Scaling up natural experimental studies: harnessing emerging technologies to transform physical activity and built environment research
Abstract Evaluating how built environment interventions influence physical activity is crucial for informing effective policies. Natural experiments provide opportunities to assess real-world interventions, yet traditional data collection methods - such as surveys, accelerometers, manual observation...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
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| Series: | International Journal of Behavioral Nutrition and Physical Activity |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12966-025-01742-7 |
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| Summary: | Abstract Evaluating how built environment interventions influence physical activity is crucial for informing effective policies. Natural experiments provide opportunities to assess real-world interventions, yet traditional data collection methods - such as surveys, accelerometers, manual observation and environmental audit tools - limit scalability due to inefficiencies, low participant engagement, and biases. As a result, there is a lack of robust, generalisable evidence to inform policymakers on the most effective built environment interventions for promoting physical activity across diverse communities. In this commentary, we outline four emerging technologies that could address these challenges: (1) smartphone applications and wearable technology for physical activity measurement, (2) geolocation data for assessing mobility patterns, (3) automated systematic observation of physical activity behaviours, and (4) automated environmental audits. We discuss how these approaches can enhance the scalability of natural experimental studies while also considering important ethical implications, including privacy, inclusivity, and community engagement. Advancing and integrating these technologies is critical for generating robust evidence to design built environments that equitably support physical activity. |
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| ISSN: | 1479-5868 |