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Sleep

Sleep is a natural, recurring state of the mind and body, characterized by altered consciousness, reduced muscle activity, and inhibited sensory activity.
It is essential for physical and mental wellbeing, allowing the body to rest, repair, and rejuvenate.
Sleep patterns and quality can be influenced by various factors, including circadian rhythms, sleep disorders, and lifestyle choices.
Proper sleep hygiene and management of sleep-related issues are crucial for optimizing overall health and performace.
Reasearchers can utilize PubCompare.ai to locte the best sleep protocols, enhance reproducibility, and improve the quality of their sleep studies.

Most cited protocols related to «Sleep»

Methodology Considerations for GT3X/+ Actigraphy

Cited 318 times

The present review focuses on 11 key methodological issues related to GT3X/+ data collection and processing criteria: (1) device placement, (2) sampling frequency, (3) filter, (4) epoch length, (5) non-wear-time definition, (6) what constitutes a valid day and a valid week, (7) registration period protocol, (8) SED and PA intensity classification, (9) PAEE algorithms, (10) sleep algorithms, and (11) step counting. Available information was classified into two different types of studies: (1) any cross-sectional, longitudinal, or intervention study which used the GT3X/+ device and met the inclusion criteria indicated in Sect. 2.3 (objective 1); and (2) studies focused on validation, calibration, or comparison of functions related to data collection or processing criteria (objective 2). Therefore, the practical considerations provided for each age group are based on the results from the validation/calibration studies (see Table 1). Furthermore, we provide a summary of all data extracted from the validation/calibration papers included in this review by age group in the Electronic Supplementary Material Appendix S1. Inclusion/exclusion criteria and analytical methods were specified in advance and registered in the PROSPERO (http://www.crd.york.ac.uk/PROSPERO/) international database of systematic reviews (CRD42016039991) [32 (link)]. The study is conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [33 (link)].

Migueles J.H., Cadenas-Sanchez C., Ekelund U., Delisle Nyström C., Mora-Gonzalez J., Löf M., Labayen I., Ruiz J.R, & Ortega F.B. (2017). Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations. Sports medicine (Auckland, N.Z.), 47(9), 1821-1845.

Publication 2017

Age Groups EPOCH protocol GZMB protein, human Medical Devices Sleep

Simultaneous Measurement of Physical Activity and Energy Expenditure

Cited 261 times

Study participants spent approximately 24-h period in a whole-room indirect calorimeter (28 (link)), and followed a structured protocol for simultaneous measurements of PA and EE. The protocol included a broad range of pursuits ranging from moderate and vigorous to light and sedentary tasks, including eating meals and snacks and self-care activities. During times (30 to 120 minutes) when no activity was specifically scheduled, the participants were asked to engage in their normal daily routine as much as possible without specific suggestions. They also recorded their activities in a diary with a detailed schedule, reporting any episodes of accidental monitor nonwear intervals and other relevant comments. Sleep was defined as the period of time spent lying on a mattress at night between 9:00 pm and 6:00 am without any significant movement as determined by the floor (force platform) in the room calorimeter. The participants were instructed how to record their activities in a provided diary with a detailed schedule and a timeline. They checked off each scheduled activity and reported any episodes of accidental monitor nonwear intervals and other relevant information (e.g. treadmill speed) or comments. During the day, staff was available for assistance and the dairy was discussed with each participant after finishing the study.
Body weight was measured to the nearest 0.01 kg with a digital scale and height was measured using a wall-mounted stadiometer. The minute-to-minute EE was calculated from the rates of oxygen consumption and carbon dioxide production (33 (link)). Nonwear EE was calculated by summing EE measured by the room calorimeter during time intervals detected as nonwear by each algorithm.
The PA was measured by commercially available Actigraph GT1M accelerometer (ActiGraph, Pensacola, FL), calibrated by the manufacturer placed on the anterior axillary line of the hip on the dominant side of the body. Among commercially available accelerometers, the Actigraph used in the present study provides consistent and high quality data, supported by its feasibility, reliability and validity (9 (link)). The monitor reports counts from the summation of the measured accelerations over a specified epoch (1 ). Actigraph data were collected at a 1-second epoch and summed as counts per minute.

Choi L., Liu Z., Matthews C.E, & Buchowski M.S. (2011). Validation of Accelerometer Wear and Nonwear Time Classification Algorithm. Medicine and science in sports and exercise, 43(2), 357-364.

Publication 2011

Acceleration Accidents Actigraphy Axilla Body Weight Carbon dioxide EPOCH protocol Human Body Light Movement Oxygen Consumption Sleep Snacks TimeLine

Sleep Disturbance and Impairment Assessment

Cited 145 times

The “full” SD and SRI item banks consisted of 27 and 16 items each. Respondents rated various aspects of their sleep over the past 7 days on 5-point scales. Most of the items used an intensity scale (not at all, a little bit, somewhat, quite a bit, very much), with a smaller number using a frequency scale (never, rarely, sometimes, often, always), and one item (S109) assessing overall sleep quality using a scale of very poor, poor, fair, good, very good. Items assessing sleep disturbance or sleep-related impairment were scored 1 to 5 with 1 for the lowest category (i.e., not at all) and 5 for the highest category (i.e., very much). In order to be consistent with PROMIS conventions, some items were reverse scored so that, for all items, higher scores corresponded to greater sleep disturbance or sleep-related impairment. Participants also completed two commonly used measures for comparative analyses, the PSQI and the ESS. The PSQI was scored based on standard procedures, with 7 component scores summed together to yield a global score with a range of 0 (good sleep quality) to 21 (poor sleep quality); only the component scores were considered in IRT analyses. The ESS contains 8 items with 4 response categories for each item. ESS items are scored 0 to 3 with 0 for the lowest category and 3 for the highest category. The score for the ESS is obtained by summing the 8 items, and has a range of 0 (no propensity for dozing during daytime activities) to 24 (high propensity for dozing during daytime activities). Demographic and global health information including global health and fatigue items were also collected, as described in Buysse et al (2010) (link).

Yu L., Buysse D.J., Germain A., Moul D.E., Stover A., Dodds N.E., Johnston K.L, & Pilkonis P.A. (2011). Development of Short Forms from the PROMIS Sleep Disturbance and Sleep-Related Impairment Item Banks. Behavioral Sleep Medicine, 10(1), 6-24.

Publication 2011

Conferences Dyssomnias Fatigue Sleep

Pediatric Actigraphy Methodological Review

Cited 96 times

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Publication 2011

Actigraphy Adolescent Adult Age Groups Child Infant Infant, Newborn Medical Devices Peer Review Preterm Infant Sleep Wrist Youth

Insomnia Severity Index: Korean Validation

Cited 92 times

The ISI comprises seven items that evaluate difficulty falling asleep and staying asleep, problems waking up too early, satisfaction with current sleep patterns, interference with daily functions, noticeability of impairment attributed to sleep problems, and distress caused by the sleep problem. Each of the ISI items is rated on a scale of 0-4; the total score ranges from 0 to 28, with a higher score indicating greater insomnia severity. The total ISI scores are divided into four subcategories: 0-7, no clinically significant insomnia; 8-14, subthreshold insomnia; 15-21, moderate insomnia; and 22-28, severe insomnia. A cutoff score of 15 has been used as the threshold for clinically significant insomnia, and a score below 8 has been used to define remission after treatment (i.e., no longer meets the criteria for insomnia).28 (link)
Linguistic validation was achieved by having two sleep specialists translate the original ISI questionnaire into Korean; the Korean version was then translated back into English by one sleep specialist and one linguist, both of whom were fluent in Korean and English. Comparison of the original ISI with the final back-translated version was performed by individuals who were fluent in both languages and who were not involved in the research study. The final ISI-K was obtained after completion of these standard procedures.

Cho Y.W., Song M.L, & Morin C.M. (2014). Validation of a Korean Version of the Insomnia Severity Index. Journal of Clinical Neurology (Seoul, Korea), 10(3), 210-215.

Publication 2014

Aftercare Dyssomnias Koreans Satisfaction Sleep Sleeplessness

Most recents protocols related to «Sleep»

Detection of Respiratory Effort and Cerebral Activation from Mandibular Movements

Not available on PMC !

Example 15

In a 15^thexample, reference is made to FIGS. 12 and 13. FIG. 12 shows an example of the first measurement signal stream F1 and of the second measurement signal stream F2 in the situation where the subject suffers a temporary disappearance of all control of cerebral origin, which is characteristic of central hypopnoea. This disappearance is characterized by the mouth opening passively because it is no longer held up by the muscles. It is therefore seen in the streams F1 and F2 that between the peaks the signal does not indicate any activity. On the other hand at the moment of the peak there is observed a high amplitude of the movement of the mandible. Toward the end of the peaks there is seen a movement that corresponds to a non-respiratory frequency, which is the consequence of cerebral activation that will then result in a micro-arousal. The digit 1 indicates the period of hypopnoea where a reduction of the flow is clearly visible on the stream F5th from the thermistor. The digits 2 and 3 indicate the disappearance of mandibular movement in the streams F1 and F2 during the period of central hypopnoea. FIG. 13 shows an example of the first measurement signal stream F1 and of the second measurement signal stream F2 in the situation where the subject experiences a prolonged respiratory effort that will terminate in cerebral activation. It is seen that the signal from the accelerometer F1 indicates at the location indicated by H a large movement of the head and of the mandible. Thereafter the stream F2 remains virtually constant whereas in that F1 from the accelerometer the level drops, which shows that there is in any event a movement of the mandible, which is slowly lowered. There then follows a high peak I that is a consequence of a change in the position of the head during the activation that terminates the period of effort. The digit 1 indicates this long period of effort marked by snoring. It is seen, as indicated by the digit 2, that the effort is increasing with time. This effort terminates, as indicated by the digit 3, in cerebral activation that results in movements of the head and the mandible, indicated by the letter I.

The analysis unit holds in its memory models of these various signals that are the result of processing employing artificial intelligence as described hereinbefore. The analysis unit will process these streams using those results to produce a report on the analysis of those results.

It was found that the accelerometer is particularly suitable for measuring movements of the head whereas the gyroscope, which measures rotation movements, was found to be particularly suitable for measuring rotation movements of the mandible. Thus cerebral activation that leads to rotation of the mandible without the head changing position can be detected by the gyroscope. On the other hand, an IMM type movement will be detected by the accelerometer, in particular if the head moves on this occasion. An RMM type movement will be detected by the gyroscope, which is highly sensitive thereto.

US11864910B2. System comprising a sensing unit and a device for processing data relating to disturbances that may occur during the sleep of a subject (2024-01-09). Sunrise SA [BE]. Inventors: Pierre Martinot [BE].

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Patent 2024

ARID1A protein, human Arousal Exhaling Fingers Gene Expression Regulation Head Head Movements Mandible Medical Devices Memory Movement Muscle Tissue Oral Cavity Respiratory Rate Sleep Thumb Vision

Central Apnoea Respiratory Monitoring

Not available on PMC !

Example 14

In a 14^thexample, reference is made to FIG. 11. FIG. 11 shows an example of the first measurement signal stream F1 and of the second measurement signal stream F2 in the situation where the subject suffers a central apnoea. The peaks F show a movement of the head and of the mandible on resumption of respiration. It is also seen that between the peaks F there is so to speak no movement of the mandible. The digit 1 indicates an absence of respiratory flow that goes hand in hand with an absence of effort, indicated by the digit 2, and activation and resumption of the effort, indicated by the digit 3.

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Patent 2024

Cell Respiration Fingers Head Movements Mandible Medical Devices Movement Respiratory Rate Sleep Sleep Apnea, Central Thumb Vision

Detection of Mixed Apnea in Mandibular Movement

Not available on PMC !

Example 13

In a 13^thexample, reference is made to FIG. 10. FIG. 10 shows an example of the first measurement signal stream F1 and of the second measurement signal stream F2 in the case where the subject suffers a mixed apnoea. As in FIG. 8, there is seen in this FIG. 10 an increase in the angular speed of the mandible at a frequency corresponding to the respiration frequency. The digit 1 indicates an absence of respiratory flow that goes hand in hand with an absence of control and of effort, indicated by the digit 2, followed by restoration of cerebral control and effort, indicated by the digit 3.

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Patent 2024

Apnea Fingers Mandible Medical Devices Respiratory Rate Sleep Thumb Vision

Cerebral Control for Respiratory Event Detection

Not available on PMC !

Example 9

In a ninth example, reference is made to table 3. Table 3 illustrates a typical behaviour of cerebral control for the detection of respiratory events and non-respiratory motor events. It can be seen that to detect an obstructive apnoea-hypopnea, the analysis unit will for example use a median and/or a mean value on the first and second flow of measurement signals. An observation time of at least two breathing cycles or 10 seconds will be preferred to make the analysis more reliable. Obstructive apnoea-hypopnea is characterized by large cerebral control amplitude at the respiratory rate that can be repeated cyclically or non-cyclically. It will end with a large mandibular movement during cerebral activation. In particular, the distribution of the amplitude values of the mandibular movement in the stream under consideration will be analyzed.

To detect breathing effort linked to arousal (RERA), the unit of analysis will proceed in the same way as described in the previous paragraph. To detect a central apnoea-hypopnea the duration of observation will also be at least two breathing cycles or 10 seconds.

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Patent 2024

Arousal Behavior Control Mandible Medical Devices Movement Neoplasm Metastasis Respiratory Rate Sleep Sleep Apnea, Central Sleep Apnea, Obstructive Vision Volition

Factors Associated with Depressive Symptoms in Older Adults

We selected a series of control variables that may be associated with depressive symptoms, including demographic characteristics [36 (link)–38 (link)] (age, gender, marital status, residence, education), health status and health behaviors [39 –42 ] (self-reported health, activities of daily living scale (ADL), smoking, drinking, sleep duration, chronic disease status), and protective factors [31 (link), 43 (link)–46 ] (health insurance, pension, employment status). For age, we selected people aged 45 and above; for marital status, we reclassified them according to the answers of the questionnaire, and considered married and living with spouse, married but not living with spouse temporarily as married; separated and no longer living with spouse, divorced, widowed, and never married as unmarried. Educational attainment was classified into five categories: no education, elementary school, middle school, high school, and college and above. Since the sleep time showed a skewed distribution, we logarithmically processed the sleep time. For chronic disease prevalence, we divided the population into five categories: no disease, one chronic disease, two chronic diseases, three chronic diseases, and four or more chronic diseases. The detailed coding of the variables is shown in Table 1.Table 1

Coding of variables

Variable	Coding
Depression	< 10 = 0, ≥10 = 1
Levels of depression	0 ~ 30
WeChat usage	Not using the WeChat =0, Using the WeChat =1
Social participation	No = 0, Yes = 1
Levels of social participation	0 ~ 10
Voluntary activities	No = 0, Yes = 1
Levels of voluntary activities	No = 0, One kind = 1, Two kinds = 2, Three kinds = 3
Recreation	No = 0, Yes = 1
Levels of recreation	No = 0, One kind = 1, Two kinds = 2, Three kinds = 3
Cultural activities	No = 0, Yes = 1
Levels of cultural activities	No = 0, One kind = 1, Two kinds = 2
Other activities	No = 0, Yes = 1
Levels of other activities	No = 0, One kind = 1, Two kinds = 2
Age	≥45
Gender	Female = 0, Male =1
Marital status	Unmarried = 0, Married = 1
Residence	Rural = 1, Urban = 2
Education	No formal education = 1, Elementary school = 2, Middle school = 3, High school = 4, College or above = 5
Self-reported health	Very poor = 1, Poor = 2, Fair = 3, Good = 4, Very good = 5
ADL	No impaired = 0, Impaired = 1
Smoke status	Still have = 1, Quit = 2, No = 3
Drink status	No = 0, Yes = 1
Sleep time	Take the log of sleep time
Employment	No = 0, Yes = 1
Pension insurance	No = 0, Yes = 1
Medical insurance	No = 0, Yes = 1
Chronic diseases	No = 0, One kind = 1, Two kinds = 2, Three kinds = 3, Four kinds and more = 4

Wang G., Duan J., Kan Q., Zhou Y., Cheng Z, & Tang S. (2023). The correlation analysis of WeChat usage and depression among the middle-aged and elderly in China: the mediating role of social participation. BMC Public Health, 23, 462.

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Publication 2023

Depressive Symptoms Disease, Chronic Gender Health Insurance Males Sleep Spouse

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The Actiwatch Spectrum is a wearable device designed to monitor activity and sleep patterns. It is a compact, lightweight, and waterproof device that can be worn on the wrist. The Actiwatch Spectrum collects data on movement, light exposure, and body position, which can be used to analyze sleep-wake cycles and activity levels.

Drosophila activity monitoring system by Trikinetics

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Wgt3x bt by ActiGraph

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More about "Sleep"

Sleep is a fundamental physiological process that is essential for overall well-being.
It is a naturally recurring state of the mind and body, characterized by reduced muscle activity, altered consciousness, and inhibited sensory perception.
This restorative state allows the body to rest, repair, and rejuvenate, preparing it for the next day's activities.
The quality and patterns of sleep can be influenced by a variety of factors, including circadian rhythms, sleep disorders, and lifestyle choices.
Proper sleep hygiene, which encompasses factors such as sleep-wake schedules, bedroom environment, and sleep-promoting behaviors, is crucial for optimizing sleep and ensuring optimal health and performance.
Researchers studying sleep can utilize a range of tools and software to gather and analyze data, such as MATLAB, Actiwatch 2, SAS 9.4, Actiwatch Spectrum, Drosophila Activity Monitoring System, ActiGraph wGT3X-BT, and SPSS software.
These tools can help researchers track sleep patterns, monitor sleep-wake cycles, and assess sleep quality, ultimately enhancing the reproducibility and accuracy of their sleep studies.
Additionally, researchers can leverage platforms like PubCompare.ai to locate the best sleep protocols from the literature, preprints, and patents, using AI-driven comparisons.
This can help researchers identify the most effective sleep-related interventions and improve the quality of their sleep studies, avoiding common mistakes and enhancing the overall research process.
By understanding the complexities of sleep and utilizing the appropriate tools and resources, researchers can contribute to the advancement of sleep science and ultimately improve the overall health and performance of individuals.