Virtual Data Collection Research Articles

Adapting for sustainability: Ensuring provision of research skills development for undergraduate medical students.

The Covid-19 pandemic forced undergraduate medical students and staff to adapt and adjust to new strategies for conducting research. The aim of this study was to investigate its impact on student research opportunities across Irish and UK medical schools and how these programmes have responded, both in terms of innovation and practical solutions. A 17-item online mixed methods survey was distributed to academic staff across 31 Irish and UK medical schools. Participants were asked about (a) the effect of the pandemic on undergraduate research teaching and project opportunities, (b) measures taken to mitigate pandemic effects on these curricular elements. Descriptive analysis was used to summarise quantitative data, and free-text responses were thematically grouped. We received a 66.74% (n = 21) response rate to our survey. Over 75% of respondents stated that the availability of clinical research, lab-based research, summer research programmes and QI/clinical audit projects was negatively impacted by Covid-19. Mitigation strategies included adaptation and adjustment in design of research projects; migration to online content delivery and collaborative platforms; virtualisation of research dissemination opportunities; increased workload for staff involved in delivery of research programmes; flexibility around assessment of research-based assignments; and importance of open communication. Covid-19 has affected traditional clinical research opportunities for medical students, particularly patient-facing projects, as well as laboratory-based research options. Mitigation strategies included the introduction of time- and cost-efficient virtual data collection methods. Some of these innovations have potential for ongoing and future development, but others are short-term responsive solutions that may prove less sustainable.

A Virtual Data Collection Model of Distributed PVs considering Spatio-Temporal Coupling and Affine Optimization Reference

The rapid development of distributed photovoltaic (DPV) made the shortage of data transmission channels and the difficulty of comprehensive coverage of measurement equipment becoming more significant. To enable a high-precision data collection of DPVs with fewer sensory devices and low computation costs, this paper proposes a virtual collection technology based on computational intelligence. To capture the spatial-temporal correlation of DPVs and find the optimal reference power station (RPS), a deep recurrent denoising autoencoder (D-RDAE)-based model is proposed in this paper. An affine artificial neural network (AANN) is constructed to tackle the uncertainty of solar radiation intensity and select RPSs. To address the high-dimensionality RPS selection, an improved honey badger algorithm (IHBA) with enhanced global search ability is proposed. The operation data of 33 DPVs in Nanjing, China, are used to train and verify the proposed method. The experimental results showed the effectiveness and superiority of the proposed method. Compared with DAE and RDAE, the deeply trained D-RDAE has the best capacity for finding the spatial-temporal correlation of DPVs. In addition, IHBA has the best global search ability compared with other 4 optimizers, and the AANN can better reduce the uncertainty of solar radiation intensity than robust and stochastic optimization techniques.

Developing Personas to Inform The Creation of Novel Interventions to Improve Participation Among People With Stroke During COVID-19

Research ObjectivesPeople living with disabilities have been found to experience reduced participation during the COVID-19 pandemic. In order to inform interventions to improve physical activity and social participation in stroke survivors, we created personas to inform the design and development of products targeting stroke patients during life-changing events such as pandemics after strokeDesignPersona development methodology using qualitative interview data. The personas were developed by mapping participants to behavioural variables identified from coded transcripts. After identifying behaviour patterns, characteristics and goals were synthesized.SettingVirtual-data collection via Zoom; community.ParticipantsCommunity-dwelling individuals who have had a stroke (n = 26) were recruited. We included participants 19 years of age or older and if they were able to communicate in English. Participants were excluded if they had moderate or severe cognitive impairment or aphasia.InterventionsN/A.Main Outcome MeasuresN/A.ResultsOur analysis identified 6 distinct personas that reflect participant behaviours and goals in regard to participation during COVID-19. 1) “The Community Provider” likes to feel involved and wants to engage in daily life. 2) “The Homebody” enjoys alone time but lacks a daily routine. This persona aims to feel motivated to accomplish personal goals. 3) “The Independent” seeks to be an active and integrated part of the community without physical or situational restrictions. 4) “The Lifestyle Person” wants to live the good life and do whatever they want, whenever they want. 5) “The Contributor” wants to feel needed and wants to be responsible for helping in the community. 6) “The Health-Literate Participator” wants to be on top of things and stay healthy for their family and themselves.ConclusionsThe personas highlight behaviours and goals in stroke patients towards physical activity and social participation during COVID-19. The personas will help guide the development of interventions to improve physical activity and social participation. The interventions will be developed based on the behaviours and goals of those living with stroke.Author(s) DisclosuresN/A.

ALWAES

For an online delivery platform, accurate physical locations of merchants are essential for delivery scheduling. It is challenging to maintain tens of thousands of merchant locations accurately because of potential errors introduced by merchants for profits (e.g., potential fraud). In practice, a platform periodically sends a dedicated crew to survey limited locations due to high workforce costs, leaving many potential location errors. In this paper, we design and implement ALWAES, a system that automatically identifies and corrects location errors based on fundamental tradeoffs of five measurement strategies from manual, physical, and virtual data collection infrastructures for online delivery platforms. ALWAES explores delivery data already collected by platform infrastructures to measure the travel time of couriers between merchants and verify all merchants' locations by cross-validation automatically. We explore tradeoffs between performance and cost of different measurement approaches. By comparing with the manually-collected ground truth, the experimental results show that ALWAES outperforms three other baselines by 32.2%, 41.8%, and 47.2%, respectively. More importantly, ALWAES saves 3,846 hours of the delivery time of 35,005 orders in a month and finds new erroneous locations that initially were not in the ground truth but are verified by our field study later, accounting for 3% of all merchants with erroneous locations.

Practical strategies for qualitative inquiry in a virtual world.

The aim of this article is to provide practical strategies for maintaining methodological rigour in executing a virtual qualitative study. Strategies are based on evidence from existing research about virtual qualitative methods and on the strategies used by the authors to convert a planned in-person qualitative, grounded theory study to an entirely virtual grounded theory study during the COVID-19 pandemic. The study began in-person in September 2019 and was converted to virtual in March 2020. Virtual data collection was completed in September 2020. This article provides a case exemplar of virtual adaptations made to a study underway when the pandemic rendered all in-person research impractical and potentially dangerous. The strategies discussed are based on our own experiences and the supporting theoretical assumptions of qualitative research, specifically grounded theory methods. Nursing scholars conducting qualitative inquiry may find these strategies helpful in continuing research activities during periods of limited access to the phenomena or persons of interest. Furthermore, these strategies allow nursing scholars to conduct rigorous, in-depth research without geographical limitations, providing greater possibilities for international collaborations and cross-institution research. Despite novel challenges, methodological adaptations that are carefully planned and purposeful allow qualitative and non-qualitative scholars to continue research activities in a fully virtual manner. This case exemplar and discussion provide practical strategies for qualitative scholars to consider while planning new studies or converting an in-person study to a virtual one. Despite the in-person nature of in-depth qualitative inquiry, a historic pandemic and a changing research environment require qualitative researchers to adapt to virtual methods while still conducting high quality, methodologically rigorous research. Qualitative scholars can use the strategies presented here to continue rigorous qualitative inquiry despite limited access to phenomena or persons.

31257 A Case Study of Needs Assessment Practices Using I-Corp Customer Discovery Protocols Alongside REDCap Surveys for CTSA Activities.

ABSTRACT IMPACT: The results from this study will improve needs assessment practices. OBJECTIVES/GOALS: The discovery phase in project development is necessary to better understand the needs and requirements of the intended market. This paper compares the outcomes of two virtual data collection methodologies, NSF I-Corps Customer Discovery interviews and REDCap surveys, for a needs assessment. METHODS/STUDY POPULATION: Clinical and Translational Science Award (CTSA) Directors and Academic Administrators across the Consortium were asked about the types of skills needed to assess clinical research professional competencies and the need for a competency-based self-assessment tool (CBST). Parallel methods were used to extract qualitative and quantitative data. The first approach was to conduct interviews using I-Corps customer discovery guidelines, and data was collected using Innovation Within software. Targeted requests were sent via cold email outreach to 102 individuals within 63 CTSA hubs. The second approach involved the use of the NJ ACTS Training and Education Offering Inventory REDCap Survey which was distributed via LISTSERV to 63 CTSA hubs. Response rates and user insights from each method were compared. RESULTS/ANTICIPATED RESULTS: Twenty-one of 63 CTSA hubs responded to the survey (response rate: 33%) while 18 of 63 hubs participated in an interview (response rate: 28%). Twenty-two individuals out of 102 were interviewed (response rate: 21%). Fifty-nine percent of interviewees and 62% of survey respondents indicated a clear need for a CBST; types of responses varied. Forty user insights were obtained from ten interviews. Two insights were gained in the survey from the eight who were prompted to fill out the free-text response. Both survey participants and interviewees indicated that communication and team science soft skills were the most important competencies. Regarding hard skills, interviewees preferred written skills while survey participants favored ‘scientific design and concept’ skills. DISCUSSION/SIGNIFICANCE OF FINDINGS: Results suggest the use of a survey or an interview for a needs assessment is dependent on several factors: need for insights, burden of time, desire to obtain quantitative vs. qualitative data, and question format. The interview was more effective than the survey in addressing the key question and obtaining insights from the intended market.

Navigating the Ethics of a Crisis.

Navigating the Ethics of a Crisis Jennifer Randles I vividly recall Friday, March 13th, 2020. It was an unseasonably warm, sunny day in Central California, and though I did not know it at the time, my last working day on campus for the foreseeable future. I attended several meetings that day related to my various roles—study principal investigator, faculty, Sociology Department Chair, and Chair of our University IRB, the Committee for the Protection of Human Subjects. Just two days earlier, I held my final in-person class meetings [End Page 70] with my students as we discussed the University's announcement that we would transition to fully virtual teaching and learning the following Monday. We knew very little about COVID-19 in mid-March 2020, but experts were already predicting the vast human toll of the pandemic and related economic recession. I wept as my students and I discussed the suffering that would likely ensue, not just for those physically affected by the virus, but also for those who would lose their jobs, access to basic needs, and life-sustaining connections to others. In the last few work hours of that Friday afternoon before I unknowingly picked up my four-year-old daughter from daycare for the final time, I perused the Centers for Disease Control and Prevention website for guidelines about the use of personal protective equipment, physical distancing, and heightened COVID-19 risks to older and immunocompromised groups. There was no way I could have envisioned this turn merely seven months earlier when I started my first term as University IRB chair. For almost 20 years prior, I had conducted ethnographic research on families in poverty and taught qualitative research methods courses. These courses always included a detailed section on research ethics and an historical overview of egregious ethical violations like those in the Tuskegee Syphilis study in which human subjects were treated much more as subject and much less as human. This was a crucial foundation, but it did not fully prepare me to enter the world of IRB administration. COVID-19 hit just as I was starting to get a handle on the details of my university's IRB policies and procedures and figuring out how best to explain risk designations and review categories to faculty PIs and oversee numerous departmental and unit IRBs. After attending several IRB bootcamps, trainings, and webinars the previous semester, I quickly learned that the IRB admin world was more complex than I had ever imagined, requiring nuanced and professionalized knowledge, not only about research, but federal regulations and creating a culture of compliance. My entrée point into this world, one I would come to see as a unique institutional realm with its own customs, norms, and language, was one of sociologist and researcher, not IRB administrative professional. I was fortunate to have had wholly positive experiences as a student collaborator and faculty principal investigator with various IRBs, and I regarded their work as crucial in facilitating ethical research. I quickly learned just how many faculty experienced IRBs as a dreaded but necessary institutional hoop through which to jump, a potential obstruction to conducting research on which their professional reputation, tenure and promotion, and students' progress through degree programs depended. With one foot still deeply embedded in that world, I knew faculty were now facing unprecedented challenges of suspending in-person research and transitioning to virtual teaching, all while facing shelter-in-place restrictions that prompted daycare and school closures and the end of social life as we previously knew it. Our prior understandings of risk and vulnerability were being shaken to the core. There was an expected uptick in modification requests from PIs who quickly pivoted their research to virtual data collection and those who devised new research projects to study the various and far-reaching impacts of COVID-19. Not only did the pandemic necessitate research redesign, it also required researchers to reassess the unique physical, psychological, emotional, social, and identity risks to human subjects. Researchers have since found themselves in a dilemma: these risks were and still are largely unknown, but we need well-designed, ethical research to identify and understand them. We are quickly...

Buried treasure or Ill-gotten spoils: the ethics of data mining and learning analytics in online instruction

This paper considers the practical applications of the article, Ethical Oversight of Student Data in Learning Analytics: A Typology Derived from a Cross-continental, Cross-institutional Perspective by Willis et al. (Educ Technol Res Dev 64: 881–901, 2016). Students engaging in online learning leave behind vast quantities of data. In 2020, the rapid shift to online learning during the global pandemic allowed virtual data collection to outpace procedures and policies that promote ethical analysis. The mere availability of data does not confer ethical collection of data. Further, analysis of data under the assumption of learning outcomes does not necessarily ensure justice or learning for students. This article offers possible applications of the heuristic by Willis et al. (Educ Technol Res Dev 64: 881–901, 2016) for ethical learning analytics in order to mitigate harm to students. It extends their work by suggesting educators consider the racialized encoding of data themselves, and argues that every act of surveillance during the pandemic creates norms for future surveillance.

Impactos psicossociais e econômicos em atletas e treinadores olímpicos/paraolímpicos de modalidades aquáticas devido ao COVID-19: percepções, discursos e perspectivas

Este artigo busca apresentar a perspectiva de 15 protagonistas do movimento olímpico e paraolímpico brasileiro sobre a forma como estão enfrentando o “Isolamento Social” devido ao COVID-19, como o adiamento de todas as competições esportivas no ano de 2020, sendo a mais importante delas os Jogos Olímpicos e Paraolímpicos de Tóquio 2020. O objetivo deste trabalho foi investigar o ponto de vista dos 15 atletas e treinadores olímpicos e paraolímpicos das modalidades aquáticas que treinavam em São Paulo sobre diversos fatores do isolamento social, como treinamento, ansiedade, perspectivas. Está é uma pesquisa qualitativa, com coleta de dados virtuais e entrevistas semiestruturadas. Discussões: Adiamento dos Jogos; Bolsa Atleta; Treinamento. Considerações: Importância do Bolsa Atleta neste momento de pandemia por conta da redução de salário e suspensão de contratos com patrocinadores; Diferença de Discurso entre o sujeito coletivo atleta e treinador sobre os impactos do COVID no treinamento; Posicionamento Político contra ações do governo federal, principalmente o Presidente da República Jair Bolsonaro.

Lunasin does not slow ALS progression: results of an open-label, single-center, hybrid-virtual 12-month trial

Objective: Lunasin, a soy peptide that reportedly alters histone acetylation in vitro, was associated with a single ALS reversal in the media. Following an ALSUntangled report, we sought to determine whether Lunasin altered histone acetylation and improved progression in people with ALS, and whether patient-centric trial design features might improve enrollment and retention. Methods: This single-center, year-long trial (NCT02709330) featured broad inclusion criteria, historical controls, primarily virtual data collection, and real-time results. Participants measured their own ALSFRS-R score, weight and perceived efficacy, and recorded these monthly on PatientsLikeMe. Blood tests at screening and month 1 assessed alterations in histone H3 and H4 acetylation. The protocol was published online, empowering patients outside the study to self-experiment. Results: Fifty participants enrolled in 5.5 months. Although this population had more advanced disease compared to other trials, retention and adherence were very high. There was no significant effect of Lunasin treatment on histone acetylation or disease progression. A cohort following our protocol outside the trial reported similar side effects and perceived effectiveness; however, their compliance with data entry was markedly lower. Conclusions: While Lunasin’s lack of efficacy is disappointing, our novel trial design had the highest ALS trial enrollment rate ever recorded, with excellent retention and adherence. Low data density from patients who are self-experimenting outside a formal protocol casts doubt on the possibility of gathering useful information from unsupervised expanded access programs or “right to try” initiatives.Trial registration: ClinicalTrials.gov identifier: NCT02709330.

Virtual Assisted Self Interviewing (VASI): An Expansion of Survey Data Collection Methods to Virtual Worlds by Means of VDCI

Changes in communication technology have allowed for the expansion of data collection modes in survey research. The proliferation of the computer has allowed the creation of web and computer assisted auto-interview data collection modes. Virtual worlds are a new application of computer technology that once again expands the data collection modes by VASI (Virtual Assisted Self Interviewing). The Virtual Data Collection Interface (VDCI) developed at Indiana University in collaboration with the German Socio-Economic Panel Study (SOEP) allows survey researchers access to the population of virtual worlds in fully immersive Heads-up Display (HUD)-based survey instruments. This expansion needs careful consideration for its applicability to the researcher’s question but offers a high level of data integrity and expanded survey availability and automation. Current open questions on the VASI method concern the optimal sampling frame and sampling procedures within a virtual world like Second Life (SL). Further multimodal studies are proposed to aid in evaluating the VDCI and placing it in the context of other data collection modes.

Surveying the Virtual World: A Large Scale Survey in Second Life Using the Virtual Data Collection Interface (VDCI)

Technology has always introduced changes in the way researchers administer surveys. A new technology known as virtual worlds has now emerged that promises to change data collection once again. Virtual worlds are persistent, online, computer-rendered spaces populated by hundreds, thousands, or even millions of people at a time. Previously, this population has only been surveyed in ways that required respondents to exit the virtual world before giving their answers. No survey method has existed whereby they could be surveyed while remaining present in the virtual space. Needless to say, this is less than ideal for any survey about the respondent's attitudes, perceptions, and behavior within the virtual world itself. This study introduces a method for solving this problem and a tool that allows surveys entirely within a virtual environment. The method is introduced as Virtual Assisted Self Interview (VASI), and the tool for implementing it, the Virtual Data Collection Interface (VDCI). The tool was created and deployed in the virtual world Second Life (SL), where users were asked questions about demographics and quality of life. The valid response numbers for the survey (N=2094) make it the largest in-virtual-world data collection seen so far. This paper discusses the VDCI and describes several different samplingmethods, as well as results that provide unique, new insights into virtual world populations. It is found, for example, that the demographic make-up of SL is unlike that of other virtual worlds. Moreover, the SL population is unlike that of other worlds in its approach to gender-switching. The limitations and new hazards of virtual world survey research are also discussed, especially survey hacking by individuals hoping to exploit the survey for financial gain. Despite the challenges, the results generally suggest that the VDCI is a valuable new research tool for obtaining representative data on virtual world population.

Surveying the Virtual World - A Large Scale Survey in Second Life Using the Virtual Data Collection Interface (VDCI)

Technology has always introduced changes in the way researchers administer surveys. A new technology known as virtual worlds has now emerged that promises to change data collection once again. Virtual worlds are persistent, online, computer-rendered spaces populated by hundreds, thousands, or even millions of people at a time. Previously, this population has only been surveyed in ways that required respondents to exit the virtual world before giving their answers. No survey method has existed whereby they could be surveyed while remaining present in the virtual space. Needless to say, this is less than ideal for any survey about the respondent’s attitudes, perceptions, and behavior within the virtual world itself. This study introduces a method for solving this problem and a tool that allows surveys entirely within a virtual environment. The method is introduced as Virtual Assisted Self Interview (VASI), and the tool for implementing it, the Virtual Data Collection Interface (VDCI). The tool was created and deployed in the virtual world Second Life (SL), where users were asked questions about demographics and quality of life. The valid response numbers for the survey (N=2094) make it the largest in-virtualworld data collection seen so far. This paper discusses the VDCI and describes several different sampling methods, as well as results that provide unique, new insights into virtual world populations. It is found, for example, that the demographic make-up of SL is unlike that of other virtual worlds. Moreover, the SL population is unlike that of other worlds in its approach to gender-switching. The limitations and new hazards of virtual world survey research are also discussed, especially survey hacking by individuals hoping to exploit the survey for financial gain. Despite the challenges, the results generally suggest that the VDCI is a valuable new research tool for obtaining representative data on virtual world population.

Virtual Assisted Self Interviewing (VASI): An Expansion of Survey Data Collection Methods to the Virtual Worlds by Means of VDCI

Changes in communication technology have allowed for the expansion of data collection modes in survey research. The proliferation of the computer has allowed the creation of web and computer assisted auto-interview data collection modes. Virtual worlds are a new application of computer technology that once again expands the data collection modes by VASI (Virtual Assisted Self Interviewing). The Virtual Data Collection Interface (VDCI) developed at Indiana University in collaboration with the German Socio-Economic Panel Study (SOEP) allows survey researchers access to the population of virtual worlds in fully immersive Heads-up Display (HUD)-based survey instruments. This expansion needs careful consideration for its applicability to the researcher's question but offers a high level of data integrity and expanded survey availability and automation. Current open questions of the VASI method are an optimal sampling frame and sampling procedures within e. g. a virtual world like Second Life (SL). Further multi-modal studies are proposed to aid in evaluating the VDCI and placing it in context of other data collection modes.

Virtual Assisted Self Interviewing (VASI): An Expansion of Survey Data Collection Methods to Virtual Worlds by Means of VDCI

Changes in communication technology have allowed for the expansion of data collection modes in survey research. The proliferation of the computer has allowed the creation of web and computer assisted auto-interview data collection modes. Virtual worlds are a new application of computer technology that once again expands the data collection modes by VASI (Virtual Assisted Self Interviewing). The Virtual Data Collection Interface (VDCI) developed at Indiana University in collaboration with the German Socio-Economic Panel Study (SOEP) allows survey researchers access to the population of virtual worlds in fully immersive Heads-up Display (HUD)-based survey instruments. This expansion needs careful consideration for its applicability to the researcher’s question but offers a high level of data integrity and expanded survey availability and automation. Current open questions on the VASI method concern the optimal sampling frame and sampling procedures within a virtual world like Second Life (SL). Further multimodal studies are proposed to aid in evaluating the VDCI and placing it in the context of other data collection modes.