Interventions for Preventing Childhood Obesity with Smartphones and Wearable Device: A Protocol for a Non-Randomized Controlled Trial

Promoting child and adolescent health through wearable technology: A systematic review

Background

Wearable technology is used in healthcare to monitor the health of individuals. This study presents an updated systematic literature review of the use of wearable technology in promoting child and adolescent health, accompanied by recommendations for future research.

Methods

This review focuses on studies involving children and adolescents aged between 2 and 18 years, regardless of their health condition or disabilities. Studies that were published from 2016 to 2024, and which met the inclusion criteria, were extracted from four academic databases (i.e. PubMed, Cochrane, Embase, and Web of Science) using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol. Data on intervention purposes, interventions deployed, intervention duration, measurements, and the main outcomes of the studies were collected.

Results

A total of 53 studies involving 14,852 participants were reviewed. They focused on various aspects, including the ownership and use of wearable devices (n = 3), the feasibility (n = 22), effectiveness (n = 4), and adherence (n = 2) of intervention strategies, or a combination of multiple aspects (n = 22). Among the interventions deployed, Fitbit was the most frequently used, featuring in 26 studies, followed by ActiGraph (n = 11). In intervention studies, the majority of studies focused on pre-morbidity prevention (n = 26) and the treatment of illnesses (n = 20), with limited attention given to postoperative monitoring (n = 4).

Conclusions

The use of wearable technology by children and adolescents has proven to be both feasible and effective for health promotion. This systematic review summarizes existing research by exploring the use of wearable technology in promoting health across diverse youth populations, including healthy and unhealthy individuals. It examines health promotion at various stages of the disease continuum, including pre-disease prevention, in-disease treatment, and postoperative monitoring. Additionally, the review provides directions for future research.

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.

Wearable sensor platforms for real-time monitoring and early warning of metabolic disorders in humans

Nowadays, the prevalence of metabolic syndromes (MSs) has attracted increasing concerns as it is closely related to overweight and obesity, physical inactivity and overconsumption of energy, making the diagnosis and real-time monitoring of the physiological range essential and necessary for avoiding illness due to defects in the human body such as higher risk of cardiovascular disease, diabetes, stroke and diseases related to artery walls. However, the current sensing techniques are inconvenient and do not continuously monitor the health status of humans. Alternatively, the use of recent wearable device technology is a preferable method for the prevention of these diseases. This can enable the monitoring of the health status of humans in different health domains, including environment and structure. The use wearable devices with the purpose of facilitating rapid treatment and real-time monitoring can decrease the prevalence of MS and long-time monitor the health status of patients. This review highlights the recent advances in wearable sensors toward continuous monitoring of blood pressure and blood glucose, and further details the monitoring of abnormal obesity, triglycerides and HDL. We also discuss the challenges and future prospective of monitoring MS in humans.

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.

Efficacy of Emerging Technologies to Manage Childhood Obesity

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.

Comparison of SpO2 and heart rate values on Apple Watch and conventional commercial oximeters devices in patients with lung disease

Lung diseases have high mortality and morbidity, with an important impact on quality of life. Hypoxemic patients are advised to use oxygen therapy to prolong their survival, but high oxygen saturation (SpO2) levels can also have negative effects. Pulse oximeters are the most common way to assess oxygen levels and guide medical treatment. This study aims to assess whether wearable devices can provide precise SpO2 measurements when compared to commercial pulse oximeters. This is a cross-section study with 100 patients with chronic obstructive pulmonary disease and interstitial lung disease from an outpatient pneumology clinic. SpO2 and heart rate data were collected with an Apple Watch Series 6 (Apple) and compared to two commercial pulse oximeters. The Bland-Altman method and interclass correlation coefficient were used to compare their values. We observed strong positive correlations between the Apple Watch device and commercial oximeters when evaluating heart rate measurements (r = 0.995, p < 0.001) and oximetry measurements (r = 0.81, p < 0.001). There was no statistical difference in the evaluation of skin color, wrist circumference, presence of wrist hair, and enamel nail for SpO2 and heart rate measurements in Apple Watch or commercial oximeter devices (p > 0.05). Apple Watch 6 is a reliable way to obtain heart rate and SpO2 in patients with lung diseases in a controlled environment.

Effects of Physical Activity and Psychological Modification-Based Intervention on Physical Fitness, Physical Activity and Its Related Psychological Variables in Female Adolescents

Background: The current study investigated the effects of an intervention incorporating physical activity and psychological modification strategy on physical fitness, physical activity levels, and psychological variables related to physical activity in female adolescents. Methods: Sixty female adolescents were recruited from H Middle School. Among them, 30 females (Mage = 14.35 years) were randomly assigned to the experimental group and the rest of 30 to the control group (Mage = 14.47 years) and voluntarily participated in the 12-week intervention. Results: The results indicated that the physical activity stage of female adolescents in the experimental group significantly increased over the 12-week intervention. The results revealed that all of psychological variables in the experimental group significantly improved over the intervention, while participants in the experimental group showed significantly higher scores on most of psychological variables, except cons, than those in the control group after the intervention. Moreover, most of physical fitness components in the experimental group significantly increased over the intervention. Conclusions: The current study confirmed that the physical activity-related psychological intervention was feasible for improving physical and psychological health among female adolescents.

Efficacy of a smartphone application-based intervention for encouraging children's healthy eating in Denmark

The growing rates of childhood obesity constitute a public health challenge worldwide. Therefore it is important to identify effective and widely applicable interventions to prevent it. This study aims to explore children's experience of using a newly developed smartphone application (app) designed to promote healthy eating and evaluate its efficacy on encouraging healthy eating. First, two focus groups were conducted to explore children's experience of using the app. Then, a quasi-experimental design was used to evaluate the app's efficacy. The children were asked to use the app for three months. Afterwards, the effect of the intervention was evaluated. 118 children aged 9 to 13 years (M=10.9, Sd=1.1) participated in the study. The children's experience of using the app was relatively positive, and they found the app easy to use. A significant increase in fruit (η2=.10) and vegetable preferences (η2=.37) and fruit intake (η2=.06) was found in the experimental group. No effects were found for vegetable intake, selfefficacy for healthy eating, or peer norms for healthy and unhealthy eating (p>0.05). The smartphone app-based intervention could potentially serve as an attractive and low-cost intervention to reach a wide population of children for the promotion of healthy eating and prevention of childhood obesity.

Big Data Warehouse for Healthcare-Sensitive Data Applications

Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project "Big Data against Childhood Obesity (BigO)" aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.

A Multidomain Approach to Assessing the Convergent and Concurrent Validity of a Mobile Application When Compared to Conventional Methods of Determining Body Composition

Determining body composition via mobile application may circumvent limitations of conventional methods. However, the accuracy of many technologies remains unknown. This investigation assessed the convergent and concurrent validity of a mobile application (LS) that employs 2-dimensional digital photography (LS2D) and 3-dimensional photonic scanning (LS3D). Measures of body composition including circumferences, waist-to-hip ratio (WHR), and body fat percentage (BF%) were obtained from 240 healthy adults using LS and a diverse set of conventional methods-Gulick tape, bioelectrical impedance analysis (BIA), and skinfolds. Convergent validity was consistently high-indicating these methods vary proportionally and can thus reliably detect changes despite individual measurement differences. The span of the Limits of Agreement (LoA) using LS were comparable to the LoA between conventional methods. LS3D exhibited high agreement relative to Gulick tape in the measurement of WHR, despite poor agreement with individual waist and hip circumferences. In BF%, LS2D exhibited high agreement with BIA and skinfold methods, whereas LS3D demonstrated low agreement. Interestingly, the low inferred bias between LS3D and DXA using existing data suggests that LS3D may have high agreement with dual-energy x-ray absorptiometry. Overall, the suitability of LS2D and LS3D to replace conventional methods must be based on an individual user's criteria.

PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables.

Knowledge Management in Healthcare Sustainability: A Smart Healthy Diet Assistant in Traditional Chinese Medicine Culture

In the past 40 years, with the changes to dietary structure and the dramatic increase in the consumption of meat products in developing countries, especially in China, encouraging populations to maintain their previous healthy eating patterns will have health, environmental, and economic co-benefits. Healthy diet education plays an important role in the promotion of people’s healthy behavior. However, in the modern age, the data regarding healthy diets available on the internet is increasing rapidly and is distributed on multiple sources. It is time-consuming for users to learn about healthy diets on the internet: they need to search data on multiple platforms, choose and integrate information, and then understand what they have learned. To help people retrieve and learn healthy diet knowledge more efficiently and comprehensively, this paper designs a knowledge graph to integrate healthy diet information on the internet and provides a semantic retrieval system. In the knowledge graph, five main concepts are defined, including food material, dish, nutritional element, symptom, and crowd, as well as the relationships among them. In addition, Chinese dietary culture elements and traditional Chinese medicine (TCM) theory are also contained in the knowledge graph. The preliminary results show that by using the system, users learn healthy diet knowledge more quickly and comprehensively and they are more inclined to have balanced diets. This work could be regarded as a retrieval and education tool, which can assist healthcare and national sustainable development.

Serial album validation for promotion of infant body weight control

Objective:

to validate the content and appearance of a serial album for children aged from 7 to 10 years addressing the topic of prevention and control of body weight.

Method:

methodological study with descriptive nature. The validation process was attended by 33 specialists in educational technologies and/or in excess of infantile weight. The agreement index of 80% was the minimum considered to guarantee the validation of the material.

Results:

most of the specialists had a doctoral degree and a graduate degree in nursing. Regarding content, illustrations, layout and relevance, all items were validated and 69.7% of the experts considered the album as great. The overall agreement validation index for the educational technology was 0.88. Only the script-sheet 3 did not reach the cutoff point of the content validation index. Changes were made to the material, such as title change, inclusion of the school context and insertion of nutritionist and physical educator in the story narrated in the album.

Conclusion:

the proposed serial album was considered valid by experts regarding content and appearance, suggesting that this technology has the potential to contribute in health education by promoting healthy weight in the age group of 7 to 10 years.