Efficacy of Emerging Technologies to Manage Childhood Obesity

Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline-A Systematic Review

Background/Objectives: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial intelligence (AI) and machine learning (ML) have been integrated into clinical practice for analyzing datasets to identify new risk factors, build predictive models, and develop personalized interventions, thereby providing useful information to healthcare professionals. This systematic review aims to evaluate the potential of AI and ML techniques in addressing the relationship between obesity, its associated health consequences, and cognitive decline. Methods: Systematic searches were performed in PubMed, Cochrane, Web of Science, Scopus, Embase, and PsycInfo databases, which yielded eight studies. After reading the full text of the selected studies and applying predefined inclusion criteria, eight studies were included based on pertinence and relevance to the topic. Results: The findings underscore the utility of AI and ML in assessing risk and predicting cognitive decline in obese patients. Furthermore, these new technology models identified key risk factors and predictive biomarkers, paving the way for tailored prevention strategies and treatment plans. Conclusions: The early detection, prevention, and personalized interventions facilitated by these technologies can significantly reduce costs and time. Future research should assess ethical considerations, data privacy, and equitable access for all.

Applications of artificial intelligence in the management of childhood obesity

Background

Childhood obesity has emerged as a significant public health challenge, with long-term implications that often extend into adulthood, increasing the susceptibility to chronic health conditions.

Objective

The objective of this review is to elucidate the applications of artificial intelligence (AI) in the prevention and treatment of pediatric obesity, emphasizing its potential to complement and enhance traditional management methods.

Methods

We undertook a comprehensive examination of existing literature to understand the integration of machine learning and other AI techniques in childhood obesity management strategies.

Results

The findings from numerous studies suggest a strong endorsement for AI's role in addressing childhood obesity. Particularly, machine learning techniques have shown considerable efficacy in augmenting current therapeutic and preventive approaches.

Conclusion

The intersection of AI with conventional obesity management practices presents a novel and promising approach to fortify interventions targeting pediatric obesity. This review accentuates the transformative capacity of AI, thereby advocating for continued research and innovation in this rapidly evolving domain.

Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of Artificial Intelligence, focused on the management of patients with obesity.

Methods

The perspectives of the authors were augmented by scientific support from published citations and integrated with information derived from search engines (i.e., Chrome by Google, Inc) and chatbots (i.e., Chat Generative Pretrained Transformer or Chat GPT).

Results

Artificial Intelligence (AI) is the technologic acquisition of knowledge and skill by a nonhuman device, that after being initially programmed, has varying degrees of operations autonomous from direct human control, and that performs adaptive output tasks based upon data input learnings. AI has applications regarding medical research, medical practice, and applications relevant to the management of patients with obesity. Chatbots may be useful to obesity medicine clinicians as a source of clinical/scientific information, helpful in writings and publications, as well as beneficial in drafting office or institutional Policies and Procedures and Standard Operating Procedures. AI may facilitate interactive programming related to analyses of body composition imaging, behavior coaching, personal nutritional intervention & physical activity recommendations, predictive modeling to identify patients at risk for obesity-related complications, and aid clinicians in precision medicine. AI can enhance educational programming, such as personalized learning, virtual reality, and intelligent tutoring systems. AI may help augment in-person office operations and telemedicine (e.g., scheduling and remote monitoring of patients). Finally, AI may help identify patterns in datasets related to a medical practice or institution that may be used to assess population health and value-based care delivery (i.e., analytics related to electronic health records).

Conclusions

AI is contributing to both an evolution and revolution in medical care, including the management of patients with obesity. Challenges of Artificial Intelligence include ethical and legal concerns (e.g., privacy and security), accuracy and reliability, and the potential perpetuation of pervasive systemic biases.

Setmelanotide optimization through fragment-growing, molecular docking in-silico method targeting MC4 receptor

Obesity has emerged as a global issue, but with the complex structures of multiple related important targets and their agonists or antagonists determined, the mechanism of ligand-protein interaction may offer new chances for developing new generation agonists anti-obesity. Based on the molecule surface of the cryo-EM protein structure 7AUE, we tried to replace D-Ala3 with D-Met in setmelanotide as the linker site for fragment-growing with De novo evolution. The simulation results indicate that the derivatives could improve the binding abilities with the melanocortin 4 receptor and the selectivity over the melanocortin 1 receptor. The improved selectivity of the newly designed derivatives is mainly due to the shape difference of the molecular surface at the orthosteric peptide-binding pocket between melanocortin 4 receptor and melanocortin 1 receptor. The new extended fragments could not only enhance the binding affinities but also function as a gripper to seize the pore, making it easier to balance and stabilize the other component of the new derivatives. Although it is challenging to synthesize the compounds designed in silico, this study may perhaps serve as a trigger for additional anti-obesity research.Communicated by Ramaswamy H. Sarma.

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

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.

Exploring the understanding of how parenting influences the children's nutritional status, physical activity, and BMI

Aim

Parental behaviors and the home environment are two of the most effective ways to adopt healthy eating and active lifestyles. For this reason, it is crucial to understand children's nutritional habits, analyze the dynamics related to parental factors, diagnose and treat childhood obesity in the early period, and prevent adulthood obesity. This study aimed to explore how parenting influences children's nutritional status, physical activity, and BMI.

Methods

The study involved 596 children with their parents. The data were collected through face-to-face interviews using the survey method. The survey consists of descriptive information (age, gender, educational status), anthropometric measurements, nutritional habits, Family Nutrition and Physical Activity Scale (FNPA), International Physical Activity Questionnaire, and 24-h dietary recall. The Mean Adequacy Ratio (MAR) was applied to assess dietary adequacy.

Results

Most mothers and fathers were overweight or obese (61.6 and 68.7%, respectively). 38.6% of boys and 23.1% of girls were overweight or obese. The FNPA score was positively correlated with MAR (p < 0.05). Multiple linear regression analysis revealed that children's BMI was negatively correlated with FNPA score, while maternal BMI and father's BMI were positively correlated (p < 0.05). Furthermore, dietary energy was not associated with the child's BMI but with dietary adequacy (p < 0.05). There was no evidence that family impacted children's physical activity.

Conclusion

This study supports that parenting influences children's dietary intake and BMI. Adequate and balanced nutrition, regardless of dietary energy, may affect children's body weight. Family plays a significant role in influencing and forming children's lifestyle-related behaviors. Children's healthy eating and physical exercise habits can be encouraged through school-based programs involving families.

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

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.

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

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.