Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study (Preprint)

BACKGROUND Remote data capture for blood glucose (BG) or blood pressure (BP) monitoring and the use of a supportive digital app are becoming the model in diabetes and hypertension chronic care. One of the goals in chronic condition management is to increase awareness and generate behavioral change in order to improve outcomes in diabetes and related comorbidities, such as hypertension. In addition, there is a lack of understanding of the association between BG and BP levels when using digital health tools. OBJECTIVE By applying a rigorous study framework to digital health data, this study investigated the relationship between BP monitoring and BG and BP levels, as well as a lagged association between BP and BG. We hypothesized that during the first 6 months of BP monitoring, BG and BP levels would decrease. Finally, we suggested a positive association between BP levels and the following month’s BG levels. METHODS In this retrospective, real-world case-control study, we extracted the data of 269 people with type 2 diabetes (T2D) who tracked their BG levels using the Dario digital platform for a chronic condition. We analyzed the digital data of the users who, in addition to BG, monitored their BP using the same app (BP-monitoring [BPM] group, n=137) 6 months before and after starting their BP monitoring. Propensity score matching established a control group, no blood pressure monitoring (NBPM, n=132), matched on demographic and baseline clinical measures to the BPM group. A piecewise mixed model was used for analyzing the time trajectories of BG, BP, and their lagged association. RESULTS Analysis revealed a significant difference in BG time trajectories associated with BP monitoring in BPM and NBPM groups (t=–2.12, P=.03). The BPM group demonstrated BG reduction improvement in the monthly average BG levels during the first 6 months (t=–3.57, P<.001), while BG did not change for the NBPM group (t=0.39, P=.70). Both groups showed similarly stable BG time trajectories (B=0.98, t=1.16, P=.25) before starting the use of the BP-monitoring system. In addition, the BPM group showed a significant reduction in systolic (t=–6.42, P<.001) and diastolic (t=–4.80, P<.001) BP during the first 6 months of BP monitoring. Finally, BG levels were positively associated with systolic (B=0.24, t=2.77, P=.001) and diastolic (B=0.30, t=2.41, P=.02) BP. CONCLUSIONS The results of this study shed light on the association between BG and BP levels and on the role of BP self-monitoring in diabetes management. Our findings also underscore the need and provide a basis for a comprehensive approach to understanding the mechanism of BP regulation associated with BG.

[1]  S. Lemon,et al.  Evaluation of a Diabetes Remote Monitoring Program Facilitated by Connected Glucose Meters for Patients With Poorly Controlled Type 2 Diabetes: Randomized Crossover Trial , 2021, JMIR Diabetes.

[2]  Abigail Hirsch,et al.  Role of Digital Engagement in Diabetes Care Beyond Measurement: Retrospective Cohort Study , 2021, JMIR diabetes.

[3]  F. Mair,et al.  Home and Online Management and Evaluation of Blood Pressure (HOME BP) using a digital intervention in poorly controlled hypertension: randomised controlled trial , 2021, BMJ.

[4]  K. Kario.  Management of Hypertension in the Digital Era , 2020, Hypertension.

[5]  T. Hesketh,et al.  The Effectiveness of Self-Management of Hypertension in Adults Using Mobile Health: Systematic Review and Meta-Analysis , 2020, JMIR mHealth and uHealth.

[6]  M. Marre.  Importance of intensive blood pressure control in type 2 diabetes: Mechanisms, treatments and current guidelines , 2020, Diabetes, obesity & metabolism.

[7]  K. Ball,et al.  Toward a Digital Platform for the Self-Management of Noncommunicable Disease: Systematic Review of Platform-Like Interventions , 2019, Journal of medical Internet research.

[8]  Tong Xu,et al.  The Effect of a Cellular-Enabled Glucose Meter on Glucose Control for Patients With Diabetes: Prospective Pre-Post Study , 2019, JMIR diabetes.

[9]  A. Khera,et al.  Correction to: 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. , 2019, Circulation.

[10]  David Kerr,et al.  Digital Diabetes Congress 2019 , 2019, Journal of diabetes science and technology.

[11]  Mark S Hawley,et al.  Smartphone Apps to Support Self-Management of Hypertension: Review and Content Analysis , 2019, JMIR mHealth and uHealth.

[12]  S. Martin,et al.  Digital health technology and mobile devices for the management of diabetes mellitus: state of the art , 2019, Diabetologia.

[13]  Gary G Bennett,et al.  The Association Between Engagement and Weight Loss Through Personal Coaching and Cell Phone Interventions in Young Adults: Randomized Controlled Trial , 2018, JMIR mHealth and uHealth.

[14]  A. Gosmanov,et al.  Hypertension Management in Diabetes: 2018 Update , 2018, Diabetes Spectrum.

[15]  T. Tsujimoto,et al.  Benefits of Intensive Blood Pressure Treatment in Patients With Type 2 Diabetes Mellitus Receiving Standard but Not Intensive Glycemic Control , 2018, Hypertension.

[16]  M. Clements,et al.  Digital Diabetes Management Application Improves Glycemic Outcomes in People With Type 1 and Type 2 Diabetes , 2018, Journal of diabetes science and technology.

[17]  L. Cloutier,et al.  Hypertension in Adults With Type 2 Diabetes: A Review of Blood Pressure Measurement Methods, Targets and Therapy. , 2018, Canadian journal of diabetes.

[18]  M. Ohishi.  Hypertension with diabetes mellitus: physiology and pathology , 2018, Hypertension Research.

[19]  Mark L. Davison,et al.  A note on estimating single-class piecewise mixed-effects models with unknown change points , 2018 .

[20]  Paul T. Williams,et al.  Effectiveness and Safety of a Novel Care Model for the Management of Type 2 Diabetes at 1 Year: An Open-Label, Non-Randomized, Controlled Study , 2018, Diabetes Therapy.

[21]  I. Raz,et al.  Digital health technology and diabetes management , 2017, Journal of diabetes.

[22]  Chandra Y Osborn,et al.  One Drop | Mobile: An Evaluation of Hemoglobin A1c Improvement Linked to App Engagement , 2017, JMIR diabetes.

[23]  I. D. de Boer,et al.  Diabetes and Hypertension: A Position Statement by the American Diabetes Association , 2017, Diabetes Care.

[24]  Praveen Raja,et al.  Effectiveness of Digital Medicines to Improve Clinical Outcomes in Patients with Uncontrolled Hypertension and Type 2 Diabetes: Prospective, Open-Label, Cluster-Randomized Pilot Clinical Trial , 2017, Journal of medical Internet research.

[25]  P. Nihoyannopoulos,et al.  Effects of Oral and Non-Insulin Injectable Antidiabetic Treatment in Hypertension: A Systematic Review. , 2017, Current pharmaceutical design.

[26]  Lucy Yardley,et al.  Developing and Evaluating Digital Interventions to Promote Behavior Change in Health and Health Care: Recommendations Resulting From an International Workshop , 2017, Journal of medical Internet research.

[27]  Jeffrey S. Gonzalez,et al.  Psychosocial factors in medication adherence and diabetes self-management: Implications for research and practice. , 2016, The American psychologist.

[28]  Hyeoun-Ae Park,et al.  A Mobile App for Hypertension Management Based on Clinical Practice Guidelines: Development and Deployment , 2016, JMIR mHealth and uHealth.

[29]  F. D'Amico,et al.  Patient Preferences for Receiving Reports of Test Results , 2015, The Journal of the American Board of Family Medicine.

[30]  G. Hillis,et al.  Effect of Lifestyle-Focused Text Messaging on Risk Factor Modification in Patients With Coronary Heart Disease: A Randomized Clinical Trial. , 2015, JAMA.

[31]  K. Rahimi,et al.  Blood pressure lowering in type 2 diabetes: a systematic review and meta-analysis. , 2015, JAMA.

[32]  Neetika Garg,et al.  A content analysis of smartphone-based applications for hypertension management. , 2015, Journal of the American Society of Hypertension : JASH.

[33]  L. Geiss,et al.  Diabetes Knowledge and Its Relationship With Achieving Treatment Recommendations in a National Sample of People With Type 2 Diabetes , 2012, Diabetes Care.

[34]  B. Cheung,et al.  Diabetes and Hypertension: Is There a Common Metabolic Pathway? , 2012, Current Atherosclerosis Reports.

[35]  G. Bernini,et al.  The Inter-Relationship between Insulin Resistance and Hypertension , 2012, Drugs.

[36]  Gary King,et al.  MatchIt: Nonparametric Preprocessing for Parametric Causal Inference , 2011 .

[37]  Russell E. Glasgow,et al.  Self-Efficacy, Problem Solving, and Social-Environmental Support Are Associated With Diabetes Self-Management Behaviors , 2010, Diabetes Care.

[38]  Mary Yannakoulia,et al.  Management of the metabolic syndrome and type 2 diabetes through lifestyle modification. , 2009, Annual review of nutrition.

[39]  Ross T Tsuyuki,et al.  A meta-analysis of the association between adherence to drug therapy and mortality , 2006, BMJ : British Medical Journal.

[40]  Ian J. Deary,et al.  Intelligence Predicts Health and Longevity, but Why? , 2004 .

[41]  C. Mogensen.  Combined high blood pressure and glucose in type 2 diabetes: double jeopardy , 1998, BMJ.