China
Africa
Explore chapters and articles related to this topic
Interactive Devices and the Internet
Published in R. S. Bridger, Introduction to Human Factors and Ergonomics, 2017
How well founded are these fears? George and Odgers point out that parents have always been worried about the effects new technologies and trends on their children and this is documented back to the 1940s with the emergence of radio, comic books, and later television and pop music. Much adolescent behavior in the digital world merely mirrors behavior in the real world. Children with more online relationships report higher offline friendship quality. Most online interaction is positive or neutral and there is a high correlation between online and offline bullying. The quality of online interaction between parents and their children mirrors the offline interaction. However, there is some evidence that constant multitasking impairs cognitive performance by increasing distractibility. Using smartphones, laptops, and so on at night or in bed (reading an e-book as opposed to a paper book) impairs sleep quality because the emitted light interferes with melatonin secretion. In general, offline factors tend to predict online outcomes and these fears can be balanced by new initiatives to deliver positive information and messages to users such as fitness apps.
Major Depressive Disorder Detection and Monitoring Using Smart Wearable Devices with Multi-Feature Sensing
Published in Govind Singh Patel, Seema Nayak, Sunil Kumar Chaudhary, Machine Learning, Deep Learning, Big Data, and Internet of Things for Healthcare, 2023
Shamla Mantri, Seema Nayak, Ritom Gupta, Pranav Bakre, Pratik Gorade, Vignesh Iyer
Another approach to predict depression from sleep is from the sleep pattern. The different phases of sleep give a rough idea about what could be a normal sleep pattern. Significant differences can be exploited to classify patients with depression symptoms. Sleep is classified into two phases: non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. Abnormalities in sleep of depressed patients are mainly indicated by increased REM sleep and reduced slow wave sleep [19]. Reduced REM latency can also be used to predict depression [20].
Fatigue Challenges in Emergency Medical Services Operations
Published in John W. Overton, Eileen Frazer, Safety and Quality in Medical Transport Systems, 2019
There are over 70 recognized sleep disorders. Common sleep disorders include sleep apnea, insomnia, restless leg syndrome, and periodic limb movement disorder. The disrupted sleep resulting from sleep disorders manifests in excessive daytime sleepiness. This affects performance, alertness, and safety and it is due to the frequent disruptions of sleep and associated sleep loss. Impairment secondary to inadequate sleep opportunities (e.g. on-call work schedule) can be worsened by an underlying sleep disorder.
Roza: a new and comprehensive metric for evaluating classification systems
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
As mentioned above, one of the systems, in which this problem is evident, is the sleep staging systems. Classification of sleep stages is very important for diagnosing and treating sleep problems. Thus, various biological signals such as electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG) are recorded during the sleep and are usually divided into 30-sec segments (epochs). Then, they are examined by experts one by one and classified into sleep stages. This process is a very tiring, time-consuming, and error-prone method (Akben and Alkan 2016; Boashash and Ouelha 2016). Thus, the software-aided classification of sleep stages will accelerate this process and significantly contribute to its accuracy (Li et al. 2016). In the literature, there are many studies based on EEG (Ronzhina et al. 2012; Kayikcioglu et al. 2015; Sharma et al. 2018; Dhok et al. 2020), and ECG (Sharma et al. 2018; 2019) for classifying sleep stages. Also, many studies have been conducted on the classification of sleep stages with only one EEG electrode. According to the R&K standard (Rechtschaffen and Kales 1968), sleep is divided into six stages: one REM (rapid eye movement) and four NonREM (no rapid eye movement) stages. NREM1 and NREM2 superficial sleep, NREM3 and NREM4 deep sleep, and REM are known as paradoxical sleep. By adding a wakefulness state to these stages, a six-class problem arises in the sleep staging system.
Evening electronic device use: The effects on alertness, sleep and next-day physical performance in athletes
Published in Journal of Sports Sciences, 2018
Maddison J. Jones, Peter Peeling, Brian Dawson, Shona Halson, Joanna Miller, Ian Dunican, Michael Clarke, Carmel Goodman, Peter Eastwood
It is often recommended that individuals avoid using electronic devices prior to sleep, as the screenlight may suppress melatonin secretion, delay the onset of sleep, shorten TST and reduce sleep efficiency (Chang et al., 2015; Fossum et al., 2014; King et al., 2014; Suganuma et al., 2007). In contrast, in the current study there was a tendency for salivary melatonin concentration to increase post-task for all 4 conditions, suggesting that using a tablet prior to sleep did not significantly inhibit melatonin release. While these findings are consistent with Rångtell et al. (2016) and Figueiro et al. (2011), other studies have shown reductions in melatonin concentration following the use of an electronic device in the evening (Bues et al., 2012; Cajochen et al., 2011; Chang et al., 2015). The discrepancy in results may be due to the duration of exposure to the electronic device, as melatonin has been significantly inhibited when electronic devices were used for 4–5 h (Bues et al., 2012; Cajochen et al., 2011; Chang et al., 2015), but not for 2 h (Figueiro et al., 2011; Rångtell et al., 2016), the latter being the case in the present study. As such, it is possible that the use of electronic devices for less than 2 h prior to bedtime may not have a negative impact on subsequent sleep.
Prevalence of sleep disorders and sleep problems in an elite super rugby union team
Published in Journal of Sports Sciences, 2019
Ian C. Dunican, Jennifer Walsh, Charles C. Higgins, Maddison J. Jones, Kathleen Maddison, John A. Caldwell, Hillman David, Peter R. Eastwood
It is estimated that approximately one third of the general population will experience a sleep disorder at some time during their life (Ohayon, 2007). Currently over 80 recognised sleep disorders are listed in the third edition of the international classification of sleep disorders (Sateia, 2014). In the general population the most common sleep disorders are obstructive sleep apnea (OSA), insomnia, and restless legs syndrome (RLS)(Adams, Appleton, Taylor, McEvoy, & Antic, 2016).