Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT04288557 |
| Other study ID # |
0713 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
January 23, 2020 |
| Est. completion date |
February 28, 2021 |
Study information
| Verified date |
April 2021 |
| Source |
University of Leicester |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Sleep behaviour has critical importance to health and wellbeing. A large body of evidence has
implicated poor sleep in all-cause mortality, and in cardiovascular and cardiometabolic 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
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.
Description:
Sleep behaviour has critical importance to health and wellbeing. Insufficient sleep duration
and poor sleep quality are independent contributors to high blood pressure and cardiovascular
disease, depression, obesity and diabetes. Given the importance of sleep duration and quality
to health, the importance of accurately monitoring sleep duration and quality in everyday
clinical practice is becoming more evident.
The 'gold standard' physiological measure of sleep is sleep polysomnography (PSG). PSG is
used to quantify measures of sleep, including length of sleep, time taken to fall asleep,
sleep efficiency. The disadvantages of sleep PSG include the need to attend a laboratory, use
of expensive equipment, specialised staff to administer PSG, and to score and interpret the
PSG outputs, which limit its use in larger, or free-living studies.
Another method to quantify indices of sleep is based on actigraphy, demonstrating 90%
agreement with polysomnography. Wrist actigraphy allows sleep assessment over several days
and measures daily sleep-wake cycles. However, the data is in the form of
manufacturer-specific activity 'counts' over a specific time window, 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, to
maximise compliance. These devices therefore collect data that could be used to estimate
sleep parameters, however to be able to use, pool or compare these data there is a need for
sleep algorithms that can be applied to datasets from different accelerometer brands. The
latest generation of accelerometers measure acceleration in universal units improving
comparability among different brands of accelerometers and allowing more control in the data
processing. Moreover, now there is a sleep detection algorithm that can applied to data from
different raw-data accelerometer brands and is freely available as a part of GGIR package in
R (software environment for statistical computing and graphics). The three widely used
raw-data accelerometer brands are the Axivity, ActiGraph and GENEActiv and ActivPAL which is
a thigh-worn accelerometer that provides a measure of posture using proprietary algorithms;
however, raw data are now available.
Studies that have validated the accelerometers with the PSG produced mixed results which can
be attributed to use of manufacturer specific sleep algorithms and different accelerometer
placement (dominant vs. non-dominant wrist vs. hip). Therefore validation of a sleep
algorithm that can be applied to different accelerometer brands against PSG warrants
investigation.
