School-Based Obesity Intervention Associated with Three Year Decrease in Student Weight Status in a Low-Income School District

Associations of Longitudinal BMI-Percentile Classification Patterns in Early Childhood with Neighborhood-Level Social Determinants of Health

Background: Understanding social determinants of health (SDOH) that may be risk factors for childhood obesity is important to developing targeted interventions to prevent obesity. Prior studies have examined these risk factors, mostly examining obesity as a static outcome variable. Methods: We extracted electronic health record data from 2012 to 2019 for a children's health system that includes two hospitals and wide network of outpatient clinics spanning five East Coast states in the United States. Using data-driven and algorithmic clustering, we have identified distinct BMI-percentile classification groups in children from 0 to 7 years of age. We used two separate algorithmic clustering methods to confirm the robustness of the identified clusters. We used multinomial logistic regression to examine the associations between clusters and 27 neighborhood SDOHs and compared positive and negative SDOH characteristics separately. Results: From the cohort of 36,910 children, five BMI-percentile classification groups emerged: always having obesity (n = 429; 1.16%), overweight most of the time (n = 15,006; 40.65%), increasing BMI percentile (n = 9,060; 24.54%), decreasing BMI percentile (n = 5,058; 13.70%), and always normal weight (n = 7,357; 19.89%). Compared to children in the decreasing BMI percentile and always normal weight groups, children in the other three groups were more likely to live in neighborhoods with higher poverty, unemployment, crowded households, single-parent households, and lower preschool enrollment. Conclusions: Neighborhood-level SDOH factors have significant associations with children's BMI-percentile classification and changes in classification. This highlights the need to develop tailored obesity interventions for different groups to address the barriers faced by communities that can impact the weight and health of children living within them.

Associations of longitudinal BMI percentile classification patterns in early childhood with neighborhood-level social determinants of health

Background

Understanding social determinants of health (SDOH) that may be risk factors for childhood obesity is important to developing targeted interventions to prevent obesity. Prior studies have examined these risk factors, mostly examining obesity as a static outcome variable.

Methods

We extracted EHR data from 2012-2019 for a children's health system that includes 2 hospitals and wide network of outpatient clinics spanning 5 East Coast states in the US. Using data-driven and algorithmic clustering, we have identified distinct BMI-percentile classification groups in children from 0 to 7 years of age. We used two separate algorithmic clustering methods to confirm the robustness of the identified clusters. We used multinomial logistic regression to examine the associations between clusters and 27 neighborhood SDOHs and compared positive and negative SDOH characteristics separately.

Results

From the cohort of 36,910 children, five BMI-percentile classification groups emerged: always having obesity (n=429; 1.16%), overweight most of the time (n=15,006; 40.65%), increasing BMI-percentile (n=9,060; 24.54%), decreasing BMI-percentile (n=5,058; 13.70%), and always normal weight (n=7,357; 19.89%). Compared to children in the decreasing BMI-percentile and always normal weight groups, children in the other three groups were more likely to live in neighborhoods with higher poverty, unemployment, crowded households, single-parent households, and lower preschool enrollment.

Conclusions

Neighborhood-level SDOH factors have significant associations with children's BMI-percentile classification and changes in classification. This highlights the need to develop tailored obesity interventions for different groups to address the barriers faced by communities that can impact the weight and health of children living within them.

Impact Statement

This study demonstrates the association between longitudinal BMI-percentile patterns and SDOH in early childhood. Five distinct clusters with different BMI-percentile trajectories are found and a strong association between these clusters and SDOH is observed. Our findings highlight the importance of targeted prevention and treatment interventions based on children's SDOH.

Physiotherapy and related management for childhood obesity: A systematic scoping review

INTRODUCTION

Despite targeted efforts globally to address childhood overweight/obesity, it remains poorly understood and challenging to manage. Physiotherapists have the potential to manage children with obesity as they are experts in movement and physical activity. However, their role remains unclear due to a lack of physiotherapy-specific guidelines. This scoping review aims to explore existing literature, critically appraising and synthesising findings to guide physiotherapists in the evidence-based management of childhood overweight/obesity.

METHOD

A scoping review was conducted, including literature up to May 2020. A review protocol exists on Open Science Framework at https://osf.io/fap8g/. Four databases were accessed including PubMed, Embase, CINAHL, Medline via OVID, with grey literature searched through google via "file:pdf". A descriptive synthesis was undertaken to explore the impact of existing interventions and their efficacy.

RESULTS

From the initial capture of 1871 articles, 263 intervention-based articles were included. Interventions included qualitative focused physical activity, quantitative focused physical activity and multicomponent interventions. Various outcome measures were utilised including health-, performance- and behaviour-related outcomes. The general trend for physiotherapy involvement with children who are obese appears to favour: 1) multicomponent interventions, implementing more than one component with environmental modification and parental involvement and 2) quantitative physical activity interventions, focusing on the quantity of bodily movement. These approaches most consistently demonstrated desirable changes across behavioural and health-related outcome measures for multicomponent and quantitative physical activity interventions respectively.

CONCLUSION

When managing children with obesity, physiotherapists should consider multicomponent approaches and increasing the quantity of physical activity, given consistent improvements in various obesity-related outcomes. Such approaches are well suited to the scope of physiotherapists and their expertise in physical activity prescription for the management of childhood obesity. Future research should examine the effect of motor skill interventions and consider the role of environmental modification/parental involvement as factors contributing to intervention success.

Improving the school food environment for the prevention of childhood obesity: What works and what doesn't

The food environment has a significant influence on dietary choices, and interventions designed to modify the food environment could contribute to the prevention of childhood obesity. Many interventions have been implemented at the school level, but effectiveness in addressing childhood obesity remains unclear. We undertook a systematic review, a meta-analysis, and meta-regression analyses to assess the effectiveness of interventions on the food environment within and around schools to improve dietary intake and prevent childhood obesity. Estimates were pooled in a random-effects meta-analysis with stratification by anthropometric or dietary intake outcome. Risk of bias was formally assessed. One hundred papers were included. Interventions had a significant and meaningful effect on adiposity (body mass index [BMI] z score, standard mean difference: -0.12, 95% confidence interval: 0.15, 0.10) and fruit consumption (portions per day, standard mean difference: +0.19, 95% confidence interval: 0.16, 0.22) but not on vegetable intake. Risk of bias assessment indicated that n = 43 (81%) of non-randomized controlled studies presented a high risk of bias in the study design by not accounting for a control. Attrition bias (n = 34, 79%) and low protection of potential contamination (n = 41, 95%) presented the highest risk of bias for randomized controlled trials. Changes in the school food environment could improve children's dietary behavior and BMI, but policy actions are needed to improve surrounding school food environments to sustain healthy dietary intake and BMI.

Healthier Choices in School Cafeterias: A Systematic Review of Cafeteria Interventions

OBJECTIVE

To describe school cafeteria interventions in terms of a behavioral economics scheme and to assess which system is more likely to be effective in improving food selection or consumption.

STUDY DESIGN

With this systematic review, we categorize cafeteria interventions using the behavioral economics theory of Kahneman into system 1 (fast and intuitive thinking) and system 2 (slow and cognitively demanding) or mixed (having elements of system 1 and system 2). Pertinent studies were identified from review of the literature of interventions performed in school and cafeteria settings in children grades K-12 within the past 5 years (2012-2017) at time of search.

RESULTS

In all, 48 of 978 studies met inclusion criteria. By defining success as a 30% improvement in a desired outcome or statistically significant reduction in body mass index, 89% of system 1, 67% of mixed (had both system 1 and 2 elements), and only 33% of system 2 interventions were successful.

CONCLUSIONS

This review found successful system 1 type school cafeteria interventions to be more common than system 2 type interventions and system 2 type interventions are less effective than system 1.

Two-Year BMI Outcomes From a School-Based Intervention for Nutrition and Exercise: A Randomized Trial

OBJECTIVES

This study examined the long-term effects on BMI of a randomized controlled trial of Students for Nutrition and Exercise, a 5-week, middle school-based obesity prevention intervention combining school-wide environmental changes, encouragement to eat healthy school cafeteria foods, and peer-led education and marketing.

METHODS

We randomly selected schools from the Los Angeles Unified School District and assigned 5 to the intervention group and 5 to a wait-list control group. Of the 4022 seventh-graders across schools, a total of 1368 students had their height and weight assessed at baseline and 2 years' postintervention.

RESULTS

A multivariable linear regression was used to predict BMI percentile at ninth grade by using BMI percentile at seventh grade, school indicators, and sociodemographic characteristics (child gender, age, Latino race/ethnicity, US-born status, and National School Lunch Program eligibility [as a proxy for low-income status]). Although the Students for Nutrition and Exercise intervention did not exhibit significant effects on BMI percentile overall, intervention students who were classified as obese at baseline (in seventh grade) showed significant reductions in BMI percentile in ninth grade (b = -2.33 percentiles; SE, 0.83; P = .005) compared with control students. This outcome translated into ∼9 pounds (∼4.1 kg) lower expected body weight after 2 years for an obese student in the intervention school at the mean height and age of the sample at baseline.

CONCLUSIONS

Multilevel school-based interventions can have long-term effects on BMI among students who are obese. Future research should examine the mechanisms by which school-based obesity interventions can affect BMI over time.

Regression to the Mean: A Commonly Overlooked and Misunderstood Factor Leading to Unjustified Conclusions in Pediatric Obesity Research

OBJECTIVE

In this paper we discuss what regression to the mean (RTM) is, the magnitude of RTM in realistic situations, interpretation of RTM, and recommendations for how to address RTM in study design.

METHODS

Public health research faces many challenges in conducting gold standard randomized, controlled trials (RCT). Although there are many threats to validity in uncontrolled trials, RTM is often overlooked or not adequately considered. RTM is a statistical phenomenon that occurs with any pair of variables that have a correlation not equal to |1.0|. With RTM, subjects' average values on an outcome variable (e.g., BMI) change in a systematic direction over time despite there being no treatment effect. Without a proper control group, changes thought to be associated with an intervention may be due entirely to RTM. Investigators may draw erroneous conclusions based on results showing greater declines in a variable among participants with higher baseline of that variable compared to those with lower baseline of that variable, and label this evidence for differential treatment efficacy.

CONCLUSIONS

Ignoring RTM can lead to unsubstantiated conclusions about the effects of treatments. These conclusions can lead to the waste of time, money, and other resources, which distract from finding appropriate interventions. When a true RCT design is not feasible, reasonable design alternatives involving nonrandomized control groups should be implemented.

Common scientific and statistical errors in obesity research

This review identifies 10 common errors and problems in the statistical analysis, design, interpretation, and reporting of obesity research and discuss how they can be avoided. The 10 topics are: 1) misinterpretation of statistical significance, 2) inappropriate testing against baseline values, 3) excessive and undisclosed multiple testing and "P-value hacking," 4) mishandling of clustering in cluster randomized trials, 5) misconceptions about nonparametric tests, 6) mishandling of missing data, 7) miscalculation of effect sizes, 8) ignoring regression to the mean, 9) ignoring confirmation bias, and 10) insufficient statistical reporting. It is hoped that discussion of these errors can improve the quality of obesity research by helping researchers to implement proper statistical practice and to know when to seek the help of a statistician.