Beyond self-monitoring technologies: actionable information?
By ucjujbl, on 18 July 2017
By Dr. Artur Direito, Research Associate at the UCL Centre for Behaviour Change
The conversation about using digital health technologies such as smartphone apps or activity trackers to promote healthy behaviours such as an active lifestyle has underscored the shortfalls of many current available tools. However, these tools are not going away and the fast pace of technology development will lead to improved features and performance in the foreseeable future.
We have not yet fully capitalised on these tools. One such example is the ability of most smartphone apps or activity trackers to set goals, enable self-monitoring, and receive feedback on behaviour (e.g. number of steps), three key effective behaviour change techniques. However, even though for some setting goals and receiving feedback may be enough to keep engaging in activity-related behaviours, it won’t suffice for most of us. Other “less fancy” forms of digital tools such as pedometers or even non-digital self-monitoring tools like scales or mirrors have been as ubiquitous or even more so than smartphones and were not a panacea for weight management.
Consider an intervention triggered by the smartphone sensing capabilities (e.g. GPS locations) that delivers notifications of opportunities for a more active lifestyle, such as a self-guided tour in a nearby park. Such intervention could be proposed depending on several factors: e.g. i) how active the individual was that day/that week; ii) whether the individual is commuting, at work, at home; iii) the individual’s perception of competence, etc. For example, for an individual with high perceived competence, a simple prompt/cue displaying a suggested route through the park to walk to the furthest public transport stop instead of the closest one may suffice. However, an individual with low perceptions of competence may require further support, such as persuasion about his/her capability, provision of emotional support, and prompting monitoring of emotional consequences of how he/she felt after attempting to perform the behaviour.
To fully harness the potential of these technologies to change behaviour, we still need to track down the optimal combination of behaviour change techniques (i.e. we can’t “pack” them all, having too many may be counterproductive and there are competing and synergetic interactions between them). While many technologies already incorporate goal setting, self-monitoring, and feedback, other techniques that could contribute with more “actionable information”, are not so common. For example, techniques that an individual could enact everyday during their “real world busy lives” to help him/her better handling everyday life clashes, such as action planning and problem solving, could perhaps add to the behaviour change potential of these technologies?
A different aspect is how well some of these behaviour change techniques are being incorporated in these technologies. The established effectiveness of techniques will be undermined if the quality of translation onto technological intervention elements is poor. How best can we deliver techniques such as problem solving and review of goals in digital tools?
Just-in-time adaptive interventions (JITAIs), through continuous capture of the individual’s behaviour and living environment, are expected to better harness these technologies. JITAIs (will) allow personalised behavioural treatments to be delivered in the individual’s living environment, at the times these are most needed, with content highly relevant to the individual’s needs. This would make them more likely to positively affect health behaviour. Together with software analytics, machine learning approaches applied to the data captured and artificial intelligence technology will allow the automation of behaviour detection and delivery of optimal intervention content. Will this provide more actionable information?
Artur is a Research Associate working on a variety of behaviour change projects within UCL’s Centre for Behaviour Change. Among these are City4Age, a EU Horizon2020 funded project aiming to improve independent living in older adults harnessing digital technologies; and CUSSH, a transdisciplinary programme aiming to identify implementable solutions for health and environmental sustainability.
He completed his PhD in Health Sciences at The University of Auckland in 2016, which investigated mobile health approaches for promoting physical activity and sedentary behaviour change. He is interested in achieving better health outcomes at population levels through health-related behaviour change.