Specific Sleep Health Domains as Predictors of Executive Function in Older Adults
Publication: The Journal of Neuropsychiatry and Clinical Neurosciences
Abstract
Objectives:
Sleep health and executive function are multifaceted constructs that decline with age. Some evidence suggests that poor sleep health may underlie declines in executive function, but this relationship is not consistently found in cognitively normal older adults. The authors systematically investigated distinct sleep health domain associations with specific aspects of executive function.
Methods:
Community-dwelling older adults completed clinical interviews, comprehensive neuropsychological assessments, and subjective sleep measures. Four sleep health domains were investigated: satisfaction/quality, sleep efficiency, sleep duration, and daytime sleepiness/fatigue. Hierarchical multiple regression analyses, adjusting for significant covariates, examined whether the sleep health domains differentially predicted executive function.
Results:
Separate analyses found that greater sleep efficiency was associated with better response inhibition, while greater daytime sleepiness/fatigue was associated with worse cognitive flexibility. Categorical differences in sleep duration indicated that average durations, compared with short and long durations, had better executive function performance across measures. Sleep satisfaction/quality was not statistically associated with executive function.
Conclusions:
These findings have implications for sleep assessment and its intervention. Routine screening of sleep duration, efficiency, and daytime fatigue may be particularly useful in identifying those at greater risk of executive dysfunction. Targeting specific problems in sleep may serve to improve cognitive control and efficiency in older adults. Future research is warranted to establish the optimal hours of sleep duration for cognitive health.
Sleep health has emerged as a critical concept in promoting physical and mental well-being across the lifespan (1). Important to the understanding of sleep health is the recognition that it is a multidimensional construct. Sleep health consists of quantitative and qualitative estimates of sleep duration, sleep efficiency, sleep timing, daytime alertness or sleepiness, and perceived sleep satisfaction (1). These sleep components have shown differential relationships with brain function and pathology, as well as cognitive performance. Substantial research links sleep health with executive function (2), which is an important domain involved in the execution of goal-directed behavior even among normally aging individuals (3). However, the literature is mixed regarding the contributions of sleep health to executive function in relatively healthy older adults (4–7).
Similar to sleep health, executive function is a multifaceted construct, and the mixed findings between these constructs may be better understood by examining specific aspects of executive function that may be more closely related to distinct sleep domains. Collectively, there is evidence that difficulty initiating sleep, poor sleep efficiency, and longer sleep duration (i.e., >8 hours) are associated with worse set-shifting, cognitive flexibility, and processing speed performance (4–10). The findings regarding shorter sleep duration (defined as <6 hours) demonstrate different patterns of associations with executive function. Some studies suggest that shorter sleep durations are associated with worse processing speed and working memory performance but have little impact on more complex executive functions, whereas other studies fail to replicate these findings (9–11). Regarding subjective global sleep quality, some evidence suggests that it is associated with lower working memory, attentional set-shifting, and abstract problem-solving performance but not with processing speed and inhibition (7). Higher daytime sleepiness is most consistently associated with worse executive function across all domains, although research shows that it particularly affects attention and processing speed (8). Finally, while some evidence has found decreased psychomotor speed and attention in those with severe insomnia (11), other research suggests that older adults with insomnia demonstrate better accuracy on inhibitory control tests compared with those without insomnia (12).
With respect to brain pathophysiology, certain sleep health domains show stronger associations with amyloid deposition in regions of the brain associated with Alzheimer’s disease (13). In particular, research shows that self-reported poor perceived sleep quality, but not sleep duration, is associated with amyloid burden in frontal lobe regions associated with Alzheimer’s disease, as well as brain areas that are thought to subserve specific memory and executive functions in cognitively intact middle-aged and older adults (13). Additionally, pathophysiological changes in key brain nuclei also appear to contribute to increased sleep problems in older adults (14).
Age-related changes in executive function and attention may be exacerbated by poor sleep health. Better characterizing the relationships between sleep health and executive function may help to detect those at risk of sleep-related problems and cognitive decline. To our knowledge, the present study is the first to systematically evaluate sleep satisfaction/quality, sleep efficiency, sleep duration, and daytime sleepiness/fatigue associations with specific aspects of executive function in older adults. In efforts to clarify the mixed findings in the literature, we aimed to determine whether the respective sleep health domains are differentially associated with performance on working memory, concept formation/problem solving, response inhibition, and cognitive flexibility. Based on previous research (5, 6, 9–12), we anticipated that sleep efficiency and sleep duration would emerge as the strongest predictors of response inhibition and cognitive flexibility. Follow-up analyses aimed to determine whether there were sleep duration group differences in executive function.
Methods
Participants
Participants were recruited as part of the Maine-Aging Behavior Learning Enhancement (M-ABLE) study. Study procedures are described in detail elsewhere (15). Briefly, the M-ABLE study uses community-based participatory research methods to enhance the recruitment of a socioeconomically diverse older adult sample. Study inclusion criteria were purposefully wide to improve the generalizability of the findings to more diverse older adults. Inclusion criteria included being 55–90 years old, willing to undergo neuropsychological assessment, and willing to provide information on socioeconomic status. Exclusion criteria included a Montreal Cognitive Assessment (MoCA) score of <18 (16), moderate to severe depression (Geriatric Depression Scale–short form score >10) (17), history of moderate to severe neurological impairments (e.g., moderate to severe traumatic brain injury), recent stroke (defined as in the past year), neurodegenerative disorder (e.g., Parkinson’s disease or Alzheimer’s disease), and physical limitations (e.g., loss of visual field or inability to hold a pencil) that prohibit cognitive testing. Individuals diagnosed with an intellectual disability, dementia disorder, or any untreated or severe psychiatric conditions (i.e., psychotic disorders) were also excluded. All procedures were approved by the institutional review board at the University of Maine.
Measures
Information on demographic characteristics was collected via a clinical interview. Medical history was collected using the clinician-administered National Alzheimer’s Coordinating Center Uniform Data Set Subject Health History measure (18). Information includes participants’ self-reported clinical history of cardiovascular disease, cerebrovascular disease, presence of neurological conditions (e.g., seizures or traumatic brain injury), psychological history (e.g., depression, anxiety, posttraumatic stress disorder), and other biological indicators of health (hypertension, hypercholesterolemia, diabetes, thyroid disease, vitamin B12 deficiency). Variables on this measure are categorized as absent, active, remote/inactive, or unknown. Enrolled participants self-reported receiving treatments for all medical conditions (e.g., depression, diabetes, hypertension, and hypercholesterolemia).
The working memory composite consisted of the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) digit span subtest (19) and the National Institute of Health Toolbox–Cognitive Battery (NIHTB-CB) List Sorting Working Memory Test (20). The WAIS-IV digit span subtest requires participants to repeat auditorily presented strings of numbers in forward, backward, and numerical order. The NIHTB-CB List Sorting test requires participants to sequence a randomly ordered series of visually presented stimuli in increasing size order. The total number of correct items on the Wisconsin Card Sorting Test Short Form (WCST-64) (21) served as an estimate of problem-solving and concept formation. The cognitive flexibility composite comprised the Trail-Making Test Part A and B (TMT-A and TMT-B) (22) and the NIHTB-CB Dimensional Change Card Sort (DCCS) test of executive function-cognitive flexibility, postswitch test (20). The TMT-A completion time (seconds) measures processing speed and visual attention, while the TMT-B total completion time (seconds) measures task-switching abilities. Reaction time on the NIHTB-CB DCCS also measures task switching and cognitive flexibility.
The response inhibition composite was formed from the Delis–Kaplan Executive Function System (D-KEFS) Color-Word Interference Test (23) and the NIHTB-CB Flanker Inhibitory Control and Attention Test (20). Total time (seconds) on D-KEFS trials 3 and 4 served as measures of cognitive inhibition and flexibility, respectively. The NIHTB-CB Flanker Test measures the test taker’s ability to attend to a visually centered target stimulus (arrow) and correctly choose its left-right orientation while simultaneously disregarding congruent or incongruent stimuli paired on either side of the target. The difference score on this task reflects the difference in choice reaction time in milliseconds between incongruent trials (e.g., surrounding arrows pointing in opposite directions from the central target arrow) and the congruent trials (e.g., surrounding arrows pointing in the same directions as the central target arrow).
We investigated four domains of sleep health—sleep satisfaction and quality, sleep efficiency, sleep duration, and daytime sleepiness and fatigue—using the Pittsburgh Sleep Quality Index (PSQI) (24), Insomnia Severity Index (ISI) (25), and the NIH Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health Scale (26). The sleep satisfaction and quality composite was composed of the PSQI component 1 (sleep quality) and the ISI total score (degree of insomnia symptoms). The ISI total scores were reverse-scored for placement on the same scale as PSQI items. The sleep efficiency composite was formed from the PSQI components 2 (sleep latency, reverse-scored) and 4 (habitual sleep efficiency). The PSQI component 3 measured sleep duration, which is based on a minimum score of 0 classified as better sleep (i.e., >7 hours of sleep per night) and a maximum score of 3 classified as worse sleep (i.e., <5 hours of sleep per night). The daytime sleepiness and fatigue composite was formed from the PSQI component 7 and the PROMIS fatigue score. The sleep domain composites were formed by first creating z scores for each measure, then summing z scores within each sleep health domain and forming averages by dividing each sum by the number of measures included in that domain.
Symptoms of depression and anxiety were measured using the PROMIS depression and anxiety subscales (27). The depression subscale is a 24-item questionnaire that evaluates mood, anhedonia (loss of interest), and other cognitive and somatic symptoms of depression within the past 7 days on a 5-point Likert scale. The anxiety subscale is a 21-item scale that evaluates the level of worry, physical tension, and anxious behavior within the past 7 days on a 5-point Likert scale.
Statistical Analyses
A priori power analyses using G*Power (28) assessed the required sample size needed to achieve a power of 0.8 and a moderate effect size (f2=0.15) (29) using linear multiple regression at an alpha level of 0.05. Results revealed that 109 participants were required with eight total predictors; this study was sufficiently powered to detect a moderate effect using linear multiple regression.
Preliminary analyses inspected data for normality by examination of descriptive statistics and visual inspection. Significant univariate outliers, defined as z scores ±3.26 (30), were “Winsorized.” To correct for normality assumption violations within the executive function measures, scores were ranked using Rankit’s rank-based method (31). Ranked scores were then converted to normally distributed scores and rescaled to have a mean of 10 and a standard deviation of 3 (i.e., scaled scores). Raw MoCA scores were transformed into z scores using the age and years of education appropriate normative data (32).
The problem-solving/concept formation composite was formed by the ranked raw total correct score of the WCST. The working memory, cognitive flexibility, and response inhibition composites were formed by summing the normalized scaled scores of each measure comprising the respective composites, redistributing the composites, and rescaling them so that each composite score was normally distributed with a mean of 10 and a standard deviation of 3. Because of the few items that made up each domain, Pearson’s r evaluated pairwise correlations between measures before composites were formed for working memory, cognitive flexibility, and response inhibition, as well as sleep satisfaction/quality, sleep efficiency, and daytime sleepiness/fatigue. Correlational analyses revealed significant associations within working memory (r=0.241, N=2), cognitive flexibility (r=0.714, N=3) and response inhibition (r=0.431, N=2) composites, as well as within sleep satisfaction/quality (r=0.768, N=2), sleep efficiency (r=0.412, N=2), and daytime sleepiness/fatigue (r=0.371, N=2) composites.
Pearson’s bivariate correlational analyses investigated associations among the demographic (age, years of education, and sex), global cognition (MoCA), sleep health, and executive function variables. Based on prior research linking anxiety and depression to sleep health (33), correlational analyses investigated associations between symptoms of anxiety and depression with sleep health and executive function measures. For the primary analyses, a series of hierarchical multiple regressions were performed in order to quantify the independent contributions of the sleep health variables on different aspects of executive function. For each model, variables were entered in three steps: demographic variables (age, sex, and education) were entered first, followed by global cognition (MoCA z scores), with the sleep health variables entered last. In order to simplify the models and increase the precision of predictions, nonsignificant predictors (i.e., p>0.05) were removed from the final models.
Follow-up trend analyses were performed to evaluate whether null findings regarding sleep duration were due to nonlinear relationships between sleep duration and executive function. Based on prior literature associating shorter and longer sleep durations with worse executive function (2), nonorthogonal quadratic comparisons evaluated differences in each aspect of executive function between recategorized sleep duration groups: less than 6 hours of sleep (N=19), 6–8 hours of sleep (N=85), and greater than 8 hours of sleep (N=11). All tests of significance were two-tailed and analyzed with SPSS (version 26.0).
Results
Participant Characteristics
Table 1 presents the demographic and clinical characteristics of the sample. One hundred fifteen participants were included in this study. Participants were predominantly female (73%; N=84) and had a broad range of education and global cognitive function. Seven percent reported a history of sleep disorder, 15% obtained ISI scores consistent with subthreshold clinical insomnia, and 7% obtained scores consistent with moderate clinical insomnia. Eleven percent of participants reported taking either over-the-counter or prescribed sleep medications. Data on the associations among the demographic, cognitive status, psychiatric, sleep health, and executive function variables are provided in the online supplement.
| Characteristic | Na | %a |
|---|---|---|
| Age (years) (mean±SD) | 70.6 | 6.5 |
| Years of education (mean±SD) | 15.7 | 2.7 |
| Montreal Cognitive Assessment score (mean±SD) | 26.3 | 2.6 |
| Diagnosis | ||
| Diabetes | 15 | 13 |
| Hyperlipidemia | 62 | 54 |
| Hypertension | 63 | 55 |
| Cardiovascular disease | 67 | 58 |
| Cerebrovascular disease | 13 | 11 |
| Thyroid disorder | 46 | 40 |
| Depression | 20 | 17 |
| Anxiety disorder | 29 | 25 |
| Sleep disorder | 8 | 7 |
a
Values represent the number and percentage of participants in the sample, unless otherwise specified.
Sleep Health and Executive Function
Hierarchal regression analyses adjusting for the statistical differences in age, years of education, and global cognition found that daytime sleepiness/fatigue was a significant predictor of cognitive flexibility. Age (β=−0.421, p<0.001), education (β=0.223, p<0.001), global cognition (β=0.255, p<0.001), and daytime sleepiness/fatigue (β=−0.159, p<0.05) each significantly contributed to the variance in cognitive flexibility performance in the model (F=3.990, df=4, 114, p<0.05). The final model is presented in Table 2.
| Model | R | R2 | Adjusted R2 | R2Δa | ΔFb | p |
|---|---|---|---|---|---|---|
| 1c | 0.519 | 0.269 | 0.256 | 0.269 | 20.631 | <0.001** |
| 2d | 0.561 | 0.315 | 0.296 | 0.046 | 7.389 | 0.008* |
| 3e | 0.582 | 0.339 | 0.315 | 0.024 | 3.990 | 0.048* |
a
R2Δ denotes change in R2.
b
ΔF denotes change in the F statistic.
c
Age and education level were variables.
d
Global cognition was a variable.
e
Daytime sleepiness/fatigue was a variable.
*
p<0.05, **p<0.001.
Hierarchical regression analyses found that global cognition and sleep efficiency but not age or education were significant predictors of the response inhibition composite. The final model indicated that global cognition (β=0.279, p<0.05) and sleep efficiency (β=0.214, p<0.05) each significantly contributed to the variance in response inhibition performance (F=6.237, df=2, 115, p<0.05; Table 3). Analyses examining the effect of the sleep health variables on the working memory and problem-solving/concept formation composites did not reveal any significant associations. No associations were found with sleep quality/satisfaction and sleep duration domains with executive function (p>0.05). In addition, no statistically significant associations were found with anxiety, depression, PSQI total score, or the ISI total score with any domain of executive function (p>0.05) (for further details, see Table S3 in the online supplement).
a
R2Δ denotes change in R2.
b
ΔF denotes change in the F statistic.
c
Global cognition was a variable.
d
Sleep efficiency was a variable.
*
p<0.05, **p<0.001.
Follow-up trend analyses evaluated for nonorthogonal quadratic relationships between executive function and three recategorized sleep duration groups: short (less than 6 hours), average (between 6 and 8 hours), and long (greater than 8 hours of sleep). Analyses revealed that the average duration group performed significantly better on the working memory (F=6.263, df=2, 107, p=0.003), problem-solving/concept formation (F=3.117, df=2, 107, p=0.048), and cognitive flexibility (F=3.190, df=2, 107, p=0.045) composites compared with the short- and long-duration sleep groups. Additionally, there was a trend-level analysis difference in the response inhibition composite scores, which again reflected better performance in the average-duration group (p=0.06). No statistical differences emerged between the short- and long-duration sleep groups on the executive function measures. Notably, there was unequal sample size distribution between the sleep duration groups; thus, these findings should be interpreted with caution.
DISCUSSION
The current study comprehensively investigated subjective sleep health domains and their respective contributions to executive function in a community-based sample of relatively healthy older adults. Our results revealed that sleep efficiency was significantly associated with response inhibition, whereas daytime sleepiness was significantly associated with cognitive flexibility. Sleep duration as measured by the PSQI was not associated with executive function; however, as will be discussed, categorical differences in sleep durations were statistically related to each of the executive function domains. Sleep health domains were not significantly associated with measures of working memory or problem-solving/concept formation. While symptoms of anxiety and depression were significantly associated with subjective perceptions of sleep health, they were not statistically associated with executive function.
Our findings align with evidence that has found statistically significant yet modest associations between sleep health and cognitive control in older adults without clinical sleep disorders (4, 8). Cognitive control has been implicated in important goal-directed behaviors such as driving, which may help to explain significant associations between poor sleep health and increased risk for automobile accidents, falling, and other changes in everyday function, including walking (34, 35). Decreased cognitive control abilities are also associated with an increased risk of dementia in older adults (36). Given the exacerbating role that sleep health has in age-related executive function changes, interventions to improve sleep health may serve as accessible and indirect ways to decrease safety risks and the risk of cognitive decline.
Although we expected sleep health would relate to other aspects of executive function based on prior evidence (6), our results did not reveal significant relationships between sleep health with working memory or concept formation. Prior research posits a dose-dependent effect between the severity and chronicity of the sleep complaint with the severity of executive function difficulty (37). It is thus possible that we were unable to reach a threshold necessary to unveil adverse effects of sleep difficulties on working memory and concept formation, given that only 7% of the sample reported a diagnosed sleep disorder. These make the observed modest associations between sleep efficiency and daytime sleepiness/fatigue with aspects of executive function even in the absence of an elevated sleep complaint all the more notable.
Executive function did not associate with sleep duration when measured by the PSQI Sleep Component 3, which categorizes having a greater number of sleep hours as being good sleep (2). However, follow-up analyses conducted based on work that suggests longer and shorter sleep durations may indicate pathological changes in brain function (9) found group differences in executive function in those with average as compared with short and long sleep durations. Specifically, those with longer and shorter sleep durations as compared with average sleep durations demonstrated worse performance on every aspect of executive function. These findings appear to suggest that the average number of hours of sleep has a somewhat global effect on executive function. They also indicate that between 6 and 8 hours may be a more optimal window for sleep duration. However, given unequal sample size distribution between the sleep duration groups, more research is warranted to try to replicate this finding.
Limitations to the present study included a racially and ethnically homogeneous sample, which reflects the nearly 95% White non-Hispanic population of Maine (38). Additionally, although we did not find significant sex differences in self-reported sleep health, it is important to note that there was a higher proportion of women in the sample, which could affect the generalizability of these findings. In addition, we did not measure sleep timing (the placement of sleep within the 24-hour day), which is an important aspect of Buysse’s Satisfaction, Alertness, Timing, Efficiency, and Duration sleep health model (1). Future research is needed to longitudinally evaluate associations between sleep health and executive function as they relate to biomarkers of cerebrovascular or neurodegenerative disease to determine potential mechanistic explanations for these relationships (39). In particular, research using polysomnography and larger sample sizes are warranted to help disentangle the effect of sleep staging and sleep duration on executive function. There is also the need to establish the optimal hours of sleep duration for sleep screening purposes, given the growing body of evidence that both short and long sleep durations have negative impacts on mental and cognitive health.
CONCLUSIONS
Our findings extend prior research linking sleep health with executive function and have potential implications for sleep assessment and its intervention. Consistent with prior work, sleep efficiency and daytime sleepiness/fatigue emerged as having the strongest relationships with cognitive control within this study. Difficulties with these sleep health domains are frequently reported by primary care patients, with approximately 31%−33% of patients reporting sleep disturbances such as poor sleep efficiency, roughly 15% reporting sleep disturbance along with daytime sleepiness/fatigue the following day, and about 10% meeting clinical diagnostic criteria for insomnia (40). Given associations observed between these sleep health factors with cognitive control, routine screening of sleep efficiency and daytime sleepiness/fatigue may be particularly useful in identifying those at greater risk of executive dysfunction and associated sequelae such as accidents and injurious falls, in addition to screening for psychiatric concerns such as anxiety and depression. Furthermore, treatments, such as cognitive-behavioral therapy for insomnia, that target specific problems in sleep may help improve cognitive control and efficiency in older adults.
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Information & Authors
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History
Received: 28 April 2021
Revision received: 24 September 2021
Accepted: 25 October 2021
Published online: 11 March 2022
Published in print: Fall 2022
Keywords
Authors
Competing Interests
The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the article.
Funding Information
This work was supported by startup funds from the University of Maine and Maine Economic Improvement Fund and a National Academy of Neuropsychology Clinical Trial grant to Dr. MacAulay.The authors report no financial relationships with commercial interests.
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