Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease

Abstract

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.

IoT-Based Unobtrusive Physical Activity Monitoring System for Predicting Dementia

“…Although there are multiple technologies and methodologies for supporting clinical diagnosis and severity assessment of neurodegenerative diseases [55][56][57], we basically eliminated all wearable-type sensors and focused on fixed and ambient sensors, especially motion sensors. We did this because the sensing technologies for real elderly populations should be in feasible and convenient in-home environments and should minimize intrusiveness on privacy.…”

“…We did this because the sensing technologies for real elderly populations should be in feasible and convenient in-home environments and should minimize intrusiveness on privacy. Many technologies described in the literature [55][56][57], such as wristband wearables and video-based sensors, didn't meet those requirements, and force and pressure sensors tend to cover only a limited small space. Several studies in the literature propose the use of motion-sensor-based monitoring systems for dementia detection in home environments.…”

Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature

“…Quantifying and characterizing these activities is emerging as critical for understanding disease risk and improving health outcomes [ 6 , 14 , 15 ]. The unique advantage of accelerometry is its ability to capture data continuously in unsupervised, free-living conditions [ 16 ]. During free-living wear however, there are circumstances that can pose challenges to data analysis such as periods of device removal (non-wear) and the potential problem of misclassifying these periods as sleep or sedentary behavior due to the shared feature of an absence in movement.…”

“…The most common window length reported in the literature is 60 minutes [ 11 , 12 , 19 ]. While 60 minutes ensures that large periods of non-wear are not misclassified as wear time, work has shown that most non-wear periods are shorter than 60 minutes (e.g., removal for a shower) and therefore, it is likely that a significant number of non-wear periods are missed using this 60-minute window length [ 16 , 26 ]. The risk associated with using non-wear algorithms that require a longer window length is an overestimation of wear time and sedentary time [ 9 , 12 ].…”

“…Recently our group evaluated adherence to daily device-wearing for remote health monitoring in older adults and persons living with neurodegenerative disease (NDD) by examining volume and patterns of non-wear [ 16 ]. Specific interest in the clinical application of wearables to aging and NDD, is driven by the need to understand daily health-related behaviors in the context of advancing age or disease to aid in health-care decision making.…”