1. INTRODUCTION
The mind and body have primitive physiological needs including respiration, growth, reproduction, nutrition, excretion and –crucial but often overlooked—the behavioral state of sleep. Sleep health is the study of how we sleep and the factors impacting this behavior. The attainment of healthy sleep is multidimensional in its nature and one of its dimensions may be operationalized as the number of hours slept per night (Buysse,2014). The recommended duration of sleep varies depending on age group, and the range for a university student (18-25 years of age) is between 7 and 9 hours of sleep (“How Much Sleep Do We Really Need?”). Despite being vital to health, over 70% of students report insufficient amounts of sleep (Hershner&Chervin,2014).
Trends for lack of sleep begin in adolescence as this period is marked by a window of vulnerability. At this time, there is a tendency to manifest risky behaviors including smoking, alcohol abuse, drug abuse, lack of exercise, poor diet, and phase delay in sleep habits, pronounced by later sleep times. Additionally, conceptions of invincibility and invulnerability may persist, as the effects of such neglect to health are long term and unapparent in the short term (Taylor&Sirois, 2014). These impressions carry through post-secondary education, and the university student is alarmingly susceptible to latency in healthy sleep (Lund, Reider, Whiting, &Prichard,2010).
2. LITERATURE REVIEW
Causes and Implications of Lack of Sleep
The factors underlying sleep loss can be broken down into biological and socio-behavioral contributions as outlined by Bryant et al (Bryant&Gomez,2015) A widely studied biological contribution is homeostatic sleep drive. This notion suggests that longer arousal during the wake state leads to an increased necessity for sleep (Bryant&Gomez,2015). Therefore, a homeostatic balance between sleep and awake states must be achieved. However, as adolescent’s progress through later stages of puberty, their bodies tend to adjust this drive for a preference towards later sleep times. Studies show that even upon sleep deprivation, during late puberty, our bodies take a longer amount of time to fall asleep. This phenomenon thus contributes to what is later outlined by Curcio et al as delayed sleep phase syndrome. Further, socio-behavioral contribution of decreased duration of sleep is shown to be a result of increased time spent on academic assignments with age, thus increasing the tendency for later sleep times. Socio-behavioral contributions relate to acute sleep deprivation and the tendency for students to perform “all-nighters,” further discussed by Hershner et al (Hershner&Chervin,2014).
Recent literature suggests that both acute sleep deprivation and chronic sleep deprivation in college students impact cognitive ability. Principally, acute and chronic sleep deprivation may be the result of (1) the prevalence of “all-nighters” among students and (2) delayed sleep phase syndromes (DSPS), respectively. In a clinical review Hershner et al highlight that it is important to note that despite 70% students reporting insufficient sleep and 82% of students reporting awareness of the adverse effects of inadequate sleep and its impact on academic performance, students do not regulate their sleep cycles. The occurrence of this paradox at the level of acute deprivation may be explained because of unapparent short term risks of lack of sleep. In one study, a sample of students given an exam were either sleep deprived for 24 hours (“pulled an all-nighter”) or obtained the recommended mean of 8 hours of sleep and were asked to report their perceived effort and motivation on the exam. The sleep deprived group reported better concentration, effort and performance however, the grades obtained were inconsistent with such perceptions as the findings showed that those who slept 8 hours performed better despite having reported perceptions otherwise (Hershner&Chervin,2014). Ultimately, the study implicates that when students do not perceive impairments after acute sleep deprivation, they will not have the motivation to change this behavior (Hershner&Chervin,2014). What remains unknown to these students is the long-term effects of sleep deprivation and sleep debt.
Literature by Curcio et al addresses students who do not receive the recommended mean of 8 hours of sleep; who, during the week have a cumulative impact of sleep deficit, otherwise known as sleep debt; and culmination of delayed sleep phase syndrome. A DSPS in students consists of two indicators: extended weekend sleep schedules and an inconsistent weekday sleep schedule (Curcio, Ferrara, &Degennaro,2006). During the week, sleep habits are characteristic of late sleep times, and early rise times inducing insufficient sleep. To account for this, students often sleep in during the weekends, however still have a late sleep time. The impact of this is a constant feeling or desire to “catch up” on sleep, but before recovery, students must return to school and the cycle of sleep between weekends and weekdays continues, thereby leaving students in a constant state of DSPS. In one study 17% of university students with a delayed sleep phase syndrome problem correlated with impaired academic performance. (Curcio, Ferrara, &Degennaro,2006). In another study, regular sleep wake patterns were operationalized as shorter sleep latencies, fewer night awakenings, later school rise times, and earlier weekend rise times. Students with such a cycle showed to have a better GPA and academic performance than students with later bedtimes and early awakenings Curcio, Ferrara, &Degennaro,2006). Therefore, a regular sleep-wake pattern is necessary for healthy sleep. To address the irregularity of sleep patterns in university students, interventions regulating sleep hygiene through sleep-monitoring have been found to be the most effective.
3. CURRENT INTERVENTIONS
Self-Monitoring Techniques
Of utmost relevance to any sleep intervention is sleep hygiene—the behaviors conducive to healthy sleep (Mairs&Mullan,2015). Several studies have manipulated sleep hygiene via self-monitoring techniques. Self-monitoring is a mechanism that “produces behavior change by creating a ‘discrepancy feedback loop.’” (Todd&Mullan,2013) This technique essentially involves keeping a diary to monitor current problematic behavior. Reflection of this diary illustrates the discrepancy between current behavior and a “desired-goal directed behavior,” (Todd&Mullan,2013) thereby cueing change. The effectiveness of this technique has been supported by data in the fields of modifying eating habits and diet extensively. Although research is inching in the field of modifying sleep behaviors, there has been evidence of success. Kroese et al conducted a study on how negative self-control—as related to poor sleep hygiene, — could be addressed by means of self-monitoring in the general population. The study involved a questionnaire (sleep diary) to be filled out weekly over the course of 1 month on frequencies of indicators of insufficient sleep. It was found that those people who are low in self-regulation, and poor sleep hygiene habits, had greater experienced insufficient sleep (Kroese, Evers, Adriaanse, &Ridder,2014). This study provides insight into how target problematic behaviors may be identified through self-monitoring.
Another study, by Todd et al utilized this technique, supplemented by response inhibition tasks, to measure the change in negative sleep hygiene prior to going to bed. This change was measured over the course of 15 days against a baseline. Results showed that self-monitoring alone promoted the reduction of anxiety and stress prior to sleep in first year university students, thus inducing improved sleep and “subsequently a range of other benefits […including…] reduced psychological strain.” (Todd&Mullan, 2013). This study as well as a third study impose that self-monitoring techniques alone are sufficient to modify sleep behaviors. In the third study, support was found for previous hypotheses that self-monitoring alone was adequate to target the most relevant and important sleep hygiene behaviors for university students. These included, “a restful sleep environment, avoiding going to bed hungry or thirsty, avoiding caffeine in the evening and avoiding stress and anxiety-provoking activities before bed.” (Mairs & Mullan, 2015) Furthermore, self-monitoring has been a relatively powerful technique seeding change in sleep behavior, particularly in the university student population; for this reason, as a university student myself, I chose to modify my own sleep behavior using this technique.
4. BEHAVIOR MODIFICATION INTERVENTION
Methods
In the intervention I implemented (n=1), the independent variable(s) were the sleep hygiene behaviors and the dependent variable was improved sleep, operationalized by the number of hours slept each night resulting as an outcome of the modified behaviors. The behaviors that were modified were chosen on the basis of the Mairs et al study and were categorized into restful, hunger/thirst, stress/anxiety, caffeine. (Mairs & Mullan 2015) Additionally, bedtime was added to the measures. Changes in number of hours slept per night were hypothesized to be contingent on the active participation of the subject modifying these behaviors before sleep. These behaviors were monitored in a sleep diary for 21 days (See Appendix A). The first seven days consisted of baseline measures where only the number of hours slept each night were recorded as the sleep-hygiene behaviors were not yet regulated. As the study was based on previous studies, I followed the instruction to encourage the modification of the sleep hygiene behaviors about one hour prior to sleep for the remaining 14 days of the study. This was cued by the “Bedtime” application on the iPhone. Through this app, I could set a bedtime and rise time ensuring eight hours of sleep, and the app would provide a reminder one hour before the set bedtime each night that the “Time for Bed” was approaching (See Appendix C).
Results
Results are outlined in Appendix B and showed that the average number of hours of sleep I obtained was 5.9hrs for Week 1, 6.6hrs for Week 2 and 7.5hrs for Week 3. Based on the hypothesis, this outcome of increase of about 48 minutes of sleep was dependent on my efforts to modify my sleep hygiene. Analysis of the weekly diaries shows I tried to make my environment restful 7 times a week, avoided hunger/thirst 3 times per week, avoided stress/anxiety 3 times per week, avoided caffeine all 7 days of the week, and set my bedtime reminder 6 times per week. In Week 3 results were comparable to Week 2 for stress/anxiety, caffeine, and bedtime and had decreased to 5 days that week for restful and increased to 4 times per week for hunger/thirst. For the 14 days that the self-monitoring intervention was implemented, at least 3 different sleep hygienes were modified prior to going to bed each day. However, on days where all 5 behaviors were modified the outcome of number of hours slept at night was relatively the same. Additionally, active effort of modifying sleep was meant to improve sleep to an average of about eight hours per night each week. The mean amount of sleep for Week 3 was only 7.5hrs of sleep. Therefore, it may be said that the self-monitoring intervention, on one individual (myself) was not effective.
5. DISCUSSION
In my experience, self-monitoring was not an effective technique as I was unable to regulate my sleep. On reason is because to engage in a behavior, an individual must find the outcome of the behavior beneficial (Eyal, Sagristano, Trope, Liberman,&Chaiken,2009). However, I found that due to academic commitments, I was unable to allow myself to sleep the recommended amount of eight hours and go to bed when I had set my bedtime for as the costs of sleeping early and not finishing my studies did not out-weight the benefit of not sleeping 8 hours. This may be a result of, as previous literature has suggested, that with increasing age, time spent on academic assignments increases (Bryant&Gomez,2015). My data shows that my sleep did increase per week with the intervention. However, this data does not consider that during the baseline week of testing, I had midterms which increased my workload and by Week 3, I did not have such a commitment thus my sleep habits may be attributed to an external factor.
Another explanation for my inability to actively modify my sleep hygiene may be that if adolescents and young adults do not see the short-term benefits of engaging in a behavior they will not participate in it (Hershner&Chervin2014) which was such a case for myself. Additionally, to be considered is lack of self-regulation. Self-control is a muscle and when work is exerted on it, it gets depleted. Practicing self-control in one domain may prevent it from success in another (Kroese, Evers, Adriaanse, & Ridder, 2014). Just as a muscle cannot contract and relax at the same time, I could not regulate my sleep while using my self-control to regulate discipline in academics.
The main implication of this study is to highlight that the costs of sleep deprivation serve as barriers to academic achievement. Sleep is implicated in cognitive functioning, and lack of sleep consequently results in poor memory, decreased learning and an overall negative impact on attention and performance (Hershner & Chervin, 2014). University is a structured environment for emerging adults, providing them with “knowledge, skills and independence… [for] successful employ[ment] and contribution to society.” (Mairs&Mullan,2015) Yet, one of the major keys to their success—sleep—is greatly compromised. My intervention may not have been successful, yet I am one individual thus these results are not generalizable to the population. However, this suggests that there is still need for more literature on how to intervene in student’s sleep behavior. One direction to consider may be self-regulation and short-term implications of lack of sleep.