Consumption and Healthy Lifestyles (CHL) and the the Laboratory of Geo-information Science and Remote Sensing (GIS)

The majority of people experience difficulties in adopting and maintaining healthy behaviors in the long run. Important hindering factors for maintenance of a healthy lifestyle are lack of motivation and lapse into old unhealthy habits. Smartphone apps have gained popularity as platform for health promotion, but have not been able yet to overcome the problem of diminishing effects over time. The development of health behavior change maintenance calls for new insights and innovative approaches. Traditionally, intervention studies are fixed over (short) time, making diminishment of effects more likely. Adaptive interventions are a novel and recommended approach in order to achieve a sustainable health behavior change. Furthermore, there is growing evidence that environmental exposures are important to consider in interventions, as person-environment interactions play an important role in health behaviors. Food intake and physical activity are for example influenced by exposure to environmental factors (Vogel et al., 2019; Brug, 2008). Since obesity and chronic diseases are increasing, prevention programs that also take environmental influences into account must be developed (Visscher & Seidell, 2001).

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). Because health behavior is complex (i.e. dynamic, multi-causal), adaptive and continuous alignment interventions are needed to better support the acceptance and maintenance of changes in health behavior over time and in different contexts.

One novel mHealth approach that seems promising for long-term behavioural change is called “just-in-time adaptive intervention” (JITAI). A JITAI is a very promising approach as it aims to provide the right type of support, at the right time, in the context that the person needs it most and is most likely to be receptive. JITAIs use mobile technology such as smartphones, sensors, and software analytics to automatically detect patient behavior and deliver intervention content that is most relevant to a patient’s needs, at the time that it is most needed and/or likely to improve health-related behaviors (Nahum-Shani et al., 2014; Spruijt-Metz & Nilsen, 2014). Despite its promising value, research is in its early stages (Hardeman et al., 2019). Relatively few JITAIs have thus far been developed and tested, especially in the domain of food intake/consumption/eating behaviour. Hence, knowledge is lacking on the optimal design principles of JITAIs, reach and acceptance of JITAIs, and effects on health and health inequalities.

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. For the analyses of movement patterns insight is required in the desired spatial and temporal scales and the location sensing techniques available. Various location sensing techniques are currently available such as GNSS (GPS), mobile phone tracking and wifi and Bluetooth tracking. Depending on the context (indoor/outdoor, required accuracy) different techniques might be required. Reliable methods to measure environmental exposure and its relation to health behaviour are lacking. Associations between exposure to environmental conditions and health-related behaviour (alcohol consumption) were mostly inconsistent within and between the different approaches to measure this exposure to environmental conditions (Morrison et al., 2019). Tests of environmental interventions are also often sub-optimal (Conner & Norman, 2017).

Reliable measurement of the influence of the context on health behaviour is needed, also in order to objectively measure the effectiveness of contextual interventions. In this proposal, we will focus on the build environment, and not the social environment. The objective of the current study is to further develop our location-based-communication app into a tool that can be used for just-in-time adaptive interventions and is also able to measure, analyse 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.

Objectives:
1.To investigate which method is most useful and reliable for measuring and mapping contextual influences and movement patterns (e.g. gps, beacons, cameras vs device e.g. smartphone, tracker etc.)
2.To be able to analyze the gathered environmental data
3.To generate meaningful movement patterns that offer insight in the behavior of people
4.To connect environmental data to behavioral data with the aim to inform/improve/evaluate effectiveness of eHealth interventions
5. To investigate the social acceptance (societal readiness) of these methods, including data sharing, especially among vulnerable people (such as low SES, low digital literacy, low health literacy)