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Effects of Deck Cadets’ Working Conditions on Quantity and Perceived Quality of Sleep Among Marine Science Students
Published in Adam Weintrit, Tomasz Neumann, Safety of Sea Transportation, 2017
Figures 4 shows the changes in sleep quality index components for all participants. The greatest impairment occurred in the field of daytime dysfunction (C7). This is followed by sleep latency, subjective sleep quality and sleep disturbance components, respectively. Individuals experiencing daytime dysfunction are forced to remain awake during driving, eating, or other social activities, and can not have enough desire to do anything. It is an undeniable fact that the educational life and social life of the students in this situation will be adversely affected. In the habitual sleep efficiency (C4) of the PSQI components, the total score fell unlike other components. Sleep efficiency is roughly calculated as the ratio of sleep time to lying time. The score increase in the sleep duration component (C3) suggests that individuals do not have an increase in sleep times. In this case, it can be mentioned that there is a decrease in lying time after the internship. It is understood that there are individuals who are experiencing sleep latency in the sample population (C2), as well as those who tend to fall asleep as soon as they reach the bed. However, when the total score changes of the components are examined, it is seen that there are no significant changes in the duration and efficiency of sleep.
Pervasive Computing and Ambient Physiological Monitoring Devices
Published in Bruno Bouchard, Smart Technologies in Healthcare, 2017
Sung Jae Isaac Chang, Jennifer Boger, Jianfeng Qiu, Alex Mihailidis
While the use of a bed as an ambient physiological monitoring system is fairly new, there have been some tests on healthy adults and people who have sleep disorders, such as those listed in Table 2.‘Coverage’ is the per cent of useful BCG that is free of movement artifacts. Note that the definition of coverage varies between researchers and between the conditions that are being monitored, leading to exacerbated differences in percentage of coverage. Regardless, the reported errors are acceptably low, showing that BCG from the bed can be used to collect the heart rate.‘Sleep efficiency’ is the ratio of time spent asleep (total sleep time) to the amount of time spent in bed.‘Sensitivity’ is true positive heartbeat (i.e. correctly detected heartbeats) divided by sum of true positive and false negative (i.e. not detecting a heartbeat when there is one) heartbeats.
Get sleep or get stumped: sleep behaviour in elite South African cricket players during competition
Published in Journal of Sports Sciences, 2020
Kayla McEwan, Jonathan Davy, Candice Jo-Anne Christie
Before the start of each condition, the players were provided with an altered version of the Core Consensus Sleep Diary (Carney et al., 2012; Appendix A). The responses from the sleep diary questions were used to determine the following sleep-related variables: bedtime (question 1; hh:mm AM/PM), sleep onset time (question 2; hh:mm AM/PM), sleep onset latency (question 3; hh:mm), wake after sleep onset (question 4; hh:mm), wake-up time (question 5; hh:mm AM), get-up time (question 6; hh:mm AM), time in bed (question 6 minus question 5; hh:mm), total sleep time (hh:mm), sleep efficiency (%), subjective sleep quality (question 7; 1 = very poor; 2 = poor; 3 = fair; 4 = good; 5 = very good), morning freshness score (question 8; 1 = not at all rested; 2 = slightly rested; 3 = somewhat rested; 4 = well-rested; 5 = very well rested) and nap duration (question 9; hh:mm). Total sleep time was calculated as: [(wake-up time – sleep onset time) – (sleep onset latency + wake after sleep onset)]. Sleep efficiency (%) was calculated as the ratio between total sleep time and time in bed. Information about substance use (alcohol and caffeine quantity and time consumed; questions 10 and 11), sleep medication (type and dose (mg); question 12), sleeping environment, injuries and illnesses (question 13) were also collected from the sleep diary.
Work and sleep quality in railway employees: an actigraphy study
Published in Ergonomics, 2020
Christin Gerhardt, Maria Undine Kottwitz, Tarsia Jana Lüdin, Dominique Gabriel, Achim Elfering
In the current study, we used a BodyMedia Sensewear Armband, including a multi-accelerometer device. Every minute, 2-axis oscillometric sensors assessed body movements. Data were analysed with BodyMedia software (Littner et al. 2003). We measured different sleep indicators, since sleep quality is a combination of both quantitative aspects and subjective impressions (Buysse et al. 1989; Harvey et al. 2008). The time participants needed to fall asleep after going to bed was coded as sleep onset latency. Sleep fragmentation was coded as the number of awakenings that lasted 5 min or longer and were preceded and followed by at least 15 min of uninterrupted sleep (Sadeh, Keinan, and Daon 2004). Sleep duration represented the time in minutes of sleep until final waking up in the morning. The percentage of time of sleep duration versus time being in bed was defined as sleep efficiency. Those objective measures were supplemented by the morning diary (‘How satisfied are you with the sleep quality of last night?’). Inaccurate measurements (e.g. malfunction of the actigraphs) were identified by visual inspection of raw data. These were coded as missing data. Data losses were small and resulted from loss of dermal contact (2.5%) or participants forgetting to wear the armband again after taking a shower (1.8%). Naps during the day were not included in the analyses.
Does self-perceived sleep reflect sleep estimated via activity monitors in professional rugby league athletes?
Published in Journal of Sports Sciences, 2018
Johnpaul Caia, Heidi R. Thornton, Vincent G. Kelly, Tannath J. Scott, Shona L. Halson, Balin Cupples, Matthew W. Driller
Sleep was assessed using wrist activity monitors collected in one-minute epochs for a minimum of three nights per participant (mean ± SD: 10.3 ± 3.9 nights). Participants wore an activity monitor on their non-dominant wrist, and were advised to wear the monitor at all times, except during training involving contact and when bathing or showering. Data derived from the monitors were used to estimate sleep duration and sleep efficiency, with sleep efficiency defined as the percentage of time in bed that was spent asleep (Lastella et al., 2015). Specifically, participants wore either an Actiwatch 2 (Philips Respironics, PA, USA) (25 athletes, 300 nights), Readiband (Fatigue Science, HI, USA) (8 athletes, 39 nights), or ActiGraph wGTX3 (ActiGraph, Pensacola, FL, USA) (30 athletes, 302 nights), with all three monitors previously showing acceptable levels of reliability and validity (Cellini, Buman, McDevitt, Ricker, & Mednick, 2013; Driller et al., 2016; Sargent, Lastella, Halson, & Roach, 2016). All athletes wore the same monitor throughout the duration of their respective monitoring period.