Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation

Abstract

BackgroundPain is one of the most prevalent health-related concerns and is among the top 3 most common reasons for seeking medical help. Scientific publications of data collected from pain tracking and monitoring apps are important to help consumers and healthcare professionals select the right app for their use.ObjectiveThe main objectives of this paper were to (1) discover user engagement patterns of the pain management app, Manage My Pain, using data mining methods; and (2) identify the association between several attributes characterizing individual users and their levels of engagement.MethodsUser engagement was defined by 2 key features of the app: longevity (number of days between the first and last pain record) and number of records. Users were divided into 5 user engagement clusters employing the k-means clustering algorithm. Each cluster was characterized by 6 attributes: gender, age, number of pain conditions, number of medications, pain severity, and opioid use. Z tests and chi-square tests were used for analyzing categorical attributes. Effects of gender and cluster on numerical attributes were analyzed using 2-way analysis of variances (ANOVAs) followed up by pairwise comparisons using Tukey honest significant difference (HSD).ResultsThe clustering process produced 5 clusters representing different levels of user engagement. The proportion of males and females was significantly different in 4 of the 5 clusters (all P ≤.03). The proportion of males was higher than females in users with relatively high longevity. Mean ages of users in 2 clusters with high longevity were higher than users from other 3 clusters (all P <.001). Overall, males were significantly older than females (P <.001). Across clusters, females reported more pain conditions than males (all P <.001). Users from highly engaged clusters reported taking more medication than less engaged users (all P <.001). Females reported taking a greater number of medications than males (P =.04). In 4 of 5 clusters, the percentage of males taking an opioid was significantly greater (all P ≤.05) than that of females. The proportion of males with mild pain was significantly higher than that of females in 3 clusters (all P ≤.008).ConclusionsAlthough most users of the app reported being female, male users were more likely to be highly engaged in the app. Users in the most engaged clusters self-reported a higher number of pain conditions, a higher number of current medications, and a higher incidence of opioid usage. The high engagement by males in these clusters does not appear to be driven by pain severity which may, in part, be the case for females. Use of a mobile pain app may be relatively more attractive to highly-engaged males than highly-engaged females, and to those with relatively more complex chronic pain problems.

Anxiety, Incentives, and Adherence to Self‐Monitoring on a Mobile Health Platform: A Naturalistic Longitudinal Cohort Study in People With Headache

“…Thus, for a subset of people with headache, adherence to the electronic headache diary was high. The rates of electronic headache diary adherence observed among all the participants in this study were similar to those described in both clinical research and commercial chronic pain self‐monitoring apps. The rates of electronic headache diary adherence observed among participants who were “completers” were higher, and more consistent with the rest of the headache literature, which predominantly includes data from clinical trials.…”

“…Interim analysis of an ongoing randomized clinical trial evaluating a behavioral migraine treatment found similar average adherence to electronic headache diaries as identified in previous trials (13% for 2 months of monitoring, 20% for 4 months of monitoring), but also found that a small number of participants (7 out of 38) accounted for a disproportionate amount of missing days . In the chronic pain literature, higher complexity of pain problems and taking more medication were associated with higher rates of adherence to a self‐monitoring app; however, little is known about the characteristics of people with headache that may be associated with adherence to electronic headache diaries. Understanding factors associated with adherence to electronic headache diaries can help identify subgroups of patients less likely to adhere, and aid in intervention development to improve adherence, to electronic headache diaries.…”

A Framework for Analyzing and Measuring Usage and Engagement Data (AMUsED) in Digital Interventions: Viewpoint (Preprint)

“…movement through pages) requires alternative methods to identify and inform subsequent statistical analysis. Therefore, techniques such as visualisation and process mining may be more informative as they can reveal patterns of usage within the data, such as workflow through an intervention, clustering by participant groups, and temporal details [10,[21][22][23][24]. In applying stage 3 of the framework, the required data format for any analytical software should be considered.…”

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

“…It is noteworthy that there are several mobile apps proving the applicability of smart mobile devices in the context of data collection scenarios, such as Manage My Pain [11] or Track Your Tinnitus [12]. Although the participants involved in respective scenarios gave positive feedback, several shortcomings could still be observed.…”