Digitally Supported Lifestyle Intervention to Prevent Type 2 Diabetes Through Healthy Habits: Secondary Analysis of Long-Term User Engagement Trajectories in a Randomized Controlled Trial

Piia Lavikainen, Elina Mattila, Pilvikki Absetz, Marja Harjumaa, Jaana Lindström, Elina Järvelä-Reijonen, Kirsikka Aittola, Reija Männikkö, Tanja Tilles-Tirkkonen, Niina Lintu, Timo Lakka, Mark van Gils, Jussi Pihlajamäki, Janne Martikainen, Piia Lavikainen, Elina Mattila, Pilvikki Absetz, Marja Harjumaa, Jaana Lindström, Elina Järvelä-Reijonen, Kirsikka Aittola, Reija Männikkö, Tanja Tilles-Tirkkonen, Niina Lintu, Timo Lakka, Mark van Gils, Jussi Pihlajamäki, Janne Martikainen

Abstract

Background: Digital health interventions may offer a scalable way to prevent type 2 diabetes (T2D) with minimal burden on health care systems by providing early support for healthy behaviors among adults at increased risk for T2D. However, ensuring continued engagement with digital solutions is a challenge impacting the expected effectiveness.

Objective: We aimed to investigate the longitudinal usage patterns of a digital healthy habit formation intervention, BitHabit, and the associations with changes in T2D risk factors.

Methods: This is a secondary analysis of the StopDia (Stop Diabetes) study, an unblinded parallel 1-year randomized controlled trial evaluating the effectiveness of the BitHabit app alone or together with face-to-face group coaching in comparison with routine care in Finland in 2017-2019 among community-dwelling adults (aged 18 to 74 years) at an increased risk of T2D. We used longitudinal data on usage from 1926 participants randomized to the digital intervention arms. Latent class growth models were applied to identify user engagement trajectories with the app during the study. Predictors for trajectory membership were examined with multinomial logistic regression models. Analysis of covariance was used to investigate the association between trajectories and 12-month changes in T2D risk factors.

Results: More than half (1022/1926, 53.1%) of the participants continued to use the app throughout the 12-month intervention. The following 4 user engagement trajectories were identified: terminated usage (904/1926, 46.9%), weekly usage (731/1926, 38.0%), twice weekly usage (208/1926, 10.8%), and daily usage (83/1926, 4.3%). Active app use during the first month, higher net promoter score after the first 1 to 2 months of use, older age, and better quality of diet at baseline increased the odds of belonging to the continued usage trajectories. Compared with other trajectories, daily usage was associated with a higher increase in diet quality and a more pronounced decrease in BMI and waist circumference at 12 months.

Conclusions: Distinct long-term usage trajectories of the BitHabit app were identified, and individual predictors for belonging to different trajectory groups were found. These findings highlight the need for being able to identify individuals likely to disengage from interventions early on, and could be used to inform the development of future adaptive interventions.

Trial registration: ClinicalTrials.gov NCT03156478; https://ichgcp.net/clinical-trials-registry/NCT03156478.

International registered report identifier (irrid): RR2-10.1186/s12889-019-6574-y.

Keywords: digital behavior change intervention; habit formation; mobile health; trajectories; type 2 diabetes; user engagement.

Conflict of interest statement

Conflicts of Interest: JM is a founding partner of ESiOR Oy and a board member of Siltana Oy. These companies were not involved in carrying out this research. PL, EM, PA, MH, JL, EJR, KA, RM, NL, TTT, TL, MVG, and JP declare no conflicts of interest.

©Piia Lavikainen, Elina Mattila, Pilvikki Absetz, Marja Harjumaa, Jaana Lindström, Elina Järvelä-Reijonen, Kirsikka Aittola, Reija Männikkö, Tanja Tilles-Tirkkonen, Niina Lintu, Timo Lakka, Mark van Gils, Jussi Pihlajamäki, Janne Martikainen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.02.2022.

Figures

Figure 1
Figure 1
Flow chart of the study population. DIGI, digital intervention group; DIGI+GROUP, group combining the digital intervention and face-to-face group coaching.
Figure 2
Figure 2
Estimated BitHabit app user engagement trajectories with their 95% CIs.
Figure 3
Figure 3
Changes (%) in risk factor levels over 12 months by user engagement trajectories. The error bars represent 95% CIs for means. P values are obtained from the analysis of covariance for the main effect of the user engagement trajectory on the change score adjusted for age, sex, and baseline value. HbA1c: glycated hemoglobin A1c.

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