Incorporating Behavioral Trigger Messages Into a Mobile Health App for Chronic Disease Management: Randomized Clinical Feasibility Trial in Diabetes

Scott Sittig, Jing Wang, Sriram Iyengar, Sahiti Myneni, Amy Franklin, Scott Sittig, Jing Wang, Sriram Iyengar, Sahiti Myneni, Amy Franklin

Abstract

Background: Although there is a rise in the use of mobile health (mHealth) tools to support chronic disease management, evidence derived from theory-driven design is lacking.

Objective: The objective of this study was to determine the impact of an mHealth app that incorporated theory-driven trigger messages. These messages took different forms following the Fogg behavior model (FBM) and targeted self-efficacy, knowledge, and self-care. We assess the feasibility of our app in modifying these behaviors in a pilot study involving individuals with diabetes.

Methods: The pilot randomized unblinded study comprised two cohorts recruited as employees from within a health care system. In total, 20 patients with type 2 diabetes were recruited for the study and a within-subjects design was utilized. Each participant interacted with an app called capABILITY. capABILITY and its affiliated trigger (text) messages integrate components from social cognitive theory (SCT), FBM, and persuasive technology into the interactive health communications framework. In this within-subjects design, participants interacted with the capABILITY app and received (or did not receive) text messages in alternative blocks. The capABILITY app alone was the control condition along with trigger messages including spark and facilitator messages. A repeated-measures analysis of variance (ANOVA) was used to compare adherence with behavioral measures and engagement with the mobile app across conditions. A paired sample t test was utilized on each health outcome to determine changes related to capABILITY intervention, as well as participants' classified usage of capABILITY.

Results: Pre- and postintervention results indicated statistical significance on 3 of the 7 health survey measures (general diet: P=.03; exercise: P=.005; and blood glucose: P=.02). When only analyzing the high and midusers (n=14) of capABILITY, we found a statistically significant difference in both self-efficacy (P=.008) and exercise (P=.01). Although the ANOVA did not reveal any statistically significant differences across groups, there is a trend among spark conditions to respond more quickly (ie, shorter log-in lag) following the receipt of the message.

Conclusions: Our theory-driven mHealth app appears to be a feasible means of improving self-efficacy and health-related behaviors. Although our sample size is too small to draw conclusions about the differential impact of specific forms of trigger messages, our findings suggest that spark triggers may have the ability to cue engagement in mobile tools. This was demonstrated with the increased use of capABILITY at the beginning and conclusion of the study depending on spark timing. Our results suggest that theory-driven personalization of mobile tools is a viable form of intervention.

Trial registration: ClinicalTrials.gov NCT04132089; https://ichgcp.net/clinical-trials-registry/NCT004122089.

Keywords: Fogg behavior model; interactive health communication application; knowledge; mHealth; messages; persuasive technology; self-efficacy; self-management; social cognitive theory; triggers.

Conflict of interest statement

Conflicts of Interest: None declared.

©Scott Sittig, Jing Wang, Sriram Iyengar, Sahiti Myneni, Amy Franklin. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 16.03.2020.

Figures

Figure 1
Figure 1
Interactive health communication application with incorporation of social cognitive theory, Fogg behavior model, and persuasive technology. IHCA: interactive health communication application; PGHD: patient-generated health data; SCT: social cognitive theory.
Figure 2
Figure 2
capABILITY resources menu.
Figure 3
Figure 3
capABILITY patient-generated health data tracking.
Figure 4
Figure 4
Patient generated health data collection.

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