Innovations In Clinical Neuroscience

JAN-FEB 2017

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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[ V O L U M E 1 4 , N U M B E R 1 – 2 , J A N U A R Y – F E B R U A R Y 2 0 1 7 ] Innovations in CLINICAL NEUROSCIENCE 35 involves a shopping task. Older adults t ook longer to complete the task and made more errors, as well as performed more poorly on a cognitive assessment. Importantly, composite test performance correlated r=0.66 with performance time i n older adults, with correlations between each of the domains of the cognitive assessment and performance time found to be significant. Importantly for the assessment of dementia-related deficits, VRFCAT time scores were most strongly correlated with verbal episodic memory of all of the neurocognitive domains. Thus, this test shows significant potential for detection of increased ADL deficits associated with the development of new-onset MCI and has considerable evidence of convergence with cognition even in healthy populations. NATURALISTIC ASSESSMENT IN REAL-WORLD ENVIRONMENTS Another recent development in assessing early stages of cognitive change has been the naturalistic assessment of everyday functioning through the use of sensor-based technology. Advancing beyond performing realistic task simulations as described above, these strategies directly measure everyday activities in the home and the community. The advent of pervasive computing technologies deployed direct-to-home (e.g., embedded sensing and computing in the home), wireless communications, and "big data" analytics provide the route to change the current, less ecologically valid, episodic clinical trial testing paradigm. This new approach has been developed and deployed in several hundred homes of seniors followed for up to seven years by the Oregon Center for Aging & Technology (ORCATECH). 48 Thus, using an in-home array of strategically placed passive infrared motion sensors, contact sensors, physiologic vital signs (e.g. pulse, body mass index [BMI]) monitoring, medication tracking with an electronic pillbox, and telephone and computer use assessments, performance of key functions has been readily derived on a continuous or near- continuous basis. With this platform deployed to multiple homes in the c ommunity, frequent assessment of relevant outcome measures could be more sensitively assessed. These metrics include total activity in a day by location, such as particular areas of the h ome (e.g., bedroom, bathroom), gait speed, number of room transitions, medication adherence, social engagement (e.g., time on the telephone, time out of home), cognitively demanding functions (e.g., typical time in computer activities, recalling to take a medication), as well as more frequent (weekly) self-report of activities that cannot be simply inferred by remote sensing (e.g., rating pain, mood, falls). Importantly, aside from the absolute or mean values that are derived, minute-to- minute or day-to-day variability also becomes inherently available. This variability itself, which cannot be obtained with the current sparse measurement techniques, may be an important indicator of early change. Using this approach, many key functions have been shown to be amenable to detecting subtle change over time in aging cohorts and those with MCI. This includes total activity, 5 0 gait, 51 sleep behaviors, 52 socialization, 53 computer use, 54 and medication adherence. 55 The dense (frequently captured), high dimensional (multi- domain) data lends itself to building comprehensive models of cognitive and functional change over time. Recent simulations using these data to develop more sensitive trial outcomes and better statistical approaches have been reported. 56,57 In this work, a major goal is to develop dramatically more efficient trials by creating new metrics sensitive to subtle changes in cognitive and functional outcomes using individual-specific distributions (as opposed to conventional group-norms). The ability to use this approach is enabled through the unobtrusively acquired in-home data, which allows the collection of enough data points (e.g., n=1,000 per subject) to generate individual-specific distributions of functional outcomes, such as computer usage and walking speed/variability, within a short duration of time (e.g., within 1–3 months). This then provides the ability to compare sample sizes required to achieve sufficient power to detect dementia p revention trial effects in two scenarios: 1) A conventional approach—annually assessed neuropsychological test scores modeled as a function of time using mixed effects models, and (2) a new approach u sing the continuous data —the likelihood of hitting subject-specific low performance thresholds modeled as a function of time using generalized mixed effects models. In the comparison of approaches, sample size estimates using the conventional approach would require approximately 2,000 subjects with a follow-up duration of three years to achieve a 30-percent effect size if the outcome is memory test scores (i.e., Logical Memory scores). If the outcome using the continuous remote sensed data is directed toward hitting an intra-individual-based low threshold of walking speed (e.g., 10th percentile of individual-specific walking speed), 263 subjects are required. For computer use (e.g., reaching the person-specific 40th percentile of low use), only 26 subjects would be required. Thus, individual- specific thresholds of low functional performance based on high-frequency, in- home monitoring data can distinguish trajectories of MCI from cognitively normal subjects with dramatically reduced sample sizes in prevention RCTs. In particular, an attractive advance might be to test candidate compounds by this method in early phases of drug development (Phases 1 and 2) to see if a clinical signal might be apparent before proceeding to large and more expensive Phase 3 trials. This approach shows great promise, especially in reducing sample sizes, which would reduce the cost of early phase studies, potentially resulting in the testing of more early-phase compounds. The technological demands of this approach are now more minimal than ever. Individuals use their own home computers or smartphones, and their activities are passively monitored. Further, physical activity monitoring is performed through the use of wearable devices, which are in common use and continuously decreasing in price. Thus, the cost of doing studies such as these is less than the typical current strategies in patient assessment, which require visits (with payments) and study coordinators and possibly psychologists to perform the assessments.

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