Apovian CM, Obesity: definition, comorbidities, causes, and Burden. Am J Managed Care. 2016;22:s176–85.
World Health Organization. Obesity and its root causes. https://www.who.int/india/Campaigns/world-obesity-day. Accessed 05 Mar 2024.
Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384:766–81.
Jaacks LM, Vandevijvere S, Pan A, McGowan CJ, Wallace C, Imamura F, et al. The obesity transition: stages of the global epidemic. The lancet. Diabetes Endocrinol. 2019;7:231–40.
Teufel F, Seiglie JA, Geldsetzer P, Theilmann M, Marcus ME, Ebert C, et al. Body-mass index and diabetes risk in 57 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 685 616 adults. Lancet. 2021;398:238–48.
World Health Organization. Obesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Accessed 03 May 2024.
Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309:71–82.
O’Neil CE, Deshmukh-Taskar P, Mendoza JA, Nicklas TA, Liu Y, Relyea G, et al. Dietary, Lifestyle, and Health Correlates of Overweight and Obesity in Adults 19 to 39 Years of Age. Am J Lifestyle Med. 2011;6:347–58.
Naja F, Itani L, Nasrallah MP, Chami H, Tamim H, Nasreddine L. A healthy lifestyle pattern is associated with a metabolically healthy phenotype in overweight and obese adults: a cross-sectional study. Eur J Nutr. 2019;59:2145–58.
Ju Q, Wu X, Li B, Peng H, Lippke S, Gan Y. Regulation of craving training to support healthy food choices under stress: A randomized control trial employing the hierarchical drift-diffusion model. Appl Psychol Health Well Being. 2024;16:1159–77.
Lee KX, Quek KF, Ramadas A. Dietary and Lifestyle Risk Factors of Obesity Among Young Adults: A Scoping Review of Observational Studies. Curr Nutr Rep. 2023;12:733–43.
Ekström S, Andersson N, Kull I, Georgelis A, Ljungman PLS, Melén E, et al. Changes in lifestyle, adiposity, and cardiometabolic markers among young adults in Sweden during the COVID-19 pandemic. BMC Public Health. 2023;23:1026.
He K, Du S, Xun P, Sharma S, Wang H, Zhai F, et al. Consumption of monosodium glutamate in relation to incidence of overweight in Chinese adults: China Health and Nutrition Survey (CHNS). Am J Clin Nutr. 2011;93:1328–36.
Hunt KJ, St. Peter JV, Malek AM, Vrana-Diaz C, Marriott BP, Greenberg D. Daily Eating Frequency in US Adults: Associations with Low-Calorie Sweeteners, Body Mass Index, and Nutrient Intake (NHANES 2007–2016). Nutrients. 2020;12:2566.
Su C, Jia XF, Wang ZH, Wang HJ, Ouyang YF, Zhang B. Longitudinal association of leisure time physical activity and sedentary behaviors with body weight among Chinese adults from China Health and Nutrition Survey 2004–2011. Eur J Clin Nutr. 2017;71:383–8.
Zhang Y, Yang J, Hou W, Arcan C. Obesity Trends and Associations with Types of Physical Activity and Sedentary Behavior in US Adults: National Health and Nutrition Examination Survey, 2007-2016. Obesity. 2020;29:240–50.
Chang T, Ravi N, Plegue MA, Sonneville KR, Davis MM. Inadequate Hydration, BMI, and Obesity Among US Adults: NHANES 2009–2012. Ann Fam Med. 2016;14:320–4.
Yuan X, Wei Y, Jiang H, Wang H, Wang Z, Dong M, et al. Longitudinal Relationship between the Percentage of Energy Intake from Macronutrients and Overweight/Obesity among Chinese Adults from 1991 to 2018. Nutrients. 2024;16:666.
Zhang X, Zhang J, Du W, Su C, Ouyang Y, Huang F, et al. Multi-Trajectories of Macronutrient Intake and Their Associations with Obesity among Chinese Adults from 1991 to 2018: A Prospective Study. Nutrients. 2021;14:13.
Yuan F, Wu M, Li W, Zhang H. The effect of self-perceived stress, the history of smoking and drinking on weight status in Chinese adults – evidence from the 2015 China Health and Nutrition Survey. Medicine. 2020;99:e21159.
Ellison-Barnes A, Yeh H-C, Pollack CE, Daumit GL, Chander G, Galiatsatos P, et al. Weighing cessation: Rising adiposity of current smokers in NHANES. Prev Med. 2023;175:107713.
Sun T, Lv J, Zhao X, Li W, Zhang Z, Nie L. In vivo liver function reserve assessments in alcoholic liver disease by scalable photoacoustic imaging. Photoacoustics. 2023;34:100569.
Liu Y, Yin H, Liu X, Zhang L, Wu D, Shi Y, et al. Alcohol use disorder and time perception: The mediating role of attention and working memory. Addict Biol. 2024;29:e13367.
White GE, Mair C, Richardson GA, Courcoulas AP, King WC. Alcohol Use Among U.S. Adults by Weight Status and Weight Loss Attempt: NHANES, 2011–2016. Am J Prev Med. 2019;57:220–30.
Fu Y, Gou W, Hu W, Mao Y, Tian Y, Liang X, et al. Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort. BMC Med. 2020;18:184.
Allen B. An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence. PLoS ONE. 2023;18:e0292341.
An R, Shen J, Wang J, Yang Y. A scoping review of methodologies for applying artificial intelligence to physical activity interventions. J Sport Health Sci. 2023;00:1–14.
Sun Z, Yuan Y, Dong X, Liu Z, Cai K, Cheng W, et al. Supervised Machine Learning: A New Method to Predict the Outcomes following Exercise Intervention in Children with Autism Spectrum Disorder. Int J Clin Health Psychol. 2023;23:100409.
Sun Z, Yuan Y, Xiong X, Meng S, Shi Y, Chen A. Predicting academic achievement from the collaborative influences of executive function, physical fitness, and demographic factors among primary school students in China: ensemble learning methods. BMC Public Health. 2024;24.
Farrahi V, Rostami M. Machine learning in physical activity, sedentary, and sleep behavior research. J Activity Sedentary Sleep Behav. 2024;3.
Farrahi V, Niemela M, Karmeniemi M, Puhakka S, Kangas M, Korpelainen R, et al. Correlates of physical activity behavior in adults: a data mining approach. Int J Behav Nutr Phys Act. 2020;17:94.
Ferreras A, Sumalla-Cano S, Martínez-Licort R, Elío I, Tutusaus K, Prola T, et al. Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight. J Med Syst. 2023;47:8.
Colmenarejo G. Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review. Nutrients. 2020;12:2466.
McCoy LG, Brenna CTA, Chen SS, Vold K, Das S. Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based. J Clin Epidemiol. 2022;142:252–7.
Lundberg SM, Lee S-I. A Unified Approach to Interpreting Model Predictions. https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf. Accessed 18 Feb 2024.
Štrumbelj E, Kononenko I. An Efficient Explanation of Individual Classifications using Game Theory. J Mach Learn Res. 2010;11:1–18.
Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B. Definitions, methods, and applications in interpretable machine learning. Proc Natl Acad Sci. 2019;116:22071–80.
Lin W, Shi S, Huang H, Wen J, Chen G. Predicting risk of obesity in overweight adults using interpretable machine learning algorithms. Front Endocrinol. 2023;14:1292167.
Jeong S, Yun SB, Park SY, Mun S. Understanding cross-data dynamics of individual and social/environmental factors through a public health lens: explainable machine learning approaches. Front Public Health. 2023;11:1257861.
Wang Y, Pan L, He H, Li Z, Cui S, Yang A, et al. Prevalence, associated factors, and gene polymorphisms of obesity in Tibetan adults in Qinghai, China. BMC Public Health. 2024;24:305.
Dettoni R, Bahamondes C, Yevenes C, Cespedes C, Espinosa J. The effect of obesity on chronic diseases in USA: a flexible copula approach. Sci Rep. 2023;13:1831.
Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32:S498–504.
Du H, Bennett D, Li L, Whitlock G, Guo Y, Collins R, et al. Physical activity and sedentary leisure time and their associations with BMI, waist circumference, and percentage body fat in 0.5 million adults: the China Kadoorie Biobank study. Am J Clin Nutr. 2013;97:487–96.
Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. Sedentary Behavior Research Network (SBRN) – Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14:75.
Huang L, Wang L, Jiang H, Wang H, Wang Z, Zhang B, et al. Trends in Dietary Carbohydrates, Protein, and Fat Intake and Diet Quality Among Chinese Adults, 1991–2015: Results from the China Health and Nutrition Survey. Public Health Nutr. 2022;26:1–31.
Cheng Z, Fu F, Lian Y, Zhan Z, Zhang W. Low-carbohydrate-diet score, dietary macronutrient intake, and depression among adults in the United States. J Affect Disord. 2024;352:125–32.
Zhang MJ, Zhang MZ, Yuan S, Yang HG, Lu GL, Chen R, et al. A nutrient-wide association study for the risk of cardiovascular disease in the China Health and Nutrition Survey (CHNS) and the National Health and Nutrition Examination Survey (NHANES). Food Funct. 2023;14:8597–603.
Risk Factor Collaboration N. C. D. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet. 2024;403:1027–50.
He H, Garcia EA. Learning from Imbalanced Data. IEEE Trans Knowl Data Eng. 2009;21:1263–84.
Rodriguez-Pardo C, Segura A, Zamorano-Leon JJ, Martinez-Santos C, Martinez D, Collado-Yurrita L, et al. Decision tree learning to predict overweight/obesity based on body mass index and gene polymporphisms. Gene. 2019;699:88–93.
Rehkopf DH, Laraia BA, Segal M, Braithwaite D, Epel E. The relative importance of predictors of body mass index change, overweight and obesity in adolescent girls. Int J Pediatr obesity: IJPO: official J Int Association Study Obes. 2011;6:e233–42.
Hammond R, Athanasiadou R, Curado S, Aphinyanaphongs Y, Abrams C, Messito MJ, et al. Predicting childhood obesity using electronic health records and publicly available data. PLoS ONE. 2019;14:e0215571.
Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From Local Explanations to Global Understanding with Explainable AI for Trees. Nat Mach Intell. 2020;2:56–67.
Quinlan JR. Induction of Decision Trees. Mach Learn. 1986;1:81–106.
Breiman L. Random Forests. Mach Learn. 2001;45:5–32.
Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Stat. 2001;29:1189–232.
Dugan TM, Mukhopadhyay S, Carroll A, Downs S. Machine Learning Techniques for Prediction of Early Childhood Obesity. Appl Clin Inf. 2015;6:506–20.
Giudici P, Raffinetti E. Shapley-Lorenz eXplainable Artificial Intelligence. Expert Syst Appl. 2021;167:114104.
Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet. 2011;378:804–14.
Bell AC, Ge K, Popkin BM. The Road to Obesity or the Path to Prevention: Motorized Transportation and Obesity in China. Obes Res. 2012;10:277–83.
Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.
Wang Y, Beydoun MA. The obesity epidemic in the United States–gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev. 2007;29:6–28.
Shi XD, He SM, Tao YC, Wang CY, Jiang YF, Feng XW, et al. Prevalence of obesity and associated risk factors in Northeastern China. Diabetes Res Clin Pract. 2011;91:389–94.
Peeters A, Barendregt JJ, Willekens F, Mackenbach JP, Al Mamun A, Bonneux L. Obesity in adulthood and its consequences for life expectancy: a life-table analysis. Ann Intern Med. 2003;138:24–32.
Zhang Y, Jiao Y, Lu K, He G. Influencing factors of overweight and obesity among Chinese adults: a meta-analysis. Chin J Public Health. 2015;31:232–5.
Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary behaviors and subsequent health outcomes in adults a systematic review of longitudinal studies, 1996–2011. Am J Prev Med. 2011;41:207–15.
Hamilton MT, Hamilton DG, Zderic TW. Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes. 2007;56:2655–67.
Popa A, Fratila O, Rus M, Aron R, Vesa C, Pantis C, et al. Risk factors for adiposity in the urban population and influence on the prevalence of overweight and obesity. Experimental Therapeutic Med. 2020;20:129–33.
Traversy G, Chaput JP. Alcohol Consumption and Obesity: An Update. Curr Obes Rep. 2015;4:122–30.
Yeomans MR, Caton S, Hetherington MM. Alcohol and food intake. Curr Opin Clin Nutr Metab Care. 2003;6:639–44.
Audrain-McGovern J, Benowitz NL. Cigarette smoking, nicotine, and body weight. Clin Pharmacol Ther. 2011;90:164–8.
World Health Organization. Tobacco. https://www.who.int/news-room/fact-sheets/detail/tobacco. Accessed 02 Jun 2024.
Noakes M, Keogh JB, Foster PR, Clifton PM. Effect of an energy-restricted, high-protein, low-fat diet relative to a conventional high-carbohydrate, low-fat diet on weight loss, body composition, nutritional status, and markers of cardiovascular health in obese women. Am J Clin Nutr. 2005;81:1298–306.
Hansen TT, Astrup A, Sjodin A. Are Dietary Proteins the Key to Successful Body Weight Management? A Systematic Review and Meta-Analysis of Studies Assessing Body Weight Outcomes after Interventions with Increased Dietary Protein. Nutrients. 2021;13.
Brandhagen M, Forslund HB, Lissner L, Winkvist A, Lindroos AK, Carlsson LM, et al. Alcohol and macronutrient intake patterns are related to general and central adiposity. Eur J Clin Nutr. 2012;66:305–13.
Tian Y, Jiang G, Chang G, Yang Y, Li Z, Gao J, et al. Analysis of key factors in diet of residents with overweight/obesity control in Tianjin. Occupation Health. 2009;25:2662–5.
Lin Y, Bolca S, Vandevijvere S, De Vriese S, Mouratidou T, De Neve M, et al. Plant and animal protein intake and its association with overweight and obesity among the Belgian population. Br J Nutr. 2011;105:1106–16.
Lin W, Shi S, Lan H, Wang N, Huang H, Wen J, et al. Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort. Endocrine. 2024;83:604–14.
Cheng X, Lin SY, Liu J, Liu S, Zhang J, Nie P et al. Does Physical Activity Predict Obesity-A Machine Learning and Statistical Method-Based Analysis. Int J Environ Res Public Health. 2021;18.
Babaei G, Giudici P, Raffinetti E. A Rank Graduation Box for SAFE AI. Expert Syst Appl. 2025;259:125239.
National Health Commission of the People’s Republic of China. Dietary Guidelines for Chinese Adult. http://www.nhc.gov.cn/. Accessed 04 Jun 2024.
Centers for Disease Control and Prevention. Obesity Strategies: What Can Be Done. https://www.cdc.gov/obesity/php/about/obesity-strategies-what-can-be-done.html. Accessed 04 Jun 2024.
Benfica M, Silva K, Oliveira M, Ribeiro T, Messa R, Tamura E, et al. Sleep duration on overweight and obesity: an overview of systematic reviews. Sleep Med. 2024;115:S63–4.
