Maintaining connectivity and enhancing communication through the use of text messaging in an undergraduate nursing programme

The purpose of this paper is to present the findings of a study that examined the use of a Short Message Service (SMS) (or ‘Text Messaging’) to enhance communication and participation with students on an undergraduate nursing programme. The ideology behind the study is based on an awareness that technology is not always recognised by nursing students as a useful aspect of their education and practice. Therefore it was considered that integrating this ubiquitous from of technology use might help them to recognise the usefulness of technology as an aid to enhance and develop more effective ways of learning and working. Short Message Service (SMS) is a communications protocol allowing the interchange of short text messages between mobile phones. It is the most widely available data application on the planet with over 2.4 billion users (Wikipedia 2008). The advantages in using SMS messaging include ‘always-on’ communication, connectivity to real world learning contexts, ‘top of mind’ direct access and ‘just for me’ personal communication (Jones & Bunting 2008). The widespread availability of mobile phones provides an opportunity to establish and maintain a sense of connectedness in helping learners to engage with their programme of study as almost everyone can participate in synchronous and asynchronous communication. Laurillard (2008) suggests that we should ‘harness technology to meet the needs of education rather than simply search for problems to which technology is the solution’. This concept has particular relevance for the learners in this study who undertake clinical learning experiences throughout their programme of study that are geographically dispersed over a large area for periods of time ranging from two to eight weeks. Text messaging was used to develop and maintain strong links between the tutor/programme director and the students throughout the duration of the programme

Recognising the potential: maximising meaningful learning in practice settings

In this symposium we critique conventional approaches to the support of learning in practice settings. In the context of the increasing complexity of health services and the consolidation of nursing as a graduate profession in the UK and internationally, we argue that these traditional approaches are not fit for purpose. Drawing in part on our experience at The Open University (OU) as a provider of part-time, distance learning pre-registration nurse education, the symposium proposes a more contemporary model of supporting learning in practice and identifies the implications for education, practice, commissioning and policy. Of relevance to practitioners, students, educationalists and commissioners, the objectives of the symposium are: • to problematise traditional constructions of how learning takes place in practice settings • to critically examine the key features of a high quality learning environment from the perspectives of students, mentors, employers and educationalists • to identify the implications for service, education, commissioning and policy

Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit

Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient’s condition and the symptom’s characteristics, while it could enable non-pharmacologic support based on rhythmic cues. This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system. The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric.Peer ReviewedPostprint (published version

Analysis of correlation between an accelerometer-based algorithm for detecting parkinsonian gait and updrs subscales

Background: Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson\u27s (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson\u27s Disease Rating Scale (UPDRS-III). Method: Seventy-five patients suffering from Parkinson\u27s disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient\u27s home. Convergence between the algorithm and the scale was evaluated by using the Spearman\u27s correlation coefficient. Results: Correlation with the UPDRS-III was moderate (rho 0.56;p<0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho 0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: "axial function, balance, and gait." The correlation between the algorithm outputs and this factor of the UPDRS-III was 0.67 (p<0.01). Conclusion: The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson\u27s disease and motor fluctuations

Detecting freezing of gait with a tri-axial accelerometer in parkinson’s disease patients

Freezing of gait (FOG) is a common motor symptom of Parkinson\u27s disease (PD), which presents itself as an inability to initiate or continue gait. This paper presents a method to monitor FOG episodes based only on acceleration measurements obtained from a waist-worn device. Three approximations of this method are tested. Initially, FOG is directly detected by a support vector machine (SVM). Then, classifier\u27s outputs are aggregated over time to determine a confidence value, which is used for the final classification of freezing (i.e., second and third approach). All variations are trained with signals of 15 patients and evaluated with signals from another 5 patients. Using a linear SVM kernel, the third approach provides 98.7 % accuracy and a geometric mean of 96.1 %. Moreover, it is investigated whether frequency features are enough to reliably detect FOG. Results show that these features allow the method to detect FOG with accuracies above 90 % and that frequency features enable a reliable monitoring of FOG by using simply a waist sensor