Currently submitted to: Journal of Medical Internet Research
Date Submitted: Aug 10, 2020
Open Peer Review Period: Aug 10, 2020 - Oct 5, 2020
(currently open for review)
Visualising patterns of engagement with a behaviour change app for alcohol reduction.
The development of behaviour change apps often follows an iterative process, where the app evolves into a more complex, dynamic or personalised intervention through cycles of research, development and implementation. Understanding how existing users engage with an app (e.g. frequency, amount, depth and duration of use) can help guide further incremental improvements. Visualisations provide a good understanding of patterns of engagement, as usage data are often longitudinal and rich.
To visualise behavioural engagement with Drink Less, a behaviour change app which aims to reduce hazardous and harmful alcohol consumption in the general adult UK population.
We explored behavioural engagement among 19,233 existing users of Drink Less. Users were included in the analytic sample if they were: from the UK; aged 18 years or over; interested in drinking less alcohol; had a baseline Alcohol Use Disorders Identification Test (AUDIT) score of 8 or above indicative of excessive drinking; and downloaded the app between 17th May 2017 to 22nd January 2019 (615 days). Measures of when sessions begin, length of sessions, time to disengagement, and patterns of use were visualised with heat maps, time-line plots, k-modes clustering analyses and Kaplan-Meier plots.
The visualisations gave us five important insights: (i) the existing daily notification, delivered at 11am, appeared to have a very strong impact on engagement in the following hour; (ii) behavioural engagement decreased over time, with 50% of users disengaging (defined as no use for seven or more consecutive days) 22 days after download; (iii) three distinct trajectories of use were identified: Engagers (24% users), Slow Disengagers (19% users) and Fast Disengagers (57% users); (iv) the depth of engagement was limited, with 85% of sessions occurring within the ‘Self-monitoring and Feedback’ module; and (v) outside the hour after the daily notification is sent, a peak of both frequency and amount of time spent per session was observed in the evenings.
Visualisations of behavioural engagement with the Drink Less app suggest that the current daily notification substantially impacts engagement. Our next research aim is to further optimise the notification policy by tailoring to contextual circumstances of individuals over time. This will be achieved by a Micro-Randomised Trial (MRT), and these visualisations were helpful in both gaining a better understanding of engagement and informing the design of the MRT.
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