Designing Conversational Agents to Support Self-Regulated Learning for Older Adults

The rapid growth of the off-the-shelf smart speakers (such as Amazon Alexa and Google Home), also called Conversational Agents (CAs), creates potential to deliver everyday life support to users at home (such as checking weather, listening to news, scheduling events). Literature demonstrated the technology acceptance of CAs among older adults (including novice users) given the low barriers to use CAs. The natural conversations among CAs and users enable the opportunities to build deeper understandings about a topic through theory-driven guided dialogues. Our study has designed the metacognition strategies in the guided dialogues of CAs to support informal self-regulated learning of health information among older adults. The study has shown the feasibility and acceptance of CAs to help older adults learn new health information on their own through these guided dialogues. Additional analyses on the feasibilities to implement different metacognitive strategies in guided dialogues in the off-the-shelf CAs were also conducted.

Connecting Older Adults With Mental Health Apps: A Survey of Provider App and Education Material Use

Many older veterans have access to mobile devices and are interested in using apps for mental health self-management, but few have ever downloaded health apps. To address the need for awareness of and access to VA mental health apps, we developed patient educational materials aimed towards older (or novice) users of mobile devices. The present study explored health care providers’ and staff’s perceptions about use of mental health mobile applications (apps) with older veterans and examined potential utility of these patient educational materials. Requestors of mobile device education materials (N = 90) were surveyed when ordering materials and again 4 months later. Baseline and follow-up surveys assessed frequency of app recommendation, and comfort recommending apps. Baseline surveys examined perceived advantages of apps; follow-up surveys examined perceived utility of the educational materials. Descriptive statistics and qualitative analysis were conducted. Most requesters (68.5%) initially were not comfortable using apps, yet perceived many advantages to using apps and hoped materials could facilitate app use. At follow-up, requestors felt more comfortable recommending apps alongside our materials. Qualitative analysis revealed perceived advantages to using the education materials. The benefits of developing and disseminating educational materials for providers to share with older veterans helped support older veterans’ app use, and potentially increased providers’ comfort with and frequency of recommending apps to their older patients. Access to educational materials can mitigate discomfort among providers in recommending apps to older users and may bring about valuable discussions about apps which support mental health.

An Architecture for Collaborative Terrain Sketching with Mobile Devices

3D terrains used in digital animations and videogames are typically created by several collaborators with a single-user application, which constrains them to update the shared terrain from their PCs, using a turn-taking strategy. Moreover, collaborators have to visualize the terrain through 2D views, confusing novice users when conceiving its shape in 3D. In this article, we describe an architecture for collaborative applications, which allow co-located users to sketch a terrain using their mobile devices concurrently. Two interaction modes are supplied: the standard one and an augmented reality-based mode, which helps collaborators understand the 3D terrain shape. Using the painting with brushesparadigm, users can modify the terrain while visualizing its shape evolution through the camera of their devices. Work coordination is promoted by enriching the 3D space with each collaborator’s avatar, which provides awareness information about identity, location, and current action. We implemented a collaborative application from this architecture that was tested by groups of users, who assessed its hedonic and pragmatic qualities in both interaction modes and compared them with the qualities of a similar Web terrain editor. The results showed that the augmented reality mode of our prototype was considered more attractive and usable by the participants.

Comparison of VividTrac, King Vision and Macintosh laryngoscopes in normal and difficult airways during simulated cardiopulmonary resuscitation among novices

Introduction Early endotracheal intubation improves neurological outcomes in cardiopulmonary resuscitation, although cardiopulmonary resuscitation is initially carried out by personnel with limited experience in a significant proportion of cases. Videolaryngoscopes might decrease the number of attempts and time needed, especially among novices. We sought to compare videolaryngoscopes with direct laryngoscopes in simulated cardiopulmonary resuscitation scenarios. Materials and methods Forty-four medical students were recruited to serve as novice users. Following brief, standardized training, students executed endotracheal intubation with the King Vision®, Macintosh and VividTrac® laryngoscopes, on a cardiopulmonary resuscitation trainer in normal and difficult airway scenarios. We evaluated the time to and proportion of successful intubation, the best view of the glottis, esophageal intubation, dental trauma and user satisfaction. Results In the normal airway scenario, significantly shorter intubation times were achieved using the King Vision® than the Macintosh laryngoscope. In the difficult airway scenario, we found that the VividTrac® was superior to the King Vision® and Macintosh laryngoscopes in the laryngoscopy time. In both scenarios, we noted no difference in the first-attempt success rate, but the best view of the glottis and dental trauma, esophageal intubation and bougie use were more frequent with the Macintosh laryngoscope than with the videolaryngoscopes. The shortest tube insertion times were achieved using the King Vision® in both scenarios. Conclusion All providers achieved successful intubation within three attempts, but we found no device superior in any of our scenarios regarding the first-attempt success rate. The King Vision® was superior to the Macintosh laryngoscope in the intubation time in the normal airway scenario and noninferior in the difficult airway scenario for novice users. We noted significantly less esophageal intubation using the videolaryngoscopes than using the Macintosh laryngoscope in both scenarios. Based on our results, the KingVision® might be recommended over the VividTrac® and Macintosh laryngoscopes for further evaluation.

Multi-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model

Objectives: Ultrasound emerges as a complement to cone-beam computed tomography in dentistry, but struggles with artifacts like reverberation and shadowing. This study seeks to help novice users recognize soft tissue, bone, and crown of a dental sonogram, and automate soft tissue height (STH) measurement using deep learning. Methods: In this retrospective study, 627 frames from 111 independent cine loops of mandibular and maxillary premolar and incisors collected from our porcine model (N = 8) were labeled by a reader. 274 premolar sonograms, including data augmentation, were used to train a multi class segmentation model. The model was evaluated against several test sets, including premolar of the same breed (n = 74, Yucatan) and premolar of a different breed (n = 120, Sinclair). We further proposed a rule-based algorithm to automate STH measurements using predicted segmentation masks. Results: The model reached a Dice similarity coefficient of 90.7±4.39%, 89.4±4.63%, and 83.7±10.5% for soft tissue, bone, and crown segmentation, respectively on the first test set (n = 74), and 90.0±7.16%, 78.6±13.2%, and 62.6±17.7% on the second test set (n = 120). The automated STH measurements have a mean difference (95% confidence interval) of −0.22 mm (−1.4, 0.95), a limit of agreement of 1.2 mm, and a minimum ICC of 0.915 (0.857, 0.948) when compared to expert annotation. Conclusion: This work demonstrates the potential use of deep learning in identifying periodontal structures on sonograms and obtaining diagnostic periodontal dimensions.

Comparison of a New Video Intubation Stylet and McGrath® MAC Video Laryngoscope for Intubation in an Airway Manikin with Normal Airway and Cervical Spine Immobilization Scenarios by Novice Personnel: A Randomized Crossover Study

The use of both a video laryngoscope and a video intubation stylet, compared with the use of a direct laryngoscope, is not only easier to learn but also associated with a higher success rate in performing endotracheal intubation for novice users. However, data comparing the two video devices used by novice personnel are rarely found in literature. Nondelayed intubation is an important condition to determine the prognosis in critically ill patients; hence, exploring intubation performance in various situations is of clinical significance. This study is aimed at comparing a video stylet and a video laryngoscope for intubation in an airway manikin with normal airway and cervical spine immobilization scenarios by novice personnel. We compared the performance of intubation by novices between the Aram Video Stylet and the McGrath® MAC video laryngoscope in an airway manikin. Thirty medical doctors with minimal experience of endotracheal intubation attempted intubation on a manikin five times with each device in each setting (normal airway and cervical spine immobilization scenarios). The order of use of the devices in each scenario was randomized for each participant. In the normal airway scenario, the Aram stylet showed a significantly higher rate of successful intubation than the McGrath® (98.7% vs. 92.0%; odds ratio (95% CI): 6.4 (1.4–29.3); p = 0.006 ). The intubation time was shorter using the Aram Stylet than that using the McGrath® video laryngoscope ( p < 0.001 ). In the cervical immobilization scenario, successful endotracheal intubation was also more frequent using the Aram stylet than with the McGrath® (96.0% vs. 87.3%; odds ratio (95% CI): 3.5 (1.3–9.0); p = 0.007 ). The Aram Stylet intubation time was shorter ( p < 0.001 ). In novice personnel, endotracheal intubation appears to be more successful and faster using the Aram Video Stylet than the McGrath® MAC video laryngoscope.

Evaluating sources of technical variability in the mechano-node-pore sensing pipeline and their effect on the reproducibility of single-cell mechanical phenotyping

Cellular mechanical properties can reveal physiologically relevant characteristics in many cell types, and several groups have developed microfluidics-based platforms to perform high-throughput single-cell mechanical testing. However, prior work has performed only limited characterization of these platforms’ technical variability and reproducibility. Here, we evaluate the repeatability performance of mechano-node-pore sensing, a single-cell mechanical phenotyping platform developed by our research group. We measured the degree to which device-to-device variability and semi-manual data processing affected this platform’s measurements of single-cell mechanical properties. We demonstrated high repeatability across the entire technology pipeline even for novice users. We then compared results from identical mechano-node-pore sensing experiments performed by researchers in two different laboratories with different analytical instruments, demonstrating that the mechanical testing results from these two locations are in agreement. Our findings quantify the expectation of technical variability in mechano-node-pore sensing even in minimally experienced hands. Most importantly, we find that the repeatability performance we measured is fully sufficient for interpreting biologically relevant single-cell mechanical measurements with high confidence.

ToxPi*GIS Toolkit: Creating, viewing, and sharing integrative visualizations for geospatial data using ArcGIS

Background: Presenting a comprehensive picture of geographic data comprising multiple factors is an inherently integrative undertaking. Visualizing such data in an interactive form is essential for public sharing and geographic information systems (GIS) analysis. The Toxicological Prioritization Index (ToxPi) framework has been used as an integrative model layered atop geospatial data, and its deployment within the dynamic ArcGIS universe would open up powerful new avenues for sophisticated, interactive GIS analysis. Objective: We propose an actively developed suite of software, the ToxPi*GIS Toolkit, for creating, viewing, sharing, and analyzing interactive ToxPi figures in ArcGIS. Methods: The ToxPi*GIS Toolkit is a collection of methods for creating interactive feature layers that contain ToxPi diagrams. It currently includes an ArcGIS Toolbox (ToxPiToolbox.tbx) for drawing geographically located ToxPi diagrams onto a feature layer, a collection of modular Python scripts that create predesigned layer files containing ToxPi feature layers from the command line, and a collection of Python routines for useful data manipulation and preprocessing. We present workflows documenting ToxPi feature layer creation, sharing, and embedding for both novice and advanced users looking for additional customizability. Results: Map visualizations created with the ToxPi*GIS Toolkit can be made freely available on public URLs, allowing users without ArcGIS Pro access or expertise to view and interact with them. Novice users with ArcGIS Pro access can create de novo custom maps, and advanced users can exploit additional customization options. The ArcGIS Toolbox provides a simple means for generating ToxPi feature layers. We illustrate its usage with current COVID-19 data to compare drivers of pandemic vulnerability in counties across the United States. Significance: Development of new features, which will advance the interests of the scientific community in many fields, is ongoing for the ToxPi*GIS Toolkit, which can be accessed from www.toxpi.org.

Assessment of the Sedia HIV Self-Test Device: Usability and Performance in the Hands of Untrained Users in Johannesburg, South Africa

The prevalence of HIV across South Africa places a strain on testing facilities. The use of HIV self-testing (HIVST) devices has been identified as a strategy to ease the burden on these facilities. The usability and performance of the Asante HIV−1/2 Oral Self-Test (Asante) (Sedia Biosciences, Portland OR, USA) device by novice users was assessed and reported on, to inform for the implementation of such devices in South Africa and elsewhere. Convenience sampling was used. Participants used the Asante HIVST device and recorded their interpretation of their results. Participants’ interpretations were compared with those of trained professionals and, thereafter, verified using the rapid diagnostic testing algorithm. Out of the eligible participants, 410 of the 524 (78.2%) were between the ages of 18–35. The usability assessment indicates that 100% of participants used the HIVST device’s information leaflet. However, 19/524 (3.6%) of participants who yielded an invalid result due to critical errors were excluded from the primary efficacy analysis. The average usability score was 98.1%. The sensitivity and specificity results were, 94.7% and 99.8%, respectively. This study shows that the Asante HIV self-test, and similar devices, can be valuable in providing convenient HIV self-testing and immediately available results. To accommodate a greater number of inexperienced users, the instructions may need to be revised.