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CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review.

ABSTRACT: Glycemic variability (GV) appears today as an integral component of glucose homeostasis for the management of type 2 diabetes (T2D). This review aims at investigating the use and relevance of GV parameters in interventional and observational studies for glucose control management in T2D. It will first focus on the relationships between GV parameters measured by continuous glucose monitoring system (CGMS) and glycemic control and T2D-associated complications markers. The second part will be dedicated to the analysis of GV parameters from CGMS as outcomes in interventional studies (pharmacological, nutritional, physical activity) aimed at improving glycemic control in patients with T2D. From 243 articles first identified, 63 articles were included (27 for the first part and 38 for the second part). For both analyses, the majority of the identified studies were pharmacological. Lifestyle studies (including nutritional and physical activity-based studies, N-AP) were poorly represented. Concerning the relationships of GV parameters with those for glycemic control and T2D related-complications, the standard deviation (SD), the coefficient of variation (CV), the mean blood glucose (MBG), and the mean amplitude of the glycemic excursions (MAGEs) were the most studied, showing strong relationships, in particular with HbA1c. Regarding the use and relevance of GV as an outcome in interventional studies, in pharmacological ones, SD, MAGE, MBG, and time in range (TIR) were the GV parameters used as main criteria in most studies, showing significant improvement after intervention, in parallel or not with glycemic control parameters' (HbA1c, FBG, and PPBG) improvement. In N-AP studies, the same results were observed for SD, MAGE, and TIR. Despite the small number of N-AP studies addressing both GV and glycemic control parameters compared to pharmacological ones, N-AP studies have shown promising results on GV parameters and would require more in-depth work. Evaluating CGMS-GV parameters as outcomes in interventional studies may provide a more integrative dimension of glucose control than the standard postprandial follow-up. GV appears to be a key component of T2D dysglycemia, and some parameters such as MAGE, SD, or TIR could be used routinely in addition to classical markers of glycemic control such as HbA1c, fasting, or postprandial glycemia.

SUBMITTER: Breyton AE

PROVIDER: S-EPMC8458933 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients' health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data. OBJECTIVE:This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. METHODS:A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. RESULTS:A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). CONCLUSIONS:This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients' self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice.

Project description:BackgroundChildren and patients with cognitive deficits may find it difficult to understand the implication of research. In the European Union (EU), clinical studies outside the EU directives concerning medicinal products or medical devices, i.e., "miscellaneous clinical studies", have no legally mandated timelines for institutional review boards' (IRB) decisions.GoalTo evaluate the review process of IRBs for two different "miscellaneous" multicenter clinical research protocols involving vulnerable subjects (children and adult stroke patients).MethodsDescriptive and comparative statistics. Protocol 1 is a prospective, multicenter, cross-sectional screening study of a symptomatic pediatric population at risk for Fabry disease involving genetic testing (NCT02152189). Protocol 2 is a prospective, multicenter, randomized, controlled, open-label, blinded endpoint post-market study to evaluate the effectiveness of stent retrievers (NCT02135926). After having obtained positive initial IRB votes at the main study site, both protocols were subsequently submitted to the remaining IRBs.ResultsProtocol 1 was submitted to 19 IRBs. No IRB objected to the study. Median time-to-final vote was 34 (IQR 10-65; range 0 to 130) days. Two IRBs accepted the coordinating center's IRB votes without re-evaluation. Changes to the informed consent documents were asked by 7/19 IRBs, amendments to the protocol by 2. Protocol 2 was submitted to 16 IRBs. Fifteen decisions were made. No IRB objected to the study. Median time-to final vote was 59 (IQR 10 to 65; range 0 to 128) days, which was not statistically significantly different compared with protocol 1 (Wilcoxon test). Two IRBs accepted a previous IRB decision and did not conduct an independent review. Eight/16 IRBs required changes to the informed consent documents; two IRBs recommended an amendment of the protocol.ConclusionBoth clinical research protocols involving vulnerable populations were well accepted. IRB workflows and decision times varied substantially. Some IRBs accepted a previous IRB decision without the necessity of another reevaluation process. Requested changes were focused on the informed consent documents. A more standardized approach across jurisdictions is desirable.

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CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review.

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