Validation of a Device for the Ambulatory Monitoring of Sleep Patterns: A Pilot Study on Parkinson's Disease

Abstract

The development of wearable devices has increase interest in the use of ambulatory methods to detect sleep disorders more objectively than those permitted by subjective scales evaluating sleep quality, while subjects maintain their usual lifestyle. This study aims to validate an ambulatory circadian monitoring (ACM) device for the detection of sleep and wake states and apply it to the evaluation of sleep quality in patients with Parkinson disease (PD). A polysomnographic validation study was conducted on a group of patients with different sleep disorders in a preliminary phase, followed by a pilot study to apply this methodology to PD patients. The ACM device makes it possible to estimate the main sleep parameters very accurately, as demonstrated by: (a) the lack of significant differences between the mean values detected by PSG and ACM in time in bed (TIB), total sleep time (TST), sleep efficiency (SE), and time awake after sleep onset (WASO); (b) the slope of the correlation lines between the parameters estimated by the two procedures, very close to 1, which demonstrates the linearity of the predictions; (c) the low bias value in the estimates obtained through ACM. Sleep in PD is associated with lower distal skin temperature, efficiency and overall sleep time; greater WASO, activity during sleep and duration of naps and a worse circadian function index. In summary, the ACM device has proven to be clinically useful to evaluate sleep in an objective manner, thanks to the integrated management of different complementary variables, having advantages over conventional actigraphy.

Living Without Temporal Cues: A Case Study

“…From triaxial acceleration, (i) acceleration of movement, (ii) wrist posture (tilt of the x-axis) and (iii) time in movement were assessed. Time in movement was calculated as the time, in periods of 0.1 s, in which a movement in any of three axes was detected, this information being particularly useful to discriminate between sleep and wake states (Madrid-Navarro et al, 2019). The sample rates were 1 Hz for wrist skin temperature and light exposure, 10 Hz for acceleration and 0.033 Hz (1 reading per epoch) for wrist position.…”

“…The scientific objective of the protocol was to study rhythmic changes in a person living in social/environmental isolation over three conditions (baseline light/dark cycle, constant dim light and a light/dark cycle with early wake up). Wrist temperature, motor activity and light exposure were assessed, and the sleepwake cycle derived, using a novel ambulatory monitoring (ACM) system (Kronowise) (Madrid-Navarro et al, 2019). Another aim of the study was to evaluate the estimation of clock time, a variable rarely assessed, in parallel with assessments of subjective sleepiness, reaction time, memory/concentration, grip strength, sprint time and mood.…”

Application of Machine Learning Methods to Ambulatory Circadian Monitoring (ACM) for Discriminating Sleep and Circadian Disorders

“…Nonetheless, it had previously shown to be highly useful and accurate for assessing circadian rhythms and sleep (Ortiz-Tudela et al, 2010. In fact, currently our Chronobiology Laboratory has implemented all temperature, motor activity, and environmental light sensors in a unique device, more modern and sophisticated that assesses simultaneously 15 different variables with a sampling rate of 10 Hz (Madrid-Navarro et al, 2019). It has been recently validated for Parkinson's disease and allows also an accurate detection of the light type, intensity, and timing the subject is exposed under ambulatory conditions (Arguelles-Prieto et al, 2019).…”

Shedding Light on Nocturnal Movements in Parkinson’s Disease: Evidence from Wearable Technologies

“…By monitoring the sleep/wake cycle, actigraphy has become the most widely used wearable technology for the assessment of nocturnal disturbances in PD patients. Although this approach relies on the measurement of limb motor activity, only a limited number of studies have specifically assessed nocturnal movements in PD through actigraphy [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Conversely, most authors used this tool to indirectly examine sleep quality through specific measures, such as the fragmentation index and sleep latency, time, and efficiency.…”

“…Actigraphy is based on the placement of a clock-like instrument, called an actigraph, which contains sensing technologies (e.g., an accelerometer and temperature, noise, and light detectors) able to monitor specific biological functions and reconstruct the sleep/wake cycle [ 12 ]. Along with the examination of sleep impairment, some previous studies using actigraphy in PD have also provided information about nocturnal limb movements in parkinsonian patients [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. More recently, innovative wearable technologies including wireless sensor networks have also been used to examine nocturnal movements involving axial body rotations in PD patients [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ].…”