Background Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). Methods Thirty-nine participants with CVD, Alzheimer’s disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson’s disease, or amyotrophic lateral sclerosis (median age 68 (45–83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. Results Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17–22 min/day), with significant differences between some locations ( p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear ( p < 0.001) and there was a small but significant increase in non-wear over the collection period ( p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners. Conclusion A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.

Introduction: Understanding synergies between neurodegenerative and cerebrovascular pathologies that modify dementia presentation represents an important knowledge gap.Methods: This multi-site, longitudinal, observational cohort study recruited participants across prevalent neurodegenerative diseases and cerebrovascular disease and assessed participants comprehensively across modalities. We describe univariate and multivariate baseline features of the cohort and summarize recruitment, data collection, and curation processes. Results:We enrolled 520 participants across five neurodegenerative and cerebrovascular diseases. Median age was 69 years, median Montreal Cognitive Assessment score was 25, median independence in activities of daily living was 100% for basic and 93% for instrumental activities. Spousal study partners predominated; participants were often male, White, and more educated. Milder disease stages predominated, yet cohorts reflect clinical presentation.

Objective: In individuals over the age of 65, concomitant neurodegenerative pathologies contribute to cognitive and/or motor decline and can be aggravated by cerebrovascular disease, but our understanding of how these pathologies synergize to produce the decline represents an important knowledge gap. The Ontario Neurodegenerative Disease Research Initiative (ONDRI), a multi-site, longitudinal, observational cohort study, recruited participants across multiple prevalent neurodegenerative diseases and cerebrovascular disease, collecting a wide array of data and thus allowing for deep investigation into common and unique phenotypes. This paper describes baseline features of the ONDRI cohort, understanding of which is essential when conducting analyses or interpreting results. Methods: Five disease cohorts were recruited: Alzheimer's disease/amnestic mild cognitive impairment (AD/MCI), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson's disease (PD), and cerebrovascular disease (CVD). Assessment platforms included clinical, neuropsychology, eye tracking, gait and balance, neuroimaging, retinal imaging, genomics, and pathology. We describe recruitment, data collection, and data curation protocols, and provide a summary of ONDRI baseline characteristics. Results: 520 participants were enrolled. Most participants were in the early stages of disease progression. Participants had a median age of 69 years, a median Montreal Cognitive Assessment score of 25, a median percent of independence of 100 for basic activities of daily living, and a median of 93 for instrumental activities. Variation between disease cohorts existed for age, level of cognition, and geographic location. Conclusion: ONDRI data will enable exploration into unique and shared pathological mechanisms contributing to cognitive and motor decline across the spectrum of neurodegenerative diseases.

White matter hyperintensity burden predicts cognitive but not motor decline inParkinson's disease. Results from the ONDRI.

Background Accelerometery is commonly used to estimate physical activity, sleep, and sedentary behavior. In free-living conditions, periods of device removal (non-wear) can lead to misclassification of behavior with consequences for research outcomes and clinical decision making. Common methods for non-wear detection are limited by data transformations (e.g., activity counts) or algorithm parameters such as minimum durations or absolute temperature thresholds that risk over- or under-estimating non-wear time. This study aimed to advance non-wear detection methods by integrating a ‘rate-of-change’ criterion for temperature into a combined temperature-acceleration algorithm. Methods Data were from 39 participants with neurodegenerative disease (36% female; age: 45–83 years) who wore a tri-axial accelerometer (GENEActiv) on their wrist 24-h per day for 7-days as part of a multi-sensor protocol. The reference dataset was derived from visual inspection conducted by two expert analysts. Linear regression was used to establish temperature rate-of-change as a criterion for non-wear detection. A classification and regression tree (CART) decision tree classifier determined optimal parameters separately for non-wear start and end detection. Classifiers were trained using data from 15 participants (38.5%). Outputs from the CART analysis were supplemented based on edge cases and published parameters. Results The dataset included 186 non-wear periods (85.5% < 60 min). Temperature rate-of-change over the first five minutes of non-wear was − 0.40 ± 0.17 °C/minute and 0.36 ± 0.21 °C/minute for the first five minutes following device donning. Performance of the DETACH (DEvice Temperature and Accelerometer CHange) algorithm was improved compared to existing algorithms with recall of 0.942 (95% CI 0.883 to 1.0), precision of 0.942 (95% CI 0.844 to 1.0), F1-Score of 0.942 (95% CI 0.880 to 1.0) and accuracy of 0.996 (0.994–1.000). Conclusion The DETACH algorithm accurately detected non-wear intervals as short as five minutes; improving non-wear classification relative to current interval-based methods. Using temperature rate-of-change combined with acceleration results in a robust algorithm appropriate for use across different temperature ranges and settings. The ability to detect short non-wear periods is particularly relevant to free-living scenarios where brief but frequent removals occur, and for clinical application where misclassification of behavior may have important implications for healthcare decision-making.