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A Personalised, Sensor-Based Smart Phone Intervention for Physical Activity and Diet – PRECIOUS N-of-1 Trial

Johanna Nurmi1, 2*, Keegan Knittle1, Christopher Helf3, Patrick Zwickl4, Pilar Lusilla Palacios5, Carmina Castellano Tejedor5, Jose Costa Requena6, Tero Myllymäki7, Niklas Ravaja1, 2 and Ari Haukkala1
  • 1 University of Helsinki, Social Psychology, Department of Social Research, Finland
  • 2 Helsinki Institute for Information Technology, Finland
  • 3 University of Vienna, Faculty of Computer Science, Entertainment Computing Group, Austria
  • 4 University of Vienna, Faculty of Computer Science, Cooperative Systems Group, Austria
  • 5 University Hospital & Research Institute Vall d'Hebron, Autonomous University of Barcelona, Psychiatry Department, CIBERSAM, Spain
  • 6 AALTO University, COMNET/ELEC School, Finland
  • 7 Firstbeat Technologies Ltd., Finland

Background: There is an urgent need for interventions which can effectively change behaviours, in order to prevent and reduce the impact of costly chronic conditions such as Type 2 diabetes and cardiovascular diseases (WHO, 2014). Smartphones offer a platform for cost-effective and broad implementation, and at the same time, via real-time tracking and sensor data, offer unprecedented possibilities for personalising interventions (Jovanov & Milenkovic, 2011). While a great number of health-related applications exist already, the content of these is rarely based on behaviour change theory, and, consequently, evidence for the effectiveness of digital behaviour change applications is minimal (Webb, Joseph, Yardley, & Michie, 2010). Even when health-related applications are theory-based, users will likely not achieve behavioural changes if they do not engage with the applications. This lack of engagement is supported by statistics: a quarter of downloaded apps were only used once (Leger, 2011). A major challenge of health care research is therefore the identification of personal treatment response, and factors which mitigate engagement and effectiveness within individuals. To address these issues, our research group, an EU-funded multi-disciplinary consortium has developed the PREventitive Care Infrastructure based On Ubiquitous Sensing (PRECIOUS) mobile application. This app targets behavioural changes in physical activity, diet, and stress, and includes both motivational and action components for each. The service design draws from evidence-based techniques in self-determination theory (SDT, Deci & Ryan, 2000), motivational interviewing (MI, Miller & Rollnick, 2002), and social cognitive theories (e.g. Schwarzer, 2008), to enhance engagement with the process of behaviour change. Through integrating sensor data, self-reported responses, and self-monitoring records, the system provides each user with a dynamic, personalised trajectory through the app, including reminders, prompts, customised support, measurement, and gamified feedback and reward mechanisms. The novelty of PRECIOUS is based on two factors: (1) a virtual individual model, synthesised from self-reported and sensor parameters, which tailors suggestions to the user, and (2) a motivational service design framework combined with gamification principles to trigger, monitor, and sustain behaviour change, especially designed for individuals with low motivation or intention to act. Aims: This study will establish the feasibility of the PRECIOUS app and examine the effects of various components of PRECIOUS on physical activity with an N-of-1 study design (Lillie et al., 2011). More specifically, the study will identify which service features/behaviour change techniques (BCTs) were most used and appreciated by individual users (Michie et al., 2013); investigate whether the use of motivational components leads to greater use of action components, and whether increased use of motivational and action components lead to increases in physical activity. Method/Results: Twelve inactive adults will be recruited to a six-week N-of-1 study testing a new smart phone application and activity bracelet. At baseline and post-treatment, participants will complete questionnaires assessing constructs related to self-determination theory and the health action process approach for both physical activity and healthy eating behaviours, and a 3-day heart rate variability measurement (FirstBeat Ltd; Helsinki, Finland) to assess activity level and stress response. Objective physical activity and sleep data will be collected throughout the study using aggregated data gathered from the smartphone’s on-board accelerometer and an activity bracelet. After completion of the study, interviews with participants will qualitatively examine feasibility and acceptability of the interventions and examine possible reasons why the intervention techniques were or were not effective for each participant. The service seeks to increase user engagement by offering e.g. ‘Conquer the city’ activity challenge, and a storyline that transports the user from one challenge to the next with a visual ‘Journey’. Motivational interviewing is built in with techniques such as user value clarification, change talk -based suggestions, information about health consequences, and discrepancy between current behaviour and goal, the whole service supporting freedom of choice. The motivational components of the app aim to increase user engagement with its action components, such as behavioural goal setting, action planning, and self-monitoring of behaviour. The novelty of this approach is the seamless integration of real-time sensor data with evidence based behavioural theories and interactive gamification. In the N-of-1 study the BCTs will be tested within the same individual by comparing intra-individual variance in physical activity when the techniques are used and not used. Qualitative analyses conducted with a narrative approach will shed light on the mechanisms behind the actions. Conclusions: The outcomes of the intervention will be the identification of components crucial to increasing engagement and activity, identification of new possibilities for intervention tailoring based on integrated sensor data, and ways that the PRECIOUS service can be improved.

Acknowledgements

This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement No: 611366

References

Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.

Jovanov, E., & Milenkovic, A. (2011). Body area networks for ubiquitous healthcare applications: opportunities and challenges. Journal of Medical Systems, 35(5), 1245–1254.

Leger, B. (2011). First impressions matter! 26% of apps downloaded in 2010 were used just once. Localytics. Retrieved from www.localytics.com/blog/2011/firstimpressions- matter-26-percent-of-apps-downloadedused- just-once/

Lillie, E. O., Patay, B., Diamant, J., Issell, B., Topol, E. J., & Schork, N. J. (2011). The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized Medicine, 8(2), 161–173.

Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., … Wood, C. E. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine, 46(1), 81–95.

Miller, W. R., & Rollnick, S. (2002). Motivational interviewing: Preparing people for change Guilford. New York.

Schwarzer, R. (2008). Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology, 57(1), 1–29.

Webb, T., Joseph, J., Yardley, L., & Michie, S. (2010). Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12(1), e4.

WHO. (2014). World Health Statistics 2014. Geneva: World Health Organization. Retrieved from http://public.eblib.com/choice/publicfullrecord.aspx?p=1741840

Keywords: Digital intervention, mHealth, physical activity, Diet, N-of-1, engagement, wearables, Sensors, Motivational Interviewing, Autonomous Motivation, intrinsic motivation

Conference: 2nd Behaviour Change Conference: Digital Health and Wellbeing, London, United Kingdom, 24 Feb - 25 Feb, 2016.

Presentation Type: Poster presentation

Topic: Academic

Citation: Nurmi J, Knittle K, Helf C, Zwickl P, Lusilla Palacios P, Castellano Tejedor C, Costa Requena J, Myllymäki T, Ravaja N and Haukkala A (2016). A Personalised, Sensor-Based Smart Phone Intervention for Physical Activity and Diet – PRECIOUS N-of-1 Trial. Front. Public Health. Conference Abstract: 2nd Behaviour Change Conference: Digital Health and Wellbeing. doi: 10.3389/conf.FPUBH.2016.01.00098

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Received: 27 Nov 2015; Published Online: 09 Jan 2016.

* Correspondence: Ms. Johanna Nurmi, University of Helsinki, Social Psychology, Department of Social Research, Helsinki, Finland, johanna.nurmi@gmail.com