Abstract
Study Objectives:
Napping might indicate deficiencies in nighttime sleep, but the relationship is not well defined. We assessed the association of nighttime sleep duration and fragmentation with subsequent daytime sleep.
Design:
Cross-sectional study.
Participants:
235 individuals (47.5% men, 29.7% black), age 80.1 (2.9) years.
Measurements and Results:
Nighttime and daytime sleep were measured with wrist actigraphy and sleep diaries for an average of 6.8 (SD 0.7) nights. Sleep parameters included total nighttime sleep (h), movement and fragmentation index (fragmentation), and total daytime sleep (h). The relationship of total nighttime sleep and fragmentation to napping (yes/no) was assessed using logistic regression. In individuals who napped, mixed random effects models were used to determine the association between the previous night sleep duration and fragmentation and nap duration, and nap duration and subsequent night sleep duration. All models were adjusted for age, race, gender, BMI, cognitive status, depression, cardiovascular disease, respiratory symptoms, diabetes, pain, fatigue, and sleep medication use. Naps were recorded in sleep diaries by 178 (75.7%) participants. The odds ratios (95% CI) for napping were higher for individuals with higher levels of nighttime fragmentation (2.1 [0.8, 5.7]), respiratory symptoms (2.4 [1.1, 5.4]), diabetes (6.1 [1.2, 30.7]), and pain (2.2 [1.0, 4.7]). Among nappers, neither sleep duration nor fragmentation the preceding night was associated with nap duration the next day.
Conclusion:
More sleep fragmentation was associated with higher odds of napping although not with nap duration. Further research is needed to determine the causal association between sleep fragmentation and daytime napping.
Citation:
Goldman SE; Hall M; Boudreau R; Matthews KA; Cauley JA; Ancoli-Israel S; Stone KL; Rubin SM; Satterfield S; Simonsick EM; Newman AB. Association between nighttime sleep and napping in older adults. SLEEP 2008;31(5):733-740.
Keywords: Napping, sleep duration, sleep fragmentation, diabetes, elderly
DAYTIME SLEEP (NAPPING) AND NOCTURNAL AWAKENINGS ARE MORE COMMON IN OLDER THAN YOUNGER ADULTS.1–9 THE ASSOCIATION BETWEEN daytime sleep, nighttime sleep, and nocturnal awakenings is not well defined. In studies with objective measures of daytime and nighttime sleep, the relationship of napping and sleep vary.10–12 Most studies have examined the effect of napping on subsequent night's sleep, with some studies showing that napping has little impact on subsequent night sleep quality or duration5,11,13 and one study reporting a negative impact on sleep quality and duration in subsequent nights.10 In addition, previous studies on the relationship between nighttime sleep and napping in older adults have been based primarily on self-report, or if in the laboratory, were conducted in small groups of individuals.
In active community-dwelling older adults, the association between disturbed nighttime sleep and whether an individual takes a nap during the day has not been examined. Further, the temporal relation between disturbed nighttime sleep and subsequent daytime napping is uncertain. The objectives of this study were to determine the sleep behaviors and other factors associated with taking a nap in older adults, and evaluate the association between nighttime sleep and subsequent daytime nap duration. We tested the hypothesis that shorter nighttime sleep and higher amounts of fragmentation would be associated with a higher likelihood of taking a nap and longer nap duration the following day. To clarify the relationship between napping and sleep on the subsequent night, we hypothesized that a longer daytime nap would be associated with a shorter nighttime sleep on the subsequent night.
MATERIALS AND METHODS
Study Population
Participants were a subgroup from the Pittsburgh site of the Health, Aging and Body Composition (Health ABC) study at the eighth year (2004–2005) clinic visit who agreed to participate in a sleep study in which they would be asked to wear a wrist actigraph continuously for one week and complete a 24-h sleep-wake diary at bedtime and wake time. The Health ABC study was designed to evaluate the relationship between weight, body composition, and various health conditions in community dwelling older adults. The study enrolled 3075 adults aged 70–79 years, 42% black, and 52% women between 1996 and 1997 in Pittsburgh, PA, and Memphis, TN. Individuals were excluded from the original study if they reported difficulty walking a quarter of a mile, climbing 10 steps without resting, or performing basic activities of daily living. They were also excluded if they had cancer under active treatment within the past 3 years or planned to move within 3 years.
Participation in the sleep study was voluntary and offered to individuals based on availability of a wrist actigraph during his/her study visit. Participants were excluded if he/she would not be able to wear the watch and/or complete the diary, used continuous positive airway pressure (CPAP) or supplemental oxygen at night, were cognitively impaired (based on participant self-reported use of cholinesterase inhibitors), were undergoing treatment for cancer, or if they had any end-stage disease. Two hundred thirty-six individuals wore wrist actigraphs and successfully completed the daily sleep-wake diary. One participant with high napping was removed from data analysis after it was determined that she was ill and was hospitalized. The institutional review board at The University of Pittsburgh approved the study and written informed consent was obtained from all participants.
Sleep
Nighttime and daytime sleep were measured by actigraphy with the Mini-Mitter Actiwatch (AW-16). The actiwatch was worn on the nondominant wrist over 7 consecutive 24-h periods starting at the clinic visit. The wrist actiwatch was set to record in 1-min epochs at a medium sensitivity level for scoring sleep and wake times (40 activity counts/min).14,15 Each participant was given a sleep-wake diary to complete concurrent with wearing the watch. On a daily basis, the participant recorded the time he/she: went to bed, tried to go to sleep, woke up in the morning, got out of bed in the morning, number of times he/she awoke during the night; and the start time and end time for each daytime nap ≥5 min. All participants were instructed in the use of the wrist actigraph and how to complete the sleep-diary during the clinic visit. Telephone support was available to participants throughout the monitoring period.
Data from the wrist actigraphs were downloaded to a personal computer where all sleep and nap intervals were manually placed on the actogram as follows. Nighttime sleep start and morning wake time (sleep end) were set based on review of the downloaded actogram and verified against the time recorded in the participants' diaries. Nap intervals were set based on the start and stop time for the nap in their sleep-wake diary using the procedure established at our laboratory. One individual (SEG) scored all of the actograms with an intrareader reliability (kappa > 0.95) based on a random sample of 10 participant actograms.
Sleep and nap measurements were analyzed with Mini-Mitter Version 5 software using a validated algorithm, set at medium sensitivity, to identify sleep and wake.14 The sleep period was defined as the time elapsed from the set sleep start to the set sleep end. The total nighttime sleep duration was the sum of all sleep epochs within the interval between the time set on the actogram for nighttime sleep and morning wake time. Sleep efficiency was calculated as the ratio of total nighttime sleep duration to the total sleep period. Wake after sleep onset was measured as the sum of all wake epochs during the sleep period and reflects the number of min that exceeded the sensitivity threshold and were scored as wake. The movement and fragmentation index (MFI), often referred to as an index of restlessness, was calculated by the software as the sum of the percent of mobile minutes plus the percent of immobile bouts less than 1 min duration divided by the number of immobile bouts for a given interval. The MFI captures all movement regardless of the intensity of the movement. Daytime sleep (nap) duration was calculated as the sum of all sleep epochs within the interval between the time set on the actogram for the start and end of the nap period.14
To determine problems with nighttime sleep, participants were asked the following questions: During a typical month how often do you have: “trouble falling asleep?”; “Wake up during the night and have difficulty getting back to sleep?”; “Wake up too early in the morning and are unable to get back to sleep?”; and “Take sleeping pills or other medication to help you sleep?” Sleep medication was defined as any medication that the subject indicated as being used primarily to induce and/or maintain sleep. Response options to these questions were: never, ≤ once a month, 2 to 4 times a month, 5 to 15 times a month, and > 15 times a month. For analysis, these variables were collapsed into: “infrequent,” consisting of never and ≤ once per month, and “frequent,” defined as ≥ 2 times per month.
Demographics and General Health
At the eighth annual clinic visit, a detailed interview and exam were conducted to obtain and update demographic and health history. Data obtained included age, sex, race, height, weight, socioeconomic variables, disease history, and medications.
Comorbid Health Conditions
Cardiovascular disease was defined as history of myocardial infarction, angina, stroke, transient ischemic attack, or congestive heart failure based on cardiovascular disease status, and updated with review of hospital records obtained during the course of the study. Diabetes was defined by self-report of the disease at the current (eighth year) visit. Respiratory disease was defined as self-report of any of the following: dyspnea on exertion, need to stop for breath when walking, history of asthma, chronic obstructive pulmonary disease, emphysema, or bronchitis at the current visit. Depression was measured with the 10-item Center for Epidemiologic Studies Depression scale.16
Other Measurements
Factors considered as potential confounders, or mediators, previously identified to be associated with sleep or napping in older adults,7,17–20 which were available in our population included: body mass index (BMI) (kg/m2); self-reported health status (excellent-good, fair, poor); cognitive status (Teng Mini-Mental Status Exam21); smoking (yes or no); or use of alcohol. Pain was defined as a response of yes to the question, “have you experienced any bodily pain in the past 30 days?” Fatigue was evaluated with a subscale from the Piper Modified Fatigue Scale (scale 0–50 highest),22 which has previously been used to evaluate overall fatigue in older adults.23 Due to the low number of current smokers (n [%] = 4 [2]) smoking status was not included in the analyses.
Statistical Analyses
Student t-tests were used to test differences in means between dichotomous variables, and chi-square analysis was used for categorical variables between individuals who did or did not nap. A series of logistic regression models were performed to obtain the odds ratios (95% confidence intervals) for taking or not taking a nap. Models were adjusted for variables associated with either sleep or napping in univariate analysis or variables that have been reported in the literature.7,17–20 The final model included age, race, BMI (kg/m2), measured night time sleep (h), measured MFI, depression, cognitive status, pain in the past 30 days, cardiopulmonary disease, respiratory disease, diabetes, fatigue, and self-reported use of medication to help sleep. Depression, cognitive status, and fatigue were treated as continuous variables for analysis purposes. Measured MFI was evaluated as a continuous variable, as well as divided into tertiles for analysis purposes. Units for continuous variables (movement and fragmentation, depression, mental status, and fatigue) approximate one standard deviation (SD). Due to the low incidence of each cardiovascular disease and respiratory disease, summary variables were created, which represented the presence or absence of any one of the cardiovascular or respiratory diseases.
To look at individual night and day data, for those participants who recorded naps, mixed models were fit to determine the association of the antecedent night measured sleep duration and MFI with the subsequent day measured nap duration. Also evaluated was the association of the antecedent night measured sleep duration, MFI, and the subsequent day measured nap duration on the measured sleep duration for the night following the nap. Participant was the random effect in the models; the same sleep variables and covariates considered in the logistic regression models above were considered as fixed effects in the mixed models. Although 179 individuals reported naps in their sleep diaries, due to the analytical modeling method, only 159 individuals were included in the final mixed model analyses. There were no significant differences between the 159 participants and the 19 participants not included. To express the strength of the associations in the mixed regression model, units of percent differences were calculated from the regression coefficients with the formula (beta*unit/mean nap duration [h]).24 All analyses were run in STATA version 8 (Stata College Station, TX), or SAS version 9 (Cary, NC). Associations were considered significant at P ≤ 0.05 unless otherwise noted.
RESULTS
Wrist actigraphs were worn for a mean of 6.8 nights (SD 0.7), with a nap of at least 5 min recorded by 178 (75.7%) participants. Demographic characteristics were similar between those who did and those did not record a nap (Table 1).
Table 1.
Characteristics of Health Aging and Body Composition Sleep Study Participants by Napping Status (N=235)
| No Recorded Naps (n=57) | Recorded Naps (n=178) | |
|---|---|---|
| Demographics | ||
| Age (yr) (mean [SD]) | 80.2 (2.8) | 80.1 (2.9) |
| Gender | ||
| Men (n=112) (n [%]) | 26 (45.6) | 86 (48.3) |
| Women (n=124) | 31 (54.4) | 92 (51.7) |
| Race | ||
| White (n=166) (n [%]) | 42 (73.7) | 124 (69.7) |
| Black (n=70) | 15 (26.3) | 54 (30.3) |
| Actigraphy measured sleep: | ||
| Nap duration (h)1 (mean [SD]) | 0 | 1.1 (0.6) |
| Sleep period (h) 1 (mean [SD]) | 7.6 (1.0) | 7.5 (1.1) |
| Nighttime sleep (h) 1 (mean [SD]) | 6.8 (1.0) | 6.5 (1.1) |
| Sleep efficiency1 (mean [SD]) | 83.4 (7.7) | 80.3 (8.6) ** |
| Movement – Fragmentation Index1 (mean [SD]) | 31.0 (11.4) | 37.7 (15.2) *** |
| Wake after sleep onset (h) 1 (mean [SD]) | 0.9 (1.0) | 1.0 (0.5) * |
| Movement & Fragmentation Index (Tertiles) (n [%]) | ||
| < 28 | 27 (47.4) | 51 (28.7) |
| ≥ 28– <40 | 19 (33.3) | 60 (33.7) |
| ≥ 40 | 11 (19.3) | 67 (37.6) * |
| Self reported sleep patterns: | ||
| Self report number of naps/week (mean [SD]) | 1.6 (2.2) | 5.3 (4.7)*** |
| Self report hours of sleep/night (mean [SD]) | 6.9 (1.4) | 6.7 (1.4) |
| Number of times up at night (sleep diary) (mean [SD]) | 1.7 (1.2) | 1.8 (0.9) |
| Trouble falling asleep2 (n [%] yes) | 23 (40.4) | 74 (41.6) |
| Wake up during the night2 (n [%] yes) | 23 (40.4) | 96 (54.2) |
| Wake up too early2 (n [%] yes) | 24 (42.1) | 77 (43.5) |
| Use medicine to help sleep2 (n [%] yes) | 8 (14.0) | 17 (9.6) |
| Daytime sleepiness2 (n (%] yes) | 25 (43.9) | 87 (48.8) |
| Health variables: | ||
| BMI (kg/m2) (mean [SD]) | 28.0 (5.2) | 28.1 (4.2) |
| Mental Status (Teng 3 MS) Score (mean [SD]) | 93.7 (5.2) | 92.8 (5.9) |
| Depression (CES D10) (mean [SD]) | 4.5 (4.0) | 4.6 (3.7) |
| Fatigue (scale 0–50)3 (mean [SD]) | 15.0 (7.9) | 18.2 (7.6)** |
| Any pain in the past 30 days (n [%] yes) | 31 (54.4) | 127 (71.4) *** |
| Respiratory symptoms4 (n [%] yes) | 16 (28.1) | 81 (46.0) ** |
| Cardiovascular disease5 (n [%] yes) | 5 (8.8) | 28 (15.7) |
| Diabetes (self report) (n [%] yes) | 2 (3.9) | 30 (18.8) ** |
| Alcoholic beverages (any) (n [%] yes) | 33 (57.9) | 84 (47.5) |
| History of falling in past year (n [%] yes) | 17 (29.8) | 53 (29.8) |
Measured with actigraphy. All values represent average duration for the period the participant wore the watch.
Self report: The number of participants who reported the symptom more than one time per month.
Actual range was 0–38. Population median = 18.
Self-report of any of the following: dyspnea on exertion, need to stop for breath when walking, history of asthma, chronic obstructive pulmonary disease, emphysema, or bronchitis.
History of myocardial infarction, angina, stroke, transient ischemic attack, or congestive heart failure at the Health ABC year 6 visit (average of 2 years earlier).
P ≤ 0.05
P ≤ 0.01
P ≤ 0.001
In those participants who recorded naps in their sleep-wake diaries, there were an average of 3.9 (SD 2.6) recorded naps over the period the participant wore the watch, with an average nap duration of 1.1 (SD 0.6) h/nap. The average duration of the sleep period and average total nighttime sleep, measured by actigraphy, were similar between the individuals who napped and those who did not nap. Individuals who napped had higher nighttime MFI, more wake after sleep onset, and poorer sleep efficiency.
Self-Reported Sleep Problems
The self-reported sleep problems—trouble falling asleep, waking up during the night, waking up too early in the morning, using medications to help sleep, or daytime sleepiness—did not differ between those participants who did or did not record naps. The average fatigue score, however, was higher in those who napped. Higher percentages of individuals who recorded naps also reported pain over the past 30 days, respiratory symptoms, or diabetes.
Probability of Napping
In the logistic regression models, to evaluate the probability of taking a nap, the unadjusted odd ratios for recording a nap were higher for individuals with higher levels of MFI, symptoms of pain in the past 30 days, respiratory symptoms, diabetes, or higher fatigue levels (Table 2). In the fully adjusted model individuals with higher MFI, self-report of any pain over the past 30 days, respiratory symptoms, diabetes and higher fatigue score had higher odds for napping that were not attenuated by adjustment for other factors.
Table 2.
Probability of Taking a Daytime Nap in the Health Aging and Body Composition Sleep Study (n = 235)1
| Unit/Referent | Unadjusted |
Adjusted – Sleep2 |
Fully Adjusted Model3 |
||||
|---|---|---|---|---|---|---|---|
| OR | (CI) | OR | CI | OR | CI | ||
| Demographics | |||||||
| Race | White | 1.2 | (0.6, 2.4) | 1.1 | (0.6, 2.2) | 1.1 | (0.4, 2.6) |
| Gender | Male | 0.9 | (0.5, 1.6) | 1.0 | (0.6, 2.0) | 1.1 | (0.5, 2.4) |
| Actigraphy measured sleep | |||||||
| Total nighttime sleep time4 | Hour | 0.9 | (0.7, 1.2) | 0.9 | (0.7, 1.2) | 0.7 | (0.5, 1.1) |
| Movement and fragmentation (tertile)4 | |||||||
| 28–40 | < 28 | 1.7 | (0.8, 3.4) | 1.8 | (0.9, 3.5) | 1.8 | (0.8, 4.0) |
| ≥ 40 | 3.2 | (1.5, 7.1) | 3.3 | (1.5, 7.3) | 2.1 | (0.8, 5.7) | |
| Health variables | |||||||
| Depression score | 1 SD5 | 1.0 | (0.8, 1.4) | 1.0 | (0.7, 1.2) | 0.7 | (0.5, 1.1) |
| Pain past 30 days (n=128) | None | 2.1 | (1.1, 3.9) | 2.1 | (1.1, 40) | 2.2 | (1.0, 4.7) |
| Respiratory symptoms (n = 98) | None | 2.2 | (1.1, 4.2) | 2.3 | (1.1, 4.8) | 2.4 | (1.1, 5.4) |
| Diabetes (n=32) | None | 5.8 | (1.3, 25.0) | 5.1 | (1.1, 22.7) | 6.1 | (1.2, 30.7) |
| Fatigue score | 1 SD5 | 1.5 | (1.1, 2.1) | 1.5 | (1.1, 2.1) | 1.6 | (1.0, 2.4) |
Only variables with a significance level of P ≤ 0.10 in univariate, logistic regression, or mixed-model analyses are shown here.
Adjusted for age, race, gender, total night time sleep (h), and tertile of movement and fragmentation index.
Adjusted for age, race, gender, total night time sleep (h), and tertile of movement and fragmentation index, self reported use of sleep medications, self-report of pain in the last 30 days, cardiovascular disease at the Health ABC year 6 visit, self-reported respiratory symptoms, self-reported diabetes, depression score, mental status, and fatigue.
Measured with Mini Mitter AW-16 and Actiware v5 software
Units for continuous variables approximate 1 standard deviation; for dichotomous variables
Association of Individuals' Night Sleep and Daytime Nap
In the mixed model analysis the association between the measured individual antecedent total nighttime sleep and MFI with the duration of the individual subsequent day nap was examined further with mixed model analysis (Table 3). Neither antecedent night sleep duration nor antecedent night MFI, was significantly associated with the subsequent day nap duration. In the fully adjusted model, self reported diabetes was associated with a 43% longer nap duration, and black race was associated with a 26% longer nap; self report of any pain was associated with 27.5% shorter nap duration. Results (not shown) were similar when the sleep interval was used in the model instead of total nighttime sleep.
Table 3.
Multivariable Association of Previous Night Sleep and the Duration of the Next Day Nap in the Health ABC Sleep Study Participants Who Reported Naps (n = 159)
| Variable | Unit/referent | Percentage difference in nap duration per unit |
Percentage difference in nap duration per unit |
||
|---|---|---|---|---|---|
| (95% CI) | (unadjusted) | (95% CI) | fully adjusted model1 | ||
| Demographics | |||||
| Race | White | 18.3 | (0.4, 76.7) | 26.0 | (2.8, 104.0) |
| Gender | Male | −3.3 | (−19.5, 27.3) | −0.6 | (−21.4, 42.9) |
| Actigraphy measured sleep | |||||
| Previous night sleep duration (h) | Hour | −1.9 | (−6.6, 5.9) | −1.0 | (−6.1, 8.6) |
| Previous night - Movement and fragmentation | 1 SD | 0.4 | (−5.2, 12.5) | −0.5 | (−6.7, 12.0) |
| Health variables | |||||
| Depression score | 1 SD | 5.5 | (−2.9. 29.4) | 6.5 | (−4.3, 36.9) |
| Bodily pain last 30 days | None | −14.2 | (−31.9, 7.4) | −27.5 | (−48.9, −12.7) |
| Respiratory symptoms | None | −9.1 | (−25.6, 15.5) | −3.5 | (−22.8, 33.5) |
| Self reported diabetes | None | 34.1 | (11.9, 119.0) | 43.0 | (18.2, 143.6) |
| Fatigue | 1 SD | 3.2 | (−5.2, 24.6) | 5.8 | (−4.8, 34.9) |
Fully adjusted mixed model included total nighttime sleep duration (hours from actigraphy), movement and fragmentation index (nightly actigraphy), age (years), race, gender, BMI (kg/m2), depression, mental status, bodily pain in the last 30 days (yes/no), cardiovascular disease at the year 6 visit (yes/no), self-reported respiratory symptoms (yes/no), self-reported diabetes (yes/no), self reported number of times up each night, fatigue score. Only variables with a significance level of P ≤ 0.10 in univariate, logistic regression, or mixed-model analyses are shown here.
2Units for continuous variables approximate 1 standard deviation; for dichotomous variables, the referent group does not have the characteristic.
Mixed model analyses were also used to examine the association between the individual measured antecedent total nighttime sleep, individual antecedent night MFI, individual subsequent day nap duration, and individual total sleep time on the subsequent night (Table 4). In the fully adjusted mixed model, each hour of previous night's sleep time was associated with a 4.1% longer sleep time the next night (nap night); and each hour of napping (the next day) was associated with 10.2% less sleep on the night of the nap. Results (not shown) were similar when the previous night sleep interval was used instead of nighttime sleep duration.
Table 4.
Multivariable Associates of Previous Night Sleep and the Following Day Nap Duration on Nap Night Sleep Duration Using Mixed Model Analysis in Health ABC Sleep Study Participants Who Reported Daytime Nap (n = 159)1
| Variable | Unit/Referent | Percentage difference in nighttime sleep duration1 per unit |
Percentage difference in nighttime sleep duration1 per unit |
||
|---|---|---|---|---|---|
| Demographics | (95% CI) | (unadjusted) | (95% CI) | adjusted for multiple variables2 | |
| Race | White | −5.4 | (−10.1, −0.7) | −8.1 | (−13.3, −2.9) |
| Gender | Male | 0.9 | (−3.5, 5.4) | 2.8 | (−1.8, 7.4) |
| Actigraphy measured sleep | |||||
| Previous night sleep duration | Hour | 1.6 | (0.8, 2.5) | 4.1 | (2.2, 5.9) |
| Previous night - Movement and fragmentation | 14.63 | 0.02 | (−1.0, 1.0) | −1.3 | (−2.8, 0.2) |
| Same day sleep (nap) duration | Hour | −2.5 | (−3.9, −1.2) | −10.2 | (−16.0, −4.3) |
| Previous night sleep duration × Nap duration | Hour | −0.1 | (−0.3, 0.1) | 1.3 | (0.4, 2.2) |
| Health variables | |||||
| Depression score | 3.83 | 0.2 | (−2.1, 2.4) | −1.0 | (−3.4, 1.4) |
| Bodily pain last 30 days | none | −1.8 | (−6.6, 3.1) | −1.5 | (−6.3, 3.2) |
| Respiratory symptoms | none | 4.4 | (−0.1, 8.8) | 4.0 | (−0.2, 8.3) |
| Self reported diabetes | none | −1.8 | (−7.7, 4.1) | −1.2 | (−6.8, 4.4) |
| Fatigue | 7.73 | −0.3 | (−2.5, 2.0) | −0.9 | (−3.2, 1.5) |
Total nighttime sleep on the night of the nap.
Fully adjusted mixed model included total nighttime sleep duration (hours from actigraphy), movement and fragmentation index (nightly actigraphy), age (years), race, gender, BMI (kg/m2), depression, mental status, bodily pain in the last 30 days (yes/no), cardiovascular disease at the year 6 visit (yes/no), self-reported respiratory symptoms (yes/no), self-reported diabetes (yes/no), times up each night, fatigue score. Only variables with a significance level of P ≤ 0.10 in univariate, logistic regression, or mixed-model analyses are shown here.
Units for continuous variables approximate 1 standard deviation; for dichotomous variables, the referent group does not have the characteristic.
DISCUSSION
The measures of disturbed sleep, MFI, wake after sleep onset, and sleep efficiency were the sleep variables that differentiated individuals who recorded naps in their sleep diaries from individuals who did not record naps. Higher amounts of nighttime MFI, the most sensitive of these 3 measures to access nighttime movement, was associated with higher odds of recording a nap in this cohort of 80-year-old adults. This association remained after adjustment for age, race, gender, and measured total sleep interval (or total sleep time). Further adjustment for self-reported use of sleep medication, BMI, depression, cognitive status, pain, respiratory symptoms, cardiopulmonary disease, diabetes, and fatigue only slightly reduced this association. The findings of more wake after sleep onset and lower sleep efficiency, while smaller in magnitude, were also significant, further supporting the potential impact that disturbed sleep has with daytime sleep. In the individuals who recorded naps, longer nap durations were associated with self-report of diabetes, and shorter nap durations were associated with self report of pain. Longer nap durations were not, however, explained by either the average measured sleep duration or nighttime MFI over the time period the participants wore the wrist actigraph.
Individuals who reported naps were differentiated from those who did not by sleep fragmentation. Frequent nighttime awakenings in older adults have been reported in multiple studies.1–3,6,7,25–27 These arousals have been associated with reduced daytime well-being, daytime sleepiness, and napping.25–28 This study supports prior research and corroborates the association between fragmented nighttime sleep and daytime napping. In addition, we were able to demonstrate that higher levels of fragmentation were associated with higher odds of having taken a nap. Whether sleep fragmentation contributes to napping, or napping contributes to sleep fragmentation is uncertain due to the cross-sectional study design. The measured interval of nighttime sleep, total nighttime sleep duration, and MFI were not found to be significantly associated with recorded nap duration. We are unable to explain this lack of association. It may be attributable to lack of power and requires further investigation.
A major factor associated with nighttime MFI, not measured in this study is sleep disordered breathing, or obstructive sleep apnea. The movement and fragmentation index has been used to provide an estimate of sleep apnea syndrome, or sleep disordered breathing. In one small study, this index has been shown to be highly correlated (r = 0.98),29 and to have high sensitivity and specificity 89% and 95% respectively,30 with polysomnography (PSG) to measure sleep apnea syndrome (or sleep disordered breathing) in a population of individuals with suspected sleep apnea. Sleep disordered breathing, defined as an apnea-hypopnea index (AHI) ≥ 10–15, has been estimated to range from 45% to 62% in adults ≥ 60 years.29,30 Fifty-nine percent of participants in this study had a MFI index ≥ 30, a value associated with severe sleep apnea syndrome in a patient population with a mean age of 52 ± 15 years.29 This suggests that a substantial proportion of these individuals might have some form of undiagnosed sleep disordered breathing that might be amenable to treatment.
Self-reported diabetes and any bodily pain in the past month were the conditions most highly associated with nap duration. A higher risk for diabetes has been associated with short (< 6 h) or long (> 9 h) sleep duration.31–34 Diabetes has also been associated with excessive daytime sleepiness,31,35 however, napping has not been demonstrated in diabetics. This study extends the relationship and suggests an association between diabetes and napping as well. The association of bodily pain and napping in our study suggests the significant impact this syndrome has on the older adults and warrants further study.
We found no association with cardiopulmonary diseases, which differs from reports in other populations of older adults where not only was a siesta more common in individuals with a past history of myocardial infarction, but the siesta was predictive of cardiovascular disease.36,37 However, it must be noted that the measure of cardiovascular disease in our study, represents disease status 2 years earlier and may not reflect current disease status.
Napping in old age has been suggested to help older adults maintain their daytime functioning at an adequate level.38,39 Naps have also been associated with improvements in objective and subjective evening sleepiness and performance.10,11,13,39 However, napping has also been associated with an increase in mortality.12,36,39,40
Whether or not older adults should nap or avoid napping is debatable. A daytime nap has been associated with activity-related health deficits and poor sleep hygiene; avoiding it to improve nighttime sleep has been advised.41 However, napping as a way to increase 24-h sleep quality, duration, or to offset insomnia has also been suggested.10,11,41 Our study demonstrated that total sleep time was significantly higher over the 24 h period during which study participants recorded a nap. A short nap can improve alertness after restricted sleep.38 Napping, as well as the ability to fall asleep, has been shown to be more common in older adults with a good night's sleep compared to those with a poor night sleep,11,31 and it was not restricted to those complaining of insomnia.
We studied a large, well-characterized cohort of adults who on average were in their ninth decade of life. Naps were based on participant self-report, and objective measures were used to quantify nap duration. Most previous studies are confined to younger individuals and performed in small controlled laboratory studies or institutionalized older adults. We examined both sleep period and actual sleep time, as well as adjusting for a number of confounding factors. There are, however, a number of limitations. The participants represent a survival cohort of the original Health ABC study of community dwelling older adults. The individuals in this ancillary study were primarily individuals still active in the community, and therefore results may differ in other populations. Measurements of nighttime and daytime sleep were obtained by actigraphy in conjunction with self-report and might differ from polysomnography measured sleep. Further, as naps were only measured if recorded in a diary this might result in underestimation of napping in the population.
CONCLUSIONS
These findings have important clinical implications and point to the need for health care providers to discuss nighttime and daytime sleep characteristics with their older patients. The pros and cons of napping in older adults are not definitive, and future research is warranted. Our study showed that nighttime fragmentation was associated with higher odds of whether or not an older adult took a nap during the day. Research to clarify the causes of fragmented nighttime sleep, as well as the cause-effect relationship between nighttime sleep fragmentation and daytime napping in older adults is needed. Clarification of these associations might help clinicians determine whether or not to recommend daytime naps to community dwelling older individuals.
ACKNOWLEDGMENTS
Supported By: NIH Contracts N01-AG-6-2101, 2103, 2106. NIA AG08415, NCI CA85264, NCI CA112035. This research was supported in part by the Intramural Research program of the NIH, National Institute on Aging. The Aging Training Grant Number: 2, T32, AG000181-16.
Footnotes
Disclosure Statement
This was not an industry supported study. Dr. Ancoli-Israel has received research support from Sepracor, and Takeda; has consulted for and/or been on the advisory board of Acadia, Cephalon, Ferring, Neurocrine Biosciences, Neurogen, Sanofi-Aventis, Sepracor, Somaxon, and Takeda; and has participated in speaking engagements for Cephalon, Neurocrine Biosciences, Sanofi-Aventis, Sepracor, and Takeda. Dr. Cauley has received research support from Merck, Eli Lilly, Pfizer, and Novartis; has received honorarium from Merck and Eli Lilly; and is on the speaker's bureau for Merck. The other authors have indicated no financial conflicts of interest.
REFERENCES
- 1.Pressman MR, Fry JM. What is normal sleep in the elderly? Clin Geriatr Med. 1988;4:71–82. [PubMed] [Google Scholar]
- 2.Bliwise DL. Kryger MH, Roth T, Dement WC. Principles and practice of sleep medicine. 4th ed. Philadelphia: W.B. Saunders Company; 2005. Normal aging. [Google Scholar]
- 3.Bliwise DL, Ansari FP, Straight L-B, Parker KP. Age changes in timing and 24 hour distribution of self-reported sleep. Am J Geriatr Psychiatry. 2005;12:1077–82. doi: 10.1176/appi.ajgp.13.12.1077. [DOI] [PubMed] [Google Scholar]
- 4.Buysse DJ, Browman BA, Monk TH, Reynolds CF, Fascizka AL, Kupfer DJ. Napping and 24-hr sleep/wake patterns in healthy elderly and younger adults. J Am Geriatr Soc. 1992;40:779–86. doi: 10.1111/j.1532-5415.1992.tb01849.x. [DOI] [PubMed] [Google Scholar]
- 5.Yoon IY, Kripke DF, Youngstedt SD, Elliot JA. Actigraphy suggests age-related differences in napping and nocturnal sleep. J Sleep Res. 2003;12:87–93. doi: 10.1046/j.1365-2869.2003.00345.x. [DOI] [PubMed] [Google Scholar]
- 6.Dement WC, Miles LE, Carskadon MA. “White paper” on sleep and aging. J Am Geriatr Soc. 1982;30:25–50. doi: 10.1111/j.1532-5415.1982.tb03700.x. [DOI] [PubMed] [Google Scholar]
- 7.Bliwise DL. Sleep in normal aging and dementia. Sleep. 1993;16:40–81. doi: 10.1093/sleep/16.1.40. [DOI] [PubMed] [Google Scholar]
- 8.Ohayon MM, Zulley J, Guilleminault C, Smirne S, Priest RG. How age and daytime activities are related to insomnia in the general population: consequences for older people. J Am Geriatr Soc. 2001;49:360–6. doi: 10.1046/j.1532-5415.2001.49077.x. [DOI] [PubMed] [Google Scholar]
- 9.Evans BD, Rogers AE. 24-Hour sleep/wake patterns in healthy elderly persons. Appl Nurs Res. 1994;7:75–83. doi: 10.1016/0897-1897(94)90036-1. [DOI] [PubMed] [Google Scholar]
- 10.Monk TH, Buysse DJ, Carrier J, Billy BD, Rose LR. Effects of afternoon “siesta” naps on sleep, alertness, performance, and circadian rhythms in the elderly. Sleep. 2001;24:680–7. doi: 10.1093/sleep/24.6.680. [DOI] [PubMed] [Google Scholar]
- 11.Campbell SS, Murphy PJ, Stauble TN. Effects of a nap on nighttime sleep and waking function in older subjects. J Am Geriatr Soc. 2005;53:48–53. doi: 10.1111/j.1532-5415.2005.53009.x. [DOI] [PubMed] [Google Scholar]
- 12.Bursztyn M, Ginsberg G, Stessman J. The siesta and mortality in the elderly: effect of rest without sleep and daytime sleep duration. Sleep. 2002;25:187–91. doi: 10.1093/sleep/25.2.187. [DOI] [PubMed] [Google Scholar]
- 13.Tamaki M, Shirota A, Tanaka H, Hayashi M, Hori T. Restorative effects of a short afternoon nap (<30 min) in the elderly on subjective mood, performance and EEG activity. Sleep Res Online. 2000;3:131–9. [PubMed] [Google Scholar]
- 14.Oakley NR. Bend, OR: Mini Mitter Co; 1997. Validation with polysomnography of the Sleepwatch sleep/wake scoring algorithm used by the Actiwatch activity monitoring system. [Google Scholar]
- 15.Mini-Mitter Co. Inc. The role of actiwarch in the study of sleep and circadian rhythms. 2004 [Google Scholar]
- 16.Irwin M, Haydan Artin K, Oxman MN. Screening for depression in the older adult: criterion validity of the 10-item Center for Epidemiological Studies Depression Scale (CES-D) Arch Intern Med. 1999;159:1701–4. doi: 10.1001/archinte.159.15.1701. [DOI] [PubMed] [Google Scholar]
- 17.Ancoli-Israel S. Sleep and aging: prevalence of disturbed sleep and treatment considerations in older adults. J Clin Psychiatry. 2005;66:24–30. [PubMed] [Google Scholar]
- 18.Neubauer DN. Sleep problems in the elderly. Am Fam Physician. 1999;59:1–11. [PubMed] [Google Scholar]
- 19.Foley DJ, Monjan AA, Brown SL. Sleep complaints among elderly persons: an epidemiologic study of three communities. Sleep. 1995;18:425–32. doi: 10.1093/sleep/18.6.425. [DOI] [PubMed] [Google Scholar]
- 20.Jacobs JM, Cohen A, Hammerman-Rozenberg R, Stessman J. Global sleep satisfaction of older people: The Jerusalem Cohort Study. J Am Geriatr Soc. 2006;54:325–9. doi: 10.1111/j.1532-5415.2005.00579.x. [DOI] [PubMed] [Google Scholar]
- 21.Teng EL, Chui HC. The Modified Mini-Mental State (3MS) examination. J Clin Psychiatry. 1987;48:314–8. [PubMed] [Google Scholar]
- 22.Piper BF, Dibble SL, Dodd MJ, Weiss MC, Slaughter RE, Paul SM. The revised Piper Fatigue Scale: Psychometric evaluation in women with breast cancer. Oncol Nurs Forum. 1998;25:677–84. [PubMed] [Google Scholar]
- 23.Liao S, Ferrell B. Fatigue in an older population. J Am Geriatr Soc. 2000;48:426–30. doi: 10.1111/j.1532-5415.2000.tb04702.x. [DOI] [PubMed] [Google Scholar]
- 24.Cauley JA, Fullman RL, Stone KL, et al. Factors associated with the lumbar spine and proximal femur bone mineral density in older men. Osteoporosis Int. 2005;16:1525–37. doi: 10.1007/s00198-005-1866-8. [DOI] [PubMed] [Google Scholar]
- 25.Carskadon MA, Brown ED, Dement WC. Sleep fragmentation in the elderly: Relationship to daytime sleep tendency. Neurobiol Aging. 1982;3:321–7. doi: 10.1016/0197-4580(82)90020-3. [DOI] [PubMed] [Google Scholar]
- 26.Stepanski E, Lamphere J, Badia P, Zorick F, Roth T. Sleep fragmentation and daytime sleepiness. Sleep. 1984;7:18–25. doi: 10.1093/sleep/7.1.18. [DOI] [PubMed] [Google Scholar]
- 27.Rosenthal L, Roehrs T, Sicklesteel J, Zorick F, Wittig R, Roth T. Periodic movements during sleep, sleep fragmentation, and sleep-wake complaints. Sleep. 1984;7:326–30. doi: 10.1093/sleep/7.4.326. [DOI] [PubMed] [Google Scholar]
- 28.Asplund R. Sleep disorders in the elderly. Drugs Aging. 1999;14:91–103. doi: 10.2165/00002512-199914020-00002. [DOI] [PubMed] [Google Scholar]
- 29.Elbaz M, Roue GM, Lofaso F, Quera Salva MA. Utility of actigraphy in the diagnosis of obstructive sleep apnea. Sleep. 2002;25:525–9. [PubMed] [Google Scholar]
- 30.Aubert-Tulkens G, Culee C, Harmant-Van Rijckevorsel K, Rodenstein DO. Ambulatory evaluation of sleep disturbance and therapeutic effects in sleep apnea syndrome by wrist activity monitoring. Am Rev Respir Dis. 1987;136:851–6. doi: 10.1164/ajrccm/136.4.851. [DOI] [PubMed] [Google Scholar]
- 31.Asplund R. Daytime sleepiness and napping amongst the elderly in relation to somatic health and medical treatment. J Intern Med. 1996;239:261–7. doi: 10.1046/j.1365-2796.1996.453806000.x. [DOI] [PubMed] [Google Scholar]
- 32.Ayas NT, White DP, Al-Delaimy WK, et al. A prospective study of self-reported sleep duration and incident diabetes in women. Diabetes Care. 2003;26:380–4. doi: 10.2337/diacare.26.2.380. [DOI] [PubMed] [Google Scholar]
- 33.Yaggi HK, Araujo AB, McKlinlay JB. Sleep duration as a risk factor for the development of type 2 diabetes. Diabetes Care. 2006;29:657–61. doi: 10.2337/diacare.29.03.06.dc05-0879. [DOI] [PubMed] [Google Scholar]
- 34.Gottlieb DJ, Punjabi NM, Newman AB, et al. Association of sleep time with diabetes mellitus and impaired glucose tolerance. Arch Intern Med. 2005;165:863–7. doi: 10.1001/archinte.165.8.863. [DOI] [PubMed] [Google Scholar]
- 35.Bixler EO, Vgontzas AN, Lin HM, Calhoun SL, Vela-Bueno A, Kales A. J Clin Endocrinol Metab. 2005. Excessive daytime sleepiness in a general population sample: the role of sleep apnea, age, obesity, diabetes and depression; pp. 4510–5. [DOI] [PubMed] [Google Scholar]
- 36.Bursztyn M, Ginsberg G, Hammerman-Rozenberg R. The siesta in the elderly: risk factor for mortality? Arch Intern Med. 1999;159:1582–6. doi: 10.1001/archinte.159.14.1582. [DOI] [PubMed] [Google Scholar]
- 37.Campos H, Siles X. Siesta and the risk of coronary heart disease: results from a population-based, case-control study in Costa Rica. Int J Epidemiol. 2000;29:429–37. [PubMed] [Google Scholar]
- 38.Tamaki M, Shirota A, Tanaka H, Hayashi M, Hori T. Aging and sleep. Effects of a daytime nap in the aged. Psychiatry Clin Neurosci. 1999;53:273–5. doi: 10.1046/j.1440-1819.1999.00548.x. [DOI] [PubMed] [Google Scholar]
- 39.Ancoli-Israel SMJ. Insomnia and daytime napping in older adults. J Clin Sleep Med. 2006;2:333–42. [PubMed] [Google Scholar]
- 40.Hays JC, Blazer DG, Foley DJ. Risk of napping: Excessive daytime sleepiness and mortality in an older community population. J Am Geriatr Soc. 1996;44:693–8. doi: 10.1111/j.1532-5415.1996.tb01834.x. [DOI] [PubMed] [Google Scholar]
- 41.Morin CM, Gramling SE. Sleep patterns and aging: comparison of older adults with and without insomnia complaints. Psychol Aging. 1989;4:290–4. doi: 10.1037//0882-7974.4.3.290. [DOI] [PubMed] [Google Scholar]