Comparing the Efficacy of a Mobile Phone-Based Blood Glucose Management System With Standard Clinic Care in Women With Gestational Diabetes: Randomized Controlled Trial

Lucy Mackillop, Jane Elizabeth Hirst, Katy Jane Bartlett, Jacqueline Susan Birks, Lei Clifton, Andrew J Farmer, Oliver Gibson, Yvonne Kenworthy, Jonathan Cummings Levy, Lise Loerup, Oliver Rivero-Arias, Wai-Kit Ming, Carmelo Velardo, Lionel Tarassenko, Lucy Mackillop, Jane Elizabeth Hirst, Katy Jane Bartlett, Jacqueline Susan Birks, Lei Clifton, Andrew J Farmer, Oliver Gibson, Yvonne Kenworthy, Jonathan Cummings Levy, Lise Loerup, Oliver Rivero-Arias, Wai-Kit Ming, Carmelo Velardo, Lionel Tarassenko

Abstract

Background: Treatment of hyperglycemia in women with gestational diabetes mellitus (GDM) is associated with improved maternal and neonatal outcomes and requires intensive clinical input. This is currently achieved by hospital clinic attendance every 2 to 4 weeks with limited opportunity for intervention between these visits.

Objective: We conducted a randomized controlled trial to determine whether the use of a mobile phone-based real-time blood glucose management system to manage women with GDM remotely was as effective in controlling blood glucose as standard care through clinic attendance.

Methods: Women with an abnormal oral glucose tolerance test before 34 completed weeks of gestation were individually randomized to a mobile phone-based blood glucose management solution (GDm-health, the intervention) or routine clinic care. The primary outcome was change in mean blood glucose in each group from recruitment to delivery, calculated with adjustments made for number of blood glucose measurements, proportion of preprandial and postprandial readings, baseline characteristics, and length of time in the study.

Results: A total of 203 women were randomized. Blood glucose data were available for 98 intervention and 85 control women. There was no significant difference in rate of change of blood glucose (-0.16 mmol/L in the intervention and -0.14 mmol/L in the control group per 28 days, P=.78). Women using the intervention had higher satisfaction with care (P=.049). Preterm birth was less common in the intervention group (5/101, 5.0% vs 13/102, 12.7%; OR 0.36, 95% CI 0.12-1.01). There were fewer cesarean deliveries compared with vaginal deliveries in the intervention group (27/101, 26.7% vs 47/102, 46.1%, P=.005). Other glycemic, maternal, and neonatal outcomes were similar in both groups. The median time from recruitment to delivery was similar (intervention: 54 days; control: 49 days; P=.23). However, there were significantly more blood glucose readings in the intervention group (mean 3.80 [SD 1.80] and mean 2.63 [SD 1.71] readings per day in the intervention and control groups, respectively; P<.001). There was no significant difference in direct health care costs between the two groups, with a mean cost difference of the intervention group compared to control of -£1044 (95% CI -£2186 to £99). There were no unexpected adverse outcomes.

Conclusions: Remote blood glucocse monitoring in women with GDM is safe. We demonstrated superior data capture using GDm-health. Although glycemic control and maternal and neonatal outcomes were similar, women preferred this model of care. Further studies are required to explore whether digital health solutions can promote desired self-management lifestyle behaviors and dietetic adherence, and influence maternal and neonatal outcomes. Digital blood glucose monitoring may provide a scalable, practical method to address the growing burden of GDM around the world.

Trial registration: ClinicalTrials.gov NCT01916694; https://ichgcp.net/clinical-trials-registry/NCT01916694 (Archived by WebCite at http://www.webcitation.org/6y3lh2BOQ).

Keywords: GDM; app; blood glucose monitoring; digital health; gestational diabetes; pregnancy.

Conflict of interest statement

Conflicts of Interest: LM, CV, and LT reports consultancy fees from Drayson Technologies who have, subsequent to this study, become the sole licensee of the GDm-health management system. LT is also on the advisory board for Drayson Technologies. LL was funded by the RCUK Digital Economy Programme and the Clarendon, Scatcherd European, and New College Graduate Scholarship schemes. ORA reports grants from Medical Research Council, grants from NIHR-HTA, grants from NIHR-HS&DR, grants from EuroQol Research Foundation, personal fees from EuroQol Research Foundation, personal fees from Oxford Pharmagenesis, outside the submitted work. JEH, KB, JB, LC, AJF, OJF, YK, JCL, and WKM have no declarations of conflicts of interest.

©Lucy Mackillop, Jane Elizabeth Hirst, Katy Jane Bartlett, Jacqueline Susan Birks, Lei Clifton, Andrew J Farmer, Oliver Gibson, Yvonne Kenworthy, Jonathan Cummings Levy, Lise Loerup, Oliver Rivero-Arias, Wai-Kit Ming, Carmelo Velardo, Lionel Tarassenko. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 20.03.2018.

Figures

Figure 1
Figure 1
CONSORT Flow Diagram for TREAT GDM.
Figure 2
Figure 2
Change in mean blood glucose.

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Source: PubMed

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