Pacific University Oregon

Background/Objectives: This pilot study aimed to evaluate the feasibility of applying the Beers criteria in the community pharmacy setting and aid pharmacists in identifying and emphasizing adverse effects from potentially inappropriate medications (PIMs) for older adults. Methods: We applied a single-center retrospective study to collect demographic and outcome data in order to analyze dispensed PIMs for older adults. We used an evaluation tool to compare warnings between pharmacy dispensing software and the Beers criteria. Descriptive statistics were computed via standard statistical software. Results: Culled from a random selection of 215 patients, the medical records from 50 subjects ≥65 years old were reviewed, including 440 of their medications. Our data demonstrated that 96% of subjects were dispensed at least one PIM, with a total of 34 different PIMs distributed at varying frequencies. A comparative analysis indicated that 74% of dispensed medications had similar, but not identical, warning profiles presented in the dispensing software and Beers criteria. Anticholinergic burden of dispensed PIMs indicated that older adults were at risk of falls and delirium. By supplementing the dispensing software with Beers criteria, we were able to create clinical communication notes for providers, patients, and pharmacy students to emphasize the role pharmacists can play to minimize PIM's adverse effects on older adults. Conclusions: Our data indicates the feasibility of implementing the Beers criteria in the community pharmacy setting. Integrating the dispensing software warnings with Beers criteria created a structured intervention strategy to prevent potential adverse effects and develop clinical communication notes to emphasize a more engaging role that the community pharmacy setting can play to optimize therapeutic outcomes for older adults.

In this study, we identify the minimal important difference for two patient-reported quality of life instruments and provide evidence for their validity in pyoderma gangrenosum (PG). Such patient-reported outcome measures are being considered as co-primary endpoints in PG clinical trials by the US Food and Drug Administration.

PurposeFalls pose a significant health risk to older adults, often resulting in adverse outcomes. Despite the recognition of effective interventions, the psychological aspect of fear of falling (FoF) remains under-addressed. The Fear of Falling Questionnaire-Revised (FFQ-R) (15 and 6-item) was developed to assess FoF. However, no Swedish version is available, necessitating its translation and evaluation of psychometric testing. This study aimed to translate the FFQ-R (15 and 6-item) and examine the psychometric properties of the Swedish versions in healthy community-dwelling adults aged ≥ 60 years.Patients and MethodsThis study used a cross-sectional design to translate and assess the psychometric properties of the FFQ-R(S) (15 and 6-item) for healthy community-dwelling older adults. Translators, experts, and community-dwelling older adults participated in the process. Confirmatory factor analysis was used to assess the fit of the model. Scale reliability was measured with ordinal α.ResultsThe translation resulted in minor changes and demonstrated satisfactory content validity. Confirmatory factor analysis showed that the FFQ-R(S) model, with four latent factors and 15 items, was not analyzable due to non-convergence. For the two-factors and six items, FFQ-R(S), the ordinal α values for the scales’ harm outcome (HO) and degree of threat (DT), measuring reliability, were 0.70 and 0.88, respectively. The confirmatory factor analysis yielded mixed fit indices, where values of standardized root mean square residual, comparative fit index, and Tucker–Lewis index suggested a good fit of the model to the sample, whereas the χ2 test and the value of root mean square error of approximation indicated a lesser good fit.ConclusionThe Swedish version of the 6-item FFQ-R demonstrates acceptable psychometric properties. Moreover, the six items align with the two factors, DT and HO. Based on these findings, we recommend using the Short FFQ-R(S) (6-item) to assess FoF in primary fall prevention efforts for community-dwelling older adults.

BackgroundTraditional cognitive assessments for older adults can be time‐consuming, resource‐intensive, and often inaccessible in primary care. The integration of electronic devices into assessment procedures could improve efficiency and access. We investigated the preliminary usability of a novel iPad‐based assessment tool for online self‐administration in an older adult sample with differing technology proficiency.MethodParticipants were 20 community‐dwelling older adults, free of neurocognitive diagnoses, aged 64‐88 (M=74.15) 65% female, 90% White, with an average of 16.25 years of education. They completed the Mobile Device Proficiency Questionnaire (MDPQ‐16), range 24 to 80 (M=58.35), indicating a wide range of proficiencies. They were provided an iPad and an instructions packet to self‐administer the cognitive assessment in office with supervision (n = 10) or at home with no supervision (n = 10). The cognitive assessment (developed with Cogni.Dx) consisted of five tests probing orientation, visuospatial memory, language, and visuomotor speed (trails). Usability was evaluated by thematically analyzing and categorizing researcher observations, participant requests for help, and participant feedback (oral and written) on difficulties encountered during setup and completion.ResultNineteen participants were able to complete the cognitive assessment (9: home, 10: office). Various usability issues emerged that are categorized into four themes: set‐up, interruptions, touchscreen interface, and extraneous concerns. In the home condition, participants had trouble activating or logging into their account and connecting the device to their home WiFi. Three more difficulties were observed in both home and office conditions. One was interrupting the testing despite instructions to complete it in a single sitting. Another challenge was the use of the touchscreen interface, particularly for tests requiring moving shapes or drawing lines where calibrating pressure of touch was observed to be very challenging. Lastly, extraneous concerns, such as pop‐ups, scrolling to see the entire page, closing out of the module by mistake, and re‐navigating to the assessment were also challenges.ConclusionPreliminary findings revealed multiple usability challenges encountered by participants in both home and office conditions. These challenges of set‐up, interruptions, touchscreen interface navigation, and managing extraneous technical issues, underscore the importance of thorough usability investigations before considering validity and widespread implementation.

Accurate forecasting of climate extremes such as floods, heatwaves, and severe storms is vital for risk mitigation and climate resilience planning. Traditional statistical models often fail to capture the nonlinear dynamics and tail dependencies inherent in such events. This paper proposes a novel hybrid framework that integrates Extreme Value Theory (EVT) with Deep Generative Models (DGMs), specifically Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), for probabilistic modeling and simulation of rare climate phenomena. EVT is employed to model the marginal distributions of extreme events using the Generalized Pareto and Generalized Extreme Value distributions. Meanwhile, DGMs learn latent representations from high-dimensional climate data and synthesize realistic, tail-aware samples. The proposed model captures both the statistical rigor of EVT and the expressive power of deep learning. Empirical evaluations are conducted using ERA5 reanalysis and satellite datasets, focusing on extreme precipitation and temperature anomalies across diverse regions. Results show that the hybrid EVT–DGM framework significantly improves tail risk estimation, return level prediction, and generative quality compared to conventional models. This approach provides a robust tool for data-driven climate risk forecasting under uncertainty.

In complex real-world environments characterized by conflicting objectives and dynamic constraints, conventional Goal Programming (GP) approaches often struggle to deliver flexible and adaptive solutions. This paper presents an AI-augmented Goal Programming framework that integrates artificial intelligence techniques—such as neural networks for demand prediction and evolutionary algorithms for multi-objective optimization—to enhance traditional GP models. The proposed approach allows real-time adjustment of priorities, constraints, and aspiration levels based on contextual data and system feedback. Case studies in healthcare logistics, renewable energy planning, and supply chain optimization demonstrate the framework’s efficacy in producing robust and high-quality solutions. Comparative numerical simulations show that the AI-enhanced GP model significantly outperforms classical GP in adaptability, accuracy, and computational efficiency. This research contributes to the fusion of intelligent systems and mathematical programming, providing a scalable decision-support methodology for addressing multifaceted challenges in applied mathematics.

Rehabilitation Engineering Research Center (RERC) on Telerehabilitation, disability, wheelchair user, TBI, rehabilitation counselor, vocational rehabilitation, researchers, clinicians, consumers, service providers, underserved populations, industry, suppliers, manufacturers, policy makers, information technology specialists, health informatics, e-health, certificate, remote assessment