Is More Always Better?: Discovering Incentivized mHealth Intervention Engagement Related to Health Behavior Trends

Abstract

Behavioral medicine is devoting increasing attention to the topic of participant engagement and its role in effective mobile health (mHealth) behavioral interventions. Several definitions of the term "engagement" have been proposed and discussed, especially in the context of digital health behavioral interventions. We consider that engagement refers to specific interaction and use patterns with the mHealth tools such as smartphone applications for intervention, whereas adherence refers to compliance with the directives of the health intervention, independent of the mHealth tools. Through our analysis of participant interaction and self-reported behavioral data in a college student health study with incentives, we demonstrate an example of measuring "effective engagement" as engagement behaviors that can be linked to the goals of the desired intervention. We demonstrate how clustering of one year of weekly health behavior self-reports generate four interpretable clusters related to participants' adherence to the desired health behaviors: healthy and steady, unhealthy and steady, decliners, and improvers. Based on the intervention goals of this study (health promotion and behavioral change), we show that not all app usage metrics are indicative of the desired outcomes that create effective engagement. As such, mHealth intervention design might consider eliciting not just more engagement or use overall, but rather, effective engagement defined by use patterns related to the desired behavioral outcome.

Keywords: adherence; clustering algorithm; college students; engagement; longitudinal study; mHealth; multiple health behaviors.

Fig. 1.

Pathway to Positive Health Outcomes: To obtain a positive distal health behavior change.

Fig. 2.

NUYou app behavioral queries interface for CVH group is shown on the left, and table to the right shows the different responses each query can take. The responses in red are considered to be non-compliant behaviors, whereas the responses in green are considered to be compliant behaviors.

Fig. 3.

NUYou app behavioral queries interface for WH group is shown on the left, and table to the right shows the different responses each query can take. The responses in red are considered to be non-compliant behaviors, whereas the responses in green are considered to be compliant behaviors.

Fig. 4.

NUYou app behavioral self-tracking and self-monitoring interface for participants in the CVH group.

Fig. 5.

This figure illustrates the framework for generating the usage patterns and proximal multiple health behavior trends.

Fig. 6.

Clusters for the behavioral query response data obtained using the k-means algorithm. The circles indicate participants from the CVH group and crosses indicate participants from the WH group. a) Shows the general change in behavior over time (slope) vs the consistency (variance) of behavior over time. b) Shows the general change in behavior over time (slope) vs the overall amplitude (mean) of behaviors. c) Shows the consistency (variance) of behavior over time vs the overall amplitude (mean) of behavior. d) Silhouette plot showing the local optimal k value (k=4).

Fig. 7.

Weekly app usage statistics for each behavioral trend clusters in the CVH group dividing the results between the health promotion and health behavior change goals. a) Number of interactions per week. b) Number of use days per week. c) Density of usage days for given thresholds.

Fig. 8.

Weekly app usage statistics for each behavioral trend clusters in the WH group dividing the results between the health promotion and health behavior change goals. a) Number of interactions per week. b) Number of use days per week. c) Density of usage days for given thresholds.