Relationship Between Reported and Measured Sleep Times
2007; American Academy of Sleep Medicine; Volume: 03; Issue: 06 Linguagem: Inglês
10.5664/jcsm.26974
ISSN1550-9397
AutoresGraciela E. Silva, James L. Goodwin, Duane L. Sherrill, Jean L. Arnold, Richard R. Bootzin, Terry Smith, Joyce A. Walsleben, Carol M. Baldwin, Stuart F. Quan,
Tópico(s)Sleep and Work-Related Fatigue
ResumoFree AccessCaffeineRelationship Between Reported and Measured Sleep TimesThe Sleep Heart Health Study (SHHS) Graciela E. Silva, Ph.D., M.P.H., James L. Goodwin, Ph.D., Duane L. Sherrill, Ph.D., Jean L. Arnold, BSEP, Richard R. Bootzin, Ph.D., Terry Smith, BSIT, Joyce A. Walsleben, Ph.D., Carol M. Baldwin, Ph.D., RN, Stuart F. Quan, M.D. Graciela E. Silva, Ph.D., M.P.H. Address correspondence to: Graciela E. Silva, Ph.D., MPH, Arizona State University, College of Nursing and Healthcare Innovation, 500 N. Third Street, Phoenix, AZ 85004-0698(602) 496-0795 E-mail Address: [email protected] College of Nursing and Healthcare Innovation Arizona State University, Phoenix, AZ Department of Medicine , James L. Goodwin, Ph.D. Arizona Respiratory Center Department of Medicine , Duane L. Sherrill, Ph.D. Arizona Respiratory Center , Jean L. Arnold, BSEP UH-Rainbow Babies and Children's Hospital, Cleveland, OH , Richard R. Bootzin, Ph.D. Department of Psychology , Terry Smith, BSIT General Clinical Research Center, University of Arizona, Tucson, AZ , Joyce A. Walsleben, Ph.D. New York University Sleep Disorders Center, New York, NY , Carol M. Baldwin, Ph.D., RN College of Nursing and Healthcare Innovation Arizona State University, Phoenix, AZ , Stuart F. Quan, M.D. Arizona Respiratory Center Department of Medicine Division of Sleep Medicine, Harvard Medical School, Boston, MA Published Online:October 15, 2007https://doi.org/10.5664/jcsm.26974Cited by:167SectionsAbstractPDF ShareShare onFacebookTwitterLinkedInRedditEmail ToolsAdd to favoritesDownload CitationsTrack Citations AboutABSTRACTStudy Objective:Subjective and objective assessments of sleep may be discrepant due to sleep misperception and measurement effects, the latter of which may change the quality and quantity of a person's usual sleep. This study compared sleep times from polysomnography (PSG) with self-reports of habitual sleep and sleep estimated on the morning after a PSG in adults.Design:Total sleep time and sleep onset latency obtained from unattended home PSGs were compared to sleep times obtained from a questionnaire completed before the PSG and a Morning Survey completed the morning after the PSG.Participants:A total of 2,113 subjects who were ≥ 40 years of age were included in this analysis.Measures and Results:Subjects were 53% female, 75% Caucasian, and 38% obese. The mean habitual sleep time (HABTST), morning estimated sleep time (AMTST), and PSG total sleep times (PSGTST) were 422 min, 379 min, and 363 min, respectively. The mean habitual sleep onset latency, morning estimated sleep onset latency, and PSG sleep onset latency were 17.0 min, 21.8 min, and 16.9 min, respectively. Models adjusting for related demographic factors showed that HABTST and AMTST differ significantly from PSGTST by 61 and 18 minutes, respectively. Obese and higher educated people reported less sleep time than their counterparts. Similarly, small but significant differences were seen for sleep latency.Conclusions:In a community population, self-reported total sleep times and sleep latencies are overestimated even on the morning following overnight PSG.Citation:Silva GE; Goodwin JL; Sherrill DL; Arnold JL; Bootzin RR; Smith T; Walsleben JA; Baldwin CM; Quan SF. Relationship between reported and measured sleep times: the sleep heart health study (SHHS). J Clin Sleep Med 2007;3(6):622-630.INTRODUCTIONSubjective and objective assessments of sleep parameters may differ due to sleep misperception and measurement effect. While subjective estimates may be biased by a subject's own sleep perception, objective assessment methods, such as PSGs, may be considered distressing, thus changing the quality and quantity of a person's usual sleep. Exposure to polysomnographic equipment, e.g., head and chest sensors, or sleeping in an unfamiliar setting such as a laboratory may interfere with the subject's habitual sleep time.Studies comparing subjective and objective estimates of sleep utilizing PSG assessments performed either at home or in the laboratory, have found that sleep time misperception is common.1–4 In these studies, subjects tended to underestimate their amount of total sleep time (TST) and overestimate the amount of sleep onset latency (SOL). In a recent study, a group of subjects with Parkinson disease showed reduced subjective sleep duration and longer SOL compared with healthy subjects.3 Another study showed that subjects with and without sleep apnea tended to overestimate SOL.2 Subjects with sleep apnea, however, made larger SOL overestimations and tended to underestimate their TST compared to those without sleep apnea. Nevertheless, consistency between subjective and objective sleep measures has also been reported.1,5 The aforementioned studies used small samples or subjects from populations with specific somatic or psychiatric disorders, and thus may not reflect the overall estimates for a community population.In addition to sleep duration and latency being altered by a number of somatic and psychiatric disorders, other social, environmental, or host factors may be influential as well. Advancing age, obesity, gender, and ethnicity have been associated with differences in sleep duration.6–9 Furthermore, lifestyle and behavior changes have influenced the amount of sleep in certain populations. It is estimated that Americans slept one and a half hours less each night in 1975 than they did in 1900, mostly attributed to the invention of electric light and to social and economic pressures.10The present study was conducted to examine the concordance between self-reported measures of TST and SOL and objective measures of sleep times as determined by ambulatory PSG, using subjects from a large multicenter community-based population. The relation between estimated and PSG sleep measures to possible host and environmental factors, as well as to the subject's perception of sleep time on the night of PSG was also assessed. This study addresses whether subjects' estimation of total sleep time and sleep onset latency differ from an objective measure of sleep and whether sleep times differences are affected by social factors and some medical conditions.METHODSThe Sleep Heart Health Study (SHHS) is a prospective multicenter cohort study designed to investigate the relationship between sleep disordered breathing and cardiovascular diseases in the United States. Details of the study design have been published elsewhere.11 Briefly, initial baseline recruitment began in 1995, enrolling 6,441 subjects over 40 years of age from several ongoing geographically distinct cardiovascular and respiratory disease cohorts that were initially assembled between 1976 and 1995.12 These included the Offspring Cohort and the Omni Cohort of the Framingham Heart Study in MA; the Hagerstown, MD, and Minneapolis, MN, sites of the Atherosclerosis Risk in Communities Study; the Hagerstown, MD, Pittsburgh, PA, and Sacramento, CA sites of the Cardiovascular Health Study; three hypertension cohorts (Clinic, Worksite, and Menopause) in New York City; the Tucson Epidemiologic Study of Airways Obstructive Diseases and the Health and Environment Study; and the Strong Heart Study (SHS) of American Indians in OK, AZ, ND, and SD. A 5-year SHHS follow-up survey took place between February 2000 and May 2003, enrolling 3,079 of the original participants. As in the baseline study, subjects were recruited to undergo an overnight home polysomnogram, completion of several questionnaires, and collection of a small amount of physical examination data. The follow-up survey including polysomnography occurred continuously throughout the recruitment window without any significant seasonal variation. Unless otherwise noted, data for the present analysis is derived from participants in the SHHS follow-up survey. However, data from participants who had follow-up PSG from the New York City site were excluded because they did not meet quality standards for the follow-up examination.All participants completed the SHHS Sleep Habits Questionnaire (SHQ).13 The SHQ contained questions regarding sleep habits, smoking status, as well as cardiovascular and respiratory problems. The habitual total sleep time (HABTST) and habitual sleep onset latency (HABSOL) during the weekdays and weekends were derived from specific questions on the SHQ. These questions were: How much sleep do you usually get at night (or in your main sleep period); on weekdays (weekends) or workdays (non-work days)?; and How long does it usually take you to fall asleep at bedtime? Weekend or weekday HABTST was used respectively according to whether the PSG was performed on a weekend or weekday. Height and weight were measured directly to determine body mass index (BMI, kg/m2). BMI was categorized into nonobese (< 30) and obese (≥ 30), according to established clinical guidelines.14SHHS participants underwent an overnight in-home PSG using the Compumedics Portable PS-2 System (Abbottsville, Victoria, Australia) administered by trained technicians. The methods for obtaining PSG data followed those used during the first SHHS examination cycle. Briefly, after a home visit was scheduled, the SHQs generally were mailed 1–2 weeks prior to the PSG home appointment. Each participant was asked to complete the questionnaire prior to the home visit at which time the SHQ was collected and verified for completeness. The home visits were performed by 2-person mixed-gender teams in visits that lasted 1.5 to 2 hours. There was emphasis on making the night of the PSG assessment as representative as possible of a usual night of sleep. Participants were asked to schedule the visit so that it would occur approximately 2 hours prior to their usual bedtime. Participants' weekday or weekend bedtime routines were encouraged to be consistent with the day of the week the visits were made.The SHHS recording montage consisted of electroencephalogram (EEG) (C4/A1 and C3/A2); right and left electrooculogram (EOG); a bipolar submental electromyogram (EMG); thoracic and abdominal excursions (inductive plethysmography bands); airflow (detected by a nasal-oral thermocouple [Protec, Woodinville, WA]), oximetry (finger pulse oximetry [Nonin, Minneapolis, MN]), ECG, and heart rate (using a bipolar ECG lead); body position (using a mercury gauge sensor); and ambient light (on/off, by a light sensor secured to the recording garment). Sensors were placed and equipment was calibrated during an evening home visit by a certified technician. Following equipment retrieval, the data, stored in real time on PCMCIA cards, were downloaded to the computers of each respective clinical site, locally reviewed, and forwarded to a central reading center (Case Western Reserve University, Cleveland, OH). Comprehensive descriptions of PSG scoring and quality assurance procedures have been previously published.15,16 In brief, sleep was scored according to guidelines developed by Rechtschaffen and Kales.17 Strict protocols were maintained in order to assure comparability between centers and technicians. Intrascorer and interscorer reliability was high.16 As in previous analyses of SHHS data, an apnea was defined as a complete or almost complete cessation of airflow (at least 10 s. Hypopneas were identified if the amplitude of a measure of flow or volume (detected by the thermocouple or thorax or abdominal inductance band signals) decreased to 10 s, but did not meet the criteria for apnea. For this study, only apneas or hypopneas associated with ≥4% oxyhemoglobin desaturation were considered in the calculation of the respiratory disturbance index (RDI4%, apneas+hypopneas per hour of total sleep time). A total of 2,113 subjects had PSGs that were of sufficient quality, in whom the time of lights out and recording time did not begin or end in a sleep state, and in whom reliable determinations of total sleep time (PSGTST) and sleep onset latency (PSGSOL) could be made.A brief morning questionnaire13 completed by participants the day after the PSG was designed to assess perceived quality of sleep on the night of the study and to record alcohol, tobacco, and caffeine use in the 4-h period before the PSG. The specific sleep questions used were: how much time do you think you actually slept last night? (AMTST), and how long did it take you to fall asleep at bedtime last night? (AMSOL).Gender, ethnicity, education, obesity, and time zone covariates were derived from data obtained from the SHHS parent cohorts. Ethnic group percentages included 75% Caucasian, 14% Native American, 6% African American, 4% Hispanic, and 1% Asian or Pacific Islander. Because of the small numbers comprising each of the ethnic categories other than Caucasians, this variable was dichotomized into Caucasians and other ethnic groups. Education was divided at the 25th and 75th quartiles and categorized into those with 16 years of education. To determine whether late evening network newscasts were associated with differences in total sleep time, time zone was assigned according to the subject's parent study location. Persons enrolled in the Sacramento, CA cohort were included in the Pacific time zone, those in the Tucson and Phoenix, AZ, and SD cohorts were included in the Mountain time zone, those in the OK and MN cohorts were included in the Central time zone, and those enrolled in the Framingham, MA, Hagerstown, MD, and Pittsburgh, PA were included in the Eastern time zone. Time zone was dichotomized into Pacific/Eastern and Mountain/Central time zones. Age was dichotomized at the mean, those ≤67 years of age, and those >67 years of age.The subject's respiratory disturbance index, self-report of chronic lung or heart diseases, and use of alcohol or caffeine were evaluated to determine if these factors affected differences in reported and measured sleep times and sleep latencies. The RDI4% was categorized into the following groups: <5, 5-<15, 15- 2 categories, i.e., education and RDI4%. Pearson's correlation coefficient with Bonferroni's correction was used to test for differences in correlation coefficients between the 3 sleep assessment measures; habitual, morning estimated, and PSG. Two separate multivariate mixed-effects linear regression models were fitted to evaluate mean differences in total sleep time and log-transformed values of sleep onset latency associated with type of sleep assessment. The dependent or outcome variables were TST in one model and SOL in the other consisting of all 3 sleep assessments values from each of the subjects. A categorical variable which specified the type of sleep assessment, either habitual, morning-estimated, or PSG was included as an independent variable in the models using PSG as the reference category. Covariates (gender, race, BMI, education, time-zone, RDI4%, chronic lung or heart disease, and alcohol or caffeine consumption) were then included as fixed effects in the models. Centers and subjects were fitted as random effects, to account for correlation within centers and serial intrasubject correlations. Predictor variables with multiple categories (>2) i.e., education and RDI4%, were entered as indicator variables. Covariates that were not significant were excluded from the final models. In these models we include centers and subjects as random effects. Subjects within centers may be similar, and thus their data may be correlated. Additionally, one has to assume that there will be correlation between the habitual, morning estimated, and PSG values on individual subjects and must be adjusted for in the analysis. Separate models were used to evaluate linear prediction of total sleep time and sleep onset latency with age. Statistical tests were performed using Intercooled Stata, version 9.0 for Windows (Stata Corporation; College Station, TX). A significance level of 0.05 was used for all statistical tests.RESULTSA total of 2,113 subjects were included in this study (mean age = 67, SD = 10 years). The demographic characteristics are presented in Table 1. The subjects were 53% female, 75% Caucasian, 38% obese, and 60% had between 12–16 years of education. Women had higher PSGTST compared to men. Caucasians had significantly higher mean HABTST and lower AMTST than all other ethnic groups combined (Table 2). Subjects who were >67 years of age had significantly lower mean AMTST and PSGTST than those who were ≤67 years of age. Obese subjects had lower mean PSGTST than nonobese subjects. Subjects with more years of education reported lower mean values for HABTST and AMTST and higher mean PSGTST than those with less years of education. There were no significant mean differences for HABTST and AMTST by RDI4% categories, chronic heart or lung disease, or alcohol consumption. However, subjects in the highest category of RDI4% had lower mean PSGTST (355 min) compared to those in the lowest category (372 min, p <0.0001). In addition, a higher percentage of obese than nonobese subjects, were found in the highest category of RDI4% (56% vs. 44%) than the lowest category (24% vs. 76%) (p < 0.0001). There were no significant differences for any of the 3 sleep measures for subjects with or without chronic lung disease. Subjects with chronic heart disease had significantly lower PSGTST than those without.Table 1 SHHS Basic Demographics%NAll2,113Gender Male47.0993 Female53.01,120Age Category ≤ 67 years52.71,114 > 67 years47.3997Ethnicity Caucasian75.21,588 Others24.8525BMI Normal ( =30%)37.8788Education 16 years25.1502Time Zone Pacific/Eastern50.11,057 Mountain/Central49.91,055RDI4% <527.7579 5 - <1539.4824 15 - 67 years42179983375†90966354‡61997BMI Nonobese ( =30%)4217777938285775358‡62788Education 16 years428*61493385*78491369*55502Time Zone Pacific/Eastern416781,045370871,021361601,057 Mountain/Central429‡701,038389‡821,041366601,055RDI4% <5416705713818256637256579 5-<15424768123808680136662824 15- =304307926637790265355§60269Chronic Lung Disease No424741,826380841,805365581,853 Yes414772573769125735862260Chronic Heart Disease No424711,649382821,637368581,673 Yes4188542937396420348‡65434Any Alcohol No422751,916380851,904364611,941 Yes422661453818314736752148Any Caffeine No422731,704377861,696365591,728 Yes42382357393†8035436064361£t-test was used to compare means between the two groups in gender, age category, ethnicity, BMI, time zone, any chronic lung or heart disease, and any alcohol or caffeine intake in each of the three sleep assessments. Comparisons between the three categories in education and RDI4% were made using one-way analysis of variance (ANOVA) in each of the three sleep assessments.†p-value <0.05 for t-test.‡p-value <0.0001 for t-test.*p-values <0.001 for one-way analysis of variance (ANOVA).§p-value 67 years of age reported higher HABSOL and AMSOL than those ≤67 years, there were no significant differences among PSGSOL for these groups. Obese subjects had significantly higher SOL for all 3 measures than nonobese subjects. Subjects with >16 years of education consistently had lower mean sleep latencies for all 3 measures than those with fewer years of education. Alcohol consumption was significantly associated with reduced sleep onset latency compared with no alcohol consumption for all 3 measures, HABSOL (15.4 vs. 16.0 min), AMSOL (17.5 vs. 21.1min), and PSGSOL (12.4 vs. 16.2 min).Table 3 SHHS Mean Sleep Onset Latency in Minutes£Habitual HABSOLAM Estimated AMSOLPolysomnogram PSGSOLMeanSDNMeanSDNMeanSDNGender Male14.52.494619.62.494015.62.3783 Female17.3‡2.51,05122.0†2.71,04316.12.4902Ethnicity Caucasian14.72.41,48219.92.61,46815.22.21,332 Others19.9‡2.551523.8†2.551518.9‡2.5353Age category ≤67 years15.12.41,08518.32.51,08015.562.42877 >67 years17.0†2.591124.3‡2.590216.392.19805BMI Nonobese ( =30%)17.3‡2.575221.6†2.674917.1†2.4593Education 16 years13.0§2.348318.5*2.548213.5*2.3373Time Zone Pacific/Eastern16.52.596422.32.694715.12.3939 Mountain/Central15.42.41,03319.6†2.51,03617†2.4745RDI4% <515.42.455719.62.654816.32.3496 5-<1516.32.477121.42.576715.82.3664 15- =3015.42.625221.1£2.525515.32.4183Chronic Lung Disease No15.72.41,74720.82.61,73315.82.31,478 Yes17.9†2.725021.12.625016.22.2207Chronic Heart Disease No15.42.41,58120.12.51,57515.82.31,335 Yes18.5‡2.541124.3†2.640316.32.2345Any Alcohol No16.02.51,83521.12.61,83116.22.31,540 Yes15.4†2.414217.5†2.514512.4‡2.2125Any Caffeine No15.62.41,62520.92.61,62215.62.31,385 Yes17.6†2.535220.52.535017.42.5281£t-test was used to compare means between the two groups in gender, age category, ethnicity, BMI, time zone, any chronic lung or heart disease, and any alcohol or caffeine intake in each of the three sleep assessments. Comparisons between the 3 categories in education and RDI4% were made using one-way analysis of variance (ANOVA) in each of the 3 sleep assessments.Means for HABSOL, AMSOL, and PSGSOL are presented here (values were log transformed for these tests, and converted back by taking the antilogarithm).†p-value <0.05 for t-test.‡p-value <0.0001 for t-test.*p-value <0.001 for ANOVA.§p-value <0.0001 for ANOVA.Correlation between HABTST and PSGTST was relatively weak although significant (r = 0.18, p < 0.0001, 95% CI: 0.14–0.22) as well as between AMTST and PSGTST (r = 0.16, p < 0.0001, 95% CI: 0.12–0.20). Correlation between HABTST and AMTST was stronger (r=0.44, p < 0.0001, 95% CI: 0.40–0.47). Correlations were also weak between the log-transformed values of HABSOL and PSGSOL (r = 0.23, p < 0.0001, 95% CI: 0.17–0.28), and between AMSOL and PSGSOL (r = 0.14, p < 0.0001, 95% CI: 0.08–0.20). Correlation between HABSOL and AMSOL was stronger (r = 0.52, p < 0.0001, 95% CI: 0.48–0.55). Thus, a number of subjects claiming to have high habitual or morning estimated values had low PSG values or vice versa.Mixed-effects linear regression models were used to evaluate the overall difference associated with type of attainment for total sleep time and sleep onset latency. Unadjusted models showed that mean PSGTST was significantly lower than mean HABTST (363 min and 422 min, p < 0.0001) and that mean PSGTST was significantly lower than mean AMTST (363 min and 379 min, p < 0.0001). Ranges for HABTST, AMTST, and PSGTST were (90–900 min, 0–720 min, and 110–519 min, respectively) (Table 4 and Figure 1). Adjusted means differed only slightly from unadjusted (Table 4). Unadjusted mean sleep onset latency was significantly different between AMSOL and PSGSOL (21.8 min and 16.9 min, p < 0.0001) but not between HABSOL and PSGSOL (16.7 min and 16.9 min, p = 0.69). Ranges for HABSOL, AMSOL, and PSGSOL were 1–300 min, 1–510 min, and 1–217 min, respectively. Adjusted models showed that on average HABTST and AMTST were higher than PSGTST by 61 and 18 min, respectively (Table 5), after adjusting for other demographic factors. Obese, higher educated people, and those with heart disease had less sleep time than their counterparts. Subjects residing in the Mountain/Central time zone slept 15 minutes more than subjects in the Pacific/Eastern time zone. Similarly, small adjusted differences, although significant, were found for sleep onset latency values (Table 6). Separate mixed models showed a decline in total sleep time of 0.5 min and an increase of 1 min in sleep onset latency for every year increased in age (Figures 2 and 3).Table 4 Adjusted and Unadjusted Means and Geometric Means for TST and SOL Values From Mixed-Effects Linear Regression Models.*HABTSTAMTSTPSGTSTMeanS.E.MeanS.E.MeanS.E.Unadjusted4223.93793.93633.9Adjusted4243.13813.23633.1HABTSTAMTSTPSGTSTMeanS.E.MeanS.E.MeanS.E.Unadjusted17.01.0421.81.0416.91.04Adjusted15.81.0220.81.0216.11.03*p < 0.0001 for HABTST and AMTST compared to PSGTST. p < 0.0001 for AMSOL compared to PSGSOL. p = not significant between HABSOL and PSGSOL.Adjusted for demographic factors listed in Tables 5 and 6.Figure 1 Unadjusted mean total sleep time (TST) and sleep onset latency (SOL) for habitual (HAB), morning estimated (AM), and polysomnogram (PSG) measures from mixed-effects linear regression models.Download FigureTable 5 Mixed-effects Linear Regression Model of Total Sleep Time in Minutes by Type of Assessment (Habitual, AM Estimated, and PSG) and Other Predictive VariablesVariablesRegression coefficientp-value95% CI*Habitual61.0<0.000157.0 – 65.0AM Estimated17.7 16 yr of education−1.10.80†−9.9 – 7.7Mountain/Central time zone14.90.0103.5 – 26.3Any Heart Disease−9.80.001−15.7 – −3.92Intercept363.9<0.0001353.3 – 374.5*CI = confidence interval. Note: PSG is the reference category for type of assessment, non-obese is the reference category for BMI, <12 years of education is the reference category for education, and Pacific/Eastern is the reference category for time-zone, no heart disease is the reference category for any heart disease.†p-value < 0.01, difference for linear contrast of coefficients between 12–16 and >16 years of education.Table 6 Mixed-effects linear regression model of sleep onset latency time in minutes by type of assessment (habitual, AM estimated, and PSG) and by predictive variablesVariablesRegression coefficientp-value95% CI*Habitual0.980.5520.93–1.04AM Estimated1.29<0.00011.22–1.37Female sex1.14 16 yr of education0.83<0.0020.74–0.94Any Heart Disease1.150.0011.06–1.24Intercept15.5<0.000113.8–17.4*CI = confidence interval. Note: Values for habitual, am estimated, and polysomnogram sleep onset latency time were log transformed for this test, anti-log values are presented here. PSG is the reference category for type of assessment, male is the reference category for sex, Caucasian is the reference category for ethnicity, 3 SDs) yielded no appreciable difference in any result. To determine whether subjects included in the analyses differed
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