CBL on FHIR: A FHIR-Based Platform for Health Professional Education.

A FHIR based platform for case-based instruction of health professions students has been developed and field tested. The system provides a non-technical case authoring tool; supports individual and team learning using digital virtual patients; and allows integration of SMART Apps into cases via its simulated EMR. Successful trials at the University of Queensland have led to adoption at the University of Melbourne.

Functional connectivity across dorsal and ventral attention networks in response to task difficulty and experimental pain.

The dorsal and ventral attention networks (DAN & VAN) provide a framework for studying attentional modulation of pain. It has been argued that cognitive demand distracts attention from painful stimuli via top-down reinforcement of task goals (DAN), whereas pain exerts an interruptive effect on cognitive performance via bottom-up pathways (VAN). The current study explores this explanatory framework by manipulating pain and task demand in combination with functional near-infrared spectroscopy (fNIRS) and Granger Causal Connectivity Analyses (GCCA). Twenty-one participants played a racing game at low and high difficulty levels with or without experimental pain (administered via a cold pressor test). Six channels of fNIRS were collected from bilateral frontal eye fields and intraparietal sulci (DAN), with right-lateralised channels at the inferior frontal gyrus and temporoparietal junction (VAN). Our first analysis revealed increased G-causality from bottom-up pathways (VAN) during the cold pressor test. However, an equivalent experience of experimental pain during gameplay increased G-causality in top-down (DAN) pathways, with the left intraparietal sulcus serving a hub of connectivity. High game difficulty increased G-causality via top-down pathways and implicated the right inferior frontal gyrus as an interhemispheric hub. Our results are discussed with reference to existing models of both networks and attentional modulation of pain.

The psychophysiological mechanisms of real-world time experience.

Our sense of time is fallible, often resulting in the sensation of time flying by quickly or dragging slowly. It has been suggested that changes in sympathetic (SNS) and parasympathetic nervous system (PNS) activity may influence the perceived passage of time, however this proposition has never been tested during real-world temporal experience. The current study directly tested the relationship between the passage of time and SNS-PNS activity in the real-world. Sixty-seven participants completed a normal day's activities whilst wearing sensors to capture electrocardiography (ECG), electrodermal activity (EDA) and movement. They also provided hourly rating of the subjective speed at which time was passing. Results revealed that greater SNS activity (e.g., increased heart rate, frequency of phasic skin conductance response) was associated with time passing more quickly. PNS activity was not related to time experience. Whilst the findings support previous suggestions that changes in physiological arousal are associated with distortions to the passage of time, the effects are small and other factors are likely to contribute to real-world temporal experience.

The Influence of Wearables on Health Care Outcomes in Chronic Disease: Systematic Review.

BACKGROUND: Chronic diseases contribute to high rates of disability and mortality. Patient engagement in chronic disease self-management is an essential component of chronic disease models of health care. Wearables provide patient-centered health data in real time, which can help inform self-management decision-making. Despite the perceived benefits of wearables in improving chronic disease self-management, their influence on health care outcomes remains poorly understood. OBJECTIVE: This review aimed to examine the influence of wearables on health care outcomes in individuals with chronic diseases through a systematic review of the literature. METHODS: A narrative systematic review was conducted by searching 6 databases for randomized and observational studies published between January 1, 2016, and July 1, 2021, that included the use of a wearable intervention in a chronic disease group to assess its impact on a predefined outcome measure. These outcomes were defined as any influence on the patient or clinician experience, cost-effectiveness, or health care outcomes as a result of the wearable intervention. Data from the included studies were extracted based on 6 key themes, which formed the basis for a narrative qualitative synthesis. All outcomes were mapped against each component of the Quadruple Aim of health care. The guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement were followed in this study. RESULTS: A total of 30 articles were included; studies reported 2446 participants (mean age: range 10.1-74.4 years), and the influence of 14 types of wearables on 18 chronic diseases was presented. The most studied chronic diseases were type 2 diabetes (4/30, 13%), Parkinson disease (3/30, 10%), and chronic lower back pain (3/30, 10%). The results were mixed when assessing the impact on a predefined primary outcome, with 50% (15/30) of studies finding a positive influence on the studied outcome and 50% (15/30) demonstrating a nil effect. There was a positive effect of 3D virtual reality systems on chronic pain in 7% (2/30) of studies that evaluated 2 distinct chronic pain syndromes. Mixed results were observed in influencing exercise capacity; weight; and biomarkers of disease, such as hemoglobin A1c, in diabetes. In total, 155 outcomes were studied. Most (139/155, 89.7%) addressed the health care outcomes component. This included pain (11/155, 7.5%), quality of life (7/155, 4.8%), and physical function (5/155, 3.4%). Approximately 7.7% (12/155) of outcome measures represented the patient experience component, with 1.3% (2/155) addressing the clinician experience and cost. CONCLUSIONS: Given their popularity and capability, wearables may play an integral role in chronic disease management. However, further research is required to generate a strong evidence base for safe and effective implementation. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021244562; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=244562.

Analysis of Biometric Sensor Data for Predicting Fatigue: A Framework Towards Reducing Work-Related Musculoskeletal Disorders in Aviation Manufacturing Workers.

Work-Related Musculoskeletal Disorders (WMSDs) transpire when injuries to the musculoskeletal system (e.g. muscles, ligaments, tendons, and nerves) occur due to high fatigue inducing work-related activities, where repetitive movements and muscle strain are prevalent. However, it is challenging to quantify the risk of injury due to the assortment of tasks that factory workers may perform. Nevertheless, wearable sensors are a viable outlet that can unobtrusively capture biometric data in order to calculate objective measures, such as fatigue, which increases the risk of developing WMSDs. This paper presents a novel wearable sensor-based ergonomic monitoring system (ErgoRelief), which has been designed to predict fatigue within the context of aviation factory work. An experiment has been undertaken whereby thirty participants completed a series of repetitive tasks whilst wearing our sensor system. Results of multiple linear regression models demonstrate a maximum Adjusted R2 Score of 0.9259.

Classification of Game Demand and the Presence of Experimental Pain Using Functional Near-Infrared Spectroscopy.

Pain tolerance can be increased by the introduction of an active distraction, such as a computer game. This effect has been found to be moderated by game demand, i.e., increased game demand = higher pain tolerance. A study was performed to classify the level of game demand and the presence of pain using implicit measures from functional Near-InfraRed Spectroscopy (fNIRS) and heart rate features from an electrocardiogram (ECG). Twenty participants played a racing game that was configured to induce low (Easy) or high (Hard) levels of demand. Both Easy and Hard levels of game demand were played with or without the presence of experimental pain using the cold pressor test protocol. Eight channels of fNIRS data were recorded from a montage of frontal and central-parietal sites located on the midline. Features were generated from these data, a subset of which were selected for classification using the RELIEFF method. Classifiers for game demand (Easy vs. Hard) and pain (pain vs. no-pain) were developed using five methods: Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Naive Bayes (NB) and Random Forest (RF). These models were validated using a ten fold cross-validation procedure. The SVM approach using features derived from fNIRS was the only method that classified game demand at higher than chance levels (accuracy = 0.66, F1 = 0.68). It was not possible to classify pain vs. no-pain at higher than chance level. The results demonstrate the viability of utilising fNIRS data to classify levels of game demand and the difficulty of classifying pain when another task is present.

Understanding Smartwatch Battery Utilization in the Wild.

Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization.

Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering.

Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering approach can be used as an annotation on raw sensor data. It improves information retrieval by reducing the search space and is based on searching only the related clusters. To further improve behavior quantification, the third algorithm identifies contrasting events withina cluster content. Two large real-world smartphone datasets have been used to evaluate our algorithms and demonstrate the utility and resource efficiency of our approach to search.

The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs).

The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that globally enrol millions of people. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms that institutions employ, such as physically attending lectures. Instead learning occurs online via recorded lecture material and online tasks. This shift has allowed more people to gain access to education, regardless of their learning background. However, despite these advancements, completion rates for MOOCs are low. The paper presents our approach to learner predication in MOOCs by exploring the impact that technology has on open learning and identifies how data about student performance can be captured to predict trend so that at risk students can be identified before they drop-out. The study we have undertaken uses the eRegister system, which has been developed to capture and analyze data. The results indicate that high/active engagement, interaction and attendance is reflective of higher marks. Additonally, our approach is able to normalize the data into consistent a series so that the end result can be transformed into a dashboard of statistics that can be used by organizers of the MOOC. Based on this, we conclude that there is a fundamental need for predictive systems within learning communities.

Educational crowdsourcing to support the learning of computer programming.

Recent advancements in technology have enabled a shift to occur in teaching and learning. We are living in a connected world where physical boundaries of attending an institution to gain an education no longer apply. There are currently thousands of courses available online that do not require formal attendance. As such, this era of "open learning" pioneers an innovative research, across multiple disciplines. One domain of knowledge where open learning can be advantageous is within computer science. This industry is now highly in demand and can benefit from open learning platforms, where students may not have the opportunity to formally attend courses but still want to enhance their skills. However, there are some significant limitations in open learning applications: how they assess the quality of learning (i.e., was it just copying or at best learning by rote) or considering individual differences among learners. In this emerging research paper, we posit the idea of an adaptive, crowdsourced, and primarily educational technology, targeted at software development students. The proposed technology caters for either individual or group learning. It differentiates itself from other tutoring and programming support technologies as it will continually monitor and assess students' performance in each phase of the education process.

Prediction of preterm deliveries from EHG signals using machine learning.

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier.

Monitoring and reducing sedentary behavior in the elderly with the aid of human digital memories.

A healthy lifestyle has the ability not only to give you more energy and help you look and feel better, but it also has the ability to help you live longer and prevent disease, such as obesity and pressure ulcers. This is particularly important for the elderly population, as a healthier lifestyle would enable independent living to occur for a longer period of time. However, providing a direct link between increasing physical activity and positive health outcomes is a problem. The effect of leading an increasing sedentary lifestyle is also not evident straightaway. Effects of this behavior often occur over years and decades, as opposed to days or months. Therefore, there is very little willingness to change, if instant results are not seen. There is a need to provide a mechanism that is able to monitor an individual and provide a visual indication of his or her behavior. It is envisioned that the area of human digital memories is capable of providing such a system. This article explores how sedentary behavior and journey information can be collected, from different environments, so that an illustration of a user's habits can be seen and changes can occur. A successful prototype has also been developed that evaluates the applicability of the approach.