Personality and Insomnia Symptoms in Older Adults: The Baltimore Longitudinal Study of Aging

Evaluating a novel 24-hour rest/activity rhythm marker of preclinical β-amyloid deposition

STUDY OBJECTIVES

To compare sleep and 24-hour rest/activity rhythms (RARs) between cognitively normal older adults who are β-amyloid-positive (Aβ+) or Aβ- and replicate a novel time-of-day-specific difference between these groups identified in a previous exploratory study.

METHODS

We studied 82 cognitively normal participants from the Baltimore Longitudinal Study of Aging (aged 75.7 ± 8.5 years, 55% female, 76% white) with wrist actigraphy data and Aβ+ versus Aβ- status measured by [11C] Pittsburgh compound B positron emission tomography. RARs were calculated using epoch-level activity count data from actigraphy. We used novel, data-driven function-on-scalar regression analyses and standard RAR metrics to cross-sectionally compare RARs between 25 Aβ+ and 57 Aβ- participants.

RESULTS

Compared to Aβ- participants, Aβ+ participants had higher mean activity from 1:00 p.m. to 3:30 p.m. when using less conservative pointwise confidence intervals (CIs) and from 1:30 p.m. to 2:30 p.m. using more conservative, simultaneous CIs. Furthermore, Aβ+ participants had higher day-to-day variability in activity from 9:00 a.m. to 11:30 a.m. and lower variability from 1:30 p.m. to 4:00 p.m. and 7:30 p.m. to 10:30 p.m. according to pointwise CIs, and lower variability from 8:30 p.m. to 10:00 p.m. using simultaneous CIs. There were no Aβ-related differences in standard sleep or RAR metrics.

CONCLUSIONS

Findings suggest Aβ+ older adults have higher, more stable day-to-day afternoon/evening activity than Aβ- older adults, potentially reflecting circadian dysfunction. Studies are needed to replicate our findings and determine whether these or other time-of-day-specific RAR features have utility as markers of preclinical Aβ deposition and if they predict clinical dementia and agitation in the afternoon/evening (i.e. "sundowning").

Psychosocial factors of insomnia in depression: a network approach

BACKGROUND

Insomnia symptoms in patients with major depressive disorder (MDD) are common and deleterious. Childhood trauma, personality traits, interpersonal distress, and social support contribute to insomnia, but how they interact to affect insomnia remains uncertain.

METHODS

A total of 791 patients with MDD completed the Insomnia Severity Index, Eysenck Personality Questionnaire, Interpersonal Relationship Comprehensive Diagnostic Scale, Childhood Trauma Questionnaire, Social Support Rating Scale and Hamilton Depression Scale-17. This study utilized network analyses to identify the central symptoms of insomnia and their associations with psychosocial factors.

RESULTS

Worrying about sleep was identified as the central symptom in the insomnia network, insomnia and associated personality network, insomnia and associated interpersonal disturbance network, insomnia and associated childhood trauma network, insomnia and associated social support network, and the integrated network of insomnia symptoms and associated psychosocial factors. In the networks of insomnia symptoms and individual psychosocial factors, most psychosocial factors (other than childhood trauma) were directly or indirectly related to insomnia symptoms; however, neuroticism was the only factor directly associated with insomnia symptoms before and after controlling for covariates. In the final integrated network of insomnia symptoms and psychosocial factors, neuroticism was a bridge node and mediated the relationships of social support and interpersonal disturbances with insomnia symptoms, which is clearly presented in the shortest pathways.

CONCLUSIONS

Worrying about sleep and neuroticism were prominent in the integrated network of insomnia symptoms and associated psychosocial factors, and the edge between them connected psychosocial factors and insomnia symptoms in MDD patients.

Personality and insomnia: A systematic review and narrative synthesis

The inherent nature of personality serves as a predisposing, and possible maintaining, factor of insomnia. However, methodological differences limit the ability to draw causal conclusions regarding the specific traits involved in the aetiology of the disorder. This systematic review of the relationship between insomnia and personality provides a narrative synthesis of the literature to date. Here, we identified N = 76 studies meeting the inclusion/exclusion criteria. The outcomes reliably evidenced the experience of insomnia to be associated with personality traits that are typically considered to be negative or maladaptive in nature. More specifically, insomnia was related to neuroticism, introversion, perfectionistic doubts and concerns, elevated personal standards, negative affect, social inhibition and avoidance, hysteria, hypochondriasis, psychasthenia, impulsive behaviour, anger, hostility, and psychopathic tendencies, schizotypal and borderline traits, reduced conscientiousness and self-directedness, and negatively perceived perception of the self. Several studies examined the role that personality plays in predicting the treatment efficacy and adherence of CBTi. Moving forward, longitudinal research, methodological consistency, the mediating role of treatment outcomes and adherence, and clinical and population representative samples should be prioritised. Methodological strengths and limitations of the literature are discussed alongside the next steps that should be taken to advance our understanding of the literature.

Sleep and Depression in Older Adults: A Narrative Review

PURPOSE OF REVIEW

The sleep-depression association has been recognized for decades. Efforts to clarify this association continue at an increasing pace. This review summarizes recent research on the sleep-depression association in older adults.

RECENT FINDINGS

Research over the past 4 years has utilized cross-sectional, longitudinal, cohort, and intervention designs to examine these associations. Short (< 7 h) and long (> 8-9 h) sleep durations and insomnia symptoms are risk factors for depression in older adults. Similarly, short sleep, long sleep, insomnia symptoms, and depression are all risk factors for poorer health in late life, including increased risk of cognitive decline, falls, and poorer quality-of-life. Intervention studies have produced mixed findings, with some studies suggesting that sleep interventions may be potentially effective in improving both insomnia and mood symptoms. Intervention studies incorporating both behavioral and physiological measures of sleep, and larger and diverse samples may enhance the field's understanding of the complex interplay between sleep and mood in older adults.

Associating sleep problems with advanced cancer diagnosis, and immune checkpoint treatment outcomes: a pilot study

BACKGROUND

Sleep problems (SP) are common in cancer patients but have not been previously assessed in patients receiving immune checkpoint inhibitors (ICI).

METHODS

We collected questionnaire data on sleep apnea risk, insomnia, and general sleep patterns. We used an adjusted multivariate Poisson regression to calculate prevalence ratios (PRs) and associated 95% confidence intervals (CIs) for associations between these SP and metastatic versus localized cancer stage (M1 vs. M0), and adjusted logistic regression models to calculate ORs for associations between SP with the number of ICI infusions completed (6 + vs. < 6).

RESULTS

Among 32 patients who received ICI treatment, the prevalence of low, intermediate, and high-risk OSA risk was 36%, 42%, and 21%, respectively. Overall, 58% of participants reported clinically significant insomnia. We did not find a significant association between intermediate or high risk OSA (vs. low risk) and metastatic cancer status (PR = 1.01 (95% CI: 0.28, 3.67)). Patients in the cohort who reported taking > 15 min to fall asleep were 3.6 times more likely to be diagnosed with metastatic cancer compared to those reporting shorter sleep latency (95% CI (1.74, 7.35)). We did not find a significant association between SP and number of ICI infusions completed.

CONCLUSION

Our data associating sleep apnea risk, insomnia, and sleep patterns with more advanced cancer encourages further exploration in larger-scale observational studies and suggests interventional clinical trials focused on sleep quality improvement that could result in better outcomes for these patients.

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets

Background

Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions.

Objective

The purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets.

Methods

In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets.

Results

Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found.

Conclusions

Our model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.