Literature DB >> 33658039

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.

Maged N Kamel Boulos1, Keumseok Koh2.   

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.

Entities:  

Keywords:  Big data; Geo-analytics; Obesity; Overweight; Smart city lifestyle sensing; Systems science; Type 2 diabetes

Year:  2021        PMID: 33658039      PMCID: PMC7926080          DOI: 10.1186/s12942-021-00266-0

Source DB:  PubMed          Journal:  Int J Health Geogr        ISSN: 1476-072X            Impact factor:   3.918


  40 in total

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2.  Prevalence and Ethnic Pattern of Diabetes and Prediabetes in China in 2013.

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Journal:  JAMA       Date:  2017-06-27       Impact factor: 56.272

3.  The spread of obesity in a large social network over 32 years.

Authors:  Nicholas A Christakis; James H Fowler
Journal:  N Engl J Med       Date:  2007-07-25       Impact factor: 91.245

Review 4.  Epigenetics across the human lifespan.

Authors:  Riya R Kanherkar; Naina Bhatia-Dey; Antonei B Csoka
Journal:  Front Cell Dev Biol       Date:  2014-09-09

5.  Mobility assessment of a rural population in the Netherlands using GPS measurements.

Authors:  Gijs Klous; Lidwien A M Smit; Floor Borlée; Roel A Coutinho; Mirjam E E Kretzschmar; Dick J J Heederik; Anke Huss
Journal:  Int J Health Geogr       Date:  2017-08-09       Impact factor: 3.918

6.  Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology.

Authors:  Trang VoPham; Jaime E Hart; Francine Laden; Yao-Yi Chiang
Journal:  Environ Health       Date:  2018-04-17       Impact factor: 5.984

7.  An overview of GeoAI applications in health and healthcare.

Authors:  Maged N Kamel Boulos; Guochao Peng; Trang VoPham
Journal:  Int J Health Geogr       Date:  2019-05-02       Impact factor: 3.918

Review 8.  Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants.

Authors: 
Journal:  Lancet       Date:  2016-04-02       Impact factor: 79.321

9.  Diabetes, obesity and COVID-19: A complex interplay.

Authors:  Prashanth Vas; David Hopkins; Michael Feher; Francesco Rubino; Martin B Whyte
Journal:  Diabetes Obes Metab       Date:  2020-07-28       Impact factor: 6.408

10.  Ethnic disparities in initiation and intensification of diabetes treatment in adults with type 2 diabetes in the UK, 1990-2017: A cohort study.

Authors:  Rohini Mathur; Ruth E Farmer; Sophie V Eastwood; Nish Chaturvedi; Ian Douglas; Liam Smeeth
Journal:  PLoS Med       Date:  2020-05-15       Impact factor: 11.069

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  4 in total

1.  Regional variation in lifestyle patterns and BMI in young children: the GECKO Drenthe cohort.

Authors:  H Marike Boezen; Rikstje Wiersma; Richard H Rijnks; Gianni Bocca; Esther Hartman; Eva Corpeleijn
Journal:  Int J Health Geogr       Date:  2022-07-01       Impact factor: 5.310

2.  The good, the bad, and the environment: developing an area-based measure of access to health-promoting and health-constraining environments in New Zealand.

Authors:  Lukas Marek; Matthew Hobbs; Jesse Wiki; Simon Kingham; Malcolm Campbell
Journal:  Int J Health Geogr       Date:  2021-04-06       Impact factor: 3.918

3.  Geographical disparities in obesity prevalence: small-area analysis of the Chilean National Health Surveys.

Authors:  Alejandro Sepúlveda-Peñaloza; Francisco Cumsille; Marcela Garrido; Patricia Matus; Germán Vera-Concha; Cinthya Urquidi
Journal:  BMC Public Health       Date:  2022-07-29       Impact factor: 4.135

4.  Exploring factors affecting individual GPS-based activity space and how researcher-defined food environments represent activity space, exposure and use of food outlets.

Authors:  Windi Lameck Marwa; Duncan Radley; Samantha Davis; James McKenna; Claire Griffiths
Journal:  Int J Health Geogr       Date:  2021-07-28       Impact factor: 3.918

  4 in total

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