Body movements during night sleep and their relationship with sleep stages are further modified in very old subjects

Giganti, Fiorenza; Ficca, Gianluca; Gori, Sara; Salzarulo, Piero

doi:10.1016/j.brainresbull.2007.07.022

Cited by 37 publications

(29 citation statements)

References 22 publications

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“…This is reflected, for example, by healthy people changing their posture, for instance, from lying on the back to lying on one side, between 10 and 30 times per night [15]. Nonetheless, too many posture changes occurring shortly after each other, that is, tossing around or staying in the same posture for less than 15 minutes at a time, indicate restless sleep of worse quality [14][15][16].…”

Section: Assessment Of Sleep Behavior Using Self-reports Polysomnogrmentioning

confidence: 99%

“…Generally, compared to younger people, older people are less active during sleep, as indicated by fewer posture changes and less activity in general [14,17]. At the same time, most laboratory and survey studies report an agerelated decrease of sleep quality as indicated by lower sleep efficiency, or more time awake while in bed, and a higher frequency of sleep disturbances [8,9,18].…”

Section: Age-related Differences In Sleep Duration and Qualitymentioning

confidence: 99%

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A New Approach for Assessing Sleep Duration and Postures from Ambulatory Accelerometry

et al. 2012

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Interest in the effects of sleeping behavior on health and performance is continuously increasing-both in research and with the general public. Ecologically valid investigations of this research topic necessitate the measurement of sleep within people's natural living contexts. We present evidence that a new approach for ambulatory accelerometry data offers a convenient, reliable, and valid measurement of both people's sleeping duration and quality in their natural environment. Ninety-two participants (14-83 years) wore acceleration sensors on the sternum and right thigh while spending the night in their natural environment and following their normal routine. Physical activity, body posture, and change in body posture during the night were classified using a newly developed classification algorithm based on angular changes of body axes. The duration of supine posture and objective indicators of sleep quality showed convergent validity with self-reports of sleep duration and quality as well as external validity regarding expected age differences. The algorithms for classifying sleep postures and posture changes very reliably distinguished postures with 99.7% accuracy. We conclude that the new algorithm based on body posture classification using ambulatory accelerometry data offers a feasible and ecologically valid approach to monitor sleeping behavior in sizable and heterogeneous samples at home.

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Section: Assessment Of Sleep Behavior Using Self-reports Polysomnogrmentioning

confidence: 99%

Section: Age-related Differences In Sleep Duration and Qualitymentioning

confidence: 99%

A New Approach for Assessing Sleep Duration and Postures from Ambulatory Accelerometry

et al. 2012

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“…In this way, the frequency of movements can help in determining whether a person is asleep or not. Frequency of movements is also different for different sleep stages and thus can be related to which sleep stage a person is currently in ( [13]). Transitions between different sleep stages also correspond to change in frequency of body movements.…”

Section: Hypothesis: Wisps Help In Sleep Monitoringmentioning

confidence: 99%

Monitoring body positions and movements during sleep using WISPs

Hoque

Dickerson

Stankovic

2010

Wireless Health 2010

101

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Sleep monitoring is very important for elderly people as inadequate and irregular sleep are often related to serious diseases such as depression and diabetes. In many cases, it is necessary to monitor the body positions and movements made while sleeping because of their relationships to particular diseases (i.e., sleep apnea and restless legs syndrome). Analyzing movements during sleep also helps in determining sleep quality and irregular sleeping patterns. This paper presents a sleep monitoring system based on the WISP platform -active RFID-based sensors equipped with accelerometers. We show how our system accurately infers fine-grained body positions from accelerometer data collected from the WISPs attached to the bed mattress. Movements and their duration are also detected by the system. We present the results of our empirical study from 10 subjects on three different mattresses in controlled experiments to show the accuracy of our inference algorithms. Finally, we evaluate the accuracy of the movement detection and body position inference for six nights on one subject, and compare these results with two baseline systems: one that uses bed pressure sensors and the other is an iPhone application.

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“…If someone is lying on the bed and does not move for a significant amount of time, then we assume the person is asleep. Frequency of movements is also different for different sleep stages and thus can be related to which sleep stage a person is currently in [90]. Transitions between different sleep stages also correspond to change in frequency of body movements.…”

Section: Designmentioning

confidence: 99%

Empath: A System for the Collection, Storage, Analysis, and Reporting of Objective Behavioral Measures

Dickerson

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Remote health monitoring deployed in homes could help streamline the efficiency of the medical system by decreasing the costs of senior care and providing preventative care to keep people out of hospitals. Besides for caring for a particular person, these systems will generate tremendous amount of aggregate data that can be used to help diagnose diseases and run longitudinal studies on epidemiology. Most systems are not yet flexible enough to handle adding additional modalities into the system or to leverage the cloud's horizontal scalablity for storage, analysis, and display of this data. is dissertation presents a novel framework that sets itself apart from existing remote health monitoring systems for its scalability and flexibility. In addition, this work advances two key technologies for home healthcare. e first is the creation and evaluation of cheap and noninvasive sleep monitoring systems and novel accelerometor-based systems from RFIDs and motes.Secondly, this thesis demonstrates that the general features from speech in the home can be a useful modality for measuring social interaction and mood and promotes a solution to existing technical problems. Ambient monitoring of speech in the home has not been successful primarily because distortion from a room's acoustics negatively impacts classification results. is work presents a novel matched-condition classifier using cuboid acoustic simulation to achieve accuracy comparable to ideal close-to-microphone conditions. Most health monitoring systems have only been tested in a lab and under very scripted scenarios. is system, however, has been used in three different applications: monitoring sleep behaviors and stress for those who suffer from severe epilepsy in a clinical study, another clinical application that studies the relationship of incontinence with sleep agitation for those suffering from Alzheimer's disease, and an in-home deployment monitoring i Abstract ii important factors of depression. We present the commonalities among these different applications and show how to adapt the system for these purposes. AcknowledgementsTo Valerie-for editing my incoherent writing, volunteering to be instrumented by all sorts of sensors, and keeping me sane these past few months. I love you! To Mom and Dad-for teaching me stay "well balanced" and to always remain intellectually curious.To Jack-who has supported me for many years and has taught me the importance of achieving excellence. Without your help, I would never have become a professor.To Enamul-without your help on all of the projects-no data would have been collected.To Ann, Jewel, Karen, and Windy-for finding patients and putting Empath into real studies.To Ben-for taking a risk with taking in an undergraduate, teaching me to be a good mentor, and encouraging me to go to graduate school. More thanks to

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Body movements during night sleep and their relationship with sleep stages are further modified in very old subjects

Cited by 37 publications

References 22 publications

A New Approach for Assessing Sleep Duration and Postures from Ambulatory Accelerometry

A New Approach for Assessing Sleep Duration and Postures from Ambulatory Accelerometry

Monitoring body positions and movements during sleep using WISPs

Empath: A System for the Collection, Storage, Analysis, and Reporting of Objective Behavioral Measures

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