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An Automatic Estimation of the Rest-Interval for MotionWatch8© Using Uniaxial Movement and Lux Data
Teresa Liu-Ambrose
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Sleep Science and Practice.
Background: Poor sleep is linked with chronic conditions common in older adults, including diabetes, heart disease, and dementia. Valid and reliable field methods to objectively measure sleep are thus greatly needed to examine how poor sleep impacts older adults. Wrist-worn actigraphy (WWA) is a common objective measure of sleep that uses motion and illuminance data to estimate sleep. The rest-interval marks the time interval between when an individual attempts to sleep and the time they get out of bed to start their day. Traditionally, the rest-interval is scored manually by trained technicians, however algorithms currently exist which automatically score WWA data, saving time and providing consistency from user-to-user. However, these algorithms ignore illuminance data and only considered motion in their estimation of the rest-interval. This study therefore examines a novel algorithm that uses illuminance data to supplement the approximation of the rest-interval from motion data.
Methods: We examined a total of 1086 days of data of 129 participants who wore the MotionWatch8© WWA for ≥14 nights of observation. Resultant sleep measures from three different parameter settings were compared to sleep measures derived following a standard scoring protocol and self-report times.
Results: The algorithm showed the strongest correlation to the standard protocol (r= 0.92 for sleep duration). There were no significant differences in sleep duration, sleep efficiency and fragmentation index estimates compared to the standard scoring protocol.
Conclusion: These results suggest that an automated rest-interval scoring method using both light exposure and acceleration data provides comparable accuracy to the standard scoring method.
Keywords
Actigraphy, Sleep, Automatic, Rest-interval
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Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryMaterial1.pdf
SM 1: Flow diagram of LO function to determine the LO times of the wearer.
- SupplementaryMaterial2.pdf
SM 2: Flow diagram of GU function to determine the GU times of the wearer.
- SupplementaryMaterial3.docx
SM 3: BL, biased-light algorithm; BM, biased-motion algorithm; LM, light-motion algorithm; IM, inactive motion; ILMM, inactive light and minimal motion; ILNM, inactive light and no motion; IL, inactive light; ALM, active light and motion; AM, active motion; AL, active light.
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Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 18 Sep, 2020
Published
On 23 Nov, 2020
No community comments so far
Editorial decision: Accept
On 15 Oct, 2020
Review #1 received
Received 05 Oct, 2020
Reviewer #1 agreed
On 28 Sep, 2020
Reviewers invited
Invitations sent on 22 Sep, 2020
Editor assigned
On 17 Sep, 2020
Submission checks complete
On 16 Sep, 2020
Editor invited
On 16 Sep, 2020
No comments provided
Review #2 received
Received 20 Aug, 2020
Editorial decision: Major revision
On 20 Aug, 2020
Review #1 received
Received 13 Aug, 2020
Reviewer #2 agreed
On 05 Aug, 2020
Reviewers invited
Invitations sent on 04 Aug, 2020
Reviewer #1 agreed
On 04 Aug, 2020
Editor assigned
On 31 Jul, 2020
First submitted
On 30 Jul, 2020
Submission checks complete
On 30 Jul, 2020
Editor invited
On 30 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
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This preprint is under consideration at Sleep Science and Practice. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
An Automatic Estimation of the Rest-Interval for MotionWatch8© Using Uniaxial Movement and Lux Data
Teresa Liu-Ambrose
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 18 Sep, 2020
Published
On 23 Nov, 2020
No community comments so far
Editorial decision: Accept
On 15 Oct, 2020
Review #1 received
Received 05 Oct, 2020
Reviewer #1 agreed
On 28 Sep, 2020
Reviewers invited
Invitations sent on 22 Sep, 2020
Editor assigned
On 17 Sep, 2020
Submission checks complete
On 16 Sep, 2020
Editor invited
On 16 Sep, 2020
No comments provided
Review #2 received
Received 20 Aug, 2020
Editorial decision: Major revision
On 20 Aug, 2020
Review #1 received
Received 13 Aug, 2020
Reviewer #2 agreed
On 05 Aug, 2020
Reviewers invited
Invitations sent on 04 Aug, 2020
Reviewer #1 agreed
On 04 Aug, 2020
Editor assigned
On 31 Jul, 2020
First submitted
On 30 Jul, 2020
Submission checks complete
On 30 Jul, 2020
Editor invited
On 30 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Sleep Science and Practice.
Background: Poor sleep is linked with chronic conditions common in older adults, including diabetes, heart disease, and dementia. Valid and reliable field methods to objectively measure sleep are thus greatly needed to examine how poor sleep impacts older adults. Wrist-worn actigraphy (WWA) is a common objective measure of sleep that uses motion and illuminance data to estimate sleep. The rest-interval marks the time interval between when an individual attempts to sleep and the time they get out of bed to start their day. Traditionally, the rest-interval is scored manually by trained technicians, however algorithms currently exist which automatically score WWA data, saving time and providing consistency from user-to-user. However, these algorithms ignore illuminance data and only considered motion in their estimation of the rest-interval. This study therefore examines a novel algorithm that uses illuminance data to supplement the approximation of the rest-interval from motion data.
Methods: We examined a total of 1086 days of data of 129 participants who wore the MotionWatch8© WWA for ≥14 nights of observation. Resultant sleep measures from three different parameter settings were compared to sleep measures derived following a standard scoring protocol and self-report times.
Results: The algorithm showed the strongest correlation to the standard protocol (r= 0.92 for sleep duration). There were no significant differences in sleep duration, sleep efficiency and fragmentation index estimates compared to the standard scoring protocol.
Conclusion: These results suggest that an automated rest-interval scoring method using both light exposure and acceleration data provides comparable accuracy to the standard scoring method.
Figure 1
Figure 2
Figure 3
Figure 4