Efficacy of Emerging Technologies to Manage Childhood Obesity

Alotaibi, Mohammed; Alnajjar, Fady; Cappuccio, Massimiliano L.; Khalid, Sumayya; Alhmiedat, Tareq; Mubin, Omar

doi:10.2147/dmso.s357176

Cited by 16 publications

(11 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Colmenarejo discussed the advantages and disadvantages of both ML and statistical methods and suggested using ML methods to have a higher prediction accuracy [ 19 ]. ML algorithms offer sophisticated and powerful techniques for analyzing critical factors to predict obesity risks and outcomes [ 6 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The World Health Organization (WHO) declared that, by 2030, 30% of deaths will be caused by preventable lifestyle-related illnesses [ 5 ]. Early preventive strategies are paramount, because children with obesity are at risk for serious lifestyle-related illnesses in adulthood [ 6 ]; therefore, determining the obesity trends of children in early life can potentially prevent future health comorbidities and extend their lives.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers

Mondal

Foysal

Norman

2023

Sensors

View full text Add to dashboard Cite

Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child’s body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child’s current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child’s growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child’s obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers

Mondal

Foysal

Norman

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…96 New mobile applications with innovative ideas are now available and look promising to enhance children's adherence to healthy lifestyle behaviors. 97 Some of these applications allow parents to have feedback regarding their child's physical activity, dietary habits and provide guidance regarding healthy eating habits and recommended daily physical activities. 98,99 Video gaming is widely used by the adolescent population.…”

Section: Emerging and Innovative Lifestyle Change Strategiesmentioning

confidence: 99%

Adolescents with Type 2 Diabetes: Overcoming Barriers to Effective Weight Management

Biggs¹,

Creo²,

Prissel³

et al. 2023

DMSO

View full text Add to dashboard Cite

The prevalence of type 2 diabetes (T2DM) among children and adolescents has remarkably increased in the last two decades, particularly among ethnic minorities. Management of T2DM is challenging in the adolescent population due to a constellation of factors, including biological, socioeconomic, cultural, and psychological barriers. Weight reduction is an essential component in management of T2DM as weight loss is associated with improvement in insulin sensitivity and glycemic status. A family centered and culturally appropriate approach offered by a multidisciplinary team is crucial to address the biological, psychosocial, cultural, and financial barriers to weight management in youth with T2DM. Lifestyle interventions and pharmacotherapy have shown modest efficacy in achieving weight reduction in adolescents with T2DM. Bariatric surgery is associated with excellent weight reduction and remission of T2DM in youth. Emerging therapies for weight reduction in youth include digital technologies, newer GLP-1 agonists and endoscopic procedures.

show abstract

“…However, conventional interventions are not always accessible due to high costs, commute inconveniences, and lack of facilities. Additionally, 80% of overweight and obese children hesitate to engage in physical activities, therefore requiring the implementation of creative interventions [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

The ENDORSE Feasibility Study: Exploring the Use of M-Health, Artificial Intelligence and Serious Games for the Management of Childhood Obesity

et al. 2023

View full text Add to dashboard Cite

Childhood obesity constitutes a major risk factor for future adverse health conditions. Multicomponent parent–child interventions are considered effective in controlling weight. Τhe ENDORSE platform utilizes m-health technologies, Artificial Intelligence (AI), and serious games (SG) toward the creation of an innovative software ecosystem connecting healthcare professionals, children, and their parents in order to deliver coordinated services to combat childhood obesity. It consists of activity trackers, a mobile SG for children, and mobile apps for parents and healthcare professionals. The heterogeneous dataset gathered through the interaction of the end-users with the platform composes the unique user profile. Part of it feeds an AI-based model that enables personalized messages. A feasibility pilot trial was conducted involving 50 overweight and obese children (mean age 10.5 years, 52% girls, 58% pubertal, median baseline BMI z-score 2.85) in a 3-month intervention. Adherence was measured by means of frequency of usage based on the data records. Overall, a clinically and statistically significant BMI z-score reduction was achieved (mean BMI z-score reduction −0.21 ± 0.26, p-value < 0.001). A statistically significant correlation was revealed between the level of activity tracker usage and the improvement of BMI z-score (−0.355, p = 0.017), highlighting the potential of the ENDORSE platform.

show abstract

Efficacy of Emerging Technologies to Manage Childhood Obesity

Cited by 16 publications

References 60 publications

Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers

Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers

Adolescents with Type 2 Diabetes: Overcoming Barriers to Effective Weight Management

The ENDORSE Feasibility Study: Exploring the Use of M-Health, Artificial Intelligence and Serious Games for the Management of Childhood Obesity

Contact Info

Product

Resources

About