Gülnaz KERVANCIOĞLU, Pınar Ongün, Gülşah KOÇ, Ahu SOYOCAK

Üniversite Öğrencilerinin Yeme Alışkanlıklarının ve İnternet Bağımlılığı Düzeylerinin Fiziksel Aktivite Düzeylerine Göre Değerlendirilmesi

Amaç: Bu çalışma, üniversite öğrencilerinin yeme davranışlarını ve internet bağımlılığı düzeylerini fiziksel aktivite düzeylerine göre belirlemeyi amaçlamaktadır. Gereç ve Yöntemler: Çalışmaya 17-25 yaşları arasında toplam 775 üniversite öğrencisi katılmıştır. Tüm öğrencilerin antropometrik ölçümleri kaydedilmiştir. Araştırmada, Üç Faktörlü Beslenme Anketi (TFEQ), Uluslararası Fiziksel Aktivite Anketi (IPAQ) ve İnternet Bağımlılığı Ölçeği kullanılmıştır. Öğrencilerin yeme alışkanlıkları ve internet bağımlılığı düzeyleri fiziksel aktivite düzeylerine göre istatistiksel olarak değerlendirilmiştir. Fiziksel aktivite düzeyi dakika, gün ve MET değerleri çarpılarak “MET-dakika / hafta” skoru elde edilmiştir. Bulgular: Üniversite öğrencilerinin TFEQ sonuçlarını fiziksel aktivite düzeylerine göre değerlendiren çalışmamızda, inaktif (sedanter) grubun kontrolsüz (p=0,047) ve duygusal yemek yeme (p=0,032) skorlarının aktif gruba göre daha yüksek olduğu gösterilmiştir. Ayrıca, çok aktif öğrencilerin yemeyi bilinçli olarak kısıtlama skorları, inaktif (p=0,001) ve minimal olarak aktif (p=0,007) olan öğrencilere göre daha yüksek olduğu saptanmıştır. İnternet bağımlılığı ölçeğinin fiziksel aktivite düzeylerine göre değerlendirilmesi sonucunda, fiziksel olarak inaktif öğrencilerin kontrol eksikliğinin (p=0,001) ve çevrimiçi kalma arzusunun (p=0,001) daha fazla olduğu, toplam internet bağımlılığı puanlarının (p=0,008) da diğer gruplara göre daha yüksek olduğu saptanmıştır. Sonuç: Bu çalışmada fiziksel aktivite yapmayan veya minimum düzeyde yapan gençlerin kontrolsüz ve duygusal yeme davranışı sergiledikleri bulunmuştur. Aynı grupta, internette çevrimiçi kalma isteği ve kontrol kaybının daha fazla olduğu gösterilmiştir

Assessment of Eating Habits and Internet Addiction Levels Based on the Physical Activity Levels in University Students

Aim: This study aims to identify eating behaviors and internet addiction levels of university students based on theirphysical activity levels.Material and Methods: 775 university student subjects, aged 17 to 25 were enrolled to the study anthropometricmeasurements were recorded for all students. In the study Three-Factor Eating Questionnaire (TFEQ), The InternationalPhysical Activity Questionnaire (IPAQ) and Internet Addiction Scale were used. The eating habits and internetaddiction levels of the students were statistically evaluated based on the physical activity levels. Physical activity levelwas calculated based on The "MET-minute/week" score was obtained by multiplying the values of minutes, days andMET.Results: Evaluating the TFEQ results based on the physical activity levels, our study demonstrated that the uncontrolled(p=0.047) and emotional eating (p=0.032) scores of the inactive group were higher compared to the active group.Cognitive restraint of eating scores of the very active students, on the other hand, were higher compared to the inactive(p=0.001) and minimally active (p=0.007) students. Assessment of internet addiction scale based on physical activitylevels showed that lack of control (p=0.001), desire to remain online more (p=0.001) and total internet addiction scores(p=0.008) were higher in inactive students compared to the other groups.Conclusion: In this study, it was found that young people who do not do physical activity or do it at a minimum levelshow uncontrolled and emotional eating behavior. It was demonstrated that the desire to stay online and loss of controlwere higher in the same group.

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1. Baysal A. Beslenme. 13. Baskı. Ankara: Hatiboğlu Basım ve Yayım; 2011.
2. Pařízková J. Dietary habits and nutritional status in adolescents in Central and Eastern Europe. Eur J Clin Nutr. 2000;54(1):S36–40.
3. Farhat T, Iannotti RJ, Simons-Morton BG. Overweight, obesity, youth, and health-risk behaviors. Am J Prev Med. 2010;38(3):258–67.
4. Simmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta‐analysis. Obes Rev. 2016;17(2):95–107.
5. Ward ZJ, Long MW, Resch SC, Giles CM, Cradock AL, Gortmaker SL. Simulation of growth trajectories of childhood obesity into adulthood. N Engl J Med. 2017;377(22):2145–53.
6. Kaya EÖ, Sarıtaş N, Yıldız K, Kaya M. Sedanter olan ve olmayan bireylerin fiziksel aktivite ve yaşam tatmin düzeyleri üzerine araştırma. Celal Bayar Üniversitesi Sağlık Bilim Enstitüsü Derg. 2018;5(3):89–94.
7. Laredo-Aguilera JA, Cobo-Cuenca AI, SantacruzSalas E, Martins MM, Rodríguez-Borrego MA, López-Soto PJ, et al. Levels of physical activity, obesity and related factors in young adults aged 18– 30 during 2009–2017. Int J Environ Res Public Health. 2019;16(20):4033.
8. Wahi G, Wilson J, Oster R, Rain P, Jack SM, Gittelsohn J, et al. Strategies for promoting healthy nutrition and physical activity among young children: priorities of two indigenous communities in Canada. Curr Dev Nutr. 2020;4(1):nzz137.
9. Jordan AB, Kramer-Golinkoff EK, Strasburger VC. Does adolescent media use cause obesity and eating disorders. Adolesc Med State Art Rev. 2008;19(3):431–49.
10. Vandewater EA, Shim M, Caplovitz AG. Linking obesity and activity level with children’s television and video game use. J Adolesc. 2004;27(1):71–85.
11.Bozkurt H, Özer S, Şahin S, Sönmezgöz E. Internet use patterns and Internet addiction in children and adolescents with obesity. Pediatr Obes. 2018;13(5):301–6.
12. Eliacik K, Bolat N, Koçyiğit C, Kanik A, Selkie E, Yilmaz H, et al. Internet addiction, sleep and healthrelated life quality among obese individuals: a comparison study of the growing problems in adolescent health. Eat Weight Disord Anorexia, Bulim Obes. 2016;21(4):709–17.
13. World Health Organisation; 2016-01 [Updated: 2020 April 1; Cited: 2020 Jul 15] [Internet]. Available from: https://www.who.int/news-room/factsheets/detail/obesity-and-overweight.
14. Shields MK, Behrman RE. Children and computer technology: analysis and recommendations. Futur Child. 2000;10(2):4-30.
15. Physical status: the use of and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser.1995;854:1-452.
16. Webster JD, Hesp R, Garrow JS. The composition of excess weight in obese women estimated by body density, total body water and total body potassium. Hum Nutr Clin Nutr. 1984;38(4):299–306.
17.Reilly D, Boyle CA, Craig DC. Obesity and dentistry: a growing problem. Br Dent J. 2009;207(4):171.
18. Thompson WR, Gordon NF, Pescatello LS. ACSM’s guidelines for exercise testing and prescription. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins; 2010.
19. Vaughan C, Schoo A, Janus ED, Philpot B, DavisLameloise N, Lo SK, et al. The association of levels of physical activity with metabolic syndrome in rural Australian adults. BMC Public Health. 2009;9(1):273.
20. De Lauzon B, Volatier JL, Martin A. A Monte Carlo simulation to validate the EAR cut-point method for assessing the prevalence of nutrient inadequacy at the population level. Public Health Nutr. 2004;7(7):893– 900.
21. Karlsson J, Persson L-O, Sjöström L, Sullivan M. Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. Int J Obes. 2000;24(12):1715.
22. Kıraç D, Kaspar EÇ, Avcılar T, Çakır ÖK, Ulucan K, Kurtel H ve ark. Obeziteyle ilişkili beslenme alışkanlıklarının araştırılmasında yeni bir yöntem “Üç faktörlü beslenme anketi.” Clin Exp Heal Sci. 2015;5(3):162–9.
23.Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sport Exerc. 2003;35(8):1381– 95.
24. Hallal PC, Victora CG. Reliability and validity of the international physical activity questionnaire (IPAQ). Med Sci Sport Exerc. 2004;36(3):556.
25. Öztürk M. Üniversitede eğitim-öğretim gören öğrencilerde uluslararası fiziksel aktivite anketinin geçerliliği ve güvenirliği ve fiziksel aktivite düzeylerinin belirlenmesi [Yüksek Lisans Tezi]. Ankara: Hacettepe Üniversitesi; 2005.
26. Hahn A, Jerusalem M. Internetsucht: Jugendliche gefangen im Netz. In: Risikoverhaltensweisen Jugendlicher. Berlin:Springer; 2001. p. 279–93.
27. Şahin C, Korkmaz Ö. İnternet bağımlılığı ölçeğinin Türkçeye uyarlanması. Selçuk Üniversitesi Ahmet Keleşoğlu Eğitim Fakültesi Derg. 2011;32:101–15.
28.Ruderman AJ. Dietary restraint: a theoretical and empirical review. Psychol Bull. 1986;99(2):247.
29. Anglé S, Engblom J, Eriksson T, Kautiainen S, Saha M-T, Lindfors P, et al. Three factor eating questionnaire-R18 as a measure of cognitive restraint, uncontrolled eating and emotional eating in a sample of young Finnish females. Int J Behav Nutr Phys Act. 2009;6(1):41.
30.Cappelleri JC, Bushmakin AG, Gerber RA, Leidy NK, Sexton CC, Lowe MR, et al. Psychometric analysis of the Three-Factor Eating QuestionnaireR21: results from a large diverse sample of obese and non-obese participants. Int J Obes. 2009;33(6):611.
31. Keskitalo K, Tuorila H, Spector TD, Cherkas LF, Knaapila A, Kaprio J, et al. The Three-Factor Eating Questionnaire, body mass index, and responses to sweet and salty fatty foods: a twin study of genetic and environmental associations. Am J Clin Nutr. 2008;88(2):263–71.
32. Konttinen H, Silventoinen K, Sarlio-Lähteenkorva S, Männistö S, Haukkala A. Emotional eating and physical activity self-efficacy as pathways in the association between depressive symptoms and adiposity indicators. Am J Clin Nutr. 2010;92(5):1031–9.
33. Kudas S, Ülkar B, Erdogan A. Ankara ili 11-12 yaş grubu çocukların fiziksel aktivite ve bazı beslenme alışkanlıkları. Spor Bilim Derg. 2005;16(1):19–29.
34.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):121–7.
35. Liu TC, Desai RA, Krishnan-Sarin S, Cavallo DA, Potenza MN. Problematic Internet use and health in adolescents: data from a high school survey in Connecticut. J Clin Psychiatry. 2011;72(6):836.
36. Wipfli BM, Rethorst CD, Landers DM. The anxiolytic effects of exercise: a meta-analysis of randomized trials and dose–response analysis. J Sport Exerc Psychol. 2008;30(4):392–410.
37.Rethorst CD, Wipfli BM, Landers DM. The antidepressive effects of exercise: a meta-analysis of randomized trials. Sport Med. 2009;39(6):491–511.
38.Resnick HE, Carter EA, Aloia M, Phillips B. Crosssectional relationship of reported fatigue to obesity, diet, and physical activity: results from the third national health and nutrition examination survey. J Clin Sleep Med. 2006;2(02):163–9.
39.Rollins BY, Riggs NR, Spruijt-Metz D, McClain AD, Chou C-P, Pentz MA. Psychometrics of the Eating in Emotional Situations Questionnaire (EESQ) among low-income Latino elementary-school children. Eat Behav. 2011;12(2):156–9.
40. Van Strien T, Ouwens MA. Effects of distress, alexithymia and impulsivity on eating. Eat Behav. 2007;8(2):251–7.
41. Khan MA, Shabbir F, Rajput TA. Effect of gender and physical activity on internet addiction in medical students. Pakistan J Med Sci. 2017;33(1):191.
42. Warbrick I, Wilson D, Boulton A. Provider, father, and bro–Sedentary Māori men and their thoughts on physical activity. Int J Equity Health. 2016;15(1):22.