scholarly journals 1025 Sleep Patterns In Head Neck Cancer Patients During Radiotherapy

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A389-A390
Author(s):  
F Gu ◽  
C Jungquist ◽  
A Sonia ◽  
L Liu ◽  
E Repasky ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Neck Cancer ◽  
Sleep Disturbances ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Sleep Patterns ◽  
Sleep Habits ◽  
End Of Treatment

Abstract Introduction Sleep disturbances are reported to be highly prevalent in head and neck cancer (HNC) patients, but no carefully assessed sleep data exists in patients with HNC undergoing concurrent chemoradiotherapy (CRT). Methods To objectively assess sleep patterns in this study population, we conducted a pilot study in 15 patients and 13 non-cancer healthy volunteers. Patients wore the wrist Actiwatch Spectrum (Philips Respironics) at week 1, 3, and 6/7 during the 7-week treatment period. Volunteers wore the Actiwatch for one week. We used the Actiware software to calculate sleep parameters. A sleep log was used as a complement to define participants’ bedtime and rise-up time. Any sleep episode scored by the software during daytime was considered as a nap. Results Compared to healthy volunteers, patients had lower overnight sleep efficiency, longer sleep onset latency and more waking time after sleep onset (WASO), indicating more difficulty falling asleep and maintaining sleep. During CRT, patients’ sleep efficiency decreased whereas latency and WASO increased, indicating possible the decrease of sleep quality. Sleep efficiency of <85% has been used previously as a cut-off for poor sleep; based on this criteria, 45% of HNC patients had poor sleep at treatment baseline, compared to 31% in non-cancer volunteers, and this proportion increased to 51% by the end of treatment. Patients had longer napping time: compared to healthy volunteers, the napping time was on average 2 hours longer at baseline, and 3 hours longer at the end of treatment, indicating unhealthy sleep habits of these patients. Conclusion Our data suggested HNC patients had severe sleep disturbances and unhealthy sleep habits, which were aggravated during CRT treatment. Support This study was supported by UL1TR001412-04, a Clinical and Translational Research Award under SUNY-Buffalo.

Links between sleep and body mass index in bipolar disorders: An exploratory study

2015 ◽  
Vol 30 (1) ◽  
pp. 89-93 ◽  
Author(s):  
C. Boudebesse ◽  
P.-A. Geoffroy ◽  
C. Henry ◽  
A. Germain ◽  
J. Scott ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Sleep Disturbances ◽  
Sleep Onset ◽  
Bipolar Disorders ◽  
Sleep Time ◽  
Sleep Efficiency ◽  
Sleep Onset Latency ◽  
Cross Sectional ◽  
Sleep Parameters

AbstractStudy objectives:Obesity and excess bodyweight are highly prevalent in individuals with bipolar disorders (BD) and are associated with adverse consequences. Multiple factors may explain increased bodyweight in BD including side effects of psychotropic medications, and reduced physical activity. Research in the general population demonstrates that sleep disturbances may also contribute to metabolic burden. We present a cross-sectional study of the associations between body mass index (BMI) and sleep parameters in patients with BD as compared with healthy controls (HC).Methods:Twenty-six French outpatients with remitted BD and 29 HC with a similar BMI completed a 21-day study of sleep parameters using objective (actigraphy) and subjective (PSQI: Pittsburgh Sleep Quality Index) assessments.Results:In BD cases, but not in HC, higher BMI was significantly correlated with lower sleep efficiency (P = 0.009) and with several other sleep parameters: shorter total sleep time (P = 0.01), longer sleep onset latency (P = 0.05), higher fragmentation index (P = 0.008), higher inter-day variability (P = 0.05) and higher PSQI total score (P = 0.004).Conclusions:The findings suggest a link between a high BMI and several sleep disturbances in BD, including lower sleep efficiency. Physiological mechanisms in BD cases may include an exaggeration of phenomena observed in non-clinical populations. However, larger scale studies are required to clarify the links between metabolic and sleep-wake cycle disturbances in BD.

Night Games and Sleep: Physiological, Neuroendocrine, and Psychometric Mechanisms

2018 ◽  
Vol 13 (7) ◽  
pp. 867-873 ◽  
Author(s):  
Laura E. Juliff ◽  
Jeremiah J. Peiffer ◽  
Shona L. Halson
Keyword(s):  
Core Temperature ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Sleep Difficulty ◽  
Sleep Complaints ◽  
Temperature State ◽  
Psychometric Measures ◽  
Cortisol Responses

Context: Night games are a regular occurrence for team-sport athletes, yet sleep complaints following night competitions are common. The mechanisms responsible for reported sleep difficulty in athletes are not understood. Methods: An observational crossover design investigating a night netball game and a time-matched rest day in 12 netball athletes was conducted to ascertain differences in physiological (core temperature), psychometric (state and trait), and neuroendocrine (adrenaline, noradrenaline, and cortisol) responses. Results: Following the night game, athletes experienced reduced sleep durations, lower sleep efficiency, early awakenings, and poorer subjective sleep ratings compared with the rest day. No differences were found between core temperature, state psychometric measures, and cortisol at bedtime. Adrenaline and noradrenaline concentrations were elevated compared with the time-matched rest day prior to (26.92 [15.88] vs 12.90 [5.71] and 232.6 [148.1] vs 97.83 [36.43] nmol/L, respectively) and following the night game (18.67 [13.26] vs 11.92 [4.56] and 234.1 [137.2] vs 88.58 [54.08] nmol/L, respectively); however, the concentrations did not correlate to the sleep variables (duration, efficiency, and sleep-onset latency). A correlation (rs = −.611) between sleep efficiency and hyperarousal (trait psychometric measure) was found. Conclusions: Athletes experienced poor sleep following a night game. Furthermore, results suggest that athletes who have a tendency toward a high trait arousal may be more susceptible to sleep complaints following a night game. These data expand knowledge and refute frequently hypothesized explanations for poor sleep following night competition. The results may also help support staff and coaches target strategies for individual athletes at a higher risk of sleep complaints.

Comorbidity of ADHD and Anxiety Disorders in School-Age Children: Impact on Sleep and Response to a Cognitive-Behavioral Treatment

2015 ◽  
Vol 22 (5) ◽  
pp. 414-424 ◽  
Author(s):  
Maxime Bériault ◽  
Lyse Turgeon ◽  
Mélanie Labrosse ◽  
Claude Berthiaume ◽  
Martine Verreault ◽  
...  
Keyword(s):  
Anxiety Disorders ◽  
Sleep Disturbances ◽  
Behavioral Treatment ◽  
Sleep Onset ◽  
Cognitive Behavioral ◽  
Sleep Onset Latency ◽  
Sleep Habits ◽  
Children With Adhd ◽  
Comorbid Anxiety ◽  
The Impact

Objective: This exploratory study measured the impact of comorbid anxiety disorders on sleep in children with ADHD and tested the effect of cognitive-behavioral therapy (CBT) on these measures. Method: Fifty-seven children (8-12 years old) were assessed with the Child Sleep Habits Questionnaire. Four groups were formed: ADHD ( n = 20), ADHD + Anxiety ( n = 20), Anxiety ( n = 8), and Healthy Controls ( n = 9). A subgroup of 10 children with ADHD + Anxiety underwent CBT for anxiety. Results: The results showed that sleep difficulties were better associated with anxiety than with ADHD. CBT reduced sleep onset latency and marginally decreased the total amount of sleep problems. Conclusion: The present study demonstrates that comorbid anxiety in ADHD children is linked with specific sleep disturbances and is sensitive to CBT aimed at reducing anxiety.

scholarly journals Sleep pattern in epilepsy patients: a polysomnographic study

2020 ◽  
Vol 56 (1) ◽  
Author(s):  
Mohamed A. Tork ◽  
Hebatallah R. Rashed ◽  
Lobna Elnabil ◽  
Nahed Salah-Eldin ◽  
Naglaa Elkhayat ◽  
...  
Keyword(s):  
Sleep Disturbances ◽  
Refractory Epilepsy ◽  
Sleep Onset ◽  
The Other ◽  
Sleep Architecture ◽  
Apnea Hypopnea Index ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Group I ◽  
Group Ii

Abstract Background Sleep disorders and epilepsy commonly exist and affect each other. Patients with epilepsy often complain of poor sleep and on the other hand, poor sleep makes epilepsy control difficult. Objectives We aimed at comparing the sleep disturbances in a group of patients with medically controlled epilepsy versus another group with medically refractory epilepsy, from the electrophysiological standpoint. Subjects and methods Sixty epilepsy patients were included; half of them with controlled epilepsy were assigned as group I, and the other half with refractory epilepsy was assigned as group II. All patients had an overnight polysomnogram and sleep EEG done. We excluded any patient with abnormal general or neurological clinical examination. Results Patients in group II, had significantly delayed sleep onset latency and REM latency. However, higher arousal index, insomnia, and periodic limb movement index were found to be significantly higher in group I. Respiratory events; as light sleep durations, were observed to be higher in Group II, in addition to apnea-hypopnea index that was significantly higher in this group. Conclusion Epilepsy affects sleep architecture and sleep-related events. Patients with refractory epilepsy suffer from more disturbance in sleep patterns. Moreover, antiepileptic drugs can have a diverse effect on sleep architecture and quality in epileptic patients.

105 Sleep Behaviors Are Differentially Associated with Eating Behavior Characteristics Based on Sex

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A43-A43
Author(s):  
Rocio Barragan ◽  
Faris Zuraikat ◽  
Victoria Tam ◽  
Samantha Scaccia ◽  
Justin Cochran ◽  
...  
Keyword(s):  
Eating Behavior ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Sleep Fragmentation ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Obesity Risk ◽  
Fragmentation Index ◽  
Behavior Characteristics

Abstract Introduction Poor sleep health is a key determinant of obesity risk, largely explained by overconsumption of energy. Eating behavior characteristics are predictive of energy intake and weight change and may link sleep with risk factors for obesity. However, the relationships between sleep and dimensions of eating behavior, and potential individual differences in these relations, are not well characterized. Elucidating these relations may aid in the development of targeted strategies to mitigate obesity risk. Therefore, we aimed to 1) evaluate whether associations of sleep were related with eating behavior characteristics, 2) explore if these associations differed by sex. Methods This was a cross-sectional analysis of 179 adults aged 20–73 y (68.7% women; 64.8% with BMI≥25 kg/m2; 27.4% minority). Sleep was assessed over 2 wk using wrist actigraphy; eating behavior characteristics (dietary restraint, disinhibition and hunger) were measured with the Three-Factor Eating Questionnaire. Linear regression models were used to evaluate associations of sleep with eating behavior characteristics, adjusting for confounding variables. In separate models, sex was added as an interaction term and analyses were stratified when interactions were significant (p<0.10). Results Variable (sleep midpoint standard deviation >60 min) vs. stable sleep timing was associated with greater tendency towards hunger (β=0.84 ± 0.39, p=0.03). When evaluated on the continuous scale, lower sleep efficiency (β=-0.13 ± 0.05; p=0.01), longer wake after sleep onset (β=0.03 ± 0.01; p=0.01) and higher sleep fragmentation index (β=0.074 ± 0.036; p=0.041) were associated with higher dietary restraint. Sex influenced associations of sleep efficiency, sleep onset latency, and sleep fragmentation index with hunger. In men, but not women, lower sleep efficiency (β=-0.15 ± 0.05; p<0.01), longer sleep onset latency (β=0.17 ± 0.07; p=0.02) and higher sleep fragmentation index (β=0.11 ± 0.04; p<0.01) were associated with greater hunger. Conclusion Objective measures of sleep were associated with eating behaviors previously linked with obesity and its risk factors. Moreover, we provide evidence of sex-specific associations between poor sleep and tendency towards hunger. Our results suggest that, particularly in men, differences in eating behavior traits may underlie susceptibility to overeating in response to poor sleep. Support (if any) Supported by NIH grants R01HL128226 and R01HL142648 and AHA grant 16SFRN27950012 (PI: St-Onge).

scholarly journals What type of mattress should be chosen to avoid back pain and improve sleep quality? Review of the literature

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gianfilippo Caggiari ◽  
Giuseppe Rocco Talesa ◽  
Giuseppe Toro ◽  
Eugenio Jannelli ◽  
Gaetano Monteleone ◽  
...  
Keyword(s):  
Systematic Review ◽  
Low Back Pain ◽  
Back Pain ◽  
Sleep Quality ◽  
Sleep Disturbances ◽  
Meta Analysis ◽  
Sleep Onset ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Low Back

AbstractEnergy spent during daily activities is recuperated by humans through sleep, ensuring optimal performance on the following day. Sleep disturbances are common: a meta-analysis on sleep quality showed that 15–30% of adults report sleep disorders, such as sleep onset latency (SOL), insufficient duration of sleep and frequently waking up at night. Low back pain (LBP) has been identified as one of the main causes of poor sleep quality. Literature findings are discordant on the type of mattress that might prevent onset of back pain, resulting in an improved quality of sleep. We conducted a systematic literature review of articles published until 2019, investigating the association of different mattresses with sleep quality and low back pain. Based on examined studies, mattresses were classified according to the European Committee for Standardization (2000) as: soft, medium-firm, extra-firm or mattresses customized for patients affected by supine decubitus. A total of 39 qualified articles have been included in the current systematic review. Results of this systematic review show that a medium-firm mattress promotes comfort, sleep quality and rachis alignment.

scholarly journals Actigraphy-Derived Sleep Is Associated with Eating Behavior Characteristics

Nutrients ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 852
Author(s):  
Rocío Barragán ◽  
Faris M. Zuraikat ◽  
Victoria Tam ◽  
Samantha Scaccia ◽  
Justin Cochran ◽  
...  
Keyword(s):  
Individual Differences ◽  
Eating Behavior ◽  
Dietary Restraint ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Sleep Fragmentation ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Fragmentation Index ◽  
Behavior Characteristics

Poor sleep is a determinant of obesity, with overconsumption of energy contributing to this relationship. Eating behavior characteristics are predictive of energy intake and weight change and may underlie observed associations of sleep with weight status and obesity risk factors. However, relationships between sleep and dimensions of eating behavior, as well as possible individual differences in these relations, are not well characterized. Therefore, the aim of this study was to evaluate whether sleep behaviors, including duration, timing, quality, and regularity relate to dietary restraint, disinhibition, and tendency towards hunger and to explore whether these associations differ by sex. This cross-sectional study included 179 adults aged 20–73 years (68.7% women, 64.8% with BMI ≥ 25 kg/m2). Sleep was evaluated by accelerometry over 2 weeks. Eating behavior dimensions were measured with the Three-Factor Eating Questionnaire. Prolonged wake after sleep onset (WASO) (0.029 ± 0.011, p = 0.007), greater sleep fragmentation index (0.074 ± 0.036, p = 0.041), and lower sleep efficiency (−0.133 ± 0.051, p = 0.010) were associated with higher dietary restraint. However, higher restraint attenuated associations of higher WASO and sleep fragmentation with higher BMI (p-interactions < 0.10). In terms of individual differences, sex influenced associations of sleep quality measures with tendency towards hunger (p-interactions < 0.10). Stratified analyses showed that, in men only, higher sleep fragmentation index, longer sleep onset latency, and lower sleep efficiency were associated with greater tendency towards hunger (β = 0.115 ± 0.037, p = 0.003, β = 0.169 ± 0.072, p = 0.023, β = −0.150 ± 0.055, p = 0.009, respectively). Results of this analysis suggest that the association of poor sleep on food intake could be exacerbated in those with eating behavior traits that predispose to overeating, and this sleep-eating behavior relation may be sex-dependent. Strategies to counter overconsumption in the context of poor quality sleep should be evaluated in light of eating behavior traits.

Actigraphy assessment of sleep quality among patients with acute myeloid leukaemia during induction chemotherapy

2018 ◽  
Vol 8 (3) ◽  
pp. 274-277 ◽  
Author(s):  
Chi-Fu Jeffrey Yang ◽  
Kelli Aibel ◽  
Ryan Meyerhoff ◽  
Frances Wang ◽  
David Harpole ◽  
...  
Keyword(s):  
Acute Myeloid Leukaemia ◽  
Sleep Quality ◽  
Myeloid Leukaemia ◽  
Induction Chemotherapy ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Acute Myeloid

ObjectivesPatients receiving induction chemotherapy for acute myeloid leukaemia (AML) anecdotally describe poor sleep, but sleep disturbances have not been well-characterised in this population. We aimed to test the feasibility of measuring sleep quality in AML inpatients using a wearable actigraphy device.MethodsUsing the Actigraph GT3X ‘watch’, we assessed the total sleep time, sleep onset latency, wake after sleep onset, number of awakenings after sleep onset and sleep efficiency for inpatients with AML receiving induction chemotherapy. We assessed patient self-reported sleep quality using the Pittsburgh Sleep Quality Index (PSQI).ResultsOf the 12 patients enrolled, 11 completed all actigraphy and PSQI assessments, demonstrating feasibility. Patients wore the Actigraph device for a mean (SD) of 15.92 (8.3) days, and actigraphy measures suggested poor sleep. Patients had a median average awakening length of 6.92 min, a median number of awakenings after sleep onset of 4 and a median sleep onset latency of 10.8 min. Actual median sleep efficiency (0.91) was high, suggesting that patients’ poor sleep was not due to insomnia but perhaps due to interruptions, such as administration of medications, lab draws and vital sign measurements.ConclusionsCollection of sleep quality data among inpatients with AML via a wearable actigraphy device is feasible. AML inpatients appear to have poor sleep quality and quantity, suggesting that sleep issues represent an area of unmet supportive care needs in AML. Further research in this areas is needed to inform the development of interventions to improve sleep duration and quality in hospitalised patients with AML.

scholarly journals The Quality, Quantity, and Intraindividual Variability of Sleep Among Students and Student-Athletes

2019 ◽  
Vol 12 (1) ◽  
pp. 43-50
Author(s):  
Cédric Leduc ◽  
Jason Tee ◽  
Jonathon Weakley ◽  
Carlos Ramirez ◽  
Ben Jones
Keyword(s):  
Sleep Quality ◽  
Student Athletes ◽  
Online Survey ◽  
Sport Participation ◽  
Sleep Onset ◽  
Intraindividual Variability ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Sleep Patterns ◽  
Sleep Habits

Background: Student-athletes are subject to significant demands due to their concurrent sporting and academic commitments, which may affect their sleep. This study aimed to compare the self-reported sleep quality, quantity, and intraindividual variability (IIV) of students and student-athletes through an online survey. Hypothesis: Student-athletes will have a poorer sleep quality and quantity and experience more IIV. Study Design: Case-control study. Level of Evidence: Level 4. Methods: Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while sleep quantity and IIV were assessed using the Consensus Sleep Diary. Initially, the PSQI and additional questions regarding sport participation habits were completed by 138 participants (65 students, 73 student-athletes). From within this sample, 44 participants were recruited to complete the sleep diary for a period of 14 days. Results: The mean PSQI score was 6.89 ± 3.03, with 65% of the sample identified as poor sleepers, but no difference was observed between students and student-athletes. Analysis of sleep patterns showed only possibly to likely small differences in sleep schedule, sleep onset latency, and subjective sleep quality between groups. IIV analysis showed likely moderate to possibly small differences between groups, suggesting more variable sleep patterns among student-athletes. Conclusion: This study highlights that sleep issues are prevalent within the university student population and that student-athletes may be at greater risk due to more variable sleep patterns. Clinical Relevance: University coaches should consider these results to optimize sleep habits of their student-athletes.

298 Sleep Stability and Academic Performance in College Students

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A119-A119
Author(s):  
Alexandria Reynolds ◽  
Madelynn Shell
Keyword(s):  
College Students ◽  
Academic Performance ◽  
Longitudinal Study ◽  
Sleep Quality ◽  
Transition To College ◽  
Sleep Efficiency ◽  
Poor Sleep ◽  
Sleep Patterns ◽  
Sleep Habits ◽  
Time Points

Abstract Introduction There is considerable research demonstrating poor sleep patterns in college students; however, few studies actually examine sleep stability over a typical undergraduate career. Considering that the transition to college involves significant shifts in independence and potentially creating a foundation of lifelong behavioral patterns, it is important to identify whether these poor sleep patterns change throughout college. Additionally, studies show that shorter sleep duration predicts poorer academic performance. In the current study, it was expected that students would report poor sleep on average, and that poorer sleep would predict worse academic performance. Methods Participants included 27 full-time first-year undergraduate students who completed an online survey every spring for four years to examine sleep habits as part of a larger longitudinal study on the transition to college at a small liberal arts school. The Pittsburgh Sleep Quality Index was used to assess total sleep time (TST), sleep efficiency, and quality; the Epworth Sleepiness Scale (ESS) was used to determine sleepiness. Semester GPA was obtained via college registrar records. Results Repeated measures ANOVAs revealed no differences in participants’ sleep variables (TST, sleep quality, sleep efficiency, and sleepiness) across all four time points. Average TST was 6.85 hours per night, and overall sleep quality (PSQI) was poor (M = 6.12). Mean sleep efficiency was 86.70%; mean ESS score was 5.35. Preliminary analyses revealed no significant differences between GPA values over the course of the four years; sleep factors did not predict GPA. Conclusion Overall, students reported short sleep, poor sleep quality, decent sleep efficiency, and borderline higher than normal daytime sleepiness. However, sleep factors and GPA were stable over all time points. These results suggest that poor sleep habits start early and continue throughout students’ college career, as opposed to developing throughout college, or starting out poor and improving. Surprisingly, preliminary results indicated that sleep factors did not predict academic performance. Limitations include subjective sleep assessments, limited testing, and small sample size; however, this longitudinal study sheds interesting light on the general sleep patterns of college students over the course of their entire academic career. Support (if any) None.

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