Dr Arantza Aldea

Human gait is a significant indicator of overall health and well-being due to its dependence on metabolic requirements. Abnormalities in gait can indicate the presence of metabolic dysfunction, such as diabetes or obesity. However, detecting these can be challenging using classical methods, which often involve subjective clinical assessments or invasive procedures. In this work, a novel methodology known as Criticality Analysis (CA) was applied to the monitoring of the gait of teenagers with varying amounts of metabolic stress who are taking part in an clinical intervention to increase their activity and reduce overall weight. The CA approach analysed gait using inertial measurement units (IMU) by mapping the dynamic gait pattern into a nonlinear representation space. The resulting dynamic paths were then classified using a Support Vector Machine (SVM) algorithm, which is well-suited for this task due to its ability to handle nonlinear and dynamic data. The combination of the CA approach and the SVM algorithm demonstrated high accuracy and non-invasive detection of metabolic stress. It resulted in an average accuracy within the range of 78.2% to 90%. Additionally, at the group level, it was observed to improve fitness and health during the period of the intervention. Therefore, this methodology showed a great potential to be a valuable tool for healthcare professionals in detecting and monitoring metabolic stress, as well as other associated disorders.

Background:  This quality improvement study, entitled Avatar-Based LEarning for Diabetes Optimal Control (ABLEDOC), explored the feasibility of delivering an educational programme to people with diabetes in Colombia. The aim was to discover how this approach could be used to improve awareness and understanding of the condition, the effects of treatment, and strategies for effective management of blood-glucose control.

Methods: Individuals with diabetes were recruited by Colombian endocrinologists to a human-centred study to co-design the educational programme, using the Double Diamond model. Participants contributed to two phases. The first phase focused on gathering unmet educational needs, and choice of curriculum. Three prototypes were developed as a result. During Phase Two, a different group of participants engaged with the programme for several weeks, before reporting back.

Results: Thirty-six participants completed a web survey during Phase One, and five were also interviewed by telephone. The majority (33 of 36, 91%) were receptive to the prospect of educational interventions, and ranked the chosen topic of hypoglycemia highly. In Phase Two, the three prototypes were tested by seventeen participants, ten of which also gave feedback in focus groups. The response was overwhelmingly positive, with 16 of 17 (94%) stating they would use a program like this again. The 3D version was the most highly rated.

Conclusion: Immersive, avatar-based programmes, delivered via smartphone, have the potential to deliver educational information that is trusted, engaging and useful. Future work includes expansion of the curriculum, evaluation with a larger group, and exploration of the prospective role of artificial intelligence in personalising this form of educational intervention. ​​​​​​​

Introduction

No studies have assessed the efficacy of telemedicine using a platform for recording and adjusting insulin doses in patients with diabetes mellitus type 2 (DM2) transitioning from inpatient to outpatient care. This study aimed to assess, in a population of patients with DM2, discharged from a tertiary referral hospital, whether treatment based on the use of an mHealth application was associated with better glycemic control at the 3-month follow-up, than standard care.

Methods

This open, randomized, controlled clinical trial included adult DM2 patients who were transitioning from inpatient to outpatient care. The efficacy and safety of patient management with and without mHealth was compared at the 3-month follow-up. The primary outcome was the change in the Glycosylated hemoglobin (HbA1c) levels. The secondary outcomes were the rates of hypoglycemic and hyperglycemic events and treatment satisfaction measured using the Insulin Treatment Satisfaction Questionnaire (ITSQ).

Results

In total, 86 patients (41 using mHealth) were included in the clinical trial. HbA1c levels showed a significant decrease in both groups. The mean HbA1c level was significantly lower in the mHealth group. Patients using mHealth showed decreased incidence rate ratios of hypoglycemia 3.0 mmol/L [

Conclusion

Using mHealth in patients with DM2 transitioning from inpatient to outpatient care improves metabolic control and may reduce the hypoglycemia rates.

Background

There are several medical devices used in Colombia for diabetes management, most of which have an associated telemedicine platform to access the data. In this work, we present the results of a pilot study evaluating the use of the Tidepool telemedicine platform for providing remote diabetes health services in Colombia across multiple devices.

Method

Individuals with Type 1 and Type 2 diabetes using multiple diabetes devices were recruited to evaluate the user experience with Tidepool over three months. Two endocrinologists used the Tidepool software to maintain a weekly communication with participants reviewing the devices data remotely. Demographic, clinical, psychological and usability data were collected at several stages of the study.

Results

Six participants, from ten at the baseline (five MDI and five CSII), completed this pilot study. Three different diabetes devices were employed by the participants: a glucose meter (Abbot), an intermittently-scanned glucose monitor (Abbot), and an insulin pump (Medtronic). A score of 81.3 in the system usability scale revealed that overall, most participants found the system easy to use, especially the web interface. The system also compared highly favourably against the proprietary platforms. The ability to upload and share data and communicate remotely with the clinicians was raised consistently by participants. Clinicians cited the lockdown imposed by the Covid-19 pandemic as a valuable test for this platform. Inability to upload data from mobile devices was identified as one of the main limitations.

Conclusion

Tidepool has the potential to be used as a tool to facilitate remote diabetes care in Colombia. Users, both participants and clinicians, agreed to recommend the use of platforms like Tidepool to achieve better disease management and communication with the health care team. Some improvements were identified to enhance the user experience.

This paper presents an overview of the usability engineering process for the development of a personalised clinical decision support system for the management of type 1 diabetes. The tool uses artificial intelligence (AI) techniques to provide insulin bolus dose advice and carbohydrate recommendations that adapt to the individual. We describe the role of human factors and user-centred design in the creation of medical systems that must adhere to international standards. We focus specifically on the formative evaluation stage of this process. The preliminary analysis of data shows promising results.

Undergraduate computing courses inevitably include a high degree of regeneration in order to keep abreast of this rapidly changing field. Introductory programming modules in particular need to adapt to changing trends and languages. Until recently, the focus of debate within the Oxford Brookes University curriculum has therefore been on the course content, but since 2012 there has been a major change in the method of delivery through the introduction of a new apprenticeship model. This paper seeks to reflect on this, and other recent changes which have led to improved student engagement and results. The data is limited however, and so the results presented here are not conclusive.

Decision-making is a crucial activity during the planning, design and operation of artefacts. To make a decision several alternatives must be evaluated and compared, which are tasks that require information, knowledge and expertise. A system that organises and manages the knowledge associated with every alternative and links ideas, arguments and issues can greatly improve and facilitate the decision making process. This paper presents how an Issue Based Information System (IBIS) implemented in Compendium (https://compendium.open.ac.uk) has been extended with new functionalities such as access to a toolkit of Multi-Criteria Decision Methods (MCDM), the ability to propagate values throughout the decision records and to perform sensitivity analysis of the recommended decisions with respect to a parameter. These additional functionalities enable the applicability of the system in the support of decisions that require not only argumentation, but also numerical evaluation of the properties of the alternatives such as those proposed during the design, planning and operation of engineering artefacts.