Digital quantification of the MMSE interlocking pentagon areas: a three-stage algorithm.

The Mini-Mental State Examination (MMSE) is a widely employed screening tool for the severity of cognitive impairment. Among the MMSE items, the pentagon copying test (PCT) requires participants to accurately replicate a sample of two interlocking pentagons. While the PCT is traditionally scored on a binary scale, there have been limited developments of granular scoring scale to assess task performance. In this paper, we present a novel three-stage algorithm, called Quantification of Interlocking Pentagons (QIP) which quantifies PCT performance by computing the areas of individual pentagons and their intersection areas, and a balance ratio between the areas of the two individual pentagons. The three stages of the QIP algorithm include: (1) detection of line segments, (2) unraveling of the interlocking pentagons, and (3) quantification of areas. A set of 497 PCTs from 84 participants including their baseline and follow-up PCTs from the Rush Memory and Aging Project was selected blinded about their cognitive and clinical status. Analysis of the quantified data revealed a significant inverse relationship between age and balance ratio (beta = - 0.49, p = 0.0033), indicating that older age was associated with a smaller balance ratio. In addition, balance ratio was associated with perceptual speed (r = 0.71, p = 0.0135), vascular risk factors (beta = - 3.96, p = 0.0269), and medical conditions (beta = - 2.78, p = 0.0389). The QIP algorithm can serve as a useful tool for enhancing the scoring of performance in the PCT.

Virtual Reality Game Selection for Traumatic Brain Injury Rehabilitation: A Therapist's Wish List for Game Developers.

This project explored the selection process of commercially available virtual reality (VR) games for traumatic brain injury rehabilitation. Occupational therapy practitioners (OTPs) developed a classification framework that they used to evaluate VR games. The classification framework focused on movements required to effectively play the game, cognitive demand, position for game play, ease in menu navigation, and perceived therapeutic applications. OTPs used the ratings to aid in game selection and identified relevant game examples that allowed customizable settings and basic navigation with a game focus on functional activities. The OTPs and the research team identified the need for further work on accessibility and adaptability of game features (e.g., difficulty and limb usage) allowing for more individualization to optimize outcomes of VR-enhanced rehabilitation. The classification framework was useful in evaluating the potential therapeutic benefit of commercially available VR games. However, trial of the game by clinicians prior to use was still warranted.

The Collaborative Aging Research Using Technology Initiative: An Open, Sharable, Technology-Agnostic Platform for the Research Community.

INTRODUCTION: 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. METHODS: 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. RESULTS: 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 (n = 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. CONCLUSION: 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.

Different Combinations of Mobility Metrics Derived From a Wearable Sensor Are Associated With Distinct Health Outcomes in Older Adults.

BACKGROUND: Gait speed is a robust nonspecific predictor of health outcomes. We examined if combinations of gait speed and other mobility metrics are associated with specific health outcomes. METHODS: A sensor (triaxial accelerometer and gyroscope) placed on the lower back, measured mobility in the homes of 1,249 older adults (77% female; 80.0, SD = 7.72 years). Twelve gait scores were extracted from five performances, including (a) walking, (b) transition from sit to stand, (c) transition from stand to sit, (d) turning, and (e) standing posture. Using separate Cox proportional hazards models, we examined which metrics were associated with time to mortality, incident activities of daily living disability, mobility disability, mild cognitive impairment, and Alzheimer's disease dementia. We used a single integrated analytic framework to determine which gait scores survived to predict each outcome. RESULTS: During 3.6 years of follow-up, 10 of the 12 gait scores predicted one or more of the five health outcomes. In further analyses, different combinations of 2-3 gait scores survived backward elimination and were associated with the five outcomes. Sway was one of the three scores that predicted activities of daily living disability but was not included in the final models for other outcomes. Gait speed was included along with other metrics in the final models predicting mortality and activities of daily living disability but not for other outcomes. CONCLUSIONS: When analyzing multiple mobility metrics together, different combinations of mobility metrics are related to specific adverse health outcomes. Digital technology enhances our understanding of impaired mobility and may provide mobility biomarkers that predict distinct health outcomes.

Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults.

INTRODUCTION: Mobility metrics derived from wearable sensor recordings are associated with parkinsonism in older adults. We examined if these metrics predict incident parkinsonism. METHODS: Parkinsonism was assessed annually in 683 ambulatory, community-dwelling older adults without parkinsonism at baseline. Four parkinsonian signs were derived from a modified Unified Parkinson's Disease Rating Scale (UPDRS). Parkinsonism was based on the presence of 2 or more signs. Participants wore a sensor on their back while performing a 32 foot walk, standing posture, and Timed Up and Go (TUG) tasks. 12 mobility scores were extracted. Cox proportional hazards models with backward elimination were used to identify combinations of mobility scores independently associated with incident parkinsonism. RESULTS: During follow-up of 2.5 years (SD = 1.28), 139 individuals developed parkinsonism (20.4%). In separate models, 6 of 12 mobility scores were individually associated with incident parkinsonism, including: Speed and Regularity (from 32 ft walk), Sway (from standing posture), and 3 scores from TUG subtasks (Posterior sit to stand transition, Range stand to sit transition, and Yaw, a measure of turning efficiency). When all mobility scores were analyzed together in a single model, 2 TUG subtask scores, Range from stand to sit transition (HR, 1.42, 95%CI, 1.09, 1.82) and Yaw from turning (HR, 0.56, 95%CI, 0.42, 0.73) were independently associated with incident parkinsonism. These results were unchanged when controlling for chronic health covariates. CONCLUSION: Mobility metrics derived from a wearable sensor complement conventional gait testing and have potential to enhance risk stratification of older adults who may develop parkinsonism.

Expanding instrumented gait testing in the community setting: A portable, depth-sensing camera captures joint motion in older adults.

BACKGROUND: Currently, it is not feasible to obtain laboratory-based measures of joint motion in large numbers of older adults. We assessed the utility of a portable depth-sensing camera for quantifying hip and knee joint motion of older adults during mobility testing in the community. METHODS: Participants were 52 older adults enrolled in the Rush Memory and Aging Project, a community-based cohort study of aging. In a subset, we compared dynamic hip and knee flexion/extension obtained via the depth-sensing camera with that obtained concurrently using a laboratory-based optoelectronic motion capture system. Then we recorded participants' annual instrumented gait assessment in the community setting with the depth-sensing camera and examined the inter-relationships of hip and knee range of motion (ROM) with mobility metrics derived from a wearable sensor and other mobility-related health measures. RESULTS: In the community, we successfully acquired joint motion from 49/52 participants using the depth-sensing camera. Hip and knee ROMs were related to diverse sensor-derived metrics of mobility performance (hip: Pearson's r = 0.31 to 0.58; knee: Pearson's r = 0.29 to 0.51), as well as daily physical activity, conventional motor measures, self-report hip and knee pain and dysfunction, mobility disability, and falls. CONCLUSIONS: The depth-sensing camera's high rate of successful data acquisition and correlations of its hip and knee ROMs with other mobility measures suggest that this device can provide a cost-efficient means of quantifying joint motion in large numbers of community-dwelling older adults who span the health spectrum.