Trajectories of Engagement with a Digital Physical Activity
Coach: Secondary Analysis of a Micro-Randomized Trial
Name of presenting author: Jan-Niklas Kramer
Name of organisation of presenting author: University of St.Gallen
Country of presenting author: Switzerland
Email address of presenting author: email@example.com
Jan-Niklas Kramer1, Florian Künzler2, Peter Tinschert1, Tobias Kowatsch1
1University of St. Gallen, Institute of Technology Management, St. Gallen, Switzerland
2ETH Zurich, Department of Management, Technology and Economics, Zurich, Switzerland
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Context: Intervention components of a MobileCoach1 based smartphone app to
promote walking were assessed in a seven-week micro-randomized trial (N = 274). In
order to make a significant contribution to public health, the app must also engage
those at higher risk for adverse health outcomes, i.e. elderly, less active or less healthy
Methods: In a secondary analysis, longitudinal trajectories of participants’ number of
daily app sessions were clustered using the k-means algorithm with an alternating
number of clusters2. An app session was defined as any interaction between a
participant and the app separated by at least five minutes between interactions. The
final number of clusters was determined based on five different clustering quality
Results: Two different clusters emerged: stable high engagement (31.3% of
participants, 7.6 (SD = 2.9) mean daily app sessions) and stable low engagement
(68.7% of participants, 1.5 (SD = 1.4) mean daily app sessions). Highly engaged
participants were older (45.8 vs. 40.1 years, p < .001, d = 0.43) and accumulated more
steps per day during the study (7373 vs. 5828 steps per day, p < .001, d = 0.57).
Clusters did not differ with regard to participants’ baseline physical activity, gender,
body mass index, self-reported health status or education.
Conclusions: A chatbot-based walking app engaged participants of a micro-randomized
trial over a period of seven weeks independent of their risk for adverse health
outcomes. Thus, participants with low risk for adverse health outcomes at baseline do
not drive high engagement with the app.
1. Kowatsch T, Volland D, Shih I, et al. Design and Evaluation of a Mobile Chat
App for the Open Source Behavioral Health Intervention Platform
MobileCoach. Paper presented at: International Conference on Design Science
Research in Information Systems; 30 May - 1 June, 2017; Karlsruhe, Germany.
2. Genolini C, Alacoque X, Sentenac M, Arnaud C. kml and kml3d: R Packages to
Cluster Longitudinal Data. Journal of Statistical Software.65(4):1-32.