Efficacy of Emerging Technologies to Manage Childhood Obesity

References

• Wolfenden L, Ezzati M, Larijani B, Dietz W. The challenge for global health systems in preventing and managing obesity. Obesity Rev. 2019;20:185–193.
   Web of Science ®Google Scholar
• World Health Organization. Facts and figures on childhood obesity. Available from: https://www.who.int/end-childhood-obesity/facts/en/. Accessed March 2021.
   Google Scholar
• Sahoo K, Sahoo B, Choudhury AK, et al. Childhood obesity: causes and consequences. J Fam Med Prim Care. 2015;4:187–192. doi:10.4103/2249-4863.154628
   PubMedGoogle Scholar
• Spear BA, Barlow SE, Ervin C, et al. Recommendations for treatment of child and adolescent overweight and obesity. Pediatrics. 2007;120(Suppl 4):S254–S288. doi:10.1542/peds.2007-2329F
   PubMed Web of Science ®Google Scholar
• Prangthip, P., Soe, Y. M., & Signar, J. F. (2021). Literature review: nutritional factors influencing academic achievement in school-age children. International Journal of Adolescent Medicine and Health, 33(2). Available from: https://www.rch.org.au/kidsinfo/fact_sheets/Nutrition_older_children/. Accessed April 17, 2022.
   Google Scholar
• Simmonds M, Burch J, Llewellyn A, et al. The use of measures of obesity in childhood for predicting obesity and the development of obesity-related diseases in adulthood: a systematic review and meta-analysis. Health Technol Assess (Rockv). 2015;19:1–336. doi:10.3310/hta19430
   PubMed Web of Science ®Google Scholar
• Shields M, Tremblay MS, Connor Gorber S, Janssen I. Abdominal obesity and cardiovascular disease risk factors within body mass index categories. Health Rep. 2012;23:7–15.
   Web of Science ®Google Scholar
• Vucenik I, Stains JP. Obesity and cancer risk: evidence, mechanisms, and recommendations. Ann NY Acad Sci. 2012;1271:37–43. doi:10.1111/j.1749-6632.2012.06750.x
   PubMed Web of Science ®Google Scholar
• Hoey H. Management of obesity in children differs from that of adults. Proc Nutr Soc. 2014;73:519–525. doi:10.1017/S0029665114000652
   Web of Science ®Google Scholar
• Jones M, Luce KH, Osborne MI, et al. Randomized, controlled trial of an internet-facilitated intervention for reducing binge eating and overweight in adolescents. Pediatrics. 2008;121:453–462. doi:10.1542/peds.2007-1173
   PubMed Web of Science ®Google Scholar
• Childhood and Adolescent Obesity. Available from: https://asmbs.org/patients/adolescent-obesity. Accessed March 2021.
   Google Scholar
• Slater A, Tiggemann M. “Uncool to do sport”: a focus group study of adolescent girls’ reasons for withdrawing from physical activity. Psychol Sport Exerc. 2010;11:619–626. doi:10.1016/j.psychsport.2010.07.006
   Web of Science ®Google Scholar
• Hammersley ML, Okely AD, Batterham MJ, Jones RA. An internet-based childhood obesity prevention program (Time2bHealthy) for parents of preschool-aged children: randomized controlled trial. J Med Internet Res. 2019;21:e11964.
   Web of Science ®Google Scholar
• Li JS, Barnett TA, Goodman E, et al. Approaches to the prevention and management of childhood obesity: the role of social networks and the use of social media and related electronic technologies: a scientific statement from the American Heart Association. Circulation. 2013;127:260–267. doi:10.1161/CIR.0b013e3182756d8e
   PubMed Web of Science ®Google Scholar
• Schulz L, Spil AAMT, de Vries SAS. Changing behavior of kids with obesity with gamified wearables. In: Wickramasinghe N, Bodendorf F, editors. Delivering Superior Health and Wellness Management with IoT and Analytics. Springer Nature Switzerland AG. Springer International Publishing; 2020: 145–169.
   Google Scholar
• Pretlow RA. Addiction to highly pleasurable food as a cause of the childhood obesity epidemic: a qualitative internet study. Eat Disord. 2011;19:295–307. doi:10.1080/10640266.2011.584803
   PubMedGoogle Scholar
• Datta AK, Sumargo A, Jackson V, Dey PP. mCHOIS: an application of mobile technology for childhood obesity surveillance. Procedia Comput Sci. 2011;5:653–660. doi:10.1016/j.procs.2011.07.085
   Google Scholar
• Daley AJ. Can exergaming contribute to improving physical activity levels and health outcomes in children? Pediatrics. 2009;124:763–771. doi:10.1542/peds.2008-2357
   PubMed Web of Science ®Google Scholar
• Radesky JS, Schumacher J, Zuckerman B. Mobile and interactive media use by young children: the good, the bad, and the unknown. Pediatrics. 2015;135:1–3. doi:10.1542/peds.2014-2251
   PubMed Web of Science ®Google Scholar
• Miller GF, Sliwa S, Michael S, et al. Evaluation of Let’s move! Active schools activation grants. Prev Med. 2018;108:36–40. doi:10.1016/j.ypmed.2017.12.024
   PubMed Web of Science ®Google Scholar
• Schuman AJ. Improving patient care: smartphones and mobile medical devices: mobile medical gadgetry is in its infancy, yet these devices can help you motivate pediatric patients and their parents to adopt healthier lifestyles. Contemp Pediatr. 2013;30:33–38.
   Google Scholar
• Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2007;2:59–77.
   PubMedGoogle Scholar
• Mirzaei G, Adeli A, Adeli H. Imaging and machine learning techniques for diagnosis of Alzheimer’s disease. Rev Neurosci. 2016;27:857–870. doi:10.1515/revneuro-2016-0029
   Web of Science ®Google Scholar
• Donsa K, Spat S, Beck P, et al. Towards personalization of diabetes therapy using computerized decision support and machine learning: some open problems and challenges. In: Holzinger A, Röcker C, Ziefle M, editors. Smart Health. Lecture Notes in Computer Science. Springer, Cham; 2015: 237–260.
   Google Scholar
• Gultepe E, Green JP, Nguyen H, et al. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J Am Med Inform Assoc. 2014;21:315–325. doi:10.1136/amiajnl-2013-001815
   Web of Science ®Google Scholar
• Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med. 2016;375:1216–1219. doi:10.1056/NEJMp1606181
   PubMed Web of Science ®Google Scholar
• Salvatore C, Cerasa A, Battista P, et al. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. Front Neurosci. 2015;9:307. doi:10.3389/fnins.2015.00307
   PubMed Web of Science ®Google Scholar
• Triantafyllidis A, Polychronidou E, Alexiadis A, et al. Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: a systematic review of the literature. Artif Intell Med. 2020;104:101844. doi:10.1016/j.artmed.2020.101844
   PubMed Web of Science ®Google Scholar
• Robinson NL, Connolly J, Hides L, Kavanagh DJ. Social robots as treatment agents: pilot randomized controlled trial to deliver a behavior change intervention. Internet Interv. 2020;21:100320. doi:10.1016/j.invent.2020.100320
   PubMedGoogle Scholar
• Cappuccio ML, Sandoval EB, Mubin O, et al. Can robots make us better humans? Int J Soc Robotics. 2020;13:7–22. doi:10.1007/s12369-020-00700-6
   Web of Science ®Google Scholar
• Sandoval EB, Mubin O. Virtuous robotics: artificial agents and the good life. Int J Soc Robotics. 2021;4:54.
   Google Scholar
• Chen JL, Wilkosz ME. Efficacy of technology-based interventions for obesity prevention in adolescents: a systematic review. Adolesc Health Med Ther. 2014;5:159–170. doi:10.2147/AHMT.S39969
   PubMedGoogle Scholar
• McMullan M, Millar R, Woodside JV. A systematic review to assess the effectiveness of technology-based interventions to address obesity in children. BMC Pediatr. 2020;20:242. doi:10.1186/s12887-020-02081-1
   Web of Science ®Google Scholar
• Nouchi R, Kawashima R. Improving cognitive function from children to old age: a systematic review of recent smart ageing intervention studies. Adv Neurosci. 2014;2014:1–15. doi:10.1155/2014/235479
   Google Scholar
• Lindberg R, Seo J, Laine TH. Enhancing physical education with exergames and wearable technology. IEEE Trans Learning Technol. 2016;9:328–341. doi:10.1109/TLT.2016.2556671
   Web of Science ®Google Scholar
• Yang HJ, Kang JH, Kim OH, et al. Interventions for preventing childhood obesity with smartphones and wearable device: a protocol for a non-randomized controlled trial. Int J Environ Res Public Health. 2017;14:184.
   Web of Science ®Google Scholar
• Argarini R, Herawati L, Irwadi I, et al. Fundamental movement skill approach to combat childhood obesity in Surabaya, Indonesia: potential effects of video games based ex ercises (Exergaming). J Talent Dev Excell. 2020;12:3026–3034.
   Google Scholar
• Adamo KB, Rutherford JA, Goldfield GS. Effects of interactive video game cycling on overweight and obese adolescent health. Appl Physiol Nutr Metab. 2010;35:805–815. doi:10.1139/H10-078
   Web of Science ®Google Scholar
• Staiano AE, Beyl RA, Guan W, et al. Home‐based exergaming among children with overweight and obesity: a randomized clinical trial. Pediatr Obes. 2018;13:724–733. doi:10.1111/ijpo.12438
   Web of Science ®Google Scholar
• Ruggiero L, Seltzer ED, Dufelmeier D, et al. 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 J. 2020;9:197–207. doi:10.1089/g4h.2019.0056
   Web of Science ®Google Scholar
• Trost SG, Sundal D, Foster GD, et al. Effects of a pediatric weight management program with and without active video games: a randomized trial. JAMA Pediatr. 2014;168:407–413. doi:10.1001/jamapediatrics.2013.3436
   Web of Science ®Google Scholar
• Staiano AE, Marker AM, Beyl RA, et al. A randomized controlled trial of dance exergaming for exercise training in overweight and obese adolescent girls. Pediatr Obes. 2017;12:120–128. doi:10.1111/ijpo.12117
   Web of Science ®Google Scholar
• Maddison R, Foley L, Ni Mhurchu C, et al. Effects of active video games on body composition: a randomized controlled trial. Am J Clin Nutr. 2011;94:156–163. doi:10.3945/ajcn.110.009142
   PubMed Web of Science ®Google Scholar
• Duman F, Kokaçya MH, Doğru E, et al. The role of active video-accompanied exercises in improvement of the obese state in children: a prospective study from Turkey. J Clin Res Pediatr Endocrinol. 2016;8:334–340. doi:10.4274/jcrpe.2284
   Web of Science ®Google Scholar
• Wagener TL, Fedele DA, Mignogna MR, et al. Psychological effects of dance‐based group exergaming in obese adolescents. Pediatr Obes. 2012;7:e68–e74. doi:10.1111/j.2047-6310.2012.00065.x
   PubMed Web of Science ®Google Scholar
• Coknaz D, Mirzeoglu AD, Atasoy HI, Alkoy S, Coknaz H, Goral K. A digital movement in the world of inactive children: favourable outcomes of playing active video games in a pilot randomized trial. Eur J Pediatr. 2019;178(10):1567–1576. doi:10.1007/s00431-019-03457-x
   PubMed Web of Science ®Google Scholar
• Gao Z, Lee JE, Zeng N, Pope ZC, Zhang Y, Li X. Home-based exergaming on preschoolers’ energy expenditure, cardiovascular fitness, body mass index and cognitive flexibility: a randomized controlled trial. J clin med. 2019;8(10):1745. doi:10.3390/jcm8101745
   Web of Science ®Google Scholar
• Espinosa-Curiel IE, Pozas-Bogarin EE, Lozano-Salas JL, et al. Nutritional education and promotion of healthy eating behaviors among Mexican children through video games: design and pilot test of FoodRateMaster. JMIR Serious Games. 2020;8:e16431. doi:10.2196/16431
   PubMed Web of Science ®Google Scholar
• Nawi AM, Jamaludin FIC. Effect of internet-based intervention on obesity among adolescents in Kuala Lumpur: a school-based cluster randomised trial. Malays J Med Sci MJMS. 2015;22:47–56.
   Google Scholar
• Del Río NG, González-González CS, Martín-González R, et al. Effects of a gamified educational program in the nutrition of children with obesity. J Med Syst. 2019;43:198. doi:10.1007/s10916-019-1293-6
   PubMed Web of Science ®Google Scholar
• Chen JL, Guedes CM, Lung AE. Smartphone-based healthy weight management intervention for Chinese American adolescents: short-term efficacy and factors associated with decreased weight. J Adolesc Health. 2019;64:443–449. doi:10.1016/j.jadohealth.2018.08.022
   Web of Science ®Google Scholar
• Rerksuppaphol L, Rerksuppaphol S. Internet based obesity prevention program for Thai school children-a randomized control trial. JCDR. 2017;11(3):SC07. doi:10.7860/JCDR/2017/21423.9368
   Web of Science ®Google Scholar
• Nyström CD, Sandin S, Henriksson P, et al. Mobile-based intervention intended to stop obesity in preschool-aged children: the MINISTOP randomized controlled trial. Am J Clin Nutr. 2017;105:1327–1335.
   Web of Science ®Google Scholar
• Fiechtner L, Kleinman K, Melly SJ, et al. Effects of proximity to supermarkets on a randomized trial studying interventions for obesity. Am J Public Health. 2016;106:557–562. doi:10.2105/AJPH.2015.302986
   PubMed Web of Science ®Google Scholar
• Lingren T, Thaker V, Brady C, et al. Developing an algorithm to detect early childhood obesity in two tertiary pediatric medical centers. Appl Clin Inform. 2016;7:693–706. doi:10.4338/ACI-2016-01-RA-0015
   Web of Science ®Google Scholar
• Ríos-Julián N, Alarcón-Paredes A, Alonso GA, et al. 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. Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE). IEEE; 2017: 1–6.
   Google Scholar
• Fergus P, Hussain A, Hearty J, et al. A machine learning approach to measure and monitor physical activity in children to help fight overweight and obesity. International Conference on Intelligent Computing; Springer, Cham; 2015: 676–688.
   Google Scholar
• Dugan TM, Mukhopadhyay S, Carroll A, Downs S. Machine learning techniques for prediction of early childhood obesity. Appl Clin Inform. 2015;6:506. doi:10.4338/ACI-2015-03-RA-0036
   PubMed Web of Science ®Google Scholar
• Singh B, Tawfik H. Machine learning approach for the early prediction of the risk of overweight and obesity in young people. International Conference on Computational Science; Springer, Cham; 2020: 523–535.
   Google Scholar
• Alotaibi M. A social robotic obesity management and awareness system for children in Saudi Arabia. Int J Online Eng. 2018;14(9):45.
   Google Scholar
• Colmenarejo G. Machine learning models to predict childhood and adolescent obesity: a review. Nutrients. 2020;12:2466. doi:10.3390/nu12082466
   Web of Science ®Google Scholar
• Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. 2009;28(6):690. doi:10.1037/a0016136
   PubMed Web of Science ®Google Scholar
• Alnajjar F, Cappuccio M, Renawi A, Mubin O, Loo CK. Personalized robot interventions for autistic children: an automated methodology for attention assessment. Int J Soc Robotics. 2021;13(1):67–82. doi:10.1007/s12369-020-00639-8
   Web of Science ®Google Scholar
• Vogan AA, Alnajjar F, Gochoo M, Khalid S. Robots, AI, and cognitive training in an era of mass age-related cognitive decline: a systematic review. IEEE Access. 2020;8:18284–18304. doi:10.1109/ACCESS.2020.2966819
   Web of Science ®Google Scholar
• Israels T, Challinor J, Howard S, Arora RH. Treating children with cancer worldwide—challenges and interventions. Pediatrics. 2015;136(4):607–610. doi:10.1542/peds.2015-0300
   PubMed Web of Science ®Google Scholar
• 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. Administration and Policy in Mental Health and Mental Health Services Research, 43(3), 309-315. Available from: https://www.betterhealth.vic.gov.au/health/ConditionsAndTreatments/diabetes-issues-for-children-and-teenagers#childrens-reactions-to-a-diabetes-diagnosis. Accessed April 17, 2022.
   Google Scholar
• Dawe J, Sutherland C, Barco A, Broadbent E. Can social robots help children in healthcare contexts? A scoping review. BMJ Paediatr Open. 2019;3:e000371. doi:10.1136/bmjpo-2018-000371
   PubMedGoogle Scholar
• Alahbabi M, Almazroei F, Almarzoqi M, et al. Avatar based interaction therapy: a potential therapeutic approach for children with Autism. 2017 IEEE International Conference on Mechatronics and Automation (ICMA); 2017: 480–484.
   Google Scholar