Faculty Bibliography

BACKGROUND:Despite fall-related injuries accounting for over two-thirds of older adult trauma injuries, fall-related injuries are more likely to be under-triaged. The Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA) is an injury risk-triage tool. This study aims to validate STTGMA's accuracy in predicting fall-related mortality among older adult trauma patients and compare its predictive accuracy with the Geriatric Trauma Outcome Score (GTOS) and the Revised Trauma Score (RTS). METHODS:Using a retrospective cohort design, we selected 6,458 older adult trauma patients (aged 65 years and older) from a single institutional trauma database (2017-2023). The primary outcome variable was in-hospital death, measured as a binary variable. The primary predictor variable was the STTGMA score, measured as a continuous variable and a four-level categorical variable. The secondary predictor variables were the GTOS and the RTS. We compared the predictive accuracy (95% confidence interval (CI)) of the STTGMA, GTOS, and RTS. We further assessed the relationships between the STTGMA risk categories and time-to-death and hospital length of stay using multivariable time-varying Cox proportional hazard analysis and multivariable quantile regression analysis, respectively. RESULTS:A total of 130 patients (2.0%) died during admission, and the median hospital length of stay was 2 days. STTGMA exhibited 84% (95% CI: 77.3-89.8) accuracy in predicting in-hospital fall-related mortality, while the GTOS and RTS both exhibited 71% diagnostic accuracies. Compared to the minimal risk category, older adult trauma patients classified as low, moderate, and high risks each had significantly longer hospital stays and adjusted mortality risks, in a dose-response pattern. CONCLUSION/CONCLUSIONS:STTGMA can accurately predict in-hospital mortality and risk-stratify the length of stay and the time to death among older adult trauma patients with fall-related injuries.

Assistive technologies can enhance safety, independence, and quality of life for people with blindness and low vision. Despite their benefits, abandonment of these technologies remains widespread, and recent research on this issue is limited. In this Perspective article, we draw on both professional experiences and relevant scientific literature to examine adoption and abandonment in the context of new artificial intelligence-powered applications. We highlight risks arising from misaligned design, inconsistent industry support, and inadequate user training. We synthesize existing knowledge on factors that influence abandonment and propose three priorities to realign assistive technology development: participatory and transdisciplinary research, integrated technology ecosystems, and socially supported engagement. Taken collectively, these priorities ensure that emerging assistive technologies better align with the needs of people with blindness and low vision, promoting lasting adoption rather than abandonment.

BACKGROUND:Visual disability is a growing problem for many middle-aged and older adults. Conventional mobility aids, such as white canes and guide dogs, have notable limitations that have led to increasing interest in electronic travel aids (ETAs). Despite remarkable progress, current ETAs lack empirical evidence and realistic testing environments and often focus on the substitution or augmentation of a single sense. OBJECTIVE:This study aims to (1) establish a novel virtual reality (VR) environment to test the efficacy of ETAs in complex urban environments for a simulated visual impairment (VI) and (2) evaluate the impact of haptic and audio feedback, individually and combined, on navigation performance, movement behavior, and perception. Through this study, we aim to address gaps to advance the pragmatic development of assistive technologies (ATs) for persons with VI. METHODS:The VR platform was designed to resemble a subway station environment with the most common challenges faced by persons with VI during navigation. This environment was used to test our multisensory, AT-integrated VR platform among 72 healthy participants performing an obstacle avoidance task while experiencing symptoms of VI. Each participant performed the task 4 times: once with haptic feedback, once with audio feedback, once with both feedback types, and once without any feedback. Data analysis encompassed metrics such as completion time, head and body orientation, and trajectory length and smoothness. To evaluate the effectiveness and interaction of the 2 feedback modalities, we conducted a 2-way repeated measures ANOVA on continuous metrics and a Scheirer-Ray-Hare test on discrete ones. We also conducted a descriptive statistical analysis of participants' answers to a questionnaire, assessing their experience and preference for feedback modalities. RESULTS:Results from our study showed that haptic feedback significantly reduced collisions (P=.05) and the variability of the pitch angle of the head (P=.02). Audio feedback improved trajectory smoothness (P=.006) and mitigated the increase in the trajectory length from haptic feedback alone (P=.04). Participants reported a high level of engagement during the experiment (52/72, 72%) and found it interesting (42/72, 58%). However, when it came to feedback preferences, less than half of the participants (29/72, 40%) favored combined feedback modalities. This indicates that a majority preferred dedicated single modalities over combined ones. CONCLUSIONS:AT is crucial for individuals with VI; however, it often lacks user-centered design principles. Research should prioritize consumer-oriented methodologies, testing devices in a staged manner with progression toward more realistic, ecologically valid settings to ensure safety. Our multisensory, AT-integrated VR system takes a holistic approach, offering a first step toward enhancing users' spatial awareness, promoting safer mobility, and holds potential for applications in medical treatment, training, and rehabilitation. Technological advancements can further refine such devices, significantly improving independence and quality of life for those with VI.

BACKGROUND:Health and healthcare disparities for surgical patients with blindness and low vision (pBLV) stem from inaccessible healthcare systems that lack universal design principles or, at a minimum, reasonable accommodations (RA). OBJECTIVES/OBJECTIVE:We aimed to identify barriers to developing and implementing RAs in the surgical setting and provide a review of best practices for providing RAs. METHODS:We conducted a search of PubMed for evidence of reasonable accommodations, or lack thereof, in the surgical setting. Articles related to gaps and barriers to providing RAs for pBLV or best practices for supporting RAs were reviewed for the study. RESULTS:Barriers to the implementation of reasonable accommodations, and, accordingly, best practices for achieving equity for pBLV, relate to policies and systems, staff knowledge and attitudes, and materials and technology. CONCLUSIONS:These inequities for pBLV require comprehensive frameworks that offer, maintain, and support education about disability disparities and RAs in the surgical field. Providing RAs for surgical pBLV, and all patients with disabilities is an important and impactful step towards creating a more equitable and anti-ableist health system.

UNav is a computer-vision-based localization and navigation aid that provides step-by-step route instructions to reach selected destinations without any infrastructure in both indoor and outdoor environments. Despite the initial literature highlighting UNav's potential, clinical efficacy has not yet been rigorously evaluated. Herein, we assess UNav against standard in-person travel directions (SIPTD) for persons with blindness or low vision (PBLV) in an ecologically valid environment using a non-inferiority design. Twenty BLV subjects (age = 38 ± 8.4; nine females) were recruited and asked to navigate to a variety of destinations, over short-range distances (<200 m), in unfamiliar spaces, using either UNav or SIPTD. Navigation performance was assessed with nine dependent variables to assess travel confidence, as well as spatial and temporal performances, including path efficiency, total time, and wrong turns. The results suggest that UNav is not only non-inferior to the standard-of-care in wayfinding (SIPTD) but also superior on 8 out of 9 metrics, as compared to SIPTD. This study highlights the range of benefits computer vision-based aids provide to PBLV in short-range navigation and provides key insights into how users benefit from this systematic form of computer-aided guidance, demonstrating transformative promise for educational attainment, gainful employment, and recreational participation.

People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive technologies for the visually impaired often struggle in real-world scenarios due to the need for constant training and lack of robustness, which limits their effectiveness, especially in dynamic and unfamiliar environments, where accurate and efficient perception is crucial. Therefore, we frame our research question in this paper as: How can we assist pBLV in recognizing scenes, identifying objects, and detecting potential tripping hazards in unfamiliar environments, where existing assistive technologies often falter due to their lack of robustness? We hypothesize that by leveraging large pretrained foundation models and prompt engineering, we can create a system that effectively addresses the challenges faced by pBLV in unfamiliar environments. Motivated by the prevalence of large pretrained foundation models, particularly in assistive robotics applications, due to their accurate perception and robust contextual understanding in real-world scenarios induced by extensive pretraining, we present a pioneering approach that leverages foundation models to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environment and providing warnings about potential risks. Specifically, our method begins by leveraging a large-image tagging model (i.e., Recognize Anything Model (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV, using prompt engineering. By combining the prompt and input image, a vision-language foundation model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing environmental objects and scenic landmarks, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method can recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV.