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Objective The purpose of the present research is to establish measurement equivalence and test differences in reliability between computerized and pencil-and-paper-based tests of spatial cognition. Background Researchers have increasingly adopted computerized test formats, but few attempt to establish equivalence for computer-based and paper-based tests. The mixed results in the literature on the test mode effect, which occurs when performance differs as a function of test medium, highlight the need to test for, instead of assume, measurement equivalence. One domain that has been increasingly computerized and is thus in need of tests of measurement equivalence across test mode is spatial cognition. Method In the present study, 244 undergraduate students completed two measures of spatial ability (i.e., spatial visualization and cross-sectioning) in either computer- or paper-and-pencil-based format. Results Measurement equivalence was not supported across computer-based and paper-based formats for either spatial test. The results also indicated that test administration type affected the types of errors made on the spatial visualization task, which further highlights the conceptual differences between test mediums. Paper-based tests also demonstrated increased reliability when compared with computerized versions of the tests. Conclusion The results of the measurement equivalence tests caution against treating computer- and paper-based versions of spatial measures as equivalent. We encourage subsequent work to demonstrate test mode equivalence prior to the utilization of spatial measures because current evidence suggests they may not reliably capture the same construct. Application The assessment of test type differences may influence the medium in which spatial cognition tests are administered.

This book constitutes the proceedings of the Third International Conference on Smart Multimedia, ICSM 2022, which was held in Marseille, France, during August 25–27, 2022.

Objective: To determine whether patients with stroke receiving rehabilitation for upper limb deficits using smart technology (video and reminder functions) demonstrate greater adherence to prescribed home exercise programmes and better functional outcomes when compared with traditional paper-based exercise prescription. Design: Randomized controlled trial comparing upper limb home exercise programmes supported by video and automated reminders on smart technology, with standard paper-based home exercise programmes. Setting: A community rehabilitation programme within a large metropolitan health service. Subjects: Patients with stroke with upper limb deficits, referred for outpatient rehabilitation. Interventions: Participants were randomly assigned to the control (paper-based home exercise programme) or intervention group (home exercise programme filmed on an electronic tablet, with an automated reminder). Both groups completed their prescribed home exercise programme for four weeks. Main measures: The primary outcome was adherence using a self-reported log book. Secondary outcomes were change in upper limb function and patient satisfaction. Results: A total of 62 participants were allocated to the intervention ( n = 30) and control groups ( n = 32). There were no differences between the groups for measures of adherence (mean difference 2%, 95% CI −12 to 17) or change in the Wolf Motor Function Test log transformed time (mean difference 0.02 seconds, 95% CI −0.1 to 0.1). There were no between-group differences in how participants found instructions ( p = 0.452), whether they remembered to do their exercises ( p = 0.485), or whether they enjoyed doing their exercises ( p = 0.864). Conclusions: The use of smart technology was not superior to standard paper-based home exercise programmes for patients recovering from stroke. This trial design was registered prospectively with the Australian and New Zealand Clinical Trials Register, ID: ACTRN 12613000786796. http://www.anzctr.org.au/trialSearch.aspx

In this study, we compared cancer patients preference for computerised (tablet/web-based) surveys versus paper. We also assessed whether the understanding of a cancer-related topic, pharmacogenomics is affected by the survey format, and examined differences in demographic and medical characteristics which may affect patient preference and understanding. Three hundred and four cancer patients completed a tablet-administered survey and another 153 patients completed a paper-based survey. Patients who participated in the tablet survey were questioned regarding their preference for survey format administration (paper, tablet and web-based). Understanding was assessed with a ‘direct’ method, by asking patients to assess their understanding of genetic testing, and with a ‘composite’ score. Patients preferred administration with tablet (71%) compared with web-based (12%) and paper (17%). Patients

A longstanding issue with knowledge bases that discuss drug-drug interactions (DDIs) is that they are inconsistent with one another. Computerized support might help experts be more objective in assessing DDI evidence. A requirement for such systems is accurate automatic classification of evidence types. In this pilot study, we developed a hierarchical classifier to classify clinical DDI studies into formally defined evidence types. The area under the ROC curve for sub-classifiers in the ensemble ranged from 0.78 to 0.87. The entire system achieved an F1 of 0.83 and 0.63 on two held-out datasets, the latter consisting focused on completely novel drugs from what the system was trained on. The results suggest that it is feasible to accurately automate the classification of a sub-set of DDI evidence types and that the hierarchical approach shows promise. Future work will test more advanced feature engineering techniques while expanding the system to classify a more complex set of evidence types.

Four experiments were conducted to test whether recent developments in display technology would suffice to eliminate the well-known disadvantages in reading from screen as compared with paper. Proofreading speed and performance were equal for a TFT-LCD and a paper display, but there were more symptoms of eyestrain in the screen condition accompanied by a strong preference for paper (Experiment 1). These results were replicated using a longer reading duration (Experiment 2). Additional experiments were conducted to test hypotheses about the reasons for the higher amount of eyestrain associated with reading from screen. Reduced screen luminance did not change the pattern of results (Experiment 3), but positioning both displays in equal inclination angles eliminated the differences in eyestrain symptoms and increased proofreading speed in the screen condition (Experiment 4). A paper-like positioning of TFT-LCDs seems to enable unimpaired reading without evidence of increased physical strain. Practitioner Summary: Given the developments in screen technology, a re-assessment of the differences in proofreading speed and performance, well-being, and preference between computer screen and paper was conducted. State-of-the-art TFT-LCDs enable unimpaired reading, but a book-like positioning of screens seems necessary to minimise eyestrain symptoms.

Clinical reasoning competency is essential for an appropriate clinical dental treatment. Among the tools for the assessment of clinical reasoning competency, computer-based testing (CBT) is considered more useful than paper-based testing (PBT), because teachers can control the timing of information given to the examinees. Such timings could possibly affect the thinking process of examinees. However, few studies reported differences of reasoning between the two testing modes. In the present study, we developed an assessment of clinical reasoning and applied it using CBT and PBT to compare the examinees' performance. The participants comprised 60 students in the fifth-year class in 2012 of the School of Dentistry, Faculty of Dentistry, Tokyo Medical and Dental University. The tests comprised 25 problems, each with four questions, totaling 100 questions. The contents of the questions were the same in CBT and PBT. The students were assigned to CBT (Group C, n = 30) and PBT (Group P, n = 30) groups, with an almost equal gender ratio in the groups. The difference between scores was analyzed with a univariate analysis of variance. No significant intergroup differences were found regarding the test duration, total score, and average score of each question. The number of problems with perfect marks was higher in Group P than in Group C (P0.05), probably because Group P students could access the information of the previous question (s) within a problem. Thus, the differences of the examinees' performance between the two testing modes were small.

To compare data based on computerized and paper versions of health status questionnaires (HSQs) for sampling patient-reported outcomes (PROs) in patients with fibromyalgia (FM). In addition, to examine associations between patient characteristics (age, education, computer experience) and differences between versions. Finally, to evaluate the acceptability of computer-based questionnaires among patients with FM.Method: The study population comprised female patients diagnosed with FM. All patients completed six HSQs: the Fibromyalgia Impact Questionnaire (FIQ), the Major Depression Inventory (MDI), the 36-item Short Form Health Survey (SF-36), the painDETECT questionnaire (PDQ), the Coping Strategies Questionnaire (CSQ), and the Generalized Anxiety Disorder Self-Assessment Questionnaire (GAD-10), both on paper and using a touch screen. One HSQ was tested at a time in a repeated randomized cross-over design. The two versions were completed with a 5-min interval and between each HSQ the participants had a 5-min break. Means, mean differences with 95% confidence intervals (CIs), medians, median differences, and intraclass correlation coefficients (ICCs) were calculated for all HSQs, including relevant subscales. Associations between patient characteristics and differences between versions were explored using Spearmans correlation coefficients.Results: Twenty women, mean age 48.4 years, participated in the study. Except for one item, ICCs between touch-screen and paper versions of the HSQs examined indicated acceptable agreement (ICC = 0.71-0.99). Overall, mean and median differences revealed no differences between versions. No significant associations were observed for patient characteristics. None of the participants preferred paper questionnaires over computerized versions.Conclusions: The computerized HSQs using a touch screen gave comparable results to answers given on paper and were generally preferred by the participants.

BACKGROUND: Computer works are associated with Musculoskeletal Disorders (MSDs). There are several methods have been developed to assess computer work risk factor related to MSDs. OBJECTIVE: This review aims to give an overview of current techniques available for pen-and-paper-based observational methods in assessing ergonomic risk factors of computer work. METHODS: We searched an electronic database for materials from 1992 until 2015. The selected methods were focused on computer work, pen-and-paper observational methods, office risk factors and musculoskeletal disorders. This review was developed to assess the risk factors, reliability and validity of pen-and-paper observational method associated with computer work. Two evaluators independently carried out this review. RESULTS: Seven observational methods used to assess exposure to office risk factor for work-related musculoskeletal disorders were identified. The risk factors involved in current techniques of pen and paper based observational tools were postures, office components, force and repetition. From the seven methods, only five methods had been tested for reliability. They were proven to be reliable and were rated as moderate to good. For the validity testing, from seven methods only four methods were tested and the results are moderate. CONCLUSION: Many observational tools already exist, but no single tool appears to cover all of the risk factors including working posture, office component, force, repetition and office environment at office workstations and computer work. Although the most important factor in developing tool is proper validation of exposure assessment techniques, the existing observational method did not test reliability and validity. Futhermore, this review could provide the researchers with ways on how to improve the pen-and-paper-based observational method for assessing ergonomic risk factors of computer work.

CONSTRUCT: In this study, we examine the differences in test performance between the paper-based and the computer-based version of the Berlin formative Progress Test. In this context it is the first study that allows controlling for students' prior performance.Computer-based tests make possible a more efficient examination procedure for test administration and review. Although university staff will benefit largely from computer-based tests, the question arises if computer-based tests influence students' test performance.A total of 266 German students from the 9th and 10th semester of medicine (comparable with the 4th-year North American medical school schedule) participated in the study (paper = 132, computer = 134). The allocation of the test format was conducted as a randomized matched-pair design in which students were first sorted according to their prior test results. The organizational procedure, the examination conditions, the room, and seating arrangements, as well as the order of questions and answers, were identical in both groups.The sociodemographic variables and pretest scores of both groups were comparable. The test results from the paper and computer versions did not differ. The groups remained within the allotted time, but students using the computer version (particularly the high performers) needed significantly less time to complete the test. In addition, we found significant differences in guessing behavior. Low performers using the computer version guess significantly more than low-performing students in the paper-pencil version.Participants in computer-based tests are not at a disadvantage in terms of their test results. The computer-based test required less processing time. The reason for the longer processing time when using the paper-pencil version might be due to the time needed to write the answer down, controlling for transferring the answer correctly. It is still not known why students using the computer version (particularly low-performing students) guess at a higher rate. Further studies are necessary to understand this finding.

Background: Major international guidelines recommend mental health screening during the perinatal period. However, substantial barriers to screening have been reported by pregnant and postpartum women and perinatal care providers. E-screening offers benefits that may address implementation challenges. Objective: The primary objective of this randomized controlled trial was to evaluate the feasibility and acceptability of Web-based mental health e-screening compared with paper-based screening among pregnant women. A secondary objective was to identify factors associated with women’s preferences for e-screening and disclosure of mental health concerns. Methods: Pregnant women recruited from community and hospital-based antenatal clinics and hospital-based prenatal classes were computer-randomized to a fully automated Web-based e-screening intervention group or a paper-based control group. Women were eligible if they spoke or read English, were willing to be randomized to e-screening, and were willing to participate in a follow-up diagnostic interview. The intervention group completed the Antenatal Psychosocial Health Assessment and the Edinburgh Postnatal Depression Scale on a tablet computer, while controls completed them on paper. All women completed self-report baseline questions and were telephoned 1 week after randomization by a blinded research assistant for a MINI International Neuropsychiatric Interview. Renker and Tonkin’s tool of feasibility and acceptability of computerized screening was used to assess the feasibility and acceptability of e-screening compared with paper-based screening. Intention-to-treat analysis was used. To identify factors associated with preference for e-screening and disclosure, variables associated with each outcome at P

Background: As eye tracking-based assessment of cognition becomes more widely used in older adults, particularly those at risk for dementia, reliable and scalable methods to collect high-quality data are required. Eye tracking-based cognitive tests that utilize device-embedded cameras have the potential to reach large numbers of people as a screening tool for preclinical cognitive decline. However, to fully validate this approach, more empirical evidence about the comparability of eyetracking-based paradigms to existing cognitive batteries is needed. Objective: Using a population of clinically normal older adults, we examined the relationship between a 30-minute Visual Paired Comparison (VPC) recognition memory task and cognitive composite indices sensitive to a subtle decline in domains associated with Alzheimer disease. Additionally, the scoring accuracy between software used with a commercial grade eye tracking camera at 60 frames per second (FPS) and a manually scored procedure used with a laptop-embedded web camera (3 FPS) on the VPC task was compared, as well as the relationship between VPC task performance and domain-specific cognitive function. Methods: A group of 49 clinically normal older adults completed a 30-min VPC recognition memory task with simultaneous recording of eye movements by a commercial-grade eye-tracking camera and a laptop-embedded camera. Relationships between webcam VPC performance and the Preclinical Alzheimer Cognitive Composite (PACC) and National Institutes of Health Toolbox Cognitive Battery (NIHTB-CB) were examined. Inter-rater reliability for manually scored tests was analyzed using Krippendorff’s kappa formula, and we used Spearman’s Rho correlations to investigate the relationship between VPC performance scores with both cameras. We also examined the relationship between VPC performance with the device-embedded camera and domain-specific cognitive performance. Results: Modest relationships were seen between mean VPC novelty preference and the PACC (r=.39, P=.007) and NIHTB-CB (r=.35, P=.03) composite scores, and additional individual neurocognitive task scores including letter fluency (r=.33, P=.02), category fluency (r=.36, P=.01), and Trail Making Test A (–.40, P=.006). Robust relationships were observed between the 60 FPS eye tracker and 3 FPS webcam on both trial-level VPC novelty preference (r=.82, P

Background Pregnant women’s perceptions of the risks and benefits during mental health screening impact their willingness to disclose concerns. Early research in violence screening suggests that such perceptions may vary by mode of screening, whereby women view the anonymity of e-screening as less risky than other approaches. Understanding whether mode of screening influences perceptions of risk and benefit of disclosure is important in screening implementation. Objective The objective of this randomized controlled trial was to compare the perceptions of pregnant women randomized to a Web-based screening intervention group and a paper-based screening control group on the level of risk and benefit they perceive in disclosing mental health concerns to their prenatal care provider. A secondary objective was to identify factors associated with women’s perceptions of risk and benefit of disclosure. Methods Pregnant women recruited from maternity clinics, hospitals, and prenatal classes were computer-randomized to a fully automated Web-based e-screening intervention group or a paper-based control. The intervention group completed the Antenatal Psychosocial Health Assessment and the Edinburgh Postnatal Depression Scale on a computer tablet, whereas the control group completed them on paper. The primary outcome was women’s perceptions of the risk and benefits of mental health screening using the Disclosure Expectations Scale (DES). A completer analysis was conducted. Statistical significance was set at P Results Of the 675 eligible women approached, 636 (94.2%) agreed to participate and were randomized to the intervention (n=305) and control (n=331) groups. There were no significant baseline differences between groups. The mode of screening was not associated with either perceived risk or benefit of screening. There were no differences in groups in the mean scores of the risk and benefit of disclosure subscales. Over three-quarters of women in both intervention and control groups perceived that mental health screening was beneficial. However, 43.1% (272/631) of women in both groups reported feeling very, moderately, or somewhat vulnerable during mental health screening. We found that women of low income, those treated previously for depression or anxiety, and those pregnant with their first child were more likely to perceive greater risk. However, these associations were very small. Conclusions Pregnant women in both the e-screening and paper-based screening groups perceived benefit and risk of disclosure similarly, suggesting that providers can implement the mode of screening that is most ideal for their clinical setting. Regardless of the mode of screening, a substantial number of women reported feeling vulnerable during mental health screening, highlighting the importance of the need to reduce women’s vulnerability throughout the screening process with strategies such as addressing women’s concerns, explaining the rationale for screening, and discussing how results will be used. Trial Registration Clinicaltrials.gov NCT01899534; https://clinicaltrials.gov/ct2/show/NCT01899534 (Archived by WebCite at http://www.webcitation.org/6tRKtGC4M)

Computer-assisted data collection offers advantages over traditional paper and pencil measures; however, little guidance is available regarding the logistics of conducting computer-assisted data collection with adolescents in group settings. To address this gap, we draw on our experiences conducting a multi-site longitudinal study of adolescent development. Structured questionnaires programmed on laptop computers using Audio Computer Assisted Self-Interviewing (ACASI) were administered to groups of adolescents in community-based and afterschool programs. Although implementing ACASI required additional work before entering the field, we benefited from reduced data processing time, high data quality, and high levels of youth motivation. Preliminary findings from an ethnically diverse sample of 265 youth indicate favorable perceptions of using ACASI. Using our experiences as a case study, we provide recommendations on selecting an appropriate data collection device (including hardware and software), preparing and testing the ACASI, conducting data collection in the field, and managing data.

The aim of this paper was to assess the validity and reliability of the touch-screen standard Portuguese version of the following patient-reported outcomes (PROs), compared with paper format, in patients with rheumatoid arthritis (RA) and spondyloarthritis: Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), Ankylosing Spondylitis Quality of Life scale (ASQoL), Short-Form 36 (SF-36), Health Assessment Questionnaire (HAQ) and visual analogue scales (VAS) measuring pain and burden of disease. Adult patients with RA and spondyloarthritis attending the Portuguese Institute of Rheumatology were recruited from March 2013 to January 2014. Patients filled the paper and touch-screen formats of the standard Portuguese versions of the PROs. Two groups of VAS were used, RA and psoriatic arthritis (Global VAS) and another specific for spondyloarthrites (Spa-VAS). Paper questionnaires were filled 15 min before touch-screen formats. Agreement between formats (validity) was assessed by intraclass correlation coefficient (ICC), while internal consistency of scales (reliability) was assessed by Cronbach's alpha. Overall, 134 patients were included with a mean age of 51 years, 74.6 % female and 57.5 % presenting RA. BASDAI, BASFI, HAQ and ASQoL showed high ICC between paper and touch-screen formats (0.977, 0.958, 0.974 and 0.940, respectively). ICC for Global VAS ranged from 0.906 to 0.921, while Spa-VAS ranged from 0.867 to 0.943. The mean ICC for all SF-36 domains was 0.889 (ICC for each domain ranged from 0.781 to 0.944). Touch-screen standard Portuguese formats of these PROs may be valid and reliable tools for PRO measurement in rheumatology.

The time, material, and staff-consuming nature of anatomy's traditional pen-and-paper assessment system, the increase in the number of students enrolling in medical schools and the ever-escalating workload of academic staff have made the use of computer-based assessment (CBA) an attractive proposition. To understand the impact of such shift in the assessment method, an experimental study evaluating its effect on students' performance was designed. Additionally, students' opinions toward CBA were gathered. Second-year medical students attending a Clinical Anatomy course were randomized by clusters in two groups. The pen-and-paper group attended two sessions, each consisting of a traditional sectional anatomy steeplechase followed by a theoretical examination, while the computer group was involved in two similar sessions conducted in a computerized environment. At the end of each of the computer sessions, students in this group filled an anonymous questionnaire. In the first session, pen-and-paper group students scored significantly better than computer-group students in both the steeplechase (mean ± standard deviation: 66.00 ± 14.15% vs. 43.50 ± 19.10%; P 0.001) and the theoretical examination (52.50 ± 12.70% vs. 39.00 ± 21.10%; P 0.001). In the second session, no statistically significant differences were found for both the steeplechase (59.50 ± 17.30% vs. 54.50 ± 17.00%; P = 0.085) and the theoretical examination (57.50 ± 13.70% vs. 54.00 ± 14.30%; P = 0.161). Besides, an intersession improvement in students' perceptions toward CBA was registered. These results suggest that, after a familiarization period, CBA might be a performance equivalent and student accepted alternative to clinical anatomy pen-and-paper theoretical and practical examinations. Anat Sci Educ 11: 124-136. © 2017 American Association of Anatomists.

The present comment concerns some doubtful results included in the above paper.

Opportunities for clinicians, researchers, and medical students to become acquainted with the three‐dimensional (3D) anatomy of the human embryo have historically been limited. This work was aimed at creating a collection of digital, printable 3D surface models demonstrating major morphogenetic changes in the embryo's external anatomy, including typical features used for external staging. Twelve models were digitally reconstructed based on optical projection tomography, high‐resolution episcopic microscopy and magnetic resonance imaging datasets of formalin‐fixed specimens of embryos of developmental stages 12 through 23, that is, stages following longitudinal and transverse embryo folding. The reconstructed replica reproduced the external anatomy of the actual specimens in great detail, and the progress of development over stages was recognizable in a variety of external anatomical features and bodily structures, including the general layout and curvature of the body, the pharyngeal arches and cervical sinus, the physiological gut herniation, and external genitalia. In addition, surface anatomy features commonly used for embryo staging, such as distinct steps in the morphogenesis of facial primordia and limb buds, were also apparent. These digital replica, which are all provided for 3D visualization and printing, can serve as a novel resource for teaching and learning embryology and may contribute to a better appreciation of the human embryonic development., This work produced digital three‐dimensional (3D) surface reconstructions of human embryo specimens of stages 12 through 23. The digital replica reproduced the external anatomy of the actual specimens in great detail, and the progress of development over stages was recognizable in a variety of external anatomical features and bodily structures. Reconstructed models are provided for 3D visualization and printing as supplementary files.

We evaluated and compared the mammographic density assessment of an artificial intelligence-based computer-assisted diagnosis (AI-CAD) program using inter-rater agreements between radiologists and an automated density assessment program. Between March and May 2020, 488 consecutive mammograms of 488 patients (56.2 ± 10.9 years) were collected from a single institution. We assigned four classes of mammographic density based on BI-RADS (Breast Imaging Reporting and Data System) using commercial AI-CAD (Lunit INSIGHT MMG), and compared inter-rater agreements between radiologists, AI-CAD, and another commercial automated density assessment program (Volpara®). The inter-rater agreement between AI-CAD and the reader consensus was 0.52 with a matched rate of 68.2% (333/488). The inter-rater agreement between Volpara® and the reader consensus was similar to AI-CAD at 0.50 with a matched rate of 62.7% (306/488). The inter-rater agreement between AI-CAD and Volpara® was 0.54 with a matched rate of 61.5% (300/488). In conclusion, density assessments by AI-CAD showed fair agreement with those of radiologists, similar to the agreement between the commercial automated density assessment program and radiologists. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10278-021-00555-x.

Motivation Genomic data repositories like The Cancer Genome Atlas, Encyclopedia of DNA Elements, Bioconductor’s AnnotationHub and ExperimentHub etc., provide public access to large amounts of genomic data as flat files. Researchers often download a subset of data files from these repositories to perform exploratory data analysis. We developed Epiviz File Server, a Python library that implements an in situ data query system for local or remotely hosted indexed genomic files, not only for visualization but also data transformation. The File Server library decouples data retrieval and transformation from specific visualization and analysis tools and provides an abstract interface to define computations independent of the location, format or structure of the file. We demonstrate the File Server in two use cases: (i) integration with Galaxy workflows and (ii) using Epiviz to create a custom genome browser from the Epigenome Roadmap dataset. Availability and implementation Epiviz File Server is open source and is available on GitHub at http://github.com/epiviz/epivizFileServer. The documentation for the File Server library is available at http://epivizfileserver.rtfd.io.

This study aimed to construct a breast ultrasound computer-aided prediction model based on the convolutional neural network (CNN) and investigate its diagnostic efficiency in breast cancer. A retrospective analysis was carried out, including 5000 breast ultrasound images (benign: 2500; malignant: 2500) as the training group. Different prediction models were constructed using CNN (based on InceptionV3, VGG16, ResNet50, and VGG19). Additionally, the constructed prediction models were tested using 1007 images of the test group (benign: 788; malignant: 219). The receiver operating characteristic curves were drawn, and the corresponding areas under the curve (AUCs) were obtained. The model with the highest AUC was selected, and its diagnostic accuracy was compared with that obtained by sonographers who performed and interpreted ultrasonographic examinations using 683 images of the comparison group (benign: 493; malignant: 190). In the model test with the test group images, the AUCs of the constructed InceptionV3, VGG16, ResNet50, and VGG19 models were 0.905, 0.866, 0.851, and 0.847, respectively. The InceptionV3 model showed the largest AUC, with statistically significant differences compared with the other models (P

Significance: Despite the ample progress made toward faster and more accurate Monte Carlo (MC) simulation tools over the past decade, the limited usability and accessibility of these advanced modeling tools remain key barriers to widespread use among the broad user community. Aim: An open-source, high-performance, web-based MC simulator that builds upon modern cloud computing architectures is highly desirable to deliver state-of-the-art MC simulations and hardware acceleration to general users without the need for special hardware installation and optimization. Approach: We have developed a configuration-free, in-browser 3D MC simulation platform—Monte Carlo eXtreme (MCX) Cloud—built upon an array of robust and modern technologies, including a Docker Swarm-based cloud-computing backend and a web-based graphical user interface (GUI) that supports in-browser 3D visualization, asynchronous data communication, and automatic data validation via JavaScript Object Notation (JSON) schemas. Results: The front-end of the MCX Cloud platform offers an intuitive simulation design, fast 3D data rendering, and convenient simulation sharing. The Docker Swarm container orchestration backend is highly scalable and can support high-demand GPU MC simulations using MCX over a dynamically expandable virtual cluster. Conclusion: MCX Cloud makes fast, scalable, and feature-rich MC simulations readily available to all biophotonics researchers without overhead. It is fully open-source and can be freely accessed at http://mcx.space/cloud.

Artificial intelligence (AI) and machine learning algorithms are changing many facets of our lives and have great potential for disease diagnosis. In this Plenary Paper, Matek et al describe training a convolutional neural network model using 171 374 bone marrow cytology images from 945 patients with various hematological diseases before validating its accuracy in classifying single cells in an independent set of 627 images. The system can automatically classify 22 classes of bone marrow leukocytes, and with further improvements, it may bring AI-aided diagnosis of bone marrow biopsy specimens closer to fruition., Key Points A data set of >170 000 microscopic images allows training neural networks for identification of BM cells with high accuracy.Neural networks outperform a feature-based approach to BM cell classification and can be analyzed with explainability and feature embedding methods., Visual Abstract, Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times every day because of a lack of data sets and trained models. We applied convolutional neural networks (CNNs) to a large data set of 171 374 microscopic cytological images taken from BM smears from 945 patients diagnosed with a variety of hematological diseases. The data set is the largest expert-annotated pool of BM cytology images available in the literature. It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species with high precision and recall. Our CNNs outcompete previous feature-based approaches and provide a proof-of-concept for the classification problem of single BM cells. This study is a step toward automated evaluation of BM cell morphology using state-of-the-art image-classification algorithms. The underlying data set represents an educational resource, as well as a reference for future artificial intelligence–based approaches to BM cytomorphology.

This article reports our experience in developing a computerized cognitive assessment method in a nurse-led clinic. The assessment and report-writing time and the accuracy of using a computer as an assessment medium were compared with assessments that use the traditional pencil-and-paper method. The attitudes of professionals and service users toward the computerized approach were also collected. The results found that the computerized approach not only helped to reduce the time spent making assessments and writing reports but also improved the accuracy of the assessment when compared with the pencil-and-paper method. Most of the professionals and service users who were interviewed responded positively toward computerized assessments. The experience gained in this study will also help us to standardize our assessment procedures, promote communication among members of the clinical team, and facilitate data management and clinical research.

Objectives Evaluation of a computerised electrocardiogram algorithm compared to the interpretation of a team of board-certified veterinary cardiologists. Materials and methods This was a cross-sectional retrospective cohort study. A total of 399 electronic canine electrocardiogram recordings screened from 1391 electrocardiograms were enrolled in the study. A panel of seven cardiologists, masked to patient information, evaluated electrocardiograms for the following: P-wave amplitude and duration; PR-interval; R-wave amplitude; QRS duration; heart rate; mean electrical axis; and final overall diagnosis for the detection of arrhythmia and any abnormal electrocardiogram anomaly. Results The sensitivity of the electrocardiogram algorithm for detecting arrhythmias was 99.7% (95% confidence intervals, CI: 98.5 to 99.9) and the specificity was 99.5% (95% CI: 98.0 to 99.9) compared to the consensus result created by panel of cardiologists. The sensitivity of the algorithm for the detection of any electrocardiogram anomaly, including abnormal measurements, was 71.3% (95% CI: 65.5 to 76.7) and the specificity was 35.1% (95% CI: 27.0 to 43.8) compared to the panel of cardiologists. Clinical significance The electrocardiogram algorithm was shown to have high sensitivity for the detection of arrhythmias, but not all electrocardiogram anomalies. The results support the use of this algorithm as a tool to aid in the triage of the electrocardiogram workflow.

Background: Work stress affects individual health and well-being. These negative effects could be mitigated through regular monitoring of employees’ stress. Such monitoring becomes even more important as the digital transformation of the economy implies profound changes in working conditions. Objective: The goal of this study was to investigate the association between computer mouse movements and work stress in the field. Methods: We hypothesized that stress is associated with a speed-accuracy trade-off in computer mouse movements. To test this hypothesis, we conducted a longitudinal field study at a large business organization, where computer mouse movements from regular work activities were monitored over 7 weeks; the study included 70 subjects and 1829 observations. A Bayesian regression model was used to estimate whether self-reported acute work stress was associated with a speed-accuracy trade-off in computer mouse movements. Results: There was a negative association between stress and the two-way interaction term of mouse speed and accuracy (mean −0.32, 95% highest posterior density interval −0.58 to −0.08), which means that stress was associated with a speed-accuracy trade-off. The estimated association was not sensitive to different processing of the data and remained negative after controlling for the demographics, health, and personality traits of subjects. Conclusions: Self-reported acute stress is associated with computer mouse movements, specifically in the form of a speed-accuracy trade-off. This finding suggests that the regular analysis of computer mouse movements could indicate work stress., Journal of Medical Internet Research, 23 (4), ISSN:1438-8871

O BESITY IS A PUBLIC HEALTH burden associated with a number of adverse health conditions and high health care costs. As an integral part of standard behavior-based therapy, selfmonitoring has been used in many lifestyle intervention programs to address the obesity epidemic. In weight management, self-monitoring usually consists of tracking dietary intake, physical activity, body weight, and/ or other health measurements. Previous research examined the efficacy of dietary self-monitoring on weight management. However, most of those studies addressed self-monitoring using traditional paper methods. The subsequent development and use of advanced technology-based self-monitoring approaches shows promising effects inweight loss.We evaluated the emerging evidence on the efficacy and adherence to different types of dietary self-monitoring, including traditional paper methods, web-based self-monitoring, and the use of mobile devices such as personal digital assistants, wearables, and tablets/mobile smartphones. Motivations and barriers to dietary selfmonitoring are described and strategies to improve adherence are identified. A discussion on future directions of dietary self-monitoring approaches in research and practice is also provided.

Objective:To assess the relative validity of a FFQ developed for the Pelotas Birth Cohort Studies.Design:Participants completed a ninety-two-food-item FFQ and then answered two 24-h recalls (24HR), one in-person interview and a second one by telephone, administered 14–28 d apart. Median and relative differences of energy, fifteen nutrients and eleven food groups were estimated based on the FFQ and the average of two 24HR. Nutrients were log-transformed and energy-adjusted using residual method. Validity was assessed by crude, energy-adjusted and de-attenuated Pearson and Lin’s concordance correlation coefficients. Agreement of quartiles and weighted κ were performed. Differences in energy and nutrient estimations between methods were plotted in Bland–Altman graphs.Setting:Pelotas, southern Brazil.Participants:Two hundred fifty-four participants randomly selected from the 1993 Pelotas Birth Cohort during the 22-year follow-up (2015).Results:The FFQ overestimated energy and most nutrients and food groups compared with the two 24HR. Energy-adjusted and de-attenuated Pearson correlation coefficients ranged from 0·21 to 0·66. The highest energy-adjusted and de-attenuated concordance correlation coefficients were observed for Ca (0·48), niacin (0·32), Na (0·29), vitamin C (0·28) and riboflavin (0·25). The percentage of nutrients classified into the same and opposite quartiles ranged from 36·5 to 60·3 %, and from 4·8 to 19·1 %, respectively. Weighted κ was moderate for Ca (0·51), beans and legumes (0·50) and milk and dairies (0·49).Conclusions:The FFQ provides a reasonable dietary intake assessment for habitual food consumption. However, the relative validity was weak for specific nutrients and food groups.

Background Facial expressions require the complex coordination of 43 different facial muscles. Parkinson disease (PD) affects facial musculature leading to “hypomimia” or “masked facies.” Objective We aimed to determine whether modern computer vision techniques can be applied to detect masked facies and quantify drug states in PD. Methods We trained a convolutional neural network on images extracted from videos of 107 self-identified people with PD, along with 1595 videos of controls, in order to detect PD hypomimia cues. This trained model was applied to clinical interviews of 35 PD patients in their on and off drug motor states, and seven journalist interviews of the actor Alan Alda obtained before and after he was diagnosed with PD. Results The algorithm achieved a test set area under the receiver operating characteristic curve of 0.71 on 54 subjects to detect PD hypomimia, compared to a value of 0.75 for trained neurologists using the United Parkinson Disease Rating Scale-III Facial Expression score. Additionally, the model accuracy to classify the on and off drug states in the clinical samples was 63% (22/35), in contrast to an accuracy of 46% (16/35) when using clinical rater scores. Finally, each of Alan Alda’s seven interviews were successfully classified as occurring before (versus after) his diagnosis, with 100% accuracy (7/7). Conclusions This proof-of-principle pilot study demonstrated that computer vision holds promise as a valuable tool for PD hypomimia and for monitoring a patient’s motor state in an objective and noninvasive way, particularly given the increasing importance of telemedicine.

Background Hip and knee osteoarthritis is ranked as the 11th highest contributor to global disability. Exercise is a core treatment in osteoarthritis. The model for physical activity–related health competence describes possibilities to empower patients to perform physical exercises in the best possible health-promoting manner while taking into account their own physical condition. Face-to-face supervision is the gold standard for exercise guidance. Objective The aim of this study was to evaluate whether instruction and guidance via a digital app is not inferior to supervision by a physiotherapist with regard to movement quality, control competence for physical training, and exercise-specific self-efficacy. Methods Patients with clinically diagnosed hip osteoarthritis were recruited via print advertisements, emails and flyers. The intervention consisted of two identical training sessions with one exercise for mobility, two for strength, and one for balance. One session was guided by a physiotherapist and the other was guided by a fully automated tablet computer-based app. Both interventions took place at a university hospital. Outcomes were assessor-rated movement quality, and self-reported questionnaires on exercise-specific self-efficacy and control competence for physical training. Participants were randomly assigned to one of two treatment sequences. One sequence started with the app in the first session followed by the physiotherapist in the second session after a minimum washout phase of 27 days (AP group) and the other sequence occurred in the reverse order (PA group). Noninferiority was defined as a between-treatment effect (gIG)

Although magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, the specificity is lower. The purpose of this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses on dynamic contrast material-enhanced MRI (DCE-MRI) by using a deep convolutional neural network (DCNN) with Bayesian optimization. Our database consisted of 56 DCE-MRI examinations for 56 patients, each of which contained five sequential phase images. It included 26 benign and 30 malignant masses. In this study, we first determined a baseline DCNN model from well-known DCNN models in terms of classification performance. The optimum architecture of the DCNN model was determined by changing the hyperparameters of the baseline DCNN model such as the number of layers, the filter size, and the number of filters using Bayesian optimization. As the input of the proposed DCNN model, rectangular regions of interest which include an entire mass were selected from each of DCE-MRI images by an experienced radiologist. Three-fold cross validation method was used for training and testing of the proposed DCNN model. The classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 92.9% (52/56), 93.3% (28/30), 92.3% (24/26), 93.3% (28/30), and 92.3% (24/26), respectively. These results were substantially greater than those with the conventional method based on handcrafted features and a classifier. The proposed DCNN model achieved high classification performance and would be useful in differential diagnoses of masses in breast DCE-MRI images as a diagnostic aid.

BackgroundCollectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. ObjectiveThe aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. MethodsFrontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. ResultsWe included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P

Motivation Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models. Results Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures. Availabilityand implementation Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Bivalent ligands are increasingly important such as for targeting G protein-coupled receptor (GPCR) dimers or proteolysis targeting chimeras (PROTACs). They contain two pharmacophoric units that simultaneously bind in their corresponding binding sites, connected with a spacer chain. Here, we report a molecular modelling tool that links the pharmacophore units via the shortest pathway along the receptors van der Waals surface and then scores the solutions providing prioritization for the design of new bivalent ligands. Results Bivalent ligands of known dimers of GPCRs, PROTACs and a model bivalent antibody/antigen system were analysed. The tool could rapidly assess the preferred linker length for the different systems and recapitulated the best reported results. In the case of GPCR dimers the results suggest that in some cases these ligands might bind to a secondary binding site at the extracellular entrance (vestibule or allosteric site) instead of the orthosteric binding site. Availability and implementation Freely accessible from the Molecular Operating Environment svl exchange server (https://svl.chemcomp.com/). Supplementary information Supplementary data are available at Bioinformatics online.

Due to the growing field of digital pathology, more and more digital histology slides are becoming available. This improves the accessibility, allows teleconsultations from specialized pathologists, improves education, and might give urologist the possibility to review the slides in patient management systems. Moreover, by stacking multiple two-dimensional (2D) digital slides, three-dimensional volumes can be created, allowing improved insight in the growth pattern of a tumor. With the addition of computer-aided diagnosis systems, pathologist can be guided to regions of interest, potentially reducing the workload and interobserver variation. Digital (3D) pathology has the potential to improve dialog between the pathologist and urologist, and, therefore, results in a better treatment selection for urologic patients. Electronic supplementary material The online version of this article (10.1007/s00345-018-2202-1) contains supplementary material, which is available to authorized users.

Background Nonadherence to medications, failure to prevent exposure to asthma triggers, lack of knowledge about using medications, and fixed mindsets contribute to poor asthma control in children. Digital learning could provide a new strategy for improving health-related outcomes in children with asthma. Objective The aim of this study is to develop and design a digital educational program, titled Module of Inhaler and Asthma Triggers for Children (MIRACLE), for Indonesian children with asthma. The program comprises an interactive narrative and a serious game. It was proposed to increase the understanding of asthma self-management, instruct on proper inhaler techniques, improve asthma control, and promote a growth mindset for children with asthma. Methods Two phases of research were conducted to develop the program. In the first phase, a literature search and two rounds of the Delphi technique were conducted to obtain agreement from an expert panel regarding elements of asthma self-management and the design of interactive narratives and a serious game. The expert panel item statements were evaluated using the content validity index (CVI). In the second phase, the SERES framework, Norma Engaging Multimedia Design, and Psychological Theory of Growth Mindset were applied to create a storyline, learn objectives, and game challenges. Results In the first phase, 40 experts were invited to participate in Delphi round 1. Forty responses were collected to generate 38 item statements that consisted of part 1, elements of asthma self-management (25 items), and part 2, design of an interactive narrative and a serious game (13 items); 38 experts were involved in Delphi round 2. In total, 24 statements in part 1 and 13 items in part 2 had item-CVI values >0.80. The average CVI was 0.9, which was considered acceptable. Four narrative plots and five game sessions were developed during the second phase. Challenges with the scenario, scoring, and feedback on asthma difficulties were designed to promote a growth mindset for learners. Conclusions We developed a culture-specific, computer-based asthma program containing an interactive narrative and a serious game to deliver asthma self-management and promote a growth mindset among Indonesian children.

Background Test anxiety often leads to poor academic performance. This study aimed to determine the effect of computer-based tests on nursing students' test anxiety. Methods This quasi-experimental study was conducted in 2016 on 39 nursing student with anxiety score under 128 on Spielberger's State-Trait Inventory (STAI). They were randomly allocated to computer-based tests (CBT) and paper-based test (PBT) group. Prior to exam, all students completed Sarason's Test Anxiety Scale (TAS). We administered CBT for students in experimental group. Data were analyzed using independent t-test and one-way ANOVA. Results Students mean test anxiety score was 11.94 and 11.32 in CBT and PBT groups, respectively. 47.4% of students in CBT group and 29.4% of those in PBT group experienced higher test anxiety, while the difference was not significant (p=0.56). Conclusions Despite, there was no significant difference between anxiety score of two groups; but students' test anxiety score was higher in CBT group. With the current increase in computer-based assessment, educational administrators must be aware of and plan for the possible unfavorable effects of computer assisted testing, such an anxiety. Future studies are needed to evaluate and compare the effect of different type of student testing such as distance testing or CBTs using new information technologies such as laptop, tablet or mobile phone on students' test anxiety and performance.

A novel microbial-electrochemical filter was designed and operated based on a combined microbial electrolysis cell and bio-trickling filter principles with the aim to maximize gas–liquid mass-transfer efficiency and minimize costs associated with bubbling biogas through liquid-filled reactor. CO2/biogas feed to the MEF was done via a computer-feedback pH control strategy, linking CO2 feed directly to the OH− production. As a result current efficiency was constant at around 100% throughout the period of experiments. CO2 from biogas was almost completely removed at cathodic pH setpoint of 8.5. Maximum CO2 removal rate was 14.6 L/L/day (equivalent to 29.2 L biogas/L/day). Net energy consumption was around 1.28 kWh/Nm3CO2 or 0.64 kWh/m3 biogas (maximum 49% energy efficiency). An ability to maintain a constant pH means elevated pH from increasing applied potential (current) is no longer an issue. The process can potentially be up-scaled and operated at a much higher current and therefore CO2 removal rate.

Author(s): Zhao, Beiqun; Tai-Seale, Ming; Longhurst, Christopher; Clay, Brian | Abstract: BackgroundPatient portals tethered to electronic health records can improve patient experience, activation, and outcomes. However, adoption of inpatient portals has been challenging. One way to potentially increase inpatient portal usage is to integrate it with a room control (RC) app on a common tablet computer.ObjectiveThe aim of this study was to perform a retrospective analysis of patient usage of an RC app provided on tablet computers in patient rooms of our new inpatient tower.MethodsWe identified all patients who were admitted for g24 hours to our new inpatient tower over a 90-day period from September 1 to November 30, 2017. After excluding newborn patients from our analysis, we then identified patients who used the RC app at least one time during their admission. We linked these data to patient demographics (including age, sex, and race) and admitting service. We then performed univariable and multivariable logistic regression to assess patterns of RC app usage.ResultsA total of 3411 patients were admitted over the course of the study period; 2242/3411 (65.73%) used the RC app during their hospitalization. Compared with white patients, other/mixed/unknown race and Asian, Hawaiian, Pacific Islander, American Indian race were significantly associated with increased use of the RC app in a multivariable analysis. Increasing age was significantly associated with increased usage of the RC app. Usage of the RC app also varied by admitting services. Compared with general medicine, bone marrow transplant and general surgery patients had increased usage of the RC app. Conversely, critical care, medical specialties, neurology, surgical subspecialties, and obstetrics/gynecology were all associated with decreased usage of the RC app.ConclusionsOur study shows that one-third of patients are not using the RC app for critical room functions. Future initiatives to increase RC usage should take these populations into consideration. Contrary to common belief, older patients may use tablet-enabled RCs just as often, if not more often, than younger patients. Certain admitting services, such as neurology and surgical subspecialties, may have had lower usage rates owing to accessibility issues. Our study allows hospitals to tailor support for specific patient populations to increase RC app usage.

Background: Safety-net systems serve patients with limited health literacy and limited English proficiency (LEP) who face communication barriers. However, little is known about how diverse safety-net patients feel about increasing clinician electronic health record (EHR) use. Objective: The aim of this study was to better understand how safety-net patients, including those with LEP, view clinician EHR use. Methods: We conducted focus groups in English, Spanish, and Cantonese (N=37) to elicit patient perspectives on how clinicians use EHRs during clinic visits. Using a grounded theory approach, we coded transcripts to identify key themes. Results: Across multiple language groups, participants accepted multitasking and silent clinician EHR use if focused on their care. However, participants desired more screen share and eye contact, especially when demonstrating physical concerns. All participants, including LEP participants, wanted clinicians to include them in EHR use. Conclusions: Linguistically diverse patients accept the value of EHR use during outpatient visits but desire more eye contact, verbal warnings before EHR use, and screen-sharing. Safety-net health systems should support clinicians in completing EHR-related tasks during the visit using patient-centered strategies for all patients.

Background Prolonged time of computer use increases the prevalence of ocular problems, including eye strain, tired eyes, irritation, redness, blurred vision, and double vision, which are collectively referred to as computer vision syndrome (CVS). Approximately 70% of computer users have vision-related problems. For these reasons, properly designed interventions for users with CVS are required. To design an effective screen intervention for preventing or improving CVS, we must understand the effective interfaces of computer-based interventions. Objective In this study, we aimed to explore the interface elements of computer-based interventions for CVS to set design guidelines based on the pros and cons of each interface element. Methods We conducted an iterative user study to achieve our research objective. First, we conducted a workshop to evaluate the overall interface elements that were included in previous systems for CVS (n=7). Through the workshop, participants evaluated existing interface elements. Based on the evaluation results, we eliminated the elements that negatively affect intervention outcomes. Second, we designed our prototype system LiquidEye that includes multiple interface options (n=11). Interface options included interface elements that were positively evaluated in the workshop study. Lastly, we deployed LiquidEye in the real world to see how the included elements affected the intervention outcomes. Participants used LiquidEye for 14 days, and during this period, we collected participants’ daily logs (n=680). Additionally, we conducted prestudy and poststudy surveys, and poststudy interviews to explore how each interface element affects participation in the system. Results User data logs collected from the 14 days of deployment were analyzed with multiple regression analysis to explore the interface elements affecting user participation in the intervention (LiquidEye). Statistically significant elements were the instruction page of the eye resting strategy (P=.01), goal setting of the resting period (P=.009), compliment feedback after completing resting (P Conclusions Based on the study results, we suggested design implications to consider when designing computer-based interventions for CVS. The sophisticated design of the customization interface can make it possible for users to use the system more interactively, which can result in higher engagement in managing eye conditions. There are important technical challenges that still need to be addressed, but given the fact that this study was able to clarify the various factors related to computer-based interventions, the findings are expected to contribute greatly to the research of various computer-based intervention designs in the future.

Clinical neuropsychologists have long underutilized computer technologies for neuropsychological assessment. Given the rapid advances in technology (e.g. virtual reality; tablets; iPhones) and the increased accessibility in the past decade, there is an on-going need to identify optimal specifications for advanced technologies while minimizing potential sources of error. Herein, we discuss concerns raised by a joint American Academy of Clinical Neuropsychology/National Academy of Neuropsychology position paper. Moreover, we proffer parameters for the development and use of advanced technologies in neuropsychological assessments.We aim to first describe software and hardware configurations that can impact a computerized neuropsychological assessment. This is followed by a description of best practices for developers and practicing neuropsychologists to minimize error in neuropsychological assessments using advanced technologies. We also discuss the relevance of weighing potential computer error in light of possible errors associated with traditional testing. Throughout there is an emphasis on the need for developers to provide bench test results for their software's performance on various devices and minimum specifications (documented in manuals) for the hardware (e.g. computer, monitor, input devices) in the neuropsychologist's practice.Advances in computerized assessment platforms offer both opportunities and challenges. The challenges can appear daunting but are a manageable and require informed consumers who can appreciate the issues and ask pertinent questions in evaluating their options.

The use of Big Data--however the term is defined--involves a wide array of issues and stakeholders, thereby increasing numbers of complex decisions around issues including data acquisition, use, and sharing. Big Data is becoming a significant component of practice in an ever-increasing range of disciplines; however, since it is not a coherent "discipline" itself, specific codes of conduct for Big Data users and researchers do not exist. While many institutions have created, or will create, training opportunities (e.g., degree programs, workshops) to prepare people to work in and around Big Data, insufficient time, space, and thought have been dedicated to training these people to engage with the ethical, legal, and social issues in this new domain. Since Big Data practitioners come from, and work in, diverse contexts, neither a relevant professional code of conduct nor specific formal ethics training are likely to be readily available. This normative paper describes an approach to conceptualizing ethical reasoning and integrating it into training for Big Data use and research. Our approach is based on a published framework that emphasizes ethical reasoning rather than topical knowledge. We describe the formation of professional community norms from two key disciplines that contribute to the emergent field of Big Data: computer science and statistics. Historical analogies from these professions suggest strategies for introducing trainees and orienting practitioners both to ethical reasoning and to a code of professional conduct itself. We include two semester course syllabi to strengthen our thesis that codes of conduct (including and beyond those we describe) can be harnessed to support the development of ethical reasoning in, and a sense of professional identity among, Big Data practitioners.

Knowledge about the total waste generated by the production of consumer goods can help raise awareness among policy-makers, producers and consumers of the benefits of closing loops in a future circular economy, avoiding unnecessary production and production steps and associated generation of large amounts of waste. In strict life cycle assessment practice, information on waste outputs from intermediate industrial processes of material and energy transformation is translated into and declared as potential environmental impacts, which are often not reported in the final results. In this study, a procedure to extract available intermediate data and perform a systematic pre-consumer waste footprint analysis was developed. The pre-consumer waste footprint concept was tested to analyse 10 generic products, which provided some novel and interesting results for the different product categories and identified a number of challenges that need to be resolved in development of the waste footprint concept. These challenges include standardised data declaration on waste in life cycle assessment, with a separation into waste categories illustrating the implicit environmental and scale of significance of waste types and quantities (e.g. hazardous waste, inert waste, waste for recycling/incineration) and establishment of a common definition of waste throughout sectors and nations.

Motivation: The growing amount of regulatory data from the ENCODE, Roadmap Epigenomics and other consortia provides a wealth of opportunities to investigate the functional impact of single nucleotide polymorphisms (SNPs). Yet, given the large number of regulatory datasets, researchers are posed with a challenge of how to efficiently utilize them to interpret the functional impact of SNP sets. Results: We developed the GenomeRunner web server to automate systematic statistical analysis of SNP sets within a regulatory context. Besides defining the functional impact of SNP sets, GenomeRunner implements novel regulatory similarity/differential analyses, and cell type-specific regulatory enrichment analysis. Validated against literature- and disease ontology-based approaches, analysis of 39 disease/trait-associated SNP sets demonstrated that the functional impact of SNP sets corresponds to known disease relationships. We identified a group of autoimmune diseases with SNPs distinctly enriched in the enhancers of T helper cell subpopulations, and demonstrated relevant cell type-specificity of the functional impact of other SNP sets. In summary, we show how systematic analysis of genomic data within a regulatory context can help interpreting the functional impact of SNP sets. Availability and Implementation: GenomeRunner web server is freely available at http://www.integrativegenomics.org/. Contact: mikhail.dozmorov@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Background No study has comprehensively investigated the association between the usage of typical screen-based electronic media devices and sleep quality in a Chinese population with individuals in a wide range of ages. Objective This study aimed to understand the characteristics of television (TV) viewing, computer usage, and mobile phone usage in a representative Chinese population in Macau and to examine their roles in predicting the variations in sleep quality. Methods This cross-sectional study was an analysis of 1500 Macau residents aged 15 to 90 years based on a community-based health needs assessment study entitled, “Healthy Living, Longer Lives.” Data collection was conducted in 7 districts of Macau from 2017 to 2018 through face-to-face interviews. The durations of daily TV viewing, computer usage, and mobile phone usage were recorded in a self-administered questionnaire. The Chinese version of the Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality. Results The prevalence of TV, computer, and mobile phone usage was 78.4% (1176/1500), 51.6% (769/1490), and 85.5% (1276/1492), respectively. The average daily hours of usage were 1.75 (1.62), 1.53 (2.26), and 2.85 (2.47) hours, respectively. Females spent more time watching TV (P=.03) and using mobile phones (P=.02) and less time on the computer (P=.04) as compared to males. Older adults were more likely to watch TV while young people spent more time using the computer and mobile phones (P for all trends5), respectively. Conclusions The mobile phone was the most popular screen-based electronic device used in the Macau population, especially among young people. “J” shape associations were observed between sleep quality and the duration of TV viewing, computer usage, and mobile phone usage, indicating that the extreme use of screen-based electronic devices predicted poorer sleep status, whereas moderate use would be acceptable.

Background Hong Kong has a high rate of electronic device (e-device; computer, smartphone, and tablet) use. However, little is known about the associations of the duration of e-device use before and after sleep with psychological symptoms. Objective This study aimed to investigate the associations of the duration of e-device use before and after sleep with psychological distress. Methods A probability-based telephone survey was conducted on 3162 Hong Kong adults (54.6% female; mean age 47.4 years, SD 18.3 years) in 2016. Multivariate linear and Poisson regressions were used to calculate adjusted regression coefficients (aBs) and prevalence ratios (aPRs) of anxiety and depressive symptoms (measured by Patient Health Questionnaire-4) for the duration from waking to the first e-device use (≥61, 31-60, 6-30, and ≤5 minutes) and the duration of e-device use before sleeping (≤5, 6-30, 31-60, and ≥61 minutes). Results The first e-device use in ≤5 (vs ≥61) minutes after waking was associated with anxiety (aB 0.35, 95% CI 0.24-0.46; aPR 1.74, 95% CI 1.34-2.25) and depressive symptoms (aB 0.27, 95% CI 0.18-0.37; aPR 1.84, 95% CI 1.33-2.54). Using e-devices for ≥61 (vs ≤5) minutes before sleeping was also associated with anxiety (aB 0.17, 95% CI 0.04-0.31; aPR 1.32, 95% CI 1.01-1.73) and depressive symptoms (aB 0.17, 95% CI 0.05-0.28; aPR 1.47, 95% CI 1.07-2.02). E-device use both ≤5 minutes after waking and for ≥61 minutes before sleeping was strongly associated with anxiety (aB 0.68, 95% CI 0.47-0.90; aPR 2.64, 95% CI 1.90-3.67) and depressive symptoms (aB 0.55, 95% CI 0.36-0.74; aPR 2.56, 95% CI 1.69-3.88). Conclusions E-device use immediately (≤5 minutes) after waking and use for a long duration (≥61 minutes) before sleeping were associated with anxiety and depressive symptoms among Chinese adults in Hong Kong.