The public health burden caused by overweight, obesity (OO) and type-2 diabetes (T2D) is very significant and continues to rise worldwide. OO and T2D are also leading risk factors for other comorbidities, including many non-communicable diseases and infectious diseases such as COVID-19. A global study estimated that about 9.7% of the world population (or 711.4 million) were obese and 4.0 million deaths were attributable to obesity in 2015, while T2D affected 463 million adults aged 20 to 79 years worldwide and caused 4.2 million deaths in 2019.
The fact that OO and T2D are unevenly distributed across different socioeconomic statuses (SES), demographic groups and geographies makes researchers and policymakers more puzzled when trying to design effective public health interventions. The causation of OO and T2D is complex and highly multifactorial rather than a mere energy intake (food) and expenditure (exercise) imbalance. But previous research into food and physical activity (PA) neighborhood environments has mainly attempted to associate body mass index (BMI) with proximity to stores selling fresh fruits and vegetables or fast food restaurants and takeaways, or with urbanisation, neighbourhood walkability factors and access to green spaces or public gym facilities, making largely naive, blanket assumptions, and crude, incomplete and often inconsistent (across similar studies) conclusions that are far from the spirit and requirements of 21st century precision public health.
We know that different people and population groups respond differently to the same food and PA environments, due to a myriad of unique individual and population group factors
We know that different people and population groups respond differently to the same food and PA environments, due to a myriad of unique individual and population group factors and their complex interplays with each other and with food and PA elements: genetic/epigenetic factors (as exemplified by these NY Times and Nutrition journal articles), metabolic factors, gut bacteria profiles, gut hormones profiles, health literacy profiles, dietary and lifestyle habits, screen viewing times, stress levels, sleep patterns, SES, local cuisine and food industry standards and regulations (e.g., food processing levels, food labeling practices, etc.), environmental air and noise pollution levels, activity spaces/histories (people are always ‘on the move’, so associating people with a single address/postcode is not always ideal in food and PA environment studies), etc.
Furthermore, the same food store, sit-in restaurant or fast-food outlet can often sell or serve both healthy and non-healthy options/portions and use both good and bad food processing and cooking methods for different products, so a simple binary classification into ‘good’ or ‘bad’ store/outlet should be avoided. Population dietary behaviors, including amounts consumed per individual snack/meal/day or per family are also important, as even the healthiest options can prove unhealthy when over consumed.
Moreover, appropriate physical exercise, whilst essential for good health and disease prevention, is not very effective for weight maintenance or loss (especially when solely relied upon), and cannot offset the effects of a bad diet. In fact, the “wrong” type of physical exercise might sometimes result in weight gain in certain individuals, and research has shown that some individuals avoid or ‘hate’ exercise because of their genetic makeup, even when living in close proximity to green spaces and public gyms.
As far as urbanisation is concerned, research also shows the gap of BMI between urban and rural is closing, mostly by an unprecedented increase in rural BMI around the world in recent years, especially in low- and middle-income regions, so it is not urbanisation that is to blame as such or at least alone.
The research we should be doing in the third decade of the 21st century should use a systems thinking approach, helped by recent advances in lifestyle sensing, big data and related technologies. This would help us to better understand and cater for this myriad of interconnected factors in our quest to design better targeted and more effective public health interventions for OO and T2D control and prevention.
The research we should be doing in the third decade of the 21st century should use a systems thinking approach
Geo-tagged big data from smartphones/apps, wearables and other sensors are enabling researchers to conduct innovative studies in OO and T2D – smartphones are not just useful for data collection; they can also be used to deliver some location-based targeted public health interventions and campaigns. Public health professionals can greatly benefit from well-conceived big data dashboards and related technologies in unveiling, and acting upon, the multifaceted challenges of OO and T2D in their target populations.
A recent International Journal of Health Geographics Editorial published in March 2021 marks the launch of a new Article Collection on the subject entitled ‘New horizons in geospatial lifestyle and food environment research‘. The Editorial introduces innovations in geospatial lifestyle and food environment research and practice in the context of OO and T2D, going beyond conventional research study designs and approaches in this field. It is hoped this new collection will initiate and stimulate further fruitful discussions among public health communities worldwide, and inspire many future ground-breaking studies about food and PA environments and population factors in OO and T2D.