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Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are increasingly initiated as treatment for type 2 diabetes due to favourable cardiorenal characteristics. However, studies have identified an increased risk of diabetic ketoacidosis (DKA). We carried out a retrospective, case-based study at East and North Herts NHS Trust between February 2018 and December 2020. Fifteen cases of SGLT2i associated DKA were identified in people with presumed type 2 diabetes;33.3% were classed as euglycaemic DKA with a blood glucose of <11mmol/L. All cases were associated with a significant precipitating factor including diarrhoea, vomiting, reduced oral intake and sepsis. One case was related to COVID-19. Two people were subsequently found to have raised islet autoantibodies suggesting type 1 diabetes or latent autoimmune diabetes in adults. It is important that awareness of SGLT2i associated DKA is raised among users and health care practitioners, including the recognition of euglycaemic DKA. Sick day rules should be emphasised and reiterated at clinical encounters. Non-specialists in primary care, oncology and in perioperative settings should be empowered to advocate for temporary withdrawal and there should be readier access to blood ketone monitoring when required. When SGLT2i associated DKA occurs, due consideration should be given to evaluate the diabetes classification and investigate the circumstances of the event. Copyright © 2023 John Wiley & Sons.Copyright © 2023 John Wiley & Sons, Ltd.
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Objectives: COVID 19 and increasing unmet needs of health technology had accelerated an adoption of digital health globally and the major categories are mobile-health, health information technology, telemedicine. Digital health interventions have various benefit on clinical efficacy, quality of care and reducing healthcare costs. The objective of the study is to identify new reimbursement policy trend of digital health medical devices in South Korea. Method(s): Official announcements published in national bodies and supplementary secondary research were used to capture policies, frameworks and currently approved products since 2019. Result(s): With policy development, several digital health devices and AI software have been introduced as non-reimbursement by utilizing new Health Technology Assessment (nHTA) pathway including grace period of nHTA and innovative medical devices integrated assessment pathway. AI based cardiac arrest risk management software (DeepCARS) and electroceutical device for major depressive disorders (MINDD STIM) have been approved as non-reimbursement use for about 3 years. Two digital therapeutics for insomnia and AI software for diagnosis of cerebral infarction were approved as the first innovative medical devices under new integrated assessment system, and they could be treated in the market. In addition, there is remote patient monitoring (RPM) reimbursement service fee. Continuous glucose monitoring devices have been reimbursed for type 1 diabetes patients by the National Health Insurance Service (NHIS) since January 2019. Homecare RPM service for peritoneal dialysis patients with cloud platform (Sharesource) has been reimbursed since December 2019, and long-term continuous ECG monitoring service fee for wearable ECG monitoring devices (ATpatch, MEMO) became reimbursement since January 2022. Conclusion(s): Although Korean government has been developed guidelines for digital health actively, only few products had been reimbursed. To introduce new technologies for improved patient centric treatment, novel value-based assessment and new pricing guideline of digital health medical devices are quite required.Copyright © 2023
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Background and aims: Onboarding of the FreeStyle Libre, an intermittently scanned continuous glucose monitoring (isCGM) device, was pre-dominantly conducted in-person prior to the Covid-19 pandemic. However, onboarding rapidly become virtual due to enforced social distancing restrictions. This audit aimed to determine if onboarding method impacted on glycaemic outcomes and engagement statistics in people living with diabetes (pwD). Method(s): PwD who started FreeStyle Libre between January 2019 and March 2022, had their mode of onboarding recorded and had >=70% data were identified and included within the audit. Glycaemic indices and engagement statistics (previous 90 day averages) were obtained from LibreView (Abbott, USA) three months after the last person was onboarded, and compared using linear models, adjusting for FreeStyle Libre duration, %active (where appropriate), age and sex. Result(s): From 1007 eligible participants (in-person 44% [n = 445];virtual 56% [n = 562]), FreeStyle Libre usage duration was greater for those onboarded in-person vs. virtually (974[891,1101) vs. 420[280,564] days [p < 0.001]). There were no significant differences in glycaemic or engagement indices between in-person and virtual onboarding methods: average glucose (10[9,11]) vs. 10[9,11])mmol/l), %time very-low (<3.0mmol/l, 0[0,1]) vs. 0[0,1]%), %time low (3.0-3.8mmol/ l, 2[1,4] vs. 2[1,4]), %time in range (3.9-10.0mmol/ l, 54[+/-17] vs. 53[+/-19]%), %time high (10.1-13.9mmol/ l, 27[21,31]) vs. 26[21,31]%), %time very-high (>13.9mmol/l, 14[6,24] vs. 15[7,26]%), %active (96[90,100] vs. 94[87,99]%) or scans/day (11[8,15] vs. 10[7,14]). Conclusion(s): There were no differences in glycaemic outcomes or engagement indices between pwD between onboarding methods. Virtual onboarding using online videos for isCGM is as equally effective as face to face.
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The Royal College of Obstetrics and Gynaecology advocated replacing OGTT with HbA1c for gestational diabetes (GDM) screening for women with risk factors during the Covid-19 pandemic. HbA1c >=48mmol/mol/random plasma glucose (RPG) >=11.1mmol/l at booking indicated diabetes, and 41-47mmol/ mol/9-11mmol/ l prediabetes or possible GDM. Testing was repeated at 26 weeks if normal previously, with HbA1c >=39mmol/mol, fasting PG >=5.6mmol/l, or RPG >=9mmol/l diagnostic for GDM. A) At her clinic booking visit at 10 weeks gestation, 36 year-old South Asian female had HbA1c 55mmol/mol/RPG 9.5mmol/l suggesting undiagnosed type 2 diabetes. Initially managed with dietary advice and home blood glucose monitoring, metformin was added when self-monitored glucose above pregnancy targets (fasting and pre-meal <5.3mmol/l or 1 h post meal <7.8mmol/l) but insulin was required later. Metformin and insulin were stopped after delivery at 38 weeks with HbA1c 50mmol/mol three months postpartum, supporting the earlier diagnosis of type 2 diabetes. B) 32 year-old White Caucasian female was screened for GDM on booking at 11 weeks as BMI 38 kg/m2. HbA1c 44mmol/mol and RPG 6.9mmol/l confirmed GDM which was managed by dietary/lifestyle changes with glucose and pregnancy targets achieved until 28 weeks when metformin added. Normal delivery at 40 weeks with HbA1c 40mmol/mol three months postpartum triggered advice on long-term dietary/lifestyle changes and annual HbA1c checks. HbA1c was useful during the pandemic but most centres reverted to OGTT for GDM screening due to a significant fall in diagnoses using HbA1c >=39mmol/mol at 26 weeks. But, HbA1c testing was advantageous at booking to diagnose type 2 diabetes earlier.
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Objectives: Insulin optimisation requires review of glucose monitoring;Covid-19 posed challenges to this. We evaluated DBm -a remote monitoring platform utilising a glucometer and smartphone app. Method(s): Evaluation was from January to November 2021. Inclusion criteria was insulin treated diabetes with HbA1c greater than 68mmol/mol. HbA1c, demographics, frequency of CBG uploads and interactions with clinicians were collected. Result(s): 97 patients were offered DBm. 48.5% used the app. There were no statistically significant differences in gender (p = 0.05), age (p = 0.36), type of diabetes (p = 0.13) or deprivation index (p = 0.96) between users and non-users. Patients of white ethnicity were less likely to use the platform (p = 0.01). Amongst users, 70% had a reduction of HbA1c of at least 5mmol/mol over six months, with a mean reduction of 25.6mmol/mol (p = 0.01). There was no difference in age (p = 0.64), gender (p = 0.4), and type of diabetes (p = 0.23) between responders and non-responders. There was also no difference in number of call back requests generated by patients (p = 0.32) or number of CBG uploads (p = 0.899) between responders and non-responders. Conclusion(s): Uptake of the remote monitoring solution was just under 50%, with no evidence of digital exclusion, although the finding that white ethnicity patients were less likely to use the system needs further exploration. Most users had improved glucose control, but there was no association with numbers of tests or call back requests. This study demonstrates that insulin optimisation can effectively be delivered using a remote glucose monitoring system. Future work will explore patient experience and patient satisfaction.
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Aims: Continuous glucose monitoring (CGM) is widely used in pregnant women with pre-gestational diabetes, but optimal targets have not been defined in gestational diabetes. Previous work identified mild hyperglycaemia in pregnant women without gestational diabetes, but with risk factors such as obesity. We aimed to examine CGM metrics and patterns of glycaemia in women with gestational diabetes compared to healthy pregnant women with comparable risk factors. Method(s): We recruited 73 healthy women with >1 risk factor (gestational diabetes excluded using Covid-19 criteria, OGTT) and 200 women with gestational diabetes (NICE and interim-Covid- 19 criteria) from antenatal clinics at 28 weeks' gestation. A Dexcom G6 CGM device was cited on the non-dominant upper arm. Result(s): Women with gestational diabetes had significantly higher weight (mean +/- SEM 95.7 kg +/- 1.3 Vs 85.4 kg +/- 2.2) and BMI (36.0 +/- 0.5 Vs 31.3 +/- 0.7) compared to healthy pregnant women (p < 0.01). Women with gestational diabetes had significantly higher mean CGM-glucose (mean +/- SEM 5.6 +/- 0.01 Vs 5.4 +/- 0.01mmol/l;p < 0.01), significantly altered time-below- range (median(IQR);1.0% (0.2-2.9) vs 2.5% (0.7-5.5);p < 0.05) and time-in- range (95.0% (91.1-97.9) vs 94.5% (87.9-96.2);p < 0.05) but comparable time-above- range to healthy women with risk factors. Diurnal glucose profiles in women with gestational diabetes were comparable to healthy women between 14:00 and 18:00, but demonstrated significant increases in glucose at all other time points during the 24-h cycle (p < 0.01). Conclusion(s): Mean CGM glucose is the most reliable CGM metric to distinguish women with gestational diabetes from healthy pregnant women with risk factors.
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Introduction: The aim was to investigate access to and the effect of intermittency scanned flash glucose monitoring (isCGM) on glycaemic control during the Covid-19 pandemic. Method(s): Data from the National Diabetes Audit from 2019 to 2021 was stratified into those who were already using isCGM on 1st April 2020 (A), those who started isCGM on or after 1st April 2020 (B), and those who did not receive isCGM (C). Logistic regression investigated the independent effects of ethnicity and deprivation on access to isCGM after adjustment for baseline covariates (age, gender, BMI, duration of diabetes, and baseline HbA1c). Ethnicity was categorized as White, Asian, Black, Mixed, and not reported. The Index of Multiple Deprivation (IMD) was divided into quintiles. Result(s): 251,620 people were identified with type 1 diabetes;88,910 (35%) had isCGM prescribed at 1st April 2020. The mean follow-up post-isCGM initiation was six months. Mean HbA1c at baseline was 67.4mmol/mol in (A), 73.6mmol/mol in (B) and 69.7mmol/mol in (C). Mean HbA1c at follow-up was 64.9mmol/mol (A) (p < 0.001), 65.5mmol/mol (p < 0.001) (B). After adjustment for age, sex, duration of diagnosis, baseline HbA1c, and BMI people with White ethnicity (OR = 1.79 p < 0.001) or in the least deprived quintile (OR = 1.54, p < 0.001) were more likely to be initiated on isCGM as compared to the black and most deprived groups. Conclusion(s): Initiating isCGM during the Covid-19 pandemic was associated with improved glycaemic control. Ethnic and socioeconomic disparities in access to isCGM were observed even during the pandemic. Ongoing work is investigating the effect of isCGM on diabetes-related hospital admissions during the pandemic.
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Background: My Diabetes My Way (MDMW) is NHS Scotland's interactive website, offering education, structured eLearning and online records access for people living with diabetes. We aimed to analyse user activity during the last 12 months. Method(s): Data were collected during the period from November 2021 to October 2022. Registration and user audit logs were analysed, observing activity across all website content and features. Result(s): An average of 62,853 pages were accessed on the public website each month. Significant activity increases were observed in December 2021 (n = 81,237). There were increased views in September 2022 (n = 76,502) and October (n = 73,039) The top five pages accessed were;Coronavirus: advice for people living with diabetes (n = 12,478), FreeStyle Libre (n = 4325), Emergency advice (n = 1576), Blood pressure-reducing your risks of complications (n = 1559) and Blood glucose monitoring and HbA1c targets (n = 1485). eLearning: During this period, 382 individuals completed one of 11 QISMET-accredited structured eLearning courses. eLearning course usage increased in relation to patient awareness activity. Social Media: There are currently 3919 Facebook and 3600 Twitter followers. Records Access: 67,655 patients had registered to access their data and 35,157 had actively accessed their records by the end of October 2022. Patient feedback remains highly positive. Conclusion(s): MDMW is a consistent and reliable resource for people with diabetes and their families to access at any time online. User statistics continue to rise, while latest development plans include the addition of new Patient Reported Outcome Measures, risk prediction features, and enhanced sharing of data with the healthcare team.
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Aim: To determine the association between Covid-19 and diabetes mellitus. Study Design: Retrospective study. Place and Duration of Study: Department of Medicine & Respiratory Physiology, Independent Medical College Faisalabad from 1st July 2022 to 31st December 2022. Methodology: Fifty five patients received at outdoor patient department of Independent University Hospital with confirmed diagnosis for Covid-19 through naso-pharyngeal reverse transcription polymerase chain reaction (RT-PCR) and aged 13-65 years were included. The complete medical files of each confirmed Covid-19 case was completely studied in relevance to diabetes mellitus association and compared with normal matched controls that only visited the OPD against the suspicion of the disease and underwent complete biochemical profiling. The baseline levels of HbA1C and glucose monitoring in each patient and control was done and compared. Result(s): The mean age of the CoVid-19 cases was 39.5+/-5.3 years while of controls as 25.65+/-4.3 years. There was an obvious significant variance in the odds ratio of Covid-19 patients and those of controls in reference to diabetes mellitus. A significant increase was observed in Odds Ratio of Covid-19 cases within the age group of 51-65 years. The Elixhauser Comorbidity Index (ECI) categories also presented, ECI >5 to be higher in Covid-19 cases than controls. Conclusion(s): There is a higher risk of diabetes new onset in Covid-19 confirmed cases as compared to matched controls.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.
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In situations where it is difficult for patients to visit hospitals, such as the coronavirus disease pandemic, it is important to more detailly predict hemoglobin A1C (HbA1c) from flash glucose monitor (FGM) data. CGM data over 14 days can be obtained from a FGM sensor;therefore, there are many options for extracting the duration from which glucose levels are derived. Thus, the extracted durations were closely studied to determine which mean glucose levels can predict HbA1c more accurately. Seventy-three outpatients with type 2 diabetes mellitus underwent HbA1c testing, wore a FGM (FreeStyle Libre Pro), and did not change diabetic treatments, on a hospital visit. FGM data over 24 h 13 days (from 00:00 on day 2 to 24:00 on day 14 [FGM attachment: day 1]) were analyzed. The mean glucose levels were calculated corresponding to the following durations: 1 day: day 2 ~ day 14 (n=13), 2 days: days 2-3 ~ days 13-14 (n=12) 12 days: days 2-13 ~ days 3-14 (n=2), 13 days: days 2-14 (n=1) [total 91 durations] (extracted mean glucose levels). Data were analyzed in all patients (n=73), in patients with hypoglycemia in the 13 days (Hypo) group (n=40), and in patients without hypoglycemia in the 13 days (Nonhypo) group (n=33). In all patients, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.76-0.86, p<0.001). HbA1c was the most significantly correlated to the mean glucose levels over 13 days (days 2-14). "Correlation coefficients between HbA1c and extracted mean glucose levels" ("r, HbA1c, EMGL") were also correlated to number of extracted days for the extracted mean glucose levels (r=0.80, p<0.001 [n=91]). In the Hypo group, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.55-0.73, p<0.001). The mean glucose levels over 13 days (days 2-14) were the most significantly correlated to HbA1c. "r, HbA1c, EMGL" correlated to the number of extracted days for the extracted mean glucose levels (r=0.68, p<0.001;Fig. 2). In the Nonhypo group, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.73-0.87, p<0.001). The mean glucose levels over 12 days (days 2-13) were the most significantly correlated to HbA1c. "r, HbA1c, EMGL" correlated to the number of extracted days for the extracted mean glucose levels (r=0.61, p<0.001). The results of the present study are consistent with that of a previous study reporting that the minimum duration needed to estimate time in range over 90 days is 14 days. In the prediction of HbA1c using data from one FGM sensor, prolonged measurement can make the glucose management indicator more accurate. Especially for patients with hypoglycemia, the importance of prolonged measurement may be applicable.
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Introduction: Lorlatinib is a third-generation tyrosine kinase inhibitor that inhibits anaplastic lymphoma kinase (ALK) and c-ros oncogene 1 (ROS1). Although 2-10% of patients with non-small cell lung cancer developed hyperglycemia in phase 2 and 3 studies of lorlatinib, only one case has subsequently reported hyperglycemia >500 mg/dL, and no cases of diabetic ketoacidosis (DKA) have been previously reported. Phase 1 trials in neuroblastoma are ongoing. Case Description: A 34-year-old woman with ALK-mutated paraspinal neuroblastoma presented with DKA 14 months after initiation of lorlatinib. Prior to starting lorlatinib, her hemoglobin A1c had been 5.0% (n: < 5.7%). After 12 months of therapy, her A1c increased to 7.8%, prompting the initiation of metformin 500 mg daily. However, two months later she was admitted for DKA with a blood glucose of 591 mg/dL (n: 65-99 mg/dL), CO2 17 mmol/L (n: 20-30 mmol/L), anion gap 18 (n: 8-12), moderate serum ketones, and 3+ ketonuria. Her A1c was 14.8%, C-peptide was 1.2 ng/mL (n: 1.1-4.3 ng/mL), and her glutamic acid decarboxylase-65 and islet antigen-2 autoantibodies were negative. She was also found to be incidentally positive for COVID-19 but was asymptomatic without any oxygen requirement. The patient's DKA was successfully treated with IV insulin infusion, and she was discharged after 3 days with insulin glargine 27 units twice daily and insulin aspart 16 units with meals. One month later, her hemoglobin A1c had improved to 9.4%, and the patient's oncologist discontinued lorlatinib due to sustained remission of her neuroblastoma and her complication of DKA. After stopping lorlatinib, her blood glucose rapidly improved, and she self-discontinued all her insulin in the following 3 weeks. One month later, she was seen in endocrine clinic only taking metformin 500 mg twice daily with fasting and post-prandial blood glucose ranging 86-107 mg/dL. Discussion(s): This is the first reported case of DKA associated with lorlatinib. This case highlights the importance of close glucose monitoring and the risk of severe hyperglycemia and DKA while on lorlatinib therapy. Discontinuation of lorlatinib results in rapid improvement of glycemic control, and glucose-lowering treatments should be promptly deescalated to avoid hypoglycemia.Copyright © 2023
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Introduction: Due to lockdown measures associated with the COVID 19 pandemic (1), there were substantial changes to healthcare delivery, including the suspension of face-to-face medical appointments, expansion of telehealth and changes to medication protocols.(2) It is important to learn from the successes and challenges of this period to ensure we adapt and improve how we support people to take medicines in the future. Aim(s): We sought to conduct a systematic review to explore the different approaches used to deliver medicines management services for people living with long term conditions (LTCs) during the pandemic and identify strategies that could be integrated into standard care. Method(s): We conducted a systematic review across 3 large databases: MEDLINE (OVID), EMBASE (OVID) and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Our research question and search strategy was developed using the PICO framework (Population: adults with LTCs, Intervention: medicines management during the COVID 19 pandemic;no comparison group. Outcome(s): any aspect relating to medicines management. Search terms relating to 'long term conditions', 'medication management' and 'COVID-19' were used. One reviewer (LM) screened all titles, s, and full texts. We included studies discussing medication management of LTCs, in patients of all ages and healthcare settings, throughout the pandemic. Primary literature sources, feasibility studies and case studies, were included. We excluded studies solely focusing on disease monitoring, or the treatment of COVID/ 'long Covid'. One reviewer performed a thematic analysis, synthesising the findings into themes and sub-themes, which were discussed with a further reviewer (CT). A critical appraisal was performed using the Critical Appraisal Skills Programme checklists. Result(s): The search returned 2365 results. After deduplication, articles were removed at the title (n=1070) (n=813) and full text (n=232) stages. 31 studies were included. Studies were conducted in India (n=6), US (n=5), international (n=4), France (n=2), Italy (n=2), and one each from China, Japan, Jordan, Mexico, Morocco, Nigeria, Romania, Saudi Arabia, Spain, UK, UK and US, and location not specified. Most studies (n=17) employed subjective methods of data collection (surveys/ questionnaires). We identified 6 themes. These were: changes in consultation type, for instance using teleconsultations and smartphone apps to monitor glucose control and diabetic management. Studies described temporary changes to treatment protocols e.g., using oral chemotherapy to reduce the need for in-person appointments and reduce the infection risk associated with intravenous administration. Control of certain conditions for example epilepsy was reduced in some studies. Patients missed doses due to drug shortages associated with disruptions in the medication supply chain, particularly in low-income countries. Finally, we identified prescribing trend changes in certain classes of medicines (e.g. reduced biologic usage due to immunosuppression risk) and an increase in patients self-medicating conditions including anxiety and depression, with associated safety risks. Conclusion(s): This review suggested that certain medical conditions such as diabetes and hypertension were more suited to remote monitoring with technological interventions such as smartphone apps. While other conditions e.g., cancer and epilepsy, demonstrated a greater need for in-person care. Countries of lower socioeconomic status were disproportionately affected by the pandemic.
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Objective: To investigate the blood glucose control of diabetic patients during the Coronavirus disease 2019 (COVID-19) epidemic, and to explore the factors affecting blood glucose. Method(s): Three hundred and fifty patients with diabetes mellitus hospitalized in the Endocrinology Department of the Second Affiliated Hospital of Air Force Military Medical University from 2017 to 2019 were selected, and we send questionnaires (a self-made questionnaire containing 39 questions, Zung anxiety self-assessment scale, Zung depression self-assessment scale) to the patients through WeChat group. After the effective questionnaires were collected, the patients were divided into good blood glucose control group (fasting blood glucose <=7 mmol/L and 2 h postprandial blood glucose <=10 mmol/L) and poor blood glucose control group (fasting blood glucose>7 mmol/L and/or 2 hours postprandial blood glucose>10 mmol/L). Chi square test or Fisher exact probability method and t test were used to compare the differences between the two groups. In Multi-factor logistic regression, the backward regression method was performed. Result(s): A total of 310 questionnaires were collected, 4 of which did not meet the requirements were eliminated, and a total of 306 valid questionnaires were analyzed. There were 108 cases (35.3%) in the well-controlled group and 198 cases (64.7%) in the poorly controlled group. Compared with well-controlled group, there was a higher percentage of patients with aged >=45 years, diabetes course >=5 years, combined with chronic complications of diabetes, weekly exercise time during the epidemic period<150 min,weekly monitoring of blood glucose frequency <=1 to 2 times and sleep disorders during the epidemic, anxiety, and depression in poorly controlled group, and there were statistically significant differences (P<0.05).The above 8 factors with P<0.05 were included in the logistic regression model. Diabetes course >=5 years, weekly exercise time during the epidemic<150 min, sleep disturbance during the epidemic, weekly monitoring of blood glucose frequency <= 1 to 2 times, depression were risk factors for poor blood glucose control (P<0.05). Conclusion(s): During the epidemic period, the blood glucose level of diabetes patients was generally high. The factors that affected blood glucose control included a long course of diabetes, short exercise time, low monitoring frequency of blood glucose, sleep disorders, and depression.Copyright © 2020 by the Chinese Medical Association.
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Objective: To investigate the blood glucose management of diabetic patients during the fight against corona virus disease 2019 (COVID-19) in Wuhan, China. Method(s): A questionnaire survey was conducted on diabetic patients receiving hypoglycemic drugs in wuhan, hubei province from February 16, 2020 to February 20, 2020. The questionnaire included participants' basic information, the management of blood glucose, and the prevention and control of COVID-19. SPSS 19.0 was used for statistical analysis, and chi2 test was used for comparison between the two groups. Result(s): A total of 152 valid questionnaires were retrieved. 86 cases (56.6%) diabetic patients achieved glycemic control. 80 cases (52.6%) could regularly monitor their blood glucose. 48 cases (31.6%) had the difficulty in the management of blood glucose for purchasing medicines. They also had the difficulties in adjusting blood glucose in the outpatient of endocrinology departments (31 cases, 20.4%), adherence to appropriate exercise (28 cases, 18.4%) and eating the balanced diet (16 cases, 10.5%). When faced with medical problems, 73 cases (48.0%) seek help from hospital out-patient clinics. Nearby pharmacies (78 case, 51.3%) or hospital outpatient (63 cases, 41.5%) were the main ways to purchase medicines for diabetic patients. 133 cases (87.5%) took medicines regularly. 39 cases (25.7%) and 17 (11.2%) diabetic patients were affected by the COVID-19 epidemic and changed or discontinued the original treatments. There was statistically significant in the proportion of discontinuation of hypoglycemic drugs between different drug treatment regiments and subgroups with diabetes course (chi2=13.30, P<0.01;chi2=8.72, P<0.05). Only 16 cases (10.5%) showed that their community health service organizations had specially trained diabetic management team. Conclusion(s): This survey suggests that the diabetic patients in Wuhan had not paid enough attention to blood glucose monitoring, and their blood glucose control standards need to be further improved. In terms of the present problems, more comprehensive blood glucose management measures need to be developed to help diabetic patients fighting against COVID-19.Copyright © 2020 by the Chinese Medical Association.
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OBJECTIVE The aim of this review was to describe how the coronavirus disease 2019 (COVID-19) lockdown affected the self-care behaviors of people living with type 2 diabetes. METHODS A systematic rapid review was conducted using four electronic databases. Studies reporting on the lock-down's impact on at least one of the self-care behaviors that were published from January 2020 through October 2021 were included. Findings were synthesized narratively, using the Association of Diabetes Care & Education Specialists ADCES7 Self-Care Behaviors as a framework. The methodological level of evidence and quality ratings of the articles were assessed using the Joanna Briggs Institute Appraisal Checklist. RESULTS Fifteen articles were included. Most studies reported on at least five of the self-care behaviors. There were reported increases in diabetes-related stress, as well as in increases in dietary intake and changes in the timing of meals. Physical activity was reported to decrease. Overall, taking medications and glycemic self-monitoring of blood glucose (SMBG) were unaffected by the lockdown. Of the studies reporting glycemic outcomes, the lockdown appeared to have little negative effect. None of the articles assessed all the self-care behaviors. The self-care behavior of SMBG was the least assessed. Most articles had a medium level of evidence and a medium to high quality rating (scores >60%). CONCLUSION The findings from this review found the COVID-19 lockdown had a variable impact on diabetes self-care behaviors. Because the potential for future COVID-19 surges and/or other virulent transmissible diseases remains a concern, health care providers should continue to address the importance of self-care behaviors to mitigate the risk of poor health outcomes in people with diabetes.Copyright © 2023 by the American Diabetes Association.
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Background and Aims: For adolescents with type 1 diabetes (T1D), self-efficacy in diabetes management is associated with better glycemic levels and improved health outcomes. We examined the impact of the COVID-19 pandemic on self-efficacy and diabetes management among adolescents with T1D. Method(s): We conducted semi-structured interviews with adolescents with T1D who were participating in an ongoing clinical trial. Adolescents (n = 24;mean age = 13.8 +/- 2.1 years;42% female;mean HbA1c = 8.7 +/- 1.7%;95% CGM users) described their confidence in their ability to manage diabetes during the pandemic. Interviews were transcribed and coded, establishing inter-reliability (kappa = .78). Adolescents' diabetes device use and HbA1c were extracted from medical records. Result(s): Most adolescents (63%) reported increased confidence over the course of the pandemic. Over half (53%) of these adolescents were already using an insulin pump, while 33% updated their method of insulin delivery over the pandemic. Many participants cited diabetes technology as an important factor in their self-management confidence. They described the additional information about blood glucose trends the technology provided as being beneficial during the pandemic, allowing them to make necessary adjustments on their own, at a time when seeing providers was inconsistent. Several participants also reported that the technology helped them adhere to recommendations regarding insulin dosing and glucose monitoring during quarantine, leading to improvements in blood glucose levels and an overall increase in self-efficacy. Conclusion(s): Findings illustrate the role of diabetes technology in the daily lives of adolescents with T1D, as well as their potential benefits during the particularly unique time of COVID-19.
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Background and Aims: COVID-19 created challenges to diabetes care and accelerated the need to optimize healthcare delivery outside of traditional settings. Due to subsequent stayat- home orders, many clinicians sought remote patient monitoring (RPM) solutions to remain engaged with their patients with diabetes (PWD) and to provide care. This study examined RPM uptake and diabetes-related outcomes during the COVID- 19 pandemic for PWD using a RPM solution. Method(s): The Glooko platform is used globally by millions of PWD and populates a real world data repository of 100+ billion data points. The analysis included diabetes device syncs from 100,000+ Glooko patient users during Year 2020. Descriptive statistics were used to evaluate trends in RPM usage and diabetes outcomes (glucose, self-monitoring, etc.). Result(s): RPM uploads increased by 36% during the "lockdown" and remained high even as clinics reopened. Five months into the pandemic, peak glucose levels on Sundays and Saturdays increased, but remained lower than pre-pandemic levels (-1.2% and -0.7%, respectively). Average glucose levels dropped early on and gradually increased over the year but with lower weekend and holiday spikes. Self-monitoring of blood glucose (SMBG) readings were within recommended range over 50% of the time and the average number of daily SMBG checks exceeded established clinical guidelines. Additional data will be presented. Conclusion(s): The Glooko RPM platform offered an important clinical tool to providers and patients during the pandemic which resulted in increased engagement, improved glucose trends, and increased self-monitoring. Remote care provided clinics and patients with necessary insights to collaborate and manage diabetes despite the lack of in-clinic visits.
ABSTRACT
The number of people with diabetes globally, is rising at an alarming rate. South Asia is one of the hot spots of the diabetes epidemic. In India alone, there are over 74 million people with diabetes today. Unfortunately, 70% of the doctors in India practice in urban areas while 70% of India's population lives in rural areas. This mismatch between the availability of health care professionals and the rapid spread of diabetes in rural areas, provides an opportunity to use technology to deliver the diabetes care to remote rural areas. The first part of this presentation will talk about a model of successful delivery of diabetes health care in rural India. The Chunampet Rural Diabetes Program was carried out in a group of 42 villages in Kancheepuram District in Tamilnadu. Using a Mobile van, a population of 27,014 individuals (86.5% of the adult population) were screened for diabetes. All those detected with diabetes were offered a follow up care at a rural diabetes centre which was set up during the project. The results were very impressive and led to good improvement in A1c levels using low cost generic drugs. The second use of technology was during the COVID - 19 pandemic and the lock down which was enforced in India and many other countries. Thankfully, Telemedicine was also legalized in India at that time. Using technology, a system was created whereby the doctor and the patient stayed at home but blood tests were arranged at home for the patient.With the results, teleconsultation was done by doctors using the Electronic Medical Records which were made available on their mobile phones. Thus, despite the lockdown, patients managed to get their tests and diabetes consultations done remotely. The third use of technology is through our network of diabetes clinics across India. Even at centres where there was no ophthalmologist, retinal photographs were obtained using a lowcost retinal camera and were uploaded for centralized diabetic retinopathy grading unit where the images were read by trained retina specialists. The eye reports were sent back to the peripheral clinics in real time. Over one year period, 25,316 individuals with diabetes could have their eyes screened for diabetic retinopathy. Only 11.4 % needed referral to an ophthalmologist for further management. Finally, the use of mobile Apps has revolutionized diabetes treatment. Recently, we have developed three diabetes related tools. 'DIA' - an AI powered chatbot to assist people through automated digital conversations, 'DIALA' - a patientfriendly mobile app and 'DIANA' - a healthcare application for precision diabetes care. The details of these three tools are briefly described below : DIA : The Conversational AI Virtual Assistant 'DIA' can interact in English with its unique conversational AI technology and intuitive interface, it has proved to be a useful solution for patients, providing complex dialogues, with quick response time and offers comprehensive solutions for patients with diabetes. DIA's uses range from scheduling appointments and reminders for visits, lab tests and teleconsultation, to addressing enquiries on available medicines, treatments, and facilities.During an emergency, health crisis or in pandemic situations, it connects with caregivers and patients to take proper action as per the seriousness of their conditions. Further, it shares notifications, updates patient engagement and special offers. In addition to this, DIA can assist patients through reminders on their medicine refill via WhatsApp or SMS notifications and even facilitate purchase and tracking of medicine orders. DIALA : 'DIALA' is a DIAbetes Lifestyle Assistant Mobile Application. This app helps deliver superior and positive patient outcomes with weight tracking, step counts, diet plan adjustment, prescription refilling, availing reports of tests done, glucose monitoring data, scheduling appointments and sends reminders. It can help to monitor one's health and manage diabetes effectively. It is currently available in Android. DIANA : An advanced machine learning tool DIANA (DIAbetes Novel subgroup Assessment) is used to classify individuals with newly detected type 2 diabetes into specific subgroups such as insulin deficient or insulin resistance forms. This tool also gives the estimates of the risk for developing diabetes complications like eye or kidney disease. This machine learning approach has been developed based on published real world clinical data and will help the clinician offer individualized care for people with diabetes. In conclusion, judicious use of technology can help to bridge the socioeconomic and geographical challenges in delivering diabetes health care in developing countries.
ABSTRACT
Background and Aims: The burden of uncontrolled DM amongst insulin users in Malaysia is great. Structured Self- Monitoring of Blood Glucose (SMBG) that are stored in cloud, simplified into visual charts, graphs coupled with a diabetes management system (DMS) that allows remote insulin titration can lead to improvement of glycemic control. Method(s): 124 Type 2 DM outpatients with HbA1C > 8% on intensive insulin therapy were recruited in this 26 weeks, multicenter, double arm, randomized controlled study. The patients were randomized to control arm which used traditional logbook and intervention arm which received remote insulin titration with a Bluetooth glucometer coupled with a DMS. The primary objective was to compare reduction of HbA1C and the secondary objective was to compare the change in Diabetes Distress Scale (DDS) between the control and intervention arm. Result(s): There was significantly higher mean reduction of HbA1C in the intervention group ;-2.016+/-1.60 versus - 1.326+/-1.51 in the control group (p = 0.027) by week 14 and was maintained till Week 26. There was no significant difference between the reduction of DDS between both groups. The mean frequency of SMBG in the intervention group was significantly higher than the control group;339.656+/-171.14 (intervention) versus 216.716+/-96.40 (control) [p < 0.001]. Conclusion(s): Remote insulin titration has been proven effective especially during COVID-19 whereby there was imminent need for reduction of physical visits to the hospital. This has led to improvement of glycemic control but could translate to lesser waiting time and reduction of cost in the long term.