Sleep hygiene of a sample of undergraduate students at a Midwestern University.
|Abstract:||Purpose. The purpose of this study was to identify sleep hygiene practices amenable to modification among undergraduate college students. Methods. Data were collected from cross-sectional samples of undergraduate students over two phases. Results. The majority of the sample received less than 7 hours of sleep. Descriptive analysis of the Sleep Hygiene Index identified potential areas for intervention targeting. Conclusion. Sleep hygiene principles have the potential to improve the sleep quality of undergraduate students.|
Stress (Psychology) (Health aspects)
Students (Health aspects)
Knowlden, Adam P.
Bernard, Amy L.
|Publication:||Name: American Journal of Health Studies Publisher: American Journal of Health Studies Audience: Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2012 American Journal of Health Studies ISSN: 1090-0500|
|Issue:||Date: Wntr, 2012 Source Volume: 27 Source Issue: 1|
|Organization:||Organization: University of Cincinnati; American Academy of Sleep Medicine|
Undergraduate students are confronted with a complex array of social, academic, and personal dynamics which have the potential to negatively influence their sleeping patterns. For many incoming undergraduate students, the family support and structure present during high school is replaced by a more disorganized lifestyle (Brown, Soper, & Buboltz, 2001). Congruent with this novel level of autonomy, students also have to cope with the stress of academic responsibility (Pilcher, Ginter, & Sadowsky, 1997). Students frequently rate their first year of college as one of the most stressful events of their lives (Wong, Cheung, Chan, Ma, & Tang, 2006); citing examinations, concerns about future success, and meeting professor expectations as salient academic stressors (Murphy, 1996). Ecological forces have a strong effect on behavior as students seek to develop social relationships and adjust to cultural norms (Vela-Bueno, Fernandez-Mendoza, & Olavarrieta-Bernardino, 2009). Researchers have noted that although any one of these factors have the capacity to negatively impact sleep patterns, the interplay between these elements increases the likelihood students will embrace self-imposed sleep restriction (Jensen, 2003).
To meet the social and academic pressures of university life, students often adopt unhealthy sleeping behaviors (Brown, Buboltz, & Soper, 2006). Adjusted sleep patterns typically involve self-imposed sleep restriction throughout the week and extended sleep duration on the weekends (Brown et al., 2001; Machado, Varella, & Andrade, 1998). This cycle often becomes more pronounced in proximity to tests and final examinations, with the potential of resulting in episodes of 24 to 48 hours of total sleep deprivation (Pilcher & Walters, 1997). Such variability in sleeping behaviors helps explain why college students frequently report symptoms consistent with delayed sleep phase syndrome (Brown et al., 2001; Lack, 1986).
In tandem with intense lifestyle changes, undergraduate students endure maturational changes as they complete the transition from adolescence to adulthood (Vela-Bueno et al., 2009). Vela-Bueno and colleagues (2009) assert that these developmental changes play an important role in some of the common sleep complaints of this cohort including disturbances of the sleep-wakefulness circadian rhythm, insufficient sleep, and excessive daytime sleepiness. The natural proclivity towards sleep difficulties in this group coupled with the stress of college life accentuates the need for sleep health education.
Several studies have reported that college women experience more sleep disturbances than college men (Brown et al., 2001; Buboltz, Brown, & Soper, 2001). Tsai and Li (2004b) observed that women had longer sleep onset latency, more nighttime awakenings, and overall poorer sleep quality than men. Lund and colleagues (2010) found women were found to report more stress-related sleep disturbances than men.
Sleep hygiene (SH) is an effective approach for improving sleep quality (Stepanski & Wyatt, 2003). SH operates under the assumption that behavioral and environmental conditions can be modified to enhance sleep quality. Inadequate SH is characterized by practices that produce increased arousal and practices that contradict the known principles of sleep organization. Studies show that SH status can be negatively impacted by substances such as caffeine, tobacco, and alcohol (AASM, 2001). Stress and excitement also contribute to reduced SH and include stimulating activities occurring within close proximity to sleep time such as intense physical activity, involved mental tasks, or social events. Environmental arousers--for example, allowing light to seep into the bedroom--are also labeled as SH disruptors. The biological processes that regulate sleep are also susceptible to inadequate SH. Spending too much time in bed, excessive variation in sleep/ wake time, and taking naps throughout the day are considered indicators of poor SH.
The majority of college students who practice poor sleeping habits are unacquainted with behaviors that promote healthy sleep. Hicks, Lucero-Gorman, Bautista, and Hicks (1999) tested the mean differences in scores of SH knowledge and SH practices scales (Lacks & Roter, 1987) among four ethnic groups of university students (n=963). Results showed that Euro-Americans scored significantly higher on both scales than each of the other three groups; however, Hicks and colleagues noted that all of the groups exhibited poor SH knowledge. The researchers pointed to the Euro-American group, which scored the highest on SH knowledge but answered only 57% of the scale's items correctly. Brown and colleagues (2006) cautioned educators to take into consideration the general lack of SH knowledge exhibited among college students as opposed to attributing students' poor sleeping habits to deviant lifestyle choices.
This study had three primary research objectives. The first objective was to identify SH practices amenable to modification among undergraduate college students. Descriptive statistics were used to address the first objective. The second objective was to determine whether sleep hygiene and gender were significant predictors of sleep behavior. To evaluate the second objective, binary logistic regression was employed to model sleep hygiene and gender on sleep behavior. The third objective was to assess whether there was a statistically and practically significant difference in sleep hygiene scores between those students who achieved adequate sleep and those who did not. An independent t-test based on adequate and inadequate sleep behavior group membership was calculated to evaluate objective three.
Demographics. For the purposes of this study, the target population included undergraduate students between the ages of 18 and 24 who were unmarried and did not reside with a parent or legal guardian. The inclusion criteria were selected to capture the SH status of traditional undergraduate students. Demographic data including age, gender, race/ethnicity, year of student, full-time/part-time enrollment status, and full-time/part-time employment status were collected from the sample.
Sleep hygiene. SH was evaluated through the Sleep Hygiene Index (SHI) developed by Mastin, Bryson, and Corwyn (2006). Permission was granted by the developers of the SHI to use the scale in this investigation. Sample items included "I take daytime naps lasting two or more hours", "I use alcohol, tobacco, or caffeine within 4 hours going to bed or after going to bed", and "I think, plan, or worry when I am in bed" (see Table 1 for a full list of items). The original format of the SHI consisted of 13-items measured by 5-point Likert scales. For the purposes of this study, the SHI scale was slightly modified to provide a broader range of response options. Subsequently, the original 5-point Likert scales were converted to 7-point Likert frequency scales (1=Always, 7=Never). In addition, the scale endpoints were reversed such that lower scores indicated a more maladaptive SH status. Items were scored from 1 to 7 and summed to provide a global SH score ranging from 13 to 91.
For addressing research objective one, qualitative labels were applied to score ranges of the SHI to evaluate degree of frequency. The SHI is a novel instrument for measuring SHI; therefore, in developing the qualitative labels there was no prior research to draw upon. However, it was determined labels would assist practitioners in intervention development. Therefore, item means were assigned qualitative weights for assessing practical importance.
Sleep duration. Sleep duration was gauged through self-report employing 24-hour recall of the previous night's sleep. Participants were asked, "In the past 24 hours, how many hours/minutes did you sleep at night time?" Responses were transformed from total hours and minutes of sleep to total minutes of sleep accrued at night time in the past 24 hours (McKnight-Eily et al., 2011). In this context, sleep duration had a potential range of 0 to 1,440 total minutes of sleep. The mean of the item scores was calculated to provide an overall sleep duration score. Scores within the range of 420 to 480 total minutes of sleep (equivalent to 7 to 8 hours) were considered to have met this study's definition of adequate sleep duration; conversely, scores outside of this range were considered to have met this study's definition of inadequate sleep duration. These operational definitions of sleep duration were based on a consensus of epidemiological research which suggests sleep durations which fall outside the range of 7 to 8 hours are associated with higher morbidity and mortality rates among adult populations (Bixler, 2009; Ferrara & De Gennaro, 2001; Gangwisch et al., 2007; Hall et al., 2008; Hublin, Partinen, Koskenvuo, & Kaprio, 2007).
Phase I data collection. Data collection spanned two phases. Phase I established test-retest reliability of the instrument. Prior to phase I data collection, a panel of six experts confirmed face, content, and readability of the instrument over a two round process. Upon incorporating feedback from the panel, the revised instrument was evaluated for stability reliability through the application of test-retest. For phase I, volunteering students (n=37) from health promotion and education classrooms completed hardcopy versions of the instrument twice, two week apart.
Phase II data collection. Phase II participants completed an electronic version of the instrument. For phase II, a power analysis was conducted to determine a sufficient sample size for logistic regression analysis. Power analysis criteria included an alpha of 0.05, a power of 0.80, and an odds ratio of 1.56. The power analysis parameters were entered into G*Power, an open source sample size calculator, which resulted in a final sample size of 197 (Faul, Erdfelder, Buchner, & Lang, 2009).
Phase II participants were recruited through a one-time electronic invitation delivered through the university e-mail system. The convenience sample was delimited by the registrar's database software to undergraduate students between 18 and 24 years old. Additional inclusion criteria for the study were satisfied through a series of inclusionary logic questions presented during the opening screens of the electronic instrument. Prior to completing the instrument, eligible participants were required to imply consent through an IRB approved electronic consent form. The consent form conveyed to participants that by affirming their consent they were permitting their responses to be used for research purposes. Respondents were encouraged to print the consent form for their records. Participation in the study was voluntary and no incentives were offered. The electronic version of the instrument was made available in February 2011. Data collection for the study ceased once the quota sample of 197 completed surveys was satisfied. Responses submitted with missing data were discarded and not considered in the analysis. All data were analyzed using the Predictive Analytics Software Statistics Grad Pack 18.0 data analysis software.
Phase I data collection included having the same group of participants (n=37) complete the instrument twice at two week intervals to assess instrument stability. Acceptable test-retest coefficient values were set a priori at 0.70. Both the SHI (r (37) = 0.871, p < 0.01) and sleep duration (r (37) = 0.806, p < 0.01) constructs exceeded the acceptable a priori criteria. Based on these findings, the instrument was determined to be stable. Internal consistency of the instrument was tested using data collected during phase II. An acceptable internal consistency coefficient value for the SHI was as established a priori at 0.70. The SHI was found to be internally reliable, exceeding the acceptable a priori criterion ([alpha] = 0.726). Responses for the item representing sleep duration were converted from total hours and minutes of sleep to total minutes of sleep accrued at night time within the past 24 hours. After this conversion, sleep duration was represented by a single item. As such, testing for internal consistency was not applicable.
To participate in this study, participants were required to be between the ages of 18 and 24. The mean age of the respondents (n=197) was 20.27 (SD=1.537). The respondent pool was comprised of 130 (66.0%) females and 67 (34.0%) males. The vast proportion of the participants were Caucasian (n=181, 91.9%). Additional racial/ethnicities accounted for in the sample included African American (n=2, 1.0%), Asian (n=5, 2.5%), multiracial (n=6, 3.0%), Hispanic (n=2, 1.0%), and other (n=1, 0.5%).
The majority of the students were full-time (n=187, 94.9%). Part-time students accounted for only a small proportion of the sample (n=10, 5.1%). Most respondents were first (n=54, 27.4%) and second year (n=54, 27.4%) students. The remaining portion of the sample was distributed across third (n=31, 15.7%), fourth (n=43, 21.8%), and fifth year and above (n=15, 7.6%) students. Concerning employment status, most participants held either part-time (n=91, 46.2%) or full-time (n=18, 9.1%) positions. A significant amount of the sample were unemployed (n=88, 44.7%).
SLEEP HYGIENE INDEX.
Respondents were requested to rate their frequency of engagement in behaviors purported to impact SH as measured by the SHI (1=Always, 7= Never). Item means were assigned qualitative weights for assessing practical importance. In this context, mean item scores from 1 to 3 were considered most important for modification (MIM), mean item scores of 4 to 5 were considered moderately important for modification (MDM), and mean item scores of 6 to 7 were considered least important for modification (LIM). Values were rounded to the nearest whole number for generalization. Table 1 provides the descriptive statistics and qualitative labels for the SHI scale items and the descriptive statistics for the SHI construct for the sample of undergraduate college students.
Examining individual items comprising the SHI, stress-related factors including "I do important work before bedtime (M=2.52, SD= 1.42; MIM)", "I think, plan, or worry when I am in bed (M= 2.61, SD=1.56; MIM)", "I do something that may wake me up before bedtime, i.e. play video games, use the internet, clean (M=2.88, SD=1.70; MIM)", and "I go to bed feeling stressed, angry, upset, or nervous (M=3.64, SD=1.64; MDM)" were the most frequently cited disruptors of positive SH status.
Irregular sleeping patterns including "I go to bed at different times from day to day (M=3.01, SD=1.71; MIM)", "I get out of bed at different times from day to day (M=3.74, SD=1.81; MDM)", "I stay in bed longer than I should two or three times a week (M=4.11, SD=1.97; MDM)", and "I take daytime naps lasting two or more hours (M=5.27, SD=1.73; LIM)" were also common among the sample.
Environmental determinants of SH including "I sleep in an uncomfortable bedroom, i.e., too bright, too stuffy, too hot, too cold, or too noisy (M=4.77, SD=1.77; LIM)", "I sleep on an uncomfortable bed i.e., poor mattress or pillow, too much or not enough blankets (M=5.14, SD=1.87; LIM)" were also influential on the samples' SH status; particularly, the item "I use my bed for things other than sleeping or sex, i.e. watch television, read, eat, or study (M=3.39, SD=2.10; MIM)".
In regards to stimulants and sedatives, the item "I use alcohol, tobacco, or caffeine within four hours of going to bed or after going to bed (M=4.39, SD=1.95; MDM)" indicated occasional use among the participants. The item "I exercise to the point of sweating within one hour of going to bed (M=6.13, SD=1.23; LIM)" was the most infrequent behavior reported by the respondents. Summated, the SHI had a possible range of 13 to 91 with higher scores reflecting a more positive SH status. The observed score for the construct was 28 to 84 with a mean of 51.59 and a standard deviation of 10.93.
In examining sleep duration, the mean minutes of total sleep at night time of the sample was 407.34 (SD=100.75). The mean sleep duration score of the sample fell below the minimal acceptable score of 420 minutes set forth in this study, indicating a large portion of the sample received less than 7 hours of total sleep in the prior 24 hours. Closer examination of the data revealed only 48 (24.37%) of the participants achieved adequate sleep duration as defined by this study. Among the pool of participants, 108 (54.80%) received insufficient sleep (<7 hours) and 41 (20.81%) obtained excessive sleep (>8 hours). Table 2 summarizes the frequencies and distributions of the adequate sleep duration construct.
RELATIONSHIP BETWEEN SLEEP HYGIENE AND SLEEP DURATION.
Logistic regression. Logistic regression analysis was performed to predict the likelihood of achieving adequate sleep duration. Total minutes of sleep were converted into a binary variable (inadequate sleep=0; adequate sleep=1) for modeling purposes. Prior to specifying the model the assumptions of absence of multicollinearity, absence of outliers, appropriate sample size, and linearity in the logit were confirmed. The Wald Chi-square test assessed the significance of SH as a predictor of sleep duration. The a priori criteria of probability of [chi square] to retain the predictor in the model was less than or equal to 0.05. Logistic regression applying the direct entry method modeled the theoretical predictors of sleep hygiene and gender on adequate sleep duration. Fit indices including the omnibus test of model coefficients, Hosmer and Lemeshow test, classification ratio, likelihood ratio statistic, and Nagelkerke's [R.sup.2] were considered in assessing model fit.
Model 1. A test of the full model with both predictors against a constant-only model was statistically significant, ([chi square] (2) = 16.443, p < 0.001) indicating that the predictors, as a set, reliability distinguished between inadequate (n=149) and adequate (n=48) sleep in the sample. The Hosmer and Lemeshow test confirmed goodness of fit for the model ([chi square] (df=7, n=197) = 6.882, p < 0.441). Classification was satisfactory with the model correctly predicting 99.3% of the sample obtaining inadequate sleep and 8.3% of the sample obtaining adequate sleep, for an overall success rate of 77.2%. The model produced a likelihood ratio statistic of 202.328. The model identified sleep hygiene (B=0.063, Wald [chi square] (1) = 13.844, p < 0.001) as a significant predictor. Gender was found to be a non-significant predictor of adequate sleep duration (B= 0.389, Wald [chi square] (1) = 1.170, p = 0.279). Subsequently, the gender variable was removed and the model re-specified.
Model 2. The re-specified model with gender removed was statistically significant, ([chi square] (1) = 15.284, p < 0.001) indicating that the predictor SH reliability distinguished between inadequate (n=149) and adequate (n=48) sleep in the sample. The Hosmer and Lemeshow test confirmed goodness of fit for the model ([chi square] (df=7, n=197) = 8.887, p < 0.261). Classification was satisfactory with the model correctly predicting 98.0% of the sample obtaining inadequate sleep and 12.5% of the sample obtaining adequate sleep, for an overall success rate of 77.2%. The final model identified SH (5=0.063, Wald [chi square] (1) = 13.618, p < 0.001) as a significant predictor of sleep duration.
SH emerged as a significant predictor of adequate sleep duration (OR=1.065, 95% CI= [1.030, 1.100]). Regarding SH, for each one unit increase in SH as measured by the SHI the odds of obtaining adequate sleep duration increased by 6.5%. Specifically, for each one unit increase in SH status, the logit of adequate sleep duration increased by 0.063 and the odds ratio increased by a factor of 1.065. The SHI construct produced a Nagelkerke's [R.sup.2] effect size of 0.111. Table 3 summarizes the fit statistics and parameter estimates for Models 1 and 2 for the theoretical predictors of SH and gender regressed on sleep duration.
T-test. An independent t-test was calculated to evaluate differences in mean scores and effect sizes of SH based on inadequate and adequate sleep duration group membership. Cohen's d was calculated to gauge effect size of the differences between groups. In interpreting Cohen's d, Cohen's criteria of: 0.20 = small, 0.50 = moderate, and 0.80 = large effect sizes were considered (Cohen, 1988). Wolf's (1986) criterion of 0.50 concerning practical/clinical significance was also referenced. Scores on the SHI differed significantly between the inadequate and adequate sleep duration groups (t (195) = -3.991, p < 0.001; Cohen's d = 0.63). Those who obtained adequate sleep reported higher SH (M=56.88 SD=12.17) than those who did not (M=49.89, SD=9.97). The effect size for SH between the two groups was moderate and practically significant.
The findings of this study agree with the literature that, as a population, undergraduate college students receive insufficient sleep (Hicks, Fernandez, & Pellegrini, 2001; Lund et al., 2010). In this investigation, sleep duration was operationally defined as participants' self-reported total minutes of sleep at night time acquired in the past 24 hours. Logistic regression analysis found that for each one unit increase in SH as measured by the SHI, the odds of obtaining adequate sleep duration increased by 6.5%. Furthermore, those participants who obtained adequate sleep reported statistically and practically higher SHI scores than those who did not.
As indicated by the findings of this study, irregular sleeping patterns were a prominent deterrent of SH. Adhering to a consistent wake/ sleep time promotes optimal sleep propensity and consolidation (Stepanski & Wyatt, 2003). In regards to SH principles, consistency of sleep-wake time is perhaps the most important; however, it is one of the most difficult for students to adopt. Practical strategies can be incorporated into sleep health education interventions to improve upon this area.
The results of this study found that stress-related factors were the most frequently cited disruptors of positive SH status. Health interventions can encourage students to replace stimulating pre-sleep stressors such as social media websites or homework with relaxing pre-sleep routines such as relaxation techniques or religious rituals. Additional stress management tools such as worry lists and journaling can mitigate racing thoughts and prepare the mind for rest (Friedman et al., 2000).
A number of environmental factors have been proposed to foster SH such as eliminating light and noise from the bedroom, regulating bedroom temperature, eliminating light-emitting bedroom clocks, ensuring the bed is comfortable and suitable for sleep, and using the bedroom exclusively for sleep (Hauri, 1991; Hicks et al., 1999). Based on the findings of this investigation, it is recommended that students receive instruction on how to cultivate a bedroom environment conducive to quality sleep. College health practitioners should specifically reinforce to students to use their beds only for sleep and not as a location to engage in stress-related activities such as homework or exam preparation.
In the current investigation, caffeine, alcohol, and tobacco usage within four hours of bed time was moderate among the participants. Nutrition education and food logs incorporated into a sleep program can assist students in monitoring caffeine, alcohol, and tobacco consumption, particularly near sleep time. Caffeine has a half-life of 6 hours therefore it is recommended to restrict caffeine consumption within 4 to 6 hours of scheduled sleep time. Alcohol has several inimical effects on SH including suppression of REM sleep, sleep fragmentation, and delay of sleep (Stepanski & Wyatt, 2003). Low doses of alcohol can assist in promoting sleep onset; however, alcohol consumed up to 6 hours prior to sleep time has been empirically verified to result in sleep fragmentation (Landolt, Roth, Dijk, & Borbely, 1996). Consequently, it is generally recommended that alcohol be avoided prior to sleep and not be employed as a hypnotic (Hauri, 1991). As well, nicotine is considered a mild stimulant and thus should be avoided prior to sleep. However, the full effect of nicotine's interaction on sleep remains unclear (Stepanski & Wyatt, 2003).
IMPLICATIONS FOR PRACTICE
The SHI is a valid and reliable tool for assessing SH and should be employed in the development and measurement of sleep health education and promotion programs. One of the chief advantages of the SHI is its brevity. The instrument is comprised of 13 items and has an estimated completion time of 5 to 7 minutes. In developing sleep health programs, interventionists should focus on modifying behaviors that contribute the most to poor SH status. In this investigation, each SHI item has been assigned a qualitative label to assist practitioners in determining the most salient behaviors to target (see Table 1). Logistic regression modeling confirmed SH as a significant predictor of adequate sleep duration; conversely, gender was not significant in the prediction of sleep duration. This outcome suggests that sleep health interventions do not need to be specifically tailored toward gender; instead, programs can remain comprehensive and targeted in a similar fashion to both men and women.
There are limitations to this study which should be considered when evaluating the findings of this report. Participants were pooled from convenience samples of undergraduate college students from a Midwestern University. Therefore, findings cannot be generalized beyond the study participants. A cross-sectional design was utilized in this investigation inhibiting the ability to infer cause-and-effect relationships between the variables. The findings of this study were based on the self-reporting accuracy, integrity, and honesty of the participants. Concurrently, misinterpretation of instrument items may have skewed participant responses. The qualitative weights assigned to the individual SHI items were based on the interpretation of the researchers. Health practitioners should consider the qualitative labels within the context of the community they serve. Sleep duration was assessed based on 24-hour recall; however, sleep is a highly complex behavior which cannot be fully reified through a single variable. Finally, the science of sleep health is in its infancy. As the field of sleep science advances, the assumptions of sleep health presented in this report may become outdated.
RECOMMENDATIONS FOR FUTURE RESEARCH
Sleep is essential for obtaining optimal health. Nevertheless, little research emphasis has been placed on this vital health topic in the fields of health promotion and education. A need exists for more sleep-related research in these fields. The SHI is a relatively novel instrument for measuring SH and requires more testing to confirm its efficacy. Future studies with more rigorous designs could assist in gauging the practicality of the SHI as a tool for measuring SH. The qualitative weights assigned to SHI items were based on the interpretation of the research team. Additional research should be done employing focus groups to improve scale descriptors. In the current study, sleep duration was gauged through a single variable; total minutes of sleep acquired in the past 24 hours. Replication of this study using 7 day sleep logs or actigraph units would reduce measurement error and strengthen future investigations.
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Adam P. Knowlden, MBA, MS, is a Graduate Assistant of the University of Cincinnati, Health Promotion & Education Program, Mailing address & Contact Information: University of Cincinnati, Health Promotion & Education Program, 526 Teachers College, P.O. Box 210068, Cincinnati, OH 45221-0068, (513) 5563878 (Phone), (513) 556-3898 (Fax), firstname.lastname@example.org (E-mail). Manoj Sharma, MBBS, MCHES, PhD, is a Professor at the University of Cincinnati, Health Promotion & Education Program & Public Health Sciences, Mailing address & Contact Information: University of Cincinnati, Health Promotion & Education Program, 527 C Teachers College, P.O. Box 210068, Cincinnati, OH 45221-0068, (513) 5563878 (Phone), (513) 556-3898 (Fax), email@example.com (E-mail). Amy L. Bernard, MCHES, PhD, is an Associate Professor at the University of Cincinnati, Health Promotion & Education Program & Public Health Sciences, Mailing address & Contact Information: University of Cincinnati, Health Promotion & Education Program, 526 D Teachers College, P.O. Box 210068, Cincinnati, OH 45221-0068, (513) 5562126 (Phone), (513) 556-3898 (Fax), (E-mail) Amy.Bernard@uc.edu
Table 1. Descriptive Statistics and Qualitative Labels for the Sleep Hygiene Index Scale Items and Descriptive Statistics for the Sleep Hygiene Index Construct for the Sample of Undergraduate College Students (n=197) Range Sleep Hygiene Possible Observed 1. Item 2. I go to bed at different 1-7 1-7 times from day to day--Always : Never 2. Item 7. I do something that may 1-7 1-7 wake me up before bedtime (e.g., play video games, use the internet, or clean)--Always : Never 3. Item 9. I use my bed for things 1-7 1-7 other than sleeping or sex (e.g., watch television, read, eat, or study)--Always : Never 4. Item 12 I do important work before 1-7 1-7 bedtime (e.g., pay bills, schedule, or study)--Always : Never 5. Item 13. I think, plan, or worry 1-7 1-7 when I am in bed --Always : Never 6. Item 3. I get out of bed at 1-7 1-7 different times from day to day --Always : Never 7. Item 5. I stay in bed longer than I 1-7 1-7 should two or three times a week --Always : Never 8. Item 6. I use alcohol, tobacco, or 1-7 1-7 caffeine within four hours of going to bed or after going to bed --Always : Never 9. Item 8. I go to bed feeling 1-7 1-7 stressed, angry, upset, or nervous --Always : Never 10. Item 1. I take daytime naps lasting 1-7 1-7 two or more hours--Always : Never 11. Item 4. I exercise to the point of 1-7 1-7 sweating within one hour of going to bed--Always : Never 12. Item 10. I sleep on an uncomfortable 1-7 1-7 bed (e.g., poor mattress or pillow, too much or not enough blankets) --Always : Never 13. Item 11. I sleep in an uncomfortable 1-7 1-7 bedroom (e.g., too bright, too stuffy, too hot, too cold, or too noisy)--Always : Never Total Sleep Hygiene Construct Score 13-91 28-84 Range Sleep Hygiene M SD QW 1. Item 2. I go to bed at different 3.01 1.71 MIM times from day to day--Always : Never 2. Item 7. I do something that may 2.88 1.70 MIM wake me up before bedtime (e.g., play video games, use the internet, or clean)--Always : Never 3. Item 9. I use my bed for things 3.39 2.10 MIM other than sleeping or sex (e.g., watch television, read, eat, or study)--Always : Never 4. Item 12 I do important work before 2.52 1.42 MIM bedtime (e.g., pay bills, schedule, or study)--Always : Never 5. Item 13. I think, plan, or worry 2.61 1.56 MIM when I am in bed --Always : Never 6. Item 3. I get out of bed at 3.74 1.81 MDM different times from day to day --Always : Never 7. Item 5. I stay in bed longer than I 4.11 1.97 MDM should two or three times a week --Always : Never 8. Item 6. I use alcohol, tobacco, or 4.39 1.95 MDM caffeine within four hours of going to bed or after going to bed --Always : Never 9. Item 8. I go to bed feeling 3.64 1.64 MDM stressed, angry, upset, or nervous --Always : Never 10. Item 1. I take daytime naps lasting 5.27 1.73 LIM two or more hours--Always : Never 11. Item 4. I exercise to the point of 6.13 1.23 LIM sweating within one hour of going to bed--Always : Never 12. Item 10. I sleep on an uncomfortable 5.14 1.87 LIM bed (e.g., poor mattress or pillow, too much or not enough blankets) --Always : Never 13. Item 11. I sleep in an uncomfortable 4.77 1.77 LIM bedroom (e.g., too bright, too stuffy, too hot, too cold, or too noisy)--Always : Never Total Sleep Hygiene Construct Score 51.59 10.93 Note. Items ranked according to qualitative weight of mean item score (QW); MIM = most important for modification; MDM = moderately important for modification; LIM = least important for modification. Qualitative weight values are rounded to the nearest whole number. Table 2. Summary of Frequency Distributions and Percentages of the Sleep Duration Variable for the Sample of Undergraduate Students (n = 197) Range Sleep Duration n % Possible Total Score 197 100.0 0-1,440 Adequate Sleep 48 24.37 420-480 Duration Inadequate Sleep 149 75.63 0-419 U Duration 481-1,440 Insufficient Sleep 108 54.80 0-420 Excessive Sleep 41 21.64 480-1,440 Range Sleep Duration Observed M SD Total Score 180-720 407.34 100.75 Adequate Sleep 420-480 441.25 20.36 Duration Inadequate Sleep 180-410 U 391.12 127.06 Duration 489-720 Insufficient Sleep 180-410 336.56 46.63 Excessive Sleep 489-720 554.24 44.55 Note: U = interval notation for union of two number sets. Table 3. Parameter Estimates for Models 1 and 2 for the Theoretical Predictors of Sleep Hygiene and Gender Regressed on Sleep Duration for the Sample of Undergraduate Students (n=197) Wald Predictor B SE Statistic OR 95% CI Model 1 Constant -4.652 0.969 23.064 * 0.010 Sleep Hygiene 0.063 0.017 13.844 * 1.065 [1.030, 1.102] Gender 0.389 0.359 1.170 1.475 [0.729, 2.982] ([double dagger]) Model 2 (Final Model) Constant -4.469 0.946 22.317 * 0.011 Sleep Hygiene 0.063 0.017 13.618 * 1.065 [1.030, 1.100] Note. * p < 0.001; ([double dagger]) = p > 0.05; CI = Confidence interval for odds ratio (OR); Final model [chi square] (2) = 16.443, p < 0.001; Final model Nagelkerke's [R.sup.2] = 0.119.
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