Methods: An extensive literature review was done using Google-Scholar and PubMed to find out scales that utilized to assess quality of life among DM patients. Four relevant scales, three diabetes specific and one general, were selected. The selected scales were carefully evaluated to find out domains that are commonly used to assess quality of life and then the items within the selected domains were reviewed to choose relevant and comprehensive items for Iraqi type 2 DM patients. Ten items were selected to formulate the quality of life scale for Iraqi DM patients (QOLSID). The content validity of QOLSID was established via an expert panel. For concurrent validity QOLSID was compared to glycosylated hemoglobin (HbA1C). For psychometric evaluation, a cross sectional study for 103 type 2 DM patients was conducted at the National Diabetes Center, Iraq. Test-retest reliability was measured by re-administering QOLSID to 20 patients 2-4 weeks later.
Results: The internal consistency of the QOLSID was 0.727. All items had a corrected total-item correlation above 0.2. There was a negative significant correlation between QOLSID score and the HbA1C level (-0.518, P = 0.000). A significant positive correlation was obtained after re-testing (0.967, P = 0.000).
Conclusion: The QOLSID is a reliable and valid instrument that can be used for assessing quality of life among Iraqi type 2 DM patients.
MATERIALS AND METHODS: Seventy-four participants, with type 1 (T1D, n = 24), type 2 (T2D, n = 11), or gestational (n = 39) diabetes, were enrolled across 13 sites (9 in United Kingdom, 4 in Austria). Average gestation was 26.6 ± 6.8 weeks (mean ± standard deviation), age was 30.5 ± 5.1 years, diabetes duration was 13.1 ± 7.3 years for T1D and 3.2 ± 2.5 years for T2D, and 49/74 (66.2%) used insulin to manage their diabetes. Sensors were worn for up to 14 days. Sensor glucose values (masked) were compared with capillary SMBG values (made at least 4 times/day).
RESULTS: Clinical accuracy of sensor results versus SMBG results was demonstrated, with 88.1% and 99.8% of results within Zone A and Zones A and B of the Consensus Error Grid, respectively. Overall mean absolute relative difference was 11.8%. Sensor accuracy was unaffected by the type of diabetes, the stage of pregnancy, whether insulin was used, age or body mass index. User questionnaires indicated high levels of satisfaction with sensor wear, system use, and comparison to SMBG. There were no unanticipated device-related adverse events.
CONCLUSIONS: Good agreement was demonstrated between the FreeStyle Libre System and SMBG. Accuracy of the system was unaffected by patient characteristics, indicating that the system is safe and accurate to use by pregnant women with diabetes.
Methods: A systematic search was conducted through PubMed/Medline, Institute of Scientific Information, and Scopus, until 2017 based on the search terms of metabolic syndrome (MetS) and cardio metabolic risk factors. Random-effect model was used to perform a meta-analysis and estimate the pooled SE, SP and correlation coefficient (CC).
Results: A total of 41 full texts were selected for systematic review. The pooled SE of greater NC to predict MetS was 65% (95% CI 58, 72) and 77% (95% CI 55, 99) in adult and children, respectively. Additionally, the pooled SP was 66% (95% CI 60, 72) and 66% (95% CI 48, 84) in adult and children, respectively. According to the results of meta-analysis in adults, NC had a positive and significant correlation with fasting blood sugar (FBS) (CC: 0.16, 95% CI 0.13, 0.20), HOMA-IR (0.38, 95% CI 0.25, 0.50), total cholesterol (TC) (0.07 95% CI 0.02, 0.12), triglyceride (TG) concentrations (0.23, 95% CI 0.19, 0.28) and low density lipoprotein cholesterol (LDL-C) (0.14, 95% CI 0.07, 0.22). Among children, NC was positively associated with FBS (CC: 0.12, 95% CI 0.07, 0.16), TG (CC: 0.21, 95% CI 0.17, 0.25), and TC concentrations (CC: 0.07, 95% CI 0.02, 0.12). However, it was not significant for LDL-C.
Conclusion: NC has a good predictive value to identify some cardiometabolic risk factors. There was a positive association between high NC and most cardiometabolic risk factors. However due to high heterogeneity, findings should be declared with caution.
METHODS: A total of 104 patients with lifestyle-controlled gestational diabetes (GDMA1) were randomized to 2-weekly or weekly 4-point per day (fasting on awakening and 2-h post-meals) SMBG. Primary outcome was the change in glycated hemoglobin (HbA1c) level from enrollment to 36 weeks of pregnancy across trial arms. The non-inferiority margin was an HbA1c increase of 0.2%.
RESULTS: The mean difference for change in HbA1c from enrollment to 36 weeks was 0.003% (95% confidence interval [CI] -0.098% to +0.093%), within the 0.2% non-inferiority margin. The change in HbA1c level increased significantly within both trial arms-0.275% ± 0.241% (P
METHODOLOGY: The Cochrane Central Register of Controlled Trials (CENTRAL) and PubMed (1985-January 2022) and trial registries for relevant randomised clinical trials were used. Relevant and published randomised clinical trials were reviewed and evaluated. The primary outcomes were anthropometry measurements, which were weight, waist circumference, body mass index (BMI), and body fat percentages. The secondary outcomes were changes in quality of life, psychological impact, lipid profile measurement, presence of adverse events, and changes in blood pressure and blood glucose. We assessed the data for risk of bias, heterogeneity, sensitivity, reporting bias, and quality of evidence.
RESULTS: 15 studies are included, involving 1161 participants. The analysis performed is based on three comparisons. For the first comparison between yoga and control, yoga reduces the waist circumference (MD -0.84, 95% CI [-5.12 to 3.44]), while there is no difference in body weight, BMI, or body fat percentages. In the second comparison between yoga and calorie restriction, yoga reduces body weight (MD -3.47, 95% CI [-6.20 to -0.74]), while there is no difference in waist circumference, BMI, or body fat percentage. In the third comparison between yoga and exercise, yoga reduces the body weight (MD -7.58, 95% CI [-11.51 to -3.65]), while there is no difference in waist circumference or BMI. For the secondary outcomes, yoga intervention reduces total cholesterol (MD -17.12, 95% CI [-32.24 to -2.00]) and triglycerides (MD -21.75, 95% CI [-38.77 to -4.73]) compared to the control group, but there is no difference compared to the calorie restriction and exercise group. There is no difference in the rest of the outcomes, which are LDL, HDL, quality of life, psychological impact, adverse events, blood pressure, and blood glucose. However, findings are not robust due to a high risk of bias and low-quality evidence.
CONCLUSION: From our review, there were methodological drawbacks and very low to moderate quality of evidence across all comparisons, and hence, it is inconclusive to say that yoga can significantly improve anthropometric parameters. More well-designed trials are needed to confirm and support the beneficial effects of yoga.
OBJECTIVE: This study aims to evaluate the effects of remote telemonitoring with team-based management on people with uncontrolled type 2 diabetes.
DESIGN: This was a pragmatic 52-week cluster-randomized controlled study among 11 primary care government practices in Malaysia.
PARTICIPANTS: People with type 2 diabetes aged 18 and above, who had hemoglobin A1c ≥ 7.5% but less than 11.0% within the past 3 months and resided in the state of Selangor.
INTERVENTION: The intervention group received home gluco-telemonitors and transmitted glucose data to a care team who could adjust therapy accordingly. The team also facilitated self-management by supporting participants to improve medication adherence, and encourage healthier lifestyle and use of resources to reduce risk factors. Usual care group received routine healthcare service.
MAIN MEASURE: The primary outcome was the change in HbA1c at 24 weeks and 52 weeks. Secondary outcomes included change in fasting plasma glucose, blood pressure, lipid levels, health-related quality of life, and diabetes self-efficacy.
RESULTS: A total of 240 participants were recruited in this study. The telemonitoring group reported larger improvements in glycemic control compared with control at the end of study (week 24, - 0.05%; 95% CI - 0.10 to 0.00%) and at follow-up (week 52, - 0.03%; - 0.07 to 0.02%, p = 0.226). Similarly, no differences in other secondary outcomes were observed, including the number of adverse events and health-related quality of life.
CONCLUSION: This study indicates that there is limited benefit of replacing telemedicine with the current practice of self-monitoring of blood glucose. Further innovative methods to improve patient engagement in diabetes care are needed.
TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02466880.