Implementation of Artificial Intelligence (AI) Machine Learning for Analysis of Physical Activity Behavior, Sedentary Behavior, and Obesity Risk
DOI:
https://doi.org/10.31949/ijsm.v4i4.11807Abstract
Background: The prevalence of obesity has become a global issue affecting all countries. Physical activity and sedentary behavior are believed to be key factors contributing to obesity. Objective: This study aims to examine the relationship between physical activity and sedentary behavior with Body Mass Index (BMI) using machine learning algorithms. Method: A total of 280 students from various programs at Universitas Pendidikan Indonesia participated in this study (101 males and 179 females), aged between 17 and 23 years. Physical activity was measured using the Actigraph GT3X accelerometer. Seven machine learning algorithms—including Naïve Bayes, Support Vector Machine (SVM), local k-nearest neighbors (KNN), Classification via Regression (CVR), decision tree, random forest, and artificial neural network (ANN)—were applied to predict obesity risk. The RapidMiner software was used for testing. Results: Based on the variables of physical activity, sedentary behavior, and demographic factors, SVM demonstrated the highest accuracy (74.22%) among the algorithms. For sensitivity and specificity, ANN and decision tree performed best, with values of 72.27% and 77.5%, respectively. Conclusion: Physical activity, total Metabolic Equivalent of Task (MET), and sedentary duration are significant predictors of obesity risk. Promoting physical activity and implementing campus policies are essential to reduce obesity prevalence among students.
Keywords:
Alghoritm, Algorithm, BMI, Machine learning, Obesity, Physical activityDownloads
References
Aldenaini, N., Alqahtani, F., Orji, R., & Sampalli, S. (2020). Trends in persuasive technologies for physical activity and sedentary behavior: a systematic review. Frontiers in artificial intelligence, 3, 7.
Ambrosio, L., Faulkner, J., Morris, J. H., Stuart, B., Lambrick, D., Compton, E., & Portillo, M. C. (2024). Physical activity and mental health in individuals with multimorbidity during COVID-19: an explanatory sequential mixed-method study. BMJ open, 14(4), e079852.
Amin, M., Kerr, D., Atiase, Y., Aldwikat, R. K., & Driscoll, A. (2023). Effect of physical activity on metabolic syndrome markers in adults with type 2 diabetes: a systematic review and meta-analysis. Sports, 11(5), 101.
Bassett, D. R., John, D., Conger, S. A., Fitzhugh, E. C., & Coe, D. P. (2015). Trends in physical activity and sedentary behaviors of United States youth. Journal of physical activity and health, 12(8), 1102-1111.
Bell, J. A., Kivimaki, M., Batty, G. D., & Hamer, M. (2014). Metabolically healthy obesity: what is the role of sedentary behaviour?. Preventive medicine, 62, 35-37.
Biddle, S. J., Bengoechea García, E., Pedisic, Z., Bennie, J., Vergeer, I., & Wiesner, G. (2017). Screen time, other sedentary behaviours, and obesity risk in adults: a review of reviews. Current obesity reports, 6, 134-147.
Biddle, S. J., García Bengoechea, E., & Wiesner, G. (2017). Sedentary behaviour and adiposity in youth: a systematic review of reviews and analysis of causality. International Journal of Behavioral Nutrition and Physical Activity, 14, 1-21.
Biener, A., Cawley, J., & Meyerhoefer, C. (2018). The impact of obesity on medical care costs and labor market outcomes in the US. Clinical chemistry, 64(1), 108-117.
Brittain, E. L., Han, L., Annis, J., Master, H., Hughes, A., Roden, D. M., ... & Ruderfer, D. M. (2024). Physical activity and incident obesity across the spectrum of genetic risk for obesity. JAMA Network Open, 7(3), e243821-e243821.
Chen, L., Liu, Q., Xu, F., Wang, F., Luo, S., An, X., ... & Liang, X. (2024). Effect of physical activity on anxiety, depression and obesity index in children and adolescents with obesity: A meta-analysis. Journal of Affective Disorders.
Cheng, X., Lin, S. Y., Liu, J., Liu, S., Zhang, J., Nie, P., ... & Xue, H. (2021). Does physical activity predict obesity—A machine learning and statistical method-based analysis. International Journal of environmental research and public Health, 18(8), 3966.
Chu, D. T., Nguyet, N. T. M., Dinh, T. C., Lien, N. V. T., Nguyen, K. H., Ngoc, V. T. N., ... & Pham, V. H. (2018). An update on physical health and economic consequences of overweight and obesity. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 12(6), 1095-1100.
Cleven, L., Krell-Roesch, J., Nigg, C. R., & Woll, A. (2020). The association between physical activity with incident obesity, coronary heart disease, diabetes and hypertension in adults: a systematic review of longitudinal studies published after 2012. BMC public health, 20, 1-15.
Du, Y., Liu, B., Sun, Y., Snetselaar, L. G., Wallace, R. B., & Bao, W. (2019). Trends in adherence to the physical activity guidelines for Americans for aerobic activity and time spent on sedentary behavior among US adults, 2007 to 2016. JAMA network open, 2(7), e197597-e197597.
Dugan, T. M., Mukhopadhyay, S., Carroll, A., & Downs, S. (2015). Machine learning techniques for prediction of early childhood obesity. Applied clinical informatics, 6(03), 506-520.
Egger, G., & Dixon, J. (2014). Beyond obesity and lifestyle: a review of 21st century chronic disease determinants. BioMed research international, 2014(1), 731685.
Ellahham, S. (2020). Artificial intelligence: the future for diabetes care. The American journal of medicine, 133(8), 895-900.
Farrahi, V., & Rostami, M. (2024). Machine learning in physical activity, sedentary, and sleep behavior research. Journal of Activity, Sedentary and Sleep Behaviors, 3(1), 5.
Farrahi, V., & Rostami, M. (2024). Machine learning in physical activity, sedentary, and sleep behavior research. Journal of Activity, Sedentary and Sleep Behaviors, 3(1), 5.
Friedrich, M. J. (2017). Global obesity epidemic worsening. Jama, 318(7), 603-603.
Gallardo-Gómez, D., Salazar-Martínez, E., Alfonso-Rosa, R. M., Ramos-Munell, J., del Pozo-Cruz, J., del Pozo Cruz, B., & Álvarez-Barbosa, F. (2024). Optimal dose and type of physical activity to improve glycemic control in people diagnosed with type 2 diabetes: a systematic review and meta-analysis. Diabetes Care, 47(2), 295-303.
Goettler, A., Grosse, A., & Sonntag, D. (2017). Productivity loss due to overweight and obesity: a systematic review of indirect costs. BMJ open, 7(10), e014632.
Haththotuwa, R. N., Wijeyaratne, C. N., & Senarath, U. (2020). Worldwide epidemic of obesity. In Obesity and obstetrics (pp. 3-8). Elsevier.
Jaacks, L. M., Vandevijvere, S., Pan, A., McGowan, C. J., Wallace, C., Imamura, F., ... & Ezzati, M. (2019). The obesity transition: stages of the global epidemic. The lancet Diabetes & endocrinology, 7(3), 231-240.
Jankowska, M. M., Schipperijn, J., & Kerr, J. (2015). A framework for using GPS data in physical activity and sedentary behavior studies. Exercise and sport sciences reviews, 43(1), 48-56.
Kańtoch, E. (2018). Recognition of sedentary behavior by machine learning analysis of wearable sensors during activities of daily living for telemedical assessment of cardiovascular risk. Sensors, 18(10), 3219.
Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, 32328-32338.
Ko, J. M., White, K. S., Kovacs, A. H., Tecson, K. M., Apers, S., Luyckx, K., ... & Cedars, A. M. (2018). Physical activity-related drivers of perceived health status in adults with congenital heart disease. The American journal of cardiology, 122(8), 1437-1442.
Kyu, H. H., Bachman, V. F., Alexander, L. T., Mumford, J. E., Afshin, A., Estep, K., ... & Forouzanfar, M. H. (2016). Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. bmj, 354.
Lavanya, T. V., & Sivaraman, K. (2024, August). A Machine Learning Approach for Predicting Physical Activity Intensity from Wearable Sensor Data. In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT) (Vol. 1, pp. 1769-1774). IEEE.
Loveday, A., Sherar, L. B., Sanders, J. P., Sanderson, P. W., & Esliger, D. W. (2015). Technologies that assess the location of physical activity and sedentary behavior: a systematic review. Journal of medical Internet research, 17(8), e192.
Malik, V. S., & Hu, F. B. (2022). The role of sugar-sweetened beverages in the global epidemics of obesity and chronic diseases. Nature Reviews Endocrinology, 18(4), 205-218.
Okunogbe, A., Nugent, R., Spencer, G., Ralston, J., & Wilding, J. (2021). Economic impacts of overweight and obesity: current and future estimates for eight countries. BMJ global health, 6(10), e006351.
Pai, A., Santiago, R., Glantz, N., Bevier, W., Barua, S., Sabharwal, A., & Kerr, D. (2024). Multimodal digital phenotyping of diet, physical activity, and glycemia in Hispanic/Latino adults with or at risk of type 2 diabetes. NPJ Digital Medicine, 7(1), 7.
Rubinger, L., Gazendam, A., Ekhtiari, S., & Bhandari, M. (2023). Machine learning and artificial intelligence in research and healthcare. Injury, 54, S69-S73.
Safaei, M., Sundararajan, E. A., Driss, M., Boulila, W., & Shapi'i, A. (2021). A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. Computers in biology and medicine, 136, 104754.
Sallis, J. F., Cerin, E., Kerr, J., Adams, M. A., Sugiyama, T., Christiansen, L. B., ... & Owen, N. (2020). Built environment, physical activity, and obesity: findings from the international physical activity and environment network (IPEN) adult study. Annual review of public health, 41(1), 119-139.
Saputra, D. S., Jajat., Damayanti, I., Sultoni, K., Ruhayati, Y., & Rahayu, N. I. (2024). Prediksi BMI Berdasarkan Level Aktivitas Fisik dengan Metode Analisis Machine Learning. Jurnal Pendidikan Kesehatan Rekreasi, 10(1), 165-175.
Silveira, E. A., Mendonça, C. R., Delpino, F. M., Souza, G. V. E., de Souza Rosa, L. P., de Oliveira, C., & Noll, M. (2022). Sedentary behavior, physical inactivity, abdominal obesity and obesity in adults and older adults: A systematic review and meta-analysis. Clinical nutrition ESPEN, 50, 63-73.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of business research, 70, 263-286.
Staudenmayer, J., He, S., Hickey, A., Sasaki, J., & Freedson, P. (2015). Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements. Journal of applied physiology, 119(4), 396-403.
Stein, N., & Brooks, K. (2017). A fully automated conversational artificial intelligence for weight loss: longitudinal observational study among overweight and obese adults. JMIR diabetes, 2(2), e8590.
Strain, T., Dempsey, P. C., Wijndaele, K., Sharp, S. J., Kerrison, N., Gonzales, T. I., ... & Wareham, N. (2023). Quantifying the relationship between physical activity energy expenditure and incident type 2 diabetes: a prospective cohort study of device-measured activity in 90,096 adults. Diabetes Care, 46(6), 1145-1155.
Strasser, B. (2013). Physical activity in obesity and metabolic syndrome. Annals of the New York Academy of Sciences, 1281(1), 141-159.
Suminski, R. R., Leonard, T., Obrusnikova, I., & Kelly, K. (2024). The impact of health coaching on weight and physical activity in obese adults: a randomized control trial. American Journal of Lifestyle Medicine, 18(2), 233-242.
Triantafyllidis, A., Polychronidou, E., Alexiadis, A., Rocha, C. L., Oliveira, D. N., da Silva, A. S., ... & Tzovaras, D. (2020). Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature. Artificial Intelligence in Medicine, 104, 101844.
Van Cauwenberg, J., De Bourdeaudhuij, I., De Meester, F., Van Dyck, D., Salmon, J., Clarys, P., & Deforche, B. (2011). Relationship between the physical environment and physical activity in older adults: a systematic review. Health & place, 17(2), 458-469.
van Poppel, M. N., Simmons, D., Devlieger, R., Van Assche, F. A., Jans, G., Galjaard, S., ... & Desoye, G. (2019). A reduction in sedentary behaviour in obese women during pregnancy reduces neonatal adiposity: the DALI randomised controlled trial. Diabetologia, 62, 915-925.
Venetsanou, F., Emmanouilidou, K., Kouli, O., Bebetsos, E., Comoutos, N., & Kambas, A. (2020). Physical activity and sedentary behaviors of young children: trends from 2009 to 2018. International Journal of Environmental Research and Public Health, 17(5), 1645.
WHO. (2018a). Obesity and overweight. Tersedia di https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight
Yang, L., Cao, C., Kantor, E. D., Nguyen, L. H., Zheng, X., Park, Y., ... & Cao, Y. (2019). Trends in sedentary behavior among the US population, 2001-2016. Jama, 321(16), 1587-1597
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Jajat Jajat, Adang Sudrazat, Mohammad Zaky, Kuston Sultoni
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
_.png){kind=logo}
_(2).png){kind=logo}
.png)
.png)
.png)
