The Use of Home-Based Nonimmersive Virtual Reality to Encourage Physical and Cognitive Exercise in People With Mild Cognitive Impairment: A Feasibility Study.

Individuals with mild cognitive impairment are at risk of cognitive and physical decline. Virtual reality (VR) exercise may provide beneficial physical and cognitive exercise. The objectives of this study were to assess the feasibility and safety of home-based VR exercise and to provide pilot data for physical and cognitive efficacy. Eleven individuals with mild cognitive impairment (seven males/four females, average 78 years old, and average 3 years since diagnosis) performed a 30-min home-based VR exercise program 5 days a week for 6 weeks. The VR platform was successfully installed in participants' homes, and all participants were able to learn the VR program and progress. Participants completed 99% of the prescribed exercise. There were no major adverse events. Most participants enjoyed the VR program and reported physical benefits; fewer reported cognitive benefits. No physical or cognitive outcome measures showed change after 6 weeks. Home-based VR exercise is safe and feasible in individuals with mild cognitive impairment.

Implementation of smart supportive dementia technology in a hospital transitional care setting using human-centred design.

Supportive smart home technology, for older adults living with dementia and their informal care partners, has shown some benefits in private homes. In this study, a supportive smart home system is being implemented in a hospital alternative level of care setting. This case report describes how a team of researchers and healthcare managers are navigating the complexities of a hospital setting, using human-centred design and implementation strategies, to facilitate the implementation and adoption of the technology.

Using Serial Trichotomization with Neuropsychological Measures to Inform Clinical Decisions on Fitness-to-Drive among Older Adults with Cognitive Impairment.

Decisions related to driving safety and when to cease driving are complex and costly. There is an interest in developing an off-road driving test utilizing neuropsychological tests that could help assess fitness-to-drive. Serial trichotomization has demonstrated potential as it yields 100% sensitivity and specificity in retrospective test samples. The purpose of this study was to test serial trichotomization using four common neuropsychological tests (Trail Making Test Part A and B, Clock Drawing Test, and Modified Mini-Mental State Examination). Test scores from 105 patients who were seen in a memory clinic were abstracted. After applying the model, participants were classified as unfit, fit, or requiring further testing, 38.1%, 25.8%, and 36.1%, respectively. This study provides further evidence that trichotomization can facilitate the assessment of fitness-to-drive.

Memory Support System in French: a Pilot Study.

Mild cognitive impairment (MCI) confers a higher risk of developing dementia. While largely preserved, instrumental activities of daily living (IADLs) may be affected to varying degrees by MCI. The Memory Support System (MSS) is a curriculum and calendar/note-taking system that has proven effective in sustaining independence in IADLs for individuals with MCI and in protecting mood among care partners. Until recently, the MSS has only been utilized among English- and Spanish-speaking samples. This study investigated the use of a translated and culturally adapted MSS in four French-speaking, community-dwelling participants with MCI and their support partners. Measures of treatment adherence, daily function, self-efficacy for memory, quality of life, mood, anxiety, and caregiver burden were assessed at baseline, treatment end, and eight-week follow-up. By treatment end and follow-up, participants with MCI showed improvement in adherence to the MSS calendar, IADLs, everyday abilities requiring memory and planning, self-efficacy, depression and anxiety symptoms, and quality of life. Care partners showed improvement in quality of life and depressive symptoms, while their caregiver burden and anxiety symptoms generally remained unchanged. Findings suggest that, with appropriate training, Francophones with MCI can and will use the MSS, and that MSS training may contribute to daily functioning and aspects of participant and care partner well-being.

Supporting compassionate use of technology to support functioning in daily activities among people with cognitive decline - a scoping review.

Cognitive decline is becoming more prevalent as population ages. Technology offers solutions to help people with cognitive decline age in place. A compassionate approach to care can promote engagement in technology use by older adults with cognitive decline and equitable access. This scoping review summarized research literature on approaches to design and selection of technology that could support a compassionate approach to technology use for daily functioning among adults with cognitive decline and their care partners. We used the framework of Arksey and O'Malley. Key words capturing constructs of compassion, technology, and cognitive decline were searched in CINAHL, Medline, and PsycINFO. Peer-reviewed articles about the design for or use of technology by persons with cognitive decline or their care partners were included. Two reviewers screened and extracted data. Data informing compassionate technology use were analysed thematically. Fifty-five included articles represented a variety of technologies and purposes with ethics being the predominant perspective (n = 15). Analysis identified four categories: 1) Person- and care partner-centered approach, 2) Tailoring design to abilities, 3) Tailoring selection and application, and 4) Training and support. Using study findings, we developed a framework for compassionate use of technology for people living with cognitive decline and their care partners.

Compassionate approach to technology design and selection for person with cognitive decline and their care partners involves supporting autonomy, and consideration of ethical issues and specific technology purposeA family-centered care with a strong relational component is important when selecting technology with people with cognitive declineHealthcare providers and industry representatives require training to understand and adapt their approach to meet the needs of individuals living with cognitive decline and their care partners.

Long-term cognitive impairment after probable delirium in long-term care residents: A population-based retrospective cohort study.

BACKGROUND: The impact of delirium on cognition has not been well-studied in long-term care (LTC) residents. This study examined changes in cognition 1 year after a probable delirium episode among LTC residents, compared to LTC residents without probable delirium. We also evaluated whether the relationship between probable delirium and cognitive change differed according to a diagnosis of dementia. METHODS: We conducted a population-based retrospective cohort study using linked health administrative data. The study population included adults aged 65+ residing in LTC in Ontario, Canada and assessed via the Resident Assessment Instrument-Minimum Dataset between January 1, 2016 and December 31, 2018. Probable delirium was ascertained via the delirium Clinical Assessment Protocol on the index assessment. Cognition was measured quarterly using the Cognitive Performance Scale (range 0-6, higher values indicate greater impairment). Cognitive decline up to 1 year after index was evaluated using multivariable proportional odds regression models. RESULTS: Of 92,005 LTC residents, 2816 (3.1%) had probable delirium at index. Residents with probable delirium had an increased odds of cognitive decline compared to those without probable delirium, with adjusted odds ratios of 1.64 (95% confidence interval [CI] 1.35-1.99), 1.56 (95% CI 1.34-1.85), 1.57 (95% CI 1.32-1.86) and 1.50 (95% CI 1.25-1.80) after 1-3, 4-6, 7-9, and 10-12 months of follow-up. Residents with probable delirium and a comorbid dementia diagnosis had the highest adjusted odds of cognitive decline (adjusted odds ratio 5.57, 95% CI 4.79-6.48) compared to those without probable delirium or dementia. Residents with probable delirium were also more likely to die within 1 year than those without probable delirium (52.5% vs. 23.4%). CONCLUSIONS: Probable delirium is associated with increased mortality and worsened cognition in LTC residents that is sustained months after the probable delirium episode. Efforts to prevent delirium in this population may help limit these adverse effects.

Potentially Inappropriate Prescribing in Long-Term Care and its Relationship With Probable Delirium.

OBJECTIVES: This study examined potentially inappropriate prescribing (PIP) of medication and its association with probable delirium among long-term care (LTC) residents in Ontario, Canada. DESIGN: Population-based cross-sectional study using provincial health administrative data, including LTC assessment data via the Resident Assessment Instrument-Minimum Dataset version 2.0 (RAI-MDS 2.0). SETTING AND PARTICIPANTS: LTC residents in Ontario between January 1, 2016, and December 31, 2019. METHODS: We used residents' first RAI-MDS 2.0 assessment in the study period as the index assessment. Probable delirium was identified via the delirium Clinical Assessment Protocol. Medication use in the 2 weeks preceding assessment was captured using medication claims data. PIP was measured using the STOPP/START criteria and 2015 Beers criteria, with residents classified as having 0, 1, 2, or 3+ instances of PIP. Relationships between PIP and probable delirium was assessed via bivariate and multivariable logistic regression models. RESULTS: The study population included 171,190 LTC residents (mean age 84.5 years, 66.8% female, 62.9% with dementia). More than half (51.8%) of residents had 1+ instances of PIP and 21% had 3+ instances of PIP according to the STOPP/START criteria; PIP prevalence was slightly lower when assessed using Beers criteria (36.5% with 1+, 11.1% with 3+). Overall, 3.7% of residents had probable delirium. The prevalence of probable delirium increased as the number of instances of PIP increased, with residents with 3+ instances of STOPP/START PIP being 1.66 times more likely (95% CI 1.56-1.77) to have probable delirium compared to those with no instances of PIP. Similar findings were observed when PIP was measured using the Beers criteria. Central nervous system (CNS)-related PIP criteria showed a stronger association with probable delirium than non-CNS-related PIP criteria. CONCLUSIONS AND IMPLICATIONS: This population-based study highlighted that PIP was highly prevalent in long-term care residents and was associated with an increased prevalence of probable delirium.

Brain vital signs as a quantitative measure of cognition: Methodological implementation in a care home environment.

Introduction: Managing cognitive function in care homes is a significant challenge. Individuals in care have a variety of scores across standard clinical assessments, such as the Mini-Mental Status Exam (MMSE), and many of them have scores that fall within the range associated with dementia. A recent methodological advance, brain vital sign monitoring through auditory event-related potentials, provides an objective and sensitive physiological measurement to track abnormalities, differences, or changes in cognitive function. Taking advantage of point-of-care accessibility, the current study evaluated the methodological feasibility, the assessment of whether a particular research method can be successfully implemented, of quantitatively measuring cognition of care home residents using brain vital signs. Secondarily, the current study examined the relationship between brain vital signs, specifically the cognitive processing associated N400 component, and MMSE scores in care home residents. Materials and methods: Brain vital signs used the established N100 (auditory sensation), P300 (basic attention), and N400 (cognitive processing) event-related potential (ERP) components. A total of 52 residents were enrolled, with all participants evaluated using the MMSE. Participants were assigned into homogeneous groups based on their MMSE scores, and were categorized into low (n = 14), medium (n = 17), and high (n = 13) MMSE groups. Both brain vital sign measures and underlying ERP waveforms were examined. Statistical analyses used partial least squares correlation (PLS) analyses in which both MMSE and age were included as factors, as well as jackknife approaches, to test for significant brain vital sign changes. Results: The current study successfully measured and analyzed standardized, quantifiable brain vital signs in a care home setting. ERP waveform data showed specific N400 changes between MMSE groups as a function of MMSE score. PLS analyses confirmed significant MMSE-related and age-related differences in the N400 amplitude (p < 0.05, corrected). Similarly, the jackknife approach emphasized the N400 latency difference between the low and high MMSE groups. Discussion and conclusion: It was possible to acquire brain vital signs measures in care home residents. Additionally, the current study evaluated brain vital signs relative to MMSE in this group. The comparison revealed significant decreasing in N400 response amplitude (cognitive processing) as a function of both MMSE score and age, as well as a slowing of N400 latency. The findings indicate that objective neurophysiological measures of impairment are detectable in care home residents across the span of MMSE scores. Direct comparison to MMSE- and age-related variables represents a critical initial step ahead of future studies that will investigate relative improvements in sensitivity, validity, reliability and related advantages of brain vital sign monitoring.

Decline in Other Instrumental Activities of Daily Living as Indicators of Driving Risk in Older Adults at an Academic Memory Clinic.

BACKGROUND: Decisions around driving retirement are difficult for older persons living with cognitive decline and their caregivers. In many jurisdictions, physicians are responsible for notifying authorities of driving risks. However, there are no standardized guidelines for this assessment. Having access to a driving risk assessment tool could help older adults and their caregivers prepare for discussions around driving retirement. This study compares the clinical profiles of older adult drivers assessed in an academic memory clinic who were referred to the driving authority to older drivers who were not with a focus on instrumental activities of daily living (iADLs). METHODS: Data on referred (R) and not-referred (NR) drivers were extracted from medical records. Elements from the medical history, cognitive history, functional abilities, Modified Mini-Mental State (3MS) examination, Trails A/B, and clock drawing were included in the analysis. Four risk factors of interest were examined in separate logistic regression analyses, adjusted for demographic variables. RESULTS: 50 participants were identified in each group. The R group was older on average than the NR. As expected, R were more likely to have Trails B scores over 3 min and have significantly abnormal clock drawing tests. R also showed lower 3MS scores and a higher average number of functional impairments (including managing appointments, medications, bills, or the television). CONCLUSION: Beyond standard cognitive tests, impairment in iADLs may help general practitioners identify at-risk drivers in the absence of standardized guidelines and tools. This finding can also inform the design of a risk assessment tool for driving and could help with approaches for drivers with otherwise borderline test results.

Driving Cessation: What Are Family Members' Experiences and What Do They Think about Driving Simulators?

BACKGROUND: Driving cessation is difficult for persons living with cognitive decline (PLWCD) and their caregivers (CG). Physicians are often required to notify authorities of driving risks, and typically base decisions on paper-based cognitive assessments and on-road tests. This study examines experiences surrounding cessation and CG's views regarding simulators in the process. METHODS: Semi-structured virtual interviews were conducted with CGs of PLWCD from an academic memory clinic. Experiences around cessation were explored first, followed by discussions regarding the simulator. Framework analysis was applied to transcribed interviews. RESULTS: Six females and two males, three children and five spouses participated. PLWCD viewed driving cessation negatively, often had difficulty understanding why, and believed cessation was temporary. CGs experienced relief and/or shock. Cessation negatively impacted the relationships between the PLWCD and both the physician and CG. Isolation, coping challenges and loss of independence were experienced by the PLWCD. The lives of caregivers were adversely affected, especially regarding driving burden and worsening mental health. CGs were generally supportive of simulators. Positives included: measurement of driving skills, method of testing, and providing an understanding regarding the driving suspension. Potential drawbacks included difficulty using the machine, testing anxiety and stress induced by a crash. Caregivers were concerned about: PLWCD's disappointment of failure, requesting to retest, and reluctance to accept the decision. CONCLUSION: PLWCD and caregivers had negative experiences related to the driving cessation. Generally, caregivers viewed implementing driving simulators positively, in a context of a practice session and support for PLWCD's potential reactions to the decision.

Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications.

Goal: The evaluation of respiratory events using audio sensing in an at-home setting can be indicative of worsening health conditions. This paper investigates the use of image-based transfer learning applied to five audio visualizations to evaluate three classification tasks (C1: wet vs. dry vs. whooping cough vs. restricted breathing; C2: wet vs. dry cough; C3: cough vs. restricted breathing). Methods: The five visualizations (linear spectrogram, logarithmic spectrogram, Mel-spectrogram, wavelet scalograms, and aggregate images) are applied to a pre-trained AlexNet image classifier for all tasks. Results: The aggregate image-based classifier achieved the highest overall performance across all tasks with C1, C2, and C3 having testing accuracies of 0.88, 0.88, and 0.91 respectively. However, the Mel-spectrogram method had the highest testing accuracy (0.94) for C2. Conclusions: The classification of respiratory events using aggregate image inputs to transfer learning approaches may help healthcare professionals by providing information that would otherwise be unavailable to them.

Probable Delirium and Associated Patient Characteristics in Long-Term Care and Complex Continuing Care: A Population-Based Observational Study.

OBJECTIVES: To estimate the prevalence of probable delirium in long-term care (LTC) and complex continuing care (CCC) settings and to describe the resident characteristics associated with probable delirium. DESIGN: Population-based cross-sectional study using routinely collected administrative health data. SETTING AND PARTICIPANTS: All LTC and CCC residents in Ontario, Canada, assessed with the Resident Assessment Instrument-Minimum Dataset (RAI-MDS) assessment between July 1, 2016, and December 31, 2016 (LTC n=86,454, CCC n=10,217). METHODS: Probable delirium was identified via the delirium Clinical Assessment Protocol on the RAI-MDS assessment, which is triggered when individuals display at least 1 of 6 delirium symptoms that are of recent onset and different from their usual functioning. RAI-MDS assessments were linked to demographic and health services utilization databases to ascertain resident demographics and health status. Multivariable logistic regression was used to identify characteristics associated with probable delirium, with adjusted odds ratios (ORs) and 95% confidence intervals (CIs) reported. RESULTS: Delirium was probable in 3.6% of LTC residents and 16.5% of CCC patients. LTC patients displayed fewer delirium symptoms than CCC patients. The most common delirium symptom in LTC was periods of lethargy (44.6% of delirium cases); in CCC, it was mental function varying over the course of the day (63.5% of delirium cases). The odds of probable delirium varied across individual demographics and health characteristics, with increased health instability having the strongest association with the outcome in both care settings (LTC: OR 30.4, 95% CI 26.2-35.3; CCC: OR 21.0, 95% CI 16.7-26.5 for high vs low instability). CONCLUSIONS AND IMPLICATIONS: There were differences in the presentation and burden of delirium symptoms between LTC and CCC, potentially reflecting differences in delirium severity or symptom identification. Several risk factors for probable delirium in LTC and CCC were identified that may be amenable to interventions to prevent this highly distressing condition.

Novel Coronavirus Cough Database: NoCoCoDa.

The current pandemic associated with the novel coronavirus (COVID-19) presents a new area of research with its own set of challenges. Creating unobtrusive remote monitoring tools for medical professionals that may aid in diagnosis, monitoring and contact tracing could lead to more efficient and accurate treatments, especially in this time of physical distancing. Audio based sensing methods can address this by measuring the frequency, severity and characteristics of the COVID-19 cough. However, the feasibility of accumulating coughs directly from patients is low in the short term. This article introduces a novel database (NoCoCoDa), which contains COVID-19 cough events obtained through public media interviews with COVID-19 patients, as an interim solution. After manual segmentation of the interviews, a total of 73 individual cough events were extracted and cough phase annotation was performed. Furthermore, the COVID-19 cough is typically dry but can present as a more productive cough in severe cases. Therefore, an investigation of cough sub-type (productive vs. dry) of the NoCoCoDa was performed using methods previously published by our research group. Most of the NoCoCoDa cough events were recorded either during or after a severe period of the disease, which is supported by the fact that 77% of the COVID-19 coughs were classified as productive based on our previous work. The NoCoCoDa is designed to be used for rapid exploration and algorithm development, which can then be applied to more extensive datasets and potentially real time applications. The NoCoCoDa is available for free to the research community upon request.

Smart home technology solution for night-time wandering in persons with dementia.

INTRODUCTION: More than half of persons with dementia will experience night-time wandering, increasing their risk of falls and unattended home exits. This is a major predictor of caregiver burnout and one of the major causes of early institutionalization. METHODS: Using smart home technologies such as sensors, smart bulbs, pressure mats and speakers, the Night-time Wandering Detection and Diversion system is designed to assist caregivers and persons with dementia that are at risk of wandering at night. Being placed in homes around Ottawa for a 12-week trial, the system allows caregivers to rest peacefully in the night, as it detects when the person with dementia gets out of bed and automatically provides cue lighting to guide them safely to the washroom. The system also uses prerecorded audio prompts, if they venture from the bedroom, only waking the caregiver when the person with dementia opens an exit door. RESULTS: Thus far, the average depression and anxiety in caregivers have been improved after the 12 weeks, and most have said that they sleep more peacefully. CONCLUSION: The system has proven successful in supporting the safety of persons with dementia as well as their caregivers.

Comparison of Silence Removal Methods for the Identification of Audio Cough Events.

Sensing technologies are embedded in our everyday lives. Smart homes typically use an Audio Virtual Assistant (AVA) (e.g. Alexa, Siri, and Google Home) interface that collects sensor information, which can provide security, assist in everyday activities and monitor health related information. One such measure is cough, changes of which can be a marker of worsening conditions for many respiratory diseases. Creating a reliable monitoring system utilizing technology that may already be present in the home (i.e. AVA) may provide an opportunity for early intervention and reductions in the number of long-term hospitalizations. This paper focuses on the optimization of the silence removal and segmentation step in an at home setting with low to moderate background noise to identify cough events. Three commonly used methods (Standard deviation (SD), Short-term Energy (SE), Zero-crossing rate (ZCR)) were compared to manual segmentations. Each method was applied to 209 audio files that were manually verified to contain at least one cough event and the average segmentation accuracy, over segmentation and under segmentation results were compared. The ZCR method had the highest accuracy (89%); however, it completely failed under moderate noise conditions. The SD method had the best combination of accuracy (86%), ability to perform under noisy conditions and low prevalence of over and under segmentation (22% and 15% respectively). Therefore, we recommend using an adaptive approach to silence removal among cough events based on the level of background noise (i.e use the ZCR method when the background noise is low and the SD method when it is higher) prior to implementation of a cough classification system.

Examining the Effect of Noise on Biosignal Estimates Extracted through Spatio-Temporal Video Processing.

Spatio-temporal video processing has been used to extract subject vital signals from optical video and, more recently, thermal video. Thermal video, in conjunction with spatio-temporal video processing can extract biosignals optical video cannot; namely temperature data, but also biosignals in poor light conditions. Video processing involves many system parameters that can result in false biosignal reporting. This paper aimed to robustly test spatio-temporal processing algorithms and determine patterns with respect to increasing noise levels. Over 500 simulated thermal videos were generated at 29 different signal frequencies representing heart rates. These videos were contaminated with 18 different levels of Gaussian noise and were used as inputs to the algorithmic system. The algorithmic system processed each video at 6 different filter widths. The results were examined individually and as a collective. Individual results were as expected; the processing resulted in an accurate heart rate estimate if the original signal was inside the filter passband. If the signal was outside of the filter passband, the processing simply amplified noise. These same patterns were observed in the cumulative results, in addition to overarching patterns with respect to noise. Two main patterns were observed; a failure threshold was determined and quantified and a pattern of error behavior beyond this threshold was quantified. The failure threshold occurred at a noise variance of approximately 500, and around this parameter value, all detected signal frequencies were approaching 1.5 Hz (90bpm). This study was able to characterize patterns of failure, which helps to prevent future false reporting.

Visual Event-Related Potentials in Mild Cognitive Impairment and Alzheimer's Disease: A Literature Review.

BACKGROUND: Cognitive deficits are correlated with increasing age and become more pronounced for people with mild cognitive impairment (MCI) and dementia caused by Alzheimer's disease (AD). Conventional methods to diagnose cognitive decline (i.e., neuropsychological testing and clinical judgment) can lead to false positives. Tools such as electroencephalography (EEG) offer more refined, objective measures that index electrophysiological changes associated with healthy aging, MCI, and AD. OBJECTIVE: We sought to review the EEG literature to determine whether visual event-related potentials (ERPs) can distinguish between healthy aging, MCI, and AD. METHOD: We searched Medline and PyscInfo for articles published between January 2005 and April 2018. Articles were considered for review if they included participants aged 60+ who were healthy older adults or people with MCI and AD, and examined at least one visually elicited ERP component. RESULTS: Our search revealed 880 records, of which 34 satisfied the inclusion criteria. All studies compared cognitive function between at least two of the three groups (healthy older adults, MCI, and AD). The most consistent findings related to the P100 and the P3b; while the P100 showed no differences between groups, the P3b showed declines in amplitude in MCI and AD. CONCLUSION: Visually elicited ERPs can offer insight into the cognitive processes that decline in MCI and AD. The P3b may be useful in identifying older adults who may develop MCI and AD, and more research should examine the sensitivity and specificity of this component when diagnosing MCI and AD.

Semi-Autonomous Vehicles as a Cognitive Assistive Device for Older Adults.

Losing the capacity to drive due to age-related cognitive decline can have a detrimental impact on the daily life functioning of older adults living alone and in remote areas. Semi-autonomous vehicles (SAVs) could have the potential to preserve driving independence of this population with high health needs. This paper explores if SAVs could be used as a cognitive assistive device for older aging drivers with cognitive challenges. We illustrate the impact of age-related changes of cognitive functions on driving capacity. Furthermore, following an overview on the current state of SAVs, we propose a model for connecting cognitive health needs of older drivers to SAVs. The model demonstrates the connections between cognitive changes experienced by aging drivers, their impact on actual driving, car sensors' features, and vehicle automation. Finally, we present challenges that should be considered when using the constantly changing smart vehicle technology, adapting it to aging drivers and vice versa. This paper sheds light on age-related cognitive characteristics that should be considered when developing future SAVs manufacturing policies which may potentially help decrease the impact of cognitive change on older adult drivers.

Influenza vaccination and decisional conflict among regulated and unregulated direct nursing care providers in long-term-care homes.

The purpose of this study was to determine whether direct nursing care providers have decisional conflict about receiving influenza vaccinations and characteristics associated with decisional conflict. The researchers used a self-administered questionnaire mailed to direct nursing care providers in two long-term-care organizations. Most direct nursing care providers in both organizations (80% and 93%, respectively) intended to get the influenza vaccine. Unregulated direct nursing care providers had more decisional conflict than regulated providers, especially related to feeling uninformed about the pros and cons of influenza vaccination. Unclear valuing of the pros and cons of influenza vaccination was related to the age of the direct care providers in both organizations. Decisional conflict and influenza vaccination practices may be determined, in part, by age and by the culture of a health care organization. A decision aid to improve knowledge and clarify values may improve decision quality and increase influenza vaccination rates.

Naturalistic Driving: A Framework and Advances in Using Big Data.

Driving is an activity that facilitates physical, cognitive, and social stimulation in older adults, ultimately leading to better physical and cognitive health. However, aging is associated with declines in vision, physical health, and cognitive health, all of which can affect driving ability. One way of assessing driving ability is with the use of sensors in the older adult's own vehicle. This paper provides a framework for driving assessment and addresses how naturalistic driving studies can assist in such assessments. The framework includes driving characteristics (how much driving, speed, position, type of road), actions and reactions (lane changes, intersections, passing, merging, traffic lights, pedestrians, other vehicles), destinations (variety and distance, sequencing and route planning), and driving conditions (time of day and season). Data from a subset of Ottawa drivers from the Candrive study is used to illustrate the use of naturalistic driving data. Challenges in using naturalistic driving big data and the changing technology in vehicles are discussed.