Prevalence of internet addiction and its effects on lifestyle choices and snacking patterns in young adults

INTRODUCTION

Internet addiction, also known as cyber addiction, represents a relatively new phenomenon not yet fully recognized by the medical community. It is part of a series of emerging health problems in the 21^st century. Researchers classify it as a form of behavioral dependence, noting that individuals with addictive behaviors are more likely to experience health morbidity, socioeconomic issues, and behavioral problems.¹

However, this widespread adoption has also led to a troubling phenomenon: internet addiction. While this potential behavioral disorder is not yet officially recognized as a distinct psychiatric condition, it has increasingly captured the attention of both researchers and the public. Moreover, the mental health decline associated with internet addiction is often exacerbated by disrupted sleep and reduced physical activity, further impacting overall well-being.^2,3

The rapid expansion of internet access and the proliferation of online activities have heightened the risk for individuals with underlying psychological vulnerabilities to develop problematic attachments to the digital realm.^3,4 Emerging research indicates that internet addiction bears many similarities to other recognized addictive disorders. It often involves compulsive and uncontrolled internet use, prioritization of online activities over real-world responsibilities, and detrimental impacts on academic, social, and occupational functioning.^5,6 Anecdotal evidence underscores these concerns, with case studies documenting students whose academic performance has suffered due to excessive immersion in the digital world, leading to neglect of their studies and physical well-being.^4,5

Adolescents are particularly vulnerable to internet addiction, which can negatively impact their social performance, psychological state, and lifestyle habits. In the past decade, internet use among adolescents has surged dramatically. In the US and Japan, 93% of adolescents aged 12 to 17 are online, while 71.8% of adolescents in China and 74.5% in India are also connected.⁴ Numerous cross-sectional studies have shown that internet addiction adversely affects various lifestyle-related factors in adolescents, such as irregular dietary habits, prolonged internet use, physical inactivity, and reduced sleep duration.^7,8

Furthermore, heavy internet use can lead to negative physical effects, including excessive weight gain or obesity. The increase in obesity over the past 10 to 20 years is linked to a shift from physically demanding lifestyles to more sedentary habits, driven by industrialization and the consumption of calorie-dense foods.⁹ Immobility is a significant concern for those who spend extensive periods online.¹⁰ Additionally, excessive internet use is associated with insomnia and disruptions in sleep patterns.^11,12

Impact on lifestyle choices

Mental health

Internet overuse can negatively impact mental health. Excessive internet use is linked to higher levels of anxiety and depression. This mental health decline is often compounded by disrupted sleep and reduced physical activity, further affecting overall well-being.²

Sleep disruption

Excessive use of electronic devices before bedtime is linked to delayed sleep onset and poor sleep quality.¹² High screen time is associated with an increased likelihood of insomnia and reduced overall sleep duration, negatively affecting daytime functioning and health.¹³

Physical inactivity

Excessive internet use is strongly associated with decreased physical activity. Young adults who spend more than four hours a day online are significantly less likely to engage in regular physical exercise.¹⁴ This sedentary behavior contributes to a range of health issues, including increased risk of obesity and cardiovascular diseases. Similarly, another study highlighted that prolonged screen time is a major factor in reducing physical activity levels and overall fitness.⁵

Impact on snacking patterns

Increased snacking and unhealthy eating

Young adults who overuse the internet are more prone to unhealthy snacking behaviors. Internet overuse is associated with higher consumption of high-calorie, low-nutrient snacks. This tendency is often driven by the convenience of online food ordering and the sedentary nature of prolonged internet use, which encourages snacking.¹

Emotional eating

Emotional eating is another consequence of excessive internet use. Individuals who spend significant time online are more likely to use food as a coping mechanism for stress and emotional distress. This pattern is further exacerbated by disrupted sleep and sedentary behavior, which are common among heavy internet users.

Obesity risk

The relationship between internet overuse, poor dietary habits, and obesity is increasingly evident. The growing prevalence of obesity among individuals who engage in prolonged internet use and exhibit poor dietary choices.¹⁵

As the prevalence of internet addiction continues to rise, numerous cross-sectional studies consistently highlight its detrimental effects on various lifestyle-related factors in adolescents. Internet addiction is associated with irregular dietary patterns, prolonged periods of internet usage, physical inactivity, insufficient sleep, and an increased likelihood of alcohol and tobacco consumption. Addressing these issues is essential for promoting healthier lifestyles among those affected by internet addiction.

Aim

This study was aimed to explore the prevalence of internet addiction among young adults and its impact on dietary behaviours.

METHODS

An exploratory study was conducted to examine the prevalence of internet addiction and dietary habits among young adults. The research was carried out from December 2023 to May 2024, involving 254 participants from Hyderabad city. The study specifically targeted young adults aged 19 to 31 years, including both male and female participants, who had been using the internet for at least the past six months. This age group was selected based on previous research indicating that young adults are particularly susceptible to internet overuse. To ensure that participants had sufficient exposure to online activities, the six-month usage criterion was established, which enhances the validity of the findings related to internet addiction. Participants were recruited through various channels, including social media platforms, local universities, and community organizations. This recruitment strategy aimed to achieve a diverse representation of young adults, considering factors such as socio-economic status, educational background, and lifestyle habits. By employing this approach, the study aimed to provide a comprehensive understanding of the prevalence of internet addiction and its impact on dietary behaviors within this demographic. Data was collected using the Korean internet addiction scale, which includes various components designed to evaluate internet addiction and associated behaviors.¹⁶ The KS scale comprises demographic information, patterns of internet addiction, and lifestyle and snacking patterns. Internet addiction organized into four subcategories: internet over-use, dependence, withdrawal, and avoidance of reality. The scale includes 20 items that assess levels of internet addiction, with responses rated on a 5-point Likert scale. The scale ranges from 0 (“not applicable”) to 5 ("always”), with intermediate values indicating varying frequencies of internet-related behaviours. Based on their scores, participants were classified into three categories: high-risk internet users, potential risk internet users, and low-risk internet users. This classification helps in understanding the severity of internet addiction and in identifying individuals who may need targeted interventions. Statistical analysis was performed using SPSS. Ethical approval was not required.

RESULTS

This demographic analysis revealed notable differences in gender, age, education, BMI, personality type, and relationship status across the varying levels of internet addiction risk. A higher proportion of females were found in the low-risk category (88.30%), while males dominate the high-risk category (31.58%). Younger individuals (19-23 years) predominantly fall into the low-risk category (77.66%). As age increases, particularly in the 23-27 age range, there was a notable rise in high-risk individuals (47.37%). Higher education levels correlate with increased representation in the high-risk category, particularly among PhD holders (26.32%). The BMI distributions were relatively stable, with most individuals categorized as having normal weight.

However, there was a slight increase in underweight individuals within the high-risk group (21.05%), indicating that high-risk individuals may experience disruptions in their eating habits related to excessive internet use. Personality types show distinct patterns. Introverts were more prevalent in the high-risk group (47.37%), while extroverts were less represented (15.79%). Ambiverts were the most common across all risk levels, suggesting that while a range of personality types can be at risk, introverts may be particularly susceptible to higher levels of addiction due to their inclination toward solitary activities. Single individuals constitute a significant majority across all risk levels (81.91% at low risk, 89.32% at potential risk, and 78.95% at high risk). The high percentage of singles, especially in the potential risk category, suggests a potential link between internet use, addiction, and social dynamics.

Individuals classified as high risk exhibit significantly higher levels of internet over-use (33.89±3.72) compared to those in the low risk (16.10±3.25) and potential risk categories (23.97±3.27). This trend indicates a progressive increase in over-use corresponding with higher risk levels. The overall mean score for the sample (19.84±7.97) falls between the low and potential risk categories, suggesting that a considerable portion of the population may experience moderate over-use. Regarding dependence, the high-risk group reports a mean score of 17.11±3.46, significantly higher than the potential risk group (11.50±2.99) and the low-risk group (7.12±1.93). This increasing trend in dependence highlights the escalating severity of addiction. The total sample mean (9.27±4.54) indicates a general tendency toward moderate dependence, although still notably lower than that seen in the high-risk group. The high-risk group scores 10.95±2.00, markedly higher than both the potential risk group (7.18±2.22) and the low-risk group (4.20±1.37). The mean score for the total sample (5.69±3.06) indicates that withdrawal symptoms were prevalent but intensify with increased risk. High-risk individuals report the highest mean score (8.89±2.83) for avoidance of reality, followed by potential risk (6.38±2.17) and low risk (4.33±1.31).

Physical activity

Physical activity levels differ significantly across risk categories. A majority (62.60% overall) of individuals report engaging in less than 30 minutes of physical activity daily, with a slight decrease in this trend as risk levels increase. Specifically, 67.02% of those at low risk and 61.17% of those at potential risk engage in minimal exercise, while only 57.89% of high-risk individuals do so. Conversely, the percentage of individuals participating in more than 60 minutes of physical activity was low overall (5.12%), but slightly higher in the higher risk groups (6.80% in potential risk and 10.53% in high risk). A higher percentage of individuals in the low-risk category report regular sleep patterns (39.36%) compared to those at potential risk (25.24%) and high risk (36.84%). However, a greater proportion of potential risk individuals have regular sleep patterns “often” (40.78%), while those at high risk show more irregular sleep patterns (36.84%). The incidence of sleeping at the workplace was highest among the high-risk group (52.63%), followed by those at potential risk (41.75%) and low risk (37.23%). Meal-skipping behaviors differ significantly with risk levels. A greater percentage of high-risk individuals skip breakfast (42.11%) compared to those at potential risk (28.16%) and low risk (15.96%). This suggests that higher internet addiction risk may be linked to poorer dietary habits, potentially due to irregular schedules or poor time management. In contrast, skipping lunch is rare across all groups, while skipping dinner is slightly more common but still relatively low overall (7.09%). Snacking during internet use is more prevalent in the high-risk group (31.58%) compared to the low-risk (18.09%) and potential risk groups (21.36%). This behavior may reflect unhealthy eating habits associated with prolonged internet use, indicating a tendency toward multitasking or sedentary behavior during online activities.

DISCUSSION

The findings of this study provide important insights into the demographic and behavioral factors associated with internet addiction among young adults. A significant gender disparity was observed, with males more likely to be categorized as high-risk users. This aligns with previous studies that have reported higher rates of internet addiction among males, potentially due to greater engagement in online gaming and other internet-based recreational activities.¹⁶ These findings suggest that gender-specific interventions may be more effective in addressing internet overuse.

Age-related trends identified in the study showed that individuals in their mid-20s exhibited higher levels of internet addiction. This may be attributed to the increased academic and professional responsibilities in this age group, which could drive excessive internet use either as a coping mechanism or due to work-related demands. Previous literature supports this, noting that stress related to transitions in early adulthood may exacerbate internet dependence.¹⁷

Interestingly, higher levels of education were associated with greater risk of internet addiction. This may reflect academic pressures and the growing reliance on digital platforms for learning and communication. Similar findings have been reported in earlier studies, where academic stress and performance anxiety were linked to increased internet use.¹⁸ These findings underscore the importance of integrating stress management programs within educational institutions.

The association between underweight status and high-risk internet use suggests a potential link between internet addiction and neglect of physical health. Prior research has indicated that internet-addicted individuals often exhibit poor nutritional habits and sedentary behaviors, which could explain the higher prevalence of underweight individuals in this group.¹⁹

Personality traits also appeared to influence internet use, with a higher prevalence of introverts among high-risk individuals. This supports earlier studies that identified introversion as a risk factor, as such individuals may prefer online interactions over face-to-face communication.²⁰ Similarly, relationship status played a role, with single individuals showing higher addiction scores, possibly due to more unstructured free time or use of the internet for social connection.

The study’s findings further highlighted the multidimensional nature of internet addiction, encompassing overuse, dependence, withdrawal, and escapism. These dimensions have been documented in earlier research, emphasizing the psychological complexity of internet addiction.⁶ High levels of dependence and withdrawal symptoms among high-risk users point to serious psychological and behavioral consequences, necessitating early identification and therapeutic interventions such as cognitive-behavioral therapy (CBT).

The use of the internet as a coping mechanism for stress was another key finding. Prior studies have established this behavior as a central feature of internet addiction, where users turn to digital engagement to manage negative emotions.²¹ This indicates the need for psychological support systems that promote healthier stress-coping strategies.

Lifestyle behaviors among high-risk users were also concerning. Decreased physical activity, poor dietary habits, and irregular sleep patterns were all significantly associated with high levels of internet use. These results are consistent with prior findings that link excessive screen time to sedentary lifestyles, disrupted circadian rhythms, and unhealthy eating behaviors.^22,23 The observed trend of breakfast skipping and increased snacking while online highlights the need for public health interventions focusing on nutritional education and healthy routine development.

Furthermore, high rates of sleeping at work among internet-addicted individuals may reflect chronic fatigue and impaired daytime functioning, affecting academic or occupational productivity. These issues have also been reported in previous studies that link internet addiction with decreased performance and well-being.²⁴

Overall, the study highlights the broad and interconnected impacts of internet addiction on psychological well-being, physical health, and daily functioning. The results support the development of targeted, multifactorial interventions that include educational, psychological, and lifestyle components aimed at young adults.

CONCLUSION

The analysis of demographic patterns and lifestyle behaviors in relation to internet addiction reveals significant insights into the factors influencing addiction risk. The data underscores that females, younger adults, and individuals with higher education levels are more frequently represented across varying risk levels. Introverts and those with less stable relationships are particularly prone to higher risk levels, highlighting the role of personality traits and social factors in internet addiction. The trend of increasing scores in internet over-use, dependence, withdrawal, and avoidance of reality with higher risk levels demonstrates a clear correlation between internet addiction severity and behavioral and psychological symptoms. Individuals at high risk exhibit markedly higher scores, reflecting more intense symptoms and reinforcing the need for specialized interventions tailored to this group. Moreover, the lifestyle patterns associated with internet addiction risk reveal a decrease in physical activity, more frequent meal skipping, and irregular sleep patterns as addiction risk increases. The tendency to sleep at the workplace among those at high risk further emphasizes the extensive impact of internet addiction on daily life and well-being. Overall, these findings suggest that addressing both demographic factors and lifestyle behaviours is essential in developing effective prevention and intervention strategies. Targeted approaches that consider individual risk profiles and associated lifestyle challenges could significantly enhance efforts to reduce internet addiction and improve overall health outcomes.

ACKNOWLEDGEMENTS

We would like to thank all the respondents who participated in the study.

Funding: No funding sources

Conflict of interest: None declared

Ethical approval: Not required

REFERENCES

1. 1. Chen SH, Weng LJ, Su YJ, Wu HM, Yang PF. Development of Chinese internet addiction scale and its psychometric study. Chinese J Psychol. 2003;45:1-38.
2. 2. Choi J, Lee HK, Kim YJ. The effects of excessive internet use on mental health: a review of current evidence. J Mental Health Res. 2023;30(2):145-59.
3. 3. Christakis DA. Internet addiction: a 21^st century epidemic? BMC Med. 2010;8:6.
4. 4. Wallace P. Internet addiction disorder and youth. Science and Society. EMBO Rep. 2014;15(1).
5. 5. Khan NT. Internet addiction: a global psychological addiction disorder. J Med Res. 2018;4(4):202-3.
6. 6. Young KS. Internet addiction: the emergence of a new clinical disorder. Cyber Psychol Behav. 2009;1(3).
7. 7. Kim JS, Chun BC. Association of Internet addiction with health promotion lifestyle profile and perceived health status in adolescents. J Prev Med Public Health. 2005;38:53-60.
8. 8. Lam LT, Peng ZW, Mai JC, Jing J. Factors associated with Internet addiction among adolescents. Cyberpsychol Behav. 2009;12:1-5.
9. 9. Nguyen TT, Collins JA, Brooks MS. Screen time and its impact on sleep: insights into insomnia and sleep duration. J Clin Sleep Med. 2022;18(8):1501-10.
10. 10. Spada MM. An overview of problematic internet use. Addict Behav. 2014; 39(1):3-6
11. 11. Shimizu T. Sleep habits and its relation to depression. Nihon Rinsho. 2012;70(7):1206-10.
12. 12. Scott H, Green AJ, Bennett LM. The impact of electronic device use on sleep patterns and quality: a comprehensive review. Sleep Med Rev. 2023;62:101-13.
13. 13. Weinsier RL, Hunter ND, O'Neal RM. Internet use and physical activity: a study of young adults. J Phys Act Health. 2022;19(4):251-60.
14. 14. Zhang Y, Li J, Wang H, Liu X. The growing prevalence of obesity among individuals with prolonged internet use and poor dietary choices. J Health Nutr. 2022;15(4):345-56.
15. 15. Kim DI, Chung YJ, Lee EA, Kim DM, Cho YM. Development of Internet addiction proneness scale-short form (KS scale). Korean J Counsell. 2008;9:1703-22.
16. 16. Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: a systematic review of epidemiological research for the last decade. Curr Pharm Design. 2013;20(25):4026-52.
17. 17. Sharma A, Sahu R, Kasar PK, Sharma R. Internet addiction among professional courses students: a study from central India. Int J Med Sci Public Health. 2014;3(9):1069-73.
18. 18. Anand N, Jain PA, Prabhu S, Thomas C, Bhat A, Prathyusha PV, et al. Internet use patterns, internet addiction, and psychological distress among engineering university students: a study from India. Indian J Psychiatr. 2018;60(4):479-85.
19. 19. Kim JH, Lau CH, Cheuk KK, Kan P, Hui HL, Griffiths SM. Predictors of heavy internet use and associations with health-promoting and health risk behaviors among Hong Kong university students. J Adolesc. 2010;33(1):215-20.
20. 20. Montag C, Reuter M. Internet addiction: Neuroscientific approaches and therapeutical interventions. Springer; 2015.
21. 21. Caplan SE. Relations among loneliness, social anxiety, and problematic internet use. Cyber Psychol Behav. 2007;10(2):234-42.
22. 22. Vandelanotte C, Sugiyama T, Gardiner P, Owen N. Associations of leisure-time internet and computer use with overweight and obesity, physical activity and sedentary behaviors: cross-sectional study. J Med Internet Res. 2009;11(3):e28.
23. 23. Tan Y, Chen Y, Lu Y, Li L. Exploring associations between problematic internet use, depressive symptoms and sleep disturbance among southern Chinese adolescents. J Behav Addict. 2016;5(3):564-9.
24. 24. Zhou Y, Li D, Li X, Wang Y, Zhao L. Internet addiction, problematic internet use, non-suicidal self-injury, and suicidal ideation among Chinese adolescents: A moderated mediation model. PLoS One. 2017;12(5):e0176716.