Clustering of South Korean Adolescents’ Health-Related Behaviors by Gender: Using a Latent Class Analysis
Abstract
:1. Introduction
2. Material and Methods
2.1. Study Participants
2.2. Health-Related Behaviors
2.3. Statistical Analysis
3. Results
3.1. Classes Selection
3.2. Classes among Male Adolescents
3.3. Classes among Female Adolescents
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Laursen, B.; Collins, W.A. Parent-child relationships during adolescence. In Handbook of Adolescent Psychology; Lerner, R.M., Steinberg, L., Eds.; Wiley: Hoboken, NJ, USA, 2009; pp. 3–42. [Google Scholar]
- Papalia, D.E.; Martoerll, G. Experience Human Development, 13th ed.; McGraw Hill: New York, NY, USA, 2015. [Google Scholar]
- Dutra, L.M.; Glantz, S.A. Thirty-day smoking in adolescence is a strong predictor of smoking in young adulthood. Prev. Med. 2018, 109, 17–21. [Google Scholar] [CrossRef] [PubMed]
- Gabel, L.; Macdonald, H.M.; Nettlefold, L.; McKay, H.A. Physical Activity, Sedentary Time, and Bone Strength from Childhood to Early Adulthood: A Mixed Longitudinal HR-pQCT study. J. Bone Miner. Res. 2017, 32, 1525–1536. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ames, M.E.; Leadbeater, B.J.; MacDonald, S.W. Health behavior changes in adolescence and young adulthood: Implications for cardiometabolic risk. Health Psychol. 2018, 37, 103. [Google Scholar] [CrossRef]
- Neumark-Sztainer, D.; Wall, M.M.; Chen, C.; Larson, N.I.; Christoph, M.J.; Sherwood, N.E. Eating, Activity, and Weight-related Problems from Adolescence to Adulthood. Am. J. Prev. Med. 2018, 55, 133–141. [Google Scholar] [CrossRef] [PubMed]
- Boden, J.; Blair, S.; Newton-Howes, G. Alcohol use in adolescents and adult psychopathology and social outcomes: Findings from a 35-year cohort study. Aust. N. Z. J. Psychiatry 2020, 54, 909–918. [Google Scholar] [CrossRef] [PubMed]
- de Looze, M.; Elgar, F.J.; Currie, C.; Kolip, P.; Stevens, G.W. Gender inequality and sex differences in physical fighting, physical activity, and injury among adolescents across 36 countries. J. Adolesc. Health 2019, 64, 657–663. [Google Scholar] [CrossRef] [Green Version]
- Miller, J.M.; Pereira, M.A.; Wolfson, J.; Laska, M.N.; Nelson, T.F.; Neumark-Sztainer, D. Are correlates of physical activity in adolescents similar across ethnicity/race and sex: Implications for interventions. J. Phys. Act. Health 2019, 16, 1163–1174. [Google Scholar] [CrossRef]
- Griffiths, S.; Murray, S.B.; Bentley, C.; Gratwick-Sarll, K.; Harrison, C.; Mond, J.M. Sex Differences in Quality of Life Impairment Associated with Body Dissatisfaction in Adolescents. J. Adolesc. Health 2017, 61, 77–82. [Google Scholar] [CrossRef] [PubMed]
- Carvalho, G.X.d.; Nunes, A.P.N.; Moraes, C.L.; Veiga, G.V.d. Body image dissatisfaction and associated factors in adolescents. Ciência Saúde Coletiva 2020, 25, 2769–2782. [Google Scholar] [CrossRef]
- Wawrzyniak, A.; Myszkowska-Ryciak, J.; Harton, A.; Lange, E.; Laskowski, W.; Hamulka, J.; Gajewska, D. Dissatisfaction with Body Weight among Polish Adolescents Is Related to Unhealthy Dietary Behaviors. Nutrients 2020, 12, 2658. [Google Scholar] [CrossRef]
- Nowak, M.; Papiernik, M.; Mikulska, A.; Czarkowska-Paczek, B. Smoking, alcohol consumption, and illicit sub-stances use among adolescents in Poland. Subst. Abuse Treat. Prev. Policy 2018, 13, 1–8. [Google Scholar] [CrossRef]
- Adolescent Alcohol-Related Behviours: Trends and Inequlities in the WHO European Region, 2002–2014; World Health Oranization Regional Office for Europe: Copenhagen, Denmark, 2018.
- Kim, D.J.; Kim, S.J. Impact of nearby smoking on adolescent smoking behavior in Korea. Medicine 2018, 97, e13125. [Google Scholar] [CrossRef] [PubMed]
- Azagba, S.; Manzione, L.; Shan, L.; King, J. Trends in Smoking Behaviors among US Adolescent Cigarette Smokers. Pediatrics 2020, 145, e20193047. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Larsen, K.; To, T.; Irving, H.M.; Boak, A.; Hamilton, H.A.; Mann, R.E.; Schwartz, R.; Faulkner, G.E. Smoking and binge-drinking among adoles-cents, Ontario, Canada: Does the school neighbourhood matter? Health Place 2017, 47, 108–114. [Google Scholar] [CrossRef] [PubMed]
- Jezewska-Zychowicz, M.; Gębski, J.; Guzek, D.; Świątkowska, M.; Stangierska, D.; Plichta, M.; Wasilewska, M. The Associations between Dietary Patterns and Sedentary Behaviors in Polish Adults (LifeStyle Study). Nutrients 2018, 10, 1004. [Google Scholar] [CrossRef] [Green Version]
- Sheldrick, M.P.R.; Tyler, R.; Mackintosh, K.A.; Stratton, G. Relationship between Sedentary Time, Physical Activity and Multiple Lifestyle Factors in Children. J. Funct. Morphol. Kinesiol. 2018, 3, 15. [Google Scholar] [CrossRef] [Green Version]
- Pearson, N.; Griffiths, P.; Biddle, S.J.; Johnston, J.P.; McGeorge, S.; Haycraft, E. Clustering and correlates of screen-time and eating behaviours among young adolescents. BMC Public Health 2017, 17, 533. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Porcu, M.; Fancesca, G. Introduction to latent class anaysis with applications. J. Early Adolesc. 2017, 37, 129–158. [Google Scholar] [CrossRef]
- Xiao, Y.; Romanelli, M.; Lindsey, M.A. A latent class analysis of health lifestyles and suicidal behaviors among US adolescents. J. Affect. Disord. 2019, 255, 116–126. [Google Scholar] [CrossRef] [PubMed]
- Champion, K.E.; Mather, M.; Spring, B.; Kay-Lambkin, F.; Teesson, M.; Newton, N.C. Clustering of multiple risk be-haviors among a sample of 18-year-old Australians and associations with mental health outcomes: A latent class analysis. Front. Public Health 2018, 6, 135. [Google Scholar] [CrossRef]
- Olson, J.S.; Hummer, R.A.; Harris, K.M. Gender and Health Behavior Clustering among U.S. Young Adults. Biodemography Soc. Biol. 2017, 63, 3–20. [Google Scholar] [CrossRef]
- Muthén, L.K.; Muthén, B.O. Mplus User’s Guide, 8th ed.; Muthén & Muthén: Los Angeles, CA, USA, 2017. [Google Scholar]
- Lo, Y.; Mendell, N.R.; Rubin, D.B. Testing the number of components in a normal mixture. Biometrika 2001, 88, 767–778. [Google Scholar] [CrossRef]
- Jedidi, K.; Ramaswamy, V.; Desarbo, W.S. A maximum likelihood method for latent class regression involving a censored dependent variable. Psychometrika 1993, 58, 375–394. [Google Scholar] [CrossRef] [Green Version]
- Gardner, L.A.; Champion, K.E.; Parmenter, B.; Grummitt, L.; Chapman, C.; Sunderland, M.; Thornton, L.; McBride, N.; The Health4Life Team the Health4Life Team; Newton, N.C. Clustering of Six Key Risk Behaviors for Chronic Disease among Adolescent Females. Int. J. Environ. Res. Public Health 2020, 17, 7211. [Google Scholar] [CrossRef] [PubMed]
- Bann, D.; Scholes, S.; Fluharty, M.; Shure, N. Adolescents’ physical activity: Cross-national comparisons of levels, distributions and disparities across 52 countries. Int. J. Behav. Nutr. Phys. Act. 2019, 16, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Chen, S.-T.; Liu, Y.; Hong, J.-T.; Tang, Y.; Cao, Z.-B.; Zhuang, J.; Zhu, Z.; Chen, P.-J. Co-existence of physical activity and sedentary behavior among children and adolescents in Shanghai, China: Do gender and age matter? BMC Public Health 2018, 18, 1287. [Google Scholar] [CrossRef] [PubMed]
- Owens, S.; Galloway, R.; Gutin, B. The Case for Vigorous Physical Activity in Youth. Am. J. Lifestyle Med. 2016, 11, 96–115. [Google Scholar] [CrossRef]
- Costigan, S.A.; Lubans, D.R.; Lonsdale, C.; Sanders, T.; del Pozo Cruz, B. Associations between physical activity intensity and well-being in adolescents. Prev. Med. 2019, 125, 55–61. [Google Scholar] [CrossRef]
- Di Nicola, M.; Ferri, V.R.; Moccia, L.; Panaccione, I.; Strangio, A.M.; Tedeschi, D.; Grandinetti, P.; Callea, A.; De-Giorgio, F.; Martinotti, G.; et al. Gender differences and psy-chopathological features associated with addictive behaviors in adolescents. Front. Psychiatry 2017, 8, 256. [Google Scholar] [CrossRef] [Green Version]
- Kim, K.; Park, H. Gender differences in the association between self-reported stress and cigarette smoking in Korean adolescents. Tob. Induc. Dis. 2016, 14, 19. [Google Scholar] [CrossRef] [Green Version]
- Rehm, J.; Shield, K.D.; Weiderpass, E. Alcohol consumption. A leading risk factor for cancer. Chem. Interact. 2020, 331, 109280. [Google Scholar] [CrossRef]
- Hagström, H.; Hemmingsson, T.; Discacciati, A.; Andreasson, A. Alcohol consumption in late adolescence is asso-ciated with an increased risk of severe liver disease later in life. J. Hepatol. 2018, 68, 505–510. [Google Scholar] [CrossRef] [PubMed]
- Rossi, M.; Jahanzaib Anwar, M.; Usman, A.; Keshavarzian, A.; Bishehsari, F. Colorectal cancer and alcohol consumption—Populations to molecules. Cancers 2018, 10, 38. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rocca, G.; Verde, A.; Gatti, U. Impact of alcohol and cannabis use on juvenile delinquency: Results from an in-ternational multi-city study (ISRD3). Eur. J. Crim. Policy Res. 2019, 25, 259–271. [Google Scholar] [CrossRef]
- Najman, J.M.; Plotnikova, M.; Horwood, J.; Silins, E.; Fergusson, D.; Patton, G.C.; Olsson, C.; Hutchinson, D.M.; Degenhardt, L.; Tait, R.; et al. Does adolescent heavier alcohol use predict young adult aggression and delinquency? Parallel analyses from four Australasian cohort studies. Aggress. Behav. 2019, 45, 427–436. [Google Scholar] [CrossRef]
- Wang, L.; Luo, J.; Gao, W.; Kong, J. The effect of Internet use on adolescents’ lifestyles: A national survey. Comput. Hum. Behav. 2012, 28, 2007–2013. [Google Scholar] [CrossRef]
- Vandelanotte, C.; Sugiyama, T.; Gardiner, P.; Owen, N.; Steele, R.; McConnon, A. 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, e28. [Google Scholar] [CrossRef]
- Hakala, P.T.; Rimpelä, A.H.; Saarni, L.A.; Salminen, J.J. Frequent computer-related activities increase the risk of neck–shoulder and low back pain in adolescents. Eur. J. Public Health 2006, 16, 536–541. [Google Scholar] [CrossRef]
- Straker, L.; Harris, C.; Joosten, J.; Howie, E.K. Mobile technology dominates school children’s IT use in an advan-taged school community and is associated with musculoskeletal and visual symptoms. Ergonomics 2018, 61, 658–669. [Google Scholar] [CrossRef] [PubMed]
- Hakala, P.T.; Saarni, L.A.; Punamäki, R.-L.; Wallenius, M.A.; Nygård, C.-H.; Rimpelä, A.H. Musculoskeletal symptoms and computer use among Finnish adolescents—Pain intensity and inconvenience to everyday life: A cross-sectional study. BMC Musculoskelet. Disord. 2012, 13, 41. [Google Scholar] [CrossRef] [Green Version]
- Jordan, V. Cochrane Corner: Coronavirus (COVID-19): Infection control and prevention measures. J. Prim. Health Care 2020, 12, 96–97. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- World Health Orgnization. Coronavirous Disease (COVID-19) Advice for the Public 2021. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public (accessed on 31 January 2021).
- World Health Orgnization. Oral Health 2020. Available online: https://www.who.int/news-room/fact-sheets/detail/oral-health (accessed on 31 January 2021).
Model Fit Indexes | Number of Classes | |||
---|---|---|---|---|
2 | 3 | 4 | 5 | |
Male (n = 1129) | ||||
AIC | 11,726.242 | 11,542.323 | 11,535.073 | 11,520.681 |
BIC | 11,831.759 | 11,703.116 | 11,751.133 | 11,792.012 |
LMR-LRT | 0 | 0 | 0.0078 | 0.2272 |
Entropy | 0.531 | 0.715 | 0.771 | 0.729 |
Female (n = 678) | ||||
AIC | 6488.399 | 6441.904 | 6428.342 | 6430.781 |
BIC | 6583.177 | 6586.327 | 6622.411 | 6674.496 |
LMR-LRT | 0 | 0.007 | 0.0443 | 0.2822 |
Entropy | 0.493 | 0.666 | 0.745 | 0.741 |
Health-Related Behaviors | Class 1 (n = 568) | Class 2 (n = 280) | Class 3 (n = 201) | Class 4 (n = 80) |
---|---|---|---|---|
Eating habits | ||||
Eating breakfast everyday | 88.5 | 54.8 | 47.8 | 47.6 |
Eating fruits and vegetables everyday | 90.4 | 32.1 | 38.4 | 52.7 |
Eating fast foods everyday | 14.6 | 20.9 | 24.1 | 29.5 |
Consuming milk and dairy product everyday | 75.7 | 34.8 | 26.8 | 32.6 |
Physical activity | ||||
Having vigorous physical activity 3 times or more per week | 70.3 | 37.7 | 40.2 | 55.4 |
Personal hygiene | ||||
Brushing teeth more than twice a day | 94.8 | 63.7 | 83.2 | 88..8 |
Washing hands before eating food or after going out | 72.2 | 0.0 | 100.0 | 40.2 |
Drinking alcohol | ||||
Having any experience of drinking alcohol in the past 30 days | 2.0 | 2.8 | 5.6 | 74.0 |
Smoking | ||||
Having any experience of smoking in the past 30 days | 1.4 | 0.0 | 0.0 | 100.0 |
Internet use | ||||
Using internet, including internet games, two hours and more per day. | 30.2 | 42.0 | 45.7 | 39.7 |
Health-Related Behaviors | Class 1 (n = 347) | Class 2 (n = 113) | Class 3 (n = 189) | Class 4 (n = 25) |
---|---|---|---|---|
Eating habits | ||||
Eating breakfast everyday | 82.4 | 34.8 | 47.8 | 61.3 |
Eating fruits and vegetables everyday | 91.9 | 27.8 | 26.8 | 59.9 |
Eating fast foods everyday | 20.7 | 47.0 | 79.8 | 50.7 |
Consuming milk and dairy product everyday | 52.8 | 27.2 | 12.5 | 38.0 |
Physical activity | ||||
Having vigorous physical activity 3 times or more per week | 37.2 | 27.6 | 20.5 | 24.1 |
Personal hygiene | ||||
Brushing teeth more than twice a day | 96.7 | 88.6 | 89.3 | 90.1 |
Washing hands before eating food or after going out | 67.9 | 38.5 | 52.9 | 49.9 |
Drinking alcohol | ||||
Having any experience of drinking alcohol in the past 30 days | 0.2 | 0.0 | 0.0 | 56.7 |
Smoking | ||||
Having any experience of smoking in the past 30 days | 0.0 | 3.2 | 1.2 | 77.7 |
Internet use | ||||
Using internet, including internet games, two hours and more per day. | 20.4 | 100.0 | 0.0 | 9.6 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chae, M.; Chung, S.J. Clustering of South Korean Adolescents’ Health-Related Behaviors by Gender: Using a Latent Class Analysis. Int. J. Environ. Res. Public Health 2021, 18, 3129. https://doi.org/10.3390/ijerph18063129
Chae M, Chung SJ. Clustering of South Korean Adolescents’ Health-Related Behaviors by Gender: Using a Latent Class Analysis. International Journal of Environmental Research and Public Health. 2021; 18(6):3129. https://doi.org/10.3390/ijerph18063129
Chicago/Turabian StyleChae, Myungah, and Sophia Jihey Chung. 2021. "Clustering of South Korean Adolescents’ Health-Related Behaviors by Gender: Using a Latent Class Analysis" International Journal of Environmental Research and Public Health 18, no. 6: 3129. https://doi.org/10.3390/ijerph18063129