To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention

Background

Smartphone users are a heterogeneous group, implying that certain user types might be distinguishable by the way they interact with a smartphone-based intervention. As these user types potentially benefit differently from an intervention, there is a need to identify them to inform future research, intervention design, and, eventually, improve intervention effectiveness. To this end, we explored 1) whether user types were distinguishable in terms of how much they used a smartphone-based intervention and experienced app engagement; and whether user types differed in 2) their intervention effects; and 3) user characteristics, i.e., HEXACO personality traits and self-efficacy.

Method

Participants were Dutch first-year university students that interacted with the FutureU app aimed at increasing future self-identification. App usage data and engagement survey data were obtained in a randomized controlled trial taking place in 2022 (n = 86). K-means++ cluster analyses were applied to identify user types based on app use and engagement. Linear discriminant analyses, ANCOVAs, and MANOVAs were conducted to assess whether the clusters differed in intervention outcomes and individual characteristics. The analyses were replicated in data obtained in an RCT taking place in 2023 with an updated version of the app (n = 106).

Results

Four user types were identified: Low use–Low engagement, Low use–High engagement, High use–Low engagement, High use–High engagement. Overall, intervention effects were strongest for the user types High engagement–High use and High engagement–Low use. No significant differences were observed in user characteristics.

Conclusion

User types can vary in their use of and engagement with smartphone-based interventions, and benefit differently from these interventions. App engagement appears to play a more significant role than previously assumed, highlighting a need for further studies on drivers of app engagement.