Self-reported sleep problems among the elderly: A latent class analysis

Latent profile analysis of depression in US adults with obstructive sleep apnea hypopnea syndrome

Objective

This study used latent profile analysis to explore the level of depression among US adults with obstructive sleep apnea hypopnea syndrome (OSAHS) symptoms and to identify different latent categories of depression to gain insight into the characteristic differences between these categories.

Methods

The data of this study were obtained from the National Health and Nutrition Examination Survey (NHANES) database, and the subjects with OSAHS symptoms were aged 18 years and older. The latent profile analysis (LPA) method was used to fit the latent depression categories in subjects with OSAHS symptoms. The chi-square test, rank sum test, and binary logistic regression were used to analyze the influencing factors of depression subgroups in subjects with OSAHS symptoms.

Results

Three latent profiles were identified: low-level (83.7%), moderate-level (14.5%) and high-level (1.8%) depression. The scores of 9 items in the high-level depression group were higher than those in the other two groups. Among them, item 4 "feeling tired or lack of energy" had the highest score in all categories.

Conclusion

Depression in subjects with OSAHS symptoms can be divided into low-level, moderate-level and high-level depression. There are significant differences among different levels of depression in gender, marital status, PIR, BMI, smoking, general health condition, sleep duration and OSAHS symptom severity.

Relationship Between Sleep Problems and Memory Impairment Among Nurses

BACKGROUND

Sleep problems are common among nurses and can lead to various health problems. Further, the relationship between multiple sleep problems and memory impairment in clinical nurses remains unknown. Therefore, in this study, we aimed to explore the relationship between sleep problems and memory impairment among nurses.

METHODS

Multistage cluster-stratified random sampling was performed from tertiary hospitals in Shandong, China. Overall, a total of 1833 nurses were included in the final analysis. The Chinese version of the Pittsburgh Sleep Quality Index and prospective and retrospective memory questionnaire were administered to the participants.

RESULTS

The sleep quality of the nurses decreased during the normal epidemic prevention and control period compared with that before the epidemic in terms of sleep duration, sleep efficiency, and sleep disturbances. Nurses who reported cumulative or specific sleep problems (e.g., high sleep latency, sleep disturbances, and daytime dysfunction) and those in the "daily disturbances" and "poor sleep quality" groups had a higher risk of memory impairment than the others.

CONCLUSION

Sleep problems might be important for memory impairment among nurses. These findings may help identify nurses at considerable risk of memory impairment in clinical practice.

Association between transition patterns of sleep problems and suicidal ideation in Chinese female nurses: A prospective study

OBJECTIVE

Suicidal ideation and sleep problems are both common in nurses. However, few longitudinal studies are available to examine the temporal association between sleep and suicidal ideation in nurses.

METHOD

Data from the Health Longitudinal Survey of Nurses in Shandong Province was analyzed, involving 623 female nurses who had completed data of concern in 2018 (T1) and 2019 (T2). Sleep problem was assessed by the Pittsburgh Sleep Quality Index, in which the transition patterns for global and specific sleep component and the cumulative number of sleep component problems were defined. Suicidal ideation was measured by the ninth item of the Patient Health Questionnaire. Binary logistic regression was used to explore the association between sleep and suicidal ideation.

RESULTS

Chronic and deteriorated global sleep problems is associated with a greater risk of suicidal ideation. For the specific component of sleep, sleep disturbance and short sleep duration are associated with a higher risk of suicidal ideation. The higher number of cumulative sleep component problems is associated with a higher risk of suicidal ideation.

CONCLUSION

Findings indicate sleep disturbance and short sleep duration may be pathways to suicidal ideation. Initiatives that target at sleep problems may be important to reduce suicidal ideation in nurses.

The sleep patterns and their associations with mental health among nursing home residents: a latent profile approach

BACKGROUND

Nursing home residents commonly experience poor sleep conditions. However, few studies have explored the potential sleep patterns among nursing home residents. This study aimed to identify the sleep patterns in nursing home residents, compare residents' characteristics across sleep patterns, and examine the relationships between sleep patterns and residents' mental health (i.e., depressive and anxiety symptoms).

METHODS

This cross-sectional study was conducted in 27 nursing homes in Jinan, China, from March to June 2018. In total, 353 participants were recruited via convenience sampling, and of which, 326 completed the survey. A latent profile analysis was performed to identify sleep patterns based on the seven dimensions of the Pittsburgh Sleep Quality Index. Bivariate analyses were conducted to compare residents' characteristics among the sleep patterns. Mixed-effects logistic regression analyses were adopted to investigate the relationships between sleep patterns and residents' mental health.

RESULTS

Three sleep patterns were identified, including 'good sleepers', 'poor sleepers without hypnotic use', and 'poor sleepers with hypnotic use'. Residents' gender, education, pain, instrumental activities of daily living, and number of chronic conditions were significantly differentiated across the sleep patterns. Compared with 'good sleepers', 'poor sleepers without hypnotic use' were significantly associated with more depressive symptoms (OR = 3.73, 95% CI = 2.09, 6.65, p < 0.001), but not with anxiety symptoms (OR = 2.04, 95% CI = 0.97, 4.29, p = 0.062); whereas 'poor sleepers with hypnotic use' had significantly more depressive (OR = 5.24, 95% CI = 2.54, 10.79, p < 0.001) and anxiety symptoms (OR = 5.02, 95% CI = 2.13, 11.83, p < 0.001).

CONCLUSIONS

This study reveals three distinct sleep patterns in nursing home residents and their significant associations with residents' mental health. These findings can inform future research to develop appropriate and tailored intervention strategies for improving sleep and promoting mental health for nursing home residents.

Characterizing Patterns of Nurses' Daily Sleep Health: a Latent Profile Analysis

BACKGROUND

Nursing is a demanding occupation characterized by dramatic sleep disruptions. Yet most studies on nurses' sleep treat sleep disturbances as a homogenous construct and do not use daily measures to address recall biases. Using person-centered analyses, we examined heterogeneity in nurses' daily sleep patterns in relation to psychological and physical health.

METHODS

Nurses (N = 392; 92% female, mean age = 39.54 years) completed 14 daily sleep diaries to assess sleep duration, efficiency, quality, and nightmare severity, as well as measures of psychological functioning and a blood draw to assess inflammatory markers interleukin-6 (IL-6) and C-reactive protein (CRP). Using recommended fit indices and a 3-step approach, latent profile analysis was used to identify the best-fitting class solution.

RESULTS

The best-fitting solution suggested three classes: (1) "Poor Overall Sleep" (11.2%), (2) "Nightmares Only" (8.4%), (3) "Good Overall Sleep" (80.4%). Compared to nurses in the Good Overall Sleep class, nurses in the Poor Overall Sleep or Nightmares Only classes were more likely to be shift workers and had greater stress, PTSD symptoms, depression, anxiety, and insomnia severity. In multivariate models, every one-unit increase in insomnia severity and IL-6 was associated with a 33% and a 21% increase in the odds of being in the Poor Overall Sleep compared to the Good Overall Sleep class, respectively.

CONCLUSION

Nurses with more severe and diverse sleep disturbances experience worse health and may be in greatest need of sleep-related and other clinical interventions.

Association between quality of life, sleep quality and mental disorders in Iranian older adults

Aging as a major challenge can affect the development and growth of countries all around the world. This study aimed to identify the subgroups of the elderly based on the quality of life (Qol), sleep quality, and common mental disorders and assess the role of demographic characteristics on the membership of participants in each latent class. This cross-sectional study was conducted on 1064 people over the age of 60 years. The sample was selected through cluster sampling in northern Iran. All participants completed six sets of checklists and questionnaires. Data analysis was performed using latent class analysis. Three latent classes were identified; namely, (1) healthy (66.8%), (2) anxious and with poor sleep quality (28.6%), and unhealthy (4.6%). Being Female significantly increased the odds of membership in classes 2 and 3 compared to class 1. Furthermore, living in urban areas increased the odds of belonging to class 2 and class 3 compared to class 1. Illiteracy was also shown to increase the odds of being in class 3 in comparison to class 1. Results from the present indicate that the co-occurrence of health problems in 33.2% of the elderly was observed in various forms. The results of this study can be used in prioritizing health programs for the elderly and emphasizing high-risk groups.

Heterogeneity in patterns of posttraumatic stress disorder symptoms and sleep disturbances among firefighters: Latent profile analyses

Firefighters are at increased risk for posttraumatic stress disorder (PTSD) symptoms and sleep disturbances due to occupational trauma exposure as well as the nature of their job (e.g., shift work, workplace stress). PTSD symptoms co-occur with sleep disturbances, including poor sleep quality, short sleep duration, and low sleep efficiency. No published studies have examined subgroups of firefighters based on PTSD symptoms and sleep disturbances. Thus, we used latent profile analysis to identify the best-fitting class solution to categorize firefighters based on endorsed PTSD symptoms and sleep disturbances and examined relations between the optimal class solution and health covariates (i.e., anger reactions, depression symptoms, emotion regulation difficulties, number of traumatic event types). The sample included 815 trauma-exposed firefighters (M_age = 38.63; 93.20% male). Results indicated three latent subgroups: High PTSD-Sleep Disturbances, Moderate PTSD-Sleep Disturbances, and Low PTSD-Sleep Disturbances. Multinomial logistic regression indicated that endorsing greater anger reactions, depression symptoms, and emotion regulation difficulties increased the chances of being in the more severe classes. Endorsing greater number of traumatic event types increased the chances of being in the Moderate vs. Low PTSD-Sleep Disturbances Classes. Findings improve our understanding of subgroups of firefighters based on PTSD and sleep disturbances and underscore the importance of addressing depression symptoms, anger management, and emotion regulation skills for firefighters reporting more severe PTSD symptoms and sleep disturbances.

Sleep profiles as a longitudinal predictor for depression magnitude and variability following the onset of COVID-19

The coronavirus disease 2019 (COVID-19) has disrupted multiple domains of life including sleep. The present study used a longitudinal dataset (N = 671) and a person-centered analytic approach - latent profile analysis (LPA) - to elucidate the relationship between sleep and depression. We used LPA to identify profiles of sleep patterns assessed by Pittsburg Sleep Quality Index (PSQI) at the beginning of the study. The profiles were then used as a predictor of depression magnitude and variability over time. Three latent profiles were identified (medicated insomnia sleepers [MIS], inefficient sleepers [IS], and healthy sleepers [HS]). MIS exhibited the highest level of depression magnitude over time, followed by IS, followed by HS. A slightly different pattern emerged for the variability of depression: While MIS demonstrated significantly greater depression variability than both IS and HS, IS and HS did not differ in their variability of depression over time. Medicated insomnia sleepers exhibited both the greatest depression magnitude and variability than inefficient sleepers and healthy sleepers, while the latter two showed no difference in depression variability despite inefficient sleepers' greater depression magnitude than healthy sleepers. Clinical implications and limitations are discussed.

Sleep heterogeneity in the third trimester of pregnancy: Correlations with depression, memory impairment, and fatigue

This study aimed to identify sleep subtypes during pregnancy using a person-centered method, explore the underlying factors of these subtypes, and investigate the associations of these subtypes with depression, memory impairment, and fatigue. Accordingly, self-report measures were administered to 1,825 pregnant women to assess demographics, prenatal factors, childhood trauma, personality traits, sleep problems, depression, memory impairment, and fatigue. Data were analyzed using latent class analysis, chi-squared tests, analysis of variance, multinomial logistical regression, and multivariate linear regression analyses. The profiles of "good sleep quality," "poor sleep efficiency," "daily disturbances," and "poor sleep quality" were identified. The results also revealed several factors underlying these subtypes that affect sleep quality: rumination, perinatal complications, high neuroticism, low resilience, history of abortion, and postgraduate education. Further, the "daily disturbances" and the "poor sleep quality" groups reported higher depression, memory impairment, and fatigue than the "good sleep quality" group. Thus, this study elucidated the heterogeneity of sleep subtypes during pregnancy in the Chinese population. Such findings may promote the development of tailored interventions for specific sleep subtypes in pregnant women.

Depressive and stress symptoms in insomnia patients predict group cognitive-behavioral therapy for insomnia long-term effectiveness: A data-driven analysis

BACKGROUND

Insomnia Disorder is characterized by high degree of phenotypic heterogeneity, that might influence treatment response.

METHODS

123 of 294 insomnia patients initially recruited (66.7% females, age=40.59±11.89) were assessed before and after group Cognitive-Behavioral Therapy for Insomnia (CBT-I), as well as at follow-up (7.8±1.6 years after the end-of-treatment). By use of latent class analysis (LCA) we identified insomnia subtypes according with baseline scores of insomnia severity and features, anxiety, depression, stress and sleepiness symptoms, circadian rhythm, and treatment effectiveness (Delta score of Insomnia Severity Index-ISI between baseline and end-of-treatment).

RESULTS

By LCA we revealed three classes: "PURE INSOMNIA", "INSOMNIA+ANXIETY+DEPRESSION+STRESS", and "INSOMNIA+ANXIETY". The improvements in insomnia severity was maintained up to 10 years after the end-of-treatment, but with differences between classes (p<0.05). Class "INSOMNIA+ANXIETY+DEPRESSION+STRESS" showed at the end-of-treatment the largest percentage of responders (63.5% = Insomnia Severity Index decrease ≥8). However, at follow up the effect was less and 48.1% had a moderate or severe insomnia (Insomnia Severity Index >14).

LIMITATIONS

The lack of a control group and the absence of a complete clinical assessment at the follow-up limit the interpretability of our results.

CONCLUSIONS

Our data driven analysis suggest insomnia heterogeneity can be categorized into sub-classes by depression, anxiety, and stress symptoms. In addition, insomnia patients with stress and depression symptoms maintained highest percentage of clinical depression at the end-of-treatment and insomnia at follow-up, in comparison with others classes. Stress and depression symptoms should be considered risk factors that play an important role in the long-term outcome of CBT-I.

Suicidal ideation among nurses: Unique and cumulative effects of different subtypes of sleep problems

BACKGROUND

Nurses have high rates of sleep problems, and higher risk of suicide than the general population. However, there is no empirical evidence showing the extent to which sleep problems among nurses are related to suicidal ideation.

METHODS

Multistage stratified cluster sampling was performed to recruit nurses from tertiary hospitals in Shandong, China. Item 9 of the Patient Health Questionnaire-9 was used to assess suicidal ideation. The Pittsburgh Sleep Quality Index was used to assess different subtypes of sleep problems.

RESULTS

Overall prevalence of suicidal ideation among the sample was 10.8%. Nurses with sleep problems were at high risk for suicidal ideation (^bOR = 1.79, 95% CI = 1.20-2.65). Among different sleep problem subtypes, only "cannot breathe comfortably" was independently related to suicidal ideation. Different subtypes of sleep problems had cumulative effects on suicidal ideation: the more subtypes of sleep problems nurses experienced, the higher the likelihood of suicidal ideation.

LIMITATIONS

The cross-sectional study design prevents inferring causation, and the use of self-report measures may lead to recall bias. Other physical/mental conditions, besides depression, were not assessed. Statistical power may have been insufficient due to the small sub-sample who reported suicidal ideation. Also, the generalizability of the results is limited, as the sample only comprised nurses in tertiary hospitals.

CONCLUSIONS

Nurses experiencing sleep problems were associated with increased likelihood of suicidal ideation. It may be beneficial to incorporate sleep problems into routine screening and intervention for suicide prevention in clinical practice.

Midlife Long-Hour Working and Later-life Social Engagement Are Associated with Reduced Risks of Mild Cognitive Impairment among Community-Living Singapore Elderly

Mild cognitive impairment (MCI), as a transitional stage between normal aging and dementia, causes cognitive decline among one-fifth of elders aged 65 years and older. Health-related lifestyles (HRL) are generally regarded as modifiable influencing factors of cognitive decline. The present study investigated how HRLs at two different life stages (one at midlife and the other at later life) affect MCI occurrence among community-dwelling elders, as part of the Diet and Healthy Aging (DaHA) study in Singapore. The frequencies of major HRL activities were compared between 119 clinical diagnosed MCI cases and 632 normal aging controls with functional cognition. The associations of HRLs with MCI were determined by multivariate logistic regression analysis and adjusted according to known factors including age, childhood education, and major chronic diseases (hypertension, stroke, diabetes, and cataracts or glaucoma). Long-hour working in midlife (adjusted OR = 0.418 with 95% CI 0.215-0.812) and social engagement in later-life (adjusted OR = 0.532 with 95% CI 0.329-0.859) were associated with reduced risks of MCI, respectively. It is important to note that those elders who had both midlife long-hour working and later-life social engagement were related to the lowest risk of MCI (adjusted OR = 0.285 with 95% CI 0.143-0.565), when compared to the least active subgroup who neither had worked long hours in midlife nor participate in social activities in later-life. Therefore, the present study demonstrated that midlife long-hour working and later-life social engagement were modifiable factors for the maintenance of cognitive functions.

Factors affecting sleep among Thai people with dementia attending an outpatient psychiatric department

Sleep quality is a well-studied aspect of overall health, especially for those who have other compounded health issues like dementia. This study identified what factors affected sleep quality among Thai people with dementia who attended an outpatient psychiatric department. While we considered factors associated with poor sleep quality, we discussed the impact that family relationship specifically has on sleep quality for this population. A cross-sectional study was conducted among 80 patient–caregiver dyads. All patients were assessed by the TMSE, a neuropsychiatric assessment (Thai Mental State Examination). The PSQI (Pittsburgh Sleep Quality Index) was administered and multiple factors affecting sleep were investigated through face-to-face interviews with each patient and caregiver dyad. Interviews were conducted by a psychiatric nurse at a hospital offering outpatient psychiatric care. A chi-square test, t test, Spearman correlation, Pearson’s correlation, and logistic regression were applied to identify statistically significant associations. Overall, we found that the prevalence of poor sleep in our target population was 70%, with a high sleep score (8.14 ± 4.20). Factors affecting sleep included mental health problems, night-time cough and urinary frequency, pain and fever during the night, sleep environment problems, and stimulant use (63.8%, 57.5%, 47.5%, 20%, and 7.5%, respectively). Other factors previously associated with poor sleep quality in other studies were not associated in this study. Those with perceived poor family relationships are at a 5.57 times greater risk for poor sleep than those with perceived good family relationships. On the contrary, those without mental health problems significantly decreased their overall risk for poor sleep. Further investigation into these associations should be studied. In conclusion, health-care providers are encouraged to consider the impact of family context on sleep quality for people with dementia.

Sleep Timing During Adolescence: A Latent Transition Analysis Approach

Objective/Background: This paper utilized a person-centered approach to examine individual differences in sleep timing cross-sectionally and prospectively in adolescents. Participants: Data from Waves 5 and 6 of the Longitudinal Study of Australian Children were used. At Wave 5, the sample included 3,552 Australian adolescents aged 12-13 years (51% males, 49% females); 84% provided data at two-year follow-up (Wave 6). Methods: Through structured interviews, adolescents provided information on sleep timing (bedtimes, sleep-onset times, and wake times) on weekends and weekdays, and relevant covariates. Latent profile analysis identified sleep profiles at Waves 5 and 6 separately; latent transition analysis examined stability and change in profiles across time. Results: Six sleep timing profiles were identified at age 12-13 years: Early Larks (n = 228; 6.4%); Larks (n = 1257; 35.4%); Intermediate (n = 1311; 36.9%); Owls (n = 351; 9.9%); Variable Owls (n = 308; 8.7%); and, Late Owls (n = 97; 2.7%). Six similar profiles were identified at age 14-15 years. The latent transition analysis indicated that the Early Larks, Larks, and Intermediate profiles were more stable over time compared with the Owls, Variable Owls, and Late Owls profiles. Higher body mass index (BMI) and longer screen time predicted transitions from Larks and Early Larks to the Owls, Variable Owls, and Late Owls profiles. Conclusions: Many adolescents showed healthy sleep timing, which continued with age. However, the Variable Owls, Owls, and Late Owls profiles reflected less healthy and more variable sleep patterns that may require targeted interventions.

Gender differences in latent classes of sleep quality in community-dwelling adults based on the Pittsburgh sleep quality index

This study investigated the latent classes of sleep quality based on the Pittsburgh Sleep Quality index (PSQI) in Chinese adults, and analyzed whether these latent classes differed between men and women. A total of 982 adults (age: 18-59 years) participated in the questionnaire survey, and the PSQI was used to assess sleep quality. Two latent classes of sleep quality were found in men, and the cut-off value of PSQI for predicting the poor sleep quality group was 6. Four latent classes were found in women, which were named 'good sleep', 'daytime dysfunction', 'inadequate sleep', and 'poor sleep', respectively. In women, the optimal cut-off values of the PSQI for predicting the poor and good sleep quality groups were 9 and 6, respectively. Women who were not included in the 'poor sleep' group but had a score not less than 2 in the 'sleep duration' or 'daytime dysfunction' dimensions of PSQI were classified into the 'inadequate sleep' group or the 'daytime dysfunction' group. The findings may benefit the identification of sleep problems and assist in more effective alleviation of these issues.