Abstract

ObjectiveWhite matter hyperintensities (WMHs) are common with age, grow over time, and are associated with cognitive and motor impairments. Mechanisms underlying WMH growth are unclear. We aimed to determine the presence and extent of decreased normal appearing white matter (NAWM) cerebral blood flow (CBF) surrounding WMHs to identify ‘WM at risk’, or the WMH CBF penumbra. We aimed to further validate cross-sectional finding by determining whether the baseline WMH penumbra CBF predicts the development of new WMHs at follow-up.MethodsSixty-one cognitively intact elderly subjects received 3 T MPRAGE, FLAIR, and pulsed arterial spin labeling (PASL). Twenty-four subjects returned for follow-up MRI. The inter-scan interval was 18 months. A NAWM layer mask, comprised of fifteen layers, 1 mm thick each surrounding WMHs, was generated for periventricular (PVWMH) and deep (DWMH) WMHs. Mean CBF for each layer was computed. New WMH and persistent NAWM voxels for each penumbra layer were defined from follow-up MRI.ResultsCBF in the area surrounding WMHs was significantly lower than the total brain NAWM, extending approximately 12 mm from both the established PVWMH and DWMH. Voxels with new WMH at follow-up had significantly lower baseline CBF than voxels that maintained NAWM, suggesting that baseline CBF can predict the development of new WMHs over time.ConclusionsA CBF penumbra exists surrounding WMHs, which is associated with future WMH expansion. ASL MRI can be used to monitor interventions to increase white matter blood flow for the prevention of further WM damage and its cognitive and motor consequences.

<b><i>Introduction:</i></b> Future digital health research hinges on methodologies to conduct remote clinical assessments and in-home monitoring. The Collaborative Aging Research Using Technology (CART) initiative was introduced to establish a digital technology research platform that could widely assess activity in the homes of diverse cohorts of older adults and detect meaningful change longitudinally. This paper reports on the built end-to-end design of the CART platform, its functionality, and the resulting research capabilities. <b><i>Methods:</i></b> CART platform development followed a principled design process aiming for scalability, use case flexibility, longevity, and data privacy protection while allowing sharability. The platform, comprising ambient technology, wearables, and other sensors, was deployed in participants’ homes to provide continuous, long-term (months to years), and ecologically valid data. Data gathered from CART homes were sent securely to a research server for analysis and future data sharing. <b><i>Results:</i></b> The CART system was created, iteratively tested, and deployed to 232 homes representing four diverse cohorts (African American, Latinx, low-income, and predominantly rural-residing veterans) of older adults (<i>n</i> = 301) across the USA. Multiple measurements of wellness such as cognition (e.g., mean daily computer use time = 160–169 min), physical mobility (e.g., mean daily transitions between rooms = 96–155), sleep (e.g., mean nightly sleep duration = 6.3–7.4 h), and level of social engagement (e.g., reports of overnight visitors = 15–45%) were collected across cohorts. <b><i>Conclusion:</i></b> The CART initiative resulted in a minimally obtrusive digital health-enabled system that met the design principles while allowing for data capture over extended periods and can be widely used by the research community. The ability to monitor and manage health digitally within the homes of older adults is an important alternative to in-person assessments in many research contexts. Further advances will come with wider, shared use of the CART system in additional settings, within different disease contexts, and by diverse research teams.

There is an urgent need to learn how to appropriately integrate technologies into dementia care. The aims of this Delphi study were to project which technologies will be most prevalent in dementia care in five years, articulate potential benefits and risks, and identify specific options to mitigate risks. Participants were also asked to identify technologies that are most likely to cause value tensions and thus most warrant a conversation with an older person with mild dementia when families are deciding about their use. Twenty-one interdisciplinary domain experts from academia and industry in aging and technology in the U.S. and Canada participated in a two-round online survey using the Delphi approach with an 84% response rate and no attrition between rounds. Rankings were analyzed using frequency counts and written-in responses were thematically analyzed. Twelve technology categories were identified along with a detailed list of risks and benefits for each. Suggestions to mitigate the most commonly raised risks are categorized as follows: intervene during design, make specific technical choices, build in choice and control, require data transparency, place restrictions on data use and ensure security, enable informed consent, and proactively educate users. This study provides information that is needed to navigate person-centered technology use in dementia care. The specific recommendations participants offered are relevant to designers, clinicians, researchers, ethicists, and policy makers and require proactive engagement from design through implementation.

Introduction: Participant retention is important to maintaining statistical power, minimizing bias, and preventing scientific error in Alzheimer disease and related dementias research. Methods: We surveyed representative investigators from NIH-funded Alzheimer’s Disease Research Centers (ADRC), querying their use of retention tactics across 12 strategies. We compared survey results to data from the National Alzheimer’s Coordinating Center for each center. We used a generalized estimating equation with independent working covariance model and empirical standard errors to assess relationships between survey results and rates of retention, controlling for participant characteristics. Results: Twenty-five (83%) responding ADRCs employed an average 42 (SD=7) retention tactics. In a multivariable model that accounted for participant characteristics, the number of retention tactics used by a center was associated with participant retention (odds ratio=1.68, 95% confidence interval: 1.42, 1.98; P<0.001 for the middle compared with the lowest tertile survey scores; odds ratio=1.59, 95% confidence interval: 1.30, 1.94; P<0.001 for the highest compared with the lowest tertile survey scores) at the first follow-up visit. Participant characteristics such as normal cognition diagnosis, older age, higher education, and Caucasian race were also associated with higher retention. Conclusions: Retention in clinical research is more likely to be achieved by employing a variety of tactics.