Identifying Key Determinants of Childhood Obesity: A Narrative Review of Machine Learning Studies

The impact of hypothetical early life interventions on rapid weight gain during infancy and body mass index at 5 and 8 years in Norway: The Norwegian Mother, Father, and Child Cohort Study (MoBa)

OBJECTIVES

This study examined the impact of hypothetical interventions on (i) early life factors (prenatal and early postnatal) on rapid weight gain during infancy, and (ii) early life factors and child energy balance-related behaviours (EBRB) on children's body mass index (BMI)-for-age at 5 and 8 years.

METHODS

Data from the Norwegian Mother, Father, and Child Cohort Study included participants aged 2 (n = 48 109), 5 (n = 18 810) and 8 (n = 10 830) years. Early life intervention variables were maternal smoking during pregnancy, maternal weight before and during pregnancy, exclusive/partial breastfeeding for 6 months, and introduction of complementary food at 6 months. Child EBRB intervention variables for the 5-year outcome included screen time, fruit and vegetable intake, and sugar-sweetened soft drink intake assessed at 3 years. For the 8-year outcome, interventions included screen time, presence of a television in the child's bedroom, sleep hours and breakfast intake assessed at 5 years. The parametric g-formula was used for analysis.

RESULTS AND CONCLUSIONS

The average population-level difference in rapid weight gain during infancy at 2 years under the intervention targeting the prenatal, early postnatal factors and these factors combined with 95% confidence intervals were -0.012 (-0.017, -0.007), -0.009 (-0.012, -0.005) and -0.020 (-0.025, -0.015), respectively. The average population-level differences in children's BMI-for-age z-score for interventions targeting early life factors, child EBRB and these factors combined were -0.225 (-0.244, -0.207), 0.02 (-0.021, 0.024) and -0.223 (-0.249, -0.196), respectively among 5-year-olds and -0.265 (-0.295, -0.236), -0.020 (-0.029, -0.011) and -0.285 (-0.315, -0.256), respectively among 8-year-olds. Our results suggested joint interventions on early life factors may reduce childhood BMI-for-age z-scores.

Exploring the multifaceted factors influencing overweight and obesity: a scoping review

Introduction

Obesity and overweight problems in public health have substantial impacts which affect the health status of individuals and community well-being and healthcare service provision worldwide. This scoping review aims to identify and classify factors from social, technological, environmental, economic and political domains which influence obesity and overweight conditions. The systematic analysis of determinants in this study generates usable information to guide public health intervention design and obesity epidemic management strategies.

Methods

The study utilized the ProQuest, ISI Web of Science, PubMed, and Scopus databases, and it also included grey literature in its analysis. The research objectives focused on identifying factors that contribute to overweight or obesity issues. The researchers used framework analysis to examine the qualitative data collected from these studies.

Results

The synthesis incorporated 121 research studies which satisfied the established criteria. This comprised 98 studies from 46 different countries, 17 studies conducted at the international level, and 6 studies involving multiple countries. Eighty-two factors influencing overweight and obesity were identified as determinants and categorized into five main categories: sociocultural, economic, technological, environmental, and political. Most of the identified determinants belong to the socio-cultural category, which demonstrates their substantial impact on lifestyle and health behaviors.

Conclusion

The implementation of public health prevention and intervention programs depends on complete knowledge of all factors that affect overweight and obesity rates. This issue needs a comprehensive approach which analyzes sociocultural aspects together with economic, technological, environmental, and political factors, as well as other policy goals within defined societal challenges. Effective solutions to resolve this situation depend on multi-sectoral collaboration to tackle obesity and promote health-enhancing factors for the entire community.

FACILITY: feeding the family-the intergenerational approach to fight obesity, a cross-sectional study protocol

Introduction

Paediatric obesity has been described by the World Health Organization as one of the most serious health challenges of the 21st century. Over the past four decades, the number of children and adolescents with obesity has increased between 10 and 12-fold worldwide.

Methods

Childhood obesity is a complex and multifactorial outcome which can be attributed to factors such as socioeconomic status, ethnicity, lifestyle and eating habits. Beside personal-children-related factors, maternal (education, food knowledge, income) and environmental ones (food environment's features and accessibility) have been proven but their influences are still worth discussion. The cross-sectional study of the "FACILITY: feeding the family-the intergenerational approach to fight obesity" project aims at estimating children prevalence of overweight and obesity and assessing the impacts of lifestyle and of socio-economic-cultural and environmental factors on overweight and obesity.

Results

Due to the current importance of developing multidisciplinary mother-child centred prevention programs, FACILITY cross-sectional study will investigate maternal and child socio-cultural, economic, environmental, health and lifestyle-related risk factors for the development of obesity.

Discussion

The knowledge gained will provide the basis to develop a "primordial prevention program" to early tackle childhood obesity.

Clinical Trial Registration

ClinicalTrials.gov, identifier (NCT06179381).

Prevalence of obesity and associated sociodemographic and lifestyle factors in Ecuadorian children and adolescents

BACKGROUND

Given the increasing prevalence of obesity in young people in Ecuador, there is a need to understand the factors associated with this condition. The aim of this study was to assess the prevalence of obesity in Ecuadorian children and adolescents aged 5-17 years and identify its associated sociodemographic and lifestyle factors.

METHODS

This cross-sectional study was conducted using data from the Encuesta Nacional de Salud y Nutrición (ENSANUT-2018). The final sample consisted of 11,980 participants who provided full information on the variables of interest.

RESULTS

The prevalence of obesity was 12.7%. A lower odd of having obesity was observed for adolescents; for those with a breadwinner with an educational level in middle/high school or higher; for each additional day with 60 or more minutes of daily moderate-to-vigorous physical activity; and for those with greater daily vegetable consumption (one, two, or three or more servings). Conversely, there were greater odds of obesity in participants from families with medium, poor, and very poor wealth and those from the coast and insular region.

CONCLUSIONS

The high prevalence of obesity in Ecuadorian children and adolescents is a public health concern. Sociodemographic and lifestyle behavior differences in young people with obesity should be considered when developing specific interventions.

IMPACT

As the prevalence of obesity among children and adolescents increases in Latin America, with a particular focus on Ecuador, it becomes crucial to delve into the factors linked to this condition and identify the most successful strategies for its mitigation. The elevated prevalence of obesity among young individuals in Ecuador raises significant public health concerns. To develop targeted interventions, it is crucial to account for sociodemographic variables and lifestyle behaviors that contribute to obesity in this population.

Validation of Variables for Use in Pediatric Obesity Risk Score Development in Demographically and Racially Diverse United States Cohorts

OBJECTIVE

To evaluate the performance of childhood obesity prediction models in four independent cohorts in the United States, using previously validated variables obtained easily from medical records as measured in different clinical settings.

STUDY DESIGN

Data from four prospective cohorts, Latinx, Eating, and Diabetes; Stress in Pregnancy Study; Project Viva; and Center for the Health Assessment of Mothers and Children of Salinas were used to test childhood obesity risk models and predict childhood obesity by ages 4 through 6, using five clinical variables (maternal age, maternal prepregnancy body mass index, birth weight Z-score, weight-for-age Z-score change, and breastfeeding), derived from a previously validated risk model and as measured in each cohort's clinical setting. Multivariable logistic regression was performed within each cohort, and performance of each model was assessed based on discrimination and predictive accuracy.

RESULTS

The risk models performed well across all four cohorts, achieving excellent discrimination. The area under the receiver operator curve was 0.79 for Center for the Health Assessment of Mothers and Children of Salinas and Project Viva, 0.83 for Stress in Pregnancy Study, and 0.86 for Latinx, Eating, and Diabetes. At a 50th percentile threshold, the sensitivity of the models ranged from 12% to 53%, and specificity was ≥ 90%. The negative predictive values were ≥ 80% for all cohorts, and the positive predictive values ranged from 62% to 86%.

CONCLUSION

All four risk models performed well in each independent and demographically diverse cohort, demonstrating the utility of these five variables for identifying children at high risk for developing early childhood obesity in the United States.

Prediction of adolescent weight status by machine learning: a population-based study

BACKGROUND

Adolescent weight problems have become a growing public health concern, making early prediction of non-normal weight status crucial for effective prevention. However, few temporal prediction tools for adolescent four weight status have been developed. This study aimed to predict the short- and long-term weight status of Hong Kong adolescents and assess the importance of predictors.

METHODS

A population-based retrospective cohort study of adolescents was conducted using data from a territory-wide voluntary annual health assessment service provided by the Department of Health in Hong Kong. Using diet habits, physical activity, psychological well-being, and demographics, we generated six prediction models for successive weight status (normal, overweight, obese and underweight) using multiclass Decision Tree, Random Forest, k-Nearest Neighbor, eXtreme gradient boosting, support vector machine, logistic regression. Model performance was evaluated by multiple standard classifier metrics and the overall accuracy. Predictors' importance was assessed using Shapley values.

RESULTS

442,898 Primary 4 (P4, Grade 4 in the US) and 344,186 in Primary 6 (P6, Grade 6 in the US) students, with followed up until their Secondary 6 (Grade 12 in the US) during the academic years 1995/96 to 2014/15 were included. The XG Boosts model consistently outperformed all other model in predicting the long-term weight status at S6 from P4 or P6. It achieved an overall accuracy of 0.72 or 0.74, a micro-averaging AUC of 0.92 or 0.93, and a macro-averaging AUC of 0.83 or 0.86, respectively. XG Boost also demonstrated accurate predictions for each predicted weight status, surpassing the AUC values obtained by other models. Weight, height, sex, age, frequency and hours of aerobic exercise were consistently the most important predictors for both cohorts.

CONCLUSIONS

The machine learning approaches accurately predict adolescent weight status in both short- and long-term. The developed multiclass model that utilizing easy-assessed variables enables accurate long-term prediction on weight status, which can be used by adolescents and parents for self-prediction when applied in health care system. The interpretable models may help to provide the early and individualized interventions suggestions for adolescents with weight problems particularly.

Physical activity on prescription for children with obesity: a focus group study exploring experiences in paediatric healthcare

Background

Insufficient physical activity is a growing public health concern and is closely linked to obesity in both adults and children. Swedish physical activity on prescription (PAP) is effective in increasing physical activity levels in adults, but knowledge about how PAP is used in paediatric healthcare is lacking. Therefore, this study aimed to explore experiences of working with PAP for children with obesity amongst paediatric staff and managers.

Methods

Seven focus group discussions with 26 participants from paediatric outpatient clinics in western Sweden were conducted. Data were analysed both inductively and deductively, framed by the Normalization Process Theory's four core constructs: coherence, cognitive participation, collective action, and reflexive monitoring.

Results

The PAP work for children with obesity was experienced to be about helping children to become physically active, and less about losing weight. Identified barriers for using PAP were the non-uniform nature of the work and a perceived lack of guidelines. Collaboration with physiotherapists and physical activity organisers outside the organisation was identified as an important facilitator. An important contextual factor for implementing PAP is the collaboration between paediatric clinics and physical activity organisers. In the transition between these stakeholders, maintaining a family-centred approach when working with PAP was experienced as challenging.

Conclusions

PAP is a well-known intervention that is inconsistently used for children with obesity. The intervention should include a family-centred approach for this patient group. It also needs to align better with existing collaborations with other healthcare units as well as with new forms of collaboration with physical activity organisers in the community.

A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance

BACKGROUND AND AIMS

Obesity is a chronic disease which can cause severe metabolic disorders. Machine learning (ML) techniques, especially deep learning (DL), have proven to be useful in obesity research. However, there is a dearth of systematic reviews of DL applications in obesity. This article aims to summarize the current trend of DL usage in obesity research.

METHODS

An extensive literature review was carried out across multiple databases, including PubMed, Embase, Web of Science, Scopus, and Medline, to collate relevant studies published from January 2018 to September 2023. The focus was on research detailing the application of DL in the context of obesity. We have distilled critical insights pertaining to the utilized learning models, encompassing aspects of their development, principal results, and foundational methodologies.

RESULTS

Our analysis culminated in the synthesis of new knowledge regarding the application of DL in the context of obesity. Finally, 40 research articles were included. The final collection of these research can be divided into three categories: obesity prediction (n = 16); obesity management (n = 13); and body fat estimation (n = 11).

CONCLUSIONS

This is the first review to examine DL applications in obesity. It reveals DL's superiority in obesity prediction over traditional ML methods, showing promise for multi-omics research. DL also innovates in obesity management through diet, fitness, and environmental analyses. Additionally, DL improves body fat estimation, offering affordable and precise monitoring tools. The study is registered with PROSPERO (ID: CRD42023475159).

Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort

BACKGROUND

The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model may suffer from overfitting or poor interpretability.

METHODS

This study employed nine commonly used machine learning methods to construct overweight risk models. The general community are the target of this study, and a total of 10,905 Chinese subjects from Ningde City in Fujian province, southeast China, participated. The best model was selected through appropriate verification and validation and was suitably explained.

RESULTS

The overweight risk models employing machine learning exhibited good performance. It was concluded that CatBoost, which is used in the construction of clinical risk models, may surpass previous machine learning methods. The visual display of the Shapley additive explanation value for the machine model variables accurately represented the influence of each variable in the model.

CONCLUSIONS

The construction of an overweight risk model using machine learning may currently be the best approach. Moreover, CatBoost may be the best machine learning method. Furthermore, combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

Predicting risk of obesity in overweight adults using interpretable machine learning algorithms

Objective

To screen for predictive obesity factors in overweight populations using an optimal and interpretable machine learning algorithm.

Methods

This cross-sectional study was conducted between June 2011 and January 2012. The participants were randomly selected using a simple random sampling technique. Seven commonly used machine learning methods were employed to construct obesity risk prediction models. A total of 5,236 Chinese participants from Ningde City, Fujian Province, Southeast China, participated in this study. The best model was selected through appropriate verification and validation and suitably explained. Subsequently, a minimal set of significant predictors was identified. The Shapley additive explanation force plot was used to illustrate the model at the individual level.

Results

Machine learning models for predicting obesity have demonstrated strong performance, with CatBoost emerging as the most effective in both model validity and net clinical benefit. Specifically, the CatBoost algorithm yielded the highest scores, registering 0.91 in the training set and an impressive 0.83 in the test set. This was further corroborated by the area under the curve (AUC) metrics, where CatBoost achieved 0.95 for the training set and 0.87 for the test set. In a rigorous five-fold cross-validation, the AUC for the CatBoost model ranged between 0.84 and 0.91, with an average AUC of ROC at 0.87 ± 0.022. Key predictors identified within these models included waist circumference, hip circumference, female gender, and systolic blood pressure.

Conclusion

CatBoost may be the best machine learning method for prediction. Combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

Parental determinants associated with early growth after the first year of life by race and ethnicity

Objective

To investigate maternal and parental factors associated with changes in children's body mass index percentile (BMI-P) from 12 to 24 months.

Methods

Data from a prospective cohort of racially and ethnically diverse mothers, fathers, and children (n = 245) were used. Changes in BMI-P from 12 to 24 months of age were examined using height and weight measurements collected at both times. Separate longitudinal mixed-effects models with maximum likelihood were introduced to examine the determinants introduced by mothers and determinants from both parents among all children, and by race and ethnicity.

Results

Models that examine maternal and parental factors showed that children's overall BMI-P decreased from 12 to 24 months [β = -4.85, 95% confidence interval (CI), -7.47 to -2.23]. Stratified tests showed that White children whose parents graduated high school or completed a 4-year college degree or higher had greater decreases in BMI-P than White children born to parents with less than high school education (β = -60.39, 95% CI, -115.05 to -5.72; β = -61.49, 95% CI, -122.44 to -0.53). Among Hispanic/Latinx children, mean BMI-P significantly decreased from 12 to 24 months (β = -7.12, 95% CI, -11.59 to -2.64). Mother's older age (β = 1.83, 95% CI, 0.29-3.36) and child female sex (β = 11.21, 95% CI, 1.61-20.82) were associated with gains in children's BMI-P, while father's older age was associated with decreases (β = -1.19, 95% CI, -2.30 to -0.08).

Conclusions

Parental determinants associated with children's early growth varied by children's sex and racial and ethnic background. Results highlight the importance of understanding racial and ethnicity-specific obesity risks and including fathers in research.

Predicting body mass index in early childhood using data from the first 1000 days

Few existing efforts to predict childhood obesity have included risk factors across the prenatal and early infancy periods, despite evidence that the first 1000 days is critical for obesity prevention. In this study, we employed machine learning techniques to understand the influence of factors in the first 1000 days on body mass index (BMI) values during childhood. We used LASSO regression to identify 13 features in addition to historical weight, height, and BMI that were relevant to childhood obesity. We then developed prediction models based on support vector regression with fivefold cross validation, estimating BMI for three time periods: 30-36 (N = 4204), 36-42 (N = 4130), and 42-48 (N = 2880) months. Our models were developed using 80% of the patients from each period. When tested on the remaining 20% of the patients, the models predicted children's BMI with high accuracy (mean average error [standard deviation] = 0.96[0.02] at 30-36 months, 0.98 [0.03] at 36-42 months, and 1.00 [0.02] at 42-48 months) and can be used to support clinical and public health efforts focused on obesity prevention in early life.

Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight

Obesity and overweight has increased in the last year and has become a pandemic disease, the result of sedentary lifestyles and unhealthy diets rich in sugars, refined starches, fats and calories. Machine learning (ML) has proven to be very useful in the scientific community, especially in the health sector. With the aim of providing useful tools to help nutritionists and dieticians, research focused on the development of ML and Deep Learning (DL) algorithms and models is searched in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol has been used, a very common technique applied to carry out revisions. In our proposal, 17 articles have been filtered in which ML and DL are applied in the prediction of diseases, in the delineation of treatment strategies, in the improvement of personalized nutrition and more. Despite expecting better results with the use of DL, according to the selected investigations, the traditional methods are still the most used and the yields in both cases fluctuate around positive values, conditioned by the databases (transformed in each case) to a greater extent than by the artificial intelligence paradigm used. Conclusions: An important compilation is provided for the literature in this area. ML models are time-consuming to clean data, but (like DL) they allow automatic modeling of large volumes of data which makes them superior to traditional statistics.

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.

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

Predicting risk of overweight or obesity in Chinese preschool-aged children using artificial intelligence techniques

OBJECTIVES

We adopted the machine-learning algorithms and deep-learning sequential model to determine and optimize most important factors for overweight and obesity in Chinese preschool-aged children.

METHODS

This is a cross-sectional survey conducted in 2020 at Beijing and Tangshan. Using a stratified cluster random sampling strategy, children aged 3-6 years were enrolled. Data were analyzed using the PyCharm and Python.

RESULTS

A total of 9478 children were eligible for inclusion, including 1250 children with overweight or obesity. All children were randomly divided into the training group and testing group at a 6:4 ratio. After comparison, support vector machine (SVM) outperformed the other algorithms (accuracy: 0.9457), followed by gradient boosting machine (GBM) (accuracy: 0.9454). As reflected by other 4 performance indexes, GBM had the highest F1 score (0.7748), followed by SVM with F1 score at 0.7731. After importance ranking, the top 5 factors seemed sufficient to obtain descent performance under GBM algorithm, including age, eating speed, number of relatives with obesity, sweet drinking, and paternal education. The performance of the top 5 factors was reinforced by the deep-learning sequential model.

CONCLUSIONS

We have identified 5 important factors that can be fed to GBM algorithm to better differentiate children with overweight or obesity from the general children, with decent prediction performance.

Implementation of physical activity on prescription for children with obesity in paediatric health care (IMPA): protocol for a feasibility and evaluation study using quantitative and qualitative methods

BACKGROUND

Physical inactivity is a main cause of childhood obesity which tracks into adulthood obesity, making it important to address early in life. Physical activity on prescription (PAP) is an evidence-based intervention that has shown good effect on physical activity levels in adults, but has not been evaluated in children with obesity. This project aims to evaluate the prerequisites, determinants, and feasibility of implementing PAP adapted to children with obesity and to explore children's, parents', and healthcare providers' experiences of PAP.

METHODS

In the first phase of the project, healthcare providers and managers from 26 paediatric clinics in Region Västra Götaland, Sweden, will be invited to participate in a web-based survey and a subset of this sample for a focus group study. Findings from these two data collections will form the basis for adaptation of PAP to the target group and context. In a second phase, this adapted PAP intervention will be evaluated in a clinical study in a sample of approximately 60 children with obesity (ISO-BMI > 30) between 6 and 12 years of age and one of their parents/legal guardians. Implementation process and clinical outcomes will be assessed pre- and post-intervention and at 8 and 12 months' follow-up. Implementation outcomes are the four core constructs of the Normalization Process Theory; coherence, cognitive participation, collective action, and reflexive monitoring; and appropriateness, acceptability, and feasibility of the PAP intervention. Additional implementation process outcomes are recruitment and attrition rates, intervention fidelity, dose, and adherence. Clinical outcomes are physical activity pattern, BMI, metabolic risk factors, health-related quality of life, sleep, and self-efficacy and motivation for physical activity. Lastly, we will explore the perspectives of children and parents in semi-structured interviews. Design and analysis of the included studies are guided by the Normalization Process Theory.

DISCUSSION

This project will provide new knowledge regarding the feasibility of PAP for children with obesity and about whether and how an evidence-based intervention can be fitted and adapted to new contexts and populations. The results may inform a larger scale trial and future implementation and may enhance the role of PAP in the management of obesity in paediatric health care in Sweden.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04847271 , registered 14 April 2021.

Prerequisites for implementing physical activity on prescription for children with obesity in paediatric health care: A cross-sectional survey

Background

Physical inactivity is a main driver of childhood obesity that tracks into adulthood, making it crucial to address early in life. Swedish physical activity on prescription (PAP) is an effective intervention for increasing physical activity levels in adults and is being implemented in primary care in Sweden. Before implementing PAP for children, both intervention effectiveness and implementation prerequisites need to be examined. Framed by the Normalization Process Theory (NPT) domains, this study aimed to investigate perceptions of PAP amongst paediatric staff and managers working with children with obesity, as well as acceptability, appropriateness, feasibility, and barriers and facilitators for implementing PAP in paediatric health care.

Methods

Staff and managers in 28 paediatric outpatient clinics in western Sweden were surveyed using validated implementation instruments and open-ended questions. Data were analysed using Mann-Whitney U tests and Kruskal-Wallis tests. Qualitative data were categorised into NPT domains.

Results

The survey response rate was 54% (125/229). Most respondents (82%) reported PAP to be familiar and many (56%) perceived it as a normal part of work; nurses and physiotherapists to a greater extent (p < 0.001). This was anticipated to increase in the future (82%), especially amongst those with the longest work experience (p = 0.012). Respondents reported seeing the potential value in their work with PAP (77%), being open to working in new ways to use PAP (94%), and having confidence in their colleagues' ability to use PAP (77%). Barriers and facilitators were found in all the NPT domains, mainly collective action and reflexive monitoring, where, for example, inadequacies of education, resources, and research on PAP for children were reported as barriers. Most respondents agreed that PAP was acceptable, appropriate, and feasible (71% to 88%).

Conclusions

PAP is familiar and perceived as an acceptable, appropriate, and feasible intervention, and by many viewed as a normal part of clinical routines in paediatric outpatient clinics in western Sweden, especially by physiotherapists and nurses. Barriers and faciliators are mainly related to collective action and reflexive monitoring. The wide acceptance demonstrates receptiveness to PAP as an intervention to promote an active lifestyle for children with obesity.