Improving the Behavioural Impact of Air Quality Alerts

Health Behavior, Pollution; Exposure

Air quality alerts, Health behaviour change, Adherence

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.

According to data released in 2014, in 2012 around 3.7 million people died prematurely in the world as a result of exposure to ambient air pollution ((WHO), 2014). Evidence has shown the negative short- and long-term effects of air pollution on both premature mortality and morbidity from cardiopulmonary disease (for an overview, see (Kelly & Fussell, 2015)). This is particularly a problem in London (Samoli et al., 2016), where levels of air pollution are quite worrying. In the UK monitoring networks measure the levels of different air pollutants, and these measurements are provided by the Department for Environment, Food & Rural Affairs (DEFRA) in the form of daily air quality indices (AQIs), together with separate health advice for at-risk groups and the general population. A recently published systematic review (D'Antoni, Smith, Auyeung, & Weinman, 2017) has found suboptimal adherence levels to health advice associated with air quality alerts, and identified several facilitators and barriers of adherence. Some of the facilitators included beliefs that air pollution can have negative health effects (i.e. perceived severity), outcome expectancies (e.g., beliefs that something can be done to reduce smog), beliefs about the health benefits of AQI adoption (i.e. response efficacy), and receiving advice from health care professionals. Barriers to adherence included: lack of understanding of the indices, being exposed to health messages that reduced both concern about air pollution and perceived susceptibility, as well as perceived lack of self-efficacy/locus of control, reliance on sensory cues and lack of time to make behavioural changes. The findings of this systematic review have informed the current research study, which aimed to improve the behavioural impact of existing air quality alerts. In particular, alternative health messages were developed based on the psychosocial factors identified in the systematic review. The purpose of the study was to test whether these theory and evidence-based alternative communication formats, compared to the official messages sent in association with the UK AQIs, maximise the behavioural impact of existing alert systems. Methods Design: This was a randomised control trail using a 2-way factorial design, with target population (2 levels: general population vs. individuals with a pre-existing health condition) and message format (2 levels: usual message format vs. alternative format) as between-factors. Qualitative data were also collected to understand the reasons for actual adherence and non-adherence. \- Theoretical framework and targeted psychosocial predictors: The COM-B model (Michie, van Stralen, & West, 2011) was used as a theoretical framework to guide in the understanding of the facilitators and barriers to behaviour change in response to air quality alerts. The control groups received usual air quality alerts and health advice based on the UK AQI messages, and the intervention groups received alternative health messages targeting knowledge about the health impact of exposure to air pollution, perceived severity of air pollution, perceived susceptibility, perceived efficacy of protective behaviours, self-efficacy, perceived negative consequences associated with protective behaviours, reliance on sensory cue, and action planning. In addition, study participants who reported having a pre-existent respiratory condition and who were randomly allocated in the intervention group, also received specific additional messages targeting beliefs about efficacy and side effects of inhalers, and medication self-efficacy. \- Targeting message specificity: Specificity refers to the extent to which a message provides a detailed description of the recommended behaviour. A meta-analysis of 18 studies (O'Keefe, 1997) found that messages providing health recommendations with a more specific description seem to be significantly more persuasive than generic recommendations (r=.10, k=18, N=11,105). Participants in the control group received the usual UK AQI message format containing less specific recommendations (e.g. advice for at risk individuals in case of high air pollution: 'Adults and children with lung problems, and adults with heart problems, should reduce strenuous physical exertion, particularly outdoors'). On the other hand, the intervention group received more specific recommendations ('Adults and children with lung problems, adults with heart problems, and older people, should reduce levels and length of physical activity outdoors. Where possible, change: travel route or exercise location (e.g. use our app to find less polluted roads or parks) or time (e.g. mornings or less polluted times)'). Control and intervention groups were compared in intended and actual behaviour change outcome measures. We predicted that the alternative format would be associated with higher behaviour change, compared to the usual format.

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