The Impact on Ambulance Mobilisations of an Increasing Age Profile of Telecare Service Users Receiving Advanced Proactive, Personalised Telecare in Spain—a Longitudinal Study 2014–2018

Advanced proactive personalised telecare services in Spain have helped service users to live independently in their own homes for longer. Concern was however noted regarding potential impacts on ambulance mobilisations as time in the service, and mean age at cessation, increased. The purpose of this study was to investigate these impacts.A longitudinal study of a telecare service user population in Spain (n = 202.1 k to 247.9 k) was undertaken using anonymised operational data collected in the delivery of proactive and personalised telecare services over the period 2014–2018.For the studied population, ambulance mobilisation on a per-person/per-annum (pp/pa) basis reduced despite the increasing age profile at cessation and with the characteristics of the population at registration remaining otherwise similar over the period. The study identified the positive correlation coefficient between ambulance mobilisations and service user’s dependency levels, and marginal negative correlation in older age bands.In conclusion, the increasing age at cessation has not correlated with an increased proportion of higher dependency service users. Indeed, the share of those over 85 years in the high dependency level decreased. This indicates that the changes in the telecare service which appear to have contributed to increased time living independently may also have helped ensure those continuing to live independently remain in lower risk bands.

Preconception and Diabetes Information (PADI) App for Women with Pregestational Diabetes: a Feasibility and Acceptability Study

Diabetes mellitus increases the risk of adverse maternal and fetal outcomes. Preconception care is vital to minimise complications; however, preconception care service provision is hindered by inadequate knowledge, resources and care fragmentation. Mobile health technology, particularly smartphone apps, could improve preconception care and pregnancy outcomes for women with diabetes. The aim of this study is to co-create a preconception and diabetes information app with healthcare professionals and women with diabetes and explore the feasibility, acceptability and preliminary effects of the app. A mixed-methods study design employing questionnaires and semi-structured interviews was used to assess preliminary outcome estimates (preconception care knowledge, attitudes and behaviours), and user acceptability. Data analysis included thematic analysis, descriptive statistics and non-parametric tests. Improvements were recorded in knowledge and attitudes to preconception care and patient activation measure following the 3-month app usage. Participants found the app acceptable (satisfaction rating was 72%), useful and informative. The app’s usability and usefulness facilitated usage while manual data input and competing priorities were barriers which participants felt could be overcome via personalisation, automation and use of daily reminders. This is the first study to explore the acceptability and feasibility of a preconception and diabetes information app for women with diabetes. Triangulated data suggest that the app has potential to improve preconception care knowledge, attitudes and behaviours. However, in order for women with DM to realise the full potential of the app intervention, particularly improved maternal and fetal outcomes, further development and evaluation is required.

Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder

Attention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, constructing such systems is a challenging task due to the complexity of attentional behavior in ASD. This paper proposes a face-based attention recognition model using two methods. The first is based on geometric feature transformation using a support vector machine (SVM) classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a convolutional neural network (CNN) approach. We conducted an experimental study on different attentional tasks for 46 children (ASD n=20, typically developing children n=26) and explored the limits of the face-based attention recognition model for participant and task differences. Our results show that the geometric feature transformation using an SVM classifier outperforms the CNN approach. Also, attention detection is more generalizable within typically developing children than within ASD groups and within low-attention tasks than within high-attention tasks. This paper highlights the basis for future face-based attentional recognition for real-time learning and clinical attention interventions.