scholarly journals Time to Take Sleeping Pills and Subjective Satisfaction among Cancer Patients

2020 ◽  
Vol 17 (3) ◽  
pp. 249-255 ◽  
Author(s):  
Soyoung Youn ◽  
Byeongil Choi ◽  
Suyeon Lee ◽  
Changnam Kim ◽  
Seockhoon Chung
Keyword(s):  
Patient Satisfaction ◽  
Cancer Patients ◽  
Sleep Onset ◽  
Onset Time ◽  
Sleeping Pills ◽  
Time In Bed ◽  
Daytime Activity ◽  
Ingestion Time ◽  
Administration Time ◽  
Time Variables

Objective We investigated the influence of the time to take hypnotics and daytime activity on patient satisfaction with sleeping pills.Methods Ninety-six cancer patients who were currently taking benzodiazepine or z-drug as hypnotics were grouped into satisfied and dissatisfied groups. The subjects’ symptoms, time to take sleeping pills, bedtime, sleep onset time, wake up time, and time in bed within 24 hours (TIB/d) were obtained.Results The satisfied group had significantly late sleeping pill ingestion time (p=0.04); significantly early wake up time (p=0.01); and significantly shorter sleep latency, TIB/d, duration from the administration of pills to sleep onset, and duration from the administration of pills to wake up time (PTW). Logistic regression analysis revealed that the significant predictors of patient satisfaction to hypnotics were less severity of insomnia [odds ratio (OR)=0.91] and the time variables, including late sleeping pill administration time (OR=1.53) and early wake up time (OR=0.57). Among the duration variables, short PTW (OR=0.30) and short TIB/d (OR=0.64) were significantly related with the satisfaction to hypnotics.Conclusion Reducing the duration from the administration of hypnotics to wake up time and TIB/d can influence the satisfaction to sleeping pills.

scholarly journals Do Parental Expectations Play a Role in Children's Sleep and Mothers' Distress? An Exploration of the Goodness of Fit Concept in 54 Mother-Child Dyads

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Helene Werner ◽  
Oskar G. Jenni
Keyword(s):  
Goodness Of Fit ◽  
Sleep Onset ◽  
Parental Expectations ◽  
Onset Time ◽  
Kindergarten Children ◽  
Parental Distress ◽  
Time In Bed ◽  
Falling Asleep ◽  
Wake Patterns ◽  
Children’S Sleep

This study describes parental expectations for sleep-wake patterns in healthy kindergarten children and explores their relation to children’s sleep quality and parental distress. Data analysis of 54 mother-child dyads (age range of the children: 4–7 years) indicated that parental expectations for children’s sleep-wake patterns differ between scheduled and free days and depend on children’s chronotype. Mothers of children with late chronotype showed less adequate expectations for children’s sleep onset time than mothers of children with early chronotype (e.g., morning types). Furthermore, children of mothers with less adequate expectations for children’s sleep onset time on scheduled days had longer settling periods during which sleep rituals may take place (r=0.31,P≤0.05), spent more time in bed than they actually sleep (r=0.35,P≤0.01), and had more frequently difficulties falling asleep (r=0.33,P≤0.01). However, less adequate expectations for children’s sleep onset time were not associated with parental distress (P>0.05). We conclude that parental expectations about their children’s sleep play a key role in understanding normal and abnormal sleep during childhood.

scholarly journals Influence of Carbohydrate on the Stages of Sleep – A Meta-Analysis

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 896-896
Author(s):  
David Benton ◽  
Anthony Bloxham ◽  
Chantelle Gaylor ◽  
Hayley Young
Keyword(s):  
Slow Wave Sleep ◽  
Meta Analysis ◽  
Sleep Onset ◽  
Onset Time ◽  
Sleep Efficiency ◽  
Glucose Levels ◽  
Funding Sources ◽  
Time In Bed ◽  
Falling Asleep ◽  
High Level

Abstract Objectives Carbohydrate is the nutrient most commonly said to influence sleep: it is proposed that a high intake increases the uptake of tryptophan by the brain, where it is metabolized into serotonin and melatonin. As this mechanism depends on the relative amount of carbohydrate and protein, studies were reviewed where diets differed in these macro-nutrients. Methods The Web of Science and Medline were interrogated using terms related to carbohydrate and sleep. Papers were retained if two diets, differing in the percentage of calories coming from carbohydrate, had been contrasted using either polysomnography or actigraphy. Measures considered with polysomnography included sleep onset time; sleep efficiency; rapid eye-movement (REM) and slow wave sleep (SWS). Measures examined from actigraphy included: sleep efficiency; duration of sleep. Meta-analysis was conducted using Review Manager 5.2 (Cochrane) using a random-effects model. Results With the polysomnography measures six studies met the inclusion criteria. A lesser consumption of carbohydrate was associated with more SWS (SMD = 0.47; CI 0.06 – 0.88; P = 0.02; I2 = 0%) and less REM (SMD = – 0.47, CI –0.87– –0.07, P = 0.02, I2 = 0%). A lower intake of carbohydrate was also associated with a shorter time before falling asleep (P = 0.03; I2 0%). Sleep efficiency is the percentage of time spent asleep, relative to the total time in bed. Using polysomnography there was a trend for better sleep efficiency to be associated with with a lower intake of carbohydrate, although it just missed significance (P = 0.06). However, using actigraphy those eating less carbohydrate were more sleep efficient (SMD = 1.25; CI 0.35 – 2.15; P = 0.007; I2 = 0%). Conclusions No study had the high level of carbohydrate needed to raise tryptophan; rather studies had enough protein to reduce the uptake of tryptophan. A novel possibility is that blood glucose levels modulate sleep. Glucose metabolism varies; it is less during SWS and greater with REM. There are many reports associating the nature of sleep with glucose tolerance. As several hormones control glucose levels, some stimulated by the level of glucose, there is a need to consider diet hormonal interactions. As SWS is believed to be restorative and aid plasticity, increasing SWS with lower carbohydrate have may functional implications. Funding Sources No external funding.

Sleeping pills administration time and subjective satisfaction among cancer patients

2017 ◽  
Vol 40 ◽  
pp. e62
Author(s):  
B. Choi ◽  
S. Youn ◽  
S. Lee ◽  
C. Kim ◽  
S. Chung
Keyword(s):  
Cancer Patients ◽  
Sleeping Pills ◽  
Subjective Satisfaction ◽  
Administration Time

Losing Sleep Over It: Sleep in Basketball Players Affected by Game But Not Training Workloads

2020 ◽  
Vol 15 (8) ◽  
pp. 1117-1124
Author(s):  
Jordan L. Fox ◽  
Aaron T. Scanlan ◽  
Robert Stanton ◽  
Cody J. O’Grady ◽  
Charli Sargent
Keyword(s):  
Heart Rate ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Sleep Onset ◽  
Onset Time ◽  
Rating Of Perceived Exertion ◽  
Self Report ◽  
Basketball Players ◽  
Time In Bed ◽  
The Impact

Purpose: To examine the impact of workload volume during training sessions and games on subsequent sleep duration and sleep quality in basketball players. Methods: Seven semiprofessional male basketball players were monitored across preseason and in-season phases to determine training session and game workloads, sleep duration, and sleep quality. Training and game data were collected via accelerometers, heart-rate monitors, and rating of perceived exertion (RPE) and reported as PlayerLoad™ (PL), summated heart-rate zones, and session RPE (sRPE). Sleep duration and sleep quality were measured using wrist-worn activity monitors in conjunction with self-report sleep diaries. For daily training sessions and games, all workload data were independently sorted into tertiles representing low, medium, and high workload volumes. Sleep measures following low, medium, and high workloads and control nights (no training/games) were compared using linear mixed models. Results: Sleep onset time was significantly later following medium and high PL and sRPE game workloads compared with control nights (P < .05). Sleep onset time was significantly later following low, medium, and high summated heart-rate-zones game workloads, compared with control nights (P < .05). Time in bed and sleep duration were significantly shorter following high PL and sRPE game workloads compared with control nights (P < .05). Following low, medium, and high training workloads, sleep duration and quality were similar to control nights (P > .05). Conclusions: Following high PL and sRPE game workloads, basketball practitioners should consider strategies that facilitate longer time in bed, such as napping and/or adjusting travel or training schedules the following day.

324 Naturalistic Characterization of Sleep in Chronic Insomnia Using a Non-Contact Sleep Measurement Device

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A130-A130
Author(s):  
Devon Hansen ◽  
Mary Peterson ◽  
Roy Raymann ◽  
Hans Van Dongen ◽  
Nathaniel Watson
Keyword(s):  
Sleep Onset ◽  
Contact Device ◽  
Chronic Insomnia ◽  
Poor Sleep ◽  
Measurement Device ◽  
Home Setting ◽  
Time In Bed ◽  
Sleep Measurement ◽  
Measurement Devices

Abstract Introduction Individuals with insomnia report poor sleep quality and non-restorative sleep, and often exhibit irregular sleep patterns over days and weeks. First night effects and logistical challenges make it difficult to measure these sleep characteristics in the laboratory. Also, sensitivity to sleep disruption from obtrusive measurement devices confounds sleep measurements in people with insomnia in their naturalistic setting. Non-contact sleep measurement devices have the potential to address these issues and enable ecologically valid, longitudinal characterization of sleep in individuals with insomnia. Here we use a non-contact device – the SleepScore Max (SleepScore Labs) – to assess the sleep of individuals with chronic insomnia, compared to healthy sleeper controls, in their home setting. Methods As part of a larger study, 13 individuals with chronic insomnia (ages 25-60y, 7 males) and 8 healthy sleeper controls (ages 21-46y, 6 females) participated in an at-home sleep monitoring study. Enrollment criteria included an age range of 18-65y and, for the insomnia group, ICSD-3 criteria for chronic insomnia with no other clinically relevant illness. Participants used the non-contact sleep measurement device to record their sleep periods each night for 8 weeks. Sleep measurements were analyzed for group differences in both means (characterizing sleep overall) and within-subject standard deviations (characterizing sleep variability across nights), using mixed-effects regression controlling for systematic between-subject differences. Results Based on the non-contact sleep measurements, individuals with chronic insomnia exhibited greater variability in bedtime, time in bed, total sleep time, sleep latency, total wake time across time in bed, wakefulness after sleep onset, sleep interruptions, and estimated light sleep, compared to healthy sleeper controls (all F&gt;5.7, P&lt;0.05). No significant differences were found for group averages and for variability in estimated deep and REM sleep. Conclusion In this group of individuals with chronic insomnia, a non-contact device used to characterize sleep naturalistically captured enhanced variability across nights in multiple aspects of sleep stereotypical of sleep disturbances in chronic insomnia, differentiating the sample statistically significantly from healthy sleeper controls. Support (if any) NIH grant KL2TR002317; research devices provided by SleepScore Labs.

scholarly journals Polysomnographic Sleep Is Associated With Time to Develop Dementia: A Study Using 19-Year VA National EHR Data

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 469-470
Author(s):  
Sara Nowakowski ◽  
Javad Razjouyan ◽  
Amir Sharafkhaneh ◽  
Mark Kunik ◽  
Aanand Naik
Keyword(s):  
Language Processing ◽  
Univariate Analysis ◽  
Sleep Onset ◽  
Onset Time ◽  
Late Time ◽  
Sleep Onset Latency ◽  
One Year ◽  
Objectively Measured ◽  
Onset Group ◽  
Dementia Onset

Abstract Few studies have longitudinally investigated the association between objectively measured sleep and time to develop dementia. This study leverages polysomnography (PSG) sleep data extracted from the VA national electronic health records (VA-EHR) to assess the association between sleep and time to develop dementia. We identified 61,165 PSG reports from the VA-EHR from 2000 to 2019 using CPT codes. Patients who developed dementia were identified using all-cause dementia ICD-9/10 codes documented on two separate visits starting one year after the PSG study until the end of 2019 in a 1-year sliding period (n=1,534). Using the first appearance of ICD-9/10 code as dementia onset time, patients were clustered into 3 groups of early-, mid-, and late time to develop dementia (mean = 2.7, 7.5, 12.8 years, respectively). Natural language processing was used to extract sleep efficiency (SE) and sleep onset latency (SOL). Univariate analysis was used to compare the groups. After adjusting for age, SE was significantly higher in the late (76%) vs early (69%) group and SOL was significantly shorter in late (21m) versus early (33m) group. SE was higher and SOL was shorter in patients who developed dementia later compared to those who developed dementia earlier. Greater sleep continuity in late dementia onset group suggests that sleep may be a modifiable risk factor that could potentially delay the onset of dementia.

scholarly journals Non-pharmacological sleep interventions for pediatric cancer patients and survivors: a systematic review protocol

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Peter L. Stavinoha ◽  
Ineke M. Olsthoorn ◽  
Maria C. Swartz ◽  
Sara Nowakowski ◽  
Stephanie J. Wells ◽  
...  
Keyword(s):  
Systematic Review ◽  
Sleep Quality ◽  
Cancer Patients ◽  
Pediatric Cancer ◽  
Sleep Disturbances ◽  
Sleep Onset ◽  
Standard Of Care ◽  
Sleep Diary ◽  
Pharmacological Interventions ◽  
Pediatric Cancer Patients

Abstract Background Sleep disturbances constitute a common complication in pediatric cancer patients and survivors and are frequently severe enough to warrant treatment. Suboptimal sleep has been associated with decreased emotional well-being and cognitive functioning and increased behavioral problems. Standardized guidelines for non-pharmacological sleep interventions for adults with cancer exist, but no standard of care intervention or standard guidelines are available to guide such intervention in pediatric cancer patients and survivors. Therefore, effective behavioral interventions for improving sleep quality need to be identified. The objective of the review is to evaluate the effect of non-pharmacological sleep interventions on sleep quality in pediatric cancer patients and survivors. Methods The review will consider studies that include children and adolescents between 0 and 18 years diagnosed with cancer or who have a history of cancer who have non-respiratory sleep disturbance. We will include experimental and quasi-experimental studies evaluating non-pharmacological interventions such as psychological interventions, technical/device interventions, interventions targeting physical activity, and complementary and alternative medicine interventions (e.g., yoga, massage, music). Interventions involving medications, ingestible supplements, products purported to work through absorption, and medical devices will be excluded. Primary outcome will be sleep quality as measured by methods including retrospective ratings, daily sleep diary, and validated questionnaires. Secondary outcomes will include total sleep time, sleep onset latency, wake after sleep onset, daytime sleepiness, and daytime sleep duration (naps) as measured by retrospective ratings, daily sleep diary, validated questionnaires, and/or actigraphy. Databases will include MEDLINE (Ovid), EMBASE (Ovid), Cochrane Library, CINAHL (Ebsco), and PsycINFO (Ovid) and will be queried from database inception to present. Two reviewers will independently screen all citations, full-text articles, and extract data. The study methodological quality will be assessed using Joanna Briggs Institute (JBI) critical appraisal tools. Data will be extracted and findings pooled and synthesized using a meta-aggregation approach via the JBI System for the Unified Management, Assessment, and Review of Information (SUMARI). If feasible, we will conduct random effects meta-analysis. Additional analyses will be conducted to explore the potential sources of heterogeneity (e.g., methodological quality, study design, outcome measures). Discussion This systematic review will synthesize and consolidate evidence on existing non-pharmacological interventions to improve sleep in pediatric cancer patients and survivors. Findings may help inform practitioners working with pediatric cancer patients and survivors experiencing sleep disturbances and is intended to identify gaps and opportunities to improve methodical quality of further non-pharmacological sleep intervention research in this population toward developing an eventual standard of care. Systematic review registration PROSPERO CRD42020200397.

507 Clinical Impression of Excessive Daytime Sleepiness (EDS) at initial Sleep Medicine (SM) consultation and its clinical correlates

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A199-A200
Author(s):  
Leon Rosenthal ◽  
Raúl Aguilar Roblero
Keyword(s):  
Lower Energy ◽  
Energy Levels ◽  
Sleep Onset ◽  
Sleep Time ◽  
Clinical Impression ◽  
Sleep Onset Latency ◽  
Convenience Sample ◽  
Time In Bed ◽  
Number Of Patients ◽  
Cardinal Symptom

Abstract Introduction EDS represents a cardinal symptom in SM. Use of subjective scales are prevalent, which have a modest correlation with the MSLT. While the Clinical Global Impression has been used in research, reports of clinical impression (CI) in medical practice are lacking. We report on the CI of EDS in a convenience sample of patients undergoing initial consultation. Methods Patients reported primary, secondary symptoms and completed the Sleep Wake Activity Inventory (SWAI) prior to Tele-Medicine consultation. A SM physician completed the assessment which included ascertainment of CI of EDS (presence S+ / absence S-). Results There were 39 ♂and 13 ♀. The CI identified 26 patients in each group (S+/S-). Age (52 [14]), BMI (33 [7]), reported time in bed, sleep time, sleep onset latency and # of awakenings did not differ. All identified a primary symptom (S-: 21, S+: 19 reported snoring or a previous Dx of OSA). Sleepiness as a 1ry or 2ry symptom was identified by 0 in the S- and by 13 in the S+ groups. Refreshing quality of sleep differed (χ2 &lt;0.05): un-refreshing sleep was reported by 7 (S-) and by 13 (S+). Naps/week: 0.7 [1.5] and 1.57 [1.5] for the S-, S+ groups respectively (p&lt;0.05). A main effect (p&lt;0.01) was documented on the SWAI. We report on the Sleepiness [SS] and Energy Level [EL] scales (lower scores on the SS reflect higher sleepiness while lower scores on EL denote higher energy). Higher sleepiness (p&lt;0.01) 43 [12] and lower energy levels 24 [6] (p&lt;0.05) were documented on the S+ group (S- 61 [17], and 18 [6] respectively). Available spouse’s Epworth score on 29 patients: S- patients 5.8 [4] and S+ 10.2 [6] (p&lt;0.05). Dx of OSA was identified among all but 1 in the S+ group. Also, Insomnia was diagnosed among 11 (S-) and 19 (S+) patients (p&lt;0.05) despite only 3 and 7 (respectively) identifying it as a presenting symptom. Conclusion While snoring or previous Dx of OSA were prevalent motivations for consultation, sleepiness and insomnia were clinically relevant among a substantial number of patients. Unrefreshing sleep, daytime naps, lower energy, and higher sleepiness were ubiquitous among S+ patients. Support (if any):

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