scholarly journals PEAR model and sleep outcomes in dementia caregivers: influence of activity restriction and pleasant events on sleep disturbances

2011 ◽  
Vol 23 (9) ◽  
pp. 1462-1469 ◽  
Author(s):  
Raeanne C. Moore ◽  
Alexandrea L. Harmell ◽  
Elizabeth Chattillion ◽  
Sonia Ancoli-Israel ◽  
Igor Grant ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Sleep Disturbance ◽  
High Activity ◽  
Sleep Latency ◽  
Subjective Sleep Quality ◽  
Activity Restriction ◽  
Subjective Sleep ◽  
Quality Sleep ◽  
Low Activity ◽  
Pleasant Events

ABSTRACTBackground:Sleep disturbance is a common consequence of providing care to a loved one with Alzheimer's disease (AD). We explored the usefulness of the Pleasant Events and Activity Restriction (PEAR) model for predicting multiple domains of sleep disturbance.Methods:Our sample consisted of 125 spousal AD caregivers. Participants completed the Pittsburg Sleep Quality Index (PSQI) and were questioned regarding the frequency with which they engaged in pleasant events and the extent to which they felt restricted in engaging in social and recreational activities in the past month. Participants were classified into one of three groups: HPLR = High Pleasant Events + Low Activity Restriction (= reference group; N = 38); HPHR/LPLR = either High Pleasant Events + High Activity Restriction or Low Pleasant Events + Low Activity Restriction (N = 52); and LPHR: Low Pleasant Events + High Activity Restriction (N = 35). These three groups were compared on the seven subscales of the PSQI.Results:Significant differences were found between the HPLR and LPHR groups on measures of subjective sleep quality, sleep latency, habitual sleep efficiency, sleep disturbance, and daytime dysfunction. Additionally, significant differences were found between the HPLR and HPHR/LPLR groups on subjective sleep quality, sleep latency, and habitual sleep efficiency, and between the HPHR/LPLR and LPHR groups on sleep disturbance and daytime dysfunction.Conclusions:This study provides broad support for the PEAR model and suggests that interventions focusing on behavioral activation may potentially provide benefits to non-affective domains including sleep.

Research Between Sleep Quality and Interpersonal Sensitivity of Chinese College Students

2020 ◽  
Author(s):  
Haiying Tang ◽  
Bao Guo ◽  
Yanzhi Lang
Keyword(s):  
College Students ◽  
Sleep Quality ◽  
Sleep Disorder ◽  
Sleep Latency ◽  
Sleep Time ◽  
Interpersonal Sensitivity ◽  
Subjective Sleep Quality ◽  
Chinese College Students ◽  
Subjective Sleep ◽  
Quality Sleep

Abstract Background To investigate and to explore the relationship between sleep quality and interpersonal sensitivity of Chinese college students.Method During the period from April 2019 to May 2019, the university students from five universities in Shanxi Province of China were selected by occasional sampling method. The research has received permission from Research Ethics Committee of ShanXi Medical University(2016010). A cross-sectional survey was conducted with the Chinese version of Pittsburgh Sleep Quality Index (PSQI), Symptom Checklist 90 (SCL-90) and the self-designed questionnaire. SPSS 25.0 was used for statistical analysis. Results A total of 901 college students were investigated. The average score of interpersonal sensitivity was 17.72±6.46, and 9.0% of college students were in the state of interpersonal sensitivity. Grade and specialty are the influencing factors of interpersonal sensitivity (p<0.05). The total PSQI score was 4.43±2.56. 28.0% of college students had poor sleep quality. Major was the influencing factor of sleep quality (p<0.001). In the multiple linear regression models, we found that daytime dysfunction, sleep disorder, subjective sleep quality, sleep latency and sleep time were the main factors affecting interpersonal sensitivity.Conclusions The higher the PSQI score, the higher the interpersonal sensitivity score. Among the dimensions of sleep quality, daytime dysfunction, sleep disorder, subjective sleep quality, sleep latency and sleep time can affect interpersonal sensitivity.

scholarly journals Association of Qi-stagnation and Subjective Sleep Characteristics with Mild Cognitive Impairment Among Non-depressed Elderly in Community: A Cross-Sectional Study

2021 ◽  
Author(s):  
Zhizhen Liu ◽  
Jingsong Wu ◽  
Youze He ◽  
Jingnan Tu ◽  
Lei Cao ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Impairment ◽  
Sleep Quality ◽  
Sleep Disturbance ◽  
Quality Index ◽  
Pittsburgh Sleep Quality Index ◽  
Sleep Latency ◽  
Subjective Sleep Quality ◽  
Subjective Sleep ◽  
Qi Stagnation

Abstract Objective: Depression and sleep disturbance is commonly reported in patients with mild cognitive impairment (MCI). However, it remains unclear whether Qi-stagnation is still a risk factor for MCI before the older adults suffer from depression. The purpose of this study was to examine the association between Qi-stagnation and subjective sleep quality with MCI among non-depressed elderly in the Chinese community.Methods: A simple random sampling method was used to abstract research subjects from 34 community elderly day care centers in Fuzhou city based on their electronic health records from March 2019 to December 2020. Intensive face-to-face interviews were conducted using tools such as Montreal cognitive function assessment, AD8 dementia screening questionnaire, Pittsburgh Sleep Quality Index, and TCM constitution assessment scale, among others to analyze the proportion of older adults with MCI who suffer from sleep disturbance and Qi-stagnation in the community. Multi-factor logistical regression was employed to analyze the association among subjective sleep quality, TCM constitution, and MCI.Results: A total of 1,268 subjects were investigated and 1,071 cases were included in this study, among which 314 cases were of MCI patients, with a morbidity of 29.3%. The proportion of individuals having Qi-deficiency (12.4%) and Qi-stagnation (11.1%) was higher in MCI patients than in the controls with normal cognitive function (P<0.05). After adjusting for age, gender, and years of education, the probability of the old with Qi-deficiency and Qi-stagnation suffering from MCI was 1.559 times [95% confidence interval (CI): 1.009–2.407] and 1.706 times (95% CI: 1.078–2.700) higher than that of the older adults without Qi-deficiency and Qi-stagnation, respectively. In the Pittsburgh sleep quality index (PSQI) scale, individuals with MCI had poorer subjective sleep quality (Z=-3.404, P=0.001), longer sleep latency (Z=-3.398, P=0.001), shorter sleep duration (Z=-2.237, P=0.025), and aggravated daytime dysfunction (Z=-3.723, P<0.001) compared with those without MCI. The intergroup differences showed no statistical significance in the three dimensions including habitual sleep efficiency, sleep disturbance, and hypnotics between groups. The results of multi-factor logistical regression showed that sleep latency [odds ratio (OR)=1.168, 95% CI: 1.016–1.342], daytime dysfunction (OR=1.261, 95% CI: 1.087–1.463), and Qi-stagnation (OR=1.449, 95% CI: 1.022–2.055) were the risk factors for MCI; the OR for older adults with sleep disturbance and Qi-stagnation suffering from MCI was 2.581 (95% CI 1.706–3.907).Conclusion: MCI patients have a higher incidence of sleep disorders and Qi-stagnation, and may show specific changes in their daytime and nighttime sleep characteristics, with the specific manifestations such as difficulty in falling asleep, easily waking up at night/ early morning, and daytime dysfunction, among others.

scholarly journals Correlation Between Sleep Quality And Quality Of Life Among Hypertensive Adults In A Mountainous Area Of Hubei Province, China

2020 ◽  
Author(s):  
Li Ran ◽  
Xuyu Chen ◽  
Mengying Li ◽  
Qi Chen ◽  
Yupeng Zhang ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Correlation Analysis ◽  
Sleep Quality ◽  
Sleep Disturbance ◽  
Life Quality ◽  
Mountainous Area ◽  
Subjective Sleep Quality ◽  
Subjective Sleep ◽  
Quality Sleep

Abstract Background: Hypertension is one of the most common and easy paroxysm diseases. Inadequately controlled hypertension has been related to poor sleep quality, which would be associated with worsening quality of life.Methods: A descriptive analyses was conducted to describe social demographic factors, while ANOVA and t-test were carried out to compare scores between different groups. The total score of life quality (MCS and PCS) was used as the dependent variable (Y), and the dimensions of sleep quality were used as the independent variable (X) for multiple line regression analysis (Stepwise) to evaluate the correlation between sleep quality and life quality.Results: The results of group comparison showed that the total PSQI score was significant at people’s residence (P<0.01). Correlation analysis indicated that subjective sleep quality, sleep disturbance, daytime dysfunction, age, concomitant diseases, and years of diagnosed hypertension had a significant association with the PCS scores (P<0.05 for all). Subjective sleep quality, sleep disturbance, daytime dysfunction, and monthly income had a significant association with the MCS scores (P<0.05 for all). Conclusion: The correlation analysis shows that sleep quality of hypertensive patients is related to quality of life. Considering the close relation among hypertension, sleep quality, and life quality, possible interventions like sleep hygiene was appealed to relieve hypertensive symptoms, promote sleep quality, and increase life quality.Trial registration: 2018-1602000-03-02

scholarly journals Prevalence and Determinants of Bad Sleep Perception among Italian Children and Adolescents

2020 ◽  
Vol 17 (24) ◽  
pp. 9363
Author(s):  
Serena Malloggi ◽  
Francesca Conte ◽  
Giorgio Gronchi ◽  
Gianluca Ficca ◽  
Fiorenza Giganti
Keyword(s):  
Sleep Quality ◽  
Depressed Mood ◽  
Behavioral Problems ◽  
Sleep Disturbances ◽  
Sleep Problems ◽  
Sleep Latency ◽  
Sleep Habits ◽  
Subjective Sleep Quality ◽  
Quality Perception ◽  
Subjective Sleep

Although sleep problems at young ages are well investigated, the prevalence of bad sleepers and the determinants of sleep quality perception remain unexplored in these populations. For this purpose, we addressed these issues in a sample of children (n = 307), preadolescents (n = 717), and adolescents (n = 406) who completed the School Sleep Habits Survey, addressing sleep quality perception, sleep habits, sleep features, daytime behavior and sleep disturbances, circadian preference, and dreaming. The sample was split in “good sleepers” and “bad sleepers”, based on the answer to the question item assessing overall subjective sleep quality. Being a bad sleeper was reported by 11.7% of the sample, with significant between-groups differences (children: 8.3%; preadolescents: 11.3%; adolescents: 15.3%; p = 0.01). At all ages, relative to good sleepers, bad sleepers showed higher eveningness, sleepiness, and depression, longer sleep latency, more frequent insufficient sleep, nocturnal awakenings, sleep–wake behavioral problems, and unpleasant dreams (all p’s ≤ 0.01). Sleep quality perception was predicted: in children, by depressed mood, eveningness, and unpleasant dreams (all p’s ≤ 0.01); in preadolescents, by sleep latency, awakening frequency, depressed mood, sufficiency of sleep, and unpleasant dreams (all p’s < 0.01); in adolescents, by awakening frequency, depressed mood, and sufficiency of sleep (all p’s < 0.001). In children, bad subjective sleep quality appears to be mainly determined by daytime psychological features, for example, depressed mood, whereas at later ages, sleep characteristics, such as frequent awakenings, add to the former determinants. This could depend on (a) the appearance, with increasing age, of objective sleep modifications and (b) a greater attention paid by adolescents to their sleep characteristics.

Sleep Quality and Cognitive Function in the Elderly Population

2020 ◽  
Author(s):  
Seyed Valiollah Mousavi ◽  
Elham Montazar ◽  
Sajjad Rezaei ◽  
Shima Poorabolghasem Hosseini
Keyword(s):  
Regression Analysis ◽  
Cognitive Function ◽  
Sleep Quality ◽  
Sleep Disorders ◽  
Elderly Population ◽  
Sleep Latency ◽  
The Elderly ◽  
Poor Sleep ◽  
Subjective Sleep Quality ◽  
Subjective Sleep

Background and Objective: Physiological process of sleep is considered as one of the influential factors of human’s health and mental functions, especially in the elderly. This research aimed at studying the association between sleep quality and the cognitive functions in the elderly population. Materials and Methods: A total of 200 elderly people (65 years and older) who were the members of retirees associa-tion in Mashhad, Iran, participated in this cross-sectional study. The participants were asked to answer the questionnaire of Pittsburgh Sleep Quality Index (PSQI) and Montreal Cognitive Assessment (MoCA) test. Correlation between the total scores of PSQI and MoCA was evaluated by Pearson correlation coefficient. In order to predict the cognitive func-tion based on different aspects of PSQI, multiple regression analysis by hierarchical method was used after removing confounding variables. Results: A significant association was found between PSQI and MoCA (P < 0.001, r = -0.55) suggesting that the com-ponents of use of sleeping medication (P < 0.001, r = -0.47), sleep disorders (P < 0.001, r = -0.37), sleep latency (P < 0.001, r = -0.34), subjective sleep quality (P < 0.001, r = -0.32), sleep duration (P < 0.001, r = -0.27), sleep effi-ciency (P < 0.001, r = -0.26), and daytime dysfunction (P < 0.001, r = -0.15) had significant negative correlation with cognitive function, and the four components of subjective sleep quality (P = 0.010, β = -0.15), sleep latency (P = 0.040, β = -0.13), sleep disorders (P = 0.010, β = -0.26), and use of sleeping medication (P = 0.010, β = -0.26) played a role in prediction of cognitive function in regression analysis. Conclusion: Poor sleep quality, sleep latency, insomnia, sleep breathing disorder, and use of sleeping medication play a determining role in cognitive function of the elderly. Thus, taking care of the sleep health is necessary for the elderly.

scholarly journals Subjective sleep quality and association with depression syndrome, chronic diseases and health-related physical fitness in the middle-aged and elderly

2021 ◽  
Author(s):  
Min-Fang Hsu ◽  
Kang-Yun Lee ◽  
Tsung-Ching Lin ◽  
Wen-Te Liu ◽  
Shu-Chuan Ho
Keyword(s):  
Depressive Symptoms ◽  
Physical Fitness ◽  
Sleep Quality ◽  
Chronic Diseases ◽  
Sleep Latency ◽  
Middle Aged ◽  
Subjective Sleep Quality ◽  
Subjective Sleep ◽  
Self Rated Health ◽  
Health Related

Abstract Background: As a complex phenomenon, sleep quality is difficult to objectively define and measure, and multiple factors related to sleep quality, such as age, lifestyle, physical activity, and physical fitness, feature prominently in older adult populations. The aim of the present study was to evaluate subjective sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and to associate sleep quality with health-related physical fitness factors, depressive symptoms, and the number of chronic diseases in the middle-aged and elderly.Methods: We enrolled a total of 283 middle-aged and elderly participants from a rehabilitation clinic or health examination department. The PSQI was used to evaluate sleep quality. The health-related fitness assessment included anthropometric and physical fitness parameters. Depressive symptoms were measured with the Center for Epidemiologic Studies Depression Scale (CES-D) short form. Data were analyzed with SPSS 18.0, and descriptive statistics and logistic regression analysis were used for the analyses.Results: Overall, 27.9% of participants in this study demonstrated bad sleepers (with a PSQI score of >5), 10.2% of study participants frequently used sleep medication to help them fall asleep, and 6.0% reported having significant depressive symptoms (with a CES-D score of ≥10). There are two major findings: (1) depression symptoms, the number of chronic diseases, self-rated health, and arthritis were significantly associated with a poor sleep quality, and (2) the 2-min step test was associated with longer sleep latency. These results confirmed that the 2-min step was associated with a longer sleep latency among the health-related physical fitness items.Conclusions: Our study found that depressive syndrome, chronic disease numbers, a poor self-rated health status, and arthritis were the main risk factors that influenced subjective sleep quality.

scholarly journals Poor Sleep and Decision-Making Disturbance Are Associated With Suicidal Ideation in Pre-natal Depression

2021 ◽  
Vol 12 ◽  
Author(s):  
Ciqing Bao ◽  
Ling Xu ◽  
Weina Tang ◽  
Shiyu Sun ◽  
Wenmiao Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Suicidal Ideation ◽  
Sleep Quality ◽  
Sleep Disturbance ◽  
Poor Sleep ◽  
Healthy Controls ◽  
Subjective Sleep Quality ◽  
Subjective Sleep ◽  
Post Natal Depression

Although many risk factors for suicidal ideation have been identified, few studies have focused on suicidal ideation and pre-natal depression. The purpose was to investigate the relationship between decision-making (DM) dysfunction and sleep disturbance on suicidal ideation in pre-natal depression. Participants included 100 women in the third trimester of pregnancy, including pregnant women with pre-natal depression who had recent suicidal ideation (n = 30), pre-natal depression without SI (n = 35) and healthy controls (n = 35). The Iowa Gambling Task (IGT) was used to evaluate the DM function and the Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep index. The Edinburgh Post-natal Depression Scale (EPDS) was used to assess suicidal ideation and the seriousness of depression. Overall, the two groups with pre-natal depression showed worse sleep quality and decreased DM function compared with healthy controls. The pre-natal depression with suicidal ideation group showed a significantly higher score in subjective sleep quality and a lower score in block 5 of IGT than the pre-natal depression without suicidal ideation group. Further correlation analysis showed that suicidal ideation positively correlated with subjective sleep quality, sleep duration, and daytime function, and negatively correlated with IGT scores. Sleep disturbance and impaired DM function may be risk factors for suicidal ideation in pre-natal depression.

scholarly journals Investigating the Sleep–Pain Relationship in Youth with Sickle Cell Utilizing mHealth Technology

2019 ◽  
Vol 44 (3) ◽  
pp. 323-332 ◽  
Author(s):  
Cecelia R Valrie ◽  
Rebecca L Kilpatrick ◽  
Kristen Alston ◽  
Krystal Trout ◽  
Rupa Redding-Lallinger ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Sickle Cell ◽  
Multilevel Models ◽  
Sleep Latency ◽  
Pain Severity ◽  
Future Research ◽  
Subjective Sleep Quality ◽  
Subjective Sleep ◽  
Ecological Momentary ◽  
Nighttime Sleep

Abstract Objectives The current study utilized mHealth technologies that were objective (e.g., sleep actigraphy and pulse oximetry) and time-sensitive (e.g., ecological momentary assessments [EMAs]) to characterize sleep in youth with sickle cell disease (SCD) and investigate the relationships between sleep variables and pain. It also investigated the influence of age on sleep and the sleep–pain relationship. Methods Eighty-eight youth with SCD (aged 8–17 years) were recruited from three regional pediatric SCD clinics. Youth completed twice daily EMAs for up to 4 weeks to assess nighttime subjective sleep quality and daily pain. They also wore a sleep actigraph for 2 weeks to assess sleep duration, sleep efficiency, and sleep latency, and a wrist-worn pulse oximeter for two nights to assess whether they had sleep apnea. Multilevel models were calculated predicting daily SCD pain using the sleep variables, age, and the interaction between age and the sleep variables. Results None of the sleep variables were related to one another. Poor subjective sleep quality during the night was related to high pain severity the next day, and high pain was related to poor subjective sleep quality that night. Older age was associated with poorer subjective sleep quality, shorter duration of nighttime sleep, and high sleep latency. Also, findings indicated that as age increased, the strength of the relationship between poor continuous subjective sleep quality and high pain severity increased. Conclusions Future research is needed to examine possible mechanisms connecting subjective sleep quality to high pain.

scholarly journals 697 Sleep Quality After COVID-19 Infection

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A272-A273
Author(s):  
Benjamin Hackett ◽  
Varun Badami ◽  
Sunil Sharma ◽  
Robert Stansbury
Keyword(s):  
Sleep Quality ◽  
Sleep Latency ◽  
Acute Infection ◽  
Secondary Data ◽  
Poor Sleep ◽  
Subjective Sleep Quality ◽  
Telephone Surveys ◽  
Subjective Sleep ◽  
Delta Sleep ◽  
Novel Virus

Abstract Introduction COVID-19 has proven to be a novel virus with significant complications to an expanding number of body systems. Hallmark characteristics of COVID-19 include substantial inflammatory response which has been linked to sleep dysregulation in previous studies. We examined the change in sleep quality after acute COVID-19 infections requiring hospitalization. Methods We performed a retrospective, single-center observational study of 20 patients with acute COVID-19 infection requiring hospitalization. Eligible patients were contacted and completed telephone surveys of the Pittsburgh Sleep Quality Index (PSQI) prior to and 1 month after hospital discharge. A score of ≥5 was indicative of poor sleep quality. Secondary data were collected from EMR. Results The mean PSQI prior to COVID-19 infection was 6.1, worsening to 10.3 one month after acute infection, denoting a delta-PSQI of 4.2 (p = 0.0004). There were noted statistically significant differences in certain components of the PSQI including: subjective sleep quality 0.8 to 1.7 (delta 0.9, p = 0.0003), sleep latency 1.25 to 1.85 (delta 0.6, p = 0.03), sleep disturbance 1.05 to 1.5 (delta 0.45, p = 0.0009), and daytime dysfunction 0.3 to 1.45 (delta 1.15, p = 0.0005). Sleep latency and daytime dysfunction accounted for the most change. Two groups declared themselves with 6 of the 20 patients having improvement/no change in PSQI, and 14 having worsening. Between these groups certain differences were seen including: Pre-infection PSQI 9.67 vs 4.57 (p = 0.009), delta global PSQI -0.83 vs 6.36 (p &lt; 0.001), delta subjective sleep quality 0.17 vs 1.2 (p = 0.002), delta sleep latency -0.3 vs 1 (p = 0.01), delta sleep duration -0.3 vs 0.93 (p = 0.02), delta sleep efficiency -0.3 vs 0.71 (p = 0.02), and delta daytime dysfunction 0.17 vs 1.57 (p = 0.006). Conclusion In our study of patients hospitalized for COVID-19 infection specific components of sleep were different following infection. Sleep latency and daytime dysfunction contributed the most to PSQI change. Two groups declared themselves based on PSQI improvement vs worsening. Those with poor sleep prior to infection continued to have poor sleep, while those without prior sleep troubles developed worsened sleep quality. Support (if any):

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