Objectives Attitudes have been widely studied as predictors of a number of social and health behaviors. However, attitudes predicting sleep outcomes have only recently been examined, despite sleep being conceptualized as an important health behavior. Prior research has demonstrated that attitudes toward sleep are associated with sleep hygiene, sleep duration and quality (Peach & Gaultney, 2017 ; Peach, Gaultney, Ruggiero, 2018 ). Sleep attitudes interact with varying demographic identities, such as age, gender, race, and perceived socioeconomic status (SES) (Ruggiero, Peach, & Gaultney, 2019 ). The present study hypothesized that (1) sleep attitudes would be indirectly associated with sleep outcomes (duration and quality) via sleep hygiene, and, (2) this indirect effect would be modified by specific demographic variables (age, gender, race, and perceived SES; moderated mediation). Method One hundred and seventy-two adults from the United States completed an anonymous survey on sleep characteristics and health. Results Results confirmed the first hypothesis, indicating that sleep attitudes were significantly and indirectly associated with both sleep duration and sleep quality via sleep hygiene. Additionally, gender and SES further modified these significant indirect effects, meaning hypothesis two was partially supported. Conclusions Results are discussed in terms of their implications for the importance and variability of sleep attitudes, and future research directions are considered.
Introduction Lockdowns associated with the COVID-19 pandemic allowed for individuals to change their schedules. Chronicity is a trait-like preference for individuals’ times of the day for activity and feeling best. As a result of the lockdowns, some individuals were able to adjust their schedule to reflect personal chronotype needs. This study examined whether chronotype predicted sleep duration and health outcomes. Methods A sample of 304 participants were recruited through Amazon’s Mechanical Turk service to fill out surveys relating to personality and health. Individuals responded with their normal bedtime and waketime for weeknights and weekends and filled out the Morningness-Eveningness Questionnaire (MEQ; Horne & Östberg, 1976). Self-reported health outcomes were measured via 9 items on the Patient Reported Outcomes Measurement Information System (PROMIS; Cella et al., 2010). Data were cleaned and analyzed via linear regressions in SPSS with age, sex, race, ethnicity and education as covariates. Results Participants reported an average of 8.52 hours of sleep (SD = 1.97 hours). 35.3% of the sample scored strong- or moderately morning-type, 54.7% were neither morning-nor evening-type and 10% scored as evening- or strong-evening types (M = 54.95; SD = 9.42). Results from the PROMIS ranged from 18 to 45 (M = 32.24, SD = 5.49). The model predicting sleep duration (R2 = .06, p = .03) produced a significant effect of ethnicity but not chronicity. Hispanic or Latino ethnicity reported shorter sleep durations relative to those who self-identified as non-Hispanic or Latino. The model predicting PROMIS (general health) scores (R2 = .14, p < .001) produced effects of education (b = .46, p = .04) and Morningness (b = .21, p < .001). People with higher educational levels and those with morning preferences reported better health. Conclusion Morningness is often associated with better self-regulation, lower risky behaviors, better physical and mental health and better sleep. During the early stages of the COVID-19 pandemic, lockdowns allowed many individuals more scheduling flexibility. As a result, sleep duration differences across chronotypes were absent, though health differences remained. Future research should continue to explore differences in sleep schedules in predicting health outcomes. Support (If Any) Charlie Reeve and the Misfits lab at UNCC.
Introduction Attitudes towards sleep have been shown to be a predictor for sleep hygiene. Sleep hygiene is the set of behaviors and conditions that promote optimal sleep, such as avoiding arousing nighttime activities, avoiding eating too close before bed, having a dark and quiet bedroom, and having a regular sleep schedule. Previous literature indicates that there are gender differences in health attitudes. This study examined whether gender differences in sleep attitudes may explain differences in sleep hygiene. Methods A sample of 172 (101 males, 71 females) individuals completed surveys through Amazon’s Mechanical Turk. Sleep attitudes were assessed using the Charlotte Attitudes Towards Sleep Scale (CATS; Peach & Gaultney, 2017). Sleep hygiene was measured using the Sleep Hygiene Practice Scale (SHPS; Lin et al; 2007; Yang et al., 2010). Males were dummy coded as 0. Other data were collected surrounding sleep outcomes, health behaviors, and demographics. Linear regression analyses were ran to examine the impact of Sleep attitudes, gender, and an interaction term on each subscale of the SHPS. Results Sleep attitudes significantly predicted each of the components of the SHPS: arousal, eating, environment, and time (b = -3.44, -2.93, -3.80, -3.04; p<.01 for each). Gender significantly predicted sleep hygiene behaviors for eating (b = -10.35, p<.05) and environment (b = -15.40, p<.05) only. The interaction term also significantly predicted sleep hygiene eating behaviors (b = 1.70 p<.05) and environmental conditions (b = 2.91, p<.05). These findings suggest that more favorable sleep attitudes lead to better sleep hygiene behaviors, and women tend to have better eating and environment related sleep hygiene behaviors. Graphs of the interactions indicated males’ sleep attitudes associated with greater differences in sleep hygiene practices, in that positive sleep attitudes predicted better eating and environment elements of sleep hygiene. Conclusion This exploratory research suggested that men’s sleep-related behaviors may be more sensitive to the role of sleep attitudes. Future research should explore causes for gender differences in sleep attitudes and seek ways to improve behaviors and outcomes that are most relevant for specific demographic groups. Support (if any) Psychological Sciences department funding
Introduction A variety of attitudes, behaviors, and health attributes can influence sleep quality. Depression and sleep quality interact bidirectionally, with depressed individuals often sleeping worse. College freshman may be prone to worse sleep and depression due to significant lifestyle changes, including sleep hygiene (a set of behaviors and conditions promoting sleep). This study sought to examine the relationship between sleep hygiene and depression in predicting sleep quality in first-year college students. Methods 165 participants were recruited to investigate sleep behaviors associated with stress, mental health, physical activity and eating as they entered college. Data were recorded using the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989), the Sleep Hygiene Practice Scale (SHPS; Lin et al., 2007; Yang et al., 2010) and the Center for Epidemiological Studies-Depression (CESD; Radloff, 1977). A simple mediation analysis was run using the PROCESS macro for SPSS (Model 4; Hayes, 2018) with age and gender as covariates to examine direct and indirect associations of depression on sleep quality via sleep hygiene practices. Results In the model predicting sleep hygiene (R2 = .33, p < .001), depression had a significant effect (b = 1.90, p < .001), suggesting individuals scoring higher for depression engaged in more unhealthy sleep hygiene behaviors. The model predicting sleep quality (R2 = .47, p < .001) had significant effects from depression (b = .11, p = .005) and sleep hygiene (b = .09, p < .001) suggesting both higher depression scores and poor sleep hygiene behaviors associate with worse sleep quality. The indirect pathway was also significant (b = .17, CI: .11 - .24), suggesting depression’s impact on sleep hygiene behaviors also contributes to sleep quality. Conclusion One connection between depression and reduced sleep quality may be indirect via maladaptive sleep hygiene. Future research should look at addressing mental health with incoming students and promoting healthy lifestyle behaviors. Support (If Any) NA
Introduction Nightmares can cause poorer sleep quality. Various mechanisms have been explored as potential treatments for nightmares, including mindfulness practices and lucid dreaming. Presently, little literature has looked at the interaction effects between mindfulness and lucid dreaming to reduce nightmare distress. Methods A sample of 275 individuals was recruited from both the United States and Thailand via social media and a student pool of research subjects at UNC Charlotte. Data were recorded on participants’ demographic information, lucid dreaming from the Lucidity and Consciousness in Dreams Scale (Voss et al., 2013), Mindfulness using the Five Facet Mindfulness Questionaire (Baer et al., 2006), Nighmares via the Nightmare Distress Questionaire (Belicki, 1992), and sleep quality using the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989). Higher scores on each measure were associated with more lucid dreaming, more mindful behaviors, more severe nightmares, and poorer sleep quality. PROCESS model 8 was run to conduct a moderated mediation analysis (Hayes, 2018). Lucid dreaming was used as the predictor; sleep quality as the outcome variable, nightmare distress as the mediator and mindfulness acted as a moderator on both the direct and indirect pathway of lucid dreaming onto the mediator and outcome. Results Mindfulness was a significant predictor at both the mediator and outcome variables. Nightmare distress was a significant predictor of sleep quality. A statistical trend (p=.054) suggests lucid dreaming may have a positive effect on nightmare distress. In the indirect path, lucid dreaming had a positive effect on PSQI scores only for individuals with low mindfulness. Conclusion The moderated mediation suggests that individuals with low mindfulness may see a decrease in sleep quality from lucid dreaming. This could be due to lucid dreaming being associated with more severe nightmares. A zero-effect size could not be ruled out of the confidence intervals for individuals of average or high mindfulness, but the data suggest that lucid dreaming alone may not help treat nightmares, but the combination of lucid dreaming and mindfulness therapies could promote lower distress and better sleep quality. Support Psychological Sciences departmental funding
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