Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies

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Basheer" }, { "@type": "Person", "name": "Anes A. Al-Sharqi" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Patients diagnosed with diabetes mellitus must monitor their Blood Glucose (BG) to continually control their glycemia. They had to undergo a blood test at least twice a day using a finger prick, and must visit a laboratory every three months for glycated hemoglobin (HbA1c). According to the World Health Organization (WHO), the constitution of Iraqi diabetes patients is 13.9%. An Internet of Things (IoT) based framework for non-invasive BG monitoring was recently developed. The aim is to enhance the Continuous Glucose Monitoring (CGM) device by using IoT technologies. Methods Starting with a review of the latest IoT technologies and some of the CGM commercial devices recommended by ISO 15197 standard. A questionnaire sheet is distributed to determine how much patients in Iraq knew about their health situation and whether they are interested in using new IoT technologies. Women patients are volunteered to check their BG using both finger prick and a CGM device three times a day for comparing. The suggestion is to connect CGM to a personal smart device that has a method controlling alarm messages instead of CGM output. Results The results show how important to educate patients about new technologies. A CGM and finger prick results are checked for similarity using statistical package. Results are presented as (mean ± SD (standard deviation)), the findings demonstrated that, significantly at P < 0.05, there were no differences between the means for tests in both methods based on the standard Ambulatory Glucose Profile (AGP) report. Conclusion This study shows how Iraqi’s patients feel when using a CGM device with new IoT technologies. CGM output is accurate but appeared every 15 min which may be uncomfortable. A method is suggested to transfer CGM output to a smart device to be controlled by algorithms, so that an alarm message is showed every 8h in a normal situation rather than every 15 min. Moreover, GMI% can be calculated every two weeks using data stored in the cloud to estimate a patient’s HbA1c level by using Markov chain. This figure how BG levels are changing in a shorter timeframe, helping in fine-tune diabetes management plans. 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F1000Research 2026, 15 :108 ( https://doi.org/10.12688/f1000research.173871.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies [version 1; peer review: 2 approved with reservations] Hanaa S. Basheer https://orcid.org/0000-0002-0900-090X 1 , Anes A. Al-Sharqi 1 Hanaa S. Basheer https://orcid.org/0000-0002-0900-090X 1 , Anes A. Al-Sharqi 1 PUBLISHED 23 Jan 2026 Author details Author details 1 Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq Hanaa S. Basheer Roles: Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Anes A. Al-Sharqi Roles: Conceptualization, Data Curation, Formal Analysis, Resources, Validation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Fallujah Multidisciplinary Science and Innovation gateway. Abstract Background Patients diagnosed with diabetes mellitus must monitor their Blood Glucose (BG) to continually control their glycemia. They had to undergo a blood test at least twice a day using a finger prick, and must visit a laboratory every three months for glycated hemoglobin (HbA1c). According to the World Health Organization (WHO), the constitution of Iraqi diabetes patients is 13.9%. An Internet of Things (IoT) based framework for non-invasive BG monitoring was recently developed. The aim is to enhance the Continuous Glucose Monitoring (CGM) device by using IoT technologies. Methods Starting with a review of the latest IoT technologies and some of the CGM commercial devices recommended by ISO 15197 standard. A questionnaire sheet is distributed to determine how much patients in Iraq knew about their health situation and whether they are interested in using new IoT technologies. Women patients are volunteered to check their BG using both finger prick and a CGM device three times a day for comparing. The suggestion is to connect CGM to a personal smart device that has a method controlling alarm messages instead of CGM output. Results The results show how important to educate patients about new technologies. A CGM and finger prick results are checked for similarity using statistical package. Results are presented as (mean ± SD (standard deviation)), the findings demonstrated that, significantly at P < 0.05, there were no differences between the means for tests in both methods based on the standard Ambulatory Glucose Profile (AGP) report. Conclusion This study shows how Iraqi’s patients feel when using a CGM device with new IoT technologies. CGM output is accurate but appeared every 15 min which may be uncomfortable. A method is suggested to transfer CGM output to a smart device to be controlled by algorithms, so that an alarm message is showed every 8h in a normal situation rather than every 15 min. Moreover, GMI% can be calculated every two weeks using data stored in the cloud to estimate a patient’s HbA1c level by using Markov chain. This figure how BG levels are changing in a shorter timeframe, helping in fine-tune diabetes management plans. READ ALL READ LESS Keywords CGM, diabetic, IoT, non-invasive BGM technologies, Markov chain Corresponding Author(s) Hanaa S. Basheer ( [email protected] ) Close Corresponding author: Hanaa S. Basheer Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Basheer HS and Al-Sharqi AA. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Basheer HS and Al-Sharqi AA. Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies [version 1; peer review: 2 approved with reservations] . F1000Research 2026, 15 :108 ( https://doi.org/10.12688/f1000research.173871.1 ) First published: 23 Jan 2026, 15 :108 ( https://doi.org/10.12688/f1000research.173871.1 ) Latest published: 05 May 2026, 15 :108 ( https://doi.org/10.12688/f1000research.173871.2 )  There is a newer version of this article available. Suppress this message for one day. I. Introduction Lifestyle medicine is the application of evidence-based lifestyle approaches to prevent and treat continuing diseases, such as diabetes. Lifestyle Medicine leads patients to live healthier by following the six advice which are: “eating a predominantly whole food, a plant-based diet, Regular physical activity, Adequate sleep, Stress management, avoidance of risky substance use, and positive social connections”. 1 According to the WHO a proportion of Iraqi diabetes patients is 13.9%. The latest technologies make a huge change in monitoring a patient’s situation with less pain, less time and less cost. In the 1970s, systems were developed to provide accurate results of the glucose level based on blood samples. This technology, called the Self-Monitoring of Blood Glucose (SMBG), allows patients with diabetes to monitor his/her glucose levels daily. Although SMBGs systems have highly predicted results, they are still painful and uncomfortable to use because they are based on fingerpicks for each test. In the 1980s, companies began to develop new devices based on CGM using non-blood tests. 2 In the 1990s, IoT healthcare was launched, and it has the ability to connect different devices that provide services through the Internet in real time. These IoT devices can be household objects or appliances, and are all called “things.” Things can collect and analyze data according to the required services. People with diabetes can live vital lives if they regularly monitor their glucose levels and keep them in range. Diabetes management strategies include education, physical activity, nutrition, medication, and lifestyle management. 3 The development of a self-aware patient agent (SPA) within a mobile agent environment was suggested in. 4 An important work was presented in 2019 showing how an auto check of glucose level for a patient can be performed using a biological sensor that can be sent through the internet to the doctor. The proposal was implemented and instrumented as an IoT system, in which all the measurements were carried out in vivo to show good benefits for patients since they follow up their conditions with less cost and painless. Its limitation is the use of bulky instruments that cannot be used during daily activities. 5 HbA1C is to measures the average amount of sugar in the blood over the past few months; for diabetic patients, the goal is to reach 7%, so this does not mean that their condition went away, but it means that their blood sugar is well managed. 6 One of the limitations of self-glucose testing is the absence of stability in (HbA1C), which is a very important parameter because it affects the nutrition plan, physical activity, and/or medication treatment. With all existing BG test tools, patients should be aware of medications that can interfere with glucometer accuracy. In 2021, a study of the correlation between vitamin D, fasting glucose, and A1c showed that the results were not significant. 7 – 9 Another study showed a significant association between BG level control and (age, education level, monthly income). 10 Technologies with education, follow-up, and care can improve the lives and health of people with diabetes. 11 Many techniques have been introduced for individual use, such as carbon nanotubes and plasmonics, but these are still ideas despite progress on the theoretical side. 12 For example, in 2022, a method based on a plasmonic nanostructure glucose sensor and gold–silver core–shell nanoparticles as the sensing platform were developed. The oxidative etching of the silver shell and the concentration of hydrogen peroxide and glucose can be determined via spectral changes, that’s why this approach can be used for diabetes diagnosis and health monitoring. 13 Another example was presented, which used a Multilayer Neural Network trained on data from a dataset of Iraqi diabetes patients obtained from the Specialized Center for Endocrine Glands and Diabetes Diseases. 14 The percentage of Iraqi diabetes patients is 13.9% and may increase by 54% by 2030; therefore, there is a need to educate patients about the importance of using IoT devices become a must by using efficient experiments, which is one of the goals of this work. There are two types of diabetes technologies, CGM and BGM. General principles for CGM devices must be considered for patients on oral medicine before use, which are 7 : • Selecting a device should be based on a person’s specific needs and skill level. • Ensure that the person receives education and training about the device before using. • People using CGM should continue with a third party such as a diabetes center for regular follow-ups. Nowadays, many techniques are developed to be simple, less painful, and non-invasive techniques for measuring blood glucose levels. Non-invasive techniques can be divided into glucose oxidase, optical spectroscopy, and dielectric spectroscopy. 15 The aim of this research is to use IoT technologies to enhance CGM devices by (1) reducing alarm beats to the minimum or buzz, when necessary, (2) calculating A1C every two weeks that reflects the overall glucose control, and helps monitor treatment effectiveness, and (3) estimating A1C for the next 14 days to show how a patient knows how his lifestyle affects his BG. II. Background Optical approaches are important for BG because they offer non-invasive, painless, and continuous monitoring alternatives to traditional finger-prick methods. In this section, a brief review of optical approaches is presented. Moreover, a review of the latest CGM commercial devices recommended by the ISO 15197 standard provides quality guidelines. A. Optical approaches for Non-invasive glucose level monitoring 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. 16 A classification for glucose level monitoring is presented in reference 17 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. Figure 1. Classification of glucose measurements according to different criteria. 17 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. 18 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. 17 , 19 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. 20 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. 21 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. 22 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%. However, IoT is considered a promising tool owing to its continuous monitoring capabilities, data analysis, and remote access features. 23 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. 19 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. 24 B. Commercial devices for non-invasive glucose level monitoring “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. 25 - GlucoTrack (Integrity Applications): The device shown in Figure 3 automatically uses personal ear clip (PEC) sensors to measure the three parameters of the earlobe tissue: ultrasonic, electromagnetic, and thermal. These parameters were found to be due to glucose-related shifts in ion concentration, density, compressibility, and hydration of both cellular and extracellular compartments of the tissue. This device is used in patients with prediabetes and type 2 diabetes mellitus. A clinical trial conducted on 17 subjects showed that 98.0% of the readings were acceptable. 26 - Eversense (Senseonics): This device is non-invasive and uses fluorescent light. However, the fact that the device needs to be under the skin and its lifetime is limited to 180 days means that additional research is required to prolong the life of the sensor. 12 Figure 4 illustrates how the sensor was inserted under the skin. 28 - Abbott Freestyle Library (CGM): This device has a handheld reader and a disposable sensor that is attached to the arm. The patient scanned the attached sensor with a meter for glucose reading, as shown in Figure 5 . The meter has built-in BG and ketone meters suitable for FreeStyle Optium blood glucose and blood ketone test strips. 29 A comparison by Galindo, R.J. and his team were made between FreeStyle CGM and a bedside point-of-care capillary glucose testing (POC). The results showed leaning for lower glucose concentrations in the CGM results. However, its clinical accuracy was acceptable. CGM detected more nocturnal and prolonged hypoglycemic episodes compared with limited daily testing with POC, indicating that this approach has failed to detect these episodes efficiently. Conclusion This study showed a good correlation between CGM taken by an Abbott lifestyle sensor and laboratory device for testing glucose values. 30 This device can also be connected to the person’s smart mobile using Bluetooth to give the patient a regular alarm about his glucose level, which automatically collects sensor glucose that provides a good indication of BG values every 15 min, but can only store 8 h of data. By observation, patients do not feel comfortable with the CGM alarm every 15 min, and the limited data storage cannot help follow the patient’s health situation. We intend to overcome these two gaps using IoT technologies, as will be discussed in the next section. 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. 32 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. 33 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. Figure 2. (a) The CoG device, (b) A cross sectional view of the CoG. 25 Figure 3. Invasive blood glucose meter GlucoTrack. 27 Figure 4. Eversion sensor insertion procedure. 28 Figure 5. Abbott lifestyle, and attached skin sensor. 31 III. Questionnaire Monitoring of blood glucose levels in patients with diabetes is an important continuous procedure. BG monitoring using the traditional method (finger prick) causes patient discomfort, potential infection, and financial burden. For these reasons, the following thoughts became a must: • Keeping the patient healthy almost all times without the need for extra assistance. • Reduce pain and infections. • Enable elder patients to make their health decisions at home and decrease the need to visit diabetic centers frequently. A questionnaire survey was distributed to 10 women with Iraqi type 2 diabetes on oral medicine, aged between 45 and 60 years. The goal is to obtain information about the daily behavior of diabetic patients in Iraq and to know if they have the desire to use new IoT technologies such as CGM devices with skin sensors. The shaded cells in Table 1 concentrate on the use of the new technology to test the BG level. The procedure first started by checking the glucose level using both the BGM by finger prick (traditional method) and the CGM sensor meter (using Abbott lifestyle device) at the same time. The questionnaire was then completed. Table 1 shows that patients with diabetes in Iraq depend on others in deciding their lifestyle and that they need to learn more about new technologies. The table shows that 9 out of 10 patients did not like finger prick testing, but 8 out of 10 patients felt uncomfortable using the CGM meter because they heard an alarm every 15 min. 34 This was the reason for starting this work because reducing the CGM device’s alarms while testing the BG level may change a patient’s mood and make him feel comfortable and willing to use CGM. The idea is to let the alarm work only when the BG is at a critical level (above 200 mg/dL or lower than 70 mg/dL), while the alarm stops when the BG is in the range (of 70–180 mg/dL). This enhancement can only be achieved by connecting CGM to a personal smart device. Table 1. The questionnaire results after testing the glucose level using both the finger prick and a CGM sensor meter for 10 women with type 2 diabetes. 34 Question Yes No Abstain Do you trust your professional who help you to make your decision 9 1 - Do you feel that diabetes affects every part of your life 10 - - Do you think that decision regarding daily diabetes care should be based only on finger prick 7 3 - Do you ask the professional about the latest way to check the glucose level 2 8 - Do you think that using two ways for testing glucose level is better than one 3 7 - Do you feel that finger prick is painful and uncomfortable 9 1 - Do you check the markets for new way to check glucose level 1 9 - Would you trust sensor meter to be used daily for checking your glucose level 5 4 1 Do you feel uncomfortable with sensor meter that show you the result of glucose level every 15 min 8 2 - Do you need more information about how sensor meter works 10 - - Would you change the way of checking your glucose level to be by sensor meter only 5 3 2 Total 100% 62.73% 34.54% 2.73% IV. Materials and methods The work is based on the use of an available CGM device in Iraqi’s market with a reasonable price (e.g., the Abbott Freestyle Library) plus a smart device where most people can use such as personal mobile devices. The main idea is to enhance the comfort of the CGM device by connecting it to a program installed in a personal smart device. The suggested method is easy to understand, so a patient can depend on its output without the need for extra assistance. The CGM device can be connected to a personal smart device by using the Bluetooth. Moreover, this connection gives the ability to benefit from the cloud storage to store large BG test results, where the stored data can be used to calculate A1C and estimate A1C (eA1C) using GMI calculation. This means that the installed program algorithms are used to control the CGM alarms and to calculate A1C and GMI every two weeks based on the stored BG test results. If GMI is < 5.7%, means that the patient had a good lifestyle during these past two weeks. A. Materials 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. 35 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. Figure 6. Abbott freestyle libre CGM and IoT technology. B. Ambulatory Glucose Profile (AGP) “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. 36 , 37 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. 36 , 38 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 . 39 Figure 7. Glucose target ranges and categories. Dx, diagnosis; DKA, diabetic ketoacidosis; Glu, glucose; Hypo, hypoglycemia; Maj, major; ER, emergency room; admit, admittance. 39 C. Methods 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. 34 (1) Avg . BG = BeforeBK BG + AfterBK BG + TwohBbed BG 3 (2) Avg . BG = ∑ i = 1 n = 96 x i 96 Table 2. The finger prick test for 10 patients in 24 hours. 34 No. Before BK (6 am-2 pm) Immediately after BK 2 hours before bedtime Total SD 1control 90 95 90 91.6 20.223 2 128 159 121 136 18.876 3 92 117 129 112.6 22.722 4 144 189 161 164.6 23.430 5 121 160 118 133 16.441 6 205 180 174 186.3 13.428 7 114 135 110 119.6 24.826 8 132 175 132 146.3 47.077 9 150 238 165 184.3 35.679 10 200 270 247 239 10.263 11 146 166 152 154.6 20.223 Mean ± SD 143.2 ± 35.627 178.9 ± 45.447 150.9 ± 40.217 157.63 ± 37.84 Table 3. The CGM test from Abbott freestyle libre for 10 patients in 24 hours. 34 No. Avg. 6 am-2 pm Avg. 2 pm-10 pm Avg. 10 pm-6 am Total SD 1control 88 90.7 90 89.56 16.941 2 124.3 154.9 127 135.4 17.683 3 89.9 111.2 125 108.7 20.232 4 142 181.3 170 164.4 22.350 5 111.3 155 125 130.4 12.503 6 192 189 169 183.3 1.184 7 120 122.1 120.1 120.7 21.101 8 130 170.3 139.3 146.5 42.111 9 149 230.3 170.6 183.3 30.008 10 198 250.9 249 232.6 8.710 11 145.1 162.3 156.1 154.5 16.941 Mean ± SD 127.73 ± 35.319 157.24 ± 43.281 142.41 ± 38.801 155.98 ± 36.64 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. Results are presented as (mean ± SD (standard deviation) SD). The independent samples t-test compares the Mean of two groups to assess the significance of differences between the groups P < 0.05. The findings of the study demonstrated that, significantly at P < 0.05, there were no differences between the means for three-time tests of patients in both methods based on the standard AGP report. 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. Figure 8. (a) Diagram (b) Scatter plot. Both shows the mean and standard deviation for finger prick and CGM tests for 10 patients in 24 hours. 34 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 . 42 (3) GMI ( % ) = 3.32 + 0 , 02392 [ meanCGMdata ( mg dl ) ] (4) eAIC ( % ) = ( GMI ( mg dl ) ) + 46.7 28.7 (5) AIC ( mg dl ) = ( 10.929 ( AIC ( % ) − 2.15 ) ) 18.05 The term mean CGM data is the average glucose level over a specific period and can be found from Equation 6 . (6) Grand Avg . BG = ∑ j = 1 14 ∑ x i i = 1 n = 96 96 j According to, 40 , 41 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. 43 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 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 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. (7) π 14 = π 0 × P 14 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. D. Solution approach 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 message will appear. 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. Table 4. A difference in laboratory-measured A1C and GMI and what it may mean. 40 A1C vs. GMI 8.0% vs. 7.8% A1C is like GMI means that the average CGM glucose level is about what would be predicted from the measured A1C. (Green color) 8.0% vs. 7.2% A1C is higher than GMI means that the average CGM glucose level is lower than what would be predicted from the measured A1C. (Yellow color) 7.2% vs. 8.0% A1C is lower than GMI means that the average CGM glucose level is higher than what would be predicted from the measured A1C. (Red color) 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. Algorithm 1. Beginning the process in personal smart device. 1: Receive BG level from CGM every t = 15 min, where (t) is the device alarm duration 2: Check BG level according to AGP 3: If 70 ≥ BG ≥ 180 then nothing happened go to step 4 Else If 181 ≥ BG ≥ 400 then alarm the patient with red color Else Alarm the patient with yellow color Endif Endif 4: t = t + 15 5: If time t = 8 hours (480 min) go to step 6 Else continue with step 1 Endif 6: If t = 24 hours (1440 min) calculate Avg. BG for every day using Equation 2 7: Go to Algorithm 2 Algorithm 2. Calculating A1C. 1: When time t = 8 hours then alarm the patient about his situation 2: Counting days (j) for every j = j + 1 3: If j < 14 days then store data and go to Algorithm 1 Else If j = 14 calculate A1C using Equations 5 and let j = 0 Endif Endif 4: Calculate GMI% using Equation 3 5: Compare the A1C% result with GMI% according to Table 4 6: Show patient the result as a colored message (either red, yellow or green) on his smart device 7: Go to Algorithm 3 Algorithm 3. Predicting GMI% for the next 14 days. 1: Use the stored GMI% for the past 14 days 2: Processing Markov Chain with three stages to predict GMI 4: If the predicted GMI is red or yellow the patient needs to control his lifestyle Else (green) means that the patient is on the right track of lifestyle Endif 5: Counting days (k) 6: If k = 90 then recall data from the cloud database to calculate GMI% and Show result to patient with colored alarm let k = 0 Else go to step 7 Endif 7: Go to Algorithm 1 V. Conclusion The work in this research discusses many subjects and can be divided into five goals: (1) First is to give an overview of non-invasive glucose level monitoring optical principles with the latest researches about it. Then, a summary of non-invasive glucose level monitoring technologies is provided. (2) To make this work a reference to the latest works in the field of diabetic diseases, where IoT technologies used to support patients individually. This done by reviewing many commercial CGM home devices with reasonable prices and granted by the WHO. Then a comparison between finger prick and CGM is done to show the CGM efficiency (all volunteers used Abbott Freestyle Libre CGM). (3) To study how Iraqi patients with type 2 diabetes feel when using a CGM device. A questionnaire was administered to 10 Iraqi women with type 2 diabetes and aged–45-60 years old. The questionnaire discussed and showed that diabetes patients in Iraq need more education about using new IoT technologies. (4) Suggesting a model with three algorithms that installed in the patient’s personal smart device and connected to a CGM device to control his/her own lifestyle. first algorithm enhances the CGM device by showing a message every 8 h in a normal situation rather than every 15 min to make it more comfortable. (5) CGM connected to smart device has the ability to store data in cloud storage, where the second algorithm uses this data to calculate GMI% every two weeks. Also, GMI% will be predicted for the next 14 days based on the Markov chain to indicates the lifestyle of the patient during the past period. In the future, we plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably. Moreover, a suggested idea can be added to estimate A1C every 3 months using a machine learning algorithm for more accurate results based on the stored outputs. Ethical considerations Ethical approval was obtained from the Ethics committee of University of Baghdad/Institute of Laser for Postgraduate Studies (Approval number: 576) signed by the chairman of the research ethics committee Prof. Dr. Abdulhadi Al-Janabi. All participants provided written informed consent after receiving a detailed explanation of the study purpose, procedures, potential risks, and benefits. Participants used CGM devices in their usual environments, such as their homes or offices, without the need to visit a laboratory or health center. All data were handled in accordance with the confidentiality and privacy guidelines. Data availability Underlying data Repository name: Questionnaire survey and comparing between FP standard test and CGM test for BG. https://doi.org/10.5281/zenodo.17914313 . 34 The project contains the following underlying data: • questionnaire.csv (summery of the questionnaire with 11 questions for 10 patients answering raw with either yes, no, or abstain) • graph.csv (Table-FP test raw, before breakfast, after breakfast, and before sleeping. Table-CGM test raw, Avg. 6am -2pm, Avg. 2pm – 10pm, and Avg. 10pm – 6am. Table-total Avg raw, and SD raw) Extended data All relevant data are included in the main manuscript and in the underlying dataset. Questionnaire . To determine whether patients are willing to change their attentions for testing their BG using new technologies). Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). References 1. Hameed EK, Al-Ameri LT: Lifestyle Medicine: A Promising tool to Restoring Health. Al-Kindy College Medical Journal. 2022; 18 (3): 165–166. Publisher Full Text 2. Shang T, Zhang JY, Thomas A, et al. : Products for monitoring glucose levels in the human body with noninvasive optical, noninvasive fluid sampling, or minimally invasive technologies. J. Diabetes Sci. Technol. 2022; 16 (1): 168–214. PubMed Abstract | Publisher Full Text 3. Wang Z: The diabetic patient software agent. Faculty of Graduate Studies and Research, University of Regina; 2012. (Doctoral dissertation). 4. Wang Z, Paranjape R: The self-aware diabetic patient software agent model. Comput. Biol. Med. 2013 Nov 1; 43 (11): 1900–1909. PubMed Abstract | Publisher Full Text 5. Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán IP, et al. : An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl. Sci. 2019; 9 (15): 3046. Publisher Full Text 6. Van't Riet E, Alssema M, Rijkelijkhuizen JM, et al. : Relationship between A1C and glucose levels in the general Dutch population: the new Hoorn study. Diabetes Care. 2010 Jan 1; 33 (1): 61–66. PubMed Abstract | Publisher Full Text 7. ElSayed NA, et al. : 7. Diabetes technology: standards of care in diabetes—2023. Diabetes Care. 2023; 46 : S111–S127. Publisher Full Text 8. Subber Z, Al-Shamma G, Hashim H: The total and free vitamin D in type 2 diabetes mellitus patients in Baghdad city. Baghdad Journal of Biochemistry and Applied Biological Sciences. 2021; 2 (02): 80–94. Publisher Full Text 9. Alobaidi ZA, Mohammed SI: Association of Disease Duration and Duration of Olanzapine Use with Blood Sugar, Blood Pressure, BMI, and Lipid Profile among Schizophrenic Patients in Iraq. Al-Rafidain Journal of Medical Sciences. 2023 Jun 5; 4 : 79–85. 2789-3219. Publisher Full Text 10. Oleiwi S, Ani AL: B.: Assessment of diabetes type 2 clients’ self-care skills toward Blood glucose level control. Kufa journal for nursing sciences. 2013; 3 (1): 187–199. 11. World Health Organization: Eastern Mediterranean Region. Reference Source 12. Villena Gonzales W, Mobashsher AT, Abbosh A: The progress of glucose monitoring - A review of invasive to minimally and non-invasive techniques, devices and sensors. Sensors. 2019; 19 (4): 800. Publisher Full Text 13. Wang J, Yue X, Zhang Y, et al. : Plasmonic Sensing of Glucose Based on Gold–Silver Core–Shell Nanoparticles. Chemosensors. 2022; 10 (10): 404. Publisher Full Text 14. Nasser ES, Dawood FAA: Diagnosis and classification of type II diabetes based on Multilayer Neural Network. Iraqi Journal of Science. 2021; 3744–3758. Publisher Full Text 15. Ahmadian N, Manickavasagan A, Ali A: Comparative assessment of blood glucose monitoring techniques: a review. J. Med. Eng. Technol. 2023; 47 (2): 121–130. PubMed Abstract | Publisher Full Text 16. Dovc K, Battelino T: Time in range centered diabetes care. The Japanese Society for Pediatric Endocrinology. 2021, January; 30 (1): 1–10. Publisher Full Text 17. Gusev M, Poposka L, Spasevski G, et al. : Noninvasive glucose measurement using machine learning and neural network methods and correlation with heart rate variability. J. Sens. 2020; 2020 : 1–13. Publisher Full Text 18. Bachache LN, Hasan JA, Al-Neam AQ: A review: non-invasive sensing system for detection glucose level. Journal of Physics: Conference Series. IOP Publishing; 2021, July; Vol. 1963 (1): p. 012125. 19. Hina A, Saadeh W: Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology-A Review. MDPI, Sensors. 2022; 22 (13): 4855. PubMed Abstract | Publisher Full Text | Free Full Text 20. Sutradhar D, Hazarika D: A review of non-invasive electromagnetic blood glucose monitoring techniques. Asian Pacific Journal of Health Sciences. 2022; 9 : 98–105. Publisher Full Text 21. Bachache LN, Al-Neami AQ, Hasan JA: Noninvasive acousto-optic glucose sensing system for diabetic COVID-19 patients. J. Eng. Sci. Technol. 2021; 16 . 22. Ramachandran S, Prakash A, Abraham B, et al. : Design and Modeling of a Glucose Optical Sensor for Diabetes Monitoring. International Journal of Intelligent Systems and Applications in Engineering. 2023; 11 (3): 570–577. 23. Lim S, Ibrahim SZ, Karim MNA, et al. : IoT-Based Blood Glucose Detection System. IEEE 6th International Conference on Electrical, Electronics and System Engineering (ICEESE). Malaysia: Shah Alam; 2023; pp. 7–11. Publisher Full Text 24. Chen J, Furukawa H: Rapid and non-invasive detection of high-thickness glucose solution concentrations using quantum cascade laser-based transmission infrared spectroscopy. Infrared Phys. Technol. 2023; 131 : 104717. 25. Segman Y(J): Device and Method for Noninvasive Glucose Assessment. J. Diabetes Sci. Technol. 2018; 12 (6): 1159–1168. PubMed Abstract | Publisher Full Text | Free Full Text 26. Lin T, Gal A, Mayzel Y, et al. : Non-invasive glucose monitoring: a review of challenges and recent advances. Curr. Trends Biomed. Eng. Biosci. 2017 Jul 31; 6 (5): 1–8. Publisher Full Text 27. Bahartan K, Horman K, Gal A, et al. : Assessing the performance of a noninvasive glucose monitor in people with type 2 diabetes with different demographic profiles. J. Diabetes Res. 2017; 2017 (1): 4393497. 28. Christiansen MP, Klaff LJ, Brazg R, et al. : A prospective multicenter evaluation of the accuracy of a novel implanted continuous glucose sensor: PRECISE II. Diabetes Technol. Ther. 2018; 20 (3): 197–206. PubMed Abstract | Publisher Full Text 29. Abbott Diabetes Care: FreeStyle Libre User’s Manual.2019. Accessed 15 October 2019. Reference Source 30. Galindo RJ, Migdal AL, Davis GM, et al. : Comparison of the FreeStyle Libre Pro flash continuous glucose monitoring (CGM) system and point-of-care capillary glucose testing in hospitalized patients with type 2 diabetes treated with basal-bolus insulin regimen. Diabetes Care. 2020; 43 (11): 2730–2735. PubMed Abstract | Publisher Full Text 31. Freestyle-libre-glucose-reader-1pc. Reference Source 32. Shuzan MNI, Chowdhury MH, Chowdhury ME, et al. : QU-GM: an IoT based glucose monitoring system from photoplethysmography, blood pressure, and demographic data using machine learning. IEEE Access. 2024; 12 : 77774–77790. 33. Almansour HHM: IAI-CGM: a novel theoretical framework for Internet of Things-enabled continuous glucose monitoring adoption for self-empowerment perspectives among Saudi patients with Type 1 Diabetes. University of Sussex; 2024. (Doctoral dissertation). 34. Basheer H: Questionnaire survey and comparing between FP standard test and CGM test for BG. [Data set]. Zenodo. 2025. Publisher Full Text 35. A CGM website. Reference Source 36. Czupryniak L, Dzida G, Fichna P, et al. : Ambulatory glucose profile (AGP) report in daily care of patients with diabetes: Practical tips and recommendations. Diabetes Therapy. 2022; 13 (4): 811–821. PubMed Abstract | Publisher Full Text 37. Battelino T, Danne T, Bergenstal RM, et al. : Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019 Aug 1; 42 (8): 1593–1603. PubMed Abstract | Publisher Full Text 38. Lin R, Brown F, Ekinci EI: The ambulatory glucose profile and its interpretation. Med. J. Aust. 2022; 217 (6): 295–298. PubMed Abstract | Publisher Full Text 39. Bergenstal RM, Ahmann AJ, Bailey T, et al. : Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: the ambulatory glucose profile. J. Diabetes Sci. Technol. 2013 Mar 1; 7 (2): 562–578. PubMed Abstract | Publisher Full Text | Free Full Text 40. Bergenstal RM, Beck RW, Close KL, et al. : Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring. Diabetes Care. 2018; 41 (11): 2275–2280. PubMed Abstract | Publisher Full Text 41. Yamada M, Okada S, Oda H, et al. : Evaluation of the relationship between glycated hemoglobin A1c and mean glucose levels derived from the professional continuous flash glucose monitoring system. Endocr. J. 2020; 67 (5): 531–536. PubMed Abstract | Publisher Full Text 42. What should my A1C goal be? by Colleen Magnani, RDN, CDCES. http 43. Lubis AR, Prayudani S, Lubis M: Analysis of the Markov Chain Approach to Detect Blood Sugar Level. Journal of Physics: Conference Series. IOP Publishing; 2019, November; Vol. 1361 (1): 012052. Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 23 Jan 2026 ADD YOUR COMMENT Comment Author details Author details 1 Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq Hanaa S. Basheer Roles: Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Anes A. Al-Sharqi Roles: Conceptualization, Data Curation, Formal Analysis, Resources, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 05 May 2026, 15:108 https://doi.org/10.12688/f1000research.173871.2 version 1 Published: 23 Jan 2026, 15:108 https://doi.org/10.12688/f1000research.173871.1 Copyright © 2026 Basheer HS and Al-Sharqi AA. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Basheer HS and Al-Sharqi AA. Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies [version 1; peer review: 2 approved with reservations] . F1000Research 2026, 15 :108 ( https://doi.org/10.12688/f1000research.173871.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 23 Jan 2026 Views 0 Cite How to cite this report: Ahmed I. Reviewer Report For: Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies [version 1; peer review: 2 approved with reservations] . F1000Research 2026, 15 :108 ( https://doi.org/10.5256/f1000research.191724.r453450 ) The direct URL for this report is: https://f1000research.com/articles/15-108/v1#referee-response-453450 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 Mar 2026 Israr Ahmed , Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.191724.r453450 The manuscript presents a system integrating a Continuous Glucose Monitoring (CGM) device with a smart-device and cloud-based architecture, proposing three algorithms for alarm optimization, A1C/GMI estimation, and short-term prediction using a Markov chain model. The study also includes a small ... Continue reading READ ALL The manuscript presents a system integrating a Continuous Glucose Monitoring (CGM) device with a smart-device and cloud-based architecture, proposing three algorithms for alarm optimization, A1C/GMI estimation, and short-term prediction using a Markov chain model. The study also includes a small questionnaire-based evaluation and comparison between finger-prick and CGM measurements. The topic is relevant and timely, particularly in the context of IoT-enabled healthcare systems. However, several substantial concerns regarding novelty, methodological rigor, statistical validation, and predictive modeling need to be addressed before the manuscript can be considered for indexing. 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. 8. The link provided in the data availability section is not valid. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Optics; Optical Sensing; Glucose Sensing; Nanotechnology; 2D materials I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ahmed I. Reviewer Report For: Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies [version 1; peer review: 2 approved with reservations] . F1000Research 2026, 15 :108 ( https://doi.org/10.5256/f1000research.191724.r453450 ) The direct URL for this report is: https://f1000research.com/articles/15-108/v1#referee-response-453450 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 05 May 2026 Hanaa Basheer , Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq 05 May 2026 Author Response Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards ... Continue reading Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. Answer: the figures resolution is limited to 300dpi as the editor requested. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. Answer: we added short conclusions as your advice at the end of each section. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. Answer: We started our work late-2024 and submit it mid-2025, so all references we include were up to date. Therefore, and as you asked in (comment 4), we added more information about the presented devices. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. Answer: more details are added for QU-GM and for the IAI-CGM) theoretical framework. We added new reference published in 2025 to show how AI involves with the healthcare fields 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. Answer: Thank you to draw attention to this point. We used pair (dependent) t-test to check the comparison between CGM and finger-prick methods 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. Answer: We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper with probability clarifying. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. Answer: We added a new discussion subsection to perform our proposed model 8. The link provided in the data availability section is not valid. Answer: We create a new dataset with valid link. Please check the revised manuscript, reference number [36] Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. Answer: the figures resolution is limited to 300dpi as the editor requested. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. Answer: we added short conclusions as your advice at the end of each section. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. Answer: We started our work late-2024 and submit it mid-2025, so all references we include were up to date. Therefore, and as you asked in (comment 4), we added more information about the presented devices. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. Answer: more details are added for QU-GM and for the IAI-CGM) theoretical framework. We added new reference published in 2025 to show how AI involves with the healthcare fields 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. Answer: Thank you to draw attention to this point. We used pair (dependent) t-test to check the comparison between CGM and finger-prick methods 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. Answer: We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper with probability clarifying. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. Answer: We added a new discussion subsection to perform our proposed model 8. The link provided in the data availability section is not valid. Answer: We create a new dataset with valid link. Please check the revised manuscript, reference number [36] Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 05 May 2026 Hanaa Basheer , Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq 05 May 2026 Author Response Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards ... Continue reading Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. Answer: the figures resolution is limited to 300dpi as the editor requested. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. Answer: we added short conclusions as your advice at the end of each section. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. Answer: We started our work late-2024 and submit it mid-2025, so all references we include were up to date. Therefore, and as you asked in (comment 4), we added more information about the presented devices. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. Answer: more details are added for QU-GM and for the IAI-CGM) theoretical framework. We added new reference published in 2025 to show how AI involves with the healthcare fields 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. Answer: Thank you to draw attention to this point. We used pair (dependent) t-test to check the comparison between CGM and finger-prick methods 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. Answer: We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper with probability clarifying. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. Answer: We added a new discussion subsection to perform our proposed model 8. The link provided in the data availability section is not valid. Answer: We create a new dataset with valid link. Please check the revised manuscript, reference number [36] Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. Answer: the figures resolution is limited to 300dpi as the editor requested. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. Answer: we added short conclusions as your advice at the end of each section. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. Answer: We started our work late-2024 and submit it mid-2025, so all references we include were up to date. Therefore, and as you asked in (comment 4), we added more information about the presented devices. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. Answer: more details are added for QU-GM and for the IAI-CGM) theoretical framework. We added new reference published in 2025 to show how AI involves with the healthcare fields 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. Answer: Thank you to draw attention to this point. We used pair (dependent) t-test to check the comparison between CGM and finger-prick methods 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. Answer: We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper with probability clarifying. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. Answer: We added a new discussion subsection to perform our proposed model 8. The link provided in the data availability section is not valid. Answer: We create a new dataset with valid link. Please check the revised manuscript, reference number [36] Competing Interests: No competing interests Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 05 May 2026 Hanaa Basheer , Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq 05 May 2026 Author Response Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards ... Continue reading Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. Answer: the figures resolution is limited to 300dpi as the editor requested. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. Answer: we added short conclusions as your advice at the end of each section. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. Answer: We started our work late-2024 and submit it mid-2025, so all references we include were up to date. Therefore, and as you asked in (comment 4), we added more information about the presented devices. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. Answer: more details are added for QU-GM and for the IAI-CGM) theoretical framework. We added new reference published in 2025 to show how AI involves with the healthcare fields 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. Answer: Thank you to draw attention to this point. We used pair (dependent) t-test to check the comparison between CGM and finger-prick methods 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. Answer: We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper with probability clarifying. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. Answer: We added a new discussion subsection to perform our proposed model 8. The link provided in the data availability section is not valid. Answer: We create a new dataset with valid link. Please check the revised manuscript, reference number [36] Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. Answer: the figures resolution is limited to 300dpi as the editor requested. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. Answer: we added short conclusions as your advice at the end of each section. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. Answer: We started our work late-2024 and submit it mid-2025, so all references we include were up to date. Therefore, and as you asked in (comment 4), we added more information about the presented devices. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. Answer: more details are added for QU-GM and for the IAI-CGM) theoretical framework. We added new reference published in 2025 to show how AI involves with the healthcare fields 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. Answer: Thank you to draw attention to this point. We used pair (dependent) t-test to check the comparison between CGM and finger-prick methods 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. Answer: We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper with probability clarifying. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. Answer: We added a new discussion subsection to perform our proposed model 8. The link provided in the data availability section is not valid. Answer: We create a new dataset with valid link. Please check the revised manuscript, reference number [36] Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 05 May 2026 Hanaa Basheer , Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq 05 May 2026 Author Response Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards ... Continue reading Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. Answer: the figures resolution is limited to 300dpi as the editor requested. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. Answer: we added short conclusions as your advice at the end of each section. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. Answer: We started our work late-2024 and submit it mid-2025, so all references we include were up to date. Therefore, and as you asked in (comment 4), we added more information about the presented devices. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. Answer: more details are added for QU-GM and for the IAI-CGM) theoretical framework. We added new reference published in 2025 to show how AI involves with the healthcare fields 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. Answer: Thank you to draw attention to this point. We used pair (dependent) t-test to check the comparison between CGM and finger-prick methods 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. Answer: We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper with probability clarifying. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. Answer: We added a new discussion subsection to perform our proposed model 8. The link provided in the data availability section is not valid. Answer: We create a new dataset with valid link. Please check the revised manuscript, reference number [36] Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. Answer: the figures resolution is limited to 300dpi as the editor requested. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. Answer: we added short conclusions as your advice at the end of each section. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. Answer: We started our work late-2024 and submit it mid-2025, so all references we include were up to date. Therefore, and as you asked in (comment 4), we added more information about the presented devices. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. Answer: more details are added for QU-GM and for the IAI-CGM) theoretical framework. We added new reference published in 2025 to show how AI involves with the healthcare fields 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. Answer: Thank you to draw attention to this point. We used pair (dependent) t-test to check the comparison between CGM and finger-prick methods 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. Answer: We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper with probability clarifying. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. Answer: We added a new discussion subsection to perform our proposed model 8. The link provided in the data availability section is not valid. Answer: We create a new dataset with valid link. Please check the revised manuscript, reference number [36] Competing Interests: No competing interests Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Abdolrazzaghi M. Reviewer Report For: Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies [version 1; peer review: 2 approved with reservations] . F1000Research 2026, 15 :108 ( https://doi.org/10.5256/f1000research.191724.r457645 ) The direct URL for this report is: https://f1000research.com/articles/15-108/v1#referee-response-457645 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 26 Feb 2026 Mohammad Abdolrazzaghi , University of Toronto, Toronto, Ontario, Canada Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.191724.r457645 Dear authors, The idea presented in this manuscript shows promise and demonstrates potential. The writing is clear, with measurements aligning well with simulations and yielding good results. However, several aspects need further clarification: Introduction ... Continue reading READ ALL Dear authors, The idea presented in this manuscript shows promise and demonstrates potential. The writing is clear, with measurements aligning well with simulations and yielding good results. However, several aspects need further clarification: Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Reference 1 Reference 2 The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes References 1. Abdolrazzaghi M, Katchinskiy N, Elezzabi A, Light P, et al.: Noninvasive Glucose Sensing in Aqueous Solutions Using an Active Split-Ring Resonator. IEEE Sensors Journal . 2021; 21 (17): 18742-18755 Publisher Full Text 2. Kazemi N, Abdolrazzaghi M, Light P, Musilek P: In–human testing of a non-invasive continuous low–energy microwave glucose sensor with advanced machine learning capabilities. Biosensors and Bioelectronics . 2023; 241 . Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: glucose sensing, microwave sensors, machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Abdolrazzaghi M. Reviewer Report For: Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies [version 1; peer review: 2 approved with reservations] . F1000Research 2026, 15 :108 ( https://doi.org/10.5256/f1000research.191724.r457645 ) The direct URL for this report is: https://f1000research.com/articles/15-108/v1#referee-response-457645 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 05 May 2026 Hanaa Basheer , Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq 05 May 2026 Author Response Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Below our answers to them all Introduction needs to be improved with ... Continue reading Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Below our answers to them all Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Answer : We did download the two papers and read them well. Both now are inserted in the introduction section with references number [15] and [16] when submitting the new draft The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? Answer : The idea of using Markov chain is adopted from reference 43. We are intended to implement the model in our future work. The state boundaries are suggested according to table 4 and reference 40. We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper. The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? Answer: this is a good point we just think about a colored alert message and a ringing bell but with your comment we will consider this important point by thinking about a second person such as a relative or a doctor to be aware of this situation immediately by sending him a copy of the alert message. This point is added as a future work to the paper. Thank you All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? Answer: Yes, we mention that as a future work (We plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably) The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Answer: I’m sorry I did not understand the comment well, but I will explain how our community think. The elderly patients in Iraq are stubborn especially with medicines and the way of treatment. They almost do not prefer any change or advise about new technology and this illustrated in table 1. 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 results are shown in table 2 and table 3. Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Below our answers to them all Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Answer : We did download the two papers and read them well. Both now are inserted in the introduction section with references number [15] and [16] when submitting the new draft The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? Answer : The idea of using Markov chain is adopted from reference 43. We are intended to implement the model in our future work. The state boundaries are suggested according to table 4 and reference 40. We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper. The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? Answer: this is a good point we just think about a colored alert message and a ringing bell but with your comment we will consider this important point by thinking about a second person such as a relative or a doctor to be aware of this situation immediately by sending him a copy of the alert message. This point is added as a future work to the paper. Thank you All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? Answer: Yes, we mention that as a future work (We plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably) The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Answer: I’m sorry I did not understand the comment well, but I will explain how our community think. The elderly patients in Iraq are stubborn especially with medicines and the way of treatment. They almost do not prefer any change or advise about new technology and this illustrated in table 1. 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 results are shown in table 2 and table 3. Competing Interests: No competing interests Close Report a concern Author Response 05 May 2026 Hanaa Basheer , Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq 05 May 2026 Author Response Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Bellow our answers to them all Introduction needs to be improved with ... Continue reading Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Bellow our answers to them all Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Answer : We did download the two papers and read them well. Both now are inserted in the introduction section with references number [15] and [16] The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? Answer : The idea of using Markov chain is adopted from reference 43. We are intended to implement the model in our future work. The state boundaries are suggested according to table 4 and reference 40. We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper. The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? Answer: this is a good point we just think about a colored alert message and a ringing bell but with your comment we will consider this important point by thinking about a second person such as a relative or a doctor to be aware of this situation directly by sending him a copy of the alert message. This point is added as a future work to the paper. Thank you All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? Answer: Yes, we mention that as a future work (We plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably) The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Answer: I’m sorry I did not understand the comment well, but I will explain how our community think. The elderly patients in Iraq are stubborn especially with medicines and the way of treatment. They almost do not prefer any change or advise about new technology and this illustrated in table 1. 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 results are shown in table 2 and table 3. Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Bellow our answers to them all Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Answer : We did download the two papers and read them well. Both now are inserted in the introduction section with references number [15] and [16] The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? Answer : The idea of using Markov chain is adopted from reference 43. We are intended to implement the model in our future work. The state boundaries are suggested according to table 4 and reference 40. We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper. The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? Answer: this is a good point we just think about a colored alert message and a ringing bell but with your comment we will consider this important point by thinking about a second person such as a relative or a doctor to be aware of this situation directly by sending him a copy of the alert message. This point is added as a future work to the paper. Thank you All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? Answer: Yes, we mention that as a future work (We plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably) The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Answer: I’m sorry I did not understand the comment well, but I will explain how our community think. The elderly patients in Iraq are stubborn especially with medicines and the way of treatment. They almost do not prefer any change or advise about new technology and this illustrated in table 1. 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 results are shown in table 2 and table 3. Competing Interests: No competing interests Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 05 May 2026 Hanaa Basheer , Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq 05 May 2026 Author Response Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Below our answers to them all Introduction needs to be improved with ... Continue reading Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Below our answers to them all Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Answer : We did download the two papers and read them well. Both now are inserted in the introduction section with references number [15] and [16] when submitting the new draft The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? Answer : The idea of using Markov chain is adopted from reference 43. We are intended to implement the model in our future work. The state boundaries are suggested according to table 4 and reference 40. We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper. The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? Answer: this is a good point we just think about a colored alert message and a ringing bell but with your comment we will consider this important point by thinking about a second person such as a relative or a doctor to be aware of this situation immediately by sending him a copy of the alert message. This point is added as a future work to the paper. Thank you All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? Answer: Yes, we mention that as a future work (We plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably) The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Answer: I’m sorry I did not understand the comment well, but I will explain how our community think. The elderly patients in Iraq are stubborn especially with medicines and the way of treatment. They almost do not prefer any change or advise about new technology and this illustrated in table 1. 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 results are shown in table 2 and table 3. Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Below our answers to them all Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Answer : We did download the two papers and read them well. Both now are inserted in the introduction section with references number [15] and [16] when submitting the new draft The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? Answer : The idea of using Markov chain is adopted from reference 43. We are intended to implement the model in our future work. The state boundaries are suggested according to table 4 and reference 40. We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper. The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? Answer: this is a good point we just think about a colored alert message and a ringing bell but with your comment we will consider this important point by thinking about a second person such as a relative or a doctor to be aware of this situation immediately by sending him a copy of the alert message. This point is added as a future work to the paper. Thank you All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? Answer: Yes, we mention that as a future work (We plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably) The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Answer: I’m sorry I did not understand the comment well, but I will explain how our community think. The elderly patients in Iraq are stubborn especially with medicines and the way of treatment. They almost do not prefer any change or advise about new technology and this illustrated in table 1. 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 results are shown in table 2 and table 3. Competing Interests: No competing interests Close Report a concern Author Response 05 May 2026 Hanaa Basheer , Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq 05 May 2026 Author Response Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Bellow our answers to them all Introduction needs to be improved with ... Continue reading Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Bellow our answers to them all Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Answer : We did download the two papers and read them well. Both now are inserted in the introduction section with references number [15] and [16] The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? Answer : The idea of using Markov chain is adopted from reference 43. We are intended to implement the model in our future work. The state boundaries are suggested according to table 4 and reference 40. We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper. The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? Answer: this is a good point we just think about a colored alert message and a ringing bell but with your comment we will consider this important point by thinking about a second person such as a relative or a doctor to be aware of this situation directly by sending him a copy of the alert message. This point is added as a future work to the paper. Thank you All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? Answer: Yes, we mention that as a future work (We plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably) The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Answer: I’m sorry I did not understand the comment well, but I will explain how our community think. The elderly patients in Iraq are stubborn especially with medicines and the way of treatment. They almost do not prefer any change or advise about new technology and this illustrated in table 1. 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 results are shown in table 2 and table 3. Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Bellow our answers to them all Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Answer : We did download the two papers and read them well. Both now are inserted in the introduction section with references number [15] and [16] The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? Answer : The idea of using Markov chain is adopted from reference 43. We are intended to implement the model in our future work. The state boundaries are suggested according to table 4 and reference 40. We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper. The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? Answer: this is a good point we just think about a colored alert message and a ringing bell but with your comment we will consider this important point by thinking about a second person such as a relative or a doctor to be aware of this situation directly by sending him a copy of the alert message. This point is added as a future work to the paper. Thank you All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? Answer: Yes, we mention that as a future work (We plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably) The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Answer: I’m sorry I did not understand the comment well, but I will explain how our community think. The elderly patients in Iraq are stubborn especially with medicines and the way of treatment. They almost do not prefer any change or advise about new technology and this illustrated in table 1. 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 results are shown in table 2 and table 3. Competing Interests: No competing interests Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 23 Jan 2026 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 05 May 26 read Version 1 23 Jan 26 read read Mohammad Abdolrazzaghi , University of Toronto, Toronto, Canada Israr Ahmed , Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Abdolrazzaghi M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 May 2026 | for Version 2 Mohammad Abdolrazzaghi , University of Toronto, Toronto, Ontario, Canada 0 Views copyright © 2026 Abdolrazzaghi M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Thanks for applying the comments. No more comments are required. Competing Interests No competing interests were disclosed. Reviewer Expertise glucose sensing, microwave sensors, machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 09 May 2026 Hanaa Basheer, Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq Dear Dr. Mohammad Thank you for your comments, they were very helpful for improving our manuscript Regards View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Abdolrazzaghi M. Peer Review Report For: Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies [version 1; peer review: 2 approved with reservations] . F1000Research 2026, 15 :108 ( https://doi.org/10.5256/f1000research.197253.r481842) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-108/v2#referee-response-481842 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Ahmed I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Mar 2026 | for Version 1 Israr Ahmed , Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates 0 Views copyright © 2026 Ahmed I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (2) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript presents a system integrating a Continuous Glucose Monitoring (CGM) device with a smart-device and cloud-based architecture, proposing three algorithms for alarm optimization, A1C/GMI estimation, and short-term prediction using a Markov chain model. The study also includes a small questionnaire-based evaluation and comparison between finger-prick and CGM measurements. The topic is relevant and timely, particularly in the context of IoT-enabled healthcare systems. However, several substantial concerns regarding novelty, methodological rigor, statistical validation, and predictive modeling need to be addressed before the manuscript can be considered for indexing. 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. 8. The link provided in the data availability section is not valid. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Optics; Optical Sensing; Glucose Sensing; Nanotechnology; 2D materials I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (2) Author Response 05 May 2026 Hanaa Basheer, Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. Answer: the figures resolution is limited to 300dpi as the editor requested. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. Answer: we added short conclusions as your advice at the end of each section. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. Answer: We started our work late-2024 and submit it mid-2025, so all references we include were up to date. Therefore, and as you asked in (comment 4), we added more information about the presented devices. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. Answer: more details are added for QU-GM and for the IAI-CGM) theoretical framework. We added new reference published in 2025 to show how AI involves with the healthcare fields 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. Answer: Thank you to draw attention to this point. We used pair (dependent) t-test to check the comparison between CGM and finger-prick methods 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. Answer: We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper with probability clarifying. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. Answer: We added a new discussion subsection to perform our proposed model 8. The link provided in the data availability section is not valid. Answer: We create a new dataset with valid link. Please check the revised manuscript, reference number [36] View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Author Response 05 May 2026 Hanaa Basheer, Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq Dear Dr. Israr Thank you for the valuable notes. We tried to give full answer for each one of them. Please check the highlighted paragraphs on the revised manuscript. Regards 1. The resolution of Figures 2a and 2d is poor and the text within the figures is not clearly readable. Please provide higher-quality images. Answer: the figures resolution is limited to 300dpi as the editor requested. 2. It would strengthen the manuscript to include a short concluding paragraph at the end of each major section (e.g., “Optical approaches for non-invasive glucose monitoring” and “Commercial devices”) summarizing the key outcomes and insights from the discussion. Answer: we added short conclusions as your advice at the end of each section. 3. Some sections would benefit from deeper discussion and inclusion of additional recent references to provide a more comprehensive and balanced overview. Answer: We started our work late-2024 and submit it mid-2025, so all references we include were up to date. Therefore, and as you asked in (comment 4), we added more information about the presented devices. 4. When discussing commercial devices (e.g., QU-GM and others), additional technical details would be helpful, such as limitations, reported accuracy, clinical validation status, and cost considerations. Answer: more details are added for QU-GM and for the IAI-CGM) theoretical framework. We added new reference published in 2025 to show how AI involves with the healthcare fields 5. In the statistical comparison between CGM and finger-prick methods, since measurements were obtained from the same patients, please clarify whether a paired statistical test would be more appropriate than an independent samples t-test. Answer: Thank you to draw attention to this point. We used pair (dependent) t-test to check the comparison between CGM and finger-prick methods 6. For the proposed Markov chain model, please provide the transition probability matrix and clarify how these probabilities were estimated. Answer: We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper with probability clarifying. 7. If available, please include quantitative validation or brief discussion of the predictive performance of the proposed model. Answer: We added a new discussion subsection to perform our proposed model 8. The link provided in the data availability section is not valid. Answer: We create a new dataset with valid link. Please check the revised manuscript, reference number [36] View more View less Competing Interests No competing interests reply Respond Report a concern Ahmed I. Peer Review Report For: Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies [version 1; peer review: 2 approved with reservations] . F1000Research 2026, 15 :108 ( https://doi.org/10.5256/f1000research.191724.r453450) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-108/v1#referee-response-453450 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Abdolrazzaghi M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 26 Feb 2026 | for Version 1 Mohammad Abdolrazzaghi , University of Toronto, Toronto, Ontario, Canada 0 Views copyright © 2026 Abdolrazzaghi M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (2) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear authors, The idea presented in this manuscript shows promise and demonstrates potential. The writing is clear, with measurements aligning well with simulations and yielding good results. However, several aspects need further clarification: Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Reference 1 Reference 2 The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes References 1. Abdolrazzaghi M, Katchinskiy N, Elezzabi A, Light P, et al.: Noninvasive Glucose Sensing in Aqueous Solutions Using an Active Split-Ring Resonator. IEEE Sensors Journal . 2021; 21 (17): 18742-18755 Publisher Full Text 2. Kazemi N, Abdolrazzaghi M, Light P, Musilek P: In–human testing of a non-invasive continuous low–energy microwave glucose sensor with advanced machine learning capabilities. Biosensors and Bioelectronics . 2023; 241 . Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise glucose sensing, microwave sensors, machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (2) Author Response 05 May 2026 Hanaa Basheer, Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Below our answers to them all Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Answer : We did download the two papers and read them well. Both now are inserted in the introduction section with references number [15] and [16] when submitting the new draft The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? Answer : The idea of using Markov chain is adopted from reference 43. We are intended to implement the model in our future work. The state boundaries are suggested according to table 4 and reference 40. We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper. The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? Answer: this is a good point we just think about a colored alert message and a ringing bell but with your comment we will consider this important point by thinking about a second person such as a relative or a doctor to be aware of this situation immediately by sending him a copy of the alert message. This point is added as a future work to the paper. Thank you All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? Answer: Yes, we mention that as a future work (We plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably) The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Answer: I’m sorry I did not understand the comment well, but I will explain how our community think. The elderly patients in Iraq are stubborn especially with medicines and the way of treatment. They almost do not prefer any change or advise about new technology and this illustrated in table 1. 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 results are shown in table 2 and table 3. View more View less Competing Interests No competing interests reply Respond Report a concern Author Response 05 May 2026 Hanaa Basheer, Photonics unit, University of Baghdad, Institute of Laser for Postgraduate Studies, Baghdad, Iraq Dear Mohammad Abdolrazzaghi Thank you for the great advice that we had from your comments. Bellow our answers to them all Introduction needs to be improved with considering the following recent relevant work and compare their potential use as wearables for BG detection: Answer : We did download the two papers and read them well. Both now are inserted in the introduction section with references number [15] and [16] The three-state Markov chain model is central to Algorithm 3, yet the manuscript provides no actual transition probability matrix (TPM), no training data, and no validation against real patient trajectories. How were the state boundaries (e.g., 7.3%–7.8% for S₀) selected, what is the sensitivity of the 14-day prediction to these thresholds? Answer : The idea of using Markov chain is adopted from reference 43. We are intended to implement the model in our future work. The state boundaries are suggested according to table 4 and reference 40. We thought that TPM is not suitable to be mentioned without implementation but after your comment the suggested TPM is added now to the paper. The proposed system suppresses CGM alarms for up to 8 hours when BG is in the 70–180 mg/dL range. Given that sudden blood sugar changes, such as nighttime hypoglycemia, can occur and resolve well within this window, what clinical safety analysis or risk assessment has been performed to justify this interval? Answer: this is a good point we just think about a colored alert message and a ringing bell but with your comment we will consider this important point by thinking about a second person such as a relative or a doctor to be aware of this situation directly by sending him a copy of the alert message. This point is added as a future work to the paper. Thank you All three algorithms are presented only as pseudocode with no actual implementation on a smart device. Can the authors provide details on the intended software architecture (e.g., mobile OS, Bluetooth protocol version, cloud platform, data encryption), and discuss how practical issues such as Bluetooth disconnection, data synchronization latency, and battery consumption would be handled to ensure reliable real-world deployment? Answer: Yes, we mention that as a future work (We plan to implement our model in a smart device to obtain real results so that we can prove the high benefit of this model in managing a patient’s BG efficiently and comfortably) The study cohort consists of only 10 women aged 45–60 with type 2 diabetes on oral medication, and no formal statistical power analysis is reported. How do the authors justify the generalizability of both the questionnaire findings and the CGM-vs-finger-prick comparison to a broader diabetic population (e.g., male patients, type 1 diabetes, insulin-dependent type 2, younger or older age groups), and what sample size would be required to achieve statistically meaningful conclusions at the reported significance level of P < 0.05? Answer: I’m sorry I did not understand the comment well, but I will explain how our community think. The elderly patients in Iraq are stubborn especially with medicines and the way of treatment. They almost do not prefer any change or advise about new technology and this illustrated in table 1. 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 results are shown in table 2 and table 3. View more View less Competing Interests No competing interests reply Respond Report a concern Abdolrazzaghi M. Peer Review Report For: Advancing Diabetic Care Through Non-Invasive Glucose Monitoring Using Optical Sensors and IoT Technologies [version 1; peer review: 2 approved with reservations] . F1000Research 2026, 15 :108 ( https://doi.org/10.5256/f1000research.191724.r457645) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-108/v1#referee-response-457645 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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