Longitudinal course of insomnia: age-related differences in subjective sleepiness and vigilance performance in a population-based sample

Effects of age on the relationship between sleep quality and cognitive performance: Findings from the Human Connectome Project-Aging cohort

BACKGROUND

The association between sleep quality and cognition is widely established, but the role of aging in this relationship is largely unknown.

OBJECTIVE

To examine how age impacts the sleep-cognition relationship and determine whether there are sensitive ranges when the relationship between sleep and cognition is modified. This investigation could help identify individuals at risk for sleep-related cognitive impairment.

SUBJECTS

Sample included 711 individuals (ages 36.00-89.83, 59.66 ± 14.91, 55.7 % female) from the Human Connectome Project-Aging (HCP-A).

METHODS

The association between sleep quality (Pittsburgh Sleep Quality Index, PSQI) and cognition (Crystallized Cognition Composite and Fluid Cognition Composite from the NIH Toolbox, the Trail Making Test, TMT, and the Rey Auditory Verbal Learning Test, RAVLT) was measured using linear regression models, with sex, race, use of sleep medication, hypertension, and years of education as covariates. The interaction between sleep and age on cognition was tested using the moderation analysis, with age as both continuous linear and nonlinear (quadratic) terms.

RESULTS

There was a significant interaction term between the PSQI and nonlinear age term (age2) on TMT-B (p = 0.02) and NIH Toolbox crystallized cognition (p = 0.02), indicating that poor sleep quality was associated with worse performance on these measures (sensitive age ranges 50-75 years for TMT-B and 66-70 years for crystallized cognition).

CONCLUSIONS

The sleep-cognition relationship may be modified by age. Individuals in the middle age to early older adulthood age band may be most vulnerable to sleep-related cognitive impairment.

Factors affecting the psychomotor vigilance of nurses working night shift

BACKGROUND

Shifts and long working hours decrease the psychomotor vigilance of healthcare workers especially when working night shifts. Working night shifts adversely affects nurses' health and impacts patient safety.

AIM

The purpose of this study is to identify factors affecting the psychomotor vigilance of nurses working night shift.

METHODS

This descriptive cross-sectional study was carried out with 83 nurses who were working in a private hospital in Istanbul and voluntarily participated in this study between April 25 and May 30, 2022. Data were collected using "Descriptive Characteristics Form," "Psychomotor Vigilance Task," "Pittsburgh Sleep Quality Index," and "Epworth Sleepiness Scale." The "STROBE checklist" for cross-sectional studies was utilized in order to report the findings of the study.

RESULTS

When time-dependent variation of the nurses' psychomotor vigilance task performances on the night shift was examined, it was observed that the nurses' mean reaction time and number of lapses increased at the end of the night shift. Age, smoking, physical activity, daily water consumption, daytime sleepiness, and sleep quality were found as the factors affecting nurses' psychomotor vigilance.

CONCLUSION

The psychomotor vigilance task performances of nurses working night shift are affected by age and a variety of behavioral factors.

IMPLICATIONS FOR NURSING AND HEALTH POLICY

Suggestions for nursing policy include the implementation of workplace health promotion programs in order to increase the attention level of nurses in order to ensure employee and patient health and safety and create a healthy working environment.

Negative life stress, sleep disturbance, and depressive symptoms: The moderating role of anterior insula activity in response to sleep-related stimuli

BACKGROUND

This study investigated the effects of anterior insula (AI) activation on the association between stress and sleep disturbance as a neurobiological basis of the trait-like degree of sleep reactivity to stress. Additionally, it examined the effects of AI activity on the association between sleep disturbance and depression.

METHODS

The participants were 48 adults. To assess AI activation in response to sleep-related stimuli (SS) compared to neutral stimuli (NS), we extracted mean AI parameter estimates for the SS-NS contrast. We examined whether the interaction between life stress and AI activation would predict sleep disturbance and whether the interaction between sleep disturbance and AI activation would predict depression.

RESULTS

At higher levels of bilateral AI activation in response to SS, higher levels of stress were associated with greater sleep disturbance (left AI x stress: b = 1.07, SE = 0.44, p < 0.05; right AI x stress: b = 1.05, SE = 0.48, p < 0.05). In addition, at higher levels of right AI activation, higher levels of sleep disturbance were associated with more severe depressive symptoms (right AI x sleep disturbance: b = 2.55, SE = 1.10, p < 0.05).

LIMITATION

This study assessed sleep quality and depressive symptoms based on self-reported questionnaires.

CONCLUSION

This study revealed moderating effects of AI activation on the association between negative life stress and sleep disturbance. Additionally, AI activation strengthened the association between sleep disturbance and depression. AI activation may reflect a crucial etiological diathesis for insomnia and stress-related disorders.

Objective Measures of Cognitive Performance in Sleep Disorder Research

Neurocognitive tests offer objective and reliable assessment of patients' status and progress. However, there is no consensus on how to use neurocognitive assessment in sleep disorder research. An effective use of neurocognitive assessment must be based on standardized practices and have a firm theoretic basis. The aim of this review is to offer an overview of how different tests have been used in the field, mapping each test onto a corresponding cognitive domain and propose how to move forward with a suggested cognitive battery of tests covering all major cognitive domains.

Fatigue and Vigilance-Related Factors in Family Caregivers of Patients With Advanced Cancer: A Cross-sectional Study

BACKGROUND

Family caregivers (FCs) commonly experience fatigue during caregiving. The factors of fatigue in the FCs of patients with advanced cancer have not yet been investigated in Taiwan.

OBJECTIVE

This study investigated potential predictors of fatigue in the FCs of patients with advanced cancer.

METHODS

A descriptive, cross-sectional study was conducted on 184 FCs. Data were collected using the Checklist Individual Strength and the palm-based psychomotor vigilance test. A linear regression model was the main statistical method for identifying the factors predictive of fatigue in FCs.

RESULTS

Subjective and objective measurements revealed that 95% of the FCs had fatigue and poor vigilance. Those who spent more time each day on caregiving tasks, had no religious beliefs, had a full- or part-time job, and had a greater caregiver burden experienced greater fatigue.

CONCLUSIONS

Fatigue and poor vigilance were common in the Taiwanese FCs of patients with advanced cancer. Family caregivers with risk factors for fatigue must be identified and given access to resources for assistance.

IMPLICATION FOR PRACTICE

Healthcare providers must proactively assess FCs for fatigue and vigilance status and provide interventions appropriate for individual needs.

Balance and Mobility in Community-Dwelling Older Adults: Effect of Daytime Sleepiness

OBJECTIVES

To examine the effect of self-reported daytime sleepiness on performance-based balance measures and self-reported balance confidence in community-dwelling older adults.

DESIGN

Cross-sectional secondary analysis of an observational cohort study designed to develop and refine measures of balance and mobility in community-dwelling older adults.

SETTING

Community.

PARTICIPANTS

Older adults (aged 78.2 ± 5.9) (n = 120).

MEASUREMENTS

The performance-based gait and balance measures included gait speed, double support time, and step width. Narrow walk, obstacle walk, and timed standing balance were also assessed. The Activities-Specific Balance Confidence Scale was included as a self-reported measure. Daytime sleepiness was defined as an Epworth Sleepiness Scale score of 9 or greater. Body mass index, fall-related comorbidities, and use of central nervous system (CNS) medications were considered as covariates.

RESULTS

Forty-five percent of participants reported daytime sleepiness. Participants reporting daytime sleepiness differed significantly from those without in gait speed (adjusted difference (standard error (SE)) -0.09 (0.04) m/s, P = .03), step width (adjusted difference (SE) 0.02 (0.01), P = .03), and self-reported balance confidence (adjusted difference (SE) -1.02 (0.38), P = .01) even after adjusting for covariates. Two-way analysis of variance of CNS medication use and daytime sleepiness showed no significant interaction effects.

CONCLUSION

Self-reported daytime sleepiness is associated with slower gait speed and poor balance confidence in community-dwelling older adults. Subjective sleep assessment should be considered when assessing balance and implementing interventions for improving balance in older adults. Further study is needed to examine the role of CNS medication use.