RESUMEN
Little is known about the correlation between subjective perception and objective measures of sleep quality in particular in the oldest-old. The aim of this study was to perform longitudinal home sleep monitoring in this age group, and to correlate results with self-reported sleep quality. This is a prospective longitudinal home sleep-monitoring study in 12 oldest-old persons (age 83-100 years, mean 93 years, 10 females) without serious sleep disorders over 1 month using a contactless piezoelectric bed sensor (EMFIT QS). Participants provided daily information about perceived sleep. Duration in bed: 264-639 min (M = 476 min, SD = 94 min); sleep duration: 239-561 min (M = 418 min, SD = 91 min); sleep efficiency: 83.9%-90.7% (M = 87.4%, SD = 5.0%); rapid eye movement sleep: 21.1%-29.0% (M = 24.9%, SD = 5.5%); deep sleep: 13.3%-19.6% (M = 16.8%, SD = 4.5%). All but one participant showed a weak (r = 0.2-0.39) or very weak (r = 0-0.19) positive or negative correlation between self-rated sleep quality and the sleep score. In conclusion, longitudinal sleep monitoring in the home of elderly people by a contactless piezoelectric sensor system is feasible and well accepted. Subjective perception of sleep quality does not correlate well with objective measures in our study. Our findings may help to develop new approaches to sleep problems in the oldest-old including home monitoring. Further studies are needed to explore the full potential of this approach.
RESUMEN
Gait abnormalities in older adults are linked to increased risks of falls, institutionalization, and mortality, necessitating accurate and frequent gait assessments beyond traditional clinical settings. Current methods, such as pressure-sensitive walkways, often lack the continuous natural environment monitoring needed to understand an individual's gait fully during their daily activities. To address this gap, we present a Lidar-based method capable of unobtrusively and continuously tracking human leg movements in diverse home-like environments, aiming to match the accuracy of a clinical reference measurement system. We developed a calibration-free step extraction algorithm based on mathematical morphology to realize Lidar-based gait analysis. Clinical gait parameters of 45 healthy individuals were measured using Lidar and reference systems (a pressure-sensitive walkway and a video recording system). Each participant participated in three predefined ambulation experiments by walking over the walkway. We observed linear relationships with strong positive correlations (R2>0.9) between the values of the gait parameters (step and stride length, step and stride time, cadence, and velocity) measured with the Lidar sensors and the pressure-sensitive walkway reference system. Moreover, the lower and upper 95% confidence intervals of all gait parameters were tight. The proposed algorithm can accurately derive gait parameters from Lidar data captured in home-like environments, with a performance not significantly less accurate than clinical reference systems.