Dr Clare Martin

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.

Background: The Patient Empowerment through Predictive Personalized Decision Support (PEPPER) system provides personalized bolus advice for people with type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system, which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalized carbohydrate recommendations, and dynamic bolus insulin constraint. We evaluated the safety and efficacy of the PEPPER system compared to a standard bolus calculator.

Methods: This was an open-labeled multicenter randomized controlled crossover study. Following 4-week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12 weeks. Participants then crossed over after a washout period. The primary end-point was percentage time in range (TIR, 3.9–10.0 mmol/L [70–180 mg/dL]). Secondary outcomes included glycemic variability, quality of life, and outcomes on the safety system and insulin recommender.

Results: Fifty-four participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3–49.8) years, diabetes duration 21.0 (11.5–26.0) years, and HbA1c 61.0 (58.0–66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 [52.1–67.8] % vs. 58.4 [49.6–64.3] %, respectively, P = 0.27). For quality of life, participants reported higher perceived hypoglycemia with the PEPPER system despite no objective difference in time spent in hypoglycemia.

Conclusions: The PEPPER system was safe, but did not change glycemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness.

Clinical trial registration:ClinicalTrials.gov NCT03849755

The Internet-based Exercise-focused Health App for Lung cancer survivors (iEXHALE) is a mobile web app being developed to provide lung cancer survivors with an algorithm-based, tailor-made, self-management programme to inform their exercise choices and improve symptom severity. The aim of this protocol paper is to detail the plan for conducting the usability study to test the effectiveness, efficiency and simplicity of an exercise-focused self-management mobile web app for lung cancer survivors. The mixed methods study will consist of three consecutive phases, each interspersed with elements of data analysis and app prototype redevelopment. The study will take place in Oxford, United Kingdom. Ethical approvals have been obtained. The study will contribute to lung cancer survivorship research and is important in the app developmental process. This study contributes to the international forum for the exchange of practice, innovation and research, increases transparency in mobile health developmental processes and contributes to the methodological evidence base.

Background: Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project.

Methods: The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are: predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycaemia; and a personalised safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycaemic control and safety system functioning were compared between the two-weeks run-in period and the end-point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses.

Results: Overall, glycaemic control improved over the evaluated period. In particular, percentage time in the hypoglycaemia range (

Conclusion: A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes.

Aims: (a) To explore how mobile technology can support self‐management in adults with type 1 diabetes (T1DM). (b) To inform a usability study in the design of a mobile application to facilitate self‐management of T1DM. Design: Qualitative exploratory design. Methods: Semi‐structured interviews were undertaken with adults with T1DM (N = 8). The data collected were analysed using a thematic analysis approach. Results: Mobile technology has the potential to support adults in their self‐management of T1DM through facilitating their decision‐making, saving time and enabling them to easily share their data with their healthcare professional. Participants identified four main visualization characteristics for technology to aid in decision‐making; relationships between inputs, trends, graphs and colours, and identified essential features such as ease of use, convenience and connectivity.

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.

Batcher’s “merge exchange” sorting network, discussed in a previous pearl (Hinze & Martin, 2016), remains one of the best practical algorithms for oblivious sorting, even almost half a century after its inception. So it is surprising that an algorithm with exactly the same level of performance, devised two decades later by Parberry (1992), has been relatively overlooked. Perhaps a reason for its lack of celebrity is that Parberry’s design is not immediately recognizable, whereas the Batcher method has a familiar ring, as a hardwired implementation of merge sort. Here we hope to rectify this imbalance by unravelling Parberry’s algorithm and uncoupling its close relationship to Batcher’s. Interestingly, Parberry derives his network using the zero-one principle (Knuth, 1998). We abandon this traditional method, in favour of a feature of comparison networks that we consider to be more fundamental: monotonicity. We shall see that this property, used before to demystify Batcher’s merger (Hinze & Martin, 2016), also helps to shed some light on Parberry’s design. To keep the pearl reasonably self-contained we start with a quick recap of the notation and Batcher’s construction.

Background: People with T1DM are confronted with self-management tasks and for which they need to develop strategies to balance the risks of long-term complications with those of hypoglycemic events. The potential of advanced and evolving technology to address these issues involves consideration of psychological and behavioral constructs alongside evaluation of the usability of devices. Access and uptake of advanced technology is further influenced by economic factors and health care provider capacity to support such interventions. Previous reviews have either focused upon clinical outcomes or descriptively scoped the literature. In addition, some have synthesized studies on adults with those on children and young people where human factors are different. Objective: The objective of this review was to describe the relationship between

human factors and adherence with technology for data logging processes in adults with T1DM and to explore the factors which influence this association. Methods: A systematic search of the literature was undertaken in accordance with the PRISMA guidelines. Quality appraisal of each study was undertaken. Data were abstracted and categorized into the themes that underpinned the human factor constructs that were examined. Results: Eighteen studies were included in the review. Six constructs emerged from the data analysis: The relationship between adherence to data logging and measurable outcomes; Satisfaction with the transition to advanced technology for self-management; Use of advanced technology and time spent on diabetes related activities; Strategies to mediate the complexities of diabetes and the use of advanced technology; Cognition in the wild and, Meanings, views and perspectives from the users of technology. Conclusions: evidence of increased treatment satisfaction was found on transition from traditional to advanced technology use (insulin pump and continuous glucose monitoring (CGM)); the most significant contributing factor was when blood glucose (BG) levels were consistently

evidence that logging of data was positively correlated with increasing age when using an app that provided meaningful feedback (regression coefficient = 55.8 recordings/ year; P = 0.009). Furthermore, there were benefits of CGM for older people in mediating complexities and their fears of hypoglycemia with reported significant differences in well-being (P= .009). Qualitative studies within the review aimed to explore the use and uptake of technology within the context of everyday lives. There were ‘frustrations’ with CGM, continuous subcutaneous insulin infusion (CSII), calibration of devices and alarms. This created implications for “body image” and the way in which “significant others” impacted on the behavior and attitude of the individual towards technology use. There were wide variations in the normal use of and interaction with technology across a continuum of sociocultural contexts, which has implications for the way in which future technologies should be designed. Many of the quantitative studies in the review were limited by small sample sizes. This may make it difficult to generalize findings to other contexts. This is further limited by a sample that was predominantly Caucasian, well-controlled and engaged with their self-care. However, the use of critical appraisal frameworks has highlighted areas where research into human factors and data logging processes of individuals could be improved. This includes engaging people in the design of the technology especially hard-to- reach or marginalized groups.

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.

The Raspberry Pi (Raspberry Pi Foundation 2014) is a small, cheap, yet powerful computer first released in 2012 by the Raspberry Pi Foundation, a UK registered charity that was founded in 2009 to support the study of basic computer science in schools. The Pi has rapidly gained popularity, partly because of its capability to control external electronic circuits with computer code.

The project described in this article began as an after school club at Wheatley Park secondary school. It was led by volunteers who were inspired by the potential of the Pi and concerned about the deficiencies in the 2013 ICT curriculum. The aims of the club were to make learning computer programming fun for the pupils and to transfer knowledge to the school’s IT staff.

The students undertook activities during the project that involved programming in Python (Python Software 2014), which most of them had not used before. The exercises involved controlling simple sound and lighting systems.

This paper describes some experiences of using fractal image compression as the subject of an assignment for a functional programming course using Haskell. The students were fascinated by the reproduction of images from their encodings and engaged well with the exercise which involved only elementary functional programming techniques.

This paper describes some experiences of using fractal image compression as the subject of an assignment for a functional programming course using Haskell. The students were fascinated by the reproduction of images from their encodings and engaged well with the exercise which involved only elementary functional programming techniques.