Perceived Stress and Sleep Quality in Midlife and Later: Controlling for Genetic and Environmental Influences

Stress and sleep quality in palliative care

OBJECTIVES

Palliative care patients experience problems in sleep quality due to stress, chronic diseases, and physical and psychosocial problems. Our aim is to determine the perceived stress and sleep quality levels of our palliative care patients and their related factors.

METHODS

This cross-sectional, descriptive research was conducted between November 2023 and February 2024 at a palliative clinic located in Ankara, Turkey, that is affiliated with a training and research hospital. In the research, a data form, the Perceived Stress Scale and the Pittsburgh Sleep Quality Index were used as data collection instruments. Kolmogorov-Smirnov and Shapiro-Wilk tests, and Mann-Whitney U and Kruskal-Wallis H tests were used for the analysis.

RESULTS

The total score on the Perceived Stress Scale was determined to be 35.81±7.45. The total score for Pittsburgh Sleep Quality Index was 13.20±3.20. Significant relationships were found between insufficient self-efficacy scores and habitual sleep efficiency, daytime dysfunction and total Pittsburgh Sleep Quality Index scores. Significant relationships were found between insufficient self-efficacy scores and sleep latency, habitual sleep efficiency, daytime dysfunction and total Pittsburgh Sleep Quality Index scores.

CONCLUSIONS

This study determined that certain descriptive characteristics of palliative care patients affect their sleep quality and perceived stress level, as well as that some components of sleep quality and perceived stress level are related. Palliative care is holistic in nature, encompassing symptom management; therefore, the importance of patients' psychosocial integrity should not be overlooked. Consequently, identifying components that may hinder symptom management and addressing the patient comprehensively will be crucial.

Study on the Profiles of Sleep Disorders, Associated Factors, and Pathways Among Gynecological Cancer Patients - A Latent Profile Analysis

Background

Gynecological cancer generally refers to malignant tumors in gynecology, commonly including cervical cancer, endometrial cancer, and ovarian cancer. Patients with gynecological cancer often suffer from sleep disorders after clinical treatment. Except for serious sleep disorders, female characteristics, family roles, and feudal beliefs make their self-stigma at a medium to high level, leading to huge pressure. This study aims to identify potential categories of sleep disorders, and analyze the relationship between self-stigma, perceived stress, and sleep disorders.

Methods

A cross-sectional study was conducted in 2021-2022. Two hundred and two patients' data were collected from ShengJing Hospital Affiliated to China Medical University in Liaoning, Shenyang by using paper questionnaires for face-to-face surveys. The survey tools included the Pittsburgh Sleep Quality Index (PSQI), the Perceived Stress Scale (PSS), and the Social Impact Scale (SIS). Potential profile analysis (LPA), multiple logistic regression analysis, and structural equation modeling (SEM) were performed by Mplus 8.3, SPSS 26.0, and Amos 24.0 statistical tools, respectively.

Results

Three latent patterns of sleep disorders were found: "Good Sleep group (42.5%)", "Sleep Deficiency group (32.4%)", and "Sleep Disturbance group (25.1%)". Patients with high perceived stress were more likely to report a moderate (OR=1.142, 95% CI: 1.061-1.230) or high (OR=1.455, 95% CI: 1.291-1.640) level of sleep disorders. Self-stigma did not have a direct effect on sleep disorders (0.055, P>0.05), but it could have indirect effect on sleep disorders through perceived stress (0.172, P<0.01).

Conclusion

The perceptions of sleep disorders among gynecological cancer patients varies and exhibits individual differences. Gynecological cancer patients who feels alienated or discriminated may cause high pressure. This internal pressure can exacerbate sleep disorders.

The importance of female reproductive history on self-reported sleep quality, mood, and urogenital symptoms in midlife

OBJECTIVE

This study investigated the importance of reproductive history on somatic and psychological symptoms in midlife women.

METHODS

A total of 503 women from 39 to 65 years of age were recruited from different localities in Slovakia. These were interviewed about their reproductive and menstrual history, sociodemographic background, and lifestyle and health status after submitting pretested questionnaires. All variables were measured by self-reporting, and multivariable logistic and ordinal regression analyses tested the associations.

RESULTS

Women who experienced miscarriage had a greater likelihood of waking early and then sleeping poorly, and they also felt unattractive in midlife. Moreover, women with two or more miscarriages were four times more likely to experience this sleep symptom than those without miscarriage (odds ratio [OR], 4.2; 95% confidence interval [CI], 1.70-10.38; P = 0.002). In addition, women with one or two children suffered significantly less often with severe depressed mood and lack of enjoyment than women with three and more children (lack of enjoyment: with one child, the OR was 0.39 [95% CI, 0.16-0.96; P = 0.041]; with two children, the OR was 0.47 [95% CI, 0.23-0.97; P = 0.040]; depressed mood: with one child, the OR was 0.32 [95% CI, 0.12-0.84; P = 0.021]). Finally, the premenopausal and perimenopausal women were less likely to experience severe vaginal dryness than those in postmenopause.

CONCLUSIONS

This cross-sectional pilot study suggests that women's reproductive history, as determined by parity and miscarriage, may be relevant to their midlife health and well-being. Future research is warranted.

Symptoms of sleep problems and adherence to dietary guidelines in older women: evidence from the Australian Longitudinal Study on Women's Health

OBJECTIVE

This study examined adherence to dietary guidelines and symptoms of sleep problems (e.g. taking a long time to fall sleep or waking up early) and their associations in a sample of older Australian women (68-73 years of age).

DESIGN

This was a population-based cross-sectional study. Adherence to the dietary guidelines was measured using a validated FFQ and reported as a diet quality score. Symptoms of sleep problems were measured using five questions and a total score was derived. Multivariate linear regression was used to investigate the association between these outcomes, adjusted for the potential confounding influence of demographic (i.e. age and marital status) and lifestyle (i.e. physical activity, stress, alcohol intake, sleep medication use) variables.

SETTING

Respondents from the 1946-1951 cohort of the Australian Longitudinal Study on Women's Health who completed Survey 9 were included.

PARTICIPANTS

Data from n 7956 older women (mean age ± sd: 70·8 ± 1·5) were included.

RESULTS

70·2 % reported having at least one symptom and 20·5 % had between 3 and 5 symptoms of sleep problems (mean score ± sd: 1·4 ± 1·4, range 0-5). Adherence to dietary guidelines was poor with an average diet quality score of 56·9 ± 10·7 (range 0-100). Better adherence to dietary guidelines was associated with fewer sleep problem symptoms (β: -0·065, 95 % CI: -0·012, -0·005) and remained significant after adjusting for confounding influences.

CONCLUSIONS

These findings support the evidence that adherence to dietary guidelines is associated with symptoms of sleep problems in older women.

From Passion to Abyss: The Mental Health of Athletes during COVID-19 Lockdown

The outbreak and pandemic of COVID-19 forced people into extreme isolation and social distancing, with significant limitations on various activity sectors, including sports. This study aimed to assess the psychological health status of athletes during sports lockdown. Additionally, we intend to verify the mediating role of sleep disorders in stress perception and subjective happiness. Our sample was composed of 1492 Portuguese athletes from eight different team sports. During sports lockdown, athletes were found to have high stress levels and low subjective happiness levels and experience sleep disorders. Finally, these results conclude that sports lockdowns harm athletes’ psychological health and well-being. Pay cuts to athletes are an extra stress factor that exacerbate these adverse effects on psychological health. Finally, sleep is a mediator variable between stress perception and subjective happiness levels. This study’s significant contributions, limitations, and future directions are discussed in the conclusion.

Monitoring Internal Training Intensity Correlated with Neuromuscular and Well-Being Status in Croatian Professional Soccer Players during Five Weeks of the Pre-Season Training Phase

This study aimed to investigate the changes in internal training intensity, well-being, and countermovement jump (CMJ) performance and to determine their relationship across five weeks of the pre-season training phase in professional soccer players. A total of 22 professional male soccer players (age = 21.7 ± 4 years, body height = 185.9 ± 6.3 cm, body weight = 79 ± 6.3 kg, BMI = 22.8 ± 1.4 kg·m−2; VO2max = 52.9 ± 3.2) from the Croatian Second League voluntary participated in this study. The players spent 2230 ± 117 min in 32 technical/tactical and strength/conditioning training sessions, mostly at the low intensity zone (61%), and played 8 friendly matches at a high intensity (>90%). A one-way repeated measure of analysis ANOVA revealed a significant difference between weeks in CMJ performance (F(1,22) = 11.8, p < 0.001), with CMJ height in weeks 4 and 5 being likely to very likely higher than that noted in week 1. Moreover, significant differences between weeks were found in all internal training intensity measures (average [F(1,22) = 74.8, p < 0.001] and accumulated weekly internal training intensity [F(1,22) = 55.4, p < 0.001], training monotony [F(1,22) = 23.9, p < 0.001], and training strain [F(1,22) = 34.5, p < 0.001]). Likewise, differences were observed for wellness status categories (fatigue [F(1,22) = 4.3, p = 0.003], sleep [F(1,22) = 7.1, p < 0.001], DOMS [F(1,22) = 5.7, p < 0.001], stress [F(1,22) = 15.6, p < 0.001]), mood [F(1,22) = 12.7, p < 0.001], and overall well-being status score (F(1,22) = 13.2, p < 0.001). Correlation analysis showed large negative correlations between average weekly internal training intensity and fatigue (r = −0.63, p = 0.002), DOMS (r = −0.61, p = 0.003), and WBI (r = −0.53, p = 0.011). Additionally, fatigue was significantly associated (large negative correlation) with accumulated weekly internal training intensity (r = −0.51, p = 0.014) and training strain (r = −0.61, p = 0.003). Small, but non-significant, correlations were found between CMJ performance and wellness status measures. These findings highlight the utility and simplicity of monitoring tools to improve athletes’ performance.

Prevalence and correlates of sleep quality in the Chinese college students with migraine: a cross-sectional study

Background: Migraineurs are often plagued by sleep disorders. The university student population is high in number and is more vulnerable to migraines. However, no study has analyzed the sleep quality of students with migraine and related contributing factors. Objective: We used the Pittsburgh Sleep Quality Index (PSQI) scale to assess the sleep of migraine patients among college students and to explore the influencing factors of sleep quality. Methods: We performed primary screening for migraine using the ID-migraine screening, and further assessed headache characteristics, sleep, anxiety, depression, and mobile phone addiction in college students with positive primary screening, then diagnosed migraine according to the third edition of the International Classification of Headache Disorders (ICHD-3). Finally, we analyzed the factors influencing sleep quality using Binary Logistic Regression Analysis. Those with scores greater than 5 points on the PSQI scale were believed to have poor sleep quality. Results: The prevalence of migraine was 6.6%. A total of 545 migraineurs were eventually included in the analysis, the incidence of poor sleep quality was 64.04%. The three factors of experiencing aura (OR = 2.966, 95%CI = 1.756-5.010, P < 0.05), anxiety (OR = 2.778, 95%CI = 1.434-5.382, P < 0.05), and high Mobile phone addiction index (MPAI) score (OR = 1.025, 95%CI = 1.002-1.049, P < 0.05) contributed enormously to poor sleep quality. Moreover, the factors of aura symptoms (OR = 3.796, 95%CI = 2.041-7.058, P < 0.05), anxiety (OR = 3.146, 95%CI = 1.473-6.719, P < 0.05), and MPAI score (OR = 1.028, 95%CI = 1.002-1.054, P < 0.05) influenced the sleep quality of female migraineurs rather than male migraineurs. Conclusions: The incidence of poor sleep quality is high among university students with migraine. Aura symptoms, anxiety, and high MPAI score influence the sleep quality of migraineurs, especially females. The proposal of prevention and intervention measures is of great importance to the physical and mental health of students with migraine. Clinical Trial Registration: identifier ChiCTR1800014343.

Effects of Work Stress and Period3 Gene Polymorphism and Their Interaction on Sleep Quality of Non-Manual Workers in Xinjiang, China: A Cross-Sectional Study

Work stress has been found to be associated with sleep quality in various occupational groups, and genetic factors such as variable number tandem repeat polymorphism in the Period3 (Per3) gene also influence the circadian sleep-wake process. Therefore, the present study aimed to evaluate the sleep quality status of non-manual workers in Xinjiang, China and to analyse the effects of work stress and Per3 gene polymorphism and their interaction on sleep quality. A cluster sampling method was used to randomly select 1700 non-manual workers in Urumqi, Xinjiang. The work stress and sleep quality of these workers were evaluated using the Effort−Reward Imbalance Inventory (ERI) and the Pittsburgh Sleep Quality Index (PSQI). Next, 20% of the questionnaire respondents were randomly selected for genetic polymorphism analysis. The polymerase chain reaction-restriction fragment length polymorphism technique was used to determine Per3 gene polymorphism. The detection rate of sleep quality problems differed between the different work stress groups (p < 0.05), suggesting that non-manual workers with high levels of work stress are more likely to have sleep quality problems. Regression analysis revealed that the Per3 gene (OR = 3.315, 95% CI: 1.672−6.574) was the influencing factor for poor sleep quality after adjusting for confounding factors, such as occupation, length of service, education, and monthly income. Interaction analysis showed that Per34/5,5/5 × high work stress (OR = 2.511, 95% CI: 1.635−3.855) had a higher risk of developing sleep quality problems as compared to Per34/4 × low work stress after adjusting for confounding factors. The structural equation modelling showed no mediating effect between work stress and Per3 gene polymorphism. The results of this study show that both work stress and Per3 gene polymorphism independently affect sleep quality of nonmanual workers from Xinjiang, and the interaction between these two factors may increase the risk of sleep quality problems. Therefore, to improve sleep quality, individuals with genetic susceptibility should avoid or reduce as much as possible self-stimulation by work-related exposures such as high levels of external work stress.

Perceived Academic Stress and Depression: The Mediation Role of Mobile Phone Addiction and Sleep Quality

BACKGROUND

Although academic stress is a well-known risk factor for students' depression, little is known about the possible psychological mechanisms underlying this association. In this study, we investigated the prevalence of depression and sleep disturbance among Chinese students, examined the relationship between perceived academic stress and depression, considered if mobile phone addiction and sleep quality is a mediator of this relationship, and tested if mobile phone addiction and sleep quality together play a serial mediating role in the influence of perceived academic stress on depression.

METHOD

A cross-sectional survey was conducted among students from September to December 2018 in Heilongjiang Province, China. The final analysis included 5,109 students. Mobile phone addiction, sleep quality, and depressive symptoms were assessed using the Mobile Phone Addiction Index, Pittsburgh Sleep Quality Index, and Center for Epidemiologic Studies-Depression scales, respectively. The serial mediation model was used to analyse the relationship between perceived academic stress, mobile phone addiction, sleep quality, and depression.

RESULTS

Among all participants, the prevalence of depressive symptoms and sleep disturbance was 28.69 and 27.95%, respectively. High school students showed the highest scores of perceived academic stress (2.68 ± 1.06), and the highest prevalence of depressive symptoms (33.14%) and sleep disturbance (36.47%). The serial mediation model indicated that perceived academic stress was a significant predictor of depression (B = 0.10, SE = 0.02, 95% CI = 0.06 - 0.13). Additionally, mobile phone addiction (B = 0.08, 95% boot CI = 0.06-0.11) and sleep quality (B = 0.27, 95% boot CI = 0.22-0.33) played a mediating role between perceived academic stress and depression. Mobile phone addiction and sleep quality together played a serial mediating role in the influence of perceived academic stress on depression (B = 0.11, 95% boot CI = 0.08-0.14). Furthermore, the indirect effect (i.e., the mediating effect of mobile phone addiction and sleep quality) was significant and accounted for 64.01% of the total effect.

CONCLUSIONS

Our research results underscore the need for stakeholders-including family members, educators, and policy makers-to take preventative intervention measures to address depression among Chinese students, especially high school students.

Changes in Smoking Behavior, Stress, and Sleep Duration Among Israeli Hospital Workers During the COVID-19 Pandemic: A Cross-sectional Study

INTRODUCTION

Being on the COVID-19 frontline could negatively impact healthcare workers mental health.

AIMS AND METHODS

We examined smoking behavior changes and the association with changes in stress levels and sleeping patterns among hospital workers during the pandemic. An online survey was conducted among employees of a large tertiary medical center in Israel. Data collected included sociodemographic characteristics, smoking status, changes in smoking behavior (for smokers only), stress levels, and sleeping duration during the pandemic, perceptions of risk for COVID-19 infection and disease severity, presence of a chronic illness, COVID-19 exposure and infection status, and involvement in treating COVID-19 patients. Multinominal logistic regression modeling assessed the effects of covariates on smoking behavior change.

RESULTS

Overall, 920 healthcare workers participated. More than half (59%) reported an increase in stress and 28% reported changes in sleep duration. Thirty-five percent of current smokers (n = 132), reported smoking more. Increased stress was associated with an increase in smoking (odds ratio [OR] = 3.45, 95% confidence interval [CI] 1.2-9.4, p = .016), and an increase in sleeping hours was significantly associated with a decrease in smoking (OR = 6.42, 95% CI 1.2-32, p = .02). Among smokers who reported perceived levels of stress to be the same or slightly higher than prepandemic, a strong inverse association was observed between sleep and smoking.

CONCLUSIONS

The mental health consequences of the pandemic, specifically for health workers, could lead to negative changes in smoking behaviors. Together with offering stress-management skills and coping strategies, mental health support should target smoking behaviors and sleep disturbances.

IMPLICATIONS

A high proportion of healthcare employees working in a large tertiary medical center in Israel reported increased stress levels during the COVID-19 pandemic. Among smokers, increased stress levels were associated with increased smoking, suggesting that smoking may be a coping mechanism for COVID-19-related stress. Offering stress-management skills and coping strategies can mitigate the negative impact on health workers' smoking behavior, and reduce stress-related increases in smoking behavior.

Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model

Background

Occupational stress is associated with adverse outcomes for medical professionals and patients. In our cross-sectional study with 136 general practices, 26.4% of 550 practice assistants showed high chronic stress. As machine learning strategies offer the opportunity to improve understanding of chronic stress by exploiting complex interactions between variables, we used data from our previous study to derive the best analytic model for chronic stress: four common machine learning (ML) approaches are compared to a classical statistical procedure.

Methods

We applied four machine learning classifiers (random forest, support vector machine, K-nearest neighbors’, and artificial neural network) and logistic regression as standard approach to analyze factors contributing to chronic stress in practice assistants. Chronic stress had been measured by the standardized, self-administered TICS-SSCS questionnaire. The performance of these models was compared in terms of predictive accuracy based on the ‘operating area under the curve’ (AUC), sensitivity, and positive predictive value.

Findings

Compared to the standard logistic regression model (AUC 0.636, 95% CI 0.490–0.674), all machine learning models improved prediction: random forest +20.8% (AUC 0.844, 95% CI 0.684–0.843), artificial neural network +12.4% (AUC 0.760, 95% CI 0.605–0.777), support vector machine +15.1% (AUC 0.787, 95% CI 0.634–0.802), and K-nearest neighbours +7.1% (AUC 0.707, 95% CI 0.556–0.735). As best prediction model, random forest showed a sensitivity of 99% and a positive predictive value of 79%. Using the variable frequencies at the decision nodes of the random forest model, the following five work characteristics influence chronic stress: too much work, high demand to concentrate, time pressure, complicated tasks, and insufficient support by practice leaders.

Conclusions

Regarding chronic stress prediction, machine learning classifiers, especially random forest, provided more accurate prediction compared to classical logistic regression. Interventions to reduce chronic stress in practice personnel should primarily address the identified workplace characteristics.

Association of job stress, CLOCK gene polymorphism and their interaction with poor sleep quality

Job stress and the Circadian Locomotor Output Cycles Kaput (CLOCK) gene could affect circadian rhythm and sleep quality. The main aim of our present study was to investigate the association of job stress, CLOCK gene polymorphism and their interaction with sleep quality in a non-clinical Chinese Han population, which has not been reported to date. Using a cross-sectional design, 450 subjects were recruited in Beijing. Sleep quality was measured with the Pittsburgh Sleep Quality Index (PSQI) and job stress was measured with the Work Stress Scale. CLOCK gene rs11932595 polymorphism was genotyped in 297 blood samples. Correlation analysis showed a close but different association of high job stress with the PSQI and its components. Analysis of variance showed significant main effects of the CLOCK gene rs11932595 polymorphism. G-allele carriers had a higher score in the PSQI, sleep duration, sleep latency and sleep disturbances. Further interaction analyses showed an ordinal interaction on sleep duration, and a disordinal interaction on daytime dysfunction. Specifically, G-allele carriers had poorer sleep duration than AA homozygotes when in high job stress, while the two subgroups displayed similar sleep duration when in low job stress, conforming to the diathesis-stress model. In comparison to G-allele carriers, AA homozygotes experienced less daytime dysfunction when in low job stress whereas more daytime dysfunction when in high job stress, fitting with the differential susceptibility model. As genetic links have been revealed, our investigation might be conducive for elucidating aetiological factors for sleep quality and targets for implementing interventions to attain good sleep quality.