Public Attitudes Towards the Use of Novel Technologies in Their Future Healthcare: A UK Survey

Background Innovation in healthcare technologies can result in more convenient and effective treatment that is less costly, but a persistent challenge to widespread adoption in health and social care is end user acceptability. The purpose of this study was to capture UK public opinions and attitudes to novel healthcare technologies (NHTs), and to better understand the factors that contribute to acceptance and future use. Methods An online survey was distributed to the UK public between April and May 2020. Respondents received brief information about four novel healthcare technologies (NHTs) in development: a laser-based tool for early diagnosis of osteoarthritis, a virtual reality tool to support diabetes self-management, a non-invasive continuous glucose monitor using microwave signals, a mobile app for patient reported monitoring of rheumatoid arthritis. They were queried on their general familiarity and attitudes to technology, and their willingness to accept each NHT in their future care. Responses were analysed using summary statistics and content analysis. Results Knowledge about NHTs was diverse, with respondents being more aware about the health applications of mobile apps (66%), followed by laser-based technology (63.8%), microwave signalling (28%), and virtual reality (18.3%). Increasing age and the presence of a self-reported medical condition favoured acceptability for some NHTs, whereas self-reported understanding of how the NHT works resulted in elevated acceptance scores across all NHTs presented. Common contributors to hesitancy were safety and risks from use. Respondents wanted more information and evidence to help inform their decisions, ideally provided verbally by a general practitioner or health professional. Other concerns, such as privacy, were NHT-specific but equally important in decision-making. Conclusions Early insight into the knowledge and preconceptions of the public about NHTs in development can assist their design and prospectively mitigate obstacles to acceptance and adoption.

Public attitudes towards the use of novel technologies in their future healthcare: A UK survey

BackgroundInnovation in healthcare technologies can result in more convenient and effective treatment that is less costly, but a persistent challenge to widespread adoption in health and social care is end user acceptability. The purpose of this study was to capture UK public opinions and attitudes to novel healthcare technologies (NHTs), and to better understand the factors that contribute to acceptance and future use.MethodsAn online survey was distributed to the UK public between April and May 2020. Respondents received brief information about four novel healthcare technologies (NHTs) in development: a laser-based tool for early diagnosis of osteoarthritis, a virtual reality tool to support diabetes self-management, a non-invasive continuous glucose monitor using microwave signals, a mobile app for patient reported monitoring of rheumatoid arthritis. They were queried on their general familiarity and attitudes to technology, and their willingness to accept each NHT in their future care. Responses were analysed using summary statistics and content analysis.ResultsKnowledge about NHTs was diverse, with respondents being more aware about the health applications of mobile apps (66%), followed by laser-based technology (63.8%), microwave signalling (28%), and virtual reality (18.3%). Increasing age and the presence of a self-reported medical condition favoured acceptability for some NHTs, whereas self-reported understanding of how the NHT works resulted in elevated acceptance scores across all NHTs presented. Common contributors to hesitancy were safety and risks from use. Respondents wanted more information and evidence to help inform their decisions, ideally provided verbally by a general practitioner or health professional. Other concerns, such as privacy, were NHT-specific but equally important in decision-making.ConclusionsEarly insight into the knowledge and preconceptions of the public about NHTs in development can assist their design and prospectively mitigate obstacles to acceptance and adoption.

An Intensive Longitudinal Study of the Association of Stress With Hyperglycemia Using Real-Time Data Collection

Caregivers of persons with dementia (PWD) are at significantly elevated risk for cardiovascular disease (CVD)s. A higher risk for diabetes is one potential mechanism of morbidity in caregivers. Diabetes has been associated with dyslipidemia, hypertension, oxidative stress, increased low-grade inflammation, and endothelial dysfunction, which all place individuals at risk for CVD. Elevated blood glucose, even in the nondiabetic range, is a significant risk marker for the development of CVD. The current study examined the semi-continuous association between stress and glucose. Participants wore a continuous glucose monitor that measured blood glucose every 5 minutes for a period of 10 days (n = 2,880/participant). Ecological Momentary Assessment (EMA) was used to measure stress, positive affect, negative affect, and dietary intake 3x/day over the 10-day period. Hierarchical linear models indicated significant within-person associations between stress and blood glucose levels (t = 3.88, df = 3.92, p = .018; R2 = 26.2%).

Evaluation of Blood Glucose Level Control in Type 1 Diabetic Patients Using Deep Reinforcement Learning

Diabetes mellitus is a disease associated with abnormally high levels of blood glucose due to lack of insulin. Combining an insulin pump and continuous glucose monitor with a control algorithm to deliver insulin is an alternative to patient self-management of insulin doses to control blood glucose levels in diabetes mellitus patients. In this work we propose a closed-loop control for blood glucose levels based on deep reinforcement learning. We describe the initial evaluation of several alternatives conducted on a realistic simulator of the glucoregulatory system and propose a particular implementation strategy based on reducing the frequency of the observations and rewards passed to the agent, and using a simple reward function. We train agents with that strategy for three groups of patient classes, evaluate and compare it with alternative control baselines. Our results show that our method is able to outperform baselines as well as similar recent proposals, by achieving longer periods of safe glycemic state and low risk.

Simposio: Nuevos horizontes en la terapéutica de la diabetes mellitus tipo 1. Infusores de insulina automatizados: ¿páncreas artificial biomecánico?

El aumento exponencial en la prevalencia de diabetes mellitus (DM) en todo el mundo incluye también el incremento de personas con DM1. Esta enfermedad se caracteriza por la destrucción autoinmune y en forma progresiva de las células beta pancreáticas, ocasionando una insuficiencia endógena de insulina e hiperglucemia que solo puede controlarse mediante la administración exógena de insulina.La insulinización intensiva con múltiples dosis de insulina es la mejor opción terapéutica para controlar la glucemia y alcanzar los objetivos de hemoglobina glicosilada A1c, con el menor número de hipoglucemias y de riesgo de complicaciones crónicas. Esto requiere de una activa y frecuente participación de la persona con DM1 y que en el tiempo es una barrera para lograr un óptimo control.En pos de alcanzar aquellos objetivos, ha sido amplio y rápido el crecimiento terapéutico tecnológico para DM1 a través del desarrollo de sensores continuos de glucosa (continuous glucose monitor, CGM) y de infusores de insulina subcutánea (continuous subcutaneous insulin infusion, CSII). Desde su aprobación, el uso de los sistemas automatizados de administración de insulina (2017) para adultos, adolescentes y niños con DM1 demostraron ser eficaces y seguros para lograr un adecuado control glucémico en condiciones nocturnas o basales. La infusión automática de insulina (IAI) o lazo cerrado (closed loop, CL) requiere del uso de CSII y CGM comunicados entre sí (por Bluetooth) e integrados funcionalmente a través de algoritmos de control que regulan la administración de insulina según la glucosa subcutánea, llevando la glucemia a rangos deseados para cada paciente. Estos algoritmos se alojan en diferentes plataformas, como un infusor de insulina o teléfono celular o bien de forma remota (Do it yourself o DIY), y desde allí controlan la infusión de insulina según la glucosa subcutánea en forma automática. Los sistemas CL pueden ser monohormonales (solo insulina) o bihormonales (insulina y glucagón), y según requieran o no la participación del paciente en la programación del bolo de insulina pre-comida, se denominan híbridos (hybrid closed loop, HCL) o totalmente automáticos (fully automatic close loop, FCL) respectivamente.Si bien las evidencias observadas en el control glucémico con estos sistemas HCL son alentadoras, los usuarios siguen experimentando la carga del recuento de carbohidratos para los bolos de insulina previo a las comidas. Los pacientes que presentan un deficiente control metabólico debido a que omiten o tardan en administrar los bolos de comida serían beneficiados con los FCL. Estos algoritmos FCL controlan la glucemia prandial sin requerir el anuncio de comida y rápidamente se anticipan y detectan en forma predictiva la variación de glucemia según la carga de carbohidratos, aliviando la carga diaria del cálculo de carbohidratos por parte de los pacientes con DM1. Pocos consorcios en el mundo están avanzando con el difícil desafío que implica la validación de estos algoritmos FCL, uno de ellos en Argentina (automatic regulator of glucose, ARG), y que permitirían alcanzar un adecuado control metabólico con la mejor calidad de vida.

The Need for Data Standards and Implementation Policies to Integrate CGM Data into the Electronic Health Record

The current lack of continuous glucose monitor (CGM) data integration into the electronic health record (EHR) is holding back the use of this wearable technology for patient-generated health data (PGHD). This failure to integrate with other healthcare data inside the EHR disrupts workflows, removes the data from critical patient context, and overall makes the CGM data less useful than it might otherwise be. Many healthcare organizations (HCOs) are either struggling with or delaying designing and implementing CGM data integrations. In this article, the current status of CGM integration is reviewed, goals for integration are proposed, and a consensus plan to engage key stakeholders to facilitate integration is presented.

Substantial Intra-Individual Variability in Post-Prandial Time to Peak in Controlled and Free-Living Conditions in Children with Type 1 Diabetes

The optimal time to bolus insulin for meals is challenging for children and adolescents with type 1 diabetes (T1D). Current guidelines to control glucose excursions do not account for individual differences in glycaemic responses to meals. This study aimed to examine the within- and between-person variability in time to peak (TTP) glycaemic responses after consuming meals under controlled and free-living conditions. Participants aged 8–15 years with T1D ≥ 1 year and using a continuous glucose monitor (CGM) were recruited. Participants consumed a standardised breakfast for six controlled days and maintained their usual daily routine for 14 free-living days. CGM traces were collected after eating. Linear mixed models were used to identify within- and between-person variability in the TTP after each of the controlled breakfasts, free-living breakfasts (FLB), and free-living dinners (FLD) conditions. Thirty participants completed the study (16 females; mean age and standard deviation (SD) 10.5 (1.9)). The TTP variability was greater within a person than the variability between people for all three meal types (between-person vs within-person SD; controlled breakfast 18.5 vs 38.9 minutes; FLB 14.1 vs 49.6 minutes; FLD 5.7 vs 64.5 minutes). For the first time, the study showed that within-person variability in TTP glycaemic responses is even greater than between-person variability.

GlucoMine

The growing popularity of wearable devices for continuous sensing has made personal health data increasingly available, yet methods for data interpretation are still a work in progress. This paper investigates potential under-utilization of wearable device data in diabetes management and develops an analytic approach - GlucoMine - to uncover individualized patterns in extended periods of such data to support and improve care. In addition, we conduct a user study with clinicians to assess and compare conventional tools used for reviewing wearable device data in diabetes management with the proposed solution. Using 3-6 months of continuous glucose monitor (CGM) data from 54 patients with type 1 diabetes, we found that: 1) the recommended practice of reviewing only short periods (e.g., the most recent 2-weeks) of CGM data based on correlation analysis is not sufficient for finding hidden patterns of poor management; 2) majority of subjects (96% in this study) had clinically-recognized episodes of recurrent adverse glycemic events observable from analysis of extended periods of their CGM data; 3) majority of clinicians (89% in this study) believe there is benefit to be gained in having an algorithm for extracting patterns of adverse glycemic events from longer periods of wearable device data. Findings from our user study also provides insights, including strengths and weakness of various data presentation tools, to guide development of better solutions that improve the use of wearable device data for patient care.