Figure - available from: International Journal of Clinical Practice
This content is subject to copyright. Terms and conditions apply.
Mean HbA1C level at baseline and after three months measured in the center.
Source publication
Background:
Telemonitoring (TM), mobile-phone technology for health, and bluetooth-enabled self-monitoring devices represent innovative solutions for proper glycemic control, compliance and monitoring, and access to providers.
Objective:
In this study, we evaluated the impact of TM devices on glycemic control and the compliance of 38 previously...
Similar publications
This randomized control trail aimed to evaluate the effects of the patient-centered pharmaceutical care (PCPC) intervention among the uncontrolled hypertensive patients. The participants were uncontrolled hypertensive patients, selected from four health-promoting hospitals in Muang Phayao district, Phayao province, Thailand. Eligible patients were...
Citations
... To facilitate regular participation in intervention programs, alternative ways of maintaining contact with patients who do not attend visits to the health facilities should be explored. A study by Farooqi and colleagues [48] reported a significant decrease in HbA1c from 10.3 ± 1.9% at baseline to 7.4 ± 1.5% after 3-month participation in telemonitoring devices of 38 previously lost to follow-up from a diabetes center as well as a weight loss 1.3 kg. Another intervention study involving diabetes education via WhatsApp to patients with diabetes in the UAE resulted in a significant improvement in glycemic control from baseline with no significant change in the control group (8.5% to 7.7% vs. 8.5 to 8.4%, respectively) after 6 months [31]. ...
Background
Type 2 diabetes mellitus is highly prevalent in the Arab Gulf countries. Despite this, limited culturally-adapted lifestyle intervention studies have been conducted in this region.
Methods
In this culturally adapted 12-month cluster randomized trial, 382 patients with type 2 diabetes, aged 20–70 years were recruited from 6 public healthcare centers (3 interventions and 3 controls) in Al Ain, United Arab Emirates. The primary outcome of this study was a change in hemoglobin A1c (HbA1c). The secondary outcomes were Body Mass Index (BMI), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, total cholesterol, dietary intake, and physical activity levels. A diet and physical activity intervention, guided by the social cognitive theory, was delivered individually and in group format to the intervention group. The control group continued receiving only their usual diabetes management care. The data were collected at baseline and 1 year after participation.
Results
The mean baseline HbA1c levels of the control and the intervention groups were 7.45 ± 0.11% and 7.81 ± 0.11%, respectively. At the end of the 12-month intervention, there was no significant difference in the changes of mean HbA1c between the intervention and the control groups. On the other hand, BMI and daily caloric intake were significantly decreased in the intervention compared to the control group by 1.18 kg/m2 (95% CI: -1.78 − -0.60) and 246 kcal (95% CI: -419.52 − -77.21), respectively, after controlling for age, gender, education, marital status, duration since diabetes diagnosis, diabetes treatment, treatment clinic, and baseline values. Sitting time during the week-end was significantly lower, difference 52.53 minutes (95% CI: 93.93 − -11.14).
Conclusions
This community-based lifestyle intervention for patients with baseline HbA1c <8% did not result in a significant decrease of HbA1c but reduced caloric intake, body weight, and weekend inactivity after controlling for the covariates.
Trial registration
This trial was registered on February 11, 2020 with Clinicaltrials.gov (NCT04264793).
... Several groups have published their CGM experience in different patient populations and clinical settings (►Table 3). [52][53][54][55][56][57] Two studies from the same tertiary care center in the UAE employed FSL-CGM in two high-risk groups fasting during Ramadan (2016). 52,53 The first prospective interventional study included 25 patients with T2D and CKD stage 3. 52 FSL-CGM data showed significantly longer duration and more frequent hypoglycemic episodes during Ramadan than non-Ramadan. ...
... Farooqi et al evaluated the impact of telemonitoring devices on glycemic control and compliance in 38 previously lost-to-follow-up patients with T2D in an interventional single-center study in Dubai. 57 Patients were provided with home-based telemedicine devices at the initial visit. The mean HbA1c decreased significantly from 10.3% at baseline to 7.4% at the end of 3 months of follow-ups (MD of À2.9%). ...
... Another group from Abu Dhabi described their experience with telemonitoring. 57 They described data on 21 individuals using FSL-CGM who were remotely connected to the diabetes clinic. Overall, glycemic control improved during the coronavirus disease 2019 (COVID-19) lockdown compared with the weeks before. ...
Introduction The United Arab Emirates (UAE), among the rest of the Arab Gulf countries, exhibits a high prevalence of diabetes, primarily type 2 diabetes (T2D).
Methods We aimed to provide an overview of the epidemiology, complications, and quality of care, including the use of technology in diabetes care. Also, we wished to explore the challenges of diabetes management and future directions in clinical practice and research. This is a focused review of the literature of selected relevant themes to serve the above objectives of the work.
Results Several epidemiological studies have documented the increased prevalence of diabetes in the native population and expatriates. The vast majority focused on T2D. The prevalence of diabetes in the UAE is estimated at 12.3% for the 20 to 79 age group. Although the high prevalence was recognized and acknowledged as a national priority, several challenges exist in standardizing care across the population. There are gaps in research about the nationwide prevalence of all forms of diabetes. Some research studies have evaluated the role of technology in diabetes care, genetic predilection to complications, and particular aspects such as diabetes during pregnancy, neonatal diabetes, monogenic diabetes, and cardiovascular risk in diabetes. UAE recently became a focal point for health-related Ramadan fasting research, including diabetes.
Conclusions Diabetes in the UAE considerably burdens the health care system. A concerted effort is needed to adopt more uniformity of diabetes care and research nationwide. This should address the use of unified methods to document the nationwide burden, explore possible differences in various epidemiological phenomena, access to health care, and impact on outcomes, and evaluation of the cost-effectiveness of different models of care.
... The observed improvements in disease-specific markers, severity of symptoms, and overall health status corroborate previous research indicating the positive impact of telemedicine on the management of chronic diseases [9]. Reductions in disease-specific markers, such as blood glucose levels or blood pressure, signify the potential for telemedicine to contribute to better health outcomes [10]. The substantial decrease in the severity of the symptoms aligns with studies that demonstrate the effectiveness of remote monitoring in the early detection and intervention of symptoms [11]. ...
Background
Telemedicine and remote patient monitoring have emerged as transformative solutions in contemporary healthcare. This study aimed to conduct a comprehensive evaluation of the impact of these technologies on healthcare delivery, focusing on patient outcomes, economic parameters, and overall satisfaction.
Methods
A prospective observational study was conducted in various healthcare facilities, involving 186 participants with chronic diseases. Inclusion criteria included patients actively using telemedicine services. Data collection methods included surveys, interviews, and review of medical records, focusing on patient demographics, clinical outcomes, and economic parameters. The intervention involved a seamless integration of telemedicine technologies into the existing health system.
Results
Primary outcomes revealed significant improvements in patient health, including a decrease in disease-specific markers (mean reduction of 12,000 to 11,000, p = 0.002), a substantial reduction in severity of symptoms (mean reduction from 3,500 to 2,500, p < 0.001), and a general improvement in health status (mean increase from 7,200 to 8,500, p < 0.001). The savings in healthcare costs were evident, with direct costs decreasing from 25,000 to 12,000 (p < 0.001) and indirect costs decreasing from <10,000 to <5,000 (p = 0.004). Secondary results demonstrated increased patient satisfaction with communication (increase from 80% to 95%, p < 0.001) and convenience of services (increase from 75% to 90%, p < 0.001). Patient satisfaction also increased significantly (from 80% to 95%, p < 0.001). Accessibility to healthcare services improved, with a reduction in geographic barriers (increase from 65% to 90%, p < 0.001) and a decrease in the frequency of healthcare utilization (decrease from 2.5 to 1.5, p < 0.001).
Conclusion
The study provides robust evidence of the positive impact of telemedicine and remote patient monitoring on healthcare delivery. Significant improvements in patient outcomes, coupled with substantial cost savings and increased satisfaction levels, underscore the transformative potential of these technologies.
... Mobile sensors for the measurement of routine blood parameters to be used in the realtime detection of various diseases are being developed rapidly with the advancements of technology [73][74][75][76]. The RBV values can be measured using a low-cost, mobile microscope, an ocular camera, and a smartphone [73]. ...
... Chan et al. [74] determined PT and INR blood values by monitoring the micro-mechanical movements of a copper particle with a proof-ofconcept using the vibration motor and camera in smartphones. Farooqi et al. [75] followed the diabetic patients with telemonitoring and Bluetooth-enabled self-monitoring devices and produced new solutions for the glycemic control of the patients. Zhang et al. [76] determined various biochemical parameters by electrochemical controls. ...
Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.
... Mobile sensors for the measurement of routine blood parameters to be used in the realtime detection of various diseases are being developed rapidly with the advancements of technology [73][74][75][76]. The RBV values can be measured using a low-cost, mobile microscope, an ocular camera, and a smartphone [73]. ...
... Chan et al. [74] determined PT and INR blood values by monitoring the micro-mechanical movements of a copper particle with a proof-ofconcept using the vibration motor and camera in smartphones. Farooqi et al. [75] followed the diabetic patients with telemonitoring and Bluetooth-enabled self-monitoring devices and produced new solutions for the glycemic control of the patients. Zhang et al. [76] determined various biochemical parameters by electrochemical controls. ...
Text available in https://www.mdpi.com/1424-8220/22/13/4820
Since February-2020, the world has embarked on an intense struggle with the COVID-19 disease, and health systems have come under a tragic pressure as the disease turned into a pandemic. The aim of this study is to determine the most effective routine-blood-values (RBV) in the diagnosis/prognosis of COVID-19 using new feature selection method for LogNNet reservoir neural network. First dataset in this study consists of a total of 5296-patients with a same number of negative and positive covid test. Second dataset consists of a total of 3899-patients with a diagnosis of COVID-19, who were treated in hospital with severe-infected (203) and mildly-infected (3696). The most important RBVs that affect the diagnosis of the disease from the first dataset were mean-corpuscular-hemoglobin-concentration (MCHC), mean-corpuscular-hemoglobin (MCH) and activated-partial-prothrombin-time (aPTT). The most effective features in the prognosis of the disease were erythrocyte-sedimentation-rate (ESR), neutrophil-count (NEU), C-reactive-protein (CRP). LogNNet-model achieved an accuracy rate of A46 = 99.5% in the diagnosis of the disease with 46 features and A3 = 99.17% with only MCHC, MCH, and aPTT features. Model reached an accuracy rate of A48 = 94.4% in determining the prognosis of the disease with 48 features and A3 = 82.7% with only ESR, NEU, and CRP features. LogNNet model demonstrated a very high disease diagnosis/prognosis of COVID-19 performance without knowing about the symptoms or history of the patients. The model is suitable for devices with low resources (3-14 kB of RAM used on the Arduino microcontroller), and is promising to create mobile health monitoring systems in the Internet of Things. Our method will reduce the negative pressures on the health sector and help doctors understand pathogenesis of COVID-19 through key futures and contribute positively to the treatment processes.
