Methods: The Iraqi Anti-Diabetic Medication Adherence Scale (IADMAS) consists of eight items. The face and content validity of the IADMAS were established via an expert panel. For convergent validity, the IADMAS was compared with the Medication Adherence Questionnaire (MAQ). For concurrent validity, the IADMAS was compared with glycosylated hemoglobin. A total of 84 patients with types 2 diabetes were recruited from a diabetes center in Baghdad, Iraq. Test-retest reliability was measured by readministering the IADMAS to the same patients 4 weeks later.
Results: Only 80 patients completed the study (response rate: 95%). Reliability analysis of the IADMAS showed a Cronbach's alpha value of 0.712, whereas that of the MAQ was 0.649. All items in the IADMAS showed no significant difference in the test-retest analysis, indicating that the IADMAS has stable reliability. There was no difference in the psychometric properties of the IADMAS and the MAQ. The sensitivity and specificity of the IADMAS were higher than that of the MAQ (100% vs 87.5% and 33.9% vs 29.7%, respectively).
Conclusion: The IADMAS developed in this study is a reliable and valid instrument for assessing antidiabetic medication adherence among Iraqi patients.
METHODS: A cross-sectional investigation was conducted at General Penang Hospital, Malaysia. Demographic criteria and laboratory tests of patients were investigated. Controlled glycemia (CG) was recognized as glycated hemoglobin (HbA1c) ≤7% depending on American Diabetes Association guidelines 2018. Charlson Comorbidity Index (CCI) was used to estimate the confounding influence of co-morbidities and predict ES-10Y. Data was managed by IBM-SPSS 23.0.
RESULTS: A total of 400 cases categorized to (44.25%) patients with CG, and (55.75%) cases had uncontrolled glycemia (UCG). HbA1c mean in CG and UCG group was (6.8 ± 0.9 vs 9.5 ± 1.6, P-value: 0.001). Fasting blood glucose was (7 ± 2.3 vs. 9.9 ± 4.3, P-value: 0.001) in CG and UCG group. CCI was (3.38 ± 2.38 vs. 4.42 ± 2.70, P-value: 0.001) and, ES-10Y was (62% vs 46.2%, p-value: 0.001) in CG vs. UCG respectively. Spearman test indicates a negative correlation between CG and CCI (r: 0.19, p-value: 0.001). Logistic regression confirmed HbA1c as a significant predictor of CCI (r2: 0.036, P-value: 0.001). CG has a positive correlation with survival (r: 0.16, P-value: 0.001) and logistic regression of survival (r2: 0.26, P-value: 0.001).
CONCLUSIONS: More than one-half of the investigated persons had UCG. Controlled HbA1c was associated with lower co-morbidities and higher ES-10Y.
OBJECTIVE: This study aimed to evaluate the impact of CMI on medication adherence and glycaemic control among patients with type 2 diabetes in Qatar.
METHODS: We developed and customised CMI for all the anti-diabetic medications used in Qatar. A randomised controlled trial in which the intervention group patients (n = 66) received the customised CMI with usual care, while the control group patients (n = 74) received usual care only, was conducted. Self-reported medication adherence and haemoglobin A1c (HbA1c ) were the primary outcome measures. Glycaemic control and medication adherence parameters were measured at baseline, 3 months, and 6 months in both groups. Medication adherence was measured using the 8-item Morisky Medication Adherence Scale (MMAS-8).
RESULTS: Although the addition of CMI resulted in better glycaemic control, this did not reach statistical significance, possibly because of the short-term follow-up. The median MMAS-8 score improved from baseline (6.6 [IQR = 1.5]) to 6-month follow-up (7.0 [IQR = 1.00]) in the intervention group. In addition, there was a statistically significant difference between the intervention and the control groups in terms of MMAS-8 score at the third visit (7.0 [IQR = 1.0]) vs 6.5 (IQR = 1.25; P-value = .010).
CONCLUSION: CMI for anti-diabetic medications when added to usual care has the potential to improve medication adherence and glycaemic control among patients with type 2 diabetes. Therefore, providing better health communication and CMI to patients with diabetes is recommended.
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.
OBJECTIVES: To compare techniques of blood glucose monitoring and their impact on maternal and infant outcomes among pregnant women with pre-existing diabetes.
SEARCH METHODS: We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (30 November 2016), searched reference lists of retrieved studies and contacted trial authors.
SELECTION CRITERIA: Randomised controlled trials (RCTs) and quasi-RCTs comparing techniques of blood glucose monitoring including SMBG, continuous glucose monitoring (CGM) or clinic monitoring among pregnant women with pre-existing diabetes mellitus (type 1 or type 2). Trials investigating timing and frequency of monitoring were also included. RCTs using a cluster-randomised design were eligible for inclusion but none were identified.
DATA COLLECTION AND ANALYSIS: Two review authors independently assessed study eligibility, extracted data and assessed the risk of bias of included studies. Data were checked for accuracy. The quality of the evidence was assessed using the GRADE approach.
MAIN RESULTS: This review update includes at total of 10 trials (538) women (468 women with type 1 diabetes and 70 women with type 2 diabetes). The trials took place in Europe and the USA. Five of the 10 included studies were at moderate risk of bias, four studies were at low to moderate risk of bias, and one study was at high risk of bias. The trials are too small to show differences in important outcomes such as macrosomia, preterm birth, miscarriage or death of baby. Almost all the reported GRADE outcomes were assessed as being very low-quality evidence. This was due to design limitations in the studies, wide confidence intervals, small sample sizes, and few events. In addition, there was high heterogeneity for some outcomes.Various methods of glucose monitoring were compared in the trials. Neither pooled analyses nor individual trial analyses showed any clear advantages of one monitoring technique over another for primary and secondary outcomes. Many important outcomes were not reported.1. Self-monitoring versus standard care (two studies, 43 women): there was no clear difference for caesarean section (risk ratio (RR) 0.78, 95% confidence interval (CI) 0.40 to 1.49; one study, 28 women) or glycaemic control (both very low-quality), and not enough evidence to assess perinatal mortality and neonatal mortality and morbidity composite. Hypertensive disorders of pregnancy, large-for-gestational age, neurosensory disability, and preterm birth were not reported in either study.2. Self-monitoring versus hospitalisation (one study, 100 women): there was no clear difference for hypertensive disorders of pregnancy (pre-eclampsia and hypertension) (RR 4.26, 95% CI 0.52 to 35.16; very low-quality: RR 0.43, 95% CI 0.08 to 2.22; very low-quality). There was no clear difference in caesarean section or preterm birth less than 37 weeks' gestation (both very low quality), and the sample size was too small to assess perinatal mortality (very low-quality). Large-for-gestational age, mortality or morbidity composite, neurosensory disability and preterm birth less than 34 weeks were not reported.3. Pre-prandial versus post-prandial glucose monitoring (one study, 61 women): there was no clear difference between groups for caesarean section (RR 1.45, 95% CI 0.92 to 2.28; very low-quality), large-for-gestational age (RR 1.16, 95% CI 0.73 to 1.85; very low-quality) or glycaemic control (very low-quality). The results for hypertensive disorders of pregnancy: pre-eclampsia and perinatal mortality are not meaningful because these outcomes were too rare to show differences in a small sample (all very low-quality). The study did not report the outcomes mortality or morbidity composite, neurosensory disability or preterm birth.4. Automated telemedicine monitoring versus conventional system (three studies, 84 women): there was no clear difference for caesarean section (RR 0.96, 95% CI 0.62 to 1.48; one study, 32 women; very low-quality), and mortality or morbidity composite in the one study that reported these outcomes. There were no clear differences for glycaemic control (very low-quality). No studies reported hypertensive disorders of pregnancy, large-for-gestational age, perinatal mortality (stillbirth and neonatal mortality), neurosensory disability or preterm birth.5.CGM versus intermittent monitoring (two studies, 225 women): there was no clear difference for pre-eclampsia (RR 1.37, 95% CI 0.52 to 3.59; low-quality), caesarean section (average RR 1.00, 95% CI 0.65 to 1.54; I² = 62%; very low-quality) and large-for-gestational age (average RR 0.89, 95% CI 0.41 to 1.92; I² = 82%; very low-quality). Glycaemic control indicated by mean maternal HbA1c was lower for women in the continuous monitoring group (mean difference (MD) -0.60 %, 95% CI -0.91 to -0.29; one study, 71 women; moderate-quality). There was not enough evidence to assess perinatal mortality and there were no clear differences for preterm birth less than 37 weeks' gestation (low-quality). Mortality or morbidity composite, neurosensory disability and preterm birth less than 34 weeks were not reported.6. Constant CGM versus intermittent CGM (one study, 25 women): there was no clear difference between groups for caesarean section (RR 0.77, 95% CI 0.33 to 1.79; very low-quality), glycaemic control (mean blood glucose in the 3rd trimester) (MD -0.14 mmol/L, 95% CI -2.00 to 1.72; very low-quality) or preterm birth less than 37 weeks' gestation (RR 1.08, 95% CI 0.08 to 15.46; very low-quality). Other primary (hypertensive disorders of pregnancy, large-for-gestational age, perinatal mortality (stillbirth and neonatal mortality), mortality or morbidity composite, and neurosensory disability) or GRADE outcomes (preterm birth less than 34 weeks' gestation) were not reported.
AUTHORS' CONCLUSIONS: This review found no evidence that any glucose monitoring technique is superior to any other technique among pregnant women with pre-existing type 1 or type 2 diabetes. The evidence base for the effectiveness of monitoring techniques is weak and additional evidence from large well-designed randomised trials is required to inform choices of glucose monitoring techniques.
METHODS: Two reviewers searched MEDLINE for studies of ≥12 weeks duration in adults with type 2 diabetes. The key search word was "gliclazide", filtered with "randomized controlled trial", "human" and "19+ years". Differences were explored in mean change in glycated hemoglobin (HbA(1c)) from baseline (primary outcome) and risk of hypoglycemia (secondary outcome) between gliclazide and other oral insulinotropic agents; and other sulfonylureas.
RESULTS: Nine out of 181 references reported primary outcomes, of which 7 reported secondary outcomes. Gliclazide lowered HbA1c more than other oral insulinotropic agents, with a weighted mean difference of -0.11% (95%, CI -0.19 to -0.03%, P=0.008, I(2)=60%), though not more than other sulfonylureas (-0.12%; 95%, CI -0.25 to 0.01%, P=0.07, I(2)=77%). Risk of hypoglycemia with gliclazide was not different to other insulinotropic agents (RR 0.85; 95%, CI 0.66 to 1.09, P=0.20, I(2)=61%) but significantly lower than other sulfonylureas (RR 0.47; 95%, CI 0.27 to 0.79, P=0.004, I(2)=0%).
CONCLUSION: Compared with other oral insulinotropic agents, gliclazide significantly reduced HbA1c with no difference regarding hypoglycemia risk. Compared with other sulfonylureas, HbA1c reduction with gliclazide was not significantly different, but hypoglycemia risk was significantly lower.
METHODS: A 6-month parallel multicenter two-arm, single-blind randomized controlled trial involving 14 pharmacists at seven primary care clinics was conducted in Johor, Malaysia. Pharmacists without prior specialized diabetes training were trained to use the tool. Patients were randomized within each center to either Simpler care (SC), receiving care from pharmacists who used the tool (n =55), or usual care (UC), receiving usual care and dispensing services (n = 69).
RESULTS: Compared with UC, SC significantly reduced HbA1c (mean reduction 1.59% [95% confidence interval {CI} -2.2, -0.9] vs 0.25% [95% CI -0.62, 0.11], respectively; P ≤ 0.001), and significantly improved systolic BP (-6.28 mmHg [95% CI -10.5, 2.0] vs 0.26 mmHg [95% CI -3.74, 0.43], respectively; P = 0.005). A significantly higher proportion of patients in the SC than UC arm reached the Malaysian guideline treatment goals for HbA1c (14.3% vs 1.5%; P = 0.020), systolic BP (80% vs 42%; P = 0.001), and low-density lipoprotein cholesterol (60.5% vs 40.4%; P = 0.046).
CONCLUSIONS: Using the Simpler tool facilitated the delivery of comprehensive evidence-based diabetes management and significantly improved clinical outcomes. The Simpler tool supported pharmacists in providing enhanced structured diabetes care.
MATERIALS AND METHODS: This was an investigator-initiated, single-center, randomized, controlled, clinical trial in patients with T2DM and DKD, comparing 12-weeks of low carbohydrate diet (<20g daily intake) versus standard low protein (0.8g/kg/day) and low salt diet. Patients in the VLCBD group underwent 2-weekly monitoring including their 3-day food diaries. In addition, Dual-energy x-ray absorptiometry (DEXA) was performed to estimate body fat percentages.
RESULTS: The study population (n = 30) had a median age of 57 years old and a BMI of 30.68kg/m2. Both groups showed similar total calorie intake, i.e. 739.33 (IQR288.48) vs 789.92 (IQR522.4) kcal, by the end of the study. The VLCBD group showed significantly lower daily carbohydrate intake 27 (IQR25) g vs 89.33 (IQR77.4) g, p<0.001, significantly higher protein intake per day 44.08 (IQR21.98) g vs 29.63 (IQR16.35) g, p<0.05 and no difference in in daily fat intake. Both groups showed no worsening of serum creatinine at study end, with consistent declines in HbA1c (1.3(1.1) vs 0.7(1.25) %) and fasting blood glucose (1.5(3.37) vs 1.3(5.7) mmol/L). The VLCBD group showed significant reductions in total daily insulin dose (39(22) vs 0 IU, p<0.001), increased LDL-C and HDL-C, decline in IL-6 levels; with contrasting results in the control group. This was associated with significant weight reduction (-4.0(3.9) vs 0.2(4.2) kg, p = <0.001) and improvements in body fat percentages. WC was significantly reduced in the VLCBD group, even after adjustments to age, HbA1c, weight and creatinine changes. Both dietary interventions were well received with no reported adverse events.
CONCLUSION: This study demonstrated that dietary intervention of very low carbohydrate diet in patients with underlying diabetic kidney disease was safe and associated with significant improvements in glycemic control, anthropometric measurements including weight, abdominal adiposity and IL-6. Renal outcomes remained unchanged. These findings would strengthen the importance of this dietary intervention as part of the management of patients with diabetic kidney disease.
RESEARCH DESIGN AND METHODS: Multinational, prospective cohort study to assess the prevalence of newborns free from major congenital malformations or perinatal or neonatal death (primary end point) following treatment with insulin detemir (detemir) versus other basal insulins.
RESULTS: Of 1,457 women included, 727 received detemir and 730 received other basal insulins. The prevalence of newborns free from major congenital malformations or perinatal or neonatal death was similar between detemir (97.0%) and other basal insulins (95.5%) (crude risk difference 0.015 [95% CI -0.01, 0.04]; adjusted risk difference -0.003 [95% CI -0.03, 0.03]). The crude prevalence of one or more congenital malformations (major plus minor) was 9.4% vs. 12.6%, with a similar risk difference before (-0.032 [95% CI -0.064, 0.000]) and after (-0.036 [95% CI -0.081, 0.009]) adjustment for confounders. Crude data showed lower maternal HbA1c during the first trimester (6.5% vs. 6.7% [48 vs. 50 mmol/mol]; estimated mean difference -0.181 [95% CI -0.300, -0.062]) and the second trimester (6.1% vs. 6.3% [43 vs. 45 mmol/mol]; -0.139 [95% CI -0.232, -0.046]) and a lower prevalence of major hypoglycemia (6.0% vs. 9.0%; risk difference -0.030 [95% CI -0.058, -0.002]), preeclampsia (6.4% vs. 10.0%; -0.036 [95% CI -0.064, -0.007]), and stillbirth (0.4% vs. 1.8%; -0.013 [95% CI -0.024, -0.002]) with detemir compared with other basal insulins. However, differences were not significant postadjustment.
CONCLUSIONS: Insulin detemir was associated with a similar risk to other basal insulins of major congenital malformations, perinatal or neonatal death, hypoglycemia, preeclampsia, and stillbirth.