Analysis of real-world capillary blood glucose data to help reduce HbA1c and hypoglycaemia in type 1 diabetes: Evidence in favour of using the percentage of readings in target and coefficient of variation.

AIMS: To examine real-world capillary blood glucose (CBG) data according to HbA1c to define proportions of CBG readings at different HbA1c levels, and evaluate patterns in CBG measurements to suggest areas to focus on with regard to self-management. METHODS: A retrospective analysis stratified 682 adults with type 1 diabetes split into quartiles based on their HbA1c . The proportions of results in different CBG ranges and associations with HbA1c were evaluated. Patterns in readings following episodes of hyperglycaemia and hypoglycaemia were examined, using glucose to next glucose reading table (G2G). RESULTS: CBG readings in the target range (3.9-10 mmol/L) increase by ~10% across each CBG quartile (31% in the highest versus 63% in the lowest quartile, p < 0.05). The novel G2G table helps the treatment-based interpretation of data. Hypoglycaemia is often preceded by hyperglycaemia, and vice-versa, and is twice as likely in the highest HbA1c quartile. Re-testing within 30 min of hypoglycaemia is associated with less hypoglycaemia, 1.6% versus 7.2%, p < 0.001, and also reduces subsequent hyperglycaemia and further hypoglycaemia in the proceeding 24 h. The coefficient of variation, but not standard deviation, is highly associated with hypoglycaemia, r = 0.71, and a CV ≤ 36% equates to 3.3% of CBG readings in the hypoglycaemic range. CONCLUSIONS: HbA1c <58 mmol/mol (7.5%) is achievable even when only ~60% of CBG readings are between 3.9-10 mmol/L. Examining readings subsequent to out-of-range readings suggests useful behaviours which people with type 1 diabetes could be supported to adhere to, both in a clinic and structured education programmes, thereby decreasing the risk of hypoglycaemia whilst also reducing hyperglycaemia and improving HbA1c .

Corrigendum: Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study.

[This corrects the article DOI: 10.3389/fcdhc.2023.1095859.].

Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study.

Background: Hypoglycemia is the most common adverse consequence of treating diabetes, and is often due to suboptimal patient self-care. Behavioral interventions by health professionals and self-care education helps avoid recurrent hypoglycemic episodes by targeting problematic patient behaviors. This relies on time-consuming investigation of reasons behind the observed episodes, which involves manual interpretation of personal diabetes diaries and communication with patients. Therefore, there is a clear motivation to automate this process using a supervised machine learning paradigm. This manuscript presents a feasibility study of automatic identification of hypoglycemia causes. Methods: Reasons for 1885 hypoglycemia events were labeled by 54 participants with type 1 diabetes over a 21 months period. A broad range of possible predictors were extracted describing a hypoglycemic episode and the subject's general self-care from participants' routinely collected data on the Glucollector, their diabetes management platform. Thereafter, the possible hypoglycemia reasons were categorized for two major analysis sections - statistical analysis of relationships between the data features of self-care and hypoglycemia reasons, and classification analysis investigating the design of an automated system to determine the reason for hypoglycemia. Results: Physical activity contributed to 45% of hypoglycemia reasons on the real world collected data. The statistical analysis provided a number of interpretable predictors of different hypoglycemia reasons based on self-care behaviors. The classification analysis showed the performance of a reasoning system in practical settings with different objectives under F1-score, recall and precision metrics. Conclusion: The data acquisition characterized the incidence distribution of the various hypoglycemia reasons. The analyses highlighted many interpretable predictors of the various hypoglycemia types. Also, the feasibility study presented a number of concerns valuable in the design of the decision support system for automatic hypoglycemia reason classification. Therefore, automating the identification of the causes of hypoglycemia may help objectively to target behavioral and therapeutic changes in patients' care.

Usability engineering in practice: developing an intervention for post-stroke therapy during a global pandemic.

This paper provides an overview of the usability engineering process and relevant standards informing the development of medical devices, together with adaptations to accommodate situations such as global pandemics where use of traditional face-to-face methods is restricted. To highlight some of those adaptations, a case study of a project developing a novel electronic rehabilitation device is referenced, which commenced in November 2020 amidst the COVID-19 pandemic. The Sheffield Adaptive Patterned Electrical Stimulation (SHAPES) project, led by Sheffield Teaching Hospitals NHS Foundation Trust (STH), aimed to design, manufacture and trial an intervention for use to treat upper arm spasticity after stroke. Presented is an outline and discussion of the challenges experienced in developing the SHAPES health technology intended for at-home use by stroke survivors and in implementing usability engineering approaches. Also highlighted, are the benefits that arose, which can offer easier involvement of vulnerable users and add flexibility in the ways that user feedback is sought. Challenges included: restricted travel; access to usual prototyping facilities; social distancing; infection prevention and control; availability of components; and changing work pressures and demands. Whereas benefits include: less travel; less time commitment; and greater scope for participants with restricted mobility to participate in the process. The paper advocates a more flexible approach to usability engineering and outlines the onward path for development and trialling of the SHAPES technology.

Protocol for a cluster randomised controlled trial of the DAFNEplus (Dose Adjustment For Normal Eating) intervention compared with 5x1 DAFNE: a lifelong approach to promote effective self-management in adults with type 1 diabetes.

INTRODUCTION: The successful treatment of type 1 diabetes (T1D) requires those affected to employ insulin therapy to maintain their blood glucose levels as close to normal to avoid complications in the long-term. The Dose Adjustment For Normal Eating (DAFNE) intervention is a group education course designed to help adults with T1D develop and sustain the complex self-management skills needed to adjust insulin in everyday life. It leads to improved glucose levels in the short term (manifest by falls in glycated haemoglobin, HbA1c), reduced rates of hypoglycaemia and sustained improvements in quality of life but overall glucose levels remain well above national targets. The DAFNEplus intervention is a development of DAFNE designed to incorporate behavioural change techniques, technology and longer-term structured support from healthcare professionals (HCPs). METHODS AND ANALYSIS: A pragmatic cluster randomised controlled trial in adults with T1D, delivered in diabetes centres in National Health Service secondary care hospitals in the UK. Centres will be randomised on a 1:1 basis to standard DAFNE or DAFNEplus. Primary clinical outcome is the change in HbA1c and the primary endpoint is HbA1c at 12 months, in those entering the trial with HbA1c >7.5% (58 mmol/mol), and HbA1c at 6 months is the secondary endpoint. Sample size is 662 participants (approximately 47 per centre); 92% power to detect a 0.5% difference in the primary outcome of HbA1c between treatment groups. The trial also measures rates of hypoglycaemia, psychological outcomes, an economic evaluation and process evaluation. ETHICS AND DISSEMINATION: Ethics approval was granted by South West-Exeter Research Ethics Committee (REC ref: 18/SW/0100) on 14 May 2018. The results of the trial will be published in a National Institute for Health Research monograph and relevant high-impact journals. TRIAL REGISTRATION NUMBER: ISRCTN42908016.

Intelligent Data-Driven Model for Diabetes Diurnal Patterns Analysis.

In type 1 diabetes, diurnal activity routines are influential factors in insulin dose calculations. Bolus advisors have been developed to more accurately suggest doses of meal-related insulin based on carbohydrate intake, according to pre-set insulin to carbohydrate levels and insulin sensitivity factors. These parameters can be varied according to the time of day and their optimal setting relies on identifying the daily time periods of routines accurately. The main issues with reporting and adjustments of daily activity routines are the reliance on self-reporting which is prone to inaccuracy and within bolus calculators, the keeping of default settings for daily time periods, such as within insulin pumps, glucose meters, and mobile applications. Moreover, daily routines are subject to change over periods of time which could go unnoticed. Hence, forgetting to change the daily time periods in the bolus calculator could contribute to sub-optimal self-management. In this paper, these issues are addressed by proposing a data-driven model for identification of diabetes diurnal patterns based on self-monitoring data. The model uses time-series clustering to achieve a meaningful separation of the patterns which is then used to identify the daily time periods and to advise of any time changes required. Further improvements in bolus advisor settings are proposed to include week/weekend or even modifiable daily time settings. The proposed model provides a quick, granular, more accurate, and personalized daily time setting profile while providing a more contextual perspective to glycemic pattern identification to both patients and clinicians.

A Deep Neural Network Application for Improved Prediction of [Formula: see text] in Type 1 Diabetes.

[Formula: see text] is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Previous studies have shown that [Formula: see text] estimates can be obtained from 5-12 weeks of daily blood glucose measurements. However, these methods suffer from accuracy limitations when applied to incomplete data with missing periods of measurements. The aim of this article is to overcome these limitations improving the accuracy and robustness of [Formula: see text] prediction from time series of blood glucose. A novel data-driven [Formula: see text] prediction model based on deep learning and convolutional neural networks is presented. The model focuses on the extraction of behavioral patterns from sequences of self-monitored blood glucose readings on various temporal scales. Assuming that subjects who share behavioral patterns have also similar capabilities for diabetes control and resulting [Formula: see text], it becomes possible to infer the [Formula: see text] of subjects with incomplete data from multiple observations of similar behaviors. Trained and validated on a dataset, containing 1543 real world observation epochs from 759 subjects, the model has achieved the mean absolute error of 4.80 [Formula: see text] mmol/mol, median absolute error of 3.81 [Formula: see text] mmol/mol and [Formula: see text] of 0.71 ± 0.09 on average during the 10 fold cross validation. Automatic behavioral characterization via extraction of sequential features by the proposed convolutional neural network structure has significantly improved the accuracy of [Formula: see text] prediction compared to the existing methods.

Enhancing public involvement in assistive technology design research.

PURPOSE: To appraise the application of accepted good practice guidance on public involvement in assistive technology research and to identify its impact on the research team, the public, device and trial design. METHODS: Critical reflection and within-project evaluation were undertaken in a case study of the development of a functional electrical stimulation device. Individual and group interviews were undertaken with lay members of a 10 strong study user advisory group and also research team members. RESULTS: Public involvement was seen positively by research team members, who reported a positive impact on device and study designs. The public identified positive impact on confidence, skills, self-esteem, enjoyment, contribution to improving the care of others and opportunities for further involvement in research. A negative impact concerned the challenge of engaging the public in dissemination after the study end. CONCLUSIONS: The public were able to impact significantly on the design of an assistive technology device which was made more fit for purpose. Research team attitudes to public involvement were more positive after having witnessed its potential first hand. Within-project evaluation underpins this case study which presents a much needed detailed account of public involvement in assistive technology design research to add to the existing weak evidence base. IMPLICATIONS FOR REHABILITATION: The evidence base for impact of public involvement in rehabilitation technology design is in need of development. Public involvement in co-design of rehabilitation devices can lead to technologies that are fit for purpose. Rehabilitation researchers need to consider the merits of active public involvement in research.

The development, preliminary validation and clinical utility of a shoe model to quantify foot and footwear kinematics in 3-D.

Functional electrical stimulation (FES) applied to the common peroneal nerve is commonly prescribed to correct both equinus and excessive foot inversion in swing and initial contact. This paper presents the development of a simple shoe model, to allow quantification of 3-D shoe (foot and footwear) kinematics in clinical situations when footwear is required, e.g. with FES systems requiring footswitches. To preliminarily validate the shoe model, barefoot 'normal' adult data (n=11) processed using validated 3-D foot models, were reprocessed with the shoe model. Outputs were compared through calculation of waveform similarity and correlation. Clinical utility of the shoe model is demonstrated through the presentation of 3-D shoe kinematics, calculated from a cohort of existing unilateral common peroneal FES users (n=16), both with and without FES. A trend of reduced inversion at mid-swing and initial contact was seen, although this was not found to be statistically significant (p≤0.0125). The shoe model was found to be practical to use in a clinical environment, and has potential to contribute to the evidence base for interventions such as common peroneal FES.

The use of hydrogel as an electrode-skin interface for electrode array FES applications.

Functional electrical stimulation is commonly used to restore function in post-stroke patients in upper and lower limb applications. Location of the electrodes can be a problem hence some research groups have begun to experiment with electrode arrays. Electrode arrays are interfaced with a thin continuous hydrogel sheet which is high resistivity to reduce transverse currents between electrodes in the array. Research using electrode arrays has all been conducted in a laboratory environment over short time periods but it is suspected that this approach will not be feasible over longer time periods due to changes in hydrogel resistivity. High resistivity hydrogel samples were tested by leaving them in contact with the skin over a seven day period. The samples became extremely conductive with resistivities reaching around 10-50 Ωm. The effect of these resistivity changes was studied using finite element analysis to solve for the stationary current quasi-static electric field gradient in the tissue. Electrical stimulation efficiency and focality were calculated for both a high and low resistivity electrode-skin interface layer at different tissue depths. The results showed that low resistivity hydrogel produced significant decreases in stimulation efficiency and focality compared to high resistivity hydrogel.