceived stress	Not stressed	1.00 (reference)		1.00 (reference)		1.00 (reference)
Depression	No	1.00 (reference)		1.00 (reference)		1.00 (reference)
Depression	Yes	2.04 (1.88, 2.21)	<0.001	2.03 (1.87, 2.21)	<0.001	2.03 (1.87, 2.20)	<0.001
Exercise	No	1.00 (reference)		-		1.00 (reference)
Exercise	Yes	0.96 (0.92, 1.00)	0.045	-		0.97 (0.93, 1.01)	0.135
Regular walking	No	-		1.00 (reference)		1.00 (reference)
Regular walking	Yes	-		0.93 (0.90, 0.97)	<0.001	0.94 (0.90, 0.97)	0.001
-2 Log L		27 357 385		27 354 445		27 352 257

Variables	Category	Age (y)
Variables	Category	19-39	p-value	40-59	p-value	≥60	p-value
Sex	Male	1.02 (0.97, 1.07)	0.534	1.07 (1.00, 1.13)	0.048	0.90 (0.80, 1.01)	0.065
Sex	Female	1.00 (reference)		1.00 (reference)		1.00 (reference)
Education level	No or primary school	1.00 (reference)		1.00 (reference)		1.00 (reference)
	Middle school	1.38 (0.75, 2.54)	0.306	1.03 (0.71, 1.50)	0.890	2.20 (1.81, 2.66)	<0.001
	High school	1.45 (0.82, 2.56)	0.203	1.72 (1.25, 2.37)	0.001	3.04 (2.56, 3.61)	<0.001
	College or higher	1.40 (0.79, 2.48)	0.246	3.01 (2.19, 4.15)	<0.001	5.16 (4.28, 6.22)	<0.001
Marital status	Married	1.00 (reference)		1.00 (reference)		1.00 (reference)
	Divorced/separated/widowed	0.82 (0.64, 1.03)	0.089	0.95 (0.87, 1.05)	0.320	0.91 (0.80, 1.04)	0.185
	Single	1.62 (1.53, 1.72)	<0.001	1.56 (1.43, 1.70)	<0.001	1.33 (0.92, 1.92)	0.125
Employment status	Self-employed	1.11 (1.01, 1.22)	0.033	0.95 (0.88, 1.03)	0.205	0.77 (0.65, 0.92)	0.003
	Salaried	1.00 (reference)		1.00 (reference)		1.00 (reference)
	Economically inactive	1.43 (1.35, 1.51)	<0.001	1.16 (1.07, 1.25)	0.000	0.79 (0.69, 0.90)	0.000
Perceived stress	Stressed	1.27 (1.20, 1.35)	<0.001	1.45 (1.36, 1.55)	<0.001	1.56 (1.36, 1.78)	<0.001
Perceived stress	Not stressed	1.00 (reference)		1.00 (reference)		1.00 (reference)
Depression	No	1.00 (reference)		1.00 (reference)		1.00 (reference)
Depression	Yes	2.08 (1.87, 2.32)	<0.001	2.16 (1.88, 2.48)	<0.001	1.50 (1.18, 1.92)	0.001
Exercise	No	1.00 (reference)		1.00 (reference)		1.00 (reference)
Exercise	Yes	0.97 (0.92, 1.03)	0.366	0.98 (0.92, 1.06)	0.629	1.03 (0.90, 1.19)	0.664
Regular walking	No	1.00 (reference)		1.00 (reference)		1.00 (reference)
Regular walking	Yes	0.96 (0.91, 1.01)	0.099	0.89 (0.84, 0.95)	<0.001	0.98 (0.88, 1.10)	0.741
No. of observations		45 161		75 312		107 343
-2 Log L		14 303 552		10 289 865		2 850 746