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.
Objective: To determine the proportion of adults treated for localized melanoma who prefer the standard scheduled visit frequency (as per Australian guideline recommendations) or fewer scheduled visits (adapted from the Melanoma Follow-up [MELFO] study of reduced follow-up).
Design, Setting, and Participants: This survey study used a telephone interview for surveillance following excision of localized melanoma at an Australian specialist center. We invited a random sample of 400 patients who had completed treatment for localized melanoma in 2014 to participate. They were asked about their preferences for scheduled follow-up, and experience of follow-up in the past 12 months. Those with a recurrent or new primary melanoma diagnosed by the time of interview (0.8-1.7 years since first diagnosis) were asked about how it was first detected and treated. SSE practices were also assessed.
Main Outcomes and Measures: Proportion preferring standard vs fewer scheduled clinic visits, median delay between detection and treatment of recurrent or new primary melanoma, and SSE practices.
Results: Of the 262 people who agreed to be interviewed, the mean (SD) age was 64.3 (14.3) years, and 93 (36%) were women. Among the 230 people who did not have a recurrent or new primary melanoma, 149 vs 81 preferred the standard vs fewer scheduled clinic visits option (70% vs 30% after adjusting for sampling frame). Factors independently associated with preferring fewer visits were a higher disease stage, melanoma on a limb, living with others, not having private health insurance, and seeing a specialist for another chronic condition. The median delay between first detection and treatment of recurrent or new primary melanoma was 7 and 3 weeks, respectively. Only 8% missed a scheduled visit, while 40% did not perform SSE or did so at greater than 3-month intervals.
Conclusions and Relevance: Some patients with melanoma may prefer fewer scheduled visits, if they are supported to do SSE and there is rapid clinical review of anything causing concern (patient-led surveillance).
METHODS: We conducted an online survey among neurosurgery residents in Indonesia, Malaysia, Philippines, Singapore, and Thailand from May 22 to 31, 2020 using Google Forms. The 33-item questionnaire collected data on elective and emergency neurosurgical operations, ongoing learning activities, and health worker safety.
RESULTS: A total of 298 of 470 neurosurgery residents completed the survey, equivalent to a 63% response rate. The decrease in elective neurosurgical operations in Indonesia and in the Philippines (median, 100% for both) was significantly greater compared with other countries (P < 0.001). For emergency operations, trainees in Indonesia and Malaysia had a significantly greater reduction in their caseload (median, 80% and 70%, respectively) compared with trainees in Singapore and Thailand (median, 20% and 50%, respectively; P < 0.001). Neurosurgery residents were most concerned about the decrease in their hands-on surgical experience, uncertainty in their career advancement, and occupational safety in the workplace. Most of the residents (n = 221, 74%) believed that the COVID-19 crisis will have a negative impact on their neurosurgical training overall.
CONCLUSIONS: An effective national strategy to control COVID-19 is crucial to sustain neurosurgical training and to provide essential neurosurgical services. Training programs in Southeast Asia should consider developing online learning modules and setting up simulation laboratories to allow trainees to systematically acquire knowledge and develop practical skills during these challenging times.
Methods: We searched seven databases up to July 2020 for randomized controlled trials investigating the effectiveness of telemedicine in the delivery of diabetes care in low- and middle-income countries. We extracted data on the study characteristics, primary end-points and effect sizes of outcomes. Using random effects analyses, we ran a series of meta-analyses for both biochemical outcomes and related patient properties.
Findings: We included 31 interventions in our meta-analysis. We observed significant standardized mean differences of -0.38 for glycated haemoglobin (95% confidence interval, CI: -0.52 to -0.23; I2 = 86.70%), -0.20 for fasting blood sugar (95% CI: -0.32 to -0.08; I2 = 64.28%), 0.81 for adherence to treatment (95% CI: 0.19 to 1.42; I2 = 93.75%), 0.55 for diabetes knowledge (95% CI: -0.10 to 1.20; I2 = 92.65%) and 1.68 for self-efficacy (95% CI: 1.06 to 2.30; I2 = 97.15%). We observed no significant treatment effects for other outcomes, with standardized mean differences of -0.04 for body mass index (95% CI: -0.13 to 0.05; I2 = 35.94%), -0.06 for total cholesterol (95% CI: -0.16 to 0.04; I2 = 59.93%) and -0.02 for triglycerides (95% CI: -0.12 to 0.09; I2 = 0%). Interventions via telephone and short message service yielded the highest treatment effects compared with services based on telemetry and smartphone applications.
Conclusion: Although we determined that telemedicine is effective in improving several diabetes-related outcomes, the certainty of evidence was very low due to substantial heterogeneity and risk of bias.
Methods: A community-based participatory research method was utilized. Two focus group discussions (FGDs) were conducted in Malaysian sign language (BIM) with a total of 10 DHH individuals. Respondents were recruited using purposive sampling. Video-recordings were transcribed and analyzed using a thematic approach.
Results: Two themes emerged: (I) challenges and scepticism of the healthcare system; and (II) features of the mHealth app. Respondents expressed fears and concerns about accessing healthcare services, and stressed on the need for sign language interpreters. There were also concerns about data privacy and security. With regard to app features, the majority preferred videos instead of text to convey information about their disease and medication, due to their lower literacy levels.
Conclusions: For an mHealth app to be effective, app designers must ensure the app is individualised according to the cultural and linguistic diversity of the target audience. Pharmacists should also educate patients on the potential benefits of the app in terms of assisting patients with their medicine-taking.
METHOD: Wounds were cleansed and debrided before using the application to photograph, document, measure and analyse the wounds. The smartphone app was oriented parallel to the plane of the wound, where possible, to obtain accurate measurements. A longitudinal study report was generated for each wound and showed the progress of the wound healing until the wound was closed.
RESULTS: A sample size of 60 patients consisting of wounds from different locations, and a total of 203 measurements and analyses were conducted over a period of seven months. The wound monitoring app proved to be effective for wound monitoring and required less than two hours' training. A report summary of wounds recorded could also be generated automatically through the dashboard. All 60 patients' cases were automatically recorded, measured and presented into reports for use in clinical analysis. There was a significant time savings (27 hours per day for a specialised care centre with 10 nurses) increase over manual wound documentation and measuring methods.
CONCLUSION: The app provided a non-contact, easy to use, reliable and accurate smart wound management solution for clinicians and physicians to track wound healing in patients. The app could also be used by patients and caregivers for home monitoring of their wounds.
METHODS: We searched PubMed, EMBASE and the Cochrane Database of Systematic Reviews from database inception to 31 August 2018 for systematic reviews and/or meta-analyses of studies that examined the impact of distal technology and reported any clinical or patient-related outcomes among people with type 1 or type 2 diabetes.
RESULTS: The umbrella review identified 95 reviews, including 162 meta-analyses with 46 unique outcomes. Evidence from meta-analyses of randomized controlled studies supports the use of distal technology, especially telehealth and mHealth (healthcare delivered by mobile technology), in people with diabetes for improving HbA1c values by 2-4 mmol/mol (0.2-0.4%). For other health outcomes, such as changes in fasting plasma glucose levels, risk of diabetic ketoacidosis or frequency of severe hypoglycaemia, the evidence was weaker. No evidence was reported for most patient-reported outcomes including quality of life, self-efficacy and medication-taking. The evidence base was poor, with most studies rated as low to very low quality.
CONCLUSION: Distal technologies were associated with a modest improvement in glycaemic control, but it was unclear if they improved major clinical outcomes or were cost-effective in people with diabetes. More robust research to improve wider outcomes in people with diabetes is needed before such technologies can be recommended as part of routine care for any patient group.