The standard SMBG test has a limitation because the blood sample gives only a fragment of the real glucose level discontinuously, meaning that SMBG cannot indicate the ongoing glucose level changes during the day, even if this test is performed frequently. Changing symptomatic hypoglycemia or hyperglycemia for a patient could be uncomfortable and make it difficult to make a correct healthcare decision. For this reason, a recent study ensured that many glucometers for self-monitoring do not meet the level of accuracy; therefore, focusing on using Continuous Glucose Monitoring (CGM) is highly considered to help a patient decide on the medicine doses. CGM provides glucose concentration measurements based on a skin sensor that measures glucose concentration in the interstitial fluid and transmits the sensor values (usually at 5–15 min intervals) to a dedicated receiver, which can be seen in real time. ¹⁸ A classification for glucose level monitoring is presented in reference ¹⁹ based on different criteria, as shown in Figure 1. Non-invasive glucose level monitoring methods are based on measuring glucose concentration based on its chemical, thermal, electrical, or optical sensing properties.

In 2021, a review of non-Invasive sensing systems for detecting glucose levels revealed a classification for the existing methods according to the sensor types. The classes include non-invasive glucose detection through the patient’s eye, finger, wrist, forearm, and abdomen. ²⁰ Light is focused on biological tissues and is affected by reflection, scattering, and transmission, according to the structure and chemical components of the tissue sample. This is the concept behind optical-based non-invasive glucose measurement methods. These properties are differentiated according to the analysis of the device used; thus, the interpretation of the glucose levels is based on the received spectrum. Many optical non-invasive methods have been analyzed to successfully test glucose levels and are classified in references. ^{19, 21} Another classification based on optical approaches divides the classes into electromechanical, electrochemical, and electromagnetic (EM) methods. This study discusses the characteristics required for efficient sensors to predict a correct decision from the collected data. The authors clarified the optical approaches based on many features. ²²

In 2021, a design was presented to test BG using optical non-invasive sensors based on finger tissue, including an infrared light-emitting diode (LED) with a wavelength of 950 nm, an ultrasound sender with 2 MHz, a sensitive IR detector, and two “Arduino” microcontrollers. The implementation of the system methodology depends on spreading the near-infrared light through the finger to obtain the glucose level. The authors did not provide the patient with the facility for auto use, as did other designs. ²³ Near-infrared NIR with LED is one of the technologies used with the associated sensor circuit to monitor the BG concentration. The experimental results of this approach and a mathematical model showed that the higher the glucose concentration, the lower the sensor output voltage. As the authors explained, this approach can be beneficial for individual patients by reducing the need for finger pricking and the pain it causes; however, further studies are needed to evaluate the accuracy and reliability of the designed system under different conditions. ²⁴ In 2023, a study considering IoT for non-invasive BG was published. The work idea is to build a system consisting of LED and NIR sensors to produce signals that are disseminated through the fingertip. A phototransistor is positioned next to the LED to detect any reflected signals and be stored in a “Thing-speak.” Mathematically analyzing the reflected signal intensity with an algorithm installed in the “Arduino” shows the relationship between glucose concentration and voltage. The experimental results of this study showed that the accuracy ranged from 1.13 to 16.41%. Another study showed that LED light and biosensors provide good results for measuring individual glucose levels. More research is required to develop non-invasive BGL systems. There is still a need to improve the hardware of the devices because most IR technologies are software. Developing a design with low power and cost that is processed in real time would lead to a non-invasive system with high performance. ²¹ A method of Fast IR spectroscopic glucose concentration measurement using a mid-infrared wavelength-swept pulsed quantum cascade laser (QCL) is adopted to measure the concentration of blood glucose using attenuated total reflection. The high power of the QCL with the proposed algorithm enables the BG measurements. The proposed method allows detection of the skin and enables the measurement of the tear film on the eyeball in the tangential direction. This has potential applications in the diagnosis and management of diabetes and minimizes the measurement time to 20 ms. ²⁵

In Conclusion, different CGM methods are depending on the sensor types that were used. The concept behind optical-based non-invasive glucose measurement methods is the light focused on biological tissues, where its reflection, scattering, and transmission are the parameters for BG measurement. Optical approaches use either spreading near-infrared light, LED and NIR sensors, LED land biosensors, or fast IR spectroscopic and mid-infrared wavelength.

“The standard ISO 15197 provides the quality guidelines, requirements, and specifications that glucose measuring devices should comply with to guarantee their suitability for human use”. The ISO guidelines evaluate each device and show whether it is suitable for commercialization. ¹² Many commercial devices in the market are approved by the ISO standard, and almost all of them use spectroscopic techniques, especially NIR. A review of some of these devices, especially those using IoT technologies. -

Non-invasive blood glucose meter (Combo Glucometer) CoG: This device is for invasive and non-invasive glucose tests, it is small, lightweight, and easy to hold. The device technology consists of a color image sensor, four LEDs (IR spectrum from 625 to 940 nm), and a digital signal processor (DSP). The control part consisted of four touch buttons, a display, an audio speaker, and a microcontroller. This device includes process management, storage, and power management. The way of using can be shown in Figure 2, the traverse light is diffused over the image sensor range, and therefore all three colors (red, green, and blue) will sense the traverse light, each one with a different sensing value and will be stored in the memory buffer. Computation was then performed using the algorithm in the DSP. ²⁶

IoT is considered a promising tool owing to its continuous monitoring capabilities, data analysis, and remote access features. ³³ In 2024, a new device, QU-GM, was proposed, which is a continuous non-invasive device based on the end-to-end Internet of Things (IoT). The QU-GM device is composed of a PPG sensor, a microcontroller, and a battery. It is a BG measurement system that uses photoplethysmography (PPG) signals, blood pressure, and demographic information. After filtering the collected data, they are sent to the backend server and reported back to the mobile application. The device can be worn as a wrist watch. The authors checked the proposed model’s prediction and ensure that it is clinically acceptable, where K-nearest neighbor model classified the severity levels with an accuracy of 98.12%. Therefore, the wristbands that can be connected to an Android mobile application were deployed in Amazon Web Server. ³⁴

Recently, IoT-enabled Continuous Glucose Monitors (IAI-CGM) framework has been used to understand user intentions to adopt Internet of Things (IoT)-enabled Continuous Glucose Monitors (CGMs). The framework was used to assess patient acceptance and use of these devices. It showed that patients need to have personal alarm systems, and IoT-CGMs have the potential to serve a useful role in the function of an alarm clock as well as provide real-time monitoring. The author concluded that in the domains of wearable technology and healthcare, the adoption of IoT-CGM is a crucial but still understudied issue. ³⁵

IoT and the interconnected sensors and devices, provides real-time data from environment. From smartwatches that monitor heart rate and activity levels to continuous glucose monitors for diabetic patients, IoT devices can present an individual’s health status. A significant challenge is presented due to the huge volume and velocity of data generated by these devices present. This is where Artificial Intelligence (AI) algorithms are started to be part of the healthcare field. Therefore, machine learning and deep learning are with great ability to analyze these massive datasets, uncover hidden patterns, and generate actionable insights. ³⁶

In conclusion, many commercial devices are small, and lightweight, but may be visible to others. Another kind of devices either they are manually used or must be injected under the skin where its lifetime is limited, and needs to be replaced. Abbott Freestyle Library CGM with wireless sensor that replaced on the skin. It is suitable for all patient from age 4 years and can be interconnected with an available smartphone application since IoT for non-invasive BG is now involved too.

When we start this work, we observed that patients do not feel comfortable with the CGM alarm every 15 min, also, the limited data storage cannot help follow the patient’s health situation. Though, our goal is to overcome these two gaps using IoT technologies.

To support our study, we distributed a questionnaire with 11 questions to determine if patients were willing to change their attentions for testing their BG using the new technologies.

The Abbott Freestyle Library (CGM) was used to monitor the BG, where each sensor lifetime was approximately 14 days. This device can be used for patients aged ≥ 4 years because of its ease of use and can be interconnected with an available smartphone application. ³⁸ The only restriction was the alarm sound every 15 min.

The suggested model is considered a new way to let the patient know his health situation only when it is critical and needs further attention by connecting the CGM device to a smart device to avoid unnecessary alarms. For more benefit from the stored BG results in the CGM device, a model is created to calculate the A1C for the patient to let him know if he is in the correct track of his health lifestyle. The personal smart device uses the cloud for extra storage space to collect BG data in a special database (DB) for GMI calculation using data from the past two weeks. The sketch in Figure 6 shows the devices and the connections between them, while the entire algorithm steps are explained in the solution approach subsection.

“AGP is considered as the standardized, practical one page report for graphically presenting a summary of glycemic control status in patients with diabetes who use continuous glucose monitoring (CGM) systems as part of their daily diabetes care.” The AGP report consists of three sections. (1)

glucose statistics and targets, (2) ambulatory glucose profile, and (3) daily glucose profile. ^{39, 40} The International Diabetes Center (DIC) developed a data analysis software program for CGM that considers the time in range (TIR) over a period of time. TIR is a metric of glycemic control that provides more actionable information than A1C alone by establishing target percentages of time in different glycemic ranges to identify the needs of diabetic populations. The target is to have a TIR of 47% and a glucose range of 70–180 mg/d. ^{39, 41} A consensus-recommended AGP report for a given patient can be generated in LibreView on a cloud-based platform ( http://www.libreview.com). This platform is available for many countries but not for Iraq, which provides the idea for presenting this work to give Iraqi patients a way to manage their own health using this work algorithm that considers the international standardization AGP. AGP shows three levels of glycemic control: in the range between 70 mg/dl and 180 mg/dl, hyperglycemia is gradient from >180 mg/dl to >400 mg/dl (22.2 mmol/liter), while hypoglycemia is considered under 70 mg/dl (3.9 mmol/liter), as shown in Figure 7. ⁴²

Initially, we tested the efficiency of the Abbott freestyle library device by comparing its results with the standard finger-prick BG test for all 10 patients for 24 h. The finger-prick test was performed three times a day (before breakfast, after breakfast, and at night before sleeping), while CGM tests the BG every 15 min. The averages of both tests based on Equation 1 for the finger pick test and Equation 2 for the CGM test are presented in Tables 2 and 3, respectively. The results in Table 3 show the average of the test results every 8 h. ³⁷ Avg . BG = BeforeBK BG + AfterBK BG + TwohBbed BG 3 (1) Avg . BG = ∑ i = 1 n = 96 x i 96 (2)

where x _i is the test value every 15 min for a day. The Statistical Package for the Social Sciences software (version 24.0) was used to perform all statistical analyses, normality of data, and parametric statistical tests.

The paired (dependent) t-test is used to assess the significance of differences between the two ways of measuring BG. The finding of the test is t = 2.274, with α = 0.05 and two-tailed test, the t _critical = 2.262 meaning that t > t _critical . Therefore, the study demonstrated that, significantly at P < 0.05, there were no differences between both methods.

Figure 8 shows the results of the comparison, where the data are almost similar for both tests, which means that the CGM device with the skin sensor that we used can measure blood glucose efficiently.

Usually, an A1C test is done every two or three months in the laboratory to check for improvement or worsening of BG; however, a significant change in BG can be known within two weeks. We used the stored data for 14 days to calculate A1C in our algorithm by using a standard formula to estimate A1C (eA1C) based on GMI, which gives an approximate value to the laboratory A1C.

The GMI is the average (mean) glucose value based on data collected by the CGM. The average glucose values from the CGM to obtain the GMI percentage can be calculated using Equation 3.

Both A1C (eA1C) and GMI provide information to maintain better control over blood sugar levels, and eA1C% can be obtained using Equation 4. The estimated A1C can then be calculated using Equation 5. ⁴³ GMI ( % ) = 3.32 + 0 , 02392 [ meanCGMdata ( mg dl ) ] (3) eAIC ( % ) = ( GMI ( mg dl ) ) + 46.7 28.7 (4) AIC ( mg dl ) = ( 10.929 ( AIC ( % ) − 2.15 ) ) 18.05 (5)

The term mean CGM data is the average glucose level over a specific period and can be found from Equation 6. Grand Avg . BG = ∑ j = 1 14 ∑ x i i = 1 n = 96 96 j (6)

According to, ^{44, 45} the GMI and A1C may not agree if the patient has acute hyperglycemia due to illness, steroid administration, or diabetic ketoacidosis; thus, GMI will be higher than the laboratory A1C measured at the same time and vice versa, where the GMI is lower than A1C if the patient suffers from hypoglycemia. This fact is used to alert the patient with a colored alarm (red, yellow, or green) to indicate hyperglycemia, hypoglycemia, or stability.

Another idea suggested to be added to this work is the use of a Markov chain to predict the GMI and A1C for the next 14 days. This can be done based on the stored data of past GMI results to allow the patient to find a healthy lifestyle. Because CGM provides glucose readings every few minutes, from this data, a calculation is made to estimate the GMI for each day using Equation 3.

The Markov chain concept was adopted from a previous study. ⁴⁶ Their idea was to collect sequential blood sugar measurements and define discrete states: hypoglycemic, normal, and hyperglycemic. A transition probability matrix (TPM) was used to show the likelihood of moving from one state to another. The model is used to predict or detect anomalies in blood sugar behavior.

The idea of our suggestion is to build a three-state ( S _i , where i = 0 to 2) model, where state S ₀ indicates good control (stable in BG), while S ₁ and S ₂ indicate moderate and poor control, respectively. The intervals based on BG average are as: 7.3% < GMI < 7.8% for S ₀, 7.8% < GMI < 8.0% for S ₁, while GMI < 7.2% or GMI > 8.0% would be for S ₂.

The state of transition is a change from one state to another based on the next period, where this transition state is a random process and is expressed in the form of probability.

The suggested TPM is as follow: P i j = [ 0.8 0.1 0.1 0.2 0.3 0.5 0.3 0.1 0.6 ] (7)

Where P _ij represents the probability of staying in the same state, while the probability of reaching state j from state i in one day is P _ij .

To calculate the state distribution after 14 days, we used Equation 7, where π 0 represents the initial state vector. This gives the probability of being in each state after 14 days. π 14 = π 0 × P 14 (8)

In this way, an estimated result can be calculated to help the patient find if he is on the right track of his lifestyle or if the lifestyle needs some changes.

This study aims to benefit from IoT technologies by connecting a CGM device to a personal smart device to transfer data between them and to be stored in a cloud database. Another benefit of this connection is that it enhances the CGM device based on the three proposed algorithms.

Algorithm 1 calculates the patient’s BG level based on AGP standardization, so that a message appears every 8 h to the smart device instead of the 15 min alarm during a stable health situation; otherwise, an emergency-colored message will appear with a ringing bell to alert the patient.

Algorithm 2 uses the data stored to calculate A1C every 14 days, based on the standard in Table 4. Then, a comparison between GMI and A1C will start, and the result is sent to the patient as a colored alarm either red, yellow, or green to indicate hyperglycemia, hypoglycemia, or being stable, respectively.

Algorithm 3 calculates the GMI every two weeks depending on the stored data, and a calculation is also performed to estimate the result for the next 14 days to help the patient to control his lifestyle. A suggested idea may be added to the algorithm to estimate A1C every 3 months for more accurate results based on the stored outputs.

The solution is based on Equation (2) to calculate the mean glucose of one day and on Equation (6) to obtain the mean CGM data, while Equations (4) and (5) are used to calculate the predicted GMI for the next 14 days based on the Markov chain model using three states, as mentioned before.

The proposed model intends to improve CGM to be more comfortable for diabetic patient. The suggested model integrates CGM data with three algorithms implemented on a patient’s smart device to support lifestyle management for individuals with type 2 diabetes. A historical CGM data stored in the cloud can be used to calculate the (GMI%) every two weeks and do an A1C test every two or three months. Markov chain can be used to predict GMI% for the following 14 days to provides an early indication of the patient’s lifestyle. The predictive capability is expected to help patients recognize patterns in their glucose levels and adjust their behavior accordingly. Next step will involve implementing the model on a smart device and validating its predictive accuracy using real patient data to quantitatively evaluate improvements in blood glucose management and user comfort.