Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review

Paper integrated microfluidic contact lens for colorimetric glucose detection

Contact lenses offer a simple, cost-effective, and non-invasive method for in situ real-time analysis of various biomarkers. Electro-chemical sensors are integrated into contact lenses for analysis of various biomarkers. However, they suffer from rigid electronic components and connections, leading to eye irritation and biomarker concentration deviation. Here, a flexible and microfluidic integrated paper-based contact lens for colorimetric analysis of glucose was implemented. Facilitating a three-dimensional (3D) printer for lens fabrication eliminates cumbersome cleanroom processes and provides a simple, batch compatible process. Due to the capillary force of the filter paper, the sample was routed to detection chambers inside microchannels, and it allowed further colorimetric detection. The paper-embedded microfluidic contact lens successfully detects glucose down to 2 mM within ∼10 s. The small dimension of the microfluidic system enables detection of glucose levels as low as 5 μl. The results show the potential of the presented approach to analyze glucose concentration in a rapid manner. It is demonstrated that the fabricated contact lens can successfully detect glucose levels of diabetic patients.

A Nanotechnology-Based Approach to Biosensor Application in Current Diabetes Management Practices

Diabetes mellitus is linked to both short-term and long-term health problems. Therefore, its detection at a very basic stage is of utmost importance. Research institutes and medical organizations are increasingly using cost-effective biosensors to monitor human biological processes and provide precise health diagnoses. Biosensors aid in accurate diabetes diagnosis and monitoring for efficient treatment and management. Recent attention to nanotechnology in the fast-evolving area of biosensing has facilitated the advancement of new sensors and sensing processes and improved the performance and sensitivity of current biosensors. Nanotechnology biosensors detect disease and track therapy response. Clinically efficient biosensors are user-friendly, efficient, cheap, and scalable in nanomaterial-based production processes and thus can transform diabetes outcomes. This article is more focused on biosensors and their substantial medical applications. The highlights of the article consist of the different types of biosensing units, the role of biosensors in diabetes, the evolution of glucose sensors, and printed biosensors and biosensing systems. Later on, we were engrossed in the glucose sensors based on biofluids, employing minimally invasive, invasive, and noninvasive technologies to find out the impact of nanotechnology on the biosensors to produce a novel device as a nano-biosensor. In this approach, this article documents major advances in nanotechnology-based biosensors for medical applications, as well as the hurdles they must overcome in clinical practice.

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection

Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.

Optical Assessment of Tear Glucose by Smart Biosensor Based on Nanoparticle Embedded Contact Lens

Diabetes is a disease condition characterized by a prolonged, high blood glucose level, which may lead to devastating outcomes unless properly managed. Here, we introduce a simple camera-based optical monitoring system (OMS) utilizing the nanoparticle embedded contact lens that produces color changes matching the tear glucose level without any complicated electronic components. Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. As a result, using in vivo mouse models and human tear samples we successfully demonstrated robust correlations across the glucose concentrations measured by three different independent techniques, validating the quantitative efficacy of the proposed OMS. For its methodological simplicity and accessibility, our findings strongly support that the innovation offered by the OMS and processing algorithm would greatly facilitate the glucose monitoring procedure and improve the overall welfare of diabetes patients.

Review of Wearable Sensor-Based Health Monitoring Glove Devices for Rheumatoid Arthritis

Early detection of Rheumatoid Arthritis (RA) and other neurological conditions is vital for effective treatment. Existing methods of detecting RA rely on observation, questionnaires, and physical measurement, each with their own weaknesses. Pharmaceutical medications and procedures aim to reduce the debilitating effect, preventing the progression of the illness and bringing the condition into remission. There is still a great deal of ambiguity around patient diagnosis, as the difficulty of measurement has reduced the importance that joint stiffness plays as an RA identifier. The research areas of medical rehabilitation and clinical assessment indicate high impact applications for wearable sensing devices. As a result, the overall aim of this research is to review current sensor technologies that could be used to measure an individual's RA severity. Other research teams within RA have previously developed objective measuring devices to assess the physical symptoms of hand steadiness through to joint stiffness. Unfamiliar physical effects of these sensory devices restricted their introduction into clinical practice. This paper provides an updated review among the sensor and glove types proposed in the literature to assist with the diagnosis and rehabilitation activities of RA. Consequently, the main goal of this paper is to review contact systems and to outline their potentialities and limitations. Considerable attention has been paid to gloved based devices as they have been extensively researched for medical practice in recent years. Such technologies are reviewed to determine whether they are suitable measuring tools.

Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST - IT Ramadan study)

OBJECTIVE

To develop a machine-based algorithm from clinical and demographic data, physical activity and glucose variability to predict hyperglycaemic and hypoglycaemic excursions in patients with type 2 diabetes on multiple glucose lowering therapies who fast during Ramadan.

PATIENTS AND METHODS

Thirteen patients (10 males and three females) with type 2 diabetes on 3 or more anti-diabetic medications were studied with a Fitbit-2 pedometer device and Freestyle Libre (Abbott Diagnostics) 2 weeks before and 2 weeks during Ramadan. Several machine learning techniques were trained to predict blood glucose levels in a regression framework utilising physical activity and contemporaneous blood glucose levels, comparing Ramadan to non-Ramadan days.

RESULTS

The median age of participants was 51 years (IQR 49-52); median BMI was 33.2 kg/m² (IQR 33.0-35.9) and median HbA1c was 7.3% (IQR 6.7-7.8). The optimal model using physical activity achieved an R² of 0.548 and a mean absolute error (MAE) of 30.30. The addition of electronic health record (ehr) information increased R² to 0.636 and reduced MAE to 26.89 and the time of the day feature further increased R² to 0.768 and reduced MAE to 20.55. Combining all the features together resulted in an optimal XGBoost model with an R² of 0.836 and MAE of 17.47. This model accurately estimated normal glucose levels in 2584/2715 (95.2%) readings and hyperglycaemic events in 852/1031 (82.6%) readings, but fewer hypoglycaemic events (48/172 (27.9%)). The optimal XGBoost model prioritized age, gender, BMI and HbA1c followed by glucose levels and physical activity. Interestingly, the blood glucose level prediction by our model was influenced by use of SGLT2i.

CONCLUSION

XGBoost, a machine learning AI algorithm achieves high predictive performance for normal and hyperglycaemic excursions, but has limited predictive value for hypoglycaemia in patients on multiple therapies who fast during Ramadan.

Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.

Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field

Early detection of physiological deterioration has been shown to improve patient outcomes. Due to recent improvements in technology, comprehensive outpatient vital signs monitoring is now possible. This is the first review to collate information on all wearable devices on the market for outpatient physiological monitoring.

A scoping review was undertaken. The monitors reviewed were limited to those that can function in the outpatient setting with minimal restrictions on the patient’s normal lifestyle, while measuring any or all of the vital signs: heart rate, ECG, oxygen saturation, respiration rate, blood pressure and temperature.

A total of 270 papers were included in the review. Thirty wearable monitors were examined: 6 patches, 3 clothing-based monitors, 4 chest straps, 2 upper arm bands and 15 wristbands. The monitoring of vital signs in the outpatient setting is a developing field with differing levels of evidence for each monitor. The most common clinical application was heart rate monitoring. Blood pressure and oxygen saturation measurements were the least common applications. There is a need for clinical validation studies in the outpatient setting to prove the potential of many of the monitors identified.

Research in this area is in its infancy. Future research should look at aggregating the results of validity and reliability and patient outcome studies for each monitor and between different devices. This would provide a more holistic overview of the potential for the clinical use of each device.

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering

In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together. The algorithm has been validated in the electrocardiographic domain for heartbeat clustering, extracting positive evidence from the heartbeat morphology and negative evidence from the distances between heartbeats. The best result obtained on the MIT-BIH Arrhythmia database yielded an error of 1.44%. In the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database database (INCARTDB), an error of 0.601% was obtained when using two electrocardiogram (ECG) leads. When increasing the number of leads to 4, 6, 8, 10 and 12, the algorithm obtains better results (statistically significant) than with the previous number of leads, reaching an error of 0.338%. To the best of our knowledge, this is the first clustering algorithm that is able to process simultaneously any number of ECG leads. Our results support the use of PN-EAC to combine different sources of information and the value of the negative evidence.

Different Types of Physical Activity and Metabolic Control in People With Type 1 Diabetes Mellitus

Background

The aim of presented cross-sectional study was to determine the association of different types of physical activity (PA) with metabolic control in people with type 1 diabetes.

Materials and Methods

A total of 109 adult subjects with type 1 diabetes were asked to complete the non-exercise activity thermogenesis (NEAT) questionnaire, the hypoglycemia questionnaire, and the World Health Organization Global PA Questionnaire (GPAQ) which was used to assess moderate PA (MPA) and vigorous PA (VPA).

Results

NEAT score (p < 0.001) and total duration of work as assessed with GPAQ (p = 0.007) were positively associated with chronic glycemic control when controlled for sex, BMI, and continuous glucose monitoring system (CGMS) use. We could not confirm such association with total leisure time PA (LTPA) assessed with GPAQ (p = 0.443), though. Multivariate regression model controlled for sex showed positive effects of HbA_{1 c} (p = 0.011) and age (p = 0.035), and negative effect of NEAT score (p = 0.001) on BMI. Systolic blood pressure was positively associated with duration of MPA (p = 0.009) and VPA (p = 0.012), but not with NEAT score (p = 0.830) when controlled for sex and BMI. NEAT score and VPA were positively associated with HDL levels when controlled for sex and BMI. Controlled for sex and BMI, higher values of VPA were significantly associated with lower levels of total cholesterol (p = 0.009) and LDL (p = 0.005).

Conclusion

Higher levels of NEAT are associated with some favorable metabolic effects in adult people with type 1 diabetes, but may also present an additional burden for them with more challenging environment regarding glycemic control.

Feature Selection for Blood Glucose Level Prediction in Type 1 Diabetes Mellitus by Using the Sequential Input Selection Algorithm (SISAL)

Feature selection is a primary exercise to tackle any forecasting task. Machine learning algorithms used to predict any variable can improve their performance by lessening their computational effort with a proper dataset. Anticipating future glycemia in type 1 diabetes mellitus (DM1) patients provides a baseline in its management, and in this task, we need to carefully select data, especially now, when novel wearable devices offer more and more information. In this paper, a complete characterization of 25 diabetic people has been carried out, registering innovative variables like sleep, schedule, or heart rate in addition to other well-known ones like insulin, meal, and exercise. With this ground-breaking data compilation, we present a study of these features using the Sequential Input Selection Algorithm (SISAL), which is specially prepared for time series data. The results rank features according to their importance, regarding their relevance in blood glucose level prediction as well as indicating the most influential past values to be taken into account and distinguishing features with person-dependent behavior from others with a common performance in any patient. These ideas can be used as strategies to select data for predicting glycemia depending on the availability of computational power, required speed, or required accuracy. In conclusion, this paper tries to analyze if there exists symmetry among the different features that can affect blood glucose levels, that is, if their behavior is symmetric in terms of influence in glycemia.

Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are "Mets" (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only "Mets" is also viable for a more immediate implementation of this correction into market devices.

Enabling the Internet of Mobile Crowdsourcing Health Things: A Mobile Fog Computing, Blockchain and IoT Based Continuous Glucose Monitoring System for Diabetes Mellitus Research and Care

Diabetes patients suffer from abnormal blood glucose levels, which can cause diverse health disorders that affect their kidneys, heart and vision. Due to these conditions, diabetes patients have traditionally checked blood glucose levels through Self-Monitoring of Blood Glucose (SMBG) techniques, like pricking their fingers multiple times per day. Such techniques involve a number of drawbacks that can be solved by using a device called Continuous Glucose Monitor (CGM), which can measure blood glucose levels continuously throughout the day without having to prick the patient when carrying out every measurement. This article details the design and implementation of a system that enhances commercial CGMs by adding Internet of Things (IoT) capabilities to them that allow for monitoring patients remotely and, thus, warning them about potentially dangerous situations. The proposed system makes use of smartphones to collect blood glucose values from CGMs and then sends them either to a remote cloud or to distributed fog computing nodes. Moreover, in order to exchange reliable, trustworthy and cybersecure data with medical scientists, doctors and caretakers, the system includes the deployment of a decentralized storage system that receives, processes and stores the collected data. Furthermore, in order to motivate users to add new data to the system, an incentive system based on a digital cryptocurrency named GlucoCoin was devised. Such a system makes use of a blockchain that is able to execute smart contracts in order to automate CGM sensor purchases or to reward the users that contribute to the system by providing their own data. Thanks to all the previously mentioned technologies, the proposed system enables patient data crowdsourcing and the development of novel mobile health (mHealth) applications for diagnosing, monitoring, studying and taking public health actions that can help to advance in the control of the disease and raise global awareness on the increasing prevalence of diabetes.

Continuous Movement Monitoring of Daily Living Activities for Prevention of Diabetic Foot Ulcer: A Review of Literature

Lower extremity ulcers represent the most ominous, feared, and costly complications of diabetes mellitus. The aim of this review is to highlight the role of daily life physical activities (PAs) and continuous movement monitoring (CMM) in the prevention of foot ulcers. Peripheral neuropathy and peripheral vascular disease are the main causes of foot ulceration and contribute, in turn, to the development of additional risk factors such as foot deformities and/or joint and muscular alterations. Moreover, a deficit of balance, posture abnormalities, followed by gait alterations, increases the risk of ulceration. PA can play a key role in the management of patients with diabetes and in the prevention of ulcers; however, even if it has been reported that some of these risk factors significantly improve after a few weeks of exercise therapy (ET), the real preventive role of ET has not yet been demonstrated. These uncertain results can occur due to some limitations in the management of the same relationship between PA and diabetic foot prevention. Technological advances during the last years enable timely management of overall daily PA. The use of these modern technologies and devices allows CMM assessment and description of daily PA even in the long term. The data collected from these devices can be used to properly manage patients' PA and thus contribute to the prevention of foot ulcers.

Applications of stem cells and bioprinting for potential treatment of diabetes

Currently, there does not exist a strategy that can reduce diabetes and scientists are working towards a cure and innovative approaches by employing stem cell-based therapies. On the other hand, bioprinting technology is a novel therapeutic approach that aims to replace the diseased or lost β-cells, insulin-secreting cells in the pancreas, which can potentially regenerate damaged organs such as the pancreas. Stem cells have the ability to differentiate into various cell lines including insulin-producing cells. However, there are still barriers that hamper the successful differentiation of stem cells into β-cells. In this review, we focus on the potential applications of stem cell research and bioprinting that may be targeted towards replacing the β-cells in the pancreas and may offer approaches towards treatment of diabetes. This review emphasizes on the applicability of employing both stem cells and other cells in 3D bioprinting to generate substitutes for diseased β-cells and recover lost pancreatic functions. The article then proceeds to discuss the overall research done in the field of stem cell-based bioprinting and provides future directions for improving the same for potential applications in diabetic research.

First Experiences With a Wearable Multisensor in an Outpatient Glucose Monitoring Study, Part I: The Users' View

BACKGROUND

Extensive past work showed that noninvasive continuous glucose monitoring with a wearable Multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions.

METHODS

To test previous findings also in uncontrolled in-clinic and outpatient conditions, a long-term study has been conducted to collect Multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking Multisensor data to glucose applied to estimate glucose trends noninvasively. The operation of a digital log book, daily semiautomated data transfer and at least 10 daily SMBG values were requested from the patient.

RESULTS

Results showed that the Multisensor is capable of indicating glucose trends. It can do so in 9 out of 10 cases either correctly or with one level of discrepancy. This means that in 90% of all cases the Multisensor shows the glucose dynamic to rapidly increase or at least increase.

CONCLUSIONS

The Multisensor and the algorithmic routine used in controlled conditions can track glucose trends in all patients, also in uncontrolled conditions. Training of the patient proved to be essential. The workload imposed on patients was significant and should be reduced in the next step with further automation. The feature of glucose trend indication was welcomed and very much appreciated by patients; this value creation makes a strong case for the justification of wearing a wearable.

The Promise and Perils of Wearable Physiological Sensors for Diabetes Management

Development of truly useful wearable physiologic monitoring devices for use in diabetes management is still in its infancy. From wearable activity monitors such as fitness trackers and smart watches to contact lenses measuring glucose levels in tears, we are just at the threshold of their coming use in medicine. Ultimately, such devices could help to improve the performance of sense-and-respond insulin pumps, illuminate the impact of physical activity on blood glucose levels, and improve patient safety. This is a summary of our experience attempting to use such devices to enhance continuous glucose monitoring-augmented insulin pump therapy. We discuss the current status and present difficulties with available devices, and review the potential for future use.

Variables to Be Monitored via Biomedical Sensors for Complete Type 1 Diabetes Mellitus Management: An Extension of the "On-Board" Concept

Type 1 diabetes mellitus (DM1) is a growing disease, and a deep understanding of the patient is required to prescribe the most appropriate treatment, adjusted to the patient's habits and characteristics. Before now, knowledge regarding each patient has been incomplete, discontinuous, and partial. However, the recent development of continuous glucose monitoring (CGM) and new biomedical sensors/gadgets, based on automatic continuous monitoring, offers a new perspective on DM1 management, since these innovative devices allow the collection of 24-hour biomedical data in addition to blood glucose levels. With this, it is possible to deeply characterize a diabetic person, offering a better understanding of his or her illness evolution, and, going further, develop new strategies to manage DM1. This new and global monitoring makes it possible to extend the "on-board" concept to other features. This well-known approach to the processing of variable "insulin" describes some inertias and aggregated/remaining effects. In this work, such analysis is carried out along with a thorough study of the significant variables to be taken into account/monitored-and how to arrange them-for a deep characterization of diabetic patients. Lastly, we present a case study evaluating the experience of the continuous and comprehensive monitoring of a diabetic patient, concluding that the huge potential of this new perspective could provide an acute insight into the patient's status and extract the maximum amount of knowledge, thus improving the DM1 management system in order to be fully functional.

Flash glucose monitoring system may benefit children and adolescents with type 1 diabetes during fasting at Ramadan

Objectives:

To assess the benefit of using the flash glucose monitoring system (FGMS) in children and adolescents with type 1 diabetes mellitus (T1DM) during Ramadan fasting.

Methods:

A prospective pilot study of 51 participants visited the pediatric diabetes clinic at King Abdulaziz University Hospital, Jeddah, Kingdom of Saudi Arabia from between June until and July 2016. The FreeStyle^® Libre™ FGMS (Abbott Diabetes Care, Alameda, CA, USA) was used. Hypoglycemia was defined as glucose values of less than 70 mg/dL, while hyperglycemia as glucose values of more than 150 mg/dL for all participants based on our institute’s protocol.

Results:

Participants were able to fast for 67.0% of the total days eligible for fasting, whereas they did not fast on 33% of the days due to either hypoglycemia (15.4%) or non-diabetes-related reasons (17.6 %). None of the participants developed severe hypoglycemia. The mean number of hyperglycemic episodes during fasting hours was 1.29, per day, which was higher than that of hypoglycemic episodes (0.7). None of the participants developed diabetic ketoacidosis (DKA). Glycemic control with mean of estimated hemoglobin A1C reading during Ramadan (8.16 ± 1.64% [pre study]) to 8.2 ± 1.63% [post study] p=0.932.

Conclusions:

Children and adolescents with T1DM who use the FGMS could fast without the risk of life-threatening episodes of severe hypoglycemia (namely seizure, coma), or DKA during Ramadan. Adequate education and good glycemic control prior to Ramadan are important strategies in combination with the use of an FGMS to achieve better outcome.

Glucose sensing in oral mucosa simulating phantom using differential absorption based frequency domain low-coherence interferometry

The superluminescent diode based differential absorption frequency domain low-coherence interferometry (FD-DALCI) technique is proposed and demonstrated for sensing physiological concentrations of glucose (0-250 mg/dl) in oral mucosa simulating phantoms (intralipid of concentrations 0.25-0.50%) with wavelengths at 1589 and 1310 nm. The proposed technique allows simultaneous measurements of refractive index based spectral shift and estimation of physiological concentration of glucose in intralipid with scattering characteristics using the differential absorption approach. The sensitivity of the glucose concentration obtained by spectral shift measurement was ≈0.016  nm/(mg/dl), irrespective of the intralipid concentration. The resolution of the glucose level was estimated to be ≈15  mg/dl in 0.25% intralipid and ≈19  mg/dl in 0.5% intralipid using the FD-DALCI technique.

Nano-enabled paper humidity sensor for mobile based point-of-care lung function monitoring

The frequency of breathing and peak flow rate of exhaled air are necessary parameters to detect chronic obstructive pulmonary diseases (COPDs) such as asthma, bronchitis, or pneumonia. We developed a lung function monitoring point-of-care-testing device (LFM-POCT) consisting of mouthpiece, paper-based humidity sensor, micro-heater, and real-time monitoring unit. Fabrication of a mouthpiece of optimal length ensured that the exhaled air was focused on the humidity-sensor. The resistive relative humidity sensor was developed using a filter paper coated with nanoparticles, which could easily follow the frequency and peak flow rate of the human breathing. Adsorption followed by condensation of the water molecules of the humid air on the paper-sensor during the forced exhalation reduced the electrical resistance of the sensor, which was converted to an electrical signal for sensing. A micro-heater composed of a copper-coil embedded in a polymer matrix helped in maintaining an optimal temperature on the sensor surface. Thus, water condensed on the sensor surface only during forcible breathing and the sensor recovered rapidly after the exhalation was complete by rapid desorption of water molecules from the sensor surface. Two types of real-time monitoring units were integrated into the device based on light emitting diodes (LEDs) and smart phones. The LED based unit displayed the diseased, critical, and fit conditions of the lungs by flashing LEDs of different colors. In comparison, for the mobile based monitoring unit, an application was developed employing an open source software, which established a wireless connectivity with the LFM-POCT device to perform the tests.

Use of Wearable Sensors and Biometric Variables in an Artificial Pancreas System

An artificial pancreas (AP) computes the optimal insulin dose to be infused through an insulin pump in people with Type 1 Diabetes (T1D) based on information received from a continuous glucose monitoring (CGM) sensor. It has been recognized that exercise is a major challenge in the development of an AP system. The use of biometric physiological variables in an AP system may be beneficial for prevention of exercise-induced challenges and better glucose regulation. The goal of the present study is to find a correlation between biometric variables such as heart rate (HR), heat flux (HF), skin temperature (ST), near-body temperature (NBT), galvanic skin response (GSR), and energy expenditure (EE), 2D acceleration-mean of absolute difference (MAD) and changes in glucose concentrations during exercise via partial least squares (PLS) regression and variable importance in projection (VIP) in order to determine which variables would be most useful to include in a future artificial pancreas. PLS and VIP analyses were performed on data sets that included seven different types of exercises. Data were collected from 26 clinical experiments. Clinical results indicate ST to be the most consistently important (important for six out of seven tested exercises) variable over all different exercises tested. EE and HR are also found to be important variables over several types of exercise. We also found that the importance of GSR and NBT observed in our experiments might be related to stress and the effect of changes in environmental temperature on glucose concentrations. The use of the biometric measurements in an AP system may provide better control of glucose concentration.

Recent Progress in Electrochemical Biosensors for Glycoproteins

This review provides an overview of recent progress in the development of electrochemical biosensors for glycoproteins. Electrochemical glycoprotein sensors are constructed by combining metal and carbon electrodes with glycoprotein-selective binding elements including antibodies, lectin, phenylboronic acid and molecularly imprinted polymers. A recent trend in the preparation of glycoprotein sensors is the successful use of nanomaterials such as graphene, carbon nanotube, and metal nanoparticles. These nanomaterials are extremely useful for improving the sensitivity of glycoprotein sensors. This review focuses mainly on the protocols for the preparation of glycoprotein sensors and the materials used. Recent improvements in glycoprotein sensors are discussed by grouping the sensors into several categories based on the materials used as recognition elements.

A Human Serum-Based Enzyme-Free Continuous Glucose Monitoring Technique Using a Needle-Type Bio-Layer Interference Sensor

The incidence of diabetes is continually increasing, and by 2030, it is expected to have increased by 69% and 20% in underdeveloped and developed countries, respectively. Therefore, glucose sensors are likely to remain in high demand in medical device markets. For the current study, we developed a needle-type bio-layer interference (BLI) sensor that can continuously monitor glucose levels. Using dialysis procedures, we were able to obtain hypoglycemic samples from commercial human serum. These dialysis-derived samples, alongside samples of normal human serum were used to evaluate the utility of the sensor for the detection of the clinical interest range of glucose concentrations (70-200 mg/dL), revealing high system performance for a wide glycemic state range (45-500 mg/dL). Reversibility and reproducibility were also tested over a range of time spans. Combined with existing BLI system technology, this sensor holds great promise for use as a wearable online continuous glucose monitoring system for patients in a hospital setting.