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dc.contributor.authorBradway, Meghan
dc.contributor.authorPfuhl, Gerit
dc.contributor.authorJoakimsen, Ragnar Martin
dc.contributor.authorRibu, Lis
dc.contributor.authorGrøttland, Astrid
dc.contributor.authorÅrsand, Eirik
dc.identifier.citationBradway M, Pfuhl G, Joakimsen RM, Ribu L, Grøttland A, Årsand E. Analysing mHealth usage logs in RCTs: Explaining participants’ interactions with type 2 diabetes self-management tools. PLoS ONE. 2018;13(8)en
dc.description.abstractBackground: The 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. Objective: The 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. Methods: The 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. Results: There 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). Conclusion: By 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.en
dc.description.sponsorshipThe EU’s ICT Policy Support Programme as part of the Competitiveness and Innovation Framework Programme( (No. 250487)and The Research Councilof Norway (norges forskningsråd, https://www. (No. 196364)funded the study designand data collection and analysis through the REgioNs of Europe WorkINg toGether for HEALTH (RENEWING HEALTH) project (https://cordis., led by Lis Ribu and EirikÅrsand.The Research Council of Norway (norges forskningsråd, https://www. funded the preparation of the manuscript and decision to publish through the “Full Flow of Health Data Between Patientsand Health Care Systems” project ( fullflow) (grant number 247974/O70), led by Eirik Årsand.The publication charges for this article have been funded by a grant from the publication fund of UiT The Arctic University of Norway ( document_id=449104)(No. 551011),led by Meghan Bradway.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Norges forskningsråd 247974en
dc.publisherPublic Library of Scienceen
dc.relation.ispartofseriesPLoS ONE;13 (8)
dc.subjectUsage logsen
dc.subjectParticipant interactionsen
dc.subjectType-2 diabetesen
dc.subjectSelf-management toolsen
dc.titleAnalysing mHealth usage logs in RCTs: Explaining participants’ interactions with type 2 diabetes self-management toolsen
dc.typeJournal articleen
dc.typePeer revieweden
dc.source.journalPLoS ONE
dc.relation.projectIDNorges forskningsråd: 247974

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