Dependence of Body Mass Index on Some Dietary Habits: An Application of Classification and Regression Tree
Background: The purpose of this study was to determine the influence of some eating habits on body mass index (BMI) using a regression model created via the classification and regression tree method (CART).
Methods: The study was conducted using a questionnaire specially developed for the study, evaluated for reliability and validity. In addition to demographics (age and sex), the questions concern the timing of the meals and the type of food consumed. The data contains records for 533 people (322 women and 211 men) aged 18 to 65 years. The survey was conducted in the period 2019-2021 in Stara Zagora, Bulgaria. Data were processed using descriptive statistics, and regression and classification data mining method CART.
Results: A CART model with a dependent variable BMI and predictors Sex, Age, Breakfast type, Breakfast time, Lunchtime, Lunch type, Dinner time, Dinner type have been created. The obtained model is statistically significant at a significance level of P<0.0001 and a coefficient of determination R2 = 0.495. The normalized importance of the factors that affect the BMI is as follows: Sex (100%), Age (61.4%), Lunch type (26. 0%), Lunchtime (18.8%), Dinner time (13.9%), and Breakfast type (13.2%). Women have a lower BMI than men. BMI increases with age.
Conclusion: The CART method allows to make a classification by the predictors used and gives opportunities for a more in-depth analysis of the reasons for the increase in BMI. The level of influence of diet and eating habits (type of food, time of consumption) on BMI was determined.
2. Sale C (2017). Nutrition and Health editorial. Nutrition and Health, 23 (2): 65-66.
3. Locke A, Schneiderhan J, Zick SM (2018). Diets for Health: Goals and Guidelines. Am Fam Physician, 97 (11): 721-728.
4. Ekmekcioglu C (2020). Nutrition and lon-gevity - From mechanisms to uncertain-ties. Crit Rev Food Sci Nutr, 60 (18): 3063-3082.
5. Link R (2020). Nutrition for Longevity Re-view: Should You Try It? Healthline, Healthline Media. https://www.healthline.com/nutrition/review-nutrition-for-longevity
6. Mõttus R, Realo A, Allik J, et al (2012). Per-sonality traits and eating habits in a large sample of Estonians. Health Psychol, 31(6): 806–814.
7. Pfeiler TM, Egloff B (2018). Examining the "Veggie" personality: Results from a rep-resentative German sample. Appetite, 120: 246–255.
8. Gifford R, Nilsson A (2014). Personal and social factors that influence pro-environmental concern and behaviour: a review. Int J Psychol, 49(3): 141–157.
9. Pfeiler M, Egloff B (2020). Personality and eating habits revisited: Associations be-tween the big five, food choices, and Body Mass Index in a representative Australian sample. Appetite, 149: 104607.
10. Sakurai M, Yoshita K, Nakamura K, et al. (2017). Skipping breakfast and 5-year changes in body mass index and waist circumference in Japanese men and women. Obes Sci Pract, 3(2): 162-170.
11. Watanabe Y, Saito I, Henmi I, et al. (2014). Skipping Breakfast is Correlated with Obesity. J Rural Med, 9(2): 51-58.
12. Okada C, Imano H, Muraki I, Yamada K, Iso H (2019). The Association of Having a Late Dinner or Bedtime Snack and Skipping Breakfast with Overweight in Japanese Women. J Obes, 2019: 2439571.
13. World Health Organization (2021). Body Mass Index - BMI. https://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi
14. Caballero B (2019). Humans against Obesity: Who Will Win? Adv Nutr, 10: S4-S9.
15. National Heart, Lung, and Blood Institute website (2019). Overweight and Obesity. https://www.nhlbi.nih.gov/health-topics/overweight-and-obesity
16. Dwivedi AK, Dubey P, Cistola DP, Reddy SY (2020). Association Between Obesity and Cardiovascular Outcomes: Updated Evidence from Meta-analysis Studies. Curr Cardiol Rep, 22 (4): 25.
17. Gray N, Picone G, Sloan F, Yashkin A (2015). Relation between BMI and diabe-tes mellitus and its complications among US older adults. South Med J, 108 (1): 29-36.
18. Lauby-Secretan B, Dossus L, Marant-Micallef C, His M (2019). Obésité et can-cer [Obesity and Cancer]. Bull Cancer, 106 (7-8): 635-646.
19. Pineda E, Sanchez-Romero LM, Brown M, et al. (2018). Forecasting Future Trends in Obesity across Europe: The Value of Improving Surveillance. Obes Facts, 11 (5): 360-371.
20. Breiman L, Friedman J, Olshen R and Stone C J (1984). Classification and Regression Trees (Monterey CA: Wadsworth and Brooks).
21. Toschke AM, Beyerlein A, von Kries R (2005). Children at high risk for over-weight: a classification and regression trees analysis approach. Obes Res, 13 (7): 1270-4.
22. Riedel C, von Kries R, Buyken AE, et al (2014). Overweight in adolescence can be predicted at age 6 years: a CART analysis in German cohorts. PLoS One, 9(3): e93581.
23. Yannakoulia M, Lykou A, Kastorini CM, et al (2016). Socio-economic and lifestyle parameters associated with diet quality of children and adolescents using classifica-tion and regression tree analysis: the DI-ATROFI study. Public Health Nutr, 19(2): 339–347.
24. Dugan TM, Mukhopadhyay S, Carroll A, Downs S (2015). Machine learning tech-niques for prediction of early childhood obesity. Appl Clin Inform, 6: 506–520.
25. Lazarou C, Karaolis M, Matalas A-L, Panag-iotakos DB (2012). Dietary patterns anal-ysis using data mining method. An appli-cation to data from the CYKIDS study. Comput Methods Programs Biomed, 108: 706–14.
26. Ríos-Julián N, Alarcón-Paredes A, Alonso GA, et al (2017). Feasibility of a screening tool for obesity diagnosis in Mexican children from a vulnerable community of Me’Phaa ethnicity in the State of Guerre-ro, Mexico. Pan American Health Care Ex-changes. 1-6.
27. Zheng Z, Ruggiero K (2017). Using ma-chine learning to predict obesity in high school students. IEEE International Confer-ence on Bioinformatics and Biomedicine (BIBM), pp. 2132-2138, doi: 10.1109/BIBM.2017.8217988.
28. Triantafyllidis A, Polychronidou E, Alexiadis A, et al (2020). Computerized decision support and machine learning applica-tions for the prevention and treatment of childhood obesity: A systematic review of the literature. Artif Intell Med, 104: 101844.
29. Lemon SC, Roy J, Clark MA, et al (2003). Classification and regression tree analysis in public health: methodological review and comparison with logistic regres-sion. Ann Behav Med, 26(3): 172–181.
30. Harrison's Principles of Internal Medicine (2018). 20th ed.
31. Kuan PX, Ho HL, Shuhaili MS, Siti AA, Gudum HR (2011). Gender differences in body mass index, body weight percep-tion, and weight loss strategies among undergraduates in Universiti Malaysia Sa-rawak. Malays J Nutr, 17 (1): 67-75.
32. Gillum RF, Sempos CT (2005). Ethnic varia-tion in validity of classification of over-weight and obesity using self-reported weight and height in American women and men: the Third National Health and Nutrition Examination Survey. Nutr J, 4: 27.
33. Lemamsha H, Randhawa G, Papadopoulos C (2019). Prevalence of Overweight and Obesity among Libyan Men and Wom-en. Biomed Res Int, 2019: 8531360.
34. Kanter R, Caballero B (2012). Global gender disparities in obesity: a review. Adv Nutr, 3 (4): 491-8.
35. Kuczmarski MF, Kuczmarski RJ, Najjar M (2001). Effects of age on validity of self-reported height, weight, and body mass index: findings from the Third National Health and Nutrition Examination Sur-vey, 1988-1994. J Am Diet Assoc, 101 (1): 28-34.
36. Peter R, Mayer B, Concin H, et al. (2015). The effect of age on the shape of the BMI–mortality relation and BMI associ-ated with minimum all-cause mortality in a large Austrian cohort. Int J Obes (Lond), 39(3): 530–534.
37. L Xu, SL Au Yeung, Schooling CM (2016). Does the optimal BMI vary by age and sex? Int J Epidemiol, 45 (1): 285–286.
38. Stanford Center on Longevity. Beyond bmi: assessing weight status as we age. https://longevity.stanford.edu/beyond-bmi-assessing-weight-status-as-we-age/
39. Bandin C, Scheer FA, Luque AJ, et al (2015). Meal timing affects glucose tolerance, substrate oxidation and circadian-related variables: A randomized, crossover trial. Int J Obes (Lond), 39: 828-833.
40. Lopez-Minguez J, Gómez-Abellán P, Garau-let M (2019). Timing of Breakfast, Lunch, and Dinner: Effects on Obesity and Metabolic Risk. Nutrients, 11 (11): 2624.
41. Health Policy Institute (2019). Obesity Among Older Americans. hpi.georgetown.edu/obesity2/.
42. Serra-Majem L, Bautista-Castaño I (2015). Relationship between bread and obesity. Br J Nutr, 113 Suppl 2:S29-35.
43. Fennell D (2015). Eating White Bread Ups Obesity Risk. Diabetes Self Management. https://www.diabetesincontrol.com/eating-white-bread-can-increase-risk-for-obesity-by-40/
44. Rautiainen S, Wang L, Lee IM, et al (2015). Higher Intake of Fruit, but Not Vegeta-bles or Fiber, at Baseline Is Associated with Lower Risk of Becoming Over-weight or Obese in Middle-Aged and Older Women of Normal BMI at Base-line. J Nutr, 145 (5): 960-8.
45. Ham E, Kim HJ (2014). Evaluation of fruit intake and its relation to body mass index of adolescents. Clin Nutr Res, 3 (2): 126-33.
46. Rosenfeld, Daniel L (2020). Gender Differ-ences in Vegetarian Identity: How Men and Women Construe Meatless Dieting. Food Qual Prefer, 81: 103859
47. Modlinska K, Adamczyk D, Maison D, Pi-sula W (2020). Gender Differences in At-titudes to Vegans/Vegetarians and Their Food Preferences, and Their Implications for Promoting Sustainable Dietary Pat-terns–A Systematic Review. Sustainability, 12 (16): 6292.
|Issue||Vol 51 No 6 (2022)|
|Body mass index (BMI) Dietary habits Classification and regression tree (CART method)|
|Rights and permissions|
|This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.|