TY - JOUR
T1 - Efficacy of Emerging Technologies to Manage Childhood Obesity
AU - Alotaibi, Mohammad
AU - Alnajjar, Fady
AU - Cappuccio, Massimiliano
AU - Khalid, Sumaya
AU - Alhmiedat, Tareq
AU - Mubin, Omar
N1 - Funding Information:
The authors would also like to acknowledge the financial support for this work from the Deanship of Scientific Research (DSR) university of Tabuk, Tabuk, Saudi Arabia, under grant no. [1440-258].
Publisher Copyright:
© 2022 Alotaibi et al.
PY - 2022
Y1 - 2022
N2 - Childhood obesity is a widespread medical condition and presents a formidable challenge for public health. Long-term treatment strategies and early prevention strategies are required because obese children are more likely to carry this condition into adulthood, increasing their risk of developing other major health disorders. The present review analyses various technological interventions available for childhood obesity prevention and treatment. It also examines whether machine learning and technological interventions can play vital roles in its management. Twenty-six studies were shortlisted for the review using various technological strategies and analysed regarding their efficacy. While most of the selected studies showed positive outcomes, there was a lack of studies using robots and artificial intelligence to manage obesity in children. The use of machine learning was observed in various studies, and the integration of social robots and other efficacious strategies may be effective for treating childhood obesity in the future.
AB - Childhood obesity is a widespread medical condition and presents a formidable challenge for public health. Long-term treatment strategies and early prevention strategies are required because obese children are more likely to carry this condition into adulthood, increasing their risk of developing other major health disorders. The present review analyses various technological interventions available for childhood obesity prevention and treatment. It also examines whether machine learning and technological interventions can play vital roles in its management. Twenty-six studies were shortlisted for the review using various technological strategies and analysed regarding their efficacy. While most of the selected studies showed positive outcomes, there was a lack of studies using robots and artificial intelligence to manage obesity in children. The use of machine learning was observed in various studies, and the integration of social robots and other efficacious strategies may be effective for treating childhood obesity in the future.
KW - artificial intelligence
KW - childhood obesity
KW - exergaming
KW - intervention
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85129508714&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129508714&partnerID=8YFLogxK
U2 - 10.2147/DMSO.S357176
DO - 10.2147/DMSO.S357176
M3 - Review article
AN - SCOPUS:85129508714
SN - 1178-7007
VL - 15
SP - 1227
EP - 1244
JO - Diabetes, Metabolic Syndrome and Obesity
JF - Diabetes, Metabolic Syndrome and Obesity
ER -