Sleep Measurement Study
Research type
Research Study
Full title
Validation of automated sleep algorithms for accelerometer data in a clinical and healthy adult population
IRAS ID
259471
Contact name
Charlotte Edwardson
Contact email
Sponsor organisation
The University of Leicester
Duration of Study in the UK
0 years, 6 months, 29 days
Research summary
Research Summary
Sleep behaviour has critical importance to health and well-being. A large body of evidence has implicated poor sleep in all-cause mortality, and in cardiovascular and cardio-metabolic risk factors. Given the importance of sleep to health, the importance of accurately monitoring sleep duration and quality is becoming more evident.
Polysomnography (PSG) is considered the gold standard for sleep assessment. Nevertheless, PSG is impractical, expensive and labour‐intensive. Another method to quantify indices of sleep is based on actigraphic measures. Wrist-worn actigraphy devices provide an indirect measure of sleep parameters e.g. total sleep time, sleep onset latency and waking time. However, the data is in the form of manufacturer-specific activity ‘counts’, making it difficult to compare the data with different accelerometer brands.
Recently wrist-worn accelerometers have become increasingly used for objective measurement of physical activity in large population studies where participants are often asked to wear them for 24 hours continuously. These devices therefore collect data that could be used to estimate sleep parameters, and now there is a sleep algorithm that can be applied to raw data from accelerometers. The three widely used raw-data wrist-worn accelerometer brands are the Axivity, ActiGraph and GENEActiv and ActivPAL which is a thigh-worn accelerometer that provides a measure of posture.
Studies that examined accuracy of estimating sleep parameters from different brands of accelerometers compared to PSG have reported conflicting results which could be due to the use of different sleep algorithms and accelerometer placement (dominant vs. non-dominant wrist vs. hip). Therefore this study will aim to validate automated sleep algorithms for research grade accelerometers against PSG in a clinical and healthy adult population.Summary of Results
This study was conducted by a PhD student, Tatiana Plekhanova as part of their research degree. This study was funded by Leicester Biomedical Research centre and Professor Kamlesh Khunti’s NIHR Senior Investigator award. The study took place at the sleep clinic at Leicester General Hospital, located on Gwendolen Road, Leicester LE5 4PW between February and January 2020.
Recently, many large research studies have started estimating sleep using activity monitors i.e., accelerometers that people wear on their wrist or thigh 24 hours a day. However, at the moment it is not clear how accurate they are at measuring sleep. The purpose of this study was to investigate how accurate accelerometers are by comparing them to polysomnography which is the most accurate method for measuring sleep. The primary objective of this study was to evaluate the criterion validity of an automated sleep detection algorithm by assessing sleep estimates from three research-grade accelerometers worn on each wrist with concurrent laboratory-based polysomnography.
Thirty-one healthy volunteers took part in the study. The study also aimed to recruit patients who had scheduled an appointment at the Leicester General Hospital Sleep Laboratory. However, because of the low response rate from the sleep clinic patients, the study recruited healthy volunteers only. This was an observational study, thus, no treatments/interventions were involved. None of the participants had any medical problems (adverse reactions).
The polysomnography assessments took place on weeknights. Participants arrived early in the evening and were fitted with three accelerometers on each wrist. After fitting the accelerometers, participants were set up for the PSG assessment. The recording began when participants expressed willingness to go to bed and ended the following morning usually between 6 and 7 am. In the morning, all accelerometers were collected from the participants. Next, all participants were fitted with a GENEActiv accelerometer on their non-dominant wrist for eight days.
The key findings from this study were: 1) The automated sleep detection algorithm applied to data from accelerometers, worn on either wrist, provides comparable measures to polysomnography of sleep (such as sleep duration); 2) The automated sleep detection algorithm provides a poor measure of wake during the sleep period.
The participants received a sleep report with a detailed information about their sleep after the study. The results from this study will inform researchers on the validity of the automated sleep detection algorithm which is now widely used within the research community.
REC name
East Midlands - Nottingham 1 Research Ethics Committee
REC reference
19/EM/0275
Date of REC Opinion
30 Sep 2019
REC opinion
Further Information Favourable Opinion