Rethinking Models of Outpatient Specialist Care in Type 2 Diabetes Using eHealth: Study Protocol for a Pilot Randomised Controlled Trial

Conventional outpatient services are unlikely to meet burgeoning demand for diabetes services given increasing prevalence of diabetes, and resultant impact on the healthcare workforce and healthcare costs. Disruptive technologies (such as smartphone and wireless sensors) create an opportunity to redesign outpatient services. In collaboration, the Department of Diabetes and Endocrinology at Brisbane Princess Alexandra Hospital, the University of Queensland Centre for Health Services Research and the Australian e-Health Research Centre developed a mobile diabetes management system (MDMS) to support the management of complex outpatient type 2 diabetes mellitus (T2DM) adults. The system comprises of a mobile App, an automated text-messaging feedback and a clinician portal. Blood glucose levels (BGL) data are automatically transferred by Bluetooth-enabled glucose meter to the clinician portal via the mobile App. The primary aim of the study described here is to examine improvement in glycaemic control of a new model of care employing MDMS for patients with complex T2DM attending a tertiary level outpatient service. A two-group, 12-month, pilot pragmatic randomised control trial will recruit 44 T2DM patients. The control group will receive routine care. The intervention group will be supported by the MDMS enabling the participants to potentially better self-manage their diabetes, and the endocrinologists to remotely monitor BGL and to interact with patients through a variety of eHealth modalities. Intervention participants will be encouraged to complete relevant pathology tests, and report on current diabetes management through an online questionnaire. Using this information, the endocrinologist may choose to reschedule the appointment or substitute it with a telephone or video-consultation. This pilot study will guide the conduct of a large-scale study regarding the capacity for a new model of care. This model utilises multimodal eHealth strategies via the MDMS to primarily improve glycaemic control with secondary aims to improve patient experience, reduce reliance on physical clinics, and decrease service delivery cost.

[1]  M. Karunanithi,et al.  Mobile-based insulin dose adjustment for type 2 diabetes in community and rural populations: study protocol for a pilot randomized controlled trial , 2019, Therapeutic advances in endocrinology and metabolism.

[2]  H. Zeeb,et al.  Effectiveness of Digital Interventions for Improving Glycemic Control in Persons with Poorly Controlled Type 2 Diabetes: A Systematic Review, Meta-analysis, and Meta-regression Analysis. , 2018, Diabetes technology & therapeutics.

[3]  M. Karunanithi,et al.  User Experience of an Innovative Mobile Health Program to Assist in Insulin Dose Adjustment: Outcomes of a Proof-Of-Concept Trial. , 2017, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[4]  Xingda Qu,et al.  Effects of consumer-oriented health information technologies in diabetes management over time: a systematic review and meta-analysis of randomized controlled trials , 2017, J. Am. Medical Informatics Assoc..

[5]  S. Newman,et al.  Quantifying beliefs regarding telehealth: Development of the Whole Systems Demonstrator Service User Technology Acceptability Questionnaire , 2017, Journal of telemedicine and telecare.

[6]  Guy Paré,et al.  Effectiveness of mHealth interventions for patients with diabetes: An overview of systematic reviews , 2017, PloS one.

[7]  Paula Forbes,et al.  mHealth applications for diabetes: User preference and implications for app development , 2016, Health Informatics J..

[8]  M. Nie,et al.  T2DM Self-Management via Smartphone Applications: A Systematic Review and Meta-Analysis , 2016, PloS one.

[9]  K. Close,et al.  Challenges in Diabetes Care: Can Digital Health Help Address Them? , 2016, Clinical Diabetes.

[10]  J. Richardson,et al.  Deriving population norms for the AQoL-6D and AQoL-8D multi-attribute utility instruments from web-based data , 2016, Quality of Life Research.

[11]  N. Kaufman,et al.  Using Digital Health Technology to Prevent and Treat Diabetes. , 2016, Diabetes technology & therapeutics.

[12]  Ruyi Huang,et al.  Utilization of a Cloud-Based Diabetes Management Program for Insulin Initiation and Titration Enables Collaborative Decision Making Between Healthcare Providers and Patients , 2015, Diabetes technology & therapeutics.

[13]  R. Istepanian,et al.  Mobile applications for diabetes management: efficacy issues and regulatory challenges. , 2015, The lancet. Diabetes & endocrinology.

[14]  B. Spring,et al.  Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention: A Scientific Statement From the American Heart Association. , 2015, Circulation.

[15]  Marlien Varnfield,et al.  Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: results from a randomised controlled trial , 2014, Heart.

[16]  J. Shaw,et al.  IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. , 2011, Diabetes research and clinical practice.

[17]  P. Harris,et al.  Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support , 2009, J. Biomed. Informatics.

[18]  E H Wagner,et al.  Chronic disease management: what will it take to improve care for chronic illness? , 1998, Effective clinical practice : ECP.

[19]  N. Kaufman,et al.  Using Digital Health Technology to Prevent and Treat Diabetes. , 2018, Diabetes technology & therapeutics.