A Mobile App for the Self-Management of Type 1 Diabetes Among Adolescents: A Randomized Controlled Trial

Background While optimal blood glucose control is known to reduce the long-term complications associated with type 1 diabetes mellitus, adolescents often struggle to achieve their blood glucose targets. However, their strong propensity toward technology presents a unique opportunity for the delivery of novel self-management interventions. To support type 1 diabetes self-management in this population, we developed the diabetes self-management app bant, which included wireless blood glucose reading transfer, out-of-range blood glucose trend alerts, coaching around out-of-range trend causes and fixes, and a point-based incentive system. Objective The primary objective was to evaluate bant ’s effect on hemoglobin A1c (HbA1c) through a randomized controlled trial (RCT). Secondary measures (eg, self-monitoring of blood glucose [SMBG]) were also collected to assess bant ’s impact on the self-management behaviors of adolescents with type 1 diabetes. Methods We enrolled 92 adolescents into a 12-month RCT, with 46 receiving usual care and 46 receiving usual care plus bant. Clinical outcome data were collected at quarterly research visits via validated tools, electronic chart review, glucometer downloads, and semistructured interviews. App satisfaction was assessed at 6 and 12 months, and at trial end, users ranked bant components based on perceived usefulness. Mobile analytics captured frequency of blood glucose uploads, which were used to categorize participants into high, moderate, low, or very low engagement levels. Results Linear mixed models showed no changes in primary and secondary clinical outcomes. However, exploratory regression analysis demonstrated a statistically significant association between increased SMBG and improved HbA1c in the intervention group. For a subgroup of bant users taking SMBG ≥5 daily, there was a significant improvement in HbA1c of 0.58% (P=.02), while the parallel subgroup in the control arm experienced no significant change in HbA1c (decrease of 0.06%, P=.84). Although app usage did diminish over the trial, on average, 35% (16/46 participants) were classified as moderately or highly engaged (uploaded SMBG ≥3 days a week) over the 12 months. Conclusion Although primary analysis of clinical outcomes did not demonstrate differences between the bant and control groups, exploratory analysis suggested that bant may positively impact the use of SMBG data and glycemic control among youth. The next generation of bant will aim to remove barriers to use, such as deploying the app directly to personal devices instead of secondary research phones, and to explore the utility of integrating bant into routine clinical care to facilitate more frequent feedback. Future evaluations of mHealth apps should consider more robust research tools (eg, ResearchKit) and alternative RCT study designs to enable more rapid and iterative evaluations, better suited to the nature of rapidly evolving consumer technology. Trial Registration ClinicalTrials.gov NCT01899274; https://clinicaltrials.gov/ct2/show/NCT01899274 (Archived by WebCite at http://www.webcitation.org/6qWrqF1yw)

[1]  Hypoglycemia in the Diabetes Control and Complications Trial , 1997, Diabetes.

[2]  J. Lachin,et al.  Mortality in Type 1 Diabetes in the DCCT/EDIC Versus the General Population , 2016, Diabetes Care.

[3]  S. Friend,et al.  The mPower study, Parkinson disease mobile data collected using ResearchKit , 2016, Scientific Data.

[4]  R. Shulman,et al.  Type 1 diabetes mellitus in childhood , 2010 .

[5]  C. de Beaufort,et al.  Lessons from the Hvidoere International Study Group on childhood diabetes: be dogmatic about outcome and flexible in approach , 2013, Pediatric diabetes.

[6]  Blake Poland,et al.  How Adolescents Use Technology for Health Information: Implications for Health Professionals from Focus Group Studies , 2003, Journal of medical Internet research.

[7]  P. Timsina,et al.  Mobile Applications for Diabetes Self-Management: Status and Potential , 2013, Journal of diabetes science and technology.

[8]  L. Tarassenko,et al.  A systematic review of telemedicine interventions to support blood glucose self‐monitoring in diabetes , 2005, Diabetic medicine : a journal of the British Diabetic Association.

[9]  J. Miller,et al.  Assessing family sharing of diabetes responsibilities. , 1990, Journal of pediatric psychology.

[10]  Morwenna Kirwan,et al.  Diabetes Self-Management Smartphone Application for Adults With Type 1 Diabetes: Randomized Controlled Trial , 2013, Journal of medical Internet research.

[11]  Cameron D. Norman,et al.  Using the internet to assist smoking prevention and cessation in schools: a randomized, controlled trial. , 2008, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[12]  Diabetes Control.  Effect of pregnancy on microvascular complications in the diabetes control and complications trial. The Diabetes Control and Complications Trial Research Group. , 2000, Diabetes care.

[13]  Milada Cvancarova Småstuen,et al.  A Mobile Health Intervention for Self-Management and Lifestyle Change for Persons With Type 2 Diabetes, Part 2: One-Year Results From the Norwegian Randomized Controlled Trial RENEWING HEALTH , 2014, JMIR mHealth and uHealth.

[14]  J. McGill,et al.  Evidence of a Strong Association Between Frequency of Self-Monitoring of Blood Glucose and Hemoglobin A1c Levels in T1D Exchange Clinic Registry Participants , 2013, Diabetes Care.

[15]  D. Marrero,et al.  A Modified Quality-of-Life Measure for Youths: Psychometric Properties , 1991, The Diabetes educator.

[16]  E. Storch,et al.  Validity and reliability of an adolescent and parent rating scale of type 1 diabetes adherence behaviors: the Self-Care Inventory (SCI). , 2009, Journal of pediatric psychology.

[17]  P. Hougaard,et al.  Analyses of the rate of decline in stimulated c-peptide 12 months after diagnosis in children with newly diag-nosed type 1 diabetes. results from the Hvidoere study group on childhood diabetes , 2010 .

[18]  Xiaomin He.  Linear Mixed Models in Clinical Trials using PROC MIXED , 2010 .

[19]  G. Eysenbach.  CONSORT-EHEALTH: Improving and Standardizing Evaluation Reports of Web-based and Mobile Health Interventions , 2011, Journal of medical Internet research.

[20]  P A Cleary,et al.  Beneficial effects of intensive therapy of diabetes during adolescence: outcomes after the conclusion of the Diabetes Control and Complications Trial (DCCT). , 2001, The Journal of pediatrics.

[21]  B. Zinman,et al.  Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. , 2005, The New England journal of medicine.

[22]  S. Murphy,et al.  The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. , 2007, American journal of preventive medicine.

[23]  Roser Bono,et al.  The effect of skewness and kurtosis on the robustness of linear mixed models , 2013, Behavior research methods.

[24]  T. Skinner,et al.  A short form of the Diabetes Quality of Life for Youth questionnaire: exploratory and confirmatory analysis in a sample of 2,077 young people with type 1 diabetes mellitus , 2006, Diabetologia.

[25]  S. Chalew.  Continuing Stability of Center Differences in Pediatric Diabetes Care: Do Advances in Diabetes Treatment Improve Outcome? The Hvidoere Study Group on Childhood Diabetes , 2008, Diabetes Care.

[26]  Wilhelm Kirch,et al.  Mobile Applications for Diabetics: A Systematic Review and Expert-Based Usability Evaluation Considering the Special Requirements of Diabetes Patients Age 50 Years or Older , 2014, Journal of medical Internet research.

[27]  Joseph A Cafazzo,et al.  Beyond the Randomized Controlled Trial: A Review of Alternatives in mHealth Clinical Trial Methods , 2016, JMIR mHealth and uHealth.

[28]  J. Cafazzo,et al.  Design of an mHealth App for the Self-management of Adolescent Type 1 Diabetes: A Pilot Study , 2012, Journal of medical Internet research.

[29]  S. Genuth,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.

[30]  Borja Martínez-Pérez,et al.  Mobile Health Applications for the Most Prevalent Conditions by the World Health Organization: Review and Analysis , 2013, Journal of medical Internet research.

[31]  J. Prochaska,et al.  Transtheoretical therapy: Toward a more integrative model of change. , 1982 .

[32]  R. Beck,et al.  Most Youth With Type 1 Diabetes in the T1D Exchange Clinic Registry Do Not Meet American Diabetes Association or International Society for Pediatric and Adolescent Diabetes Clinical Guidelines , 2013, Diabetes Care.

[33]  C. de Beaufort,et al.  Continuing Stability of Center Differences in Pediatric Diabetes Care: Do Advances in Diabetes Treatment Improve Outcome? , 2007, Diabetes Care.

[34]  C. Quinn,et al.  Cluster-Randomized Trial of a Mobile Phone Personalized Behavioral Intervention for Blood Glucose Control , 2011, Diabetes Care.

[35]  W. Velicer,et al.  The Transtheoretical Model of Health Behavior Change , 1997, American journal of health promotion : AJHP.

[36]  Joyce M Lee,et al.  The Promise and Peril of Mobile Health Applications for Diabetes and Endocrinology , 2013, Pediatric diabetes.

[37]  John B. Horrigan,et al.  Pew Internet & American Life Project , 2002 .

[38]  G. Hartvigsen,et al.  Features of Mobile Diabetes Applications: Review of the Literature and Analysis of Current Applications Compared Against Evidence-Based Guidelines , 2011, Journal of medical Internet research.

[39]  S. Edirippulige,et al.  Using mobile technology to motivate adolescents with type 1 diabetes mellitus: A systematic review of recent literature , 2015, Journal of telemedicine and telecare.

[40]  Lawrence A Leiter,et al.  Canadian Diabetes Association Clinical Practice Guidelines Expert Committee , 2013 .

[41]  N. Consedine,et al.  Kindness Matters: A Randomized Controlled Trial of a Mindful Self-Compassion Intervention Improves Depression, Distress, and HbA1c Among Patients With Diabetes , 2016, Diabetes Care.

[42]  P. Bingley,et al.  Trends in childhood type 1 diabetes incidence in Europe during 1989–2008: evidence of non-uniformity over time in rates of increase , 2012, Diabetologia.

[43]  H. Bilo,et al.  Differences in interpretation of haemoglobin A1c values among diabetes care professionals. , 2014, The Netherlands journal of medicine.

[44]  H. Mortensen,et al.  Insulin injection regimens and metabolic control in an international survey of adolescents with type 1 diabetes over 3 years: results from the Hvidore study group , 2002, European Journal of Pediatrics.

[45]  M. Fisher,et al.  Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) , 2007 .

[46]  David M Maahs,et al.  Current State of Type 1 Diabetes Treatment in the U.S.: Updated Data From the T1D Exchange Clinic Registry , 2015, Diabetes Care.