34 research outputs found

    A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the Remote Early Detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial

    Get PDF
    OBJECTIVES: It is currently thought that most-but not all-individuals infected with SARS-CoV-2 develop symptoms, but the infectious period starts on average 2 days before the first overt symptoms appear. It is estimated that pre- and asymptomatic individuals are responsible for more than half of all transmissions. By detecting infected individuals before they have overt symptoms, wearable devices could potentially and significantly reduce the proportion of transmissions by pre-symptomatic individuals. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests [to determine if there are antibodies against the SARS-CoV-2 in the blood] or SARS-CoV-2 infection tests such as polymerase chain reaction [PCR] or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the following two algorithms to detect first time SARS-CoV-2 infection including early or asymptomatic infection: • The algorithm using Ava bracelet data when coupled with self-reported Daily Symptom Diary data (Wearable + Symptom Data Algo; experimental condition) • The algorithm using self-reported Daily Symptom Diary data alone (Symptom Only Algo; control condition) In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. TRIAL DESIGN: The trial is a randomized, single-blinded, two-period, two-sequence crossover trial. The study will start with an initial learning phase (maximum of 3 months), followed by period 1 (3 months) and period 2 (3 months). Subjects entering the study at the end of the recruitment period may directly start with period 1 and will not be part of the learning phase. Each subject will undergo the experimental condition (the Wearable + Symptom Data Algo) in either period 1 or period 2 and the control condition (Symptom Only Algo) in the other period. The order will be randomly assigned, resulting in subjects being allocated 1:1 to either sequence 1 (experimental condition first) or sequence 2 (control condition first). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. PARTICIPANTS: The trial will be conducted in the Netherlands. A target of 20,000 subjects will be enrolled. Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. This results in approximately 6500 normal-risk individuals and 3500 high-risk individuals per sequence. Subjects will be recruited from previously studied cohorts as well as via public campaigns and social media. All data for this study will be collected remotely through the Ava COVID-RED app, the Ava bracelet, surveys in the COVID-RED web portal and self-sampling serology and PCR kits. More information on the study can be found in www.covid-red.eu . During recruitment, subjects will be invited to visit the COVID-RED web portal. After successfully completing the enrolment questionnaire, meeting eligibility criteria and indicating interest in joining the study, subjects will receive the subject information sheet and informed consent form. Subjects can enrol in COVID-RED if they comply with the following inclusion and exclusion criteria: Inclusion criteria: • Resident of the Netherlands • At least 18 years old • Informed consent provided (electronic) • Willing to adhere to the study procedures described in the protocol • Must have a smartphone that runs at least Android 8.0 or iOS 13.0 operating systems and is active for the duration of the study (in the case of a change of mobile number, the study team should be notified) • Be able to read, understand and write Dutch Exclusion criteria: • Previous positive SARS-CoV-2 test result (confirmed either through PCR/antigen or antibody tests; self-reported) • Current suspected (e.g. waiting for test result) COVID-19 infection or symptoms of a COVID-19 infection (self-reported) • Participating in any other COVID-19 clinical drug, vaccine or medical device trial (self-reported) • Electronic implanted device (such as a pacemaker; self-reported) • Pregnant at the time of informed consent (self-reported) • Suffering from cholinergic urticaria (per the Ava bracelet's user manual; self-reported) • Staff involved in the management or conduct of this study INTERVENTION AND COMPARATOR: All subjects will be instructed to complete the Daily Symptom Diary in the Ava COVID-RED app daily, wear their Ava bracelet each night and synchronize it with the app each day for the entire period of study participation. Provided with wearable sensor and/or self-reported symptom data within the last 24 h, the Ava COVID-RED app's underlying algorithms will provide subjects with a real-time indicator of their overall health and well-being. Subjects will see one of three messages, notifying them that no seeming deviations in symptoms and/or physiological parameters have been detected; some changes in symptoms and/or physiological parameters have been detected and they should self-isolate; or alerting them that deviations in their symptoms and/or physiological parameters could be suggestive of a potential COVID-19 infection and to seek additional testing. We will assess the intraperson performance of the algorithms in the experimental condition (Wearable + Symptom Data Algo) and control conditions (Symptom Only Algo). Note that both algorithms will also instruct to seek testing when any SARS-CoV-2 symptoms are reported in line with those defined by the Dutch national institute for public health and the environment 'Rijksinstituut voor Volksgezondheid en Milieu' (RIVM) guidelines. MAIN OUTCOMES: The trial will evaluate the use and performance of the Ava COVID-RED app and Ava bracelet, which uses sensors to measure breathing rate, pulse rate, skin temperature and heart rate variability for the purpose of early and asymptomatic detection and monitoring of SARS-CoV-2 in general and high-risk populations. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests, PCR tests and/or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each of the following two algorithms to detect first-time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava bracelet data when coupled with the self-reported Daily Symptom Diary data and the algorithm using self-reported Daily Symptom Diary data alone. In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. The protocol contains an additional twenty secondary and exploratory objectives which address, among others, infection incidence rates, health resource utilization, symptoms reported by SARS-CoV-2-infected participants and the rate of breakthrough and asymptomatic SARS-CoV-2 infections among individuals vaccinated against COVID-19. PCR or antigen testing will occur when the subject receives a notification from the algorithm to seek additional testing. Subjects will be advised to get tested via the national testing programme and report the testing result in the Ava COVID-RED app and a survey. If they cannot obtain a test via the national testing programme, they will receive a nasal swab self-sampling kit at home, and the sample will be tested by PCR in a trial-affiliated laboratory. In addition, all subjects will be asked to take a capillary blood sample at home at baseline (between month 0 and 3.5 months after the start of subject recruitment), at the end of the learning phase (month 3; note that this sampling moment is skipped if a subject entered the study at the end of the recruitment period), period 1 (month 6) and period 2 (month 9). These samples will be used for SARS-CoV-2-specific antibody testing in a trial-affiliated laboratory, differentiating between antibodies resulting from a natural infection and antibodies resulting from COVID-19 vaccination (as vaccination will gradually be rolled out during the trial period). Baseline samples will only be analysed if the sample collected at the end of the learning phase is positive, or if the subject entered the study at the end of the recruitment period, and samples collected at the end of period 1 will only be analysed if the sample collected at the end of period 2 is positive. When subjects obtain a positive PCR/antigen or serology test result during the study, they will continue to be in the study but will be moved into a so-called COVID-positive mode in the Ava COVID-RED app. This means that they will no longer receive recommendations from the algorithms but can still contribute and track symptom and bracelet data. The primary analysis of the main objective will be executed using the data collected in period 2 (months 6 through 9). Within this period, serology tests (before and after period 2) and PCR/antigen tests (taken based on recommendations by the algorithms) will be used to determine if a subject was infected with SARS-CoV-2 or not. Within this same time period, it will be determined if the algorithms gave any recommendations for testing. The agreement between these quantities will be used to evaluate the performance of the algorithms and how these compare between the study conditions. RANDOMIZATION: All eligible subjects will be randomized using a stratified block randomization approach with an allocation ratio of 1:1 to one of two sequences (experimental condition followed by control condition or control condition followed by experimental condition). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence, resulting in approximately equal numbers of high-risk and normal-risk individuals between the sequences. BLINDING (MASKING): In this study, subjects will be blinded to the study condition and randomization sequence. Relevant study staff and the device manufacturer will be aware of the assigned sequence. The subject will wear the Ava bracelet and complete the Daily Symptom Diary in the Ava COVID-RED app for the full duration of the study, and they will not know if the feedback they receive about their potential infection status will only be based on the data they entered in the Daily Symptom Diary within the Ava COVID-RED app or based on both the data from the Daily Symptom Diary and the Ava bracelet. NUMBERS TO BE RANDOMIZED (SAMPLE SIZE): A total of 20,000 subjects will be recruited and randomized 1:1 to either sequence 1 (experimental condition followed by control condition) or sequence 2 (control condition followed by experimental condition), taking into account their risk level. This results in approximately 6500 normal-risk and 3500 high-risk individuals per sequence. TRIAL STATUS: Protocol version: 3.0, dated May 3, 2021. Start of recruitment: February 19, 2021. End of recruitment: June 3, 2021. End of follow-up (estimated): November 2021 TRIAL REGISTRATION: The Netherlands Trial Register on the 18th of February, 2021 with number NL9320 ( https://www.trialregister.nl/trial/9320 ) FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this letter serves as a summary of the key elements of the full protocol

    On the effects of the fix geometric constraint in 2D profiles on the reusability of parametric 3D CAD models

    Get PDF
    [EN] In order to be reusable, history-based feature-based parametric CAD models must reliably allow for modifications while maintaining their original design intent. In this paper, we demonstrate that relations that fix the location of geometric entities relative to the reference system produce inflexible profiles that reduce model reusability. We present the results of an experiment where novice students and expert CAD users performed a series of modifications in different versions of the same 2D profile, each defined with an increasingly higher number of fix geometric constraints. Results show that the amount of fix constraints in a 2D profile correlates with the time required to complete reusability tasks, i.e., the higher the number of fix constraints in a 2D profile, the less flexible and adaptable the profile becomes to changes. In addition, a pilot software tool to automatically track this type of constraints was developed and tested. Results suggest that the detection of fix constraint overuse may result in a new metric to assess poor quality models with low reusability. The tool provides immediate feedback for preventing high semantic level quality errors, and assistance to CAD users. Finally, suggestions are introduced on how to convert fix constraints in 2D profiles into a negative metric of 3D model quality.The authors would like to thank Raquel Plumed for her support in the statistical analysis. This work has been partially funded by Grant UJI-A02017-15 (Universitat Jaume I) and DPI201784526-R (MINECO/AEI/FEDER, UE), project CAL-MBE. The authors also wish to thank the editor and reviewers for their valuable comments and suggestions that helped us improve the quality of the paper.González-Lluch, C.; Company, P.; Contero, M.; Pérez Lopez, DC.; Camba, JD. (2019). On the effects of the fix geometric constraint in 2D profiles on the reusability of parametric 3D CAD models. International Journal of Technology and Design Education. 29(4):821-841. https://doi.org/10.1007/s10798-018-9458-zS821841294Ait-Aoudia, S., & Foufou, S. (2010). A 2D geometric constraint solver using a graph reduction method. Advances in Engineering Software, 41(10), 1187–1194. https://doi.org/10.1016/j.advengsoft.2010.07.008 .Ault, H. K. (1999). Using geometric constraints to capture design intent. Journal for Geometry and Graphics, 3(1), 39–45.Ault, H. K. (2004). Over-constrained, under-constrained or just right? Goldilocks evaluates DOF of sketched profiles. Paper presented at American Society for Engineering Education, 59th annual midyear meeting past, present and future? Williamsburg, November 21–23.Ault, H. K., Bu, L., & Liu, K. (2014). Solid modeling strategies-analyzing student choices. Paper presented at proceedings of the 121st ASEE annual conference and exposition, Indianapolis, June 15–18.Ault, H. K., & Fraser, A. (2013). A comparison of manual vs. online grading for solid models. Paper presented at 120th ASEE annual conference and exposition, Atlanta, GA, June 23–26, 2013, Paper ID #7233.Barbero, B. R., Pedrosa, C. M., & Samperio, R. Z. (2016). Learning CAD at university through summaries of the rules of design intent. International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-016-9358-z .Bodein, Y., Bertrand, R., & Caillaud, E. (2014). Explicit reference modeling methodology in parametric CAD system. Computers in Industry, 65(1), 136–147. https://doi.org/10.1016/j.compind.2013.08.004 .Bouma, W., Fudos, I., Hoffmann, C., Cai, J., & Paige, R. (1995). Geometric constraint solver. Computer-Aided Design, 27(6), 487–501. https://doi.org/10.1016/0010-4485(94)00013-4 .Briggs, J. C., Hepworth, A. I., Stone, B. R., Cobum, J. Q., Jensen, C. G., & Red, E. (2015). Integrated, synchronous multi-user design and analysis. Journal of Computing and Information Science in Engineering, 15(3), 031002. https://doi.org/10.1115/1.4029801 .Buckley, J., Seery, N., & Canty, D. (2017). Heuristics and CAD modelling: An examination of student behaviour during problem solving episodes within CAD modelling activities. International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-017-9423-2 .Camba, J. D., & Contero, M. (2015). Assessing the impact of geometric design intent annotations on parametric model alteration activities. Computers in Industry, 71, 35–45. https://doi.org/10.1016/j.compind.2015.03.006 .Camba, J. D., Contero, M., & Company, P. (2016). Parametric CAD modeling: An analysis of strategies for design reusability. Computer-Aided Design, 74, 18–31. https://doi.org/10.1016/j.cad.2016.01.003 .Camba, J. D., Contero, M., & Company, P. (2017). CAD reusability and the role of modeling information in the MBE context. Model-based enterprise summit 2017. National Institute of Standards and Technology (NIST), Gaithersburg, MD, April 3–7. MBE17-020. https://www.nist.gov/file/361581 .Cheng, Z., & Ma, Y. (2017). A functional feature modeling method. Advanced Engineering Informatics, 33, 1–15. https://doi.org/10.1016/j.aei.2017.04.003 .Cheng, Z., Xie, Y., & Ma, Y. (2018). Graph centrality analysis of feature dependencies to unveil modeling intents. Computer-Aided Design and Applications. https://doi.org/10.1080/16864360.2018.1441236 .Chester, I. (2007). Teaching for CAD expertise. International Journal of Technology and Design Education, 17, 23–35. https://doi.org/10.1007/s10798-006-9015-z .Company, P., Contero, M., Otey, J., & Plumed, R. (2015). Approach for developing coordinated rubrics to convey quality criteria in CAD training. Computer-Aided Design, 63, 101–117. https://doi.org/10.1016/j.cad.2014.10.00 .Company, P., & González-Lluch, C. (2013). CAD 3D con SolidWorks ® Tomo I: Diseño básico. Publicacions de la Universitat Jaume I. (Colección Sapientia, Núm. 86). http://cad3dconsolidworks.uji.es .Contero, M., Company, P., Vila, C., & Aleixos, N. (2002). Product data quality and collaborative engineering. IEEE Computer Graphics Applications, 22(3), 32–42. https://doi.org/10.1109/MCG.2002.999786 .Dixon, B. M., & Dannenhoffer, J. F., III. (2014). Geometric sketch constraint solving with user feedback. Journal of Aerospace Information Systems, 11(5), 316–325. https://doi.org/10.2514/1.I010110 .Fudos, I., & Hoffmann, C. M. (1997). A graph-constructive approach to solving systems of geometric constraints. ACM Transactions on Graphics, 16(2), 179–216. https://doi.org/10.1145/248210.248223 .Ge, J. X., Chou, S. C., & Gao, X. S. (1999). Geometric constraint satisfaction using optimization methods. Computer-Aided Design, 31(14), 867–879. https://doi.org/10.1016/S0010-4485(99)00074-3 .González-Lluch, C., Company, P., Contero, M., Camba, J. D., & Colom, J. (2017a). A case study on the use of model quality testing tools for the assessment of MCAD models and drawings. International Journal of Engineering Education, 33(5), 1643–1653.González-Lluch, C., Company, P., Contero, M., Camba, J. D., & Plumed, R. (2017b). A survey on 3D CAD model quality assurance and testing tools. Computer-Aided Design, 83, 64–79. https://doi.org/10.1016/j.cad.2016.10.003 .Hamade, R. F. (2009). Profiling the desirable CAD trainee: Technical background, personality attributes, and learning preferences. Journal of Mechanical Design, 131(12), 121009–121019. https://doi.org/10.1115/1.4000455 .Hekman, K. A., & Gordon, M. T. (2013). Automated grading of first year student CAD work. Paper presented at the 120th ASEE annual conference and exposition 2013, Atlanta, GA, June 23–26. Paper ID #6379.Hepworth, A., Tew, K., Trent, M., Ricks, R., Jensen, C. G., & Red, E. R. (2014). Model consistency and conflict resolution with data preservation in multi-user computer aided design. Journal of Computing and Information Science in Engineering, 14(2), 021008. https://doi.org/10.1115/1.4026553 .Jackson, C., & Buxton, M. (2007). The design reuse benchmark report: Seizing the opportunity to shorten product development. Boston: Aberdeen Group.Joan-Arinyo, R., Soto-Riera, A., Vila-Marta, S., & Vilaplana-Pastó, J. (2003). Transforming an under-constrained geometric constraint problem into a well-constrained one. Paper presented at proceedings of ACM SM03, Seatle, June 16–20.Kirstukas, S. J. (2016). Development and evaluation of a computer program to assess student CAD models. Paper presented at ASEE annual conference and exposition, New Orleans, June 26.Kramer, G. (1991). Using degrees of freedom analysis to solve geometric constraint systems. Paper presented at proceedings of the first ACM symposium on solid modeling foundations and CAD/CAM applications 1991, Austin, June 05–07.Kwon, S., Kim, B. C., Mun, D., & Han, S. (2015). Graph-based simplification of feature-based three-dimensional computer-aided design models for preserving connectivity. Journal of Computing and Information Science in Engineering, 15(3), 031010. https://doi.org/10.1115/1.4030748 .Leea, J. Y., & Kimb, K. (1998). A 2-D geometric constraint solver using DOF-based graph reduction. Computer-Aided Design, 30(11), 883–896. https://doi.org/10.1016/S0010-4485(98)00045-1 .Mata Burgarolas, N. (1997). Solving incidence and tangency constraints in 2D. Technical report LSI-97-3R, Departament LiSI, Universitat Politècnica de Catalunya.Petrina, S. (2003). Two cultures of technical courses and discourses: The case of computer aided design. International Journal of Technology and Design Education, 13, 47–73.Race, P. (2001). The lecturers toolkit—A practical guide to learning, teaching and assessment. Great Britain: Glasgow.Red, E., French, D., Jensen, G., Walker, S. S., & Madsen, P. (2013). Emerging design methods and tools in collaborative product development. Journal of Computing and Information Science in Engineering, 13(3), 031001. https://doi.org/10.1115/1.4023917 .Robertson, B. F., Walther, J., & Radcliffe, D. (2007). Creativity and the use of CAD tools: Lessons for engineering design education from industry. Journal of Mechanical Design, 129(7), 753–760. https://doi.org/10.1115/1.2722329 .Stone, B., Salmon, J., Eves, K., Killian, M., Wright, L., Oldroyd, J., et al. (2017). A multi-user computer-aided design competition: Experimental findings and analysis of team-member dynamics. Journal of Computing and Information Science in Engineering, 17(3), 031003. https://doi.org/10.1115/1.4035674 .Summers, J. D., & Shah, J. J. (2010). Mechanical engineering design complexity metrics: Size, coupling, and solvability. Journal of Mechanical Design, 132(2), 21004–21015. https://doi.org/10.1115/1.4000759 .Szewczyk, J. (2003). Difficulties with the novices’ comprehension of the computer-aided design (CAD) interface: Understanding visual representations of CAD tools. Journal of Engineering Design, 14(2), 169–185. https://doi.org/10.1080/0954482031000091491

    Absolute single-photoionization cross sections of Se2+: Experiment and theory

    No full text
    Absolute single-photoionization cross-section measurements for Se2+ ions were performed at the Advanced Light Source at Lawrence Berkeley National Laboratory using the merged-beams photo-ion technique. Measurements were made at a photon energy resolution of 24±3 meV in the photon energy range 23.5-42.5 eV, spanning the ground state and low-lying metastable state ionization thresholds. To clearly resolve the resonant structure near the ground-state threshold, high-resolution measurements were made from 30.0 to 31.9 eV at a photon energy resolution of 6.7±0.7 meV. Numerous resonance features observed in the experimental spectra are assigned and their energies and quantum defects tabulated. The high-resolution cross-section measurements are compared with large-scale, state-of-the-art theoretical cross-section calculations obtained from the Dirac Coulomb R-matrix method. Suitable agreement is obtained over the entire photon energy range investigated. These results are an experimental determination of the absolute photoionization cross section of doubly ionized selenium and include a detailed analysis of the photoionization resonance spectrum of this ion
    corecore