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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 subse-quent 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 lab-oratory, 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 sub-sequent 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 as-sociation 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 partici-

SLEEP IN OLDER ADuLTS

Association between Nighttime Sleep and Napping in Older AdultsSuzanne E. Goldman, PhD1; Martica Hall, PhD2; Robert Boudreau, PhD3; Karen A. Matthews, PhD2; Jane A. Cauley, DrPH3; Sonia Ancoli-Israel, PhD4; Katie L. Stone, PhD5; Susan M. Rubin, MPH6; Suzanne Satterfield, PhD7; Eleanor M. Simonsick, PhD8; Anne B. Newman, MD, MPH3

1Department of Neurology, Sleep Disorders Program, Vanderbilt University Medical Center, Nashville, TN; 2Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA; 3Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA; 4Department of Psychiatry, University of California, San Diego, CA; 5California Pacific Medical Center Research Institute, San Francisco, CA; 6Department of Epidemiology and Biostatistics, University of California, San Francisco, CA; 7Department of Preventive Medicine, University of Tennessee, Memphis, TN; 8Clinical Research Branch, National Institute on Aging, Baltimore, MD

Study Objectives: Napping might indicate deficiencies in nighttime sleep, but the relationship is not well defined. We assessed the asso-ciation 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 mea-sured 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 fragmenta-tion and nap duration, and nap duration and subsequent night sleep duration. All models were adjusted for age, race, gender, BMI, cogni-tive 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]), dia-betes (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 fragmenta-tion and daytime napping.Keywords: Napping, sleep duration, sleep fragmentation, diabetes, elderlyCitation: 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.

Disclosure StatementThis 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 Bio-sciences, 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 re-ceived 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.

Submitted for publication May, 2007Accepted for publication January, 2008Address correspondence to: Dr. Suzanne E. Goldman, Department of Neurology-Sleep Disorders Program, Vanderbilt University Medical Cen-ter, 1301 Medical Center Drive, Room B-727, Nashville, TN 37232; E-mail: Suzanne.e.goldman@vanderbilt.edu

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pate 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 exclud-ed from the original study if they reported difficulty walking a quarter of a mile, climbing 10 steps without resting, or perform-ing 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 con-tinuous positive airway pressure (CPAP) or supplemental oxy-gen 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 suc-cessfully 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 institu-tional 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 peri-ods 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 partici-pant 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 per-sonal 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 us-ing 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 ac-tograms.

Sleep and nap measurements were analyzed with Mini-Mit-ter Version 5 software using a validated algorithm, set at me-

dium 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 pe-riod and reflects the number of min that exceeded the sensitivity threshold and were scored as wake. The movement and frag-mentation index (MFI), often referred to as an index of restless-ness, 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 sub-ject indicated as being used primarily to induce and/or main-tain 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 col-lapsed 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 medica-tions.

Comorbid Health Conditions

Cardiovascular disease was defined as history of myocardial infarction, angina, stroke, transient ischemic attack, or conges-tive 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 exer-tion, need to stop for breath when walking, history of asthma, chronic obstructive pulmonary disease, emphysema, or bron-chitis 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

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older adults,7,17-20 which were available in our population in-cluded: 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 num-

ber 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 be-tween 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

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)

1Measured with actigraphy. All values represent average duration for the period the participant wore the watch.2Self report: The number of participants who reported the symptom more than one time per month.3Actual range was 0–38. Population median = 18.4Self-report of any of the following: dyspnea on exertion, need to stop for breath when walking, history of asthma, chronic obstructive pul-monary disease, emphysema, or bronchitis.5History 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

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the strength of the associations in the mixed regression model, units of percent differences were calculated from the regres-sion 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%) par-ticipants. Demographic characteristics were similar between those who did and those did not record a nap (Table 1).

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 morn-ing, 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, respira-tory symptoms, or diabetes.

to obtain the odds ratios (95% confidence intervals) for tak-ing or not taking a nap. Models were adjusted for variables as-sociated 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 re-spiratory diseases.

To look at individual night and day data, for those partici-pants 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 du-ration 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 logis-tic 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

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 CIDemographicsRace 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 sleepTotal 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 variablesDepression 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)

1Only variables with a significance level of P ≤ 0.10 in univariate, logistic regression, or mixed-model analyses are shown here.2Adjusted for age, race, gender, total night time sleep (h), and tertile of movement and fragmentation index.3Adjusted for age, race, gender, total night time sleep (h), and tertile of movement and fragmentation index, self reported use of sleep medica-tions, 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.4Measured with Mini Mitter AW-16 and Actiware v5 software5Units for continuous variables approximate 1 standard deviation; for dichotomous variables

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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 simi-lar when the previous night sleep interval was used instead of nighttime sleep duration.

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 indi-viduals 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-report-ed 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 night-time awakenings in older adults have been reported in multiple

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, symp-toms of pain in the past 30 days, respiratory symptoms, dia-betes, 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 fa-tigue score had higher odds for napping that were not attenu-ated by adjustment for other factors.

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). Nei-ther antecedent night sleep duration nor antecedent night MFI, was significantly associated with the subsequent day nap du-ration. 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.

Mixed model analyses were also used to examine the asso-ciation between the individual measured antecedent total night-time sleep, individual antecedent night MFI, individual subse-quent 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

Table 3—Multivariable Association of Previous Night Sleep and the Duration of the Next Day Nap in the Health ABC Sleep Study Partici-pants Who Reported Naps (n = 159)

Variable Unit/referent Percentage difference Percentage difference in nap duration per unit in nap duration per unit (95% CI) (unadjusted) (95% CI) fully adjusted model1

DemographicsRace 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 sleepPrevious 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 variablesDepression 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)

1Fully 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 character-istic.

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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 asso-ciated 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 predic-tive of cardiovascular disease.36,37 However, it must be noted that the measure of cardiovascular disease in our study, repre-sents 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 objec-tive 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 in-somnia has also been suggested.10,11,41 Our study demonstrated

studies.1-3,6,7,25-27 These arousals have been associated with re-duced daytime well-being, daytime sleepiness, and napping.25-28 This study supports prior research and corroborates the associa-tion 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 tak-en 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 dura-tion. 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 ap-nea. 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.

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 Percentage difference in nighttime sleep duration1 per unit sleep duration1 per unitDemographics (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 sleepPrevious 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 x Nap duration Hour −0.1 (−0.3, 0.1) 1.3 (0.4, 2.2)Health variablesDepression 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)

1. Total nighttime sleep on the night of the nap.2. Fully adjusted mixed model included total nighttime sleep (hours from actigraphy), movement and fragmentation index (from nightly ac-tigraphy), 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.3. Units for continuous variables approximate 1 standard deviation; for dichotomous variables, the referent group does not have the charac-teristic.

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that total sleep time was significantly higher over the 24 h pe-riod 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 com-plaining 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 labora-tory 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 mea-sured 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 indi-viduals.

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.

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