Effects of Patient-Driven Lifestyle Modification Using Intermittently Scanned Continuous Glucose Monitoring in Patients With Type 2 Diabetes: Results From the Randomized Open-label PDF Study

Hun Jee Choe, Eun-Jung Rhee, Jong Chul Won, Kyong Soo Park, Won-Young Lee, Young Min Cho, Hun Jee Choe, Eun-Jung Rhee, Jong Chul Won, Kyong Soo Park, Won-Young Lee, Young Min Cho

Abstract

Objective: To investigate the effects of patient-driven lifestyle modification using intermittently scanned continuous glucose monitoring (isCGM) in patients with type 2 diabetes (T2D).

Research design and methods: We conducted a 12-week, open-label, randomized controlled trial. A total of 126 participants were 1:1 randomized to either the intervention group (structured education + isCGM) or the control group (standard care with blood glucose monitoring). The Self-Evaluation Of Unhealthy foods by Looking at postprandial glucose (SEOUL) algorithm was developed and applied to aid structured education in guiding patients to follow healthy eating behavior depending on the postprandial glycemic response. The primary end point was the change in HbA1c level from baseline.

Results: Implementation of the SEOUL algorithm with isCGM was associated with greater improvement in HbA1c than with standard care (risk-adjusted difference -0.50%, 95% CI -0.74 to -0.26, P < 0.001). Participants in the intervention group had a greater reduction in fasting blood glucose and body weight (-16.5 mg/dL, 95% CI -30.0 to -3.0, P = 0.017; -1.5 kg, 95% CI -2.7 to -0.3, P = 0.013, respectively). The score sum for the Korean version of the revised Summary of Diabetes Self-Care Activities Questionnaire increased in both groups but to a greater extent in the intervention group (mean difference 4.8, 95% CI 1.7-8.0, P = 0.003). No severe hyperglycemia or hypoglycemia was reported in either group of patients.

Conclusions: Patient-driven lifestyle modification primarily focused on eating behavior using isCGM effectively lowered HbA1c levels in patients with T2D.

Trial registration: ClinicalTrials.gov NCT04932928.

© 2022 by the American Diabetes Association.

Figures

Figure 1
Figure 1
The SEOUL algorithm. Participants are encouraged to continue eating a healthy meal with tolerable glycemic response after consuming the food and should avoid an unhealthy meal that provokes postprandial hyperglycemia. If hyperglycemia is detected after consuming a meal that is generally considered to be healthy, reducing the amount of food is recommended; the amount of unhealthy food should also be reduced even if it does not generate hyperglycemia on ingestion. Decisions on lifestyle modification will be made on an individual basis according to the SEOUL algorithm.
Figure 2
Figure 2
Consort diagram.
Figure 3
Figure 3
Glycemic outcome according to treatment groups. A: Box plot of HbA1c (%) in the intervention and control groups at V1 and V2. HbA1c levels were significantly decreased in the intervention group at 12 weeks. B: Cumulative distribution of HbA1c (%) levels at V1. C: Cumulative distribution of HbA1c (%) levels at V2. *P < 0.05, **P < 0.01, ***P < 0.001. ns, not significant.

References

    1. American Diabetes Association Professional Practice Committee . 5. Facilitating behavior change and well-being to improve health outcomes: Standards of Medical Care in Diabetes—2022. Diabetes Care 2022;45(Suppl. 1):S60–S82
    1. Franz MJ, MacLeod J, Evert A, et al. . Academy of Nutrition and Dietetics Nutrition Practice Guideline for Type 1 and Type 2 Diabetes in Adults: systematic review of evidence for medical nutrition therapy effectiveness and recommendations for integration into the nutrition care process. J Acad Nutr Diet 2017;117:1659–1679
    1. Evert AB, Boucher JL, Cypress M, et al. .; American Diabetes Association . Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care 2013;36:3821–3842
    1. Zeevi D, Korem T, Zmora N, et al. . Personalized nutrition by prediction of glycemic responses. Cell 2015;163:1079–1094
    1. Mendes-Soares H, Raveh-Sadka T, Azulay S, et al. . Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. JAMA Netw Open 2019;2:e188102.
    1. Edelman SV, Argento NB, Pettus J, Hirsch IB. Clinical implications of real-time and intermittently scanned continuous glucose monitoring. Diabetes Care 2018;41:2265–2274
    1. Perez-Guzman MC, Shang T, Zhang JY, Jornsay D, Klonoff DC. Continuous glucose monitoring in the hospital. Endocrinol Metab (Seoul) 2021;36:240–255
    1. Haak T, Hanaire H, Ajjan R, Hermanns N, Riveline JP, Rayman G. Flash glucose-sensing technology as a replacement for blood glucose monitoring for the management of insulin-treated type 2 diabetes: a multicenter, open-label randomized controlled trial. Diabetes Ther 2017;8:55–73
    1. Beck RW, Riddlesworth TD, Ruedy K, et al. .; DIAMOND Study Group . Continuous glucose monitoring versus usual care in patients with type 2 diabetes receiving multiple daily insulin injections: a randomized trial. Ann Intern Med 2017;167:365–374
    1. Vigersky RA, Fonda SJ, Chellappa M, Walker MS, Ehrhardt NM. Short- and long-term effects of real-time continuous glucose monitoring in patients with type 2 diabetes. Diabetes Care 2012;35:32–38
    1. Martens T, Beck RW, Bailey R, et al. .; MOBILE Study Group . Effect of continuous glucose monitoring on glycemic control in patients with type 2 diabetes treated with basal insulin: a randomized clinical trial. JAMA 2021;325:2262–2272
    1. Aleppo G, Beck RW, Bailey R, et al. .; MOBILE Study Group; Type 2 Diabetes Basal Insulin Users: The Mobile Study (MOBILE) Study Group . The effect of discontinuing continuous glucose monitoring in adults with type 2 diabetes treated with basal insulin. Diabetes Care 2021;44:2729–2737
    1. Korean Diabetes Association . Diabetes education, 2022. Accessed 3 November 2021. Available from
    1. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group; Tamborlane WV, Beck RW, Bode BW, et al. . Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med 2008;359:1464–1476
    1. Ehrhardt NM, Chellappa M, Walker MS, Fonda SJ, Vigersky RA. The effect of real-time continuous glucose monitoring on glycemic control in patients with type 2 diabetes mellitus. J Diabetes Sci Technol 2011;5:668–675
    1. Dunn TC, Xu Y, Hayter G, Ajjan RA. Real-world flash glucose monitoring patterns and associations between self-monitoring frequency and glycaemic measures: a European analysis of over 60 million glucose tests. Diabetes Res Clin Pract 2018;137:37–46
    1. Bolinder J, Antuna R, Geelhoed-Duijvestijn P, Kröger J, Weitgasser R. Novel glucose-sensing technology and hypoglycaemia in type 1 diabetes: a multicentre, non-masked, randomised controlled trial. Lancet 2016;388:2254–2263
    1. Castellana M, Parisi C, Di Molfetta S, et al. . Efficacy and safety of flash glucose monitoring in patients with type 1 and type 2 diabetes: a systematic review and meta-analysis. BMJ Open Diabetes Res Care 2020;8:e001092
    1. World Health Organization Regional Office for the Western Pacific International Association for the Study of Obesity and the International Obesity Task Force . The Asia-Pacific Perspective: Redefining Obesity and Its Treatment. 2002
    1. Kim EK, Kwak SH, Baek S, et al. . Feasibility of a patient-centered, smartphone-based, diabetes care system: a pilot study. Diabetes Metab J 2016;40:192–201
    1. Kim EK, Kwak SH, Jung HS, et al. . The effect of a smartphone-based, patient-centered diabetes care system in patients with type 2 diabetes: a randomized, controlled trial for 24 weeks. Diabetes Care 2019;42:3–9
    1. Chang S, Song M. The validity and reliability of a Korean version of the Summary of Diabetes Self-Care Activities Questionnaire for older patients with type 2 diabetes. J Korean Acad Adult Nurs 2009;21:235–244

Source: PubMed

Подписаться