ArticleJMIR public health and surveillance2024
Untapped Potential of Unobtrusive Observation for Studying Health Behaviors.
Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Long-term impacts of co-designed sustainable park improvements on physical activity and other wellbeing behaviours: a 7-year natural experimental study in a deprived urban area.The international journal of behavioral nutrition and physical activity · 2026Article
- Community physical activity environments and physical activity levels among older adults: a hierarchical analysis of environmental dimensions.Frontiers in public health · 2026Article
- Method for Observing SMOKing and vaping bEhaviours (MOSMOKE): development and validation of a systematic observation tool.BMJ open · 2025Article
- "Walking a Day in My Shoes": A Clinical Shadowing Program to Enhance Medical Students' Understanding of Chronic Disease Management Beyond Clinical Settings.Clinics and practice · 2025Article
- Association Between Social Distancing Compliance and Public Place Crowding During the COVID-19 Pandemic: Cross-Sectional Observational Study Using Computer Vision to Analyze Surveillance Footage.JMIR public health and surveillance · 2025Observational
- Association between nutrition literacy and cognitive impairment among older adults in Bengbu, China.Frontiers in aging neuroscience · 2025Article
- Using Video Cameras to Assess Physical Activity and Other Well-Being Behaviors in Urban Environments: Feasibility, Reliability, and Participant Reactivity Studies.JMIR public health and surveillance · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Improving the environment is an important upstream intervention to promote population health by influencing health behaviors such as physical activity, smoking, and social distancing. Examples of promising environmental interventions include creating high-quality green spaces, building active transport infrastructure, and implementing urban planning regulations. However, there is little robust evidence to inform policy and decision makers about what kinds of environmental interventions are effective and for which populations. In this viewpoint, we make the case that this evidence gap exists partly because health behavior research is dominated by obtrusive methods that focus on studying individual behavior and that are less suitable for understanding environmental influences. In contrast, unobtrusive observation can assess how behavior varies in different environmental contexts. It thereby provides valuable data relating to how environments affect the behavior of populations, which is often useful knowledge for effectively and equitably tackling population health challenges such as obesity and noncommunicable diseases. Yet despite a long history, unobtrusive observation methods are currently underused in health behavior research. We discuss how developing the use of video technology and automated computer vision techniques can offer a scalable solution for assessing health behaviors, facilitating a more thorough investigation of how environments influence health behaviors. We also reflect on the important ethical challenges associated with unobtrusive observation and the use of these emerging video technologies. By increasing the use of unobtrusive observation alongside other methods, we strongly believe this will improve our understanding of the influences of the environment on health behaviors.
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What OpenQuestion holds
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 OpenQuestion graph.