The impact of structured self-monitoring of blood glucose on glycaemic variability in non-insulin treated type 2 diabetes: The SMBG study, a 12-month randomised controlled trial

Williams, David M.; Parsons, Sharon; Dunseath, Gareth; Stephens, Jeffrey W.; Luzio, Stephen; Owens, David R.

doi:10.1016/j.dsx.2020.01.006

Cited by 12 publications

(9 citation statements)

References 28 publications

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“…As previously mentioned, in many studies for patients with non–insulin-treated DMT2, the value of SMBG is inconsistent [ 8 , 11 , 22 - 24 ]. Some studies have demonstrated that SMBG was effective in controlling blood glucose [ 8 , 22 , 23 , 25 ], whereas other studies claimed that SMBG was not effective [ 12 , 26 ].…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, previous research articles have shown that SMBG is helpful for patients with diabetes mellitus type 1 in controlling blood sugar level. Furthermore, one randomized controlled trial (RCT) recruited patients with diabetes mellitus type 2 (DMT2) and observed them for at least 12 months, and the results suggested that SMBG improves diabetes control [ 11 ]. However, for patients with non–insulin-treated DMT2, the value of SMBG was inconsistent among the studies [ 11 - 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, one randomized controlled trial (RCT) recruited patients with diabetes mellitus type 2 (DMT2) and observed them for at least 12 months, and the results suggested that SMBG improves diabetes control [ 11 ]. However, for patients with non–insulin-treated DMT2, the value of SMBG was inconsistent among the studies [ 11 - 15 ]. The difference may be due to the different research designs or targets of diabetes type in the studies.…”

Section: Introductionmentioning

confidence: 99%

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Real-world Benefits of Diabetes Management App Use and Self-monitoring of Blood Glucose on Glycemic Control: Retrospective Analyses

Chang¹,

Tu²,

Chiou³

et al. 2022

JMIR Mhealth Uhealth

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Background Among self-care measures, the self-monitoring of blood glucose (SMBG) is a critical component for checking blood glucose levels. In addition, there is growing evidence suggesting that digital technologies are being adopted as an additional method for health care systems to increase patient contact. However, for patients with non–insulin-treated diabetes mellitus type 2 (DMT2), the value of SMBG was inconsistent among studies, and the evidence for digital technologies from real-world clinical practice is still limited. Objective Our study aimed to assess patients with non–insulin-treated DMT2 who were receiving care from a single clinic and analyze whether the use of a diabetes management app and SMBG behavior would affect glycemic control in a real-world clinical setting. Methods We collaborated with a large clinic focused on diabetes care in Taiwan that had been using the Health2Sync mobile app and web-based Patient Management Platform to collect the data. The patients were divided into 2 groups (app-engaged-user group and only-data-uploader group) according to different activities in the app, and blood glucose was recorded every month from 1 to 6 months after registration in the app. A sample of 420 patients was included in the analysis, and a linear mixed model was built to investigate which factors affected the patients’ blood glucose percentage change. Results Using the mixed model coefficient estimates, we found that the percentage change was significantly negative when the only-data-uploader group was set as the baseline (t=–3.873, df=1.81 × 104; P<.001 for the patients of the app-engaged-user group). We found that for patients with shorter diabetes duration, their blood glucose decreased more than patients with longer diabetes duration (t=2.823, df=1.71 × 104; P=.005 for the number of years of diabetes duration). In addition, we found that for younger patients, their blood glucose decreased more than older patients (t=2.652, df=1.71 × 104; P=.008 for the age of the patients). Furthermore, the patients with an education level of junior high school or lower saw a significantly greater decrease in blood glucose percentage change than the patients with an education level of senior high school or higher (t=4.996, df=1.72 × 104; P<.001 for the patients with an education level of senior high school or higher). We also found that the count of blood glucose measured enlarged the decrease along the interaction months (t=–8.266, df=1.97 × 104; P<.001 for the nth month × the count of blood glucose in the nth month). Lastly, the gender of the patients did not significantly affect the percentage change (t=0.534, df=1.74 × 104; P=.59 for female patients). Conclusions Our analysis showed the following: the blood glucose percentage change of the patients in the app-engaged-user group dropped more than that in the only-data-uploader group; shorter diabetes duration is associated with a steeper decrease in the patients’ blood glucose percentage change; the percentage decrease in blood glucose change in younger patients is greater than older patients; the blood glucose percentage change of the patients with an education level of junior high school or lower dropped more than those with an education level of senior high school or higher; and the more frequently the patients test SMBG each month, the greater the decrease in the patients’ blood glucose percentage. Further studies can be performed to consider the differences in daily behaviors such as exercise and diet across the patients and whether these factors could have vital effects on glycemic control.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-world Benefits of Diabetes Management App Use and Self-monitoring of Blood Glucose on Glycemic Control: Retrospective Analyses

Chang¹,

Tu²,

Chiou³

et al. 2022

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Moreover, the benefits are greater when SMBG data are used to adjust the prescription of diabetes medications (-0.3% [-0.4 to 0.1], data from 3 RCTs). The efficacy of structured SMBG was further highlighted by the recent SMBG study in 231 patients with non-insulin-treated T2D, reporting improvements in all measures of glycemic variability with structured monitoring [14].…”

Section: Introductionmentioning

confidence: 99%

Structured self-monitoring of blood glucose is associated with more appropriate therapeutic interventions than unstructured self-monitoring: A novel analysis of data from the PRISMA trial

Molfetta¹,

Bosi²,

Ceriello³

et al. 2021

Diabetes Research and Clinical Practice

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To investigate the relationship between single therapeutic interventions and indicators of glycemic control in the PRISMA trial, a large study comparing the effects of intensive structured SMBG (ISM) vs. active control (AC) in non-insulin-treated type 2 diabetes (T2D).Methods: Information was collected at four time points, corresponding to months 3, 6, 9, and 12 and visits 2, 3, 4, and 5, respectively. Data on therapeutic interventions, HbA 1c levels and the number of hypoglycemic episodes at each visit were analyzed.Results: Intensification of drug therapy occurred in 20.3% vs. 15.6%, and no change in 71.8% vs. 78.7% of visits for the ISM and AC groups, respectively. On the other hand, de-intensification and redistribution of drugs and/or drug dose occurred in a similar proportion of visits. Intensification of drug therapy in both groups was associated with significant reductions in HbA 1c vs. the previous visit, while de-intensification of therapy led to a significant increase in HbA 1c in the AC group only. Conclusions. Our data strongly support that structured SMBG has clinical value in reducing HbA 1c in non-insulin-treated T2D and suggest that this clinical benefit may be mediated by more appropriate and timely changes in drug therapy.

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“…Diabetes management programs have shown to be as or more effective than usual care in providing a significant reduction in hemoglobin A 1c (HbA 1c ) [ 8 - 10 ]. Additionally, structured self-monitoring of blood glucose (SMBG) has been observed to improve glycemic variability and provide greater self-efficacy in management by helping an individual understand lifestyle behaviors’ impact on blood glucose (BG) values over time [ 2 , 11 ]. However, while advances in diabetes treatment options, diabetes management programs, new technologies to support self-management, and the rise in digital health are rushing the market, half of individuals with diabetes have an HbA 1c value of 7.0% or higher and struggle to obtain consistent glycemic control [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

Kamath¹,

Kappaganthu²,

Painter³

et al. 2022

JMIR Form Res

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Background Diabetes management is complex, and program personalization has been identified to enhance engagement and clinical outcomes in diabetes management programs. However, 50% of individuals living with diabetes are unable to achieve glycemic control, presenting a gap in the delivery of self-management education and behavior change. Machine learning and recommender systems, which have been used within the health care setting, could be a feasible application for diabetes management programs to provide a personalized user experience and improve user engagement and outcomes. Objective This study aims to evaluate machine learning models using member-level engagements to predict improvement in estimated A1c and develop personalized action recommendations within a remote diabetes monitoring program to improve clinical outcomes. Methods A retrospective study of Livongo for Diabetes member engagement data was analyzed within five action categories (interacting with a coach, reading education content, self-monitoring blood glucose level, tracking physical activity, and monitoring nutrition) to build a member-level model to predict if a specific type and level of engagement could lead to improved estimated A1c for members with type 2 diabetes. Engagement and improvement in estimated A1c can be correlated; therefore, the doubly robust learning method was used to model the heterogeneous treatment effect of action engagement on improvements in estimated A1c. Results The treatment effect was successfully computed within the five action categories on estimated A1c reduction for each member. Results show interaction with coaches and self-monitoring blood glucose levels were the actions that resulted in the highest average decrease in estimated A1c (1.7% and 1.4%, respectively) and were the most recommended actions for 54% of the population. However, these were found to not be the optimal interventions for all members; 46% of members were predicted to have better outcomes with one of the other three interventions. Members who engaged with their recommended actions had on average a 0.8% larger reduction in estimated A1c than those who did not engage in recommended actions within the first 3 months of the program. Conclusions Personalized action recommendations using heterogeneous treatment effects to compute the impact of member actions can reduce estimated A1c and be a valuable tool for diabetes management programs in encouraging members toward actions to improve clinical outcomes.

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The impact of structured self-monitoring of blood glucose on glycaemic variability in non-insulin treated type 2 diabetes: The SMBG study, a 12-month randomised controlled trial

Abstract: The impact of structured self-monitoring of blood glucose on glycaemic variability in non-insulin treated type 2 diabetes: the SMBG study, a 12-month randomised controlled trial

Cited by 12 publications

References 28 publications

Real-world Benefits of Diabetes Management App Use and Self-monitoring of Blood Glucose on Glycemic Control: Retrospective Analyses

Real-world Benefits of Diabetes Management App Use and Self-monitoring of Blood Glucose on Glycemic Control: Retrospective Analyses

Structured self-monitoring of blood glucose is associated with more appropriate therapeutic interventions than unstructured self-monitoring: A novel analysis of data from the PRISMA trial

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

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