JMIR Publications

ABSTRACT

Background:

Maintaining engagement with digital apps is a key concern when developing effective digital health interventions. Previous research typically examines engagement through a single-event outcome, such as time to first disengagement. However, users may repeatedly stop using and later return to the app, experiencing multiple disengagement and re-engagement cycles. Understanding the factors associated with re-engagement is valuable and may help inform the optimization and personalization of digital intervention strategies.

Objective:

This study aimed to characterize re-engagement with Drink Less, an alcohol reduction app, as a recurrent-event process and to examine its associations with baseline characteristics and recent app-use history.

Methods:

We analyzed data from 19,233 users of the Drink Less app who had a baseline Alcohol Use Disorders Identification Test (AUDIT) score of at least 8 on the 0 to 40 scale, indicative of drinking at hazardous or harmful levels. Disengagement was defined as 7 or more consecutive days without app use and a disengagement episode began when this threshold was reached. Re-engagement was defined as the first recorded app-use session after entry into that episode. The Prentice-Williams-Peterson Gap-Time (PWP-GT) model was used to examine associations between baseline characteristics, recent app-use history, and re-engagement. Recent app-use history was represented by previous gap duration, measured as the number of days from entry into the immediately preceding disengagement episode to the corresponding re-engagement that ended it; and previous engagement duration, defined as the number of days from that re-engagement to entry into the current disengagement episode.

Results:

In the baseline characteristics model, users in the Harmful AUDIT category (16-19) were more likely to re-engage after disengagement than those in the Hazardous category (8-15; HR 1.0431, 95% CI 1.0026-1.0852; P=.04). Older age was also associated with a greater likelihood of returning to the app (HR 1.0043 per year, 95% CI 1.0029-1.0058; P<.001), whereas users in manual employment (HR 0.8437, 95% CI 0.8000-0.8899; P<.001) and other employment categories (HR 0.9436, 95% CI 0.9016-0.9876; P=.01) were less likely to re-engage than those in non-manual employment. There was no clear evidence of a difference by sex. In separate lagged variable models, longer previous gap duration was negatively associated with re-engagement (HR 0.9921 per day, 95% CI 0.9913-0.9930; P<.001), whereas longer previous engagement duration was positively associated with re-engagement (HR 1.0078 per day, 95% CI 1.0067-1.0089; P<.001). All comparisons accounted for the other covariates in the respective model and were conditional on disengagement episode order.

Conclusions:

Re-engagement with Drink Less was significantly associated with age and employment, as well as with users’ recent app-use history. Together, these findings could inform the development and testing of targeted support both at registration and as the app learns about users’ behavior.