Journal of Pediatric Psychology Advance Access originally published online on December 12, 2007
Journal of Pediatric Psychology 2008 33(4):387-395; doi:10.1093/jpepsy/jsm125
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Sleep Quality in Young Adults with Very Low Birth Weight—the Helsinki Study of Very Low Birth Weight Adults
1National Public Health Institute, Helsinki, Finland, 2Hospital for Children and Adolescents, Helsinki University Central Hospital, Helsinki, Finland, 3Department of Psychology, Helsinki, Finland, and 4Department of Public Health, University of Helsinki, Helsinki, Finland
All correspondence concerning this article should be addressed to Sonja Strang-Karlsson, MD, Hospital for Children and Adolescents, Biomedicum 2, Tukholmankatu 8A, Helsinki University Central Hospital, PO Box 448, FI-00029, Helsinki, Finland. E-mail: sonja.strang{at}helsinki.fi
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Objective To assess the relationship between very low birth weight (VLBW; <1,500 g) and quality and amount of sleep in young adults. Methods We compared 89 VLBW and 78 term-born 19- to 26-year-old adults, by actigraphy and the Basic Nordic Sleep Questionnaire. Results There were no group differences in sleep quality or amount (p's >.15), although VLBW adults went to bed on average 36 min earlier (95% confidence interval 6–66 min). Shorter gestational age was related to longer sleep latency both within VLBW (standardized regression coefficient β = –.36, p =.040) and term-born adults (β = –.25, p =.029). Conclusion Adults with VLBW had similar quality and amount of sleep as those born at term, although VLBW adults went to bed earlier, suggesting an advanced sleep phase. Within each group, a lower gestational age was related to a longer sleep onset.
Key words: gestational age; prematurity; sleep; sleep disturbances; very low birth weight.
Over the last decades, dramatic advances in the field of neonatology have taken place. Along with the introduction of modern neonatal intensive care, survival rates of preterm infants with very low birth weight (VLBW; birth weight <1,500 g) have improved remarkably and the first VLBW infants who experienced the early years of modernized care are now young adults. Hence, the possible effects of their distinctive neonatal history on their long-term health and well-being are increasingly relevant.
Previous research indicates that at least in childhood, VLBW infants have on average poorer cognitive performance and are more likely to develop psychiatric and behavioral disorders such as depression and attention deficit/hyperactivity disorder (Bhutta, Cleves, Casey, Cradock, & Anand, 2002
; Botting, Powls, Cooke, & Marlow, 1997; Levy-Shiff et al., 1994; Saigal, Pinelli, Hoult, Kim, & Boyle, 2003). Recent evidence suggests that severe prematurity is also associated with risk factors of adult cardiovascular disease, such as elevated blood pressure (Doyle, Faber, Callanan, & Morley, 2003; Hack, Schluchter, Cartar, & Rahman, 2005; Hovi et al., 2007) and impaired glucose regulation (Hovi et al., 2007) in young adult life. Independently of prematurity, all the aforementioned phenomena have been related to sleep disturbances, suggesting that disrupted sleep could possibly be one mechanism either mediating or modifying the link between VLBW and its consequences.
Yet, little is known about the effects of VLBW on human sleep beyond the neonatal period. Research findings to date are inconclusive, while some studies do indicate that sleep disturbances characterize former preterm infants in childhood (Gössel-Symank, Grimmer, Korte, & Siegmund, 2004; Rosen et al., 2003), others do not (Iglowstein, Latal Hajnal, Molinari, Largo, & Jenni, 2006; Wolke, Meyer, Ohrt, & Riegel, 1995; Wolke, Sohne, Riegel, Ohrt, & Österlund, 1998). One possible underlying mechanism is that insults early in life may affect the later rhythmicity through programming of the fetal suprachiasmatic nuclei, the biological clock of the brain, although it is beyond the scope of prevailing knowledge to state whether this could alter sleep behavior in adulthood (Kennaway, 2002).
Our major aim was to assess sleep quality in young adults with VLBW, which to our knowledge has not been studied before. Our secondary aim was to evaluate whether possible relationships are modified by other perinatal factors such as length of gestation or relative birth weight.
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Methods |
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Participants
The Helsinki Study of Very Low Birth Weight Adults is a cohort study, described in detail previously (Hovi et al., 2007
). For the present study, we invited 255 young adults with VLBW (birth weight <1,500 g), born between 1978 and 1985 and treated at the neonatal intensive care unit of the Helsinki University Central Hospital, and a comparison group of 314 term-born individuals. The comparison group was selected from the records of all consecutive births of the hospitals, as the next available singleton infant who was of the same gender, was born at term (gestational age 
37 weeks) and who was not small for gestational age (SGA; 
–2 SD). In 2004–2005, at the age of 18–27 years, the participants attended a clinical study encompassing detailed assessment of their psychological and physical health. The participation rate for those invited to the study was 65.1% (n = 166) in the VLBW group and 54.8% (n = 172) in the comparison group. The participants and nonparticipants had similar gestational ages at birth, birth weights, relative birth weights, and rates of maternal pre-eclampsia. In the VLBW group, participants and nonparticipants furthermore did not differ in terms of broncopulmonary dysplasia, SGA status, age at discharge from hospital, days of oxygen treatment, or days on mechanical ventilation. However, the VLBW participants were less likely to have been diagnosed with cerebral palsy at age 15 months than the VLBW non-participants (6 vs. 19%, p =.005). Each participant gave a written informed consent, and the study protocol was approved by the Ethics Committee for Children and Adolescents’ Diseases and Psychiatry at the Helsinki University Central Hospital. From this study sample, a subsample of 211 (of which 89 VLBW and 78 term-borns with valid recordings) underwent actigraphy. All study participants were offered an actigraph, provided that there was one available. The participants who received an actigraph did not differ from the rest of the study sample with regard to maternal or perinatal variables (data not shown).
Actigraphy Measures
Objective sleep assessment was performed using the Actiwatch AW4 model (Cambridge Neurotechnology Ltd, UK). An actigraph is a small, wrist-worn device that registers body movements. It is considered a valid and reliable method for measuring sleep in population studies (Littner et al., 2003). The AW4 model and the sleep–wake scoring algorithm used in the current study have been validated against polysomnography in adults by Kushida et al. (2001), who reported a correspondence between the two methods of over 90%. The scoring algorithm is incorporated in the sleep analysis software (Actiwatch Activity & Sleep Analysis V 5.42 software) provided by the manufacturer. It separates sleep from wakefulness by analyzing activity in a specific epoch taking into account the surrounding epochs. The epoch is scored as sleep unless the activity exceeds a certain threshold, which in turn is determined by the selected sensitivity.
The participants were asked to wear the actigraph on their nondominant wrist continuously for a minimum of 3 days except when taking showers, swimming, or going to sauna. They kept a sleep log and were asked to report bed times (closing the eyes), awakening times (opening the eyes) as well as monitor removal times in the log, and to press the actigraph event button simultaneously. Both the aforementioned sources of information were considered when scoring the data, however, we prioritized the sleep periods defined by "pressing the button" since we found them more accurate in the age group of our study sample.
Actigraphy Data Analysis Procedure
Nocturnal activity data were scored with Actiwatch Activity & Sleep Analysis V 5.42 software, using medium sensitivity operation mode and 1 min epochs as recommended by the manufacturer. The analysis window was set to start at least 20 min before bed time and to end at least 20 min after awakening time, to ensure that the whole sleeping period was covered. The person performing the scoring was unaware of group assignment.
During the scoring procedure, the complete activity data plot was visually inspected in order to recognize significant discrepancies between sleep log, markers from button pressings, and the activity pattern. Individual nights were excluded from further sleep analysis (a) if the actigraph was not in use or was taken off, (b) information on bedtime was missing, (c) information on wake-up time was missing and the activity pattern was not unequivocally interpretable, (d) if the sleep log information was missing and the event markers ("button pressing") were not unequivocally interpretable, (e) if both sleep log information and event markers were missing although the actigraph had been in use, and (f) if the registered activity data did not correspond with the sleep log or the event markers.
In total, 27 (12.8%) individuals were excluded from further analyses; three due to technical problems with the actigraph, and 24 because reliable nights were lacking according to the aforementioned criteria. There were 17 (8.1%) individuals with one reliable night and 167 (79.1%) with two or more (ranging from 2 to 9 reliable nights). The data were averaged for each study participant over the nights that were considered valid, and mean estimates were used in the analyses.
The following actigraph-derived sleep measurements were used: Sleep duration, defined as the estimated total sleep length minus wake time; sleep latency, the estimated time before falling asleep; sleep efficiency, the percentage of minutes asleep whilst in bed thus including sleep latency; and fragmentation index, an indicator of restlessness. Additionally, we used data on bed times and wake-up times; this information was gathered from the actigraph registration marks or the sleep log.
Questionnaire and Clinical Measures
Data on pregnancy, delivery, and postnatal course were collected from hospital records. The birth weights were transformed into standardized birth weight scores according to Finnish growth charts (Pihkala, Hakala, Voutilainen, & Raivio, 1989). The VLBW individuals were then classified as either AGA (birth weight SD –2.0) or small for gestational age (SGA; birth weight SD <–2.0). The ponderal index at birth, a measure of thinness, was calculated as kilograms per cubic meter. In conjunction with a clinical examination, preceding the actigraph recording, the subjects’ weight and height were measured. Body mass index (BMI) was calculated as weight in kilograms divided by height in square meters. The subjects completed a questionnaire enquiring about their medical history and socio-economic background. Sleep was subjectively assessed using a 21-item questionnaire, the Basic Nordic Sleep Questionnaire (BNSQ) (Partinen & Gislason, 1995). The BNSQ refers to the sleep quality during the antecedent 3 months. Depressive symptoms were assessed using the Beck Depression Inventory (BDI), composed of 21 items with four statements, each statement reflecting varying degree of symptom severity on a scale from 0 to 3 (Beck, Steer, & Garbin, 1988).
Statistical Analysis
Crude group differences were assessed by use of the t-test or 
2-test. To assess the effects of gestational age and birth measures, Pearson's correlation coefficients were calculated. Skewed outcome variables were normalized using logarithmic transformation when appropriate. To assess the effects of gestational age and birth measures, while controlling for confounding, multivariate linear regression models were computed using parental educational attainment (dummy coded using the least educated groups as the reference), gender, age, BDI score (logarithmically transformed), standardized birth weight, and current smoking as covariates. Smoking was used as a covariate because nicotine is known as a stimulant (Boutrel & Koob, 2004) and BDI score because it was related to SGA status as well as the sleep parameters. In the models, all variables were estimated separately for the VLBW group and the comparison group due to the study design. All tests were two-sided and the 
-level was set at.05. The statistical analyses were performed using SPSS 13.0.1 and 14.0.2 for Windows.
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The socio-demographic data and clinical characteristics of the participants are shown in Table I. Neurosensory disability was reported by 11 participants: One was blind, seven had cerebral palsy and two had other disability (all in the VLBW group), and one had severe hearing deficit (in the comparison group). Within the VLBW group, 32 (36.0%) individuals were SGA (relative birth weight <–2 SD).
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Duration and Quality of Sleep in VLBW and Term-born Young Adults
As shown in Table II, VLBW individuals did not differ from term-borns in sleep duration, sleep efficiency, fragmentation index, or sleep latency (all p's >.15). The average bed time was, however, 36 min earlier among the VLBW individuals (p =.019). This difference remained statistically significant after controlling for parental education, gender, age, BDI score, standardized birth weight, and current smoking (p =.031). If additionally controlling for getting drunk frequently, which theoretically could be related to altered bed times, the results remained similar (p =.030). In the VLBW group, 21.3% (n = 19) had long sleep latency (dichotomized at 80th percentile) as compared to the 16.7% (n = 13) in the comparison group, but this difference was not significant (p =.44). Among the VLBW adults, maternal pre-eclampsia, SGA status (serving as a proxy for intrauterine growth retardation), and multiple pregnancy were not related to any of the sleep parameters (all p's >.09).
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Sleep Quantity, Quality, and Neurosensory Disability
Individuals who reported neurosensory disability (n = 11) had longer sleep latency than those who did not (geometric mean 24.3, SD 1.8, vs. 9.67, SD 2.7 min), producing a mean difference of 60.2% [95% confidence interval (CI): 26.9–78.4, p =.003]. Likewise, their sleep duration (mean 6.50 ± 0.84 SD vs. 7.22 ± 0.85 SD hours) was on average 0.72 hr shorter (95% CI: 0.20–1.25; p =.007), and their sleep efficiency (79.1 ± 4.5 vs. 82.6 ± 5.2%) was 3.6 percentage points lower (95% CI: 0.4–6.8; p =.030). They did not differ with regard to bed times, wake-up times or fragmentation index (p's >.31). When excluding individuals who reported neurosensory disability from the group comparisons between VLBW and term-born adults (Table II), the results remained essentially the same (data not shown).
Sleep Quantity, Quality, and Birth Measures
We performed separate analyses in the VLBW and term-born groups to assess whether sleep quality is related to gestational age and standardized birth weight. We found no relationship with sleep duration, sleep efficiency, and fragmentation index (all p's >.12).
Sleep latency, however, correlated with gestational age at birth among the term-born adults (Pearson correlation coefficient r = –.32, p =.004), whereas among VLBW adults the nonadjusted correlation did not reach statistical significance (r = –.14, p =.20) (Fig. 1). When adjusting for confounding variables (BDI score, parental education, age, gender, current smoking, and standardized birth weight) in multivariate linear regression models (Table III), this relationship became statistically significant in the VLBW group (p =.040) and remained significant in the term-born comparison group (p =.029). In the VLBW group, the relationship weakened somewhat after exclusion of individuals with neurosensory disability (–13.3% change in sleep latency associated with one unit increase in gestational age, 95% CI: –26.8 to 2.5, p =.094). Controlling for the number of registered nights accepted for analyses did not significantly affect the results shown in Table III. When analyzing the data as a continuum, with the groups pooled, gestational age correlated significantly with sleep latency (r = –.16, p =.043) (Fig. 1). After additional adjustment for prematurity and covariates mentioned in Table III, gestational age remained significantly associated with sleep latency: one unit increase in gestational age corresponded to a –14.7% (95% CI: –23.3 to –5.1) change in sleep latency, standardized regression coefficient β = –.90, p =.004.
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Standardized birth weight, ponderal index at birth and birth weight were not related to sleep latency in either group (when adjusting for BDI score, parental education, age, gender, current smoking, and gestational age).
Questionnaire-derived Sleep Measures
As shown in Table IV, the self-reported data from the BNSQ supported the actigraphy-derived findings. VLBW and term-born individuals did not differ in sleep duration or sleep latency (p's >.14), although signs of sleep phase advance was indeed seen; VLBW individuals reported an earlier wake-up time both on free days and on working days (p =.049 vs. p =.035), and earlier bed time on free days (p =.062) when compared to their term-born peers.
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Discussion |
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We found no differences in sleep duration, latency, efficiency, or fragmentation index between VLBW and term-born adults. Participants who reported neurosensory disability exhibited distinctively poorer sleep than others. VLBW adults went to bed earlier, possibly suggestive of a phase advance in sleep rhythmicity. Separate comparisons within the VLBW and term-born groups showed that in both groups, a shorter duration of gestation was associated with a longer sleep latency.
An unequivocal strength of the study is the well-characterized study cohort with VLBW adults and term-born controls. The VLBW group represents the first generation of VLBW infants exposed to modern neonatal intensive care. The use of two complementary methods, objective assessment by means of actigraphy and subjective assessment by questionnaire, provides a valid and reliable estimate of sleep quality, particularly in larger-scale studies where the use of polysomnography is impractical and cost-ineffective.
The major weakness of this study is the number of excluded participants due to incomplete bookkeeping of sleep habits. To avoid misclassification, we decided to exclude recordings with lacking sleep logs as low-activity periods may falsely be scored as sleep. Even though such a scoring procedure is conservative and may reduce our ability to detect subtle effects, we considered it necessary in order to keep the data analysis process as objective as possible. Moreover, although in our study participants and nonparticipants did not differ with regard to most perinatal variables, participation bias cannot be ruled out.
We found no difference in actigraph-derived measures of sleep quality between VLBW and term-born adults. Although we are unaware of any previous sleep studies in VLBW adults, our findings are consistent with those by Iglowstein et al. (2006), who did not find any differences in sleep onset, duration, or sleep efficiency between 10-year-old preterm children (n = 139) and term-born controls (n = 75). Likewise, Wolke et al. (1995, 1998) demonstrated no difference in sleeping behavior based upon parental report among preterm infants at age 4.8 month. It is, however, possible that differences in sleep are present at an earlier age. Gössel-Symank et al. (2004) demonstrated less restful sleep, shorter daytime sleep, and trends towards shorter night time sleep duration among a small sample (n = 17) of VLBW infants at age 20 months. Hoppenbrouwers et al. (2005) examined sleep architecture in preterm infants during the very first months of life, and found signs of delayed maturation among preterm infants born at the earliest gestational ages when compared to preterms born at later gestational ages, even though they were examined at the same postmenstrual age.
The earlier bed time we found among VLBW adults suggests an advanced sleep phase, although this finding should be interpreted with caution since the study was not designed to detect alterations in the circadian rhythm, which requires different methodology. An advanced sleep phase is however also supported by the fact that the VLBW adults reported earlier bed times in the questionnaire as well, although again we did not use a morningness–eveningness questionnaire specifically designed to assess circadian rhythm. This is consistent with a previous study reporting increased propensity for morningness-types among teenagers aged 13 born late preterm (Natale et al., 2005).
The negative correlation we found between gestational age and sleep onset difficulties in adulthood is intriguing. It is noteworthy that this correlation was seen as well within the VLBW as within the term-born group, and when the groups were pooled and analyzed as a continuum. We can only speculate about the mechanisms, which may very well be different within the two groups. One possibility is that the finding reflects early programming of the hypothalamic–pituitary–adrenal axis (HPAA), which is tightly involved in, among other things, the regulation of parturition. This is in accordance with previous data demonstrating that in subjects born at term, normal variations in gestational age are associated with differences in HPAA function in adult life (Kajantie et al., 2002, 2003). It remains uncertain whether this potential underlying mechanism is identical in the VLBW group, since the numerous adverse perinatal events associated with a very preterm birth may perhaps exert stronger influence on adult outcomes than the shorter duration of gestation itself.
There is a tight interplay between the HPAA and the regulation of circadian rhythmicity (Swaab, Bao, & Lucassen, 2005). Kennaway (2002) indeed wondered whether gestational length could be associated with an increased amplitude of melatonin rhythm in adult life, and whether length of gestation could impact on adult sleep. Premature infants, who had suffered from intrauterine growth retardation, developed melatonin rhythmicity slower than term-borns (Kennaway, Goble, & Stamp, 1996). In a previous study on young adults, the overnight excretion of the urinary melatonin metabolite 6-sulphatoxymelatonin correlated positively with body size at birth and negatively with gestational age (Kennaway et al., 2001). Whether this phenomenon is related to the longer sleep latency seen in our study among individuals with lower gestational age, or to the lack of association between sleep latency and ponderal index at birth (although examined at different levels of adult BMI), is however difficult to determine because no clear relationship between urinary 6-sulfatoxymelatonin and sleep latency has been established (Mahlberg & Kunz, 2007).
Arisen from observations on a relationship between birth weight and subsequent risk for cardiovascular diseases, the "developmental origin of health and disease" hypothesis has gained interdisciplinary recognition. The concept refers to programming or developmental plasticity, which means an organism being able to adjust its adult phenotype based on environmental cues at sensitive stages of development. A body of evidence from animal experiments shows that modest interventions, such as maternal stress or malnutrition during the fetal or early postnatal periods cause life-long changes in the structure and function of many organs including the brain, with related alterations in behavior (reviewed by Kofman, 2002; Weinstock, 2001). These alterations are likely to affect sleep patterns as well. Direct evidence is available from animal studies: For example, Datta and colleagues (2000) found alterations in sleep quality and quantity in prenatally malnourished rats.
What could be the underlying mechanism for the potential association between prematurity and altered sleep phase in adulthood? The suprachiasmatic nucleus continues to mature after birth (Swaab, 1995). Thus, it could be sensitive to programming by the often extreme conditions of neonatal intensive care after preterm birth. This period is also characterized by the absence of the effects of maternal hormones including melatonin. In addition, excessive and constant light in the NICU, which was routinely applied during the period when our study participants were born, may affect infant sleep. In mice, constant light in the postnatal period had both immediate and long-lasting disruptive consequences on the developing biological clock (Ohta, Mitchell, & McMahon, 2006).
We conclude that in this sample, sleep quality in young adults with VLBW is similar to that of their term-born peers. Therefore, differences in disease risk factors that have been observed between VLBW and term-born adults are unlikely to be explained solely by differences in sleep quality. VLBW adults, however, on average go to sleep earlier. Moreover, shorter gestational length is associated with longer sleep latency. Although they need to be replicated, these results suggest that the regulation of circadian rhythmicity and sleep may be programmed during early life.
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This study was financially supported by grants from the Academy of Finland, the Children's Castle Hospital Foundation, the Finnish Medical Society Duodecim, Finska Läkaresällskapet, the Finnish Foundation for Pediatric Research, the Finnish Special Governmental Subsidary for Health Sciences, the Jalmari and Rauha Ahokas Foundation, the Juho Vainio Foundation, the Finnish National Graduate School of Clinical Investigation, the Novo Nordisk Foundation, the Päivikki and Sakari Sohlberg Foundation, the Signe and Ane Gyllenberg Foundation, the Yrjö Jahnsson Foundation, the Research Foundation for Orion Corporation, the Pediatric Graduate School, University of Helsinki, the Perklén Foundation, and the Wilhelm and Else Stockmann Foundation. We owe sincere gratitude to the study participants and to our research nurses Paula Nyholm, Anne Kaski, Marita Suni, and Hilkka Puttonen, and to Sigrid Rostén and Jarkko Volanen for data management.
Conflicts of interest: None declared.
Received April 1, 2007; revision received October 26, 2007; accepted November 16, 2007
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