The intersection of technology, ethnicity, deprivation, and the covid-19 pandemic: Lessons from the National Diabetes Audit

Introduction: The aim was to investigate access to and the effect of intermittency scanned flash glucose monitoring (isCGM) on glycaemic control during the Covid-19 pandemic. Method(s): Data from the National Diabetes Audit from 2019 to 2021 was stratified into those who were already using isCGM on 1st April 2020 (A), those who started isCGM on or after 1st April 2020 (B), and those who did not receive isCGM (C). Logistic regression investigated the independent effects of ethnicity and deprivation on access to isCGM after adjustment for baseline covariates (age, gender, BMI, duration of diabetes, and baseline HbA1c). Ethnicity was categorized as White, Asian, Black, Mixed, and not reported. The Index of Multiple Deprivation (IMD) was divided into quintiles. Result(s): 251,620 people were identified with type 1 diabetes;88,910 (35%) had isCGM prescribed at 1st April 2020. The mean follow-up post-isCGM initiation was six months. Mean HbA1c at baseline was 67.4mmol/mol in (A), 73.6mmol/mol in (B) and 69.7mmol/mol in (C). Mean HbA1c at follow-up was 64.9mmol/mol (A) (p < 0.001), 65.5mmol/mol (p < 0.001) (B). After adjustment for age, sex, duration of diagnosis, baseline HbA1c, and BMI people with White ethnicity (OR = 1.79 p < 0.001) or in the least deprived quintile (OR = 1.54, p < 0.001) were more likely to be initiated on isCGM as compared to the black and most deprived groups. Conclusion(s): Initiating isCGM during the Covid-19 pandemic was associated with improved glycaemic control. Ethnic and socioeconomic disparities in access to isCGM were observed even during the pandemic. Ongoing work is investigating the effect of isCGM on diabetes-related hospital admissions during the pandemic.

Monitoring Engagement in Online Classes Through WiFi CSI

Due to the Covid-19 pandemic, people have been forced to move to online spaces to attend classes or meetings and so on. The effectiveness of online classes depends on the engagement level of students. A straightforward way to monitor the engagement is to observe students' facial expressions, eye gazes, head gesticulations, hand movements, and body movements through their video feed. However, video-based engagement detection has limitations, such as being influenced by video backgrounds, lighting conditions, camera angles, unwillingness to open the camera, etc. In this work, we propose a non-intrusive mechanism of estimating engagement level by monitoring the head gesticulations through channel state information (CSI) of WiFi signals. First, we conduct an anonymous survey to investigate whether the head gesticulation pattern is correlated with engagement. We then develop models to recognize head gesticulations through CSI. Later, we plan to correlate the head gesticulation pattern with the instructor's intent to estimate the students' engagement. © 2023 IEEE.

Flash glucose monitoring: The story so far and the journey ahead

Just a few years ago FreeStyle Libre (FSL) was a rarely encountered device, used only by a select few people with type 1 diabetes who could afford to self-fund it. This small disc has a small cannula under the skin which allows for interstitial glucose measurements and remains in situ for 14 days. Over the last 4 years the number of people with access to this life-changing technology on the National Health Service (NHS) has increased rapidly. Although there were barriers to implementing access and encouraging uptake of this technology, including systems, healthcare professionals and the users themselves, innovative interventions from NHS England and diabetes organisations ensured those who stood to gain the most benefit were not impeded in their access, with a particular emphasis on enabling FSL use in those who are often the hardest to reach. This article reviews the impact of FSL on type 1 diabetes care in England, the key events to date and the lessons learnt that can be applied in the future for newer diabetes technologies. Copyright © 2022 BMJ Publishing Group. All rights reserved.

Detection of COVID-19 Through Thermal and Voice Sensing Using Smartphone

Since the end of 2019, the world has been caught in the crisis of the COVID-19 which is a serious epidemic disease. This paper seeks to come up with a fast and efficient COVID-19 detection and monitoring easy to use system which can be used in the facilities of densely populated areas, such as community centers and school clinics, to quickly identify suspected COVID-19 patients. This system could detect the probability of a person getting infected by COVID-19 using an android smartphone and thermal camera. Three types of data are collected from users: breathe sound, thermal video, and health status. Generally, the breathe audio and thermal video are preprocessed into two-time series, which indicate the breath status of the user. Then, the two series are inputted into the Bidirectional Gated Recurrent Unit (BI-GRU) neural network model separately to get the infection rates. Since the real data is difficult to get due to privacy reasons, a synthetic dataset is generated based on mathematical equations to train the model. For health status, the application requires the user to fill a questionnaire and calculates an infection rate through a medical prediction model. Finally, the two values from the machine learning model and the infection rate from the user report are added together with weight to calculate the final predictive infection rate. © 2022 IEEE.

An Integrated Single Device Framework for Combined Face Recognition and Covid-19 Detection Using Thermal Infrared Imagery

Temperature detection aiming for Covid-19 prevention is widely in demand from 2020. A system combining Covid-19 detection and authentication plays a significant role in ensuring safety and security. Nevertheless, after reviewing the literature, it is found that most of such systems existing contain two devices including a visible light camera for recognition and a thermal camera for temperature detection. In this paper, we propose an integrated single device framework based on a thermal camera, which combines authentication and Covid-19 detection using thermal infrared imagery. It features customizing machine learning models for face recognition, temperature detection with thermal images captured by the thermal camera and sending warnings to the house owner remotely if necessary. The whole work is carried out from two aspects: framework design and simulation. The framework designing part concerns modules involved and allocating the modules into edge devices and servers. For the software implementation part, work is generally divided into the initialization phase for training the machine learning model and the authentication phase for face recognition and temperature detection. © 2022 IEEE.

Application of IoT in Smart Epidemic Management in context of Covid-19

The main reason which makes epidemics so dangerous and difficult to contain is their highly infectious nature. In the case of Covid-19 also, data shows that its contagious nature is increasing along with its various mutant strains. One of the primary methods adopted to fight the pandemic has been to break the infection chain and thus reduce the rate of persons getting infected every day, through lockdowns, self-isolation, social distancing, and other measures. But although there are already many existing epidemic models, to predict and track the spread of the disease, it is evident from the difference in the rates of infection and fatalities in different countries, that a uniform set of parameters is not sufficient to accurately predict the curves. In this paper, we have suggested some additional benchmarks that could be considered and at a higher granularity for more accurate predictions at more local levels. We also propose an IoT-based framework for the collection of such types of data through smartphones for more consolidated information to be made available to the authorities, for the effective management of epidemics. The framework also issues warnings to other users through smartphones if the app detects the presence of a potentially infected person within close range.

A Novel Framework for Predicting the Spread of COVID-19 by Contact Tracing Through Smartphone

In recent times, COVID-19 is the most severe epidemic disease and it needs to be controlled as soon as possible. Promising ideas and mathematical models have been proposed to predict the number of infected people in a particular timeline and project its development tendency. In this paper, to further increase the accuracy of prediction, we propose a new model named AMSD model at an agent scale by combining three models that are widely used in this field: the social network model, the mobility model, and the Susceptible-Exposed-Infected-Recovered (SEIR) model. Initially, the mobility model could identify people's mobility patterns on weekdays and weekends, and during the day and at night. Then, by combining this model with the social network model, we could classify people by their social connections in the network, with a more accurate prediction of infected people. The basic SEIR model is enhanced to find the spread/growth of viruses between and within people and has four stages from susceptible to recovered. AMSD model, as the combination of these three models is a more comprehensive approach to better present and predict the propagation of COVID-19, which involves many more important social factors. © 2021 IEEE

Clinico-virological-epidemiological characteristics in Covid positive clusters among frontline Health care workers and non-health care workers – A Hospital based study

Background:Ample of studies have been carried out on the causative agent, pattern of illness, treatment options which mainly concern regarding the patients and general population affected from COVID -19, however few studies have fo- cused on its adverse effects on front line health care workers ( HCW ) and other employees of health care facilities. The present retrospective study was planned to analyse the clinico-viro-epidemiological profile of different covid clusters in HCWs and non-health care employees of AIIMS, Bhubaneswar. Methods:A hospital based retrospective study was carried out on the HCWs and other employees of AIIMS, Bhubanes- war, who tested positive SARS-CoV-2 infection by RT-PCR test. The clinical and demographic information were analysed with corresponding virological data of the patient. Results:Of the 671 employees of AIIMS, Bhubaneswar who tested positive for SARS-CoV-2, 92 were from eight clusters that could be traced. The eight clusters involved 4 clusters each from both the HCWs group containing 66 individuals and non-HCWs group with 32. Male to female ratio was 2.5:1. Maximum 55(59.7%) individuals belonged to 20 -30yrs age group followed by 30-40yrs 28(30.4%) and least 3(3.2%) in 50-60yrs. Asymptomatic COVID positive individuals were more as compared to symptomatic in all the age groups. All the individuals with cycle threshold value (CT) ≤ 20 were symptomatic;of the 21 persons with CT value 21-30, seven were symptomatic and 14 were asymptomatic. Majority with >30 CT value (35/44) were asymptomatic. Conclusions:Frontline HCWs are constantly at increased risk of getting infection, but the disease burden and post -covid stigma can be substantially decreased among non-HCWS if COVID appropriate behaviour are strictly implemented and followe

Influence of temperature, humidity on COVID positivity in Odisha

Background:The spread of COVID 19 has not been uniform across various states of India, which encounters significant spatio-temporal variations in the climatic conditions. As, seasonal cycle plays a dynamic role in spread of respiratory In- fections, we aimed to ascertain the Influence of temperature, humidity and seasonal variability on COVID positivity in a tertiary care testing hospital of Odisha Methods:Samples collected from patients attending AIIMS, Bhubaneswar and from other districts for detection of Covid-19 were tested at our lab by RTPCR. A retrospective month wise comparative analysis of the Covid -19 positivity rate of samples tested during the months of March to November 2020 was done with temperature and humidity Results:Out of 56,874 samples tested, 9,484(16.6%) were positive by real time reverse transcriptase PCR. As Odisha is a costal state it has high humidity and temperature as compared to rest of India. The mean humidity along with the mean temperature were com- pared to COVID positivity [Formula presented] Conclusions:In our study period over 9 months, Mon- soon months showed surge in positive cases peaking in August and September, and autumn months showed a downward trend.

AutoNotes: A Touch-Free Blink-Based Interactive Model for Generation of Notes from Lecture Videos

The recent explosion of interest in online courses can directly be attributed to the current pandemic which has collated to the adoption of Massive Open Online Courses. While the enforced social distancing protocol inevitably demands the involvement of technology in raising awareness, it further requires the imposition of preventive restrictions on touch-based systems. Visual gestures like simple blinks can account for a significant subset of possible actions in mobile application domains. The proposed model introduces a novel approach that facilitates the automated generation of relevant lecture notes and web-linked keywords from videos through a blink-controlled interface. Functions like auto-video pausing and auto-closing of the application based on eye attribute tracking have been integrated into the system for handling context switching. The experimental results reveal 84.17 usability score, an average of 89.55% of different types of blink detection accuracy, 91.56% of text generation accuracy and 70.41% of keyword detection accuracy along with low false positives and false negatives under different lighting conditions. © 2021, The Institution of Engineers (India).

Imaging Goa’s B2B sales dilemma: provoke or justify

Learning outcomes: Appreciate changing contours of business to business (B2B) purchase and how sellers should adapt their selling style and promotions. Case overview/synopsis: In the past two decades, imaging Goa (IG) and Azim Shaikh had weathered many business crises. However, as the COVID 19 pandemic unfolded, he became aware of critical fault lines in his B2B selling model. IG offered customised digital display solutions, but its primary source of revenue was B2B selling of interactive flat panel display (IFPD) devices. It, respectively, controlled about 35% and 3% of the market share of IFPD sales, respectively, in Goa and western India. IG’s success in the B2B segment was because of Shaikh’s ability to build strong relationships and customised solutions in an emerging market context. To deal with the COVID pandemic, the Indian Government had imposed a country-wide lockdown, which forced organisations to adopt work from home. This, in turn, created a pull for IFPDs. Yet, very soon Shaikh realised, in the new normal, there was a growing mismatch between his selling efforts and outcomes. Though overall revenue had not fallen much, but the veteran seller had started doubting his tried and tested relational solution selling model. Case dilemma involves the selection of appropriate selling approaches e.g. solution, insight or tiebreaker selling for different situations. This case also offers an opportunity to discuss, how to use online channels to complement B2B selling. Complexity Academic Level: This teaching case study is suitable for the graduate-level programme in marketing management. Supplementary materials: Teaching Notes are available for educators only. Subject code: CSS 8: Marketing © 2021, Emerald Publishing Limited.

Celebrating talents of the type 1 diabetes community during covid-19 pandemic

Background: 'ArT1st' was created (11/2019) by Professor Kar, bringing 9 artistic people with type 1 diabetes (PWDs) and diabetes healthcare professionals (HCPs). The aim for ArT1st was to provide peer support within type 1 diabetes community via art, and to showcase the community's artistic talents through a live event in 2020. With covid-19 pandemic, lockdown became a time of anxiety and loneliness, affecting mental health. With the live ArT1st event postponed, to help uplift the mood and reduce social isolation, the ArT1st team created an online project for the type 1 diabetes community. Methods: • ArT1st email address and website created to receive/house contributions • Social media accounts created • Daily posts across social media -artistic contributions + summary about artist (diabetes journey/ community involvement, how art helped with diabetes/mental health/pandemic) Results: 167 contributions of music, fashion, photography, craft, poetry, dance, drama, painting received worldwide over 2/12: all ages, PWDs, HCPs, carers, professional/amateur artists. • Social media data: Twitter-498 followers/447 tweets, Facebook-1358 friends, Instagram-365 followers/273 posts • Twitter activity: 371.3k impressions over 77 days-average 4.8k impressions/day. Average-11 retweets/day, 38 likes/ day, 3 comments/day. Interest extended beyond community, highlighting issues PWD face daily. Many positive comments about uplifting mood, great support network. Friendships and art collaborations developed between PWDs, and between PWDs and HCPs. Conclusion: ArT1st is first of its kind as a form of peer support for the type 1 diabetes community and celebrating artistic talents. ArT1st promoted new online peer support & friendships for the community, through the power of art;and provided an innovative outlet during the pandemic.

Guidelines for the management of diabetes in care homes during the Covid-19 pandemic.

The National Diabetes Stakeholders Covid-19 Response Group was formed in early April 2020 as a rapid action by the Joint British Diabetes Societies for Inpatient Care, Diabetes UK, the Association of British Clinical Diabetologists, and Diabetes Frail to address and support the special needs of residents with diabetes in UK care homes during Covid-19. It was obvious that the care home sector was becoming a second wave of Covid-19 infection and that those with diabetes residing in care homes were at increased risk not only of susceptibility to infection but also to poorer outcomes. Its key purposes included minimising the morbidity and mortality associated with Covid-19 and assisting care staff to identify those residents with diabetes at highest risk of Covid-19 infection. The guidance was particularly created for care home managers, other care home staff, and specialist and non-specialist community nursing teams. The guidance covers the management of hyperglycaemia by discussion of various clinical scenarios that could arise, the management of hypoglycaemia, foot care and end of life care. In addition, it outlines the conditions where hospital admission is required. The guidance should be regarded as interim and will be updated as further medical and scientific evidence becomes available.

Guidelines for the management of diabetes services and patients during the COVID-19 pandemic.

The UK National Diabetes Inpatient COVID Response Group was formed at the end of March 2020 to support the provision of diabetes inpatient care during the COVID pandemic. It was formed in response to two emerging needs. First to ensure that basic diabetes services are secured and maintained at a time when there was a call for re-deployment to support the need for general medical expertise across secondary care services. The second was to provide simple safe diabetes guidelines for use by specialists and non-specialists treating inpatients with or suspected of COVID-19 infection. To date the group, comprising UK-based specialists in diabetes, pharmacy and psychology, have produced two sets of guidelines which will be continually revised as new evidence emerges. It is supported by Diabetes UK, the Association of British Clinical Diabetologists and NHS England.