JMIR Publications

ABSTRACT

Background:

There is limited understanding of which components to include in digital interventions for 24-hour movement behaviors (physical activity, sleep and sedentary behavior). For intervention effectiveness, user engagement is important. This can be enhanced by a user-centered design, such as exploring and integrating user preferences for intervention techniques and features.

Objective:

This study examined adult user preferences for techniques and features in mobile applications for 24-hour movement behaviors, per movement behaviors and by users’ behavioral intention and adoption

Methods:

An online survey was completed by 86 participants (mean age=37.4y ± 9.2, 57% female). Behavior change techniques were based on a validated taxonomy v2 by Michie and colleagues, and engagement features was based on list extracted from literature. Behavioral data were collected with Fitbit trackers. Correlations, (repeated measures) ANOVA and independent sample T-tests were used to examine associations and differences between and within users, by type of health domain, users’ behavioral intention and adoption.

Results:

An online survey was completed by 86 participants (mean age=37.4y ± 9.2, 57% female). Behavior change techniques were based on a validated taxonomy v2 by Michie and colleagues, and engagement features was based on list extracted from literature. Behavioral data were collected with Fitbit trackers. Correlations, (repeated measures) ANOVA and independent sample T-tests were used to examine associations and differences between and within users, by type of health domain, users’ behavioral intention and Preferences were generally highest for information on the health consequences of movement behavior (84-98%; self-monitoring (84-97%); behavioral feedback (77-92%); insight in healthy lifestyles (80-91%); and tips and instructions (79-85%). While the same ranking was found for techniques across health behaviors, preferences were stronger on all but one BCT for physical activity than for one or both of the other health behaviors. Although techniques seem to fit user preferences for addressing physical activity very well, supplemental techniques may thus be able to even better address preferences for sleep and sedentary behavior. In addition to what is commonly included in apps, sleep apps should also consider giving tips for sleep. SB apps may wish to include more self-regulation and goal-setting techniques. In general, very few differences were found by users’ intentions or adoption to change a particular movement behavior. Apps should provide more self-monitoring (P=.03), information on behavior-health outcome (P=.048) and feedback (P=.04), and incorporate social support (P=.048) to help those who are further removed from a healthy sleep. A virtual coach (P<.001) and video modelling (P=.004) may provide appreciated support to those who are less physically active. PA self-monitoring appealed more to those with an intention to change PA (P=.03). Social comparison (top2 box preferences 45-54%) and support features (top2 box preferences 27-41%) are generally not high on users’ agenda and may not be needed from an engagement point of view. Engagement features may not be very relevant for user engagement (top2 box preferences 1-55%), but should be examined in future research with a less reflective method.

Conclusions:

These findings provide guidance to the design of digital 24-hour movement behavior interventions. As 24-hour movement guidelines are increasingly being adopted in several countries, our study findings are timely to support the design of interventions to meet these guidelines.