scholarly journals Accelerating Research With Technology: Rapid Recruitment for a Large-Scale Web-Based Sleep Study (Preprint)

2018 ◽  
Author(s):  
Sean Deering ◽  
Madeline M Grade ◽  
Jaspreet K Uppal ◽  
Luca Foschini ◽  
Jessie L Juusola ◽  
...  
Keyword(s):  
Large Scale ◽  
Wearable Device ◽  
Sleep Study ◽  
Web Based ◽  
Enrollment Rate ◽  
Participant Engagement ◽  
Novel Approach ◽  
Complete Study ◽  
Conducting Research ◽  
Daytime Function

BACKGROUND Participant recruitment can be a significant bottleneck in carrying out research studies. Connected health and mobile health platforms allow for the development of Web-based studies that can offer improvement in this domain. Sleep is of vital importance to the mental and physical health of all individuals, yet is understudied on a large scale or beyond the focus of sleep disorders. For this reason and owing to the availability of digital sleep tracking tools, sleep is well suited to being studied in a Web-based environment. OBJECTIVE The aim of this study was to investigate a method for speeding up the recruitment process and maximizing participant engagement using a novel approach, the Achievement Studies platform (Evidation Health, Inc, San Mateo, CA, USA), while carrying out a study that examined the relationship between participant sleep and daytime function. METHODS Participants could access the Web-based study platform at any time from any computer or Web-enabled device to complete study procedures and track study progress. Achievement community members were invited to the study and assessed for eligibility. Eligible participants completed an electronic informed consent process to enroll in the study and were subsequently invited to complete an electronic baseline questionnaire. Then, they were asked to connect a wearable device account through their study dashboard, which shared their device data with the research team. The data were used to provide objective sleep and activity metrics for the study. Participants who completed the baseline questionnaires were subsequently sent a daily single-item Sleepiness Checker activity for 7 consecutive days at baseline and every 3 months thereafter for 1 year. RESULTS Overall, 1156 participants enrolled in the study within a 5-day recruitment window. In the 1st hour, the enrollment rate was 6.6 participants per minute (394 per hour). In the first 24 hours, the enrollment rate was 0.8 participants per minute (47 participants per hour). Overall, 1132 participants completed the baseline questionnaires (1132/1156, 97.9%) and 1047 participants completed the initial Sleepiness Checker activity (1047/1156, 90.6%). Furthermore, 1000 participants provided activity-specific wearable data (1000/1156, 86.5%) and 982 provided sleep-specific wearable data (982/1156, 84.9%). CONCLUSIONS The Achievement Studies platform allowed for rapid recruitment and high study engagement (survey completion and device data sharing). This approach to carrying out research appears promising. However, conducting research in this way requires that participants have internet access and own and use a wearable device. As such, our sample may not be representative of the general population.

Insights from a survey “comments” section: extending research on doctoral well-being

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anna Sverdlik ◽  
Lynn Mcalpine ◽  
Nathan Hall
Keyword(s):  
Doctoral Students ◽  
Thematic Analysis ◽  
Large Scale ◽  
Doctoral Education ◽  
Doctoral Degree ◽  
Well Being ◽  
Web Based ◽  
Content Type ◽  
Novel Approach ◽  
Process Work

Purpose The purpose of this study is to better understand the declines in doctoral students’ mental and physical health while pursuing their doctoral degrees, by revealing the major themes of students’ voluntary comments following a survey that primed students to reflect on these topics. Design/methodology/approach The present study used qualitative thematic analysis to uncover themes in doctoral students’ voluntary comments on a large-scale, web-based survey of graduate students’ motivation and well-being. Findings A thematic analysis revealed six major emerging themes: timing in the degree process, work-life balance, health/well-being changes, impostor syndrome, the supervisor and hopelessness. Research limitations/implications The themes uncovered in the present study contribute to the literature by highlighting important underexplored topics (e.g. timing in the degree process, hopelessness) in doctoral education research and they are discussed and situated in the context of existing literature. Practical implications Implications for doctoral supervisors and departments are discussed. Social implications The present study highlights some pressing concerns among doctoral students, as articulated by the students themselves and can contribute to the betterment of doctoral education, thereby reducing attrition, improving the experiences of doctoral students and possibly affording more candidates to achieve a doctoral degree. Originality/value The present study makes the above-mentioned contributions by taking a novel approach and analyzing doctoral students’ voluntary comments (n = 607) on a large-scale, web-based survey. Thus, while some of the themes were primed by the survey itself, the data represent issues/concerns that students perceived as important enough to comment about after already having completed a lengthy questionnaire.

scholarly journals Accelerating Research With Technology: Rapid Recruitment for a Large-Scale Web-Based Sleep Study

10.2196/10974 ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. e10974 ◽  
Author(s):  
Sean Deering ◽  
Madeline M Grade ◽  
Jaspreet K Uppal ◽  
Luca Foschini ◽  
Jessie L Juusola ◽  
...  
Keyword(s):  
Large Scale ◽  
Sleep Study ◽  
Web Based

Web-based Exploration of Annotated Multi-Layered Relightable Image Models

2021 ◽  
Vol 14 (2) ◽  
pp. 1-29
Author(s):  
Alberto Jaspe-Villanueva ◽  
Moonisa Ahsan ◽  
Ruggero Pintus ◽  
Andrea Giachetti ◽  
Fabio Marton ◽  
...  
Keyword(s):  
Large Scale ◽  
Region Of Interest ◽  
Material Models ◽  
Web Based ◽  
Single View ◽  
Multi Scale ◽  
Presentation Modes ◽  
Novel Approach ◽  
Image Models ◽  
Spatially Varying

We introduce a novel approach for exploring image-based shape and material models registered with structured descriptive information fused in multi-scale overlays. We represent the objects of interest as a series of registered layers of image-based shape and material data. These layers are represented at different scales and can come out of a variety of pipelines. These layers can include both Reflectance Transformation Imaging representations, and spatially varying normal and Bidirectional Reflectance Distribution Function fields, possibly as a result of fusing multi-spectral data. An overlay image pyramid associates visual annotations to the various scales. The overlay pyramid of each layer is created at data preparation time by either one of the three subsequent methods: (1) by importing it from other pipelines, (2) by creating it with the simple annotation drawing toolkit available within the viewer, and (3) with external image editing tools. This makes it easier for the user to seamlessly draw annotations over the region of interest. At runtime, clients can access an annotated multi-layered dataset by a standard web server. Users can explore these datasets on a variety of devices; they range from small mobile devices to large-scale displays used in museum installations. On all these aforementioned platforms, JavaScript/WebGL2 clients running in browsers are fully capable of performing layer selection, interactive relighting, enhanced visualization, and annotation display. We address the problem of clutter by embedding interactive lenses. This focus-and-context-aware (multiple-layer) exploration tool supports exploration of more than one representation in a single view. That allows mixing and matching of presentation modes and annotation display. The capabilities of our approach are demonstrated on a variety of cultural heritage use-cases. That involves different kinds of annotated surface and material models.

Using Web-based Testing for Large-Scale Assessment

2013 ◽  
Author(s):  
Laura S. Hamilton ◽  
Stephen P. Klein ◽  
William Lorie
Keyword(s):  
Large Scale ◽  
Web Based ◽  
Large Scale Assessment ◽  
Scale Assessment

Impact of COVID-19 on Nuclear Medicine in Germany, Austria and Switzerland: An International Survey in April 2020

2020 ◽  
Vol 59 (04) ◽  
pp. 294-299 ◽  
Author(s):  
Lutz S. Freudenberg ◽  
Ulf Dittmer ◽  
Ken Herrmann
Keyword(s):  
Nuclear Medicine ◽  
Health Systems ◽  
Personal Protective Equipment ◽  
Large Scale ◽  
Diagnostic Procedures ◽  
Protective Equipment ◽  
Current Crisis ◽  
Web Based ◽  
Public Versus Private ◽  
The Impact

Abstract Introduction Preparations of health systems to accommodate large number of severely ill COVID-19 patients in March/April 2020 has a significant impact on nuclear medicine departments. Materials and Methods A web-based questionnaire was designed to differentiate the impact of the pandemic on inpatient and outpatient nuclear medicine operations and on public versus private health systems, respectively. Questions were addressing the following issues: impact on nuclear medicine diagnostics and therapy, use of recommendations, personal protective equipment, and organizational adaptations. The survey was available for 6 days and closed on April 20, 2020. Results 113 complete responses were recorded. Nearly all participants (97 %) report a decline of nuclear medicine diagnostic procedures. The mean reduction in the last three weeks for PET/CT, scintigraphies of bone, myocardium, lung thyroid, sentinel lymph-node are –14.4 %, –47.2 %, –47.5 %, –40.7 %, –58.4 %, and –25.2 % respectively. Furthermore, 76 % of the participants report a reduction in therapies especially for benign thyroid disease (-41.8 %) and radiosynoviorthesis (–53.8 %) while tumor therapies remained mainly stable. 48 % of the participants report a shortage of personal protective equipment. Conclusions Nuclear medicine services are notably reduced 3 weeks after the SARS-CoV-2 pandemic reached Germany, Austria and Switzerland on a large scale. We must be aware that the current crisis will also have a significant economic impact on the healthcare system. As the survey cannot adapt to daily dynamic changes in priorities, it serves as a first snapshot requiring follow-up studies and comparisons with other countries and regions.

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

scholarly journals ‘CTRL’: an online, Dynamic Consent and participant engagement platform working towards solving the complexities of consent in genomic research

2021 ◽  
Author(s):  
Matilda A. Haas ◽  
Harriet Teare ◽  
Megan Prictor ◽  
Gabi Ceregra ◽  
Miranda E. Vidgen ◽  
...  
Keyword(s):  
Large Scale ◽  
Genomic Medicine ◽  
International Standards ◽  
Security And Privacy ◽  
Genomic Research ◽  
Return Of Results ◽  
Future Research ◽  
Design And Development ◽  
Participant Engagement ◽  
Dynamic Consent

AbstractThe complexities of the informed consent process for participating in research in genomic medicine are well-documented. Inspired by the potential for Dynamic Consent to increase participant choice and autonomy in decision-making, as well as the opportunities for ongoing participant engagement it affords, we wanted to trial Dynamic Consent and to do so developed our own web-based application (web app) called CTRL (control). This paper documents the design and development of CTRL, for use in the Australian Genomics study: a health services research project building evidence to inform the integration of genomic medicine into mainstream healthcare. Australian Genomics brought together a multi-disciplinary team to develop CTRL. The design and development process considered user experience; security and privacy; the application of international standards in data sharing; IT, operational and ethical issues. The CTRL tool is now being offered to participants in the study, who can use CTRL to keep personal and contact details up to date; make consent choices (including indicate preferences for return of results and future research use of biological samples, genomic and health data); follow their progress through the study; complete surveys, contact the researchers and access study news and information. While there are remaining challenges to implementing Dynamic Consent in genomic research, this study demonstrates the feasibility of building such a tool, and its ongoing use will provide evidence about the value of Dynamic Consent in large-scale genomic research programs.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

2021 ◽  
Author(s):  
Silvia Huber ◽  
Lars B. Hansen ◽  
Lisbeth T. Nielsen ◽  
Mikkel L. Rasmussen ◽  
Jonas Sølvsteen ◽  
...  
Keyword(s):  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Novel Approach

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

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