Publications by authors named "R Douglas Bruce"

Exercise is a powerful tool for disease prevention and rehabilitation. Commercially available virtual reality (VR) devices and apps offer an immersive platform to gamify exercise and potentially enhance physiological and psychological benefits. However, no work has compared immersive exercise to closely matched 2D screen-based equivalents with the same visual and auditory stimuli.

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Background: Robust and well-defined data collection is important when using electronic patient-reported outcome measures (ePROMs) in clinical studies. Questions have been raised as to whether older age may be a barrier to data collection due to patients' unfamiliarity with electronic devices. Older adults may also have underlying health conditions that affect their ability to fill out patient-reported outcome measures (PROMs) on electronic devices.

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Introduction: There are minimal evidence-based outcomes from clinical trials for medical-dental integration. This formative work and pilot study is a precursor to a larger cluster-randomized clinical trial in adult primary care practices to test an electronic health record (EHR) structured workflow for primary care providers (physicians/nurse practitioners [NPs]) and medical staff (medical assistants [MAs] and nurses) with oral health (OH) screening and referral for Medicaid-enrolled adults ≥55 years.

Methods: This study was conducted in 2 practices with providers, medical staff, and older adults.

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The muscle metaboreflex effect on pulmonary ventilation (V̇) regulation is more apparent during rhythmic exercise than rest, possibly because this reflex interacts with other mechanisms regulating V̇ during voluntary contractions, such as central command. Therefore, we tested whether one part of central command, the descending component of motor execution (i.e.

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Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS's efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.

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