based on CC0 public domain image from https://pixy.org/82620/
The public health burden caused by overweight, obesity (OO) and type 2 diabetes (T2DM) is very significant and continues to increase worldwide. OO and T2DM are also major risk factors for other comorbidities, including many noncommunicable diseases and infectious diseases such as COVID-19[female[feminine. A global to study estimated that about 9.7% of the world’s 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 at 79 in the world and 4.2 million deaths in 2019.
The fact that OO and T2DM are unevenly distributed across different socioeconomic status (SES), demographic groups and geographies makes researchers and policy makers more perplexed when trying to design effective public health interventions. The causation of OO and T2DM is complex and highly multifactorial rather than a simple imbalance of energy intake (food) and expenditure (exercise). But previous research on neighborhood environments related to diet and physical activity (PA) has primarily attempted to associate body mass index (BMI) with proximity to stores selling fresh fruit and vegetables or from fast food restaurants and take out, or to urbanization, to walkers and green spaces or public gyms, making largely naive and general assumptions, and crude, incomplete and often inconsistent conclusions ( in similar studies) which fall far short of the spirit and demands of 21st century precision public health.
We know that different people and population groups respond to the same food and PA environments differently, due to a myriad of unique individual and population group factors.
We know that different people and different population groups respond to the same food and PA environments differently, due to a myriad of unique individual and population group factors and their complex interactions with each other and with foods and PA elements: genetic / epigenetic factors (exemplified by these New York Times and Nutrition Newspaper articles), metabolic factors, intestinal bacteria profiles, gut hormone profiles, health literacy profiles, eating and lifestyle habits, screen viewing time, 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, move them ”, so associating people with a single address / zip code is not always ideal in PA food and environmental studies) , etc.
Additionally, the same food store, on-site restaurant, or fast food establishment can often sell or serve healthy and unhealthy options / portions and use both good and bad food processing methods. and cooking for different products, so a simple binary classification. in a “good” or “bad” store / point of sale should be avoided. The eating behaviors of the population, including the amounts consumed per snack / meal / day or per family, are also important, as even the healthiest options can turn out to be unhealthy when eaten too much.
In addition, proper physical exercise, while essential for good health and disease prevention, is not very effective for weight maintenance or loss (especially when used only) and cannot compensate. the effects of a bad nutrition. In fact, the “Wrong” type of physical exercise can sometimes cause some people to gain weight, and research has shown that some people avoid or “ hate ” exercise due to their genetic makeup, even when living near green spaces and public gymnasiums.
With regard to urbanization, research also shows that the difference in BMI between urban and rural is narrowing, mainly by a increase in rural BMI in the world in recent years, especially in low- and middle-income regions, so it is not urbanization 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 advancements in lifestyle detection, big data and related technologies. This would help us better understand and respond to this myriad of interconnected factors in our quest to design better targeted and more effective public health interventions for the control and prevention of OO and T2DM.
The research we should be doing in the third decade of the 21st century should use a systems thinking approach
Big data geolocated from smartphones / apps, clothes and other sensors enable researchers to conduct innovative OO and T2D studies – smartphones are not only useful for data collection; they can also be used to implement targeted location-based public health interventions and campaigns. Public health professionals can greatly benefit from well-designed Big Data dashboards and associated technologies to uncover and act on the multifaceted challenges of OO and T2DM in their target populations.
A recent International Journal of Health Geography Editorial published in March 2021 marks the launch of a new collection of articles on the subject titled ‘New horizons in geospatial lifestyle and food environment research“. The editorial features 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 domain. It is hoped that this new collection will initiate and stimulate new fruitful discussions between public health communities around the world and inspire many future groundbreaking studies on food and PA environments and demographic factors in OO and T2DM.