Background: Dementia is a syndrome that affects a person’s ability to understand and express information. The higher prevalence of vision and/or hearing losses among persons with dementia in long-term care (LTC) facilities interferes with the ability of nurses to provide optimal care because communication is compromised. Therefore, the detection and screening for sensory impairment is of the utmost importance in LTC facilities; however, there is currently no agreement among nursing professionals on how to best identify such losses for the purpose of further referral, and the need for a validated screening measure suitable for nurses in LTC facilities is clear. The present project aims to close this gap by investigating the screening recommendations of vision- and hearing-care professionals working with clients affected by dementia.
Methods: Eleven experts in audiology, optometry, deafblindness, and technology participated in individual semi-structured interviews on the topic of tools and strategies that can be used to screen individuals with dementia for sensory loss. Interview transcripts were coded by two evaluators using verbal agreement and consensus building.
Results: Three main themes emerged from the interviews with experts: barriers, facilitators, and strategies. Barriers to sensory screening were often mentioned, particularly impaired communication and lack of staff cooperation. Facilitators consisted uniquely of people, such as family members, intervenors, and nurses. Strategies for sensory screening in this population consisted of improving communication through repetition and encouragements; considerations based on familiarity; and inferring an impairment on the basis of patient behaviour. Few of our interviewees were knowledgeable on the topic of screening apps.
Conclusions: Our findings, to be integrated with a similar environmental scan conducted among LTC nurses, can inform the administration of sensory impairment screening tests among a population with dementia in order to optimize care.
Background: The perceptions surrounding assistive technology have been shown to be increasingly stigmatizing in older adult populations. This stigmatization can lead individuals to the abandonment of the assistive device. Until now, the methods of identifying or predicting the stigma surrounding assistive technology has mostly been qualitative in nature. Here we present a novel quantitate and qualitative research study that uses neuro-cognitive (psychophysics and EEG) and eye tracking technology, in addition to a new questionnaire to investigate the stigma associated with assistive devices. Therefore, this approach plays a major role in understanding and predicting the neural and physiological correlates associated to stigma.
Methods: Thirty-four older adults (>50 years) took part in the study. To determine the psychophysiological predictors of stigma surrounding assistive technologies, we monitored brain activity using EEG, heart rate and eye movements using an eye-tracker while participants viewed a series of images containing either an older or younger individual in different social scenarios (e.g., talking to doctor, at coffee shop). In each scenario, the individual uses either no assistive device, a low stigmatizing device (e.g., iPad), or a high stigmatizing device (e.g., electronic magnifier).
Results: Here we present preliminary analysis of the eye movement data. Analysis shows that in comparison to images that contained a low stigmatizing device, in images that contain high stigmatizing devices, the latency to fixate the device is shorter, first fixation duration is longer, and the total number of fixations on the device are higher. The environment that the devices is used in has no effect on eye movement metrics.
Conclusions: Although the sample size is small, and based on a healthy older-adult population, these initial observations would indicate that latency to fixate and first fixation duration are predictors of stigma associated with assistive devices. Future research should expand this prediction to those actively using assistive devices, and how the measures predict abandonment over time.
Abstract: The most prominent causes of loss of vision in individuals over 50 years include age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR). While it is important to screen for these diseases effectively, current eye care is not properly doing so for much of the population, resulting in unfortunate visual disability and high costs for patients. Innovative functional testing can be unified with other screening methods for a more robust and safer screening and prediction of disease. The goal in the creation of functional testing modalities is to develop highly sensitive screening tests that are easy to use, accessible to all users, and inexpensive. The tests herein are deployed on an iPad with easily understood and intuitive instructions for rapid, streamlined, and automatic administration. These testing modalities could become highly sensitive screenings for early detection of potentially blinding diseases. The applications from our collaborators at AMA Optics include a cone photostress recovery test for detection of AMD and diabetic macular edema (DME), brightness balance perception for optic nerve dysfunction and especially glaucoma, color vision testing which is a broad screening tool, and visual acuity test. Machine learning with the combined structural and functional data will optimize identification of disease and prediction of outcomes. Here, we review and assess various tests of visual function that are easily administered on a tablet for screening in primary care. These user-friendly and simple screening tests allow patients to be identified in the early stages of disease for referral to specialists, proper assessment and treatment.
