Analysing mHealth usage logs in RCTs: Explaining participants’ interactions with type 2 diabetes self-management tools

Abstract

BackgroundThe Introduction of mobile health (mHealth) devices to health intervention studies challenges us as researchers to adapt how we analyse the impact of these technologies. For interventions involving chronic illness self-management, we must consider changes in behaviour in addition to changes in health. Fortunately, these mHealth technologies can record participants’ interactions via usage-logs during research interventions.ObjectiveThe objective of this paper is to demonstrate the potential of analysing mHealth usage-logs by presenting an in-depth analysis as a preliminary study for using behavioural theories to contextualize the user-recorded results of mHealth intervention studies. We use the logs collected by persons with type 2 diabetes during a randomized controlled trial (RCT) as a use-case.MethodsThe Few Touch Application was tested in a year-long intervention, which allowed participants to register and review their blood glucose, diet and physical activity, goals, and access general disease information. Usage-logs, i.e. logged interactions with the mHealth devices, were collected from participants (n = 101) in the intervention groups. HbA1c was collected (baseline, 4- and 12-months). Usage logs were categorized into registrations or navigations.ResultsThere were n = 29 non-mHealth users, n = 11 short-term users and n = 61 long-term users. Non-mHealth users increased (+0.33%) while Long-term users reduced their HbA1c (-0.86%), which was significantly different (P = .021). Long-term users significantly decreased their usage over the year (P < .001). K-means clustering revealed two clusters: one dominated by diet/exercise interactions (n = 16), and one dominated by BG interactions and navigations in general (n = 40). The only significant difference between these two clusters was that the first cluster spent more time on the goals functionalities than the second (P < .001).ConclusionBy comparing participants based upon their usage-logs, we were able to discern differences in HbA1c as well as usage patterns. This approach demonstrates the potential of analysing usage-logs to better understand how participants engage during mHealth intervention studies.

Exploring People’s Candidacy for Mobile Health–Supported HIV Testing and Care Services in Rural KwaZulu-Natal, South Africa: Qualitative Study

“…The use of mobile communication technologies (mHealth) in chronic disease management has grown significantly over the years. mHealth interventions have the potential to decentralize access to health care and make it convenient, particularly in resource-constrained settings [18-22]. It is well documented that mHealth interventions, such as SMS text messaging, have been used to support management of diseases, such as hypertension and diabetes, and to support HIV treatment [20,23,24].…”

The Service User Technology Acceptability Questionnaire: Psychometric Evaluation of the Norwegian Version

“…Of the 101 participants who were randomized to the 2 intervention groups, 74.3% (75/101) completed the SUTAQ questionnaire. Other results from the RCT are reported in detail elsewhere [9-12].…”

User Engagement With Smartphone Apps and Cardiovascular Disease Risk Factor Outcomes: Systematic Review (Preprint)

“…The study characteristics are summarized in Multimedia Appendix 2 . Of the 24 studies, 17 used an RCT design [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], 4 used a retrospective analysis [33][34][35][36], and 3 used a single-group pre-and posttest quasi-experimental design [37][38][39]. Although 20 studies used an RCT or single-group preand-posttest quasi-experimental design, it was the correlational studies nested within these larger parent studies that were of interest for this review.…”

“…Of the 17 RCT studies, 14 conducted an a priori power analysis. Of these 14 RCTs, 3 detected a significant difference in the primary outcome between groups [21,23,27], 9 did not detect a significant difference in the primary outcome between groups [16,17,22,[24][25][26]29,31,32], and 2 did not report the results of the difference in the primary outcome between groups [18,30]. One of the studies that did not detect a significant difference between groups did not achieve the target sample size [32].…”

“…Thirteen studies reported sample races [17][18][19][21][22][23][24][25][26][27][28]32,39], with 7 consisting largely of White participants [18,19,22,[24][25][26]28]. Seventeen studies reported on sample educational level, with the majority of the participants within each individual study having attended some college [16][17][18][19][20][21][22][23][24][25][26][28][29][30][31][32]38,39]. Ten studies limited their sample to participants who were overweight or obese [17,19,[21][22][23][24][25][26]28,35], and another 11 studies reported enrolling participants with a baseline mean or majority percentage BMI indicating overweight or obes...…”