ClinicalTrials.gov
public full text
Public Domain
Improving the Behavioural Impact of Air Quality Alerts
The evidence shows that adherence to air quality advice to adopt protective behaviours during pollution episodes is suboptimal, and that the traditional strategy of simply informing people about high pollution episodes is not effective. The aim of the present study was to investigate how to improve the behavioural impact of existing air quality alert messages through a systematic manipulation of key communication variables, including perceived susceptibility, self-efficacy, response efficacy, planning, message specificity, etc. Users of an existing air quality alert smartphone application in London, who agreed to take part in the study, were randomly allocated to a control group (i.e. receiving usual health advice associated with the official UK Air Quality Index) or an intervention group receiving health advice associated with air quality alerts in an alternative format (i.e. targeting key variables). Both intended and actual adherence behaviours were investigated. Qualitative data were also collected to understand the reasons for not adopting protective behaviours in response to receiving a real air pollution alert. Implications of this study include the potential to increase protective behaviours in the general population during air pollution episodes through the development of more effective communication strategies provided via existent air quality alert systems.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Empirical Analysis of Emotional Health Awareness and Social Media Usage Intensity#241346
- Short Active Lives Survey in Chronic Lung Conditions#875145
- Internet-based Mindfulness and Exposure Treatment for Atopic Dermatitis#1476877
- Clustering of self-reported social and environmental risk factors identifies vulnerable subgroups with increased asthma morbidity.#662903
- Large Language Model based air quality monitoring and localized alert generation#1011666
Record · ID 1496742
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive