Continuous glucose monitoring (CGM) provides real-time assessment of glucose levels and may be beneficial in reducing hypoglycemia in older adults with type 1 diabetes.
To determine whether CGM is ...effective in reducing hypoglycemia compared with standard blood glucose monitoring (BGM) in older adults with type 1 diabetes.
Randomized clinical trial conducted at 22 endocrinology practices in the United States among 203 adults at least 60 years of age with type 1 diabetes.
Participants were randomly assigned in a 1:1 ratio to use CGM (n = 103) or standard BGM (n = 100).
The primary outcome was CGM-measured percentage of time that sensor glucose values were less than 70 mg/dL during 6 months of follow-up. There were 31 prespecified secondary outcomes, including additional CGM metrics for hypoglycemia, hyperglycemia, and glucose control; hemoglobin A1c (HbA1c); and cognition and patient-reported outcomes, with adjustment for multiple comparisons to control for false-discovery rate.
Of the 203 participants (median age, 68 interquartile range {IQR}, 65-71 years; median type 1 diabetes duration, 36 IQR, 25-48 years; 52% female; 53% insulin pump use; mean HbA1c, 7.5% SD, 0.9%), 83% used CGM at least 6 days per week during month 6. Median time with glucose levels less than 70 mg/dL was 5.1% (73 minutes per day) at baseline and 2.7% (39 minutes per day) during follow-up in the CGM group vs 4.7% (68 minutes per day) and 4.9% (70 minutes per day), respectively, in the standard BGM group (adjusted treatment difference, -1.9% (-27 minutes per day); 95% CI, -2.8% to -1.1% -40 to -16 minutes per day; P <.001). Of the 31 prespecified secondary end points, there were statistically significant differences for all 9 CGM metrics, 6 of 7 HbA1c outcomes, and none of the 15 cognitive and patient-reported outcomes. Mean HbA1c decreased in the CGM group compared with the standard BGM group (adjusted group difference, -0.3%; 95% CI, -0.4% to -0.1%; P <.001). The most commonly reported adverse events using CGM and standard BGM, respectively, were severe hypoglycemia (1 and 10), fractures (5 and 1), falls (4 and 3), and emergency department visits (6 and 8).
Among adults aged 60 years or older with type 1 diabetes, continuous glucose monitoring compared with standard blood glucose monitoring resulted in a small but statistically significant improvement in hypoglycemia over 6 months. Further research is needed to understand the long-term clinical benefit.
ClinicalTrials.gov Identifier: NCT03240432.
Long‐term monitoring with optical fibers has moved into the focus of attention due to the applicability for medical measurements. Within this Review, setups of flexible, unobtrusive body‐monitoring ...systems based on optical fibers and the respective measured vital parameters are in focus. Optical principles are discussed as well as the interaction of light with tissue. Optical fiber‐based sensors that are already used in first trials are primarily selected for the section on possible applications. These medical textiles include the supervision of respiration, cardiac output, blood pressure, blood flow and its saturation with hemoglobin as well as oxygen, pressure, shear stress, mobility, gait, temperature, and electrolyte balance. The implementation of these sensor concepts prompts the development of wearable smart textiles. Thus, current sensing techniques and possibilities within photonic textiles are reviewed leading to multiparameter designs. Evaluation of these designs should show the great potential of optical fibers for the introduction into textiles especially due to the benefit of immunity to electromagnetic radiation. Still, further improvement of the signal‐to‐noise ratio is often necessary to develop a commercial monitoring system.
This Review deals with possibilities in health‐care monitoring by means of polymer optical fiber sensing. Fundamentals of optics and different optical fiber sensing techniques are discussed followed by a materials section and the various available noninvasive applications in which optical fibers can be used. The last section includes approaches for textile integration to allow for wearable long‐term monitoring systems.
The recent trend in electrocardiogram (ECG) device development is towards wireless body sensors applied for patient monitoring. The ultimate goal is to develop a multi-functional body sensor that ...will provide synchronized vital bio-signs of the monitored user. In this paper, we present an ECG sensor for long-term monitoring, which measures the surface potential difference between proximal electrodes near the heart, called differential ECG lead or differential lead, in short. The sensor has been certified as a class IIa medical device and is available on the market under the trademark Savvy ECG. An improvement from the user's perspective-immediate access to the measured data-is also implemented into the design. With appropriate placement of the device on the chest, a very clear distinction of all electrocardiographic waves can be achieved, allowing for ECG recording of high quality, sufficient for medical analysis. Experimental results that elucidate the measurements from a differential lead regarding sensors' position, the impact of artifacts, and potential diagnostic value, are shown. We demonstrate the sensors' potential by presenting results from its various areas of application: medicine, sports, veterinary, and some new fields of investigation, like hearth rate variability biofeedback assessment and biometric authentication.
Continuous glucose monitoring (CGM) is becoming widely accepted as an adjunct to diabetes management. Compared to standard care, CGM can provide detailed information about glycaemic variability in an ...internationally standardised ambulatory glucose profile, enabling more informed user and clinician decision making. We aimed to review the evidence, user experience and cost-effectiveness of CGM.
A literature search was conducted by combining subject headings 'CGM' and 'flash glucose monitoring', with key words 'type 1 diabetes' and 'type 2 diabetes', limited to '1999 to current'. Further evidence was obtained from relevant references of retrieved articles.
There is a strong evidence for CGM use in people with type 1 diabetes, with benefits of reduced glycated haemoglobin and hypoglycaemia, and increased time in range. While the evidence for CGM use in type 2 diabetes is less robust, similar benefits have been demonstrated. CGM can improve diabetes-related satisfaction in people with diabetes (PWD) and parents of children with diabetes, as well as the clinician experience. However, CGM does have limitations including cost, accuracy and perceived inconvenience. Cost-effectiveness analyses have indicated that CGM is a cost-effective adjunct to type 1 diabetes management that is associated with reduced diabetes-related complications and hospitalisation.
Continuous glucose monitoring is revolutionising diabetes management. It is a cost-effective adjunct to diabetes management that has the potential to improve glycaemic outcomes and quality of life in PWD, especially type 1 diabetes.
Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of ...low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM
10
and PM
2.5
) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM
10
–PM
2.5
sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (
n
= 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM
2.5
(normalized root-mean-square error 9–24%) and PM
10
(10–37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM
2.5
(
R
2
0.47–0.86) than PM
10
(0.24–0.56). The correlations (
R
2
) between the 24-h PM
2.5
averages from the sensors and reference instruments were 0.63–0.87 for continuous monitoring and 0.69–0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM
2.5
averages after correcting for RH.
Compared with the limited capability of ground-level monitoring, remote sensing provides useful image data at a moderate or high spatial or temporal resolution with global coverage for monitoring of ...air pollutants, e.g., aerosol optical depth (AOD) observations from the MODIS for fine particulate matter (PM2.5) and Ozone Monitoring Instrument (OMI) nitrogen dioxide (NO2) vertical columns for ground-level NO2 concentration. However, the extensive nonrandom missingness of OMI-NO2 data (e.g., an approximate per-pixel missing proportion of 59% for mainland China in 2015) due to cloud contamination or high reflectance limits applicability of these data in estimation of ground-level NO2. This paper proposes the use of a full residual deep learning method to impute missing satellite-borne NO2 data (OMI-NO2) and to estimate (map) ground-level NO2 with uncertainty (coefficient of variation) at a high spatial (1 × 1 km2) and temporal (daily) resolution. For the large study region (mainland China except Hainan Province), the presented method achieved robust performance with a stable learning efficiency (mean test R2: 0.98 with a small standard deviation of 0.01; mean test RMSE: 0.42 × 1015 molecules/cm2) for imputation of OMI-NO2. In the model, the coordinates and elevation were used to capture the spatial variability of the OMI-NO2 columns, and fused meteorological grid data and planetary boundary layer height and ozone data from GEOS-FP were used to capture spatiotemporal variability of OMI-NO2. The evaluation with ground in situ NO2 measurements showed considerable contribution of the complete (raw observed and imputed) OMI-NO2 columns, meteorology and traffic variables to inference of ground-level NO2 (test R2: 0.82; test RMSE: 8.80 μg/m3). The complete grids of OMI-NO2 columns showed natural and smooth spatial transitions between the raw observed and imputed values. The surfaces of predicted NO2 concentration not only showed consistent distributions with OMI-NO2 at a regional and temporal scale, but also presented local spatial gradients of ground-level NO2. OMI-NO2 can be downscaled and imputed to be used as an important predictor to improve the estimation of high-resolution ground-level NO2. The reliable estimates of ground-level NO2 concentration with uncertainty can reduce the bias in estimates of NO2 exposure and subsequently evaluations of its health effects.
Display omitted
•A coarse spatial resolution and extensive missingness limit applications of OMI-NO2.•Full residual network was used to make reliable imputation of missing OMI-NO2.•Traffic and land-use variables were used to capture local gradient of ground NO2.•Bagging of base models was done to improve high-res nationwide prediction of NO2.•Predicted NO2 showed spatiotemporal distributions and local gradients.
Wireless technology development has increased rapidly due to it's convenience and cost effectiveness compared to wired applications, particularly considering the advantages offered by Wireless Sensor ...Network (WSN) based applications. Such applications exist in several domains including healthcare, medical, industrial and home automation. In the present study, a home-based wireless ECG monitoring system using Zigbee technology is considered. Such systems can be useful for monitoring people in their own home as well as for periodic monitoring by physicians for appropriate healthcare, allowing people to live in their home for longer. Health monitoring systems can continuously monitor many physiological signals and offer further analysis and interpretation. The characteristics and drawbacks of these systems may affect the wearer's mobility during monitoring the vital signs. Real-time monitoring systems record, measure, and monitor the heart electrical activity while maintaining the consumer's comfort. Zigbee devices can offer low-power, small size, and a low-cost suitable solution for monitoring the ECG signal in the home, but such systems are often designed in isolation, with no consideration of existing home control networks and smart home solutions. The present study offers a state of the art review and then introduces the main concepts and contents of the wireless ECG monitoring systems. In addition, models of the ECG signal and the power consumption formulas are highlighted. Challenges and future perspectives are also reported. The paper concludes that such mass-market health monitoring systems will only be prevalent when implemented together with home environmental monitoring and control systems.
Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable ...growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.
Adolescents and young adults with type 1 diabetes exhibit the worst glycemic control among individuals with type 1 diabetes across the lifespan. Although continuous glucose monitoring (CGM) has been ...shown to improve glycemic control in adults, its benefit in adolescents and young adults has not been demonstrated.
To determine the effect of CGM on glycemic control in adolescents and young adults with type 1 diabetes.
Randomized clinical trial conducted between January 2018 and May 2019 at 14 endocrinology practices in the US including 153 individuals aged 14 to 24 years with type 1 diabetes and screening hemoglobin A1c (HbA1c) of 7.5% to 10.9%.
Participants were randomized 1:1 to undergo CGM (CGM group; n = 74) or usual care using a blood glucose meter for glucose monitoring (blood glucose monitoring BGM group; n = 79).
The primary outcome was change in HbA1c from baseline to 26 weeks. There were 20 secondary outcomes, including additional HbA1c outcomes, CGM glucose metrics, and patient-reported outcomes with adjustment for multiple comparisons to control for the false discovery rate.
Among the 153 participants (mean SD age, 17 3 years; 76 50% were female; mean SD diabetes duration, 9 5 years), 142 (93%) completed the study. In the CGM group, 68% of participants used CGM at least 5 days per week in month 6. Mean HbA1c was 8.9% at baseline and 8.5% at 26 weeks in the CGM group and 8.9% at both baseline and 26 weeks in the BGM group (adjusted between-group difference, -0.37% 95% CI, -0.66% to -0.08%; P = .01). Of 20 prespecified secondary outcomes, there were statistically significant differences in 3 of 7 binary HbA1c outcomes, 8 of 9 CGM metrics, and 1 of 4 patient-reported outcomes. The most commonly reported adverse events in the CGM and BGM groups were severe hypoglycemia (3 participants with an event in the CGM group and 2 in the BGM group), hyperglycemia/ketosis (1 participant with an event in CGM group and 4 in the BGM group), and diabetic ketoacidosis (3 participants with an event in the CGM group and 1 in the BGM group).
Among adolescents and young adults with type 1 diabetes, continuous glucose monitoring compared with standard blood glucose monitoring resulted in a small but statistically significant improvement in glycemic control over 26 weeks. Further research is needed to understand the clinical importance of the findings.
ClinicalTrials.gov Identifier: NCT03263494.
The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their ...physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.
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