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Four of the most prominent chronic diseases are linked by common and preventable behavioural risk factors: unhealthy diet, physical inactivity and tobacco use. In 2002, major chronic diseases accounted for almost 60% of all deaths and 43% of the global burden of disease. Prevention of chronic diseases is thus urgently needed and should be focused on controlling the risk factors (WHO, 2002). Especially a sub-optimal diet was found to be an important risk factor for disability-adjusted life years and deaths worldwide, and in the Netherlands.

 

Interventions that focus on lifestyle-related changes are needed to prevent the development of chronic diseases. However, the majority of people experience difficulties in adopting and maintaining healthy behaviors in the long run. Research shows that health behaviors such as physical activity and sleep, eating behaviors and smoking lapses vary from day to day at the individual level, often in response to a dynamic interplay of intra-individual (e.g., motivation), inter-individual (i.e., social support) and environmental/contextual factors (Chevance, Perski, & Hekler, 2020). There is growing evidence that environmental exposures are important to consider in interventions. Since obesity and chronic diseases are increasing, prevention programs that also take environmental influences into account must be developed (Visscher & Seidell, 2001).

 

Adaptive interventions are a novel and recommended approach in order to achieve a sustainable health behavior change. One novel mHealth approach that seems promising for long-term behavioural change is called “just-in-time adaptive intervention” (JITAI). JITAIs adapt to day-to-day variations in people and the environment, which will increase engagement and acceptance. This is essential for long term behaviour change. Despite its promising value, research is in its early stages (Hardeman et al., 2019). Relatively few JITAIs have thus far been developed and tested.

 

To overcome the lack of available JITAIs, we developed an innovative app for observation in naturalistic settings within the VIDI project of Prof.dr. Emely de Vet (How to navigate a tempting food environment: from explicit directions to hidden cues; 452-14-014). This app communicates through bluetooth with beacons: devices that can be placed in an indoor food environment and that allow for communication through mobile phones based on the exact location (e.g., in the candy corridor in the supermarket). This Location Based Communication app (LBC) can be used as a just-in-time intervention by sending prompts to the participant when reaching a beacon. The objective of the proposed study is to further develop this location-based-communication app into a tool that can be used for just-in-time adaptive interventions in the broader contextual environment and is also able to measure, analyze and map movement patterns, the enabling and hindering contextual factors on health behaviours in different contexts, and to generate meaningful movement patterns that offer insight in the behavior of people. This tool also needs to be socially accepted by end-users.

 

Spatial/movement patterns in different environments can be an indicator of health behavior; a change in these spatial patterns can demonstrate the effect of interventions in these places. Reliable methods to measure environmental exposure and its relation to health behaviour are lacking. Measuring and comparing movement patterns before and after sending prompts is a very novel, relevant and objective method to measure the effectiveness of JITAIs. The method that we propose will objectively and unobtrusively measure changes in spatial patterns and is created in co-design with relevant stakeholders. This method is therefore also suitable for vulnerable people who might experience difficulties in reading and answering questions, and will also be accepted by them through their involvement in the development phase. By using the same app for the intervention (e.g. sending prompts) and for measuring the effectiveness, usability and therefore acceptance will be increased. By actively involving relevant stakeholders in the development phase, such as public health practitioners, experts from various domains and policy makers, we create large support which will lead to better implementation of this tool in the future.

 

Once developed and tested, this tool has the potential to inform policy makers on important decisions regarding the build environment and health-related approaches in an attempt to reduce chronic diseases. Our aim is that this tool can be applied in different contexts (e.g. shopping districts, urban environments, low SES neighborhoods), for different lifestyle behaviours (such as food intake/consumption, alcohol consumption, physical activity, sleep, stress), and that it can be used with various target groups (e.g. elderly, low SES, youth, migrants etc.).

 

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