The rapid increase in obesity rates across the world over the past few decades poses a big problem. This increase has not only proved expensive, costing nearly $275 billion annually, but there is also a social cost. The stigma associated with being overweight makes it harder for obese people to be healthy—not to mention stresses them out.
A lot of time and money has been spent over the past 30 years trying to create a better understanding of the factors behind this increase.
Take research into the relationship between where we live, and how much we weigh. Among the findings: Neighborhoods that promote walking have fewer obese residents, and having more fast-food restaurants nearby is associated with a two-plus pound weight gain for an average-sized man.
But don’t start mapping out the healthiest places to live yet—there is also a lot of research contradicting these results. One paper found that having at least one fast food restaurant near your home was associated with a 50% lower likelihood of obesity. To complicate things further, various other papers have found no relationship whatsoever between neighborhoods and obesity.
Why is this research so confusing? In part, because much of it relies on people knowing their exact height and weight—which they often don’t. This has become a big problem for population-level studies of obesity.
While most people have a rough idea of their dimensions, few seem to know them exactly. Most research finds that people are off by small amounts—they overestimate their height by less than an inch, and underestimate their weight by less than 4 pounds.
This may seem like small potatoes, but unfortunately it ends up being a big problem for obesity research, which requires precise measurements. When a lot of people get their height and weight a little wrong, it can add up to inaccurate, if not downright confusing, results.
One study found that when people reported their own height and weight, obesity prevalence was underestimated by 9%, while another found that 18% of women and 22% of men were put in the wrong BMI category when they self-reported.
It turns out that self-reported data make it really hard to discern how neighborhoods affect obesity.
Let’s say you are doing a study to figure out if having a park in your neighborhood reduces obesity. Given this self-report problem, we cannot accurately compare obesity rates in a neighborhood near a park to rates in a neighborhood that isn’t near a park. There is no way to know how neighborhoods impact obesity if you cannot evaluate obesity rates in different neighborhoods.
Why can’t we just fix this problem by, for example, adjusting self-reported data with a corrective algorithm? If we knew exactly how wrong people are about their height and weight we probably could. Unfortunately, the problem is not that simple. Research shows that the degree to which people are off about their height and weight varies across demographics, including race, gender, and especially age.
And while we do have some ideas about the extent of these demographic discrepancies, recent research shows that people in different states get their self-report wrong by different amounts.
The bigger problem here is that, if we cannot figure out what neighborhoods (and by extension, which built environment features) are related to obesity, we will never know what we should change to improve people’s health. For example, there is some evidence from planned communities in Austin, TX, that changes in the built environment, including better sidewalks and increased land-use mix, can help people lose weight. But without knowing which specific built environment factors are causing this weight loss, it’s really hard to justify rearranging the cities we live in.
It is important to note, though, that this is not a problem for all types of obesity research. Research that tries to change the way people behave or tests surgical or medicinal solutions to obesity, typically measure people’s height and weight instead of asking them for it. It’s worth noting, however, that collecting these types of direct measurements for a representative sample of the whole population can be prohibitively expensive.
Though researchers have found highly suggestive relationships between the built environment and obesity, if we really want to know how neighborhoods affect body weight, we have to overcome the costs and start collecting height and weight data directly.