Adamo, K.B., Rutherford, J.A., & Goldfield, G.S. (2010). Effects of interactive video game cycling on overweight and obese adolescent health. Appl Physiol Nutr Metab 35:80515. doi: 10.1139/H10-078.
Alahbabi, M., Almazroei, F., Almarzoqi, M., Almeheri A., et al. (2017). Avatar based interaction therapy: A potential therapeutic approach for children with Autism. In: 2017 IEEE international conference on mechatronics and automation (ICMA). Takamatsu, Japan: IEEE Press4804. doi: 10.1109/ICMA.2017.8015864.
Alotaibi, M., Alnajjar, F., Cappuccio, M., Khalid, S., Alhmiedat, T., Mubin, O. (2022). Efficacy of emerging technologies to manage childhood obesity. Diabetes Metab Syndr Obes, 15:122744. doi: 10.2147/DMSO.S357176.
Argarini, R., Herawati, L., Irwadi, I., Putri, E.A., Sari, G.M. (2020). Fundamental movement skill approach to combat childhood obesity in Surabaya, Indonesia: Potential effects of video games based exercises (Exergaming). Journal of Talent Development and Excellence, 12:302634
Bhavnani, S.P., Narula, J., Sengupta, P.P. (2016). Mobile technology and the digitization of healthcare. Eur Heart J, 37:142838. doi: 10.1093/eurheartj/ehv770.
Buttussi, F., Chittaro, L. (2008). MOPET: A context-aware and user-adaptive wearable system for fitness training. Artif Intell Med, 42:15363. doi: 10.1016/j.artmed. 2007.11.004.
Cheng, E.R., Steinhardt, R., Ben Miled, Z. (2022). Predicting childhood obesity using machine learning: Practical considerations. BioMedInformatics, 2:184203. doi: 10.3390/ biomedinformatics2010012
Colmenarejo, G. (2020). Machine learning models to predict childhood and adolescent obesity: A Review. Nutrients, 12:2466. doi: 10.3390/nu12082466.
Di Martino, F., Delmastro, F., Dolciotti, C. (2023). Explainable AI for malnutrition risk prediction from m-health and clinical data. arXiv preprint arXiv: 2305.19636.
Dugan, T.M., Mukhopadhyay, S., Carroll, A., Downs, S. (2015). Machine learning techniques for prediction of early childhood obesity. Appl Clin Inform, 6:50620. doi: 10.4338/ACI-2015-03-RA-0036.
Espinosa-Curiel, I.E., Pozas-Bogarin, E.E., Lozano-Salas, J.L., Martínez-Miranda, J., Delgado-Pérez, E.E., Estrada-Zamarron, L.S. (2020). Nutritional education and promotion of healthy eating behaviors among Mexican children through video games: Design and pilot test of food rate master. JMIR Serious Games, 8:e16431. doi: 10.2196/16431.
Fiechtner, L., Kleinman, K., Melly, S.J., Sharifi, M., Marshall, R., Block, J., et al. (2016). Effects of proximity to supermarkets on a randomized trial studying interventions for obesity. Am J Public Health, 106:55762. doi: 10.2105/AJPH.2015.302986.
Goh, G., Tan, N.C., Malhotra, R., Padmanabhan, U., Barbier, S., Allen, J.C Jr, et al. (2015). Short-term trajectories of use of a caloric-monitoring mobile phone app among patients with type 2 diabetes mellitus in a primary care setting. J Med Internet Res, 17:e33. doi: 10.2196/jmir. 3938.
Gray, L.A., Hernandez Alava, M., Kelly, M.P., Campbell, M.J. (2018). Family lifestyle dynamics and childhood obesity: Evidence from the millennium cohort study. BMC Public Health, 18:500. doi: 10.1186/s12889-018-5398-5.
Henriksson, H., Alexandrou, C., Henriksson, P., Henström, M., Bendtsen, M., Thomas K., et al. (2020). MINISTOP 2.0: A smartphone app integrated in primary child health care to promote healthy diet and physical activity behaviours and prevent obesity in preschool-aged children: Protocol for a hybrid design effectiveness-implementation study. BMC Public Health, 20:1756. doi: 10.1186/s12889-020-09808-w.
Iacob, C., Harrison, R., Faily, S. (2014). Online reviews as first class artifacts in mobile app development. In: Memmi G, Blanke U, editors. Mobile Computing, Applications, and Services. MobiCASE 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Vol. 130. Springer, Cham, Available from: https://link.springer.com/chapter/10.1007/978-3-319-05452-0_4. [Last accessed on 2023 Mar 01].
Keles, E., Bagci, U. (2023). The past, current, and future of neonatal intensive care units with artificial intelligence. arXiv preprint arXiv: 2302.00225.
Leiva, A. (2018). MVP for Android: how to organize the presentation layer. Antonio Leiva. https://antonioleiva.com/mvp-android/. [Last accessed on 2022 Dec 27]
Lindberg, R., Seo, J., Laine, T. (2016). Enhancing physical education with exergames and wearable technology. IEEE Transactions on Learning Technologies, 9:32841. doi: 10.1109/TLT.2016.2556671
Lingren, T., Thaker, V., Brady, C., Namjou, B., Kennebeck, S., Bickel, J., et al. (2016). Developing an algorithm to detect early childhood obesity in two tertiary pediatric medical centers. Appl Clin Infor, 7:693706. doi: 10.4338/ACI-2016-01-RA-0015.
Lisowska, A., Wilk, S., Peleg, M. (2023). Personalizing digital health behavior change interventions using machine learning and domain knowledge. arXiv preprint arXiv: 2304.03392.
Magro, D.O., Geloneze, B., Delfini, R., Pareja B.C., Callejas, F., Pareja, J.C. (2008). Long-term weight regain after gastric bypass: A 5-year prospective study. Obes Surg,18:64851. doi: 10.1007/s11695-007-9265-1.
Marmett, B., Carvalho, R.B., Fortes, M.S., Cazella, S.C. (2018). Artificial intelligence technologies to manage obesity. VITTALLE-Revista de Ciências da Saúde, 30:739. doi: 10.14295/vittalle.v30i2.7654
Matsushita, F.Y., Krebs, V.L.J., Carvalho, W.B. (2022). Artificial intelligence and machine learning in pediatrics and neonatology healthcare. Rev Assoc Med Bras, 68:74550. doi: 10.1590/1806-9282.20220177.
Mohsen, F., Al-Absi H.R., Yousri, N.A., Hajj N.E., Shah, Z. (2023). Artificial intelligence-based methods for precision medicine: Diabetes risk prediction. arXiv preprint arXiv: 2305.16346.
Montalbano, L., Augello, A., Pilato, G., La Grutta, S. (2023). Social robots to improve therapeutic adherence in pediatric asthma. arXiv preprint arXiv: 2306.04422.
Nyström, C.D., Sandin, S., Henriksson, P., Henriksson, H., Trolle-Lagerros, Y., Larsson, C., et al. (2017). Mobile-based intervention intended to stop obesity in preschool-aged children: The MINISTOP randomized controlled trial. Am J Clin Nutr, 105:132735. doi: 10.3945/ajcn. 116.150995.
Pang, X., Forrest, C.B., Lê-Scherban, F., Masino, A.J. (2021). Prediction of early childhood obesity with machine learning and electronic health record data. Int J Med Inform, 150:104454. doi: 10.1016/j.ijmedinf. 2021.104454.
Ríos-Julián, N., Alarcón-Paredes, A., Alonso, G.A., Hernández-Rosales, D., Guzmán-Guzmán, I.P. (2017). Feasibility of a screening tool for obesity diagnosis in Mexican children from a vulnerable community of Me'Phaa ethnicity in the state of Guerrero, Mexico. 2017 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges, GMEPE/PAHCE. doi: 10.1109/GMEPE-PAHCE.2017.7972105.
Ruggiero, L., Seltzer, E.D., Dufelmeier, D., McGee Montoya, A., Chebli, P. (2020). MyPlate picks: Development and initial evaluation of feasibility, acceptability, and impact of an educational exergame to help promote healthy eating and physical activity in children. Games Health, 9:197207. doi: 10.1089/g4h. 2019.0056.
Staiano, A.E., Beyl, R.A., Guan, W., Hendrick C.A., Hsia, D.S., Newton, R.L. (2018). Home-based exergaming among children with overweight and obesity: A randomized clinical trial. Pediatr Obes, 13:72433. doi: 10.1111/ijpo. 12438.
Stephens, T.N., Joerin, A., Rauws, M., Werk, L.N. (2019). Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med, 9:4407. doi: 10.1093/tbm/ibz043.
The CHICA System | Children's Health Services Research Center | IU School of Medicine. https://medicine.iu.edu/pediatrics/specialties/health-services/child-health-informatics-research-development-lab/the-chica-system. [Last accessed on 2022 Dec 27]
Vogan, A.A., Alnajjar, F., Gochoo, M., Khalid, S. (2020). Robots, AI, and cognitive training in an era of mass age-related cognitive decline: A systematic review. IEEE Access, 8:18284304. doi: 10.1109/ACCESS.2020.2966819
Wolpert, M., Curtis-Tyler, K., Edbrooke-Childs, J. (2016). A qualitative exploration of patient and clinician views on patient reported outcome measures in child mental health and diabetes services. Adm Policy Ment Health, 43:30915. doi: 10.1007/s10488-014-0586-9.
Yang, H.J., Kang JH, Kim OH, Choi M, Oh M, Nam J, et al. (2017). Inte rventions for preventing childhood obesity with smartphones and wearable device: A protocol for a non-randomized controlled trial. Int J Environ Res Public Health, 14:184. doi: 10.3390/ijerph 14020184.