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Implications of napping into and beyond kindergarten on sleep, diet, and the awakening cortisol response

Dissertation
Author: Alyssa Anne Cairns
Abstract:
This study is an examination of sleep distribution, dietary intake, and endocrine function of caregiver-reported Nap and Non-Nap Groups of children before and after they transition to an all-day kindergarten where napping is reduced or eliminated. Measures were assessed the summer prior to kindergarten, within two weeks, and after a month of the transition to kindergarten. The study revealed that the transition to kindergarten was associated with changes in sleep and dietary intake. Endocrine function remained stable as children transitioned to kindergarten. On average, Nap and Non-Nap Groups equally lost total sleep time as they transitioned to kindergarten. However, the Nap Group lost nap sleep, whereas the Non-Nap Group lost nighttime sleep. Children experienced an advance in weekday and weekend sleep periods. The sleep quality data were consistent with the notion that the transition to kindergarten was associated with an increase in sleepiness. On average, children reduced their breakfast intake as they transitioned to kindergarten. Also, changes in sleep were proportional to changes in breakfast consumption.

TABLE OF CONTENTS ABSTRACT …………………………………………………………………………….. ii ACKNOWLEDGMENTS ……………………………………………………………… iii LIST OF ILLUSTRATIONS ...………………………….………………………............ vi LIST OF TABLES ................………………………………………………………...… vii CHAPTER I. INTRODUCTION ………………………………………………………….. 1 The Epidemic of Overweight and Obesity Short Sleep is a Risk Factor of Obesity in Children Sleep and Obesity May be Causally Related Quantification of Sleep Sleep/Wake Distribution during Childhood Napping in Childhood Napping and the Awakening Cortisol Response Napping and Dietary Intake Statement of Purpose

II. METHODOLOGY ………………………………………………………... 23 Participants Materials Procedure Analyses III. RESULTS …………………………………………………………………. 34 Sleep Duration, Timing, and Quality The Awakening Cortisol Response Dietary Intake Sleep Duration and Dietary Intake IV. DISCUSSION……………………………………………………………... 48 Summary Changes in Sleep as Children Transition to Kindergarten Sleep and Dietary Intake The Awakening Cortisol Response Limitations and Future Directions

v

APPENDIXES ……………………………………………...………………………….. 59 REFERENCES ………………………………………………..…………………….…. 70

vii

LIST OF TABLES Table 1. Sample Characteristics for Study Groups …………………………………..... 24 2. Changes in Nap Sleep as Children Transition from Pre-K to Kindergarten …. 36

vi

LIST OF ILLUSTRATIONS Figure 1. Endocrine Hypothesis of Sleep and Obesity ……………………….………..… 9

2. Cross Lag Panel Analysis Model of Changes in Sleep and Diet …….......…… 33

3. Changes in Total Sleep Duration (Nap and Nocturnal Sleep) for the Nap and Non-Nap Group as They Transitioned to Kindergarten..................................... 37

4. Changes in Nocturnal Sleep for the Nap and Non-Nap Group as They Transitioned to Kindergarten............................................................................. 38

5. Changes in Rise Time for Nap and Non-Nap Groups as they Transitioned to Kindergarten...................................................................................................... 39

6. Changes in Bedtime in the Nap and Non-Nap Group as they Transitioned to Kindergarten...................................................................................................... 40

7. Sleep Quality in Nap and Non-Nap Groups as Children Transition from Preschool to kindergarten................................................................................. 42

8. The Awakening Cortisol Response in Nap and Non-Nap Groups as They Transition from Preschool to Kindergarten............................................. 44

9. Dietary Intake in Nap and Non-Nap Groups as Children Transition from Preschool to Kindergarten................................................................................ 46

10. Cross-lagged Panel Analyses for Weekday Total Sleep Duration

(Nap and Nocturnal Sleep) and Breakfast Calories......................................................... 47

1 CHAPTER I INTRODUCTION The Epidemic of Overweight and Obesity Adult overweight and obesity, defined as a body mass index (BMI) of ≥ 25 and 30, respectively have been identified as epidemic in the United States, with prevalence estimates doubling in the past twenty years. Adult overweight and obesity is associated with several health problems such as cardiovascular disease, hyperlipidemia, hypertension, and type II diabetes (Pender & Pories, 2005). Overweight and obesity burden the healthcare system and strain economic resources. The total annual cost for overweight and obesity ranges from $70 billion to $100 billion (Wellman & Friedberg, 2002). Moreover, overweight and obesity are the most common health problems for children in our society (National Center for Health Statistics [NCHS], 20032004; Strauss & Pollack, 2001). The National Health and Nutrition Examination Survey (a general population survey conducted by the Center for Disease Control in 2006) suggested that the current prevalence of childhood obesity (defined as ≥ 95 th BMI percentile for age) is approximately 17%; triple that of thirty years ago (Ogden et al., 2006). Further, the survey elucidated that current prevalence estimates of obesity for young children (2 to 5 years of age) is approximately 10% (Ogden, Carroll, Curtin, Lamb, & Flegal, 2010). Childhood obesity is associated with adultlike comorbidities, such as hypertension, type II diabetes, obstructive sleep apnea (OSA), and dyslipidemia (Sorof & Daniels, 2002). One possible reason for the surge in adult obesity is the increased prevalence of childhood obesity.

2 Research suggests that childhood obesity is a significant risk factor for the development of obesity in adolescence and adulthood (Serdula et al., 1993). For example, a sixyear study following Chinese children from age 9 to 15 suggested that overweight children were 2.8 times more likely to become overweight adolescents (≥ 85 th gender/age specific BMI percentile) (Wang, Ge, & Popkin, 2000). Likewise, a U.S. study following participants from birth to age 35 revealed the odds ratio (OR) for overweight 5yearolds (≥ 85 th gender/age specific BMI overweight percentile) classified as overweight at 35 (BMI > 26) to be approximately 3 times that of normal weight 5yearolds (Guo, Roche, Chumlea, Gardner, & Siervogel, 1994). Weight is determined by a complex interplay of genes, metabolism, behavior, environment, culture, and socioeconomic status (CDC, 2007). According to the Avon Cohort Study (a longitudinal study of 8,234 children from birth to 7 years of age) children born to two obese parents were over 10 times more likely to be obese at age 7 than those born to normal weight individuals (Reilly et al., 2005). There is considerable racial/ethnic variation in overweight and obesity in the United States. According to a metaanalytic review of obesity in the U.S over the past 30 years, prevalence of overweight (≥85 th age/gender specific BMI percentile) for nonHispanic White, non Hispanic Black, and Mexican American children ages 6 to 19 was 28.2%, 35.4%, and 39.9%, respectively (Wang & Beydoun, 2007). Major lifestyle risk factors for childhood overweight and obesity include sedentary behavior (e.g. T.V. viewing), parental education, and habitual sleep duration. Research suggests that TV viewing ≥3 hours per day is associated with a 1.4to 2.8fold increase in BMI (Chaput, Brunet, & Tremblay, 2006; Reilly et al., 2005; Sekine et al., 2002). According to the Quebec en Forme Project

3 (a crosssectional study of 422 Canadian children 5 to 10 years of age), children by parents with lower education were 1.7 times more likely to be overweight or obese (Chaput et al., 2006). However, of the factors in the lifestyle domain, short habitual sleep duration may be one of the most important. Short Sleep is a Risk Factor for Obesity in Children Both crosssectional and longitudinal studies have found short habitual sleep duration to be an important lifestyle risk factor for childhood overweight and obesity because (1) it is present over other known risk factors and (2) it is potentially modifiable, unlike many other risk factors (Lumeng et al., 2007; Reilly et al., 2005; Sekine et al., 2002; Snell, Adam, & Duncan, 2007; Taheri, 2006; von Kries, Toschke, Wurmser, Sauerwald, & Koletzko, 2002). In the Toyama birth cohort study of 8,274 six and seven yearold Japanese children, a doseresponse relationship was found between childhood obesity and short sleeping hours. Short sleeping hours (<10 hours) and obesity (≥ BMI of 96.9 and 98.2 for males and females, respectively) (Cole, Bellizzi, Flegal, & Dietz, 2000) were determined by caregiverreported time in bed and physicianreported height and weight. Holding constant parental obesity and several lifestyle factors, children sleeping between 9 and 10 hours, 8 and 9 hours, and < 8 hours were 1.49, 1.89, and 2.87 times more likely to be obese than children sleeping > 10 hours a night (Sekine et al., 2002). Notable are the cohort studies providing additional information about important aspects of sleep distribution. In the Quebec en Forme Project of 422 five to ten yearold children, short weekday sleep duration was associated with obesity and overweight status. Short sleep duration (≤11.5 hours) and overweight/obesity (≥85 th and ≥95 gender/age specific BMI percentile respectively) was determined by caregiverreported time in bed

4 and teacherassessed height and weight. Holding constant parental obesity and several lifestyle factors, children sleeping between 10.5 to 11.5 and 8 to 10 hours per weekday night were 1.42 and 3.45 times more likely to be overweight or obese (Chaput et al., 2006). Likewise, a cohort study of 6,862 five and six yearold Bavarian children revealed that ≥11.5 relative to ≤10 hours of weekday sleep was associated with a reduced risk of obesity (≥ 97 th gender/age specific BMI percentile respectively) by about half. This relationship was present controlling for parental obesity and lifestyle factors (von Kries et al., 2002). Longitudinal studies provide causerelated information about the role of short sleep duration in the development of overweight and obesity because they more adequately control for confounding variables, most importantly a child’s baseline weight. One study analyzed data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development where researchers longitudinally examined 785 children in the 3 rd and 6 th grades (9 to 12 years of age). Total sleep duration (nap and nocturnal sleep) was obtained via caregiver report and overweight status was determined by height/weight measurements taken by research assistants (≥95 th gender/age specific percentile). Independent of the child’s weight in the 3 rd grade, shorter sleep duration at grade 3 was associated with overweight status at grade 6. For every additional 1 hour of sleep in the 3 rd grade, the child was 40% less likely to be overweight in the 6 th grade (controlling for gender, racial identity, and several lifestyle factors) (Lumeng et al., 2007). The second longitudinal study was based on the first and seconds waves of the Child Development Supplement of the Panel Survey of Income Dynamics, a representative sample of 2,281 US families with children 3 to 18 years of

5 age. Sleep duration was obtained via caregiver or selfreport and overweight status was determined by height/weight measurements taken by interviewers (≥gender/age specific BMI overweight centile). In comparison to children sleeping 9 to 9.9 hours at time 1, children sleeping 11 hours or more were 17.1% less likely to be overweight at time 2. In other words, every additional hour of sleep time at time 1 was associated with a reduction in BMI at time 2 by .75 (holding constant gender, racial identity and several lifestyle factors) (Snell et al., 2007). Research suggests there may be developmentallysensitive periods for weight gain during childhood (Adair, 2008; Dietz, 1997). Body mass typically rises during infancy, declines to its lowest point around 5 to 6 years of age, and rapidly accelerates thereafter (Siervogel, Roche, Guo, Mukherjee, & Chumlea, 1991). According to Dietz (1997), the point at which BMI starts to accelerate during early childhood is termed the “adiposity rebound (AR).” Research suggests that the timing of the AR is a risk factor for development of obesity in adulthood over and above the influence of parental obesity and child’s BMI (Adair, 2008). For example, a retrospective cohort study of 390 U.S. individuals revealed that children with early (<4.8 years) AR were 2.8 times more likely to be obese in adulthood than those with an average AR (between 4.8 and 6.2 years of age) (Whitaker, Pepe, Wright, Seidel, & Dietz, 1998). This period may play a role in the development of future overweight because it corresponds with the time children typically become autonomous enough to make their own food choices (Dietz, 1997; Rolland Cachera et al., 1987; Whitaker et al., 1998). If lifestyle factors that influence food intake are changed (i.e. sleep loss during this sensitive period), it may lead to an earlier expression of the AR (Whitaker et al., 1998).

6 Overweight and obesity have proven difficult to treat in adulthood. Behavioral, pharmacological, or surgical modalities are typically utilized (Daniels, 2005). Although these strategies can be effective in the shortterm (Anderson, Konz, Frederich, & Wood, 2001), research suggests that individuals typically rebound to their pretreatment weight within 5 years (Anderson, Vichitbandra, Qian, & Kryscio, 1999; Anderson, Backer, Stockholm, & Quaade, 1984; Brownell & Jeffrey, 1987). For example, a longitudinal study of 114 obese adults on a restricted calorie diet revealed that within three years, participants regained 73.4% of their lost weight. Further, only 25% maintained their post diet weight after 7 years (Anderson et al., 1999). Therefore, preventative interventions during periods of vulnerability may be the ideal approach to addressing the problem of obesity in our society. Sleep and Obesity May be Causally Related It is possible that short sleep duration and/or other sleep distribution variables may be directly related to overweight and obesity and that alterations in sleep may bring about changes in the probability of or the extent of overweight and obese. That is, research may show that simple caregiver education and guidance interventions may be sufficient to bring about a change in how much children sleep, and thus buffer against adverse metabolic changes and poor diet choices. The findings reviewed in the previous section are consistent with hypotheses that sleep duration may directly contribute to weight gain including three hypotheses focused on reduced level of activity, rapid eye movement (REM) sleep, and endocrine processes. A reduced activity hypothesis holds that when sleep loss occurs, overall activity level and thus energy expenditure, is reduced. Although this hypothesis has not been systematically explored, it is plausible that reduced

7 activity occurs secondarily to daytime sleepiness (Taheri, 2006). A REM sleep deprivation hypothesis suggests that short sleep periods may result in REM sleep loss and that REM loss leads to increased caloric intake. This hypothesis stems from the proposed connection between REM sleep and motivated behavior (Kleitman, 1963; Vogel, 1975). REM sleep deprivation has been found to produce 34%100% increases in food intake over baseline levels in both cats and rats (Kushida, Bergmann, & Rechtschaffen, 1989; Vogel, 1975). No studies have directly assessed the effects of longterm REM deprivation on food intake in humans. However, patients with OSA syndrome and severe REM sleep deprivation have been reported to weigh more at the time of diagnosis than OSA patients with comparable respiratory disturbance but without REM deprivation. Moreover, the REMdeprived patients in this study lost more weight during the first year of treatment than patients without REM deprivation and the weight loss was proportional to the amount of REM sleep rebound seen with treatment of the OSAS (Peszka, Harsh, & Hartwig, 1998). The REM sleep deprivation hypothesis is especially relevant to the study of the link between sleep and changes in diet in young children as early school start times (i.e. wake times) may result in curtailing the sleep period at the time when REM sleep is most likely to occur (Carskadon & Dement, 2000). Endocrine hypotheses suggest that habitual sleep loss results in physiological changes that lead to increased caloric intake and/or changes in metabolism and future weight gain. Although the relationship between sleep loss (i.e. stress) and food intake is complicated, three principle hormones are involved: leptin, ghrelin, and cortisol (see Figure 1). It is welldocumented that shortand longterm sleep deprivation changes the ratio of leptin to ghrelin (Copinschi, 2005; Spiegel, Knutson, Leproult, Tasali, & Van

8 Cauter, 2005; Spiegel, Tasali, Penev, & Van Cauter, 2004; Taheri, Lin, Austin, Young, & Mignot, 2004). Ghrelin is a gutderived hormone that increases hunger (Spiegel et al., 2004) and food intake (Tolle et al., 2002; Wren et al., 2001), whereas leptin is a hormone secreted by adipocytes (fat cells) and decreases hunger (Spiegel et al., 2004) and food intake (Licinio et al., 2004). Intravenous (IV) administration of ghrelin has been found to increase hunger ratings in fasted individuals by 32% in comparison to controls (Wren et al., 2001). Moreover, IV administration of ghrelin in both fasted humans and sated rats has been found to increase food intake by 28% (Wren et al., 2001) and 140% (Wren et al., 2000), respectively. Individuals born with leptin deficiency (a genetic mutation in the leptin gene) tend to have an elevated appetite and BMI. Leptin replacement therapy has shown to decrease food intake (calories) by 49% over a twoweek period (Licinio et al., 2004). Shortand longterm sleep loss also increases circulating cortisol. For example, in a sample of 33 adult males, participants exposed to partial sleep deprivation (4 hours in bed) and total sleep deprivation had a 37% and 45% higher basal cortisol production (i.e. averaged over the next day) in comparison to those not sleepdeprived (Leproult, Copinschi, Buxton, & Van Cauter, 1997). The relationship between sleep and cortisol has also been observed in children. For example, in a study of 91 healthy Brazilian children ages 45 days to 36 months, shorter sleep duration was associated with elevated morning cortisol values (Silva, Mallozi, & Ferrari, 2007). Studies have shown a relationship between sleep loss, elevated cortisol, and hunger (Epel, Lapidus, McEwen, & Brownell, 2001; Gluck, Geliebter, Hung, & Yahav, 2004); however, this pathway is less understood. In relation to sleep deprivation, cortisol may be most important in the

9 formation of glucose intolerance, insulin resistance, and subsequent obesity (Copinschi, 2005; Khani & Tayek, 2001). Metabolically, cortisol functions to elevate blood glucose levels (via gluconeogenesis) and mobilize fatty acids to increase energy available to the organism when stressed (Gunnar, 1989; Guyton & Hall, 1996). If energy is not used, glucose and fatty acids get stored in adipose tissue for future use. This is an adaptive process unless the stressor habitually results in inactivity, where weight gain can follow (Guyton & Hall, 1996). For example, numerous studies of shift work show that habitual short sleep is related to elevated BMI and glucose intolerance secondary to sustained high cortisol levels (Di Lorenzo et al., 2003; Ishizaki et al., 2004). The possible causal link between short sleep and changes in diet establishes the need for further research on the determinants of sleep loss during childhood.

Figure 1. Endocrine Hypothesis of Sleep Loss and Obesity Ghrelin

Leptin

Cortiso l

CHRONIC

SLEEP LOSS Gluconeogenesis

HUNGER & FOOD INTAKE INSULIN RESISTANCE

GLUCOSE INTOLERANCE OBESITY

10 Quantification of Sleep Adult sleep is characterized by two predominant states distinct from wakefulness, REM and nonREM sleep. NonREM and REM sleep alternate through the night in a cyclical pattern (termed “sleep architecture”) (Carskadon & Dement, 2000). Each sleep state is associated with distinctive levels of arousal, brain activity, and muscle tone. Non REM sleep is categorized into four distinct stages. The stages represent gradations in depth of sleep and difficulty of arousal, with stage I being the lightest and stage IV (slow wave sleep) being the deepest (Davis, Parker, & Montgomery, 2004). Children sleep more than adults and distribute their sleep differently. Moreover, their sleep architecture is markedly different from adults mostly attributable to the dominance of slow wave sleep over other stages (Carskadon & Dement, 2000). The gold standard for measuring sleep in children and adults is polysomnography (PSG) because it allows for quantification of stages and states of sleep. Although PSG is considered the gold standard, all night recordings are expensive, time and labor consuming, and do not assess sleep in the natural environment (Sivan, 2005). Activity recording is a newer, more common procedure for discriminating sleep/wake states in children. Activity monitoring can be done with a device called an actigraphy. The actigraphy is a small, computerized movement detector that is worn around the wrist or leg and is a nonintrusive way to measure naturally occurring sleep throughout the day (Acebo et al., 1999). Data can be obtained on total sleep time, sleep onset, wake time, nocturnal arousals and awakenings, latency to sleep reinitiation, sleep efficiency, and timing and duration of naps (Sadeh, Lavie, Scher, Tirosh, & Epstein, 1991).

11 Sleep/Wake Distribution during Childhood From infancy to young adulthood, sleep amount, distribution, and architecture is developing and changing (Davis, Parker, & Montgomery, 2004). On average, a newborn infant sleeps 16 to 20 hours each day (Mindell & Owens, 2003). Sleep typically occurs in five to six periods around the 24hour clock. Sleep architecture in the infant is characterized by periods of wake, active sleep (REM), and quiet sleep (NREM) (Davis et al., 2004). During the next few years, sleep begins to be distributed into a primary nocturnal period and two daytime naps, one in the morning (which disappears around the age of 2 years) and one in the afternoon (Iglowstein, Jenni, Molinari, & Largo, 2003; Weissbluth, 1995). Sleep duration over the 24hour period declines steadily during childhood to approximately 11 hours by the age of 5 years (Mindell & Owens, 2003). It is generally acknowledged that between the ages of 2 and 5 years, afternoon naps are given up and an adultlike pattern of sleep distribution emerges with a consolidated period of sleep occurring only at night (Sheldon, Spire, & Levy, 1992). However, some research suggests there are racial/ethnic differences in nap tendency (Lavigne et al., 1999) in that Black children tend to give up their naps more gradually than White children and many are still napping into their school years (Crosby, LeBourgeois, & Harsh, 2005). Sleep architecture is not organized into an adultlike pattern until about the age of 5 years (Davis et al., 2004). Understanding of the agerelated changes in sleep and wakefulness and the wide individual differences in the rate and extent of these changes is limited. Studies of adults and adolescents have identified the importance of both biological and contextual variables (Sadeh & Anders, 1993).

12 Biological Mechanisms The twoprocess model of sleep/wake regulation proposes that sleep and wake propensity in humans is determined by both a circadian process C and a homeostatic process S (Borbely, 1982). Circadian rhythms refer to the endogenous rhythms that control a myriad of physiological and behavioral functions; characterized by a periodicity (period) of approximately 24 hours. Organized by the suprachiasmatic nucleus (SCN) of the hypothalamus, circadian rhythms collectively regulate sleep/wake cycles, temperature, hormone secretion, and modulate physical activity and eating behaviors. The SCN entrains our circadian rhythms to the 24hour clock by receiving light inputs from the retina of the eye and transmitting the signals to the rest of the brain and body. The circadian timing system is mostly independent of prior waking and sleep. Process S represents homeostatic sleep pressure and accumulates as the time from waking is lengthened, and dissipates in nocturnal and diurnal sleep (i.e. during a nap) (Borbely & Achermann, 1992). To illustrate the dissipation of process S during a nap, research has shown the level of slow wave activity (a marker of sleep pressure) in the subsequent nocturnal sleep episode to be reduced following a daytime nap (Feinberg et al., 1985; Knowles, Coulter, Wahnon, Reitz, & MacLean, 1990). Although little is known about maturational effects on the underlying biological mechanisms, Process C and process S presumably interact to influence the timing of children’s sleep and wake periods including naps. Circadian sleep phase refers to where the sleep period is positioned within the 24hour clock. There are individual differences in the timing of sleep. Some individuals biologically prefer to go to sleep/wake early and function optimally during morning hours, whereas others prefer to go to bed/wake late

13 and function best during the evening hours. This biological preference for either the morning or evening is termed chronotype (Roenneberg, WirzJustice, & Merrow, 2003). Misalignment of the sleep/wake cycle within the circadian rhythm causes circadian dysfunction. Those that commonly experience circadian dysfunction are individuals that work shift work or travel across time zones (i.e. jet lag) (Lu & Zee, 2006). Circadian dysfunction is associated with a host of negative consequences, including sleep/wake difficulties (Doghramji, 2004), performance decrements (Giannotti, Cortesi, Sebastiani, & Ottaviano, 2002), and alterations in metabolic processes (Di Lorenzo et al., 2003). Sleep onset difficulties occur when sleep is attempted at a time of circadian tendency for wake. For example, research on shift workers has shown that sleeping during a time of circadian tendency for wakefulness is associated with less restorative sleep (Doghramji, 2004). Likewise, reduced alertness and wakemaintenance difficulties occur when wakefulness is attempted at a time of circadian tendency for sleep. For example, individuals with evening chronotypes tend to experience reduced alertness and performance during early morning hours (Giannotti et al., 2002). The most “severe” type of evening chronotype, or Delayed Sleep Phase Syndrome (DSPS), is a circadian rhythm disorder that occurs when the sleep phase is notably delayed relative to conventional sleep/wake times (Baker & Zee, 2000). Individuals with DSPS tend to have shortened sleep periods on weekdays and longer sleep periods on weekends. Shortened weekday sleep periods are mainly attributable to early rise times (because of social obligations) yet still late bedtimes (because of circadiangoverned processes) (Roenneberg et al., 2003). Circadian dysfunction is also associated with alterations in metabolic processes and risk for overweight and obesity. A comparison between 319 healthy shift and non

14 shift working Italian males revealed that shiftwork was associated with risk of obesity, elevated BMI (d = .39), and reduced glucose tolerance. This relationship was present while controlling for several lifestyle risk factors (Di Lorenzo et al., 2003). Further, a study of 2,824 day and 826 shift workers revealed that shift work was associated with markers for insulin resistance in those younger than 50 years of age (controlling for several lifestyle risk factors). Markers for insulin resistance included high blood pressure, high fasting blood sugar, and high triglycerides (Nagaya, Yoshida, Takahashi, & Kawai, 2002). Contextual Variables Biological mechanisms operate in the context of environmental variables (i.e. family, medical status, physical environment, and psychosocial functioning) (Lebourgeois, 2003). These contextual variables are mediated or moderated by caregiver/child sleeprelated behavior (i.e. sleep hygiene). Contextual variables importantly influence the distribution, amount, and quality of sleep. Contextual factors that lead to sleep and circadianrelated problems in adulthood and adolescence are well known. In adults, lifestyle/occupational factors (i.e. shift work) lead to insufficient and poorly timed sleep. During adolescence, an interaction between changes in the biological regulation of the sleepwake cycle and psychosocial demands is thought to perpetuate sleep loss and circadian dysfunction (Colten & Altevogt, 2006). Little is known of the contextual factors that lead to insufficient or poorly timed sleep during childhood but it is likely that biologically and/or behaviorallyshaped preferences for timing of sleep/wake periods are at times incompatible with sleep/wake schedules dictated by family or school schedules. A specific concern of the research proposed here is that a napping pattern

15 shaped prior to school years may be incompatible with the demands on wakefulness associated with attendance of an allday kindergarten that does not allow napping. Specifically, this incompatibility may have adverse consequences affecting the longterm health and wellbeing of the child. As of 2005, a survey of prekindergarten and kindergarten napping policies in the U.S. revealed that only 28% of all 50 states had policies regarding naptime (Daniel & Lewin, 2005). Because the majority of states have no policy on napping, and there may be a trend to eliminate nap opportunities from children in kindergartens and even preschools (Trejos, 2004), there is a need for systematic research on the consequences of nap restriction from children that are still napping. Napping in Childhood We do not have a complete understanding of why some children continue to nap and others do not. There may be a number of processes involved; however those in the biological, family/cultural, and psychosocial realm may have the broadest influence. It is possible that napping children simply require more sleep and are unable to meet their total sleep need in one nocturnal sleep period and/or circadian processes that strengthen the tendency for wakefulness may be immature. Also, the caregiver’s home environment or culture may influence napping because of a strong preference for (i.e. siesta cultures) or against napping. Last, persistent napping may reflect lower adaptability to new sleep/wake routines. The popular belief is that children in the U.S. stop napping by the time they reach school age; however recent findings reveal that a significant proportion of children continue to nap into and beyond kindergarten (Crosby et al., 2005; Lavigne et al., 1999).

Full document contains 94 pages
Abstract: This study is an examination of sleep distribution, dietary intake, and endocrine function of caregiver-reported Nap and Non-Nap Groups of children before and after they transition to an all-day kindergarten where napping is reduced or eliminated. Measures were assessed the summer prior to kindergarten, within two weeks, and after a month of the transition to kindergarten. The study revealed that the transition to kindergarten was associated with changes in sleep and dietary intake. Endocrine function remained stable as children transitioned to kindergarten. On average, Nap and Non-Nap Groups equally lost total sleep time as they transitioned to kindergarten. However, the Nap Group lost nap sleep, whereas the Non-Nap Group lost nighttime sleep. Children experienced an advance in weekday and weekend sleep periods. The sleep quality data were consistent with the notion that the transition to kindergarten was associated with an increase in sleepiness. On average, children reduced their breakfast intake as they transitioned to kindergarten. Also, changes in sleep were proportional to changes in breakfast consumption.