ULEARN: Personalised Learner’s Profile Based On Dynamic Learning Style Questionnaire

The file attached to this record is the author's final peer reviewed version.E-Learning recommender system effectiveness re- lies upon their ability to recommend appropriate learning con- tents according to the learner learning style and preferences. An effective approach to handle the learner preferences is to build an efficient learner profile in order to gain adaptation and individualisation of the learning environment. It is usually necessary to know learning style and preferences of the learner on a domain before adapting the learning process and course content. This study focuses on identifying the learning styles of students in order to adapt the learning process and course content. ULEARN is an adaptive recommender learning system designed to provide learners with personalised learning environment such as course learning objects that match their adaptive profile. This paper presents the algorithm used in ULEARN to reduce dynamically the number of questions in Felder-Silverman learning style ques- tionnaire used to initialise the adaptive learner profile. Firstly, the questionnaire is restructured into four groups, one for each learning style dimension; and a study is carried out to determine the order in which questions will be asked in each dimension. Then an algorithm is built upon this ranking of questions to calculate dynamically the initial learning style of the user as they go through the questionnaire

Human identification via unsupervised feature learning from UWB radar data

This paper presents an automated approach to automatically distinguish the identity of multiple residents in smart homes. Without using any intrusive video surveillance devices or wearable tags, we achieve the goal of human identification through properly processing and analyzing the received signals from the ultra-wideband (UWB) radar installed in indoor environments. Because the UWB signals are very noisy and unstable, we employ unsupervised feature learning techniques to automatically learn local, discriminative features that can incorporate intra-class variations of the same identity, and yet reflect differences in distinguishing different human identities. The learned features are then used to train an SVM classifier and recognize the identity of residents. We validate our proposed solution via extensive experiments using real data collected in real-life situations. Our findings show that feature learning based on K-means clustering, coupled with whitening and pooling, achieves the highest accuracy, when only limited training data is available. This shows that the proposed feature learning and classification framework combined with the UWB radar technology provides an effective solution to human identification in multi-residential smart homes

Managing obesity through mobile phone applications: a state-of-the-art review from a user-centred design perspective

Evidence has shown that the trend of increasing obesity rates has continued in the last decade. Mobile phone applications, benefiting from their ubiquity, have been increasingly used to address this issue. In order to increase the applications’ acceptance and success, a design and development process that focuses on users, such as User-Centred Design, is necessary. This paper reviews reported studies that concern the design and development of mobile phone applications to prevent obesity, and analyses them from a User-Centred Design perspective. Based on the review results, strengths and weaknesses of the existing studies were identified. Identified strengths included: evidence of the inclusion of multidisciplinary skills and perspectives; user involvement in studies; and the adoption of iterative design practices. Weaknesses included the lack of specificity in the selection of end-users and inconsistent evaluation protocols. The review was concluded by outlining issues and research areas that need to be addressed in the future, including: greater understanding of the effectiveness of sharing data between peers; privacy; and guidelines for designing for behavioural change through mobile phone applications

Improving Adherence and Clinical Outcomes in Self-Guided Internet Treatment for Anxiety and Depression: A 12-Month Follow-Up of a Randomised Controlled Trial

Background: A recent paper reported the outcomes of a study examining a new self-guided internet-delivered treatment, the Wellbeing Course, for symptoms of anxiety or depression. This study found the intervention resulted in significant symptom reductions. It also found that automated emails increased treatment completion and clinical improvements in a subsample with elevated anxiety and depression. Aims: To examine the clinical outcomes and the effect of automated emails at 12 months post-treatment. Method: Participants, who were randomly allocated to a Treatment Plus Automated Emails Group (TEG; n = 100), a standard Treatment Group (TG; n = 106) or delayed-treatment Waitlist Control Group (Control; n = 51), were followed up at 12 months post-treatment. Eighty-one percent, 78% and 87% of participants in the TEG, TG and treated Waitlist Control Group provided symptom data at 12-month follow-up, respectively. The primary outcome measures were the Patient Health Questionnaire-9 Item Scale (PHQ-9) and the Generalized Anxiety Disorder-7 Item Scale (GAD-7).Results: Significant improvements in symptoms of anxiety and depression were observed over time in both the TEG and TG (Fs >69, ps .05), and were associated with large effect sizes. No statistically significant differences in symptoms were found between the TEG and TG at post-treatment, 3-month or 12-month follow-up. Previously reported symptom differences between TEG and TG participants with comorbid symptoms were no longer present at 12-month follow-up (ps >.70).Conclusions: The overall benefits of the Wellbeing Course were sustained at 12-month follow-up. Although automated emails facilitated Course completion and reductions in symptoms for participants with comorbid anxiety and depression from pre-post treatment, these differences were no longer observed at 12-month follow-up. The results indicate that automated emails promote more rapid treatment response for people with elevated and comorbid symptoms, but may not improve longer term outcomes

Identification and thermochemical analysis of high-lignin feedstocks for biofuel and biochemical production

Background - Lignin is a highly abundant biopolymer synthesized by plants as a complex component of plant secondary cell walls. Efforts to utilize lignin-based bioproducts are needed. Results - Herein we identify and characterize the composition and pyrolytic deconstruction characteristics of high-lignin feedstocks. Feedstocks displaying the highest levels of lignin were identified as drupe endocarp biomass arising as agricultural waste from horticultural crops. By performing pyrolysis coupled to gas chromatography-mass spectrometry, we characterized lignin-derived deconstruction products from endocarp biomass and compared these with switchgrass. By comparing individual pyrolytic products, we document higher amounts of acetic acid, 1-hydroxy-2-propanone, acetone and furfural in switchgrass compared to endocarp tissue, which is consistent with high holocellulose relative to lignin. By contrast, greater yields of lignin-based pyrolytic products such as phenol, 2-methoxyphenol, 2-methylphenol, 2-methoxy-4-methylphenol and 4-ethyl-2-methoxyphenol arising from drupe endocarp tissue are documented. Conclusions - Differences in product yield, thermal decomposition rates and molecular species distribution among the feedstocks illustrate the potential of high-lignin endocarp feedstocks to generate valuable chemicals by thermochemical deconstruction

The eClinical Care Pathway Framework: A novel structure for creation of online complex clinical care pathways and its application in the management of sexually transmitted infections.

Despite considerable international eHealth impetus, there is no guidance on the development of online clinical care pathways. Advances in diagnostics now enable self-testing with home diagnosis, to which comprehensive online clinical care could be linked, facilitating completely self-directed, remote care. We describe a new framework for developing complex online clinical care pathways and its application to clinical management of people with genital chlamydia infection, the commonest sexually transmitted infection (STI) in England.Using the existing evidence-base, guidelines and examples from contemporary clinical practice, we developed the eClinical Care Pathway Framework, a nine-step iterative process. Step 1: define the aims of the online pathway; Step 2: define the functional units; Step 3: draft the clinical consultation; Step 4: expert review; Step 5: cognitive testing; Step 6: user-centred interface testing; Step 7: specification development; Step 8: software testing, usability testing and further comprehension testing; Step 9: piloting. We then applied the Framework to create a chlamydia online clinical care pathway (Online Chlamydia Pathway).Use of the Framework elucidated content and structure of the care pathway and identified the need for significant changes in sequences of care (Traditional: history, diagnosis, information versus Online: diagnosis, information, history) and prescribing safety assessment. The Framework met the needs of complex STI management and enabled development of a multi-faceted, fully-automated consultation.The Framework provides a comprehensive structure on which complex online care pathways such as those needed for STI management, which involve clinical services, public health surveillance functions and third party (sexual partner) management, can be developed to meet national clinical and public health standards. The Online Chlamydia Pathway's standardised method of collecting data on demographics and sexual behaviour, with potential for interoperability with surveillance systems, could be a powerful tool for public health and clinical management.UKCRC Translational Infection Research (TIR) Initiative supported by the Medical Research Council, eSTI2 Consortium (Grant Number G0901608)

Personal Informatics, Self-Insight, and Behavior Change: A Critical Review of Current Literature

Personal Informatics (PI) systems allow users to collect and review personally relevant information. The purpose commonly envisioned for these systems is that they provide users with actionable, data-driven self-insight to help them change their behavioral patterns for the better. Here, we review relevant theory as well as empirical evidence for this ‘Self-Improvement Hypothesis’. From a corpus of 6568 only 24 studies met the selection criteria of being a peer-reviewed empirical study reporting on actionable, data-driven insights from PI data, using a ‘clean’ PI system with no other intervention techniques (e.g. additional coaching) on a non-clinical population. First results are promising—many of the selected articles report users gaining actionable insights—but we do note a number of methodological issues that make these results difficult to interpret. We conclude that more work is needed to investigate the Self-Improvement Hypothesis and provide a set of recommendations for future work

Statistical design and analysis in trials of proportionate interventions: a systematic review

Background: In proportionate or adaptive interventions, the dose or intensity can be adjusted based on individual need at predefined decision stages during the delivery of the intervention. The development of such interventions may require an evaluation of the effectiveness of the individual stages in addition to the whole intervention. However, evaluating individual stages of an intervention has various challenges, particularly the statistical design and analysis. This review aimed to identify the use of trials of proportionate interventions and how they are being designed and analysed in current practice. Methods: We searched MEDLINE, Web of Science and PsycINFO for articles published between 2010 and 2015 inclusive. We considered trials of proportionate interventions in all fields of research. For each trial, its aims, design and analysis were extracted. The data synthesis was conducted using summary statistics and a narrative format. Results: Our review identified 44 proportionate intervention trials, comprising 28 trial results, 13 protocols and three secondary analyses. These were mostly described as stepped care (n=37) and mainly focussed on mental health research (n=30). The other studies were aimed at finding an optimal adaptive treatment strategy (n=7) in a variety of therapeutic areas. Further terminology used included adaptive intervention, staged intervention, sequentially multiple assignment trial or a two-phase design. The median number of decision stages in the interventions was two and only one study explicitly evaluated the effect of the individual stages. Conclusions: Trials of proportionate staged interventions are being used predominantly within the mental health field. However, few studies consider the different stages of the interventions, either at the design or the analysis phase, and how they may interact with one another. There is a need for further guidance on the design, analyses and reporting across trials of proportionate interventions