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Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.

Human Factors Engineering (HFE) is an applied discipline that uses a wide range of methodologies to better the design of systems and devices for human use. Underpinning all human factors design is the maxim to fit the human to the task/machine/system rather than vice versa. While some HFE methods such as task analysis and anthropometrics remain relatively fixed over time, areas such as human-technology interaction are strongly influenced by the fast-evolving technological trend. In times of big data, human factors engineers need to have a good understanding of topics like machine learning, advanced data analytics, and data visualization so that they can design data-driven products that involve big data sets. There is a natural lag between industrial trends and HFE curricula, leading to gaps between what people are taught and what they will need to know. In this paper, we present the results of a survey involving HFE practitioners (N=101) and we demonstrate the need for including data science and machine learning components in HFE curricula.

In Canada, over 15,000 residents of long-term care have died from COVID-19 since the start of the pandemic representing 59 percent of all COVID-19 deaths (National Institute of Ageing, 2021). Urgent research and subsequent applied action are needed to save life and quality of life including the presence of family (CFHI, 2020). Social and physical frailty are major systemic patient safety gaps and are challenges for most healthcare organizations. This practitioner-led panel of experienced human factors, implementation science and healthcare experts used a case study of a project at North York General Hospital’s Seniors’ Health Centre in Toronto to discuss how these challenges can be addressed with serious games. The project discussed used games that aim to reduce social and physical frailty through exercise while interacting with remote families. Lessons learned to-date and challenges observed, in rapidly implementing safety and human factors programs intended to create resilient residents in a real healthcare context were discussed.

In today’s digital economy, the Internet of Things (IoT) has connected devices, humans, and everyday objects to each other in ways that were unimaginable before. Vast amounts of data are collected everywhere and disrupting how we design systems and products. Data science and emerging technologies offer challenges and opportunities for early-career human factors professionals who are looking to grow their careers and their human factors practice. In this paper, we report on a survey to assess the perspectives of students currently studying human factors. The survey items examined shortfalls in current human factors education with re-spect to relevance to industry trends. The survey results show that students see a need to include more relevant subjects in data science, as well as opportunities to learn trending industry problems, hands-on experience with real-life projects, prior to graduation.

Older adults living in long-term care facilities typically receive insufficient exercise and have long periods of the day when they are not doing anything other than sitting or lying down, watching television, or ruminating (Wilkinson et al., 2017). We developed an intervention called the Experiential Centivizer, which provides residents with opportunities to use a driving simulator, watch world travel videos, and engage in exercise. We assessed the impact of the intervention on residents of a long-term care home in Fredericton, NB, Canada. In this paper, we report on the results observed and highlight the lessons learned from implementing a technological intervention within a long-term care setting. Practical and research recommendations are also discussed to facilitate future intervention implementation in long-term care.

Objective We examine the relationships between contemporary progress in on‐road vehicle automation and its coherence with an envisioned “autopia” (automobile utopia) whereby the vehicle operation task is removed from all direct human control. Background The progressive automation of on‐road vehicles toward a completely driverless state is determined by the integration of technological advances into the private automobile market; improvements in transportation infrastructure and systems efficiencies; and the vision of future driving as a crash‐free enterprise. While there are many challenges to address with respect to automated vehicles concerning the remaining driver role, a considerable amount of technology is already present in vehicles and is advancing rapidly. Methods A multidisciplinary team of experts met to discuss the most critical challenges in the changing role of the driver, and associated safety issues, during the transitional phase of vehicle automation where human drivers continue to have an important but truncated role in monitoring and supervising vehicle operations. Results The group endorsed that vehicle automation is an important application of information technology, not only because of its impact on transportation efficiency, but also because road transport is a life critical system in which failures result in deaths and injuries. Five critical challenges were identified: driver independence and mobility, driver acceptance and trust, failure management, third-party testing, and political support. Conclusion Vehicle automation is not technical innovation alone, but is a social as much as a technological revolution consisting of both attendant costs and concomitant benefits.

In this paper we explore how memories of experience with streaming video affect Quality of Experience (QoE) indicators that are of interest to service providers and marketers. Since observations of experience are time consuming, and the effects of technical quality (TQ) are difficult to entangle from content quality (CQ), we examined the impact of a visualization methodology for assessing experiences. A study was carried out to examine how well overall technical quality (TQ) judgments for a sequence of visualized video experience (a picture of a red video playbar with yellow portions indicating disrupted video in place of actually viewed video) would correspond to overall TQ judgments made after watching a sequence of actual videos. Sequencing effects found in overall TQ ratings, made after viewing visualizations (with their overlaid disruptions) were similar to sequencing effects found after viewing actual videos. However, the sequencing effects after viewing the visualizations were less pronounced than the corresponding sequencing effects that were found after viewing actual videos. Sequences of both visualized and actually viewed videos showed significant negative end effect and trend effects (both positive and negative). There was also evidence that sequencing effects respond to relative change in TQ rather than absolute TQ.

Objectives Recognition of delirium in the emergency department (ED) is poor. Our objectives were to assess: (1) the diagnostic accuracy of the Predicting Emergency department Delirium with an Interactive Computer Tablet (PrEDICT) "serious game" to identify older ED patients with delirium compared to clinical recognition and (2) the feasibility of the PrEDICT application compared to existing tests of attention. Design Prospective observational study. Setting ED of a Canadian tertiary care center. Participants We included ED patients, aged 70 years and older, with a minimum 4-hour stay. We excluded anyone with critical illness, communication barriers, and visual impairment or those unable to use a computer tablet. None had prevalent delirium by ED clinicians' routine clinical assessment. Measurements Participants were asked to tap targets on a tablet at four difficulty levels. Time and accuracy were automatically recorded. Other measures included the Confusion Assessment Method, the Delirium Severity Index, the Digit Vigilance Test (DVT), and the Choice Reaction Test (CRT). Results We enrolled 203 patients. Their average age was 80.6 years, 49.8% were female, and their average ED length of stay was 15.9 hours. Sixteen subjects had clinically unrecognized delirium, and 14 of them completed the PrEDICT game (87.5%). We developed a threshold score with 100% sensitivity (95% confidence interval [CI] = 76.8%-100.0%) and 59.7% specificity (95% CI = 52.3%-66.6%) to identify patients with clinically unrecognized delirium. The area under the curve was 0.86 (95% CI = 0.77-0.94). Completion rates were 196/203 (96.6%) for the PrEDICT serious game compared to 128/203 (63.1%) for the CRT and 51/203 (25.1%) for the DVT. Conclusion Older ED patients were able to use our serious game, including 87.5% of those with clinically unrecognized delirium. The PrEDICT application has potential to act as a sensitive screening tool to identify older ED patients with clinically unrecognized delirium. J Am Geriatr Soc 67:2370-2375, 2019.

Clustering prior to regression analysis improves the accuracy of prediction in clinical decision making. However, most previously described methods focused on numerical data only. This paper investigated how well textual features can improve the accuracy of regression predictions. Preliminary diagnosis, diagnosis summary, and drug names used in prescriptions as provided in the MIMIC II dataset were used to derive textual features. We proposed the bag-of-entities indexing method, which relies on named entity recognition, a machine learning technique used for locating and identifying words into predefined classes. The proposed technique captured meaningful phrases from texts in health records and represented them in numerical vector format. Dimensionality of the data space was reduced using principal component analysis. The additional well-tuned textual features were then combined with existing numerical features in using cluster-boosted regression to predict patient mortality in ICU. The experimental results showed prediction improvement obtained from textual features over the use of numerical features only. We found that using the proposed indexing method outperformed traditional word-vector representation approaches (bag-of-words and bag-of-bigrams) as well as a state-of-the-art approach (Doc2vec) in terms of resulting accuracy in predicting death status. Moreover, instead of directly interpreting, the identifiable individual features were grouped into types and summarized. The summarized de-identified data of textual features handled by the proposed framework can support predictive classification while also reducing privacy concerns. Grouping of similar patients based on their electronic health records also benefits physicians through the improved differential diagnosis and effective treatment planning.

This paper reports on the Patient Safety Research and Application Competition that was held in conjunction with the 2021 International Ergonomics Association Conference. The objectives of this competition were to: (1) Formulate research problem statements and innovative solutions to improve patient safety through the application of human factors/ergonomics (HF/E) to the healthcare system, (2) Showcase how the HF/E approach to this topic can lead to a useful, usable, and satisfying user experience while simultaneously improving outcomes relating to both functional and non-functional requirements, and (3) Provide an effective way of engaging students and early career researchers in IEA activities and initiatives. After reviewing the patient safety topics that motivated the design competition, we then report on the work carried out by the two finalists in the competition, and discuss lessons learned. We propose the continued use of design competitions in the future to motivate and showcase ergonomic problem-solving design by students and early career researchers and practitioners.

Traditional Human Factors Engineering (HFE) education focuses on bridging the gap between human and system design. Given the rapid, and accelerating technological advancement, particularly in areas such as machine learning and data science, how should HFE education adapt to better equip students for working in industry? This project sought to identify and understand the gap between HFE education and industry needs by surveying human factors engineering students and practitioners concerning their impressions of the gap and how it can be addressed.

IntroductionThe global COVID-19 pandemic continues to have wide-ranging implications for health, including psychological well-being. A growing corpus of research reviews has emerged on the topic of psychological resilience in the context of the pandemic. However, this body of work has not been systematically reviewed for its quality, nor with respect to findings on the effectiveness of tools and strategies for psychological resilience. To this end, a meta-review protocol is proposed with the following objectives: (1) identify review work on the topic of psychological resilience during COVID-19; (2) assess the quality of this review work using A MeaSurement Tool to Assess systematic Reviews; (3) assess the risk of bias in this work; (4) generate a narrative summary of the key points, strengths and weaknesses; (5) identify the psychological resilience strategies that have been reviewed; (6) identify how these strategies have been evaluated for their effectiveness; (7) identify what outcomes were measured and (8) summarise the findings on strategies for psychological resilience so far, providing recommendations, if possible.Methods and analysisA systematic meta-review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews for Protocols and Joanna Briggs Institute umbrella review guidelines. Electronic searches of general databases, especially Web of Science, Scopus and PubMed, will be conducted. Only results from January 2020 onwards will be considered, coinciding with the COVID-19 pandemic. Only results in English will be included. Descriptive statistics, thematic analysis and narrative summaries describing the nature of the reviewed work and evaluation of psychological resilience strategies will be carried out.Ethics and disseminationEthical approval is not needed for systematic review protocols. The results of the meta-review will be published in an international peer-reviewed journal. The raw and summarised data will be shared in the journal or other open venues.PROSPERO registration numberCRD42021235288.

Data exfiltration is a serious threat to organizations. Such exfiltrations cause breach events that can lead to millions of dollars of loss. Perimeter defense is not enough by itself since successful exploits from insiders can also be very damaging. Internal network user activities need to be monitored to detect malicious actions. Automatic machine learning methods can be applied for network anomaly detection, but they create a lot of false alarms. Domain experts can identify malicious users, but they are unable to process large volumes of data. Interactive machine learning (iML) deals with this tradeoff by creating an efficient collaboration between domain experts and machine learning algorithms. Previous research in iML has focused mainly on collaboration with non-experts. The design and requirements for expertise-driven iML have yet to be delineated for cybersecurity applications. In this research, we proposed an Active Learning (AL) model trained with outputs from a liberal (outputting many false alarms as well as possible hits) anomaly detection (AD) criterion to study expert-iML collaboration in anomaly detection. The results showed that: iML in this context can prune false alarms and minimize misses; the performance/compatibility tradeoff that typically occurs in conventional machine learning updates may be less salient in iML. We suggest that compatibility between experts and algorithms can be improved by presenting information about feature relevance during the training process.

Accurately predicting the time to an event of interest is an important problem in a wide range of real-world applications. However, prediction is often difficult because many medical datasets have a large number of unlabeled (“censored”) instances because labeling is costly and time consuming. Survival analysis focuses on labeled data to predict the time to an event of interest, such as time of death, or conversion to a different stage in a progressive disease. Grouping structure, which naturally exists in medical datasets, can be exploited to improve generalization performance by learning multiple related survival prediction tasks for subgroups collaboratively. Thus a multi-task learning framework can connect multiple survival prediction tasks (for different subgroups) and learn them simultaneously. In order to take into account both censored information, as well as discover the grouping structure, we propose a novel cluster-boosted multitask learning framework for survival analysis that boosts survival prediction performance. We develop an efficient algorithm and demonstrate the performance of the proposed cluster-boosted multi-task survival analysis method on The Cancer Genome Atlas (TCGA) dataset. Our results show that the proposed approach can significantly improve prediction performance in survival analysis while also identifying different subgroups of cancer patients.

New technologies, such as serious games and ambient activities, are being developed to address problems of under-stimulation, anxiety, and agitation in millions of people living with dementia in long term care homes. Frequent interactions with instrumented versions of these technologies may not only be beneficial for long term care residents, but may also provide a valuable new set of multifaceted data related to the health status of residents over time. In this paper, we develop a model for health monitoring in healthcare environments and we report on two studies that show how medically relevant data can be collected from elderly residents and emergency department patients in an unobtrusive way. The first study shows how data related to cognitive abilities can be collected from elderly emergency department patients and the second study shows how detailed data on a range of factors can be collected from ambient activity units designed to provide engaging interactions for long term care residents. In summary, this paper proposes the use of new technologies to transform long term care from a data poor to a data rich environment, where the health status of long term care residents and elderly patients is more closely monitored.

This study measured Session Quality of Experience (SQE) by letting people look at visual proxies of a video play bar without having to watch the actual video, with its associated impairments and/or failures. We supplemented a laboratory study of 10 participants with two further online samples of 14, and 20, participants respectively, obtained using online surveys. Cluster analysis was carried out on the entire data set of 44 participants and clusters were obtained representing differences in sensitivity to pricing and to types of disruption. Crucially, there was no significant dependency between samples and cluster demonstrating that visualization can be used in a crowdsourcing, as well as a laboratory, context. This study demonstrates the combined use of crowdsourcing, visualizations of service experience, and cluster analysis to create more efficient, and nuanced, interpretations of SQE.

There has been considerable research on developing symptom clusters for cancer patients but finding a consistent and replicable typology of patients in terms of the kinds of symptoms they experience remains elusive (e.g., [1]). One reason for lack of consensus may be that analyses are unfocused, and use different combinations of clustering variables. This paper identifies subgroups of similar cancer patients within a sample of 18,535 patients, based on the Edmonton Symptom Assessment System (ESAS) and also a set of inpatient and outpatient variables using linked administrative sources of Ontario healthcare data. K-means cluster analysis was performed on cancer patients having only one primary cancer type, and who had at least one emergency department (ED) visit. Only variables that had a Pearson correlation of at least 0.1 with the number of days until visiting an ED, after an assessment, were included in the cluster analysis. While information about next emergency department visit was not included as a clustering variable, the number of patient types was chosen so as to minimize the mean absolute error of predictions of next emergency department visits by cancer patients. The next emergency department visit, after the last assessment date of each patient, was predicted for each cluster solution. The cluster solution with maximum accuracy/minimum MAE was used to derive the final set of patient types. With the help of physicians, and guided by the results of these analyses, a description of each patient type was created. Based on our results we grouped cancer patients into four types that differ in terms of type of cancer, stage of cancer, and symptomology. Implications for symptom management and reduction of ED visits are discussed.

Purpose. This study evaluates the predictive ability of patient types (clusters of similar patients) in identifying cancer patients at high risk for emergency department (ED) visits within one year (365 days) following their index date. A descriptive and retrospective cohort study of 45,356 unique cancer patients with only one primary cancer type and at least one ED visit was done using linked administrative sources of health care data. Methods. Three outcomes were investigated in this study. First, the time of ED visit following an index date was predicted using multiple linear regression. Second, those patients who visited an ED within seven days of their index date were detected using logistic regression. In addition to predicting an emergency department visit, vital status of patients was also predicted using logistic regression. We implemented the linear/logistic regression first on unclustered raw data and then on clustered data. The results of these two analyses were then compared. Conclusion. Clustering was found to contribute to a modest improvement in prediction accuracy for all three outcome variables. The results are discussed in terms of the predictive ability of patient types in development of clinical support tools with respect to privacy of patients and their implications for better allocation of resources to cancer patients.

In this paper, individually reported long-term care (LTC) quality indicators have been supplemented with the composite Qindex measure and applied to 614 homes in Ontario, Canada. This study (1) describes the overall quality performance of LTC homes across five years (2012-2017) and (2) determines if organizational factors impact quality performance. The results demonstrate significant, continuous sector-wide improvement in overall quality performance (as assessed by the Qindex) over time and significant differences in quality based on home size, operator size and ownership. This paper positions the Qindex, a global metric of quality, as a valuable tool for quality measurement and management in the LTC sector.

This thesis introduces a new method to measure subjective ratings of technical quality (TQ) in disrupted video (DV). Current measurement methods are subject to extraneous factors such as delays introduced by the streaming software/hardware and the impact of video content. The proposed method is designed to supplement current methods by avoiding the confounding effects of content on TQ ratings and by reducing the time and effort necessary to run experiments. The new method replaces actual video with images representative of videos that contain various disruptions. In the research reported in this thesis, I ran an experiment with 37 participants to explore the viability of assessing TQ using visualizations instead of actual videos. The experiment compared ratings made after actually watching videos that varied in TQ versus ratings made after viewing the TQ visualizations, without actually seeing corresponding videos with associated impairments or failures. The results of the experiment showed that, with appropriate training, ratings of TQ made, after viewing the visualizations only, were similar to TQ ratings made after actually watching videos with corresponding impairments or failures.%%%%M.A.S.

The rise in aging populations worldwide places a focus on shifting healthcare needs to match changing demographics. Older adults have a higher risk of becoming frail and losing functional abilities (e.g., walking and bathing) as well as the ability to perform daily activities such as shopping and cooking. Providing care and appropriate interventions to assist older adults with frailty is contingent upon identifying these individuals through effective screening. Frailty is often characterized by self-reports of exhaustion, weakness, slowing, and low physical activity. Older adults at risk of becoming frail often enter the healthcare system through emergency services (e.g., calling 911 or presenting at an emergency department), and screening should target these entry points. This paper discusses the design process, and usability findings associated with a tablet-based battery of frailty measures for assessing functional and cognitive states in elderly adults while being admitted to emergency care. This research is focusing on the use of digital technologies as a medium for physical and mental frailty assessment in emergency care. A diverse group of healthcare users is envisaged including paramedics, physicians, and research personnel, as well as end-users such as elderly patients and their caregivers. We describe the development and usability of the tablet-based frailty assessment system and we report on the concurrent validity of frailty measures.

Purpose: Although extensive research has been carried out on the determinants of mobile or wearable health care technology (mHealth), as well as on its acceptance by patients and other health care providers, very little research has been done on physiotherapists' perspectives on the use of mHealth in their current or future practice. The aims of this study were to (1) explore the attitudes of physiotherapists toward mHealth using a modified technology acceptance model questionnaire, (2) understand the applications and delivery paradigms that are most desirable, and (3) assess the content validity of the questionnaire. Method: The questionnaire was administered online. Participants (n=76) were recruited using snowball and convenience sampling. Data were analyzed using factor analysis and partial least-squares path modelling. Results: Results indicate that perceived usefulness and perceived ease of use were related to early adoptive behaviour among participants. We found no evidence that age, gender, experience, or practice setting influenced early adoptive behaviour. Participants demonstrated favourable attitudes toward mHealth tools in clinical practice. Conclusions: This article provides initial insights into factors that are likely to be significant determinants of adoption of mHealth among physiotherapists. Further work, including qualitative research, will help to identify personal and institutional factors that will improve the acceptance of mHealth.

Background Systematic reviews are infrequently used by health care managers (HCMs) and policy-makers (PMs) in decision-making. HCMs and PMs co-developed and tested novel systematic review of effects formats to increase their use. Methods A three-phased approach was used to evaluate the determinants to uptake of systematic reviews of effects and the usability of an innovative and a traditional systematic review of effects format. In phase 1, survey and interviews were conducted with HCMs and PMs in four Canadian provinces to determine perceptions of a traditional systematic review format. In phase 2, systematic review format prototypes were created by HCMs and PMs via Conceptboard©. In phase 3, prototypes underwent usability testing by HCMs and PMs. Results Two hundred two participants (80 HCMs, 122 PMs) completed the phase 1 survey. Respondents reported that inadequate format (Mdn = 4; IQR = 4; range = 1–7) and content (Mdn = 4; IQR = 3; range = 1–7) influenced their use of systematic reviews. Most respondents (76%; n = 136/180) reported they would be more likely to use systematic reviews if the format was modified. Findings from 11 interviews (5 HCMs, 6 PMs) revealed that participants preferred systematic reviews of effects that were easy to access and read and provided more information on intervention effectiveness and less information on review methodology. The mean System Usability Scale (SUS) score was 55.7 (standard deviation [SD] 17.2) for the traditional format; a SUS score

In this paper we describe the development of a user interface for a liquid handling robot. We describe the user interface design process that we carried out, beginning with requirements analysis and rapid prototyping, followed by heuristic evaluation. We then demonstrate the resulting cloud interface where the robot is controlled remotely in a teleoperation mode to program common protocols in chemistry, and biology, laboratories. We describe the main characteristics of the web interface, focusing on solution strategies used to expose features for programming experiments, remotely. We also report on the System Usability Scale (SUS) scores obtained by testing the interface with 15 experts in the fields of chemistry, and biology.

Many countries are aging societies. Since abilities generally deteriorate with age, technologies can assist older adults in their daily life. Loss of cognitive status is particularly severe in cases of dementia, with around 70% (according to Alzheimers.net) of dementia cases involving Alzheimer's Dementia (AD), a progressive and currently incurable disease. There is considerable research on AD with thousands of relevant publications being added to the PubMed online database every year. The knowledge incorporated in this large body of work is spread across hundreds of thousands of pages of text, making it difficult to distill and mobilize that knowledge in terms of treatments and guidelines. Text mining technology may assist in distilling knowledge from the vast corpus of research literature on Alzheimer's dementia. In this paper, we apply the Named Entity Recognition (NER) system, a text mining (TM) method used to group words into classes, in order to extract useful information from free texts. We present findings concerning how well NER can extract information from a corpus of AD research publications.

Results of an empirical study on the privacy concerns, attitudes, and behaviour of personal health record (PHR) users are presented. The study addressed the following questions: (1) What are the factors influencing privacy concerns of PHR users? (2) To what extent do PHR users read privacy agreements and which factors are in play when they fail to read them. (3) Are the behaviour and attitudes of PHR users consistent with respect to reading privacy agreements and using privacy settings? We infer from the study results that the factors influencing privacy concerns of general online users (as reported in literature) also apply to PHR users. In spite of privacy concerns, 60% of the respondents in our study reported not reading privacy agreements. PHR users who were highly concerned about privacy did not report changing their default privacy settings (as offered in a typical social networking website) in a manner consistent with their stated attitudes towards privacy. Based on the issues identified in the study we conclude with a number of design recommendations for privacy-friendly PHR systems.

Simple tools are needed to support differential diagnosis in a cognitively complex setting. Tools that show similar patients' diagnoses and treatment trajectories might provide useful clinical decision support for emergency physicians who use a case-based reasoning approach. However, privacy concerns that arise with indirect use of electronic health records (EHRs) must be addressed. The authors present a method to abstract a collection of EHRs into a set of summarized patient types and demonstrate its use on a database of medical records.

Introduction: Cognitive screening in settings such as emergency departments (ED) is frequently carried out using paper-and-pencil tests that require administration by trained staff. These assessments often compete with other clinical duties and thus may not be routinely administered in these busy settings. Literature has shown that the presence of cognitive impairments such as dementia and delirium are often missed in older ED patients. Failure to recognize delirium can have devastating consequences including increased mortality (Kakuma et al., 2003). Given the demands on emergency staff, an automated cognitive test to screen for delirium onset could be a valuable tool to support delirium prevention and management. In earlier research we examined the concurrent validity of a serious game, and carried out an initial assessment of its potential as a delirium screening tool (Tong et al., 2016). In this paper, we examine the test-retest reliability of the game, as it is an important criterion in a cognitive test for detecting risk of delirium onset. Objective: To demonstrate the test-retest reliability of the screening tool over time in a clinical sample of older emergency patients. A secondary objective is to assess whether there are practice effects that might make game performance unstable over repeated presentations. Materials and Methods: Adults over the age of 70 were recruited from a hospital ED. Each patient played our serious game in an initial session soon after they arrived in the ED, and in follow up sessions conducted at 8-h intervals (for each participant there were up to five follow up sessions, depending on how long the person stayed in the ED). Results: A total of 114 adults (61 females, 53 males) between the ages of 70 and 104 years (M = 81 years, SD = 7) participated in our study after screening out delirious patients. We observed a test-retest reliability of the serious game (as assessed by correlation r-values) between 0.5 and 0.8 across adjacent sessions. Conclusion: The game-based assessment for cognitive screening has relatively strong test-retest reliability and little evidence of practice effects among elderly emergency patients, and may be a useful supplement to existing cognitive assessment methods.

In this paper, we describe an Over-The-Top (OTT) video Quality of Experience (QoE) subjective evaluation experiment that was carried out to examine variations in the way subjects assess viewing experiences. The experiment focuses on different level of impairment and failure types, using 5-point measurement scales. Clustering is used to differentiate between unreliable and reliable participants, where reliability is defined in terms of criteria such as consistency of rating and ability to distinguish between qualitative differences in level of impairments. The results show that clustering a data set that is augmented with unreliable pseudo-participants can provide a new and improved perspective on individual differences in video QoE assessment.

In this paper, we study the relationships between Quality of Service (QoS) and Quality of Experience (QoE) in a session-based Over-The-Top (OTT) video service. A number of Performance Metrics (PMs) with and without the existence of failures during a video are examined. As QoE factors, Technical Quality (TQ) and Acceptability are used. We analyze the correlation between QoS performance metrics and QoE factors, and find new PMs should be employed because failures are included in QoE evaluation. We also summarize the relationships between QoS metrics and QoE factors through machine learning approaches. Using decision tree, we have a general idea about the relationships between PMs and QoE factors. We also understand the impact caused by failures and the value of rating scales.

Sharing of personal health information is subject to multiple constraints, which may dissuade some organizations from sharing their data. Summarized deidentified data, such as that derived from k -means cluster analysis, is subject to far fewer privacy-related constraints. In this paper, we examine the extent to which analysis of clustered patient types can match predictions made by analyzing the entire dataset at once. After reviewing relevant literature, and explaining how data are summarized in each cluster of similar patients, we compare the results of predicting death, and length of stay (LOS) in the ICU 1 1 ICU: Intensive care unit.

Quality of Experience (QoE) is a crucial guiding factor for network management of an end-to-end service session. The network provider can control the resources allocated to sessions and in doing so, influence the Technical Quality (TQ), which covers the technical aspects of signal quality during the session. On the other hand, the network provider has no control over the Content Quality (CQ), which pertains to the user's level of interest in a particular video. Together TQ and CQ influence the Overall eXperience (OX) in a session. In this paper, we present results from a user subjective study in which the impact of TQ and CQ on OX was investigated for Over-The-Top (OTT) video sessions from the perspective of a network provider. This perspective places a focus on those elements of QoE that can be controlled by the provider. Various studies have shown that very high interest in a content can strongly influence QoE independent of other factors, so our study uses videos that are neutral with respect to content. We assess the TQ, CQ and OX for video sessions that contain Integrity impairments (in the form of image freezing) and failures in terms of session Accessibility and Retainability. Our findings indicate that TQ and CQ have a strong impact on OX in the presence of impairments, but no failures. On the other hand, TQ is the main determinant of OX when failures are present.

Memories of experience are influenced by a peak-end effect [13]. Memories are modified to emphasize the final portions of an experience, and the peak positive, or negative, portion of that experience. We examine peak-end effects on judged Technical Quality (TQ) of online video. In two studies, sequences of different types of video disruption were varied so as to manipulate the peak-end effect of the experiences. The first experiment demonstrated an end effect, plus a possible peak effect involving negative, but not positive, experience. The second study manipulated payment conditions so that some sessions were structured as requiring payment to watch the video. The second study also distinguished between a peak effect and a possible sequence effect. Evidence was again found for an end effect, with a secondary effect of sequence, but no evidence was found for a peak effect independent of sequencing.

Emotion is critical to how health-related information is perceived and acted upon. This paper shares empirical and methodological insights from an eHealth multimedia usability study involving participants with type 2 diabetes, using a typical sample size for usability studies: n=10. Skin conductance sensors and a slider were used to assess emotional responses toward videos on disease complications. The videos featuring peers elicited trends of stronger emotional responses compared to the other video formats, for both emotion measures. Clustering was used to investigate patterns in slider usage; participants clustered into types characterized as: ‘responsive’, ‘judging’, and ‘unresponsive’. We examined slider and arousal data to estimate immersion and found that half of participants who moved the slider minimally experienced skin conductance spikes, indicating high immersion. We discuss implications for understanding high-impact eHealth multimedia and for measuring immersion and emotions using small samples common in usability studies.

The rapid development of blogs as a social networking tool has greatly increased the number of people who are expressing themselves in text published online. As yet little is known about the social psychology of online interaction using recently developed Web 2.0 functionalities. To what extent do principles of social psychology carry over into the online domain and how can appropriate use of those principles assist in activities such as community building, e-commerce, marketing, and personalization of services? There would seem to be a large number of novel research questions that can be posed with respect to online interaction. One pertinent question concerns the extent to which compatibility of personality influences online interaction. We investigated this question in terms of hypotheses derived from the personality research literature, using a two-part experiment. In the first study, eight participants wrote blogs in two distinct genres (personal diaries and commentaries) and rated their own personalities. In the second study 12 different participants judged the personality of authors as implied in the blog texts created in the first study. Those participants also completed a personality questionnaire and rated their attraction to the blog text author. Readers of the blog corpus were able to consistently judge the personality of the writers based solely on the text that they wrote. Moreover, they followed a well-established social rule regarding interpersonal attraction in real-life interaction. Blog readers were significantly more attracted to blog writers with more similar personalities-offering support that in a blog environment, ''birds of a feather flock together.'' Emotion word use in the blog corpus correlated with writer's personality, suggesting that online personality may be signalled by linguistic cues. The two most popular genres of blog writing, personal journal and commentary, differed in how accurately readers judged author personality traits. Based on the results obtained it is suggested that personality is an important determinant and that further research on how people communicate with blogs will be relevant to social network analysis and to marketing.

Web 2.0 tools in general, and Web video in particular, provide new ways for activists to express their viewpoints to a broad audience. In this paper we deployed tools that have been used to find subgroups automatically in social networks and applied them to the problem of distinguishing between two sides of a controversial issue based on patterns of online interaction. We explored the problem of distinguishing between anti- and pro-vaccination activists based on a social network of videos and associated comments posted on YouTube. Videos for the analysis were selected by submitting the term “vaccination” to a search on YouTube. A content analysis of the selected videos was then performed (Keelan et al, 2007) to classify videos as pro- or anti-vaccination. Then, a modified version of the SCAN method (Chin and Chignell, 2008) for identifying cohesive subgroups in social networks was applied to the social network inferred from the discussions about the videos. Results showed that a cohesive subgroup of anti-vaccination people existed in discussions around anti-vaccination videos, whereas discussions around pro-vaccination videos included both anti-vaccination and pro-vaccination people. Implications of the method and results for more general delineation of types of medical activism and the opposing camps within those camps are discussed.

As a first step in the development of community trackers for large-scale online interaction, this paper shows how cohesive subgroup analysis using the Social Cohesion Analysis of Networks (SCAN; Chin and Chignell 2008) and Data-Intensive Socially Similar Evolving Community Tracker (DISSECT; Chin and Chignell 2010) methods can be applied to the problem of identifying cohesive subgroups and tracking them over time. Three case studies are reported, and the findings are used to evaluate how well the SCAN and DISSECT methods work for different types of data. In the largest of the case studies, variations in temporal cohesiveness are identified across a set of subgroups extracted from the inferred social network. Further modifications to the DISSECT methodology are suggested based on the results obtained. The paper concludes with recommendations concerning further research that would be beneficial in addressing the community tracking problem for online data.

The problem of identifying cohesive subgroups in social hypertext is reviewed. A computationally efficient three-step framework for identifying cohesive subgroups is proposed, referred to as the Social Cohesion Analysis of Networks (SCAN) method. In the first step of this method (Select), people within a social network are screened using a level of network centrality to select possible subgroup members. In the second step (Collect), the people selected in the first step are collected into subgroups identified at each point in time using hierarchical cluster analysis. In the third step (Choose), similarity modeling is used to choose cohesive subgroups based on the similarity of subgroups when compared across different points in time. The application of this SCAN method is then demonstrated in a case study where a subgroup is automatically extracted from a social network formed based on the online interactions of a group of about 150 people that occurred over a two-year period. In addition, this paper also demonstrates that similarity-based cohesion can provide a different, and in this case more compelling, subgroup representation than a method based on splitting a hierarchical clustering dendrogram using an optimality criterion.

Point of care access to electronic medical records may provide clinicians with the information they want when they need it and may in turn improve patient safety. Yet providing an electronic medical record on handheld devices presents many usability challenges, and it is unclear whether clinicians will use them.An iterative design process for the development and evaluation of a prototype of a mobile electronic medical record was performed. Usability sessions were conducted in which physicians were asked to 'think aloud' while working through clinical scenarios using the prototype. Verbal protocol analysis, which consists of coding utterances, was conducted on the transcripts from the sessions and common themes were extracted.Usability sessions were held with five family physicians and four internists with varying levels of computer expertise. Physicians were able to use the device to complete 52 of 54 required tasks. Users commented that it was intuitive (9/9), would increase accessibility (5/9) but for them to use it, it would need the system to be fast and time-saving (5/9). Users had difficulty entering information (5/9) and reading the screen (4/9). In terms of functionality, users had concerns about completeness of information (6/9), details of ordering (5/9) and desired billing functionality (5/9) and integration with other systems (4/9).While physicians can use mobile electronic medical records in realistic scenarios, certain requirements likely need to be met including a fast system with easy data selection, easy data entry and improved display before widespread adoption occurs.

The impact of notifications on driving performance is a critical safety concern (Lee & Strayer, 2004). This study examined how interruptions (including phone calls) impair simulated driving performance, and how individual differences mediate the effect of those interruptions. Consistent with our hypothesis, field dependent participants answered phone calls more quickly and with less consideration of the difficulty of the current driving situation than more field independent participants. Further, a post-hoc analysis showed that, on average, field dependents with small operating spans crashed twice as often as the other drivers. Thus the detrimental effect of a field dependent style on managing interruptions while driving is likely worsened for people with low working memory capacity. It is suggested that future research should investigate the safety implications of individual differences in interruption handling ability. Such research is needed to support ongoing efforts to develop guidelines and legislation concerning the use of distracting information technologies (such as cell phones) in automobiles.

This paper describes a study of content and communication processes in multidisciplinary meetings of healthcare professionals, using a Human Factors engineering approach. The goal of the study, which took place in a teaching hospital Toronto, Canada, was to understand the processes of collaboration and information exchange that take place in the meetings, and to identify potential supports for the meetings, including the use of information technology. The methodology included data collection through observation of the multidisciplinary patient care meetings, and quantitative analysis of verbal exchanges in those meetings. The results suggested the need for a standardized approach to information exchange using structured language.

Consumer acceptance is of great interest in the adoption of novel multimedia products and services. A number of factors can greatly influence the customer experience during a video session, impacting the acceptability of the product or service. Factors such as the Technical Quality (TQ) which covers the technical aspects of the signal quality can be controlled from the network provider. On the other hand, the network provider has no control on the subject's level of interest in a video, the Content Quality (CQ). Together TQ and CQ influence the Overall eXperience (OX) of the subject and, in the case of Over The Top (OTT) video, in a video session. In this paper, we present results from a user subjective study in which the impact of TQ and OX on the acceptability was investigated for OTT video sessions. To minimize the CQ impact on OX, the videos were carefully selected to have relatively neutral content. We assess the impact of TQ and OX on acceptability for videos containing impairment and failure events during lifecycle, thus affecting Accessibility, Retainability and Integrity. Our experimental results indicate that TQ and OX have a strong impact on acceptability.

This paper describes Empathic Tutoring System (ETS) which uses character agents for online learning. Eye movement tracking and other physiological measures are used to personalize character agent behaviors (affective and instruction) in an e-learning environment. A prototype system reacts to learner's eye information in real-time, recording eye gaze and pupil dilation data (plus heart rate and skin conductance) during learning. Based on these measures, character agents inferred the attentional and motivational status of the learner and responded accordingly with affective and instructional behaviors. Character agents engage and direct the learner's attention while providing both generalized system help and personalized advice about the learning content. Feedbacks from preliminary usability studies may suggest that e-learning character agents reacting to eye gaze and physiological measures may heighten l earner concentration and lead to more effective learning.

The Evidence at the Point of Care (EPoCare) project (Takeshita, Davis, Straus, 2002) is examining how to put clinical evidence on mobile devices to meet physicians' information needs at the point of care. Forty-seven physicians, including family physicians, general internists and medical residents, participated in usability testing with prototypes implemented on two different form factors (a tablet-style PC and a PDA-style handheld). During the testing physicians were asked to describe their impression of the prototype and to indicate their agreement with 20 statements concerning the prototype implementations. The three user groups differed in how they valued device portability and screen size. Family physicians wanted larger screen size and were less concerned with portability, while internists wanted portability and were less concerned with screen size. Residents wanted both portability and large screen size. Cluster analysis was used to identify four clusters of physicians, varying by age, amount of time in clinical practice, and frequency of use of search engines and medical databases. Differences in preferences about format of information were present amongst these clusters. This study demonstrates how large sample usability testing can be used to develop customization strategies for mobile applications, for designing both form factor and content.

Privacy has been identified as a key issue in a variety of domains, including electronic commerce and public policy. While there are many discussions of privacy issues from a legal and policy perspective, there is little information on the structure of privacy as a psychometric construct. Our goal is to develop a method for measuring attitudes towards privacy that can guide the design and personalization of services. This paper reports on the development of an initial version of the PAQ. Four privacy attitudes are identified based on the factor structure of the PAQ. Cluster analysis is used to identify potential stereotypes with respect to attitudes towards privacy amongst different groups of people. Version 1.0 of the PAQ is presented in an Appendix as a 36 item questionnaire that measures the four privacy attitudes of personal information, monitoring, exposure and protection.

With advances in computer graphics, a number of innovative approaches to information visualization have been developed (e.g., Card et al, 1991). Some of these approaches create a mapping between information and corresponding structure in a virtual world. The resulting virtual worlds can be fully three dimensional (3D) or they can be implemented as a series of 2D birds-eye "snapshots" that are traversed as if they were in 3D, using operations such as panning and zooming interactively (2.5D). This paper reports a study that contrasted 3D and 2.5D performance for people with differing levels of spatial and structure learning ability. Four data collection methods were employed: search task scoring; subjective questionnaires; navigational activity logging and analysis; and administration of tests for spatial and structure-learning abilities. Analysis of the results revealed statistically significant effects of user abilities, and information environment designs. Overall, this research did not find a performance advantage for using a 3D rather than a 2.5D virtual world. In addition, users in the lowest quartile of spatial ability had significantly lower search performance in the 3D environment. The findings suggest that individual differences in traits such as spatial ability may be important in determining the usability and acceptability of 3D environments.

In spite of the powerful functionality and message integration offered, unified communication (UC) has yet to be widely adopted as a solution that meets the demands and complexities of modern communicators. By understanding individual differences, requirements, and preferences, it should be possible to design better communication interfaces and services. This study examined the characteristics of communication-related preferences among users of a three-month UC trial. Survey data from 123 respondents were analyzed. A series of factor analyses identified 19 communication traits, 7 of which were particularly interesting. A cluster analysis suggested three types of communication styles: Low-tech, Power, and Strategic. Future research will attempt to relate the current findings to actual communication behaviour. The creation of a communication preference inventory (CPI) is also discussed.

Measurement of video Quality of Experience is needed to enable telecommunication service operators to provide acceptable video viewing experience to their customers. Without accurate Quality of Experience measurement, it is hard to predict customer satisfaction. Accessibility and Retain ability failures are important factors that can affect the experience of viewing video on the Internet. Most of the published work in the assessment of Video Quality of Experience has focused on the quality of video viewing sessions without considering the impact of Accessibility and Retain ability failures. In this paper, we focus on the Quality of Experience for video sessions in the presence of Retain ability failures. We primarily evaluate video Quality of Experience for videos with temporary interruptions, called Integrity impairments, as well as with permanent interruptions called Retain ability failures. A subjective study was conducted to observe the effect of Retain ability failure on video Quality of Experience. The results of the subjective study suggest that videos with Retain ability failures are rated much lower than videos without any failure. An initial linear model for QoE of videos subject to Retain ability failures is introduced.

Background and Objectives The implementation of electronic remote blood issue (ERBI) may provide safety and efficiency gains for transfusion medicine. This systematic review's objective was to assess whether ERBI affects incidents of adverse events, time taken for blood issue and delivery, and cross-match to transfusion ratios, among other measures of safety and efficiency. The review also sought to uncover barriers and facilitators of ERBI implementation. Materials and Methods We searched four aggregated electronic databases (Medline, EMBASE, CINAHL and BIOSIS) up to 19 July 2012, with an updated search performed on 5 March 2014 for studies on ERBI. No specific study design criteria were used in the initial inclusion due to the low number of studies on ERBI. Results A total of 4758 citations were initially identified; after 1844 duplicates were removed, 2612 citations were excluded on the basis of the abstract. Two reviewers evaluated a total of 302 full-text articles independently; of these, seven citations were eligible for inclusion. An updated search and the authors' own collections confirmed an additional five citations, totalling 13 citations and six studies within these. Conclusion There is insufficient evidence to demonstrate whether ERBI significantly impacts safety and efficiency of blood transfusion and delivery processes. Rigorously designed studies to assess safety and efficiency outcomes are required using proxy or corollary measures. A number of positive results were reported, however, and most studies included suggestions for facilitating ERBI implementation.