ArticleInternational journal of health geographics2021
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.
Article in International journal of health geographics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Pharos: a mobile GIS application for network assessment and ground-truth mapping to support mHealth study design in low-resource settings.International journal of geographical information science : IJGIS · 2026Article
- Sociodemographic characteristics predict land use patterns by farmers near a protected area in Madagascar.Scientific reports · 2026Article
- The application of artificial intelligence in health policy: a scoping review.BMC health services research · 2023Article
- Research Trends in Motivation and Weight Loss: A Bibliometric-Based Review.Healthcare (Basel, Switzerland) · 2023Review
- Applications and Challenges of Telemedicine: Privacy-Preservation as a Case Study.Archives of Iranian medicine · 2023Review
- Association of neighborhood physical activity facilities with incident cardiovascular disease.International journal of health geographics · 2023Article
- Deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data for enhancing obesity estimation.International journal of health geographics · 2022Article
- Participatory mapping to address neighborhood level data deficiencies for food security assessment in Southeastern Virginia, USA.International journal of health geographics · 2022Article
- Geographical disparities in obesity prevalence: small-area analysis of the Chilean National Health Surveys.BMC public health · 2022Article
- Regional variation in lifestyle patterns and BMI in young children: the GECKO Drenthe cohort.International journal of health geographics · 2022Article
- Editorial: Data science and health economics in precision public health.Frontiers in public health · 2022Article
- Exploring factors affecting individual GPS-based activity space and how researcher-defined food environments represent activity space, exposure and use of food outlets.International journal of health geographics · 2021Article
- The good, the bad, and the environment: developing an area-based measure of access to health-promoting and health-constraining environments in New Zealand.International journal of health geographics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.