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

The Ongoing Impact of COVID-19 on Pediatric Obesity

In the developed world, pediatric obesity (PO) has been a major health concern since the last century, and this condition may lead to detrimental life-long physical and mental comorbidities. Currently, its prevalence has increased in low- and middle-income countries and in many high-income countries. Thus, the provision of effective and tailored care for children and their families has become vital. The social consequences of the COVID-19 pandemic are known everywhere, and among these, it has been argued that the COVID-19 pandemic has had a major impact on PO. Overall, the growth of PO over the last decade has been enhanced by the pandemic. During the COVID-19 pandemic, children, adolescents and young adults gained weight as the pediatric population dealt with sedentary lifestyles and changes in food habits. In this review, we want to highlight the impact that the COVID-19 pandemic had on PO.

Parenting Styles and Psychosocial Factors of Mother-Child Dyads Participating in the ENDORSE Digital Weight Management Program for Children and Adolescents during the COVID-19 Pandemic

Childhood obesity is a complex disease with multiple biological and psychosocial risk factors. Recently, novel digital programs were developed with growing evidence for their effectiveness in pediatric weight management studies. The ENDORSE platform consists of mobile applications, wearables, and serious games for the remote management of childhood obesity. The pilot studies included 50 mothers and their children aged 6-14 years and resulted in a clinically significant BMI z-score reduction over 4 to 5 months. This secondary analysis of the ENDORSE study focuses on parenting styles and psychosocial factors.

METHODOLOGY

Semi-structured clinical interviews were conducted with all participating mothers pre-and post-intervention. The Parenting Styles and Dimensions Questionnaire (PSDQ) evaluated the mothers' parenting styles. The psychosocial functioning of the participating children was assessed with the parental version of the Strengths and Difficulties Questionnaire (SDQ). The relationship between parenting styles, psychosocial parameters, and weight outcomes was investigated using a linear regression analysis.

RESULTS

Weight-related stigma at school (56%), body image concerns (66%), and difficulties in family relationships (48%) were the main concerns documented during the initial psychological interviews. According to the SDQ, there was a significant decrease in children's conduct problems during the study's initial phase (pre-pilot group). A decrease in maternal demandingness (i.e., strict parenting style) was associated with a decrease in BMI z-score (beta coefficient = 0.314, p-value = 0.003).

CONCLUSION

Decreasing parental demandingness was associated with better weight outcomes, highlighting the importance of assessing parenting factors in pediatric weight management programs.

Differences in segmental hair cortisol concentration analysis among children and adolescents with overweight and obesity, their parents, and normal weight peers

PURPOSE

Dysregulation of the stress system via incidental long exposure to glucocorticoids (GCs) can lead to weight gain. In addition, family and maternal stress can also have an impact on children's weight. Hair is used in several studies to evaluate cortisol (GC) levels in children and adolescents with excess weight as a retrospective stress biomarker, depending on the hair length the cortisol measurement depicting different time periods. We aimed to investigate whether there is a difference among segmental hair cortisol concentration (HCC) analysis between children and adolescents with overweight and obesity, their mothers, and normal weight peers.

METHODS

This study recruited 25 children aged 6-14 years with a body mass index (BMI) ≥ 85th centile and their mothers, as well as 20 children of the same age with a BMI < 85th centile. Hair cortisol concentration was measured using electrochemiluminescence immunoassay.

RESULTS

Segmental HCC analysis exhibited gradually decreasing values in all participants as segments of hair were more distantly located from the scalp. A positive correlation was found between BMI z-score and HCC of the first segment of hair in children and adolescents with elevated BMI (b = 1.84, p = 0.033), as well as with maternal HCC / of an only child (b = 15.77, p = 0.01). There were no associations between mother-child dyads and children and adolescents of different BMI groups, even though minors with excess weight exhibited higher HCC levels in all segments of hair in comparison to their normal weight counterparts.

CONCLUSIONS

Hair cortisol of all participants exhibited a gradually declining concentration. More studies with larger samples and more sensitive methods of analysis are warranted in order to draw firmer conclusions.

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.

The Impact of the ENDORSE Digital Weight Management Program on the Metabolic Profile of Children and Adolescents with Overweight and Obesity and on Food Parenting Practices

Childhood obesity is a serious public health problem worldwide. The ENDORSE platform is an innovative software ecosystem based on Artificial Intelligence which consists of mobile applications for parents and health professionals, activity trackers, and mobile games for children. This study explores the impact of the ENDORSE platform on metabolic parameters associated with pediatric obesity and on the food parenting practices of the participating mothers. Therefore, the metabolic parameters of the 45 children (mean age: 10.42 years, 53% girls, 58% pubertal, mean baseline BMI z-score 2.83) who completed the ENDORSE study were evaluated. The Comprehensive Feeding Practices Questionnaire was used for the assessment of food parenting practices. Furthermore, regression analysis was used to investigate possible associations between BMI z-score changes and changes in metabolic parameters and food parenting practices. Overall, there was a statistically significant reduction in glycated hemoglobin (mean change = −0.10, p = 0.013), SGOT (mean change = −1.84, p = 0.011), and SGPT (mean change = −2.95, p = 0.022). Emotional feeding/food as reward decreased (mean change −0.21, p = 0.007) and healthy eating guidance increased (mean change = 0.11, p = 0.051). Linear regression analysis revealed that BMI z-score change had a robust and significant correlation with important metabolic parameters: HOMA-IR change (beta coefficient = 3.60, p-value = 0.046), SGPT change (beta coefficient = 11.90, p-value = 0.037), and cortisol change (beta coefficient = 9.96, p-value = 0.008). Furthermore, healthy eating guidance change had a robust negative relationship with BMI z-score change (beta coefficient = −0.29, p-value = 0.007). Conclusions: The Endorse digital weight management program improved several metabolic parameters and food parenting practices.