Factors Affecting Quality of Sleep in Intensive Care Unit

Background: The etiology of sleep disruption in intensive care unit is poorly known and often ignored complication. It is caused by the environmental factors especially pain, noise, diagnostic testing and human interventions that cause sleep disruption. Light, medications and activities related to patient care interfere with patient's ability to have good sleep. There are multi-factorial environmental etiologies for disruption of sleep in ICU. Objective: The objective of this study was to evaluate the factors disturbing the sleep quality in intensive care unit (ICU) admitted patients. Methodology: A cross sectional study was designed involving 150 patients admitted in intensive care unit and high dependency unit of Gulab Devi Chest Hospital. The duration of study was from September 2015 to March 2016. The questionnaire was made and filled with the help of patients. The data was analyzed using SPSS version 16.00. Results: Mean age of patients was 50.46+10.96 with maximum age of 65 and minimum age of 30 years. There was 53.33% male patients and 46.67% females participating in this study. The sleep quality was significantly poor in ICU than at home. After analysis, 54.67% patients were with poor quality of sleep due to pain and 48.67% were due to noise of environmental stimuli. The other factors were alarms, light and loud talking. Conclusion: Current study shows that reduced sleep quality is a common problem in ICU with multi-factorial etiologies. Patient reported the poor sleep quality in ICU due to environmental issues that are potentially modifiable. Conclusion: Current study shows that reduced sleep quality is a common problem in ICU with multi-factorial etiologies. Patient reported the poor sleep quality in ICU due to environmental issues that are potentially modifiable

Factors affecting the sleep of one-year-olds : a pilot study using objective monitoring of New Zealand infants : a thesis presented in partial fulfilment of the requirements of the degree of Master of Science in Psychology at Massey University, Wellington, New Zealand

Sleep takes time to mature and in infancy the structure and cycle of sleep differs greatly to that of adults. Data concerning normative sleep of infants is lacking due to few studies using objective measures. Factors affecting infants' sleep are both intrinsic and extrinsic in nature. The causes of problematic sleep are not well understood. This study aimed to pilot a methodology involving 1 week of actigraphy monitoring of 1-year-olds, as well as collecting normative data concerning sleep and sleep ecology through questionnaires and diaries. Potential factors contributing to sleep quantity, quality and maturation were investigated. Sleeping problems were reported in 35% of the sample of 52 Wellington infants. Current breastfeeding, time awake at night, and poor evening mood were all associated with problem sleep. Short sleep duration and more instances of being put to bed were also significant predictors of reporting problem sleep. Infants were typically rated in a poorer mood and exhibited more bedtime problems at the weekend. Longer sleep onset latencies and poorer sleep efficiency were identified by actigraphy on weekend evenings. The timing of sleep did not differ between genders or between week days and weekends, or childcare and non-childcare days. Mixed model analysis of variance indicated that the maturation and quality of sleep were significantly correlated with age and stages of cognitive and motor development. Sleep duration did not correlate with ponderal index, possibly due to the young age group as well as underrepresentation of short sleeping or overweight infants. Results support previous studies in western societies and autonomous sleeping is common. Potential mechanisms behind relationships between sleep and feeding, temperament and development are discussed. Strengths and limitations of methods and procedures are assessed. Actigraphic recording of 1-year-olds is demonstrated to be a useful and reliable tool for studying sleep of infants and the results contribute to normative data. Future studies in NZ should consider recruiting a more representative sample and incorporate a longitudinal design to further assess the relationships highlighted here and in previous research

The Sleep Condition Indicator: a clinical screening tool to evaluate insomnia disorder

Objective: Describe the development and psychometric validation of a brief scale (the Sleep Condition Indicator (SCI)) to evaluate insomnia disorder in everyday clinical practice.<p></p> Design: The SCI was evaluated across five study samples. Content validity, internal consistency and concurrent validity were investigated.<p></p> Participants: 30 941 individuals (71% female) completed the SCI along with other descriptive demographic and clinical information.<p></p> Setting: Data acquired on dedicated websites.<p></p> Results: The eight-item SCI (concerns about getting to sleep, remaining asleep, sleep quality, daytime personal functioning, daytime performance, duration of sleep problem, nights per week having a sleep problem and extent troubled by poor sleep) had robust internal consistency (α≥0.86) and showed convergent validity with the Pittsburgh Sleep Quality Index and Insomnia Severity Index. A two-item short-form (SCI-02: nights per week having a sleep problem, extent troubled by poor sleep), derived using linear regression modelling, correlated strongly with the SCI total score (r=0.90).<p></p> Conclusions: The SCI has potential as a clinical screening tool for appraising insomnia symptoms against Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria.<p></p&gt

Neural reactivations during sleep determine network credit assignment.

A fundamental goal of motor learning is to establish the neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this 'credit assignment' problem. Using neuroprosthetic learning, in which we could control the causal relationship between neurons and behavior, we found that sleep-dependent processing was required for credit assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Notably, we observed a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non-causal activity. Decoupling of spiking to slow oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep is essential for establishing sparse activation patterns that reflect the causal neuron-behavior relationship

Sleep

There is much received wisdom on infant sleep and new parents will find that just about everyone they speak to has an opinion – where, how much, how often. For parents, understanding infant sleep and adapting to new patterns and behaviours can be one of the biggest challenges in the early years. Unsurprisingly, sleep is one of the main concerns presented by parents to child and family health nurses. By giving parents information about sleep, they can be better prepared to promote and support healthy sleep patterns in their infants (Middlemiss, 2004). During sleep we all go through cycles of deep and light sleep. An adult’s sleep cycle lasts around 90 minutes, but an infant’s cycle is shorter, lasting 20 to 50 minutes. Deep sleep is quiet sleep; babies are mostly still and breathe evenly, but will sometimes jerk or startle. During light, active sleep, babies look restless, groan, sometimes open their eyes and even wake up completely. The amount of time we spend in each phase of sleep varies depending on age. Newborns spend about half their sleeping time in a light, active sleep, but by three years old, only one third of sleep time is active. This continues to reduce as children grow older. Understanding the physiological basics of sleep – cycles, patterns, phases and how much we need at different ages – can help health professionals and parents make better sense of infant sleep behaviours. For example, frequent night waking can be a problem for some parents but is in fact a normal part of an infant’s sleep cycle. There’s even an argument that night waking serves protective functions by allowing frequent feeding and creating the opportunity for emotional reconnection and brain stimulation. It may be helpful for parents to focus on improving their infant’s ability to self-settle rather than on the frequent waking.&nbsp

A Fully Polynomial-Time Approximation Scheme for Speed Scaling with Sleep State

We study classical deadline-based preemptive scheduling of tasks in a computing environment equipped with both dynamic speed scaling and sleep state capabilities: Each task is specified by a release time, a deadline and a processing volume, and has to be scheduled on a single, speed-scalable processor that is supplied with a sleep state. In the sleep state, the processor consumes no energy, but a constant wake-up cost is required to transition back to the active state. In contrast to speed scaling alone, the addition of a sleep state makes it sometimes beneficial to accelerate the processing of tasks in order to transition the processor to the sleep state for longer amounts of time and incur further energy savings. The goal is to output a feasible schedule that minimizes the energy consumption. Since the introduction of the problem by Irani et al. [16], its exact computational complexity has been repeatedly posed as an open question (see e.g. [2,8,15]). The currently best known upper and lower bounds are a 4/3-approximation algorithm and NP-hardness due to [2] and [2,17], respectively. We close the aforementioned gap between the upper and lower bound on the computational complexity of speed scaling with sleep state by presenting a fully polynomial-time approximation scheme for the problem. The scheme is based on a transformation to a non-preemptive variant of the problem, and a discretization that exploits a carefully defined lexicographical ordering among schedules