| Summary: | Background
Non-communicable diseases (NCDs) place severe financial strain on global health resources. Diabetes mellitus, the second most prevalent NCD, has been attributed to 8.4% of deaths worldwide for adults aged 20-79 years (International Diabetes Federation, 2013) with physical inactivity attributable to 7% of cases (Lee et al., 2012). The recent surge in commercially available wearable technology has begun to allow individuals to self-monitor their physical activity and sedentary behaviour as well as the physiological response to these behaviours (e.g., health markers such as glucose levels). Equipped with feedback obtained from such wearables, individuals are better able to understand the relationship between the lifestyle behaviours they take (e.g. going for a walk after dinner) and health consequences (e.g. less glucose excursions (area under the curve)). However, in order to achieve true behaviour change, the feedback must be optimised. Innovative communications research suggest that health messages (and in our case feedback) that activates brain regions such as the medial prefrontal cortex (Falk, Berkman, Mann, Harrison & Lieberman, 2010) can predict and are associated with successful behaviour change. Fortunately, functional magnetic resonance imaging (fMRI) can map this neural activity whilst individuals receive various forms of personalised feedback. Such insight into the optimisation of feedback can improve the design and delivery of future behaviour change interventions.
Aim
Examine neural activity in response to personalised feedback in order to identify health messages most potent for behaviour change.
Methods
A mixed gender sample of 30 adults (aged 30-65 years) will be recruited through campus advertisements at Loughborough University, UK. Physical activity and sedentary behaviour will be assessed using waist-worn ActiGraph GT3x-BT accelerometer (100Hz) and LUMO posture sensor (30Hz), respectively. Both devices will be removed for sleep and water-based activities and are valid measures of activity (Santos-Lozano et al., 2013) and sedentary behaviour (Rosenberger, Buman, Haskell, McConnell & Carstensen, 2015). Continuous (15 minute epochs) interstitial glucose levels will be measured through a minimally-invasive 5mm flexible fibre (Freestyle Libre) inserted into the posterior brachium. This device will be worn throughout the whole day, including sleep and water-based activities, and has been validated against venous sampling (Bailey, Bode, Christiansen, Klaff & Alva, 2015). All devices will be returned using internal mail or freepost services after 14 days of wear. Personalised feedback messages will be extracted from the devices. Accelerometry will generate feedback in relation to time spent in activity intensities (e.g. moderate-to-vigorous) in the context of current UK physical activity guidelines and frequently used goal setting targets (e.g. 10,000 steps per day). The LUMO will generate feedback on time spent sitting and the number of sit-to-stand transitions in the context of international sedentary behaviour guidelines. The Freestyle Libre will generate feedback in relation to time spent in the normal glucose range (4.0-6.0mmol/L); average glucose concentration; number of hyperglycaemic and hypoglycaemic events; and level of daily glycaemic variation. Participants will then attend a one-off appointment to undergo task-based fMRI. Imaging data of the whole-brain will be acquired via a 32-channel phased-array head coil on a Discovery MR750w 3.0T MR system. The fMRI investigation will monitor brain activation whilst messages are presented to each participant in a counterbalanced blocked design. Exposure to each stimulus (5 seconds) will be compared to the reference stimulus (5 seconds); shown between each block of stimuli. Following the fMRI, participants will provide self-reported effectiveness scores via a four-point Likert scale (1-4; higher the number, higher the perceived level of effectiveness) for each feedback message (Falk, Berkman & Lieberman, 2012) with scores summed to rank each message within and between feedback topics.
Results
The primary outcome of the study will be the level of neural activity in the medial prefrontal cortex in response to physical activity, sedentary behaviour and glucose feedback messages. Secondary outcomes will be to (i) analyse fMRI scans to identify other areas of the brain that are activated by the stimuli; (ii) compare neural activity between participants grouped by levels of physical activity, sedentary behaviour and glycaemic control; and (iii) evaluate self-reported scales of feedback message effectiveness.
Conclusion
The study will identify the most neurologically stimulating messaging types for behaviour change relating to physical activity, sedentary behaviour and glucose levels. Findings will inform an intervention which will use the identified personalised feedback platforms to attempt to change activity, sedentary behaviour and/or tighten glycaemic control.
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