Abstract
Background
Whether daytime napping may be a risk factor for dementia, in addition to nighttime sleep disturbances, remains unclear. This study investigated longitudinal associations of napping characteristics with incident dementia among U.S. older adults.
Methods
Data came from the National Health and Aging Trends Study (NHATS). Napping characteristics were self-reported in a sleep module at round 3 (2013), including (1) frequency (non-napper, infrequent, frequent); (2) intention (non-napper, intentional, unintentional); (3) duration (non-napper, ≤30 minutes, >30 minutes). Dementia classification was established at each round using a validated algorithm, and possible/probable dementia served as our outcome. We included 995 participants free of possible/probable dementia at round 3 who were followed up to round 13 (2023). Discrete-time complementary log-log models were used for hazard ratio (HR) of incident possible/probable dementia associated with napping characteristics, adjusting for demographic, health, and sleep variables (duration, daytime sleepiness, and obstructive sleep apnea risk).
Results
When compared to non-nappers, frequent nappers had 1.48 times the hazard of developing incident possible/probable dementia (95% confidence interval [CI]: 1.14, 1.94). Unintentional napping was associated with incident possible/probable dementia (HR = 1.39, 95% CI: 1.06, 1.81). No associations were found for infrequent or intentional napping. Napping ≤30 minutes was associated with possible/probable dementia (HR = 1.40, 95% CI: 1.06, 1.85), but the association of napping >30 minutes was attenuated with additional adjustment for sleep variables (HR = 1.28, 95% CI: 0.96, 1.69).
Conclusions
Daytime napping may be a modifiable risk factor for dementia if supported by further evidence. Future studies with detailed napping characteristics and longer follow-up are needed.
Keywords: Dementia, Cognitive aging, Sleep, Epidemiology
Introduction
The burden of dementia is growing in the U.S. Currently, 6.9 million U.S. older adults are estimated to live with dementia, a number that will likely double by 2060.1 Dementia has major physical, cognitive, psychosocial, and economic effects on patients and their families, and no cure yet exists.2–4 Up to 45% of global dementia cases might be prevented by addressing modifiable risk factors.5 Therefore, identification of modifiable risk factors is critical to delay the onset of dementia.
Nighttime sleep disturbances are an increasingly recognized risk factor for dementia that are prevalent among older adults and modifiable.6 Daytime napping refers to sleep episodes occurring in the daytime. Daytime napping is also important to consider in relation to dementia. Napping is more common among older versus younger adults and can be related to nighttime sleep in various ways.7 For example, naps may be due to disturbed nighttime sleep including occult or diagnosed sleep disorders that cause excessive daytime sleepiness, and therefore play a compensatory role; however, napping may contribute to nighttime sleep disturbances (eg, sleep onset or maintenance problems) by decreasing homeostatic sleep drive.
Prior evidence of associations between napping and cognitive outcomes remains inconsistent.8–12 While some longitudinal studies report that longer and more frequent daytime naps increase dementia risk,8,9,12 other studies report null associations or even potentially protective effects of short napping.10,11 Notably, the studies that used actigraphic measures of napping tended to find significant associations, although use of actigraphy to detect daytime sleep is controversial, particularly in older adults, given the increased prevalence of sedentary wakefulness during the day that can be miscategorized as sleep.13,14 Several systematic reviews and meta-analyses on napping and cognitive health have also reached inconsistent conclusions. Some have suggested an association between longer napping duration and cognitive impairment, a null association, or potential benefits of short or moderate napping duration on cognitive function.15–17 Napping has different implications across cultures, and inconsistencies in the literature may partially reflect differences in geographic context and social norms related to napping behaviors.15 A further complicating factor is reverse causation; napping may also be due to neurological changes, such as degeneration of wake-promoting neurons associated with tau pathology that results in excessive daytime sleepiness and, hence, in naps.18,19
Longitudinal evidence is needed to better establish the temporal sequencing of the napping-dementia association and clarify the underlying mechanisms. Evidence from large population-based cohorts can help identify subpopulations among whom the napping-dementia link is stronger versus weaker. In addition, napping intention has rarely been examined in previous studies, yet it may have important implications for reasons for napping (habits versus excessive daytime sleepiness due to nighttime sleep disturbances) and underlying neurological changes and other health conditions that contribute to dementia. Studies in which different napping characteristics are examined, including nap frequency, intention, and duration, may help advance our understanding of the role of napping in dementia risk.
We investigated the longitudinal association between napping characteristics and incident possible/probable dementia over 10 years of follow-up among a nationally representative cohort of U.S. Medicare beneficiaries aged 65 years and older. We hypothesized that napping characteristics, especially frequent, unintentional, and long naps, are associated with a higher dementia risk among older adults.
Methods
Study population
This study used data from the National Health and Aging Trends Study (NHATS), a longitudinal panel survey of Medicare beneficiaries aged 65 years and older in the United States that has been conducted annually since 2011. Participants complete annual in-person interviews on cognitive, physical, social, and functional status. Written informed consent was obtained from all participants, and the study protocols were approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board. Only de-identified publicly available data were used for this study.
Among 5799 participants at NHATS round 3 (2013), a subset of 1623 participants received an additional sleep module, including questions about napping characteristics (Figure 1).20 Round 3 therefore served as our study baseline. When compared to the 1623 participants who participated in the sleep module, the remaining 4176 participants at round 3 were older but were similar in other demographics and health characteristics (Table S1). A total of 1577 participants in the sleep module completed napping questions. NHATS has developed a validated algorithm for identifying individuals with probable and possible dementia at each round.21 We excluded 50 participants with missing dementia classification at round 3, and 117 participants with missing covariates (N = 9: education; N = 1: smoking; N = 36: body mass index [BMI]; N = 4: health conditions; N = 14: Patient Health Questionaire-2 [PHQ-2]; N = 4: Generalized Anxiety Disorder-2 [GAD-2]; N = 45: sleep duration; N = 2: daytime sleepiness; N = 2: obstructive sleep apnea [OSA] risk), leaving a total of 1410 participants at round 3 (Figure 1). We did not impute missing covariates due to the relatively low proportion of missing data (7.7%) and the lack of auxiliary variables to support a valid multiple imputation model for covariates such as OSA risk. Of the 1410 participants, 995 were free of baseline possible/probable dementia (primary outcome) and had at least one follow-up round between rounds 4-13. When compared to the 995 participants, the remaining 628 participants of the sleep module were older, less likely to self-identify as non-Hispanic White, had lower levels of educational attainment, lower BMI, and greater depressive and anxiety symptoms (Table S2). For secondary outcomes of impairments in orientation, memory, and executive function, 1110, 1056, and 1115 participants, respectively, were included in the study population.
Figure 1.
Flowchart of study population derivation, the National Health and Aging Trends Study (NHATS) rounds 3-13 (2013-2023). STOP-BANG, Snoring, Tiredness, Observed apneas, high blood Pressure, BMI, Age, Neck circumference, Gender.
Dementia classification
The NHATS dementia classification algorithm classified participants as having no, possible, and probable dementia at each round.21 Probable dementia was defined as meeting any of the following criteria: (1) a diagnosis of dementia or Alzheimer’s disease reported by a participant or a proxy respondent; (2) a score of 2 or above indicating informant-reported dementia on the Ascertain Dementia 8-item (AD8) administered during the proxy interview22; (3) impairment (≤1.5 standard deviation [SD] below the mean) in at least two of the three cognitive domains (orientation; memory; and executive function). Possible dementia was defined as having impairment in one cognitive domain. We used a composite outcome of possible/probable dementia as our primary outcome. A sensitivity analysis was conducted examining probable dementia as the outcome.
Impairments in orientation, memory, and executive function served as secondary outcomes. Orientation was assessed by recalling date, month, year, and day of the week, as well as naming the President and Vice President (score range 0-8; ≤3: impairment). Memory was assessed by immediate and delayed 10-word recall (score range 0-20; ≤3: impairment). Executive function was assessed by the Clock-drawing Test (score range 0-5; ≤1: impairment).
Napping characteristics
Napping frequency was defined based on the question “In the last month, how often did you take naps during the day?” Participants who answered “never” or “rarely” were categorized as non-nappers, those who answered “some days” were categorized as infrequent nappers, and those who answered “most days” or “every day” were categorized as frequent nappers. These non-nappers were consistently included as the reference group in all analyses of napping frequency, intention, and duration.
Among nappers, we further defined napping intention based on the question “In general, were these naps planned, or did you fall asleep without meaning to?” Nappers who answered “naps planned” were classified as intentional nappers, and those who answered “fell asleep without meaning to” or “both (some planned/some not)” were classified as unintentional nappers.
Among nappers, we also defined napping duration based on the question, “On average, how long were these naps?” Responses were categorized as ≤30 minutes or >30 minutes for analysis.
Covariates
Demographic information included age in years (65-69; 70-74; 75-79; 80-84; 85-89; 90+), sex (male; female), race/ethnicity (non-Hispanic White; non-Hispanic Black; Hispanic or other groups including American Indian, Asian, Native Hawaiian, Pacific Islander, other specified, and more than one race combined), and education (less than high school; high school graduate; above high school).
For smoking status, participants were asked, “Did you ever smoke cigarettes regularly, at least 1 cigarette a day?” at round 1 and “Do you smoke cigarettes now?” at rounds 2 and 3. Those answering no at rounds 1, 2, and 3 were considered never smokers, and all others were considered ever smokers. Self-reported height and weight at round 3 were used to calculate continuous BMI in kg/m2. We calculated the number of health conditions as a count variable, including self-reported diagnoses of hypertension, diabetes, heart attack, heart disease, stroke, lung disease, and cancer, and analyzed as 0, 1, 2, or ≥3. PHQ-2 is a 2-item screening tool for depression in which participants reported the frequency of “having little interest or pleasure in doing things” and “feeling down, depressed, or hopeless” over the last month.23 Total PHQ-2 score ranges from 0-6, and a higher score indicates greater depressive symptomatology. GAD-2 is a 2-item screening tool for generalized anxiety disorder in which participants reported the frequency of “feeling nervous, anxious, or on edge” and “being unable to stop or control worrying.”24 Total GAD-2 score ranges from 0-6, and a higher score indicates greater anxiety symptoms.
Three sleep characteristics at round 3 were included as they can impact daytime napping behaviors. Sleep duration in hours was self-reported. Participants self-reported their frequency of daytime sleepiness and were classified as having daytime sleepiness if they answered “some days,” “most days,” or “every day” versus not if they answered “never” or “rarely.” A modified version of the STOP-BANG (Snoring, Tiredness, Observed apneas, high blood Pressure, BMI, Age, Neck circumference, Gender) score in NHATS was computed to screen for OSA risk (score range 0-7; ≥3: elevated OSA risk).25–27
Statistical analysis
For descriptive analyses, we compared unweighted participant characteristics by napping characteristics using Pearson’s chi-squared test for categorical variables and Kruskal-Wallis test for continuous variables.
The complex survey design of NHATS was incorporated using survey weights to obtain nationally representative estimates in regression analyses. To examine if baseline (round 3) napping characteristics were associated with development of incident possible/probable dementia (our primary outcome) during 10 years of follow-up (rounds 4-13), we used discrete-time complementary log-log models. No violation of the proportional hazards assumption was observed. A total of 995 participants in our study population free of baseline possible/probable dementia and with baseline and ≥1 follow-up round were followed for incident possible/probable dementia, death, loss to follow-up, or end of follow-up at round 13 (2023), whichever occurred first. To further address potential reverse causation, we excluded dementia cases during the first 5 years of follow-up and examined the associations of napping characteristics with incident possible/probable dementia at or after round 8. A sensitivity analysis with probable dementia as the outcome was conducted (N = 1109). Given the association between napping and mortality, dementia events may not be observed due to death. We therefore conducted a competing risk analysis to estimate the cause-specific hazard of death without possible/probable dementia by napping characteristics using discrete-time complementary log-log models.28,29
As our secondary outcomes, we investigated whether baseline napping characteristics were associated with the development of incident impairments in orientation (N = 1110), memory (N = 1056), or executive function (N = 1115) during 10 years of follow-up using discrete-time complementary log-log models.
Model 1 adjusted for age, sex, race/ethnicity, and education. Model 2 further adjusted for smoking, BMI, number of health conditions, and depressive and anxiety symptoms. Model 3 further adjusted for sleep duration, daytime sleepiness, and the modified STOP-BANG score for OSA risk to examine whether the napping-dementia association was independent of sleep characteristics. In model 3, as sex and hypertension are inherently categorical variables and were included as such in the calculation of the STOP-BANG, we removed sex and hypertension as covariates for adjustment.
In exploratory stratified analyses, we investigated whether the associations between napping characteristics and incident possible/probable dementia differ by sleep characteristics (sleep duration ≥7 vs <7 hours; having daytime sleepiness vs not; elevated vs low OSA risk) and demographics (age 65-74 vs 75-84 vs 85+ years; male vs female; non-Hispanic White vs non-Hispanic Black vs Hispanic/Other). For each model, we additionally included an interaction term between napping characteristics and moderators.
Analyses were conducted using Stata version 18.0 (StataCorp LLC, College Station, TX). A two-sided p-value <.05 was considered statistically significant. We prespecified our primary analysis to examine the associations of three napping characteristics (frequency, intention, and duration) with incident possible/probable dementia, and in light of limited and inconsistent prior findings, we did not adjust for multiple comparisons.
Results
Participant characteristics by napping characteristics
Among 995 participants in our primary analysis for possible/probable dementia, 482 (48.4%) were categorized as non-nappers, 292 (29.3%) were categorized as infrequent nappers, and 221 (22.2%) were categorized as frequent nappers (Table 1). Compared to non-nappers and infrequent nappers, frequent nappers had higher BMI, a greater number of health conditions, higher depressive symptoms, and were more likely to have daytime sleepiness. Compared to non-nappers, both infrequent and frequent nappers were more likely to be males and have a higher OSA risk (ie, higher modified STOP-BANG score).
Table 1.
Participant characteristics by napping frequency, the National Health and Aging Trends Study (NHATS) rounds 3-13 (2013-2023).
| Characteristicb | Total N = 995 | Napping frequencya |
p-Valuec | ||
|---|---|---|---|---|---|
| Non-napper | Infrequent napper | Frequent napper | |||
| N = 482 | N = 292 | N = 221 | |||
| Age (year), N (%) | |||||
| 65-69 | 133 (13.4) | 63 (13.1) | 43 (14.7) | 27 (12.2) | .18 |
| 70-74 | 267 (26.8) | 140 (29.0) | 78 (26.7) | 49 (22.2) | |
| 75-79 | 213 (21.4) | 109 (22.6) | 57 (19.5) | 47 (21.3) | |
| 80-84 | 198 (19.9) | 94 (19.5) | 59 (20.2) | 45 (20.4) | |
| 85-89 | 121 (12.2) | 45 (9.3) | 42 (14.4) | 34 (15.4) | |
| 90+ | 63 (6.3) | 31 (6.4) | 13 (4.5) | 19 (8.6) | |
| Sex, N (%) | |||||
| Male | 398 (40.0) | 150 (31.1) | 143 (49.0) | 105 (47.5) | <.001 |
| Female | 597 (60.0) | 332 (68.9) | 149 (51.0) | 116 (52.5) | |
| Race/ethnicity, N (%) | |||||
| White, non-Hispanic | 775 (77.9) | 373 (77.4) | 236 (80.8) | 166 (75.1) | .54 |
| Black, non-Hispanic | 160 (16.1) | 80 (16.6) | 42 (14.4) | 38 (17.2) | |
| Hispanic/otherd | 60 (6.0) | 29 (6.0) | 14 (4.8) | 17 (7.7) | |
| Education, N (%) | |||||
| Less than high school | 194 (19.5) | 88 (18.3) | 52 (17.8) | 54 (24.4) | .27 |
| High school graduate | 249 (25.0) | 117 (24.3) | 78 (26.7) | 54 (24.4) | |
| Above high school | 552 (55.5) | 277 (57.5) | 162 (55.5) | 113 (51.1) | |
| Smoking, N (%) | |||||
| Never | 499 (50.2) | 250 (51.9) | 145 (49.7) | 104 (47.1) | .49 |
| Ever | 496 (49.8) | 232 (48.1) | 147 (50.3) | 117 (52.9) | |
| Body mass index (kg/m2), median (IQR) | 27.0 (24.0, 31.0) | 26.6 (24.0, 29.5) | 27.1 (23.8, 32.5) | 28.0 (24.4, 31.9) | .002 |
| Number of health conditionse, N (%) | |||||
| 0 | 176 (17.7) | 105 (21.8) | 43 (14.7) | 28 (12.7) | .01 |
| 1 | 370 (37.2) | 185 (38.4) | 107 (36.6) | 78 (35.3) | |
| 2 | 276 (27.7) | 120 (24.9) | 87 (29.8) | 69 (31.2) | |
| ≥3 | 173 (17.4) | 72 (14.9) | 55 (18.8) | 46 (20.8) | |
| Patient Health Questionnaire-2, median (IQR) | 0.0 (0.0, 1.0) | 0.0 (0.0, 1.0) | 0.0 (0.0, 1.0) | 0.0 (0.0, 2.0) | .01 |
| Generalized Anxiety Disorder-2, median (IQR) | 0.0 (0.0, 1.0) | 0.0 (0.0, 1.0) | 0.0 (0.0, 1.0) | 0 (0.0, 1.0) | .43 |
| Nighttime sleep duration (hour), median (IQR) | 7.0 (6.0, 8.0) | 7.0 (6.0, 8.0) | 7.0 (6.0, 8.0) | 7.0 (6.0, 8.0) | .66 |
| Daytime sleepiness, N (%) | |||||
| Never | 325 (32.7) | 206 (42.7) | 65 (22.3) | 54 (24.4) | <.001 |
| Rarely | 297 (29.8) | 169 (35.1) | 86 (29.5) | 42 (19.0) | |
| Some days | 283 (28.4) | 87 (18.0) | 114 (39.0) | 82 (37.1) | |
| Most days | 59 (5.9) | 11 (2.3) | 21 (7.2) | 27 (12.2) | |
| Every day | 31 (3.1) | 9 (1.9) | 6 (2.1) | 16 (7.2) | |
| Modified STOP-BANG score, median (IQR) | 3.0 (2.0, 3.0) | 2.0 (2.0, 3.0) | 3.0 (2.0, 4.0) | 3.0 (2.0, 4.0) | <.001 |
| Incident possible/probable dementia, N (%) | 417 (41.9) | 182 (37.8) | 122 (41.8) | 113 (51.1) | .004 |
| Follow-up period (year), median (IQR) | 5.0 (2.0, 10.0) | 6.0 (2.0, 10.0) | 5.0 (2.0, 9.0) | 4.0 (2.0, 8.0) | <.001 |
Abbreviations: IQR, interquartile range; STOP-BANG, Snoring, Tiredness, Observed apneas, high blood Pressure, BMI, Age, Neck circumference, Gender
Participants were categorized based on the question “In the last month, how often did you take naps during the day? Would you say every day, most days, some days, rarely, or never?”. Participants were categorized as non-nappers if they answered “never” or “rarely,” infrequent nappers if they answered “some days,” and frequent nappers if they answered “most days” or “every day.”
Participant characteristics were presented as number (percentage) for categorical variables and median (IQR) for continuous variables. For descriptive purposes, this table presented unweighted characteristics in the study population.
p-values were calculated using Pearson’s chi-squared test for categorical variables and Kruskal-Wallis test for continuous variables.
Other race/ethnicity groups included American Indian/Asian/Native Hawaiian/Pacific Islander/Other specified/More than one race.
Health conditions included self-reported hypertension, diabetes, heart attack, heart disease, stroke, lung disease, and cancer.
In addition to 482 (48.4%) non-nappers, 228 (22.9%) were categorized as intentional nappers, and 285 (28.6%) were categorized as unintentional nappers (Table S3). Compared to non-nappers, intentional and unintentional nappers were more likely to be males. Compared to non-nappers, unintentional nappers had a lower level of educational attainment, higher BMI, a greater number of health conditions, higher depressive symptoms, and higher OSA risk, and were more likely to have daytime sleepiness, and intentional nappers were more likely to self-identify as non-Hispanic White.
For napping duration, 482 (48.4%) were non-nappers, and among nappers, 231 (23.2%) had napping duration ≤30 minutes, and 282 (28.3%) had napping duration >30 minutes (Table S4). Compared to non-nappers and those napping for ≤30 minutes, nappers with duration >30 minutes had a greater number of health conditions and higher depressive symptoms. Compared to non-nappers, participants napping for ≤30 and >30 minutes were both more likely to be males, have higher BMI, daytime sleepiness, and higher OSA risk.
Napping characteristics and incident dementia
Among 995 participants, 417 (41.9%) developed incident possible/probable dementia over a median follow-up of 5 years, including 182 (37.8%) among 482 non-nappers (median follow-up = 6 years), 122 (41.8%) among 292 infrequent nappers (median follow-up = 5 years), and 113 (51.1%) among 221 frequent nappers (median follow-up = 4 years). When compared to non-nappers, frequent, but not infrequent napping, was associated with a higher risk of developing incident possible/probable dementia, after adjusting for demographics in model 1 (hazard ratio [HR] = 1.65, 95% confidence interval [CI]: 1.28, 2.12), health covariates in model 2 (HR = 1.58, 95% CI: 1.23, 2.04), and sleep characteristics in model 3 (HR = 1.48, 95% CI: 1.14, 1.94) (Table 2).
Table 2.
Multivariable-adjusted associations of napping characteristics with incident possible/probable dementia, the National Health and Aging Trends Study (NHATS) rounds 3-13 (2013-2023) (N = 995).a
| Napping characteristics | N Outcome/NTotal | Model 1b |
Model 2c |
Model 3d |
|||
|---|---|---|---|---|---|---|---|
| HR (95% CI) | p-Value | HR (95% CI) | p-Value | HR (95% CI) | p-Value | ||
| Napping frequency | |||||||
| Non-napper | 182/482 | Ref. | – | Ref. | – | Ref. | – |
| Infrequent napper | 122/292 | 1.30 (0.99, 1.72) | .06 | 1.26 (0.94, 1.70) | .12 | 1.22 (0.92, 1.62) | .17 |
| Frequent napper | 113/221 | 1.65 (1.28, 2.12) | <.001 | 1.58 (1.23, 2.04) | .001 | 1.48 (1.14, 1.94) | .004 |
| Napping intention | |||||||
| Non-napper | 182/482 | Ref. | – | Ref. | – | Ref. | – |
| Intentional napper | 98/228 | 1.37 (1.02, 1.83) | .04 | 1.32 (0.97, 1.80) | .08 | 1.28 (0.95, 1.72) | .11 |
| Unintentional napper | 137/285 | 1.53 (1.20, 1.95) | .001 | 1.47 (1.15, 1.89) | .003 | 1.39 (1.06, 1.81) | .02 |
| Napping duration | |||||||
| Non-napper | 182/482 | Ref. | – | Ref. | – | Ref. | – |
| ≤30 min | 107/231 | 1.45 (1.09, 1.92) | .01 | 1.46 (1.11, 1.94) | .01 | 1.40 (1.06, 1.85) | .02 |
| >30 min | 128/282 | 1.46 (1.13, 1.88) | .01 | 1.35 (1.01, 1.79) | .04 | 1.28 (0.96, 1.69) | .09 |
Abbreviations: CI, confidence interval; HR, hazard ratio; Ref, reference.
We estimated the HR of incident possible/probable dementia by napping characteristics using a discrete-time complementary log-log model. Complex survey design was taken into account by applying sampling weights.
Model 1 adjusted for age, sex, race/ethnicity, and education.
Model 2 adjusted for Model 1 covariates, smoking status, body mass index, number of health conditions (hypertension, diabetes, heart attack, heart disease, stroke, lung disease, and cancer), Patient Health Questionnaire-2, and Generalized Anxiety Disorder-2.
Model 3 adjusted for Model 2 covariates, sleep duration in hours, frequency of daytime sleepiness, and the modified STOP-BANG score. As sex and hypertension were included in the same modeling form in the modified STOP-BANG score, they were not separately adjusted for in the model.
A total of 98 (43.0%) out of 228 intentional nappers (median follow-up = 5 years) and 137 (48.1%) out of 285 unintentional nappers (median follow-up = 4 years) developed possible/probable dementia. Intentional napping was associated with incident possible/probable dementia after adjusting for demographics (HR = 1.37, 95% CI: 1.02, 1.83) (Table 2). The association was attenuated with further adjustment for health covariates (HR = 1.32, 95% CI: 0.97, 1.80) and sleep characteristics (HR = 1.28, 95% CI: 0.95, 1.72). Unintentional napping was associated with incident possible/probable dementia, independent of demographics, health covariates, and sleep characteristics (Model 1: HR = 1.53, 95% CI: 1.20, 1.95; Model 2: HR = 1.47, 95% CI: 1.15, 1.89; Model 3: HR = 1.39, 95% CI: 1.06, 1.81).
For napping duration, 107 (46.3%) out of 231 participants napping ≤30 minutes (median follow-up = 4.0 years) and 128 (45.4%) out of 282 participants napping >30 minutes (median follow-up = 4.5 years) developed possible/probable dementia. Napping ≤30 minutes was associated with higher risk of incident possible/probable dementia across all models when compared to non-nappers (Model 1: HR = 1.45, 95% CI: 1.09, 1.92; Model 2: HR = 1.46, 95% CI: 1.11, 1.94; Model 3: HR = 1.40, 95% CI: 1.06, 1.85) (Table 2). Napping >30 minutes was associated with possible/probable dementia when adjusting for demographics (HR = 1.46, 95% CI: 1.13, 1.88) and health covariates (HR = 1.35, 95% CI: 1.01, 1.79). The association was attenuated with additional adjustment for sleep characteristics (HR = 1.28, 95% CI: 0.96, 1.69).
When only considering possible/probable dementia cases at or after round 8, our inferences remained similar for the associations of frequent napping (HR = 1.75, 95% CI: 1.22, 2.52) and unintentional napping (HR = 1.40, 95% CI: 0.95, 2.06) with dementia, after adjusting for demographics, health covariates, and sleep characteristics (Table S5). The association between napping ≤30 minutes and incident dementia was attenuated, however (HR = 1.24, 95% CI: 0.83, 1.85).
In the sensitivity analysis only considering probable dementia as the outcome, frequent napping was similarly associated with probable dementia across all models (Table S6). Both intentional and unintentional napping were associated with probable dementia after adjusting for demographics, but the associations were attenuated when further adjusting for health covariates and sleep characteristics. Napping ≤30 minutes was not associated with probable dementia, while napping >30 minutes was similarly associated with probable dementia after adjusting for demographics and health covariates.
In the competing risk analysis, napping characteristics were not associated with deaths without possible/probable dementia after adjusting for demographics, health covariates, and sleep characteristics (Table S7).
Napping characteristics and incident impairments in orientation, memory, and executive function
We did not find associations between napping characteristics and developing incident impairment in orientation (Table S8).
Frequent napping, but not infrequent napping, was associated with a higher risk of developing impairment in memory, independent of adjustment for demographics, health covariates, and sleep characteristics (Model 1: HR = 1.58, 95% CI: 1.15, 2.17; Model 2: HR = 1.49, 95% CI: 1.08, 2.07; Model 3: HR = 1.49, 95% CI: 1.07, 2.08) (Table S9). Unintentional nappers, when compared to non-nappers, were more likely to develop impairment in memory (Model 1: HR = 1.50, 95% CI: 1.10, 2.03; Model 2: HR = 1.42, 95% CI: 1.05, 1.93; Model 3: HR = 1.42, 95% CI: 1.02, 1.98). No associations were found for intentional nappers. Napping ≤30 minutes, but not >30 minutes, was associated with memory impairment across all models (Model 1: HR = 1.49, 95% CI: 1.04, 2.13; Model 2: HR = 1.50, 95% CI: 1.05, 2.16; Model 3: HR = 1.47, 95% CI: 1.00, 2.15).
Frequent napping was similarly associated with impairment in executive function (Model 1: HR = 1.54, 95% CI: 1.02, 2.30; Model 2: HR = 1.48, 95% CI: 0.99, 2.19; Model 3: HR = 1.65, 95% CI: 1.12, 2.42) (Table S10). The associations of intentional and unintentional napping were not statistically significant in most models, except for intentional napping after adjustment for demographics, health covariates, and sleep characteristics (HR = 1.57, 95% CI: 1.02, 2.41). When compared to non-nappers, nappers with duration >30 minutes were more likely to have impairment in executive function, but the association was only statistically significant after additional adjustment for sleep characteristics in model 3 (Model 1: HR = 1.41, 95% CI: 0.97, 2.06; Model 2: HR = 1.37, 95% CI: 0.94, 2.01; Model 3: HR = 1.50, 95% CI: 1.02, 2.20).
Associations between napping characteristics and incident dementia stratified by sleep characteristics and demographics
In exploratory stratified analyses, we did not find differential associations of napping characteristics and incident possible/probable dementia by sleep duration (Figure 2, panel A; Table S11), daytime sleepiness (Figure 2, panel B; Table S12), and OSA risk (Figure 2, panel C; Table S13).
Figure 2.
Multivariable-adjusted association of napping characteristics with incident possible/probable dementia by (A) Sleep duration (<7 vs ≥7 hours); (B) Daytime sleepiness (yes vs no); (C) Obstructive sleep apnea risk (elevated [modified STOP-BANG score ≥3] vs low [modified STOP-BANG score <3]), the National Health and Aging Trends Study (NHATS) rounds 3-13 (2013-2023) (N = 995). We estimated the hazard ratio of incident possible/probable dementia by napping characteristics using a discrete-time complementary log-log model. Complex survey design was taken into account by applying sampling weights. Models included napping, moderator, and an interaction term between napping and moderator. Models adjusted for age, sex, race/ethnicity, education, smoking status, body mass index, number of health conditions (hypertension, diabetes, heart attack, heart disease, stroke, lung disease, and cancer), Patient Health Questionnaire-2, and Generalized Anxiety Disorder-2. STOP-BANG, Snoring, Tiredness, Observed apneas, high blood Pressure, BMI, Age, Neck circumference, Gender.
The associations between napping characteristics and incident possible/probable dementia did not differ by age (Figure 3, panel A; Table S14) or sex (Figure 3, panel B; Table S15). For race/ethnicity, the associations of infrequent napping (HR = 4.46, 95% CI: 1.47, 13.51; p-interaction = .01) and napping >30 minutes (HR = 2.85, 95% CI: 1.16, 7.01; p-interaction = .05) with possible/probable dementia were only significant among Hispanic/Other race/ethnicity groups (Figure 3, panel C; Table S16). However, the sample size of the Hispanic/Other group was small.
Figure 3.
Multivariable-adjusted association of napping characteristics with incident possible/probable dementia by (A) Age (65-74 vs 75-84 vs 85+ years); (B) Sex (male vs female); (C) Race/Ethnicity (Non-Hispanic White vs Non-Hispanic Black vs Hispanic/Other), the National Health and Aging Trends Study (NHATS) rounds 3-13 (2013-2023) (N = 995). We estimated the hazard ratio of incident possible/probable dementia by napping characteristics using a discrete-time complementary log-log model. Complex survey design was taken into account by applying sampling weights. Models included napping, moderator, and an interaction term between napping and moderator. Models adjusted for age, sex, race/ethnicity, education, smoking status, body mass index, number of health conditions (hypertension, diabetes, heart attack, heart disease, stroke, lung disease, and cancer), Patient Health Questionnaire-2, and Generalized Anxiety Disorder-2.
Discussion
In a nationally representative sample of 995 U.S. older adults enrolled in Medicare, frequent and unintentional napping were associated with a higher risk of developing new-onset possible/probable dementia over ten years, independent of demographic, health, and sleep characteristics. Both napping ≤30 minutes and >30 minutes had a higher risk of developing possible/probable dementia when compared to non-nappers. The associations were robust to adjustment for demographic and health covariates, but with additional adjustment for sleep characteristics, only napping ≤30 minutes was associated with possible/probable dementia.
The relationship between napping and cognitive health remains unclear. One prior study was conducted among 2751 community-dwelling older men (mean age = 76 years) enrolled in the Osteoporotic Fractures in Men (MrOS) Sleep Study, and the study found that actigraphy-assessed napping duration of ≥120 minutes was associated with a 66% higher risk of developing cognitive impairment, as compared to napping <30 minutes.8 Another study among 1401 participants (mean age = 81 years) of the Rush Memory and Aging Project reported an association between longer actigraphy-assessed naps and risk of developing Alzheimer’s dementia, in which participants napping ≥1 hour/day had approximately 40% increased risk compared to those who napped <1 hour/day.9 A study in the UK Biobank found that self-reported frequent napping (often/all of the time) was associated with a higher dementia risk over 11 years among 397 777 adults aged 38-73 years.12 Other studies, however, have reported different results. For example, one study using the Million Women Study data reported a null association between self-reported daytime napping and dementia risk among 830 716 women in the UK (mean age = 60 years).11 Another study in Japan reported a lower cumulative incidence of cognitive decline associated with <30 minutes of self-reported daytime napping as compared to no napping among 389 community-dwelling older adults (mean age = 75 years).10 Other napping characteristics, such as napping intention, have been studied less often. In a previous cross-sectional study using data from NHATS rounds 3 or 4, unintentional nappers showed worse cognitive performance than intentional nappers, whereas our study found an elevated risk of incident dementia between rounds 3-13 among unintentional nappers.20 As with sleep disturbances, the association between napping and dementia may be bidirectional, such that neurological changes that adversely affect cognitive function may also lead to increased daytime napping.9 While our study focused on examining napping as a potential risk factor for dementia, and our sensitivity analysis addressing reverse causation by restricting to incident dementia cases occurring after 5 years yielded similar inferences, future studies should further disentangle the potential bidirectionality of the napping-dementia association.
Inconsistencies among the findings of studies of napping and dementia might be attributed to heterogeneity in study design. Objective assessment methods (eg, actigraphy) might more accurately capture napping characteristics, although it can be controversial to use actigraphic sleep algorithms validated on nighttime polysomnography to quantify daytime sleep, particularly in older adults who commonly engage in sedentary behavior that can be difficult to distinguish from napping.13,14 Self-reported napping, however, reflects individuals’ perceptions of napping characteristics and captures different constructs. This can be further complicated by cultural perspectives on napping. Whether napping is culturally accepted and encouraged and is a part of daily routine can influence how individuals report their napping characteristics. A systematic review and meta-analysis reported overall longer daytime napping associated with cognitive impairment, but when stratified by region, the association was found in North America and Europe but not in Asia.15 In addition, underlying health conditions (eg, cognitive impairment, depression) that could influence the self-report of napping may confound the napping-dementia association.
Inconsistencies in findings from prior studies may also be attributable to differences in population characteristics, such as age, sex, and race/ethnicity, which could be important moderators of the napping-dementia association. Sex and racial/ethnic differences in sleep health have been widely reported; for example, women may be more likely to report sleep disturbances and shorter daytime napping, and racial/ethnic minorities may be more likely to have shorter sleep duration and poorer sleep quality.30–32 Also, females and racial/ethnic minorities experience a disproportionate burden of dementia.33,34 Our stratified analyses were exploratory due to small subgroups, and we did not find a clear pattern of moderation of the napping-dementia association by demographic characteristics. Thus, further examination in larger cohorts with diverse population characteristics is needed.
Daytime napping may reflect a need to compensate for poor nighttime sleep. The relationship between daytime napping, nighttime sleep, and dementia is likely to be complex. For example, daytime sleepiness may drive napping and has been associated with dementia.35,36 Other sleep disturbances such as insomnia and OSA may also contribute to daytime napping via increased daytime sleepiness.37,38 Thus, we performed two parts of analyses. First, we adjusted for sleep characteristics including sleep duration, daytime sleepiness, and OSA risk in addition to demographic and health variables. The associations of frequent and unintentional napping as well as napping ≤30 minutes with possible/probable dementia were robust to the adjustment, suggesting that daytime napping might be a risk factor for dementia, in addition to nighttime sleep disturbances. However, the attenuated association between napping >30 minutes and possible/probable dementia may reflect that longer napping is more relevant to compensating for nighttime sleep, and therefore may reflect underlying associations between nighttime sleep disturbances and dementia. Our second part of the analysis examined sleep characteristics as potential moderators of the napping-dementia association, but the association did not differ by sleep characteristics. Future studies need to characterize daytime napping and nighttime sleep and consider incorporating objective assessments to further disentangle the contribution of nighttime sleep disturbances to the napping-dementia association. Our study has both napping and nighttime sleep based on self-report, and objective measures may yield different results. In addition, we were only able to adjust for OSA risk based on the modified STOP-BANG questionnaire, and daytime sleepiness was based on a single self-reported question. Studies with polysomnography-based OSA diagnoses and daytime sleepiness assessed via validated questionnaires such as the Epworth Sleepiness Scale would enhance the literature in this domain.
Cognitive health outcomes vary across studies. Previous research on napping has relied on different batteries of neuropsychological tests, hospital records, or adjudicated cognitive diagnoses. The napping-dementia association might vary by the cognitive domains measured in studies and the duration of follow-up. One systematic review suggested potential beneficial effects of napping on psychomotor function and working memory based on intervention studies, but with limited current evidence.17 The present study used incident possible/probable dementia based on a validated algorithm in NHATS and also investigated different cognitive domains as secondary outcomes. While algorithmic approaches to dementia classification are limited by the lack of adjudication by a clinician, they reduce time and effort in large national cohorts and offer opportunities for earlier detection of dementia. Overall, we found consistent associations of napping characteristics with memory impairment and possible/probable dementia, but not for orientation and executive function domains. Our neuropsychological test battery is limited, and the orientation domain (based on recall of time and name) and executive function domain (based on the Clock Drawing Test) might not be as sensitive in capturing more subtle cognitive deficits, as compared to the memory domain (based on 10-word recall).39,40 In addition, our primary outcome combined possible and probable dementia; when only examining probable dementia, which captured more severe cognitive impairment, the association of frequent napping persisted, but not for unintentional napping and napping ≤30 minutes. Different napping characteristics may therefore have varying sensitivity in capturing progression along the dementia continuum. Future longitudinal population-based studies with more comprehensive assessments of napping and cognitive health, along with longer follow-up periods, will help clarify and resolve inconsistencies in the current evidence.
Our study has several limitations. The napping characteristics were based solely on self-report and might be subject to recall bias, although the approach is consistent with usual clinical practice. Further, dementia was based on a validated algorithm, but adjudicated diagnoses were not available, and we were not able to investigate potential differential associations of napping characteristics with different dementia etiologies (eg, Alzheimer’s dementia and vascular dementia). Also, our secondary outcomes of impairment in three cognitive domains were based on a limited number of neuropsychological tests. Nonetheless, this study contributes longitudinal evidence over 10 years of follow-up in a diverse national cohort to the inconsistent literature on napping and dementia. The assessment of different napping characteristics, adjustment for demographic, health, and sleep variables, and investigation of potential moderation should be considered in future studies to further elucidate the contribution of napping to dementia risk.
In conclusion, in a nationally-representative sample of older Medicare beneficiaries, frequent and unintentional napping were associated with a higher risk of developing incident possible/probable dementia over 10 years. Napping ≤30 minutes and >30 minutes were both associated with subsequent development of possible/probable dementia, though the association for >30 minutes of napping was not independent of sleep characteristics. In addition to nighttime sleep disturbances, daytime napping might be another modifiable risk factor to be targeted among older adults for primary prevention of dementia, if supported by further causal evidence. Even if this association is not causal, napping may be a useful marker of future dementia risk to guide subsequent prevention studies among high-risk populations.
Supplementary material
Supplementary material is available at The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences online.
Funding
This study was supported in part by the National Institute on Aging (R01AG079391, K02AG095384, P30AG028740, U01AG032947, K23AG064036, and 5T32AG027668-19).
Conflicts of interest
J.L. is funded by NIH/NIA. H.A. is funded by NIH/NIA. E.M.W.’s institution received research funding from the AASM Foundation, Department of Defense, Merck, NIH/NIA, ResMed, the ResMed Foundation, and the SRS Foundation. E.M.W. served as a scientific consultant to Axsome Therapeutics, DayZz, Eisai, EnsoData, Idorsia, Merck, Nox Health, Primasun, Purdue, and ResMed and is inventor/equity shareholder in WellTap. J.S.A.’s institution received research funding from NIH and the Department of Defense. A.M. is funded by NIH and reports income from Eli Lilly, Zoll, Powell Mansfield, LIvanova and Sunrise. A.M. is co-founder with equity in Clairyon, a small startup unrelated to this topic and Resmed gave a philanthropic donation to UCSD. C.N.K. has served as a paid consultant to Jazz Pharmaceuticals. A.P.S. is supported in part by the NIH/NIA. He has served as a consultant to Sequoia Neurovitality, BellSant, Inc., Amissa, Inc., and Synaptic Health, LLC. C.N.K. and A.P.S. serve on the editorial board for The Journals of Gerontology, Series A: Medical Sciences. No other disclosures were reported. The other authors declare no conflict.
Supplementary Material
Acknowledgments
The authors thank the NHATS staff and participants for their important contributions.
Contributor Information
Kening Jiang, Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States.
Alden L Gross, Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States; Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland, United States.
Catriona Tingsea Wu, Department of Medicine, Taipei Medical University, Taipei, Taiwan.
Junxin Li, School of Nursing, Johns Hopkins University, Baltimore, Maryland, United States.
Halima Amjad, Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland, United States; Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States.
Emerson M Wickwire, Division of Pulmonary, Critical Care and Sleep Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States.
Jennifer S Albrecht, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, United States.
Atul Malhotra, Division of Pulmonary, Critical Care and Sleep Medicine, University of California, San Diego, School of Medicine, La Jolla, California, United States.
Chien-Yu (Irene) Tseng, Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, United States.
Marc Kaizi-Lutu, Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States.
Chunyu Liu, Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States.
Christopher N Kaufmann, Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, Florida, United States.
Adam P Spira, Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States; Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland, United States; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, United States.
Data availability
This study used publicly available data from the National Health and Aging Trends Study (NHATS), accessible at https://www.nhats.org.
Author contributions
Christopher N. Kaufmann and Adam P. Spira are considered co-senior authors of this work.
Kening Jiang (Conceptualization [lead], Formal analysis [lead], Methodology [lead], Writing—original draft [lead], Writing—review & editing [lead]), Alden L. Gross (Methodology [equal], Writing—review & editing [equal]), Catriona Tingsea Wu (Methodology [equal], Writing—review & editing [equal]), Junxin Li (Methodology [equal], Writing—review & editing [equal]), Halima Amjad (Methodology [equal], Writing—review & editing [equal]), Emerson M. Wickwire (Methodology [equal], Writing—review & editing [equal]), Jennifer S. Albrecht (Methodology [equal], Writing—review & editing [equal]), Atul Malhotra (Methodology [equal], Writing—review & editing [equal]), Chien-Yu (Irene) Tseng (Methodology [equal], Writing—review & editing [equal]), Marc Kaizi-Lutu (Methodology [equal], Writing—review & editing [equal]), Chunyu Liu (Methodology [equal], Writing—review & editing [equal]), Christopher N. Kaufmann (Conceptualization [equal], Methodology [equal], Supervision [equal], Writing—review & editing [equal]), and Adam P. Spira (Conceptualization [equal], Methodology [equal], Supervision [equal], Writing—review & editing [equal])
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
This study used publicly available data from the National Health and Aging Trends Study (NHATS), accessible at https://www.nhats.org.