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Dollars to donuts..>8 patients in P2a had sleep issues.

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Dollars to donuts..>8 patients in P2a had sleep issues.

Poster from CTAD 2018:


, Richard Joules1
, Janet Munro1
, Iain Simpson1
, Robin
Wolz1,2, Yves Dauvilliers3 ((1) IXICO Plc, London, UK; (2) Imperial
College London, London - UK; (3) Sleep Unit, Department Neurology,
Centre Hospitalier Universitaire, Montpellier, INSERM 1061 -
Background: Sleep disturbances are common in people living
with dementia. Reduced and fragmented sleep can impact
pathology, worsen other symptoms, and significantly affect
daytime functioning. Additionally, poor sleep quality may
be a risk factor of many neurological conditions, including
Alzheimer’s disease (Ju et al. 2014). While polysomnography
(PSG) remains the gold standard for sleep assessment, it is
costly, often recorded on an isolated night with no daytime
assessment, and may not be feasible in many clinical trials.
The increasing availability of wearable sensor technology
offers a practical, non-intrusive means of collecting continuous
sleep-wake data, over several days. This provides “digital
biomarkers” from real world settings that can be used to
predict risk, detect symptoms and monitor changes in sleep
and activity. Objectives: The aim of this study was to examine
relationships between actigraphy-derived and self-reported
sleep diary measurements within an epidemiology study cohort
of elderly subjects. Methods: We analyzed actigraphy and sleep
diary data, available for 22 subjects (Age: Mean=80.6, SD=9.9;
68% female) from a study of sleep difficulties, lifestyle factors
and general health in the Centre Hospitalier Universitaire,
Montpellier - France. All subjects were asked to wear an Axivity
3-axis accelerometry biosensor device (http://axivity.com/) on
their non-dominant wrist for 14 nights and to complete sleep
diaries for the same 2-week period. IXICO’s fully integrated
wearables work-flow (Figure 1) was employed for data analysis.
This incorporated quality control (QC) for periods of perceived
device “non-wear”. In-bed periods were estimated with the
McVeigh algorithm (McVeigh et al. 2016) and sleep and wake
periods with the Cole-Kripke (CK) algorithm (Cole et al. 1992).
Correlations between the actigraphy- and self-reported sleep
measurements were assessed sample-wise, for all nights with
both types of data available. As part of a quantitative QC,
unrealistic night-time in-bed periods of <2.5 and >13 hours
(N=17) were excluded from subsequent analysis, as well as
self-reported nights with <1 hour of sleep (N=2). This resulted
in 141 nights’ data for the statistical analysis. Correlations
(Pearson’s R/Spearman’s Rho) were calculated between four
variables available from both actigraphy and the diaries: sleep
efficiency/quality, total time in bed, night-time in-bed sleep
and number of awakenings. Results: Actigraphy-derived sleep
efficiency correlated with self-reported sleep quality at r = .16
(p = .06; Figure 2). McVeigh-estimated times in bed at night
and self-reported times from into-bed to out-of-bed correlated
at r = .32 (p = .0001). CK-estimated in-bed sleep times and selfreported times from lights-out to awakening (minus sleep onset
latency) correlated at r = .15 (p = .07). As expected, the number
of estimated night-time awakenings was considerably higher
from actigraphy (median=13, IQR=11) than from the diaries
(median=2, IQR=2), with these measurements correlated at r
= .06 (p = .51). Conclusions: The relatively low correlations
between sleep diaries and actigraphy measures reported here
may implicate poor reliability and inaccuracy of self-reported
sleep measures (see e.g. Goelema et al. 2017). However, as selfreported awakenings may include lying still in bed, the higher
number of actigraphy-derived awakenings may be partially
explained by interpretation of the minute-wise sleep/no-sleep
labeling from the algorithm: a period of wakefulness being
split into several if quiet wakefulness is labelled as asleep. With
sleep efficiency, we have previously shown in the same cohort
that measures derived from 1-night accelerometry recordings
using the CK algorithm (as above) correlated at r = .61 (p =
.003) with simultaneous PSG (Wolz et al. 2017). Using a welldesigned work-flow, actigraphy can provide objective realworld measures of sleep and activity from data collected over 14
days or longer, with minimal patient discomfort and acceptable
QC failure rates. Although PSG can be used to assess sleep
stages, apnea/hypopnea indexes and periodic leg movements,
the advantages of actigraphy over traditional PSG include the
well-tolerated continuous recording in real life settings and
relative cost-effectiveness. The current results suggest that
compared to sleep diaries, actigraphy can offer an attractive
and more reliable alternative for the measurement of signs
and symptoms of disease, or the evaluation of therapeutic
effects. Customized data analytics, including disease-specific
models, have the potential to detect sleep disturbance more
accurately than widely used algorithms such as the CK, which
may incorrectly interpret periods of quiet wakefulness as sleep.
In the Wolz et al. (2017) 1-night study, we found the IXICO
Deep Learning Sleep (DLS) algorithm to outperform the CK
in estimating PSG-derived sleep efficiency (DLS: r = .84, p <
.0001 vs. CK: r = .61, p = .003). Our approach of combining
actigraphy with advanced data analytics shows promise for
providing improved biomarkers of sleep, circadian rhythm
and activity outcomes in clinical trials. In future work, we
will employ the DLS algorithm to estimate sleep efficiency in
elderly populations and neurodegenerative disease cohorts
and will extend the presented statistical analysis to include
daytime naps and an assessment of variability between night
and day activity/sleep over the 14 day period. References:Cole
et al. (1992). Automatic sleep/wake identification from wrist
activity. Sleep, 12(5), 461–9. Goelema et al. (2017). Determinants
of perceived sleep quality in normal sleepers. Behavioural
Sleep Medicine, (20):1-10. Ju, et al. (2014). Sleep and Alzheimer
disease pathology--a bidirectional relationship. Nature Reviews
Neurology, 10(2), 115–9. McVeigh et al. (2016). Validity of an
automated algorithm to identify waking and in-bed wear time
in hip-worn accelerometer data collected with a 24 h wear
protocol in young adults. Physiological Measurement, 37(10),
1636–52. Wolz et al. (2017). Extracting digital biomarkers of
sleep from 3-axis accelerometry using deep learning. The
Journal of Prevention of Alzheimer’s Disease, 4(4), P81.


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