New research reveals that major cities see lower obesity and impulsiveness rates, discovering how healthy life, education and mental health is shaping healthier urban populations.
Study: Investigate the link between impulsiveness and obesity through urban levels. Image credit: Markus Mainka / Shutterstock
In a recent article published in the journal PLOS complex systemsThe researchers explored the link between impulsiveness and obesity in 915 cities in the United States. Their results indicate that obesity and impulsiveness, measured by the prevalence of hyperactivity disorder with attention deficit (ADHD), were less frequent in large cities. ADHD seemed to influence the levels of obesity, the lifestyle acting as a moderating factor.
Background
Obesity is an increasing global health crisis, especially in the United States, where its prevalence should increase considerably by 2030. While various factors such as behavior, genetics and the environment contribute to obesity, impulsiveness, defined as acting without provident, has become a key psychological factor.
Although impulsiveness can be adaptive in certain contexts, excessive impulsiveness is linked to poor food choices and weight gain. ADHD, a clinical form of impulsivity, has shown associations consistent with obesity in epidemiological, genetic and pharmacological studies through various populations, including Dutch and Korean children.
However, most research neglect the way in which environmental characteristics, in particular those of urban circles, can influence this link. Obesogenic environments vary from one city to another, including limited access to physical activity, healthy foods and social support.
Urban Science, which studies how the city is on the scale of the size of the population, offers tools to explore this complexity. Urban laws reveal how health results, such as obesity and mental disorders, change with the size of the city. The study hypothesized that the lower prevalence of ADHD in major cities could result from increased genetic diversity or better access in mental health, although these explanations remain speculative.
About the study
In this study, researchers applied a new method of causal inference to understand how ADHD and urban characteristics influence obesity in American cities. The study also analyzes data at the individual level of more than 19,000 children to ensure robustness.
The study used data sets at the individual level and at the city level to explore how factors such as physical activity, obesity, ADHD, food insecurity, education and access to mental health care are linked to the size of the urban population and to the other.
City data included physical inactivity, adult obesity, access to mental health services, college education and food insecurity. These data were grouped into 915 micropolitan and metropolitan areas.
Individual data included health and demographic data on a random selected child (10 to 17 years) per household. The variables included the category of body mass index (BMI), physical activity (days / week), the severity of ADHD, food insufficiency of households, the use of mental health services and the level of education of the guards. The final set of data included more than 19,000 children after cleaning.
The laws on urban scaling have been modeled using the regression of ordinary least squares (OLS) on data transformed into logarithm, with coherent standard errors by heteroscedasticity. The GINI index (adapted to negative values) has measured intra-state inequality in health and social indicators.
The causal relationships between the variables were deducted using the Peter-Clark algorithm, which identifies associations suggesting causal links by testing conditional independence. Although useful, this method does not suppose hidden variables or feedback loops, which cannot always be maintained. The study avoided combining individual and city information in causal models due to differences in data type, age groups and missing location information.
Results
At the city level (915 American cities), the analysis of the urban scale revealed that ADHD in children, adult obesity and physical inactivity have all extended by the size of the population, indicating a lower prevalence per capita in large cities.
On the other hand, access to mental health services and its college studies have evolved superlinearly, being more frequent in large cities, while food insecurity has evolved linearly. In particular, small cities presented probabilities of physical inactivity up to 30% higher compared to the largest.
Using metropolitan indicators adjusted on the scale, the team applied a causal discovery algorithm to discover key associations: physical inactivity has led to increased obesity and the prevalence of ADHD was associated with higher physical inactivity and food insecurity.
The availability of mental health providers has reduced physical inactivity, while college education was associated with better access in mental health and less food insecurity. These links are correlational but align with known organic paths, such as brain circuits regulating the control of impulses and genes linked to dopamine.
At the individual level (data of more than 19,000 children), the models reflected those found in cities. The severity of ADHD was correlated with less physical activity and a greater BMI, suggesting both direct (for example, poor food) and indirect (for example, reduction in exercise) between ADHD and obesity.
In addition, the researchers noted the protective nature of adult education in households, linked to better access to mental health care, lower food insufficiency and healthier BMI in children, although perhaps also falling time of physical activity.
Conclusions
This study shows that global well-being increases with city size: obesity, food insecurity, ADHD and inactivity decrease in large cities, while college education and access to mental health care increases.
Causal analysis suggests that ADHD leads to obesity by reduced physical activity. Collegial education and food security indirectly reduce obesity by encouraging more physical activity.
Individual information supports these models at the city level, highlighting ADHD and control of impulses in obesity, with potential biological links involving brain function (for example, the anterior cingular cortex) and genetic factors such as dopaminergic signaling.
The strength of the study lies in the combination of city data on a large scale with information at the individual level. The limitations include hypotheses in causal algorithm, potential hidden variables, incompatible age ranges and the inability to link individuals to specific cities. The emphasis on American data also limits generalization, although international studies suggest broader relevance.
However, the results suggest that targeted policies promoting physical activity and education can help reduce obesity, especially in smaller or bad communities.