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How to meaningfully integrate behavioural and psychological data to outline behaviour change strategies: a case study applied to physical activity

By ucjubil, on 21 February 2017

Over the past years, thanks to the innovation in technology, we can assist to the widespread devices aimed at monitoring behavioural and physiological data in real time. The way in which such devices collect the data is becoming ever easier and more reliable. Among them, activity trackers are very widespread and accessible. They are mainly based on GPS, tri-axial accelerometer or heart rate monitors. Often such monitoring systems are connected to or directly built-in to people’s smartphones in order to provide the user with useful and meaningful information about their physical activity. The process of translating the collected raw data into graphical and informative readable outputs is usually done by dedicated mobile apps. Monitoring these behaviours has some advantages in promoting the behaviour change. Assessing and evaluating the behaviour is the first step to define and implement more suitable strategies to change it. However, each behaviour is often the consequence of some conscious and automatic psychological processes that are influenced by a specific behaviour or an environmental stimulus. Health-related behaviours are largely associated with contextual psychological cognitions and emotional states that continuously fluctuate. Thus, by integrating behavioural and psychological information, it is possible to better understand their reciprocal influence and to predict how behaviour and psychological correlates might evolve over time. Digital technology may be a powerful tool in performing these functions as it can not only monitor behaviours, it can also:

  1. Assess real time psychological variables that are contextually associated with the specific behaviour. It can be defined as a digital Ecological Momentary Assessment (EMAs).
  2. Elaborate pertinent and tailored outputs based on the integration of both quantitative (people’s behaviour) and qualitative (psychological variables associated with the behaviour) input data.

A case study framed in Bandura’s self-efficacy theory to increase physical activity

In the psychological literature it has been consistently shown that self-efficacy (the belief in one’s capability to organise and execute the courses of action required to produce given attainments) is a predictor and a mediator of the adoption and maintenance of physical activity. Since people’s past physical activity achievements constitute a powerful source of self-efficacy, there is a reciprocal influence between self-efficacy and physical activity. As a consequence, the selection of any specific behavioural goal should be set on the basis of both these two variables in order to gradually support both the increasing of self-efficacy and the achievement of successful experiences. In the proposed case study, we focused on the mentioned association between physical activity achievement and self-efficacy in order to define a tailored goal-setting strategy. The proposed digital computational model is integrated into a mobile app and it focuses on levels of self-efficacy that change dynamically in response to day-to-day achievements of personalized goals. More specifically, self-efficacy is assessed after each physical activity session so that it is possible to obtain both a physical activity (regardless of whether the goal is accomplished or not) and a self-efficacy (low or high) evaluation. This constitutes the input values that permit the computational model to automatically generate the following physical activity goals (output data) on the basis of their specific scores.

Slide1In the current era the most behaviours are largely quantified and monitored. The result is the collection of a large amount of behavioural data that need to be interpreted. The real time integration of behavioural data with psychological information might be the key to better understand how behaviour evolve over time.  Which kinds of behaviours might benefit from that? And which are the most useful psychological correlates in explaining the behaviour? Are they the same across all the behaviours?

BIO: Dario Baretta is a PhD student in the Department of Psychology, University of Milano-Bicocca, Italy. He is interested in understanding how digital technologies support and influence behaviour change, with a specific focus on physical activity. His PhD project aims to develop a mobile app for the promotion of physical activity among a sedentary adult population.

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