Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing
Abstract The public health burden caused by overweight, obesity (OO) and type-2 diabetes (T2D) is very significant and continues to rise worldwide. The causation of OO and T2D is complex and highly multifactorial rather than a mere energy intake (food) and expenditure (exercise) imbalance. But previ...
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| Format: | Article |
| Language: | English |
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BMC
2021-03-01
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| Series: | International Journal of Health Geographics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12942-021-00266-0 |
| _version_ | 1818590497142210560 |
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| author | Maged N. Kamel Boulos Keumseok Koh |
| author_facet | Maged N. Kamel Boulos Keumseok Koh |
| author_sort | Maged N. Kamel Boulos |
| collection | DOAJ |
| description | Abstract The public health burden caused by overweight, obesity (OO) and type-2 diabetes (T2D) is very significant and continues to rise worldwide. 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) neighbourhood environments has mainly focused on associating body mass index (BMI) with proximity to stores selling fresh fruits and vegetables or fast food restaurants and takeaways, or with neighbourhood walkability factors and access to green spaces or public gym facilities, making largely naive, crude and inconsistent assumptions and conclusions that are far from the spirit of 'precision and accuracy public health'. 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 (genetic/epigenetic, metabolic, dietary and lifestyle habits, health literacy profiles, screen viewing times, stress levels, sleep patterns, environmental air and noise pollution levels, etc.) and their complex interplays with each other and with local food and PA settings. Furthermore, the same food store or fast food outlet can often sell or serve both healthy and non-healthy options/portions, so a simple binary classification into 'good' or 'bad' store/outlet should be avoided. 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. The research we should be doing in the third decade of the twenty-first century should use a systems thinking approach, helped by recent advances in sensors, big data and related technologies, to investigate and consider all these factors in our quest to design better targeted and more effective public health interventions for OO and T2D control and prevention. |
| first_indexed | 2024-12-16T09:57:29Z |
| format | Article |
| id | doaj.art-b4256e82773c425187f18caea0496e51 |
| institution | Directory Open Access Journal |
| issn | 1476-072X |
| language | English |
| last_indexed | 2024-12-16T09:57:29Z |
| publishDate | 2021-03-01 |
| publisher | BMC |
| record_format | Article |
| series | International Journal of Health Geographics |
| spelling | doaj.art-b4256e82773c425187f18caea0496e512022-12-21T22:35:53ZengBMCInternational Journal of Health Geographics1476-072X2021-03-0120111010.1186/s12942-021-00266-0Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doingMaged N. Kamel Boulos0Keumseok Koh1School of Information Management, Sun Yat-Sen UniversityDepartment of Geography, The University of Hong Kong, Pokfulam RDAbstract The public health burden caused by overweight, obesity (OO) and type-2 diabetes (T2D) is very significant and continues to rise worldwide. 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) neighbourhood environments has mainly focused on associating body mass index (BMI) with proximity to stores selling fresh fruits and vegetables or fast food restaurants and takeaways, or with neighbourhood walkability factors and access to green spaces or public gym facilities, making largely naive, crude and inconsistent assumptions and conclusions that are far from the spirit of 'precision and accuracy public health'. 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 (genetic/epigenetic, metabolic, dietary and lifestyle habits, health literacy profiles, screen viewing times, stress levels, sleep patterns, environmental air and noise pollution levels, etc.) and their complex interplays with each other and with local food and PA settings. Furthermore, the same food store or fast food outlet can often sell or serve both healthy and non-healthy options/portions, so a simple binary classification into 'good' or 'bad' store/outlet should be avoided. 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. The research we should be doing in the third decade of the twenty-first century should use a systems thinking approach, helped by recent advances in sensors, big data and related technologies, to investigate and consider all these factors in our quest to design better targeted and more effective public health interventions for OO and T2D control and prevention.https://doi.org/10.1186/s12942-021-00266-0Smart city lifestyle sensingBig dataGeo-analyticsSystems scienceOverweightObesity |
| spellingShingle | Maged N. Kamel Boulos Keumseok Koh Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing International Journal of Health Geographics Smart city lifestyle sensing Big data Geo-analytics Systems science Overweight Obesity |
| title | Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing |
| title_full | Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing |
| title_fullStr | Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing |
| title_full_unstemmed | Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing |
| title_short | Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing |
| title_sort | smart city lifestyle sensing big data geo analytics and intelligence for smarter public health decision making in overweight obesity and type 2 diabetes prevention the research we should be doing |
| topic | Smart city lifestyle sensing Big data Geo-analytics Systems science Overweight Obesity |
| url | https://doi.org/10.1186/s12942-021-00266-0 |
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