Neonatal Sleep Predicts Attention Orienting and Distractibility

Abstract

Objective: Children with sleep disorders tend to experience attention problems, yet little is known about the relationship between sleep and attention in early development. This prospective follow-up study investigated the longitudinal relationships between neonatal sleep, attention, and distraction in infants born preterm. Method: We used actigraphy and sleep-wake diaries in the neonatal intensive care unit (NICU, N = 65), attention orienting in a visual-recognition-memory task (VRM) at age 4 months, and structured observation of attention and distractibility at age 18 months. Results: Infants with poorer neonatal sleep (n = 31) exhibited longer first gaze durations in the VRM at 4 months and longer distraction episodes at 18 months relative to neonatal controls who slept well (p < .01). Hierarchical regression models support relations between neonatal sleep and gaze behavior at 4 months and distractibility at 18 months; moreover, alterations in orienting attention at 4 months predicted the likelihood of being distracted during the second year of life. Conclusion: Findings underscore the importance of early sleep-wake and attention regulation in the development of distraction in infants born preterm.

Keywords

infant prematurity attention sleep

The importance of sleep for physical well-being and cognitive processes has been extensively documented; however, researchers have only recently become interested in studying sleep to understand developmental processes. Sleep, arousal, and attention all share physiological, neuroanatomic, clinical, and developmental characteristics (Dahl, 1996). Clinical populations that experience attention-regulation deficits such as ADHD often experience specific sleep disturbances (Silvestri et al., 2009), and recent studies have shown that sleep problems may be associated with attention problems in childhood (O’Callaghan et al., 2010). Little is known about the relationship between sleep and attention in low-risk childhood populations. In this respect, the understudied developmental phase of infancy is of particular interest. Longitudinal prospective studies of low-risk neonates may provide a window into the characteristics of the relationship between sleep and attention.

Arousal Modulation: Effects on Regulation of Sleep and Attention

Sleep–wake rhythms serve as a basis for arousal modulation (Geva & Feldman, 2008a) through noradrenergic, serotonergic, and histaminergic networks to promote sleep and wakefulness (Aston-Jones, Chen, Zhu, & Oshinsky, 2001). The ascending noradrenergic system is thought to be involved in the attainment of physiological and psychological states of wakefulness and/or reactivity to internal vital cues (Sawaguchi, Franco, Groswasser, & Kahn, 2003) and enables anticipatory readiness to external stimuli (Colombo, 2001). Its connections with the sleep–wake circadian regulators—such as the suprachiasmatic nucleus, the dorsomedial hypothalamic nuclei, and the locus ceruleus—regulate alertness, vigilance, attention, learning, and memory (Aston-Jones, 2005). Indeed, neonatal research indicates the role of arousal in attention (DiPietro, Costigan, & Pressman, 2002; Geva & Feldman, 2008b); thus, it is plausible that sleep, arousal, and attention are already interlinked in infancy (Colombo, 2001).

Sleep–Wake Regulation and Preterm Development

Sleep-related structural and functional developments occurring in the second half of pregnancy enable near-term fetuses to experience arousal fluctuations and sleep episodes (DiPietro et al., 2002). This development is partly endogenously driven (Mirmiran, Baldwin, & Ariagno, 2003) but is not necessarily an expression of a free-running internal clock. It may instead represent the reactivity of fetal arousal to maternal entrainment signals of day–night rhythms, such as responses to changes in rhythmic auditory signals, bowel movements, or heart rate (HR; DiPietro et al., 2002; Frank et al., 2006). This relationship implies that state regulation emerges through the mediation of fetal capacities to attend to sensations in the environment, thereby setting the groundwork for the hypothesis that neonatal sleep–wake characteristics may already be linked with attention regulation in infancy and affect the development of attentional orienting and distraction later on.

Prematurity As a Model for Studying the Relationship Between Arousal and Distractibility

Studies indicate that the cyclicity of neonatal sleep behaviors emerges in most at 25 to 30 weeks post-conception (M. S. Scher, Johnson, & Holditch-Davis, 2005). Hence, premature infants (who are typically born relatively soon after this period, as compared with full-term infants) may show some susceptibility to distractibility in this newly emerging system. As such, low-risk infants who are born preterm may serve as a model to test the hypothesis regarding relationships involving arousal distractibility, a relationship that may be more elusive in the general population of neonates.

According to Kinney (2006), during the period of 30 to 40 gestational weeks, the brain still continues with its marked linear growth spurt (Guihard-Costa & Larroche, 1990), dendritic arborization processes, laminar organization, significant axonal growth, and white-matter formation (Haynes et al., 2005). Preterm infants born during this period not only lack sufficient maturation but also face excessive stimulation in the extrauterine environment because they are no longer shielded by the uterus in the late-term period. Many are also exposed to relatively strong lights, sounds, smells, and pressure signals in neonatal intensive care units (NICUs), experiences from which full-term infants are typically spared (Als, 1986). This intense and unexpected stimulation incurred at such an immature phase may compromise the development of the arousal–attention relationship. Indeed, many infants born preterm have increased susceptibility to attention deficits as they mature (Gurevitz, Geva, Varon, & Leitner, 2014). Specifically, studies have shown that prematurity is related to difficulties in sustaining and modulating attention arousal in infancy (Gardner, Karmel, & Magnano, 1992; Geva, Gardner, & Karmel, 1999), affecting orienting behavior (van de Weijer-Bergsma, Wijnroks, & Jongmans, 2008), and sustaining attention (Richards, 1994). Moreover, prematurity has been related to proneness to difficulties with focused attention (FA) because of increased distractibility (Anderson et al., 2011; Lawson & Ruff, 2004; van de Weijer-Bergsma et al., 2008).

Research on distractibility in infancy is just emerging. Distraction research is a growing field because it facilitates understanding of multiple psychopathological processes (Harvey, Winters, Weintraub, & Neale, 1981), including ADHD (Arnsten, 2006), learning disabilities, accident proneness (Vollrath, Landolt, & Ribi, 2003), and compromised welfare. However, little is known about the infant-related risk conditions that lay the groundwork for later proneness to distractibility.

Advancements in understanding the role of the medial reticular formation’s ascending pathways using positron emission tomography have demonstrated its gating activity, which enables selective attention (Kinomura, Larsson, Gulyás, & Roland, 1996; Schiff & Plum, 2000) and regulates gaze control as a response to arousing stimulation (Perkins, Warren, & May, 2009). This suggests that infants with less-regulated neonatal sleep–wake states—which are thought to involve reticular formation pathways—may be more prone to experiencing difficulties in gaze regulation (Colombo, Mitchell, Coldren, & Freesman, 1991; Reynolds, Guy, & Dantong, 2010) and distractibility (Ruff, Capozzoli, & Saltarelli, 1996).

Neonatal Sleep–Wake Activity Assessment

Assessment of sleep–wake activity in neonates is possible using actigraphy (Cole, Kripke, Gruen, Mullaney, & Gillin, 1992; Gertner et al., 2002; Kushida et al., 2001; Sadeh, Acebo, Seifer, Aytur, & Carskadon, 1995; Sadeh, Alster, Urbach, & Lavie, 1989; Sadeh, Dark, & Vohr, 1996; Sadeh, Hauri, Kripke, & Lavie, 1995). The actigraph continuously records and sums limb movements over given epochs. Through the use of a specially developed algorithm for infants, the motility levels can be computer scored into sleep–wake states (Sadeh et al., 1996; Sadeh, Hauri, et al., 1995). Its parametric and objective output is considered advantageous over parental reports, particularly when sleep efficiency (SE) is the target measure (Sadeh, 2004). Actigraphy has been validated in infants younger than 6 months using behavioral state evaluations and polysomnography (So, Buckley, Adamson, & Horne, 2005), with high sensitivity values of 96.2 (1.1%) in the neonatal period (So et al., 2005); furthermore, it enables a double-blind experimental paradigm. Actigraphy has been used successfully, specifically with preterm newborns in nurseries (Chang, Anderson, & Lin, 2002; Gertner et al., 2002; So et al., 2005).

Operational Hypotheses

The overall framework for this study was built on the notion that neonatal sleep–wake regulation patterns might be related to the infant’s later emerging competence at attention regulation (Geva & Feldman, 2008b). In other words, infants with low neonatal SE were expected to exhibit longer first gaze lengths while familiarizing themselves with the visual recognition memory (VRM) targets at age 4 months and longer gazes at salient distractors at 18 months compared with infants having higher neonatal SE. Finally, neonatal SE and first gaze length in the VRM task were expected to predict gaze length at salient distractors and the likelihood of missing distractors in the 2nd year of life.

Method

Participants

Sixty-five infants born preterm at a Level-III Safra Children’s hospital, Sheba Medical Center, were enrolled in a prospective double-blind longitudinal study. A low-risk sample was selected, thereby limiting potential effects of neurological damage and high psychosocial risk on arousal. The participants had a mean gestational age (GA) of 32.4 ± 1.4 weeks (range = 30-35.1 weeks) and a mean birth weight of 1,637 ± 349.7 g (range = 1,010-2,540 g). Of the infants, 47% were girls. The medical exclusion criteria included the presence of intraventricular hemorrhage Grades II-IV, perinatal asphyxia, or encephalopathy (Sarnat Hb, 1976, #1850), or metabolic, genetic, or syndromic disease. Given the possible relationships between stress and sleep regulation, the psychosocial exclusion criteria included teenage pregnancy, single parenthood, and unemployment of both parents. All mothers were aged >21 years, living with the infant’s father, and did not report using psychoactive drugs or psychiatric medication. The families were middle class by national standards. The Institutional Review Board of the Medical Center approved the study, and informed consent was obtained from all participants. Of the approached mothers, 30% declined to participate, citing time constraints, partner’s refusal, or not feeling ready to deal with developmental issues. Five more infants were discharged prior to the anticipated date and were therefore excluded from this segment of the study. These families did not differ from the participating families on any of the demographic or medical variables.

Procedure

Approximately 2 to 3 days prior to discharge from the NICU (GA = 37.4 ± 1.5 weeks), a sleep–wake assessment was conducted through actigraph and diary records (Sadeh, Hauri, et al., 1995). All participants were tested in the same NICU room and were exposed to the same lighting, handling, and noise conditions. The families were then invited to the lab when the infants were 4 months old (age corrected for prematurity) for a gaze-orienting VRM task (Jones, Pascalis, Eacott, & Herbert, 2011) and a structured attention observation at 18 months. Both tasks took place mid-morning at the lab (Geva et al., 1999) in a naturally lit room after a short “warm-up” phase in the testing room with the infant’s mother and a trained experimenter. Procedural considerations with regard to each of the three tasks of the study are detailed below.

Neonatal Sleep–Wake Activity Assessment

Assessment of sleep–wake activity was conducted using an actigraph (Actiwatch-AW4, Cambridge Neurotechnology Ltd., UK; Cole et al., 1992; Gertner et al., 2002; Kushida et al., 2001; Sadeh, Acebo, et al., 1995; Sadeh et al., 1989; Sadeh et al., 1996; Sadeh, Hauri, et al., 1995). The actigraph was attached to each infant’s right ankle. Ambulatory data were collected for 48 consecutive hours before discharge from the NICU. Data were analyzed using Actiwatch Activity & Sleep Analysis V 5.42 software, utilizing an algorithm developed and validated specifically for infants, including newborns (Sadeh, Acebo, et al., 1995). The published agreement rate between actigraphy, automatic sleep–wake scoring, and parallel scorings from direct observations and respiration pads is high in infant samples (88.9%; Sadeh, Acebo, et al., 1995). Specific algorithms were developed for newborn and young infants to estimate the probability of each state in an each scored époque (Sadeh, 1995, #1517). The algorithms included the following parameters: nzw = minutes with zero activity, ntl = minutes with low activity (i.e., nonzero but lower than 100 counts), nth = number of minutes with high activity (equal or greater than 100 counts) in the scored minute plus the 5 min that precede it and follow it, s5 = standard deviation (SD) of the window of the scored minutes plus the 5 min preceding it, ml = mean activity level of the scored minute and the preceding minute, and lw4 = the lowest activity count during the window that includes the scored minute plus the following 4 min (Sadeh, 1995, #1517).

The algorithm for probability of awake epoch (PAW) = −25.638 + I .714 × nzw + 3.0168 × ntl + 4.064′nth + .1066 × s5 + .0386 × ml − .016 × lw4.

Whereas the probably of an active sleep epoch (PAS) = 5.134 + I .696 × nzw + 2.062 × ntl +.9568 × nth + .058S × s5 + .00556 × ml + .0105″lw4.

The validation of these specific, automatic, algorithmic categorizations of newborns’ states has been reported to be high for the agreement of active sleep states with behavioral observations (e.g., 82.8% [n = 240] for awake states and 74.4% [n = 677] for active sleep states; Sadeh, 1995).

To differentiate “spontaneous” changes in state from artifacts related to external sources, care teams were asked to fill out concurrent behavioral sleep logs (in 15-min epochs), as advised by Acebo and colleagues (2000). The logs included the timings and lengths of all periods of handling, such as holding, feeding, positioning, checking, treating, and bathing. As the actigraph does not deal well with external motion or differentiate it from endogenous motion, data from these logs and NICU records were later synchronized with the actigraphy data, and periods of exogenous motion, removal of the actigraph from the infant’s ankle (i.e., during bathing), and lack of correspondence between registered activity and the sleep log or event markers (Acebo et al., 2000) were excluded from further analysis.

The actigraphy-derived measures previously reported to be useful in neonates born preterm were collected; these include SE, which is the percentage of minutes coded by the algorithm as sleep (Hoppenbrouwers et al., 2005; A. Scher et al., 1995). Specifically, SE is the percentage of time within sleep bouts (i.e., between sleep onset and final awakening) spent asleep (de Souza et al., 2003), mean activity level is the average number of zero crossings of the piezoelectric beam (Sadeh et al., 1996), sleep latency is the estimated time before falling asleep (Strang-Karlsson et al., 2008), and the percentage of time in the awake state is defined as suggested by Hoppenbrouwers et al. (2005). These measures demonstrate good test–retest reliability (Sadeh, 2011, #1851).

Attention orienting at 4 months

Gaze-orienting times during free observation were measured by using an infant-controlled single-trial VRM task (Geva et al., 1999; Harel, Gordon, Geva, & Feldman, 2011). The age of 4 months was selected because this task has been shown to elicit orienting responses (Nelson & Collins, 1992) to differences between stimuli (Colombo, Wayne Mitchell, Dodd, Coldren, & Horowitz, 1989) and responses to novelty (Geva et al., 1999; Reynolds & Richards, 2005) at this age. The task comprised of a 60-s familiarization phase (Richards, 1997), in which the infant passed the 10-s familiarization criterion followed without delay by a testing stage with fixed 20-s test trials (Reynolds & Richards, 2005). All participants attained the familiarization criterion within the allotted period. During the testing phase, the infant was presented with a novel 12.7 × 9.3 cm² stimulus (a black and white graphic pair of patterns; Harel et al., 2011; Rose, 1980) displayed on a 31 × 23 cm² computer screen with a gray background. The infant sat on the mother’s lap 55 to 60 cm from the screen. A Studio software program fixed the frequencies of the presentation types and sides each at 50%. Trials were initiated when the infant was decidedly looking at the monitor, and looking times were judged by an observer according to a method comparable with that of Reynolds and Richards (2005). The VRM is a widely used paradigm that is considered to be characterized by modest reliability, good discriminant validity, good predictive validity, and significant cross-age continuity (Rose, Feldman, & Jankowski, 2004). As the focus of the current hypothesis was orienting attention in the presence of changing demands, first gaze length during the familiarization and test phases and novelty scores were the primary dependent measures.

Structured observation of attention at 18 months

The FA paradigm developed by Ruff and Rothbart (1996) and later employed and modified by Oakes, Tellinghuisen, and Tjebkes (2000) was used (Gavrilov, Rotem, Ofek, & Geva, 2012). In this task, an experimenter presented a target toy within the child’s reach, maintained an inviting and calm expression, and maintained his or her gaze on the target toy. No explicit instruction was given. The spontaneous activity was recorded online by an observer who stood behind a one-sided mirror facing the infant, and the procedure was videotaped for reliability testing and behavioral coding (setup is depicted in Appendix A).

This task included four randomly presented trials of 2 min each. Each trial had a target toy, that is, a stuffed animated orange, Legos, a plastic turtle with geometric parts to insert, or a complex bead track (Appendix B). Each trial was presented with a randomly selected distracting condition: (a) no distraction, (b) a low-saliency visual distracter (a looming red ball), (c) a low-saliency auditory distracter (a soft guitar tune, 40 dB hearing level [HL]), and (d) a high-saliency bimodal distracter (a bouncing red ball coupled with a bouncing noise, 40 dB HL) that has been shown to be a highly effective distracter (Bahrick & Lickliter, 2000; Miller, Rodger, Bucolo, Greer, & Kimble, 2010). To avoid a fixed tempo, the 4-s-long distractors were presented with random interstimulus intervals every 7 to 11 s. An average of 8 and no more than 10 distractors were presented in each block. Dependent measures of divided attention efficiency and distractibility included the latency to direct gaze at distractors, the frequency of missed distractors, and the average gaze length and total fixation time at the toy and the distractors. The interrater reliability coefficients for the timed gazes on six pilot tapes were greater than 90%.

Statistical Analysis Approach

Descriptive statistics (means and SDs) of neonatal sleep, attention orienting at 4 months, and distractibility measures were computed, as were Pearson’s correlations between the neonatal measures, early attention measures, and distractibility measures at age 18 months. The participants were then divided into two groups as a function of neonatal sleep measures. As all sleep measures were strongly intercorrelated, the median SE score was used as a classification criterion for 90% risk, as suggested by Leitner and colleagues (2002) in their study of young children born with intrauterine growth restriction; this procedure yielded a good sleepers group (n = 34) and a poor sleepers group (n = 31).

Finally, the predictive power of GA, neonatal sleep, gaze orienting at age 4 months, and distractibility at age 18 months were tested with three planned hierarchical regression models to predict gaze length at the target toy, distractors, and distracter misses.

Results

Descriptive analysis of actigraphy is detailed in Table 1. The table shows the means, SDs, and ranges of the neonatal sleep measures. The table also indicates that infants in the poor sleepers group were not significantly different from those in the good sleepers group in terms of neonatal characteristics, such as GA, birth weight, or parental measures. Moreover, the results of the neonatal neurobehavioral examination (Karmel & Gardner, 2005), which include measures of the evolution of 17 neonatal sensorimotor reactivity items, showed that the infants in both groups were low risk and performed well on the evaluation items, except for a difference in the propensity for hypertonic legs, such that six children in the poor sleepers group (vs. none in the good sleepers group) had hypertonic legs in the neonatal period (χ² = 7.325, p < .009).

Table 1.

Means and Standard Errors of the Neonatal Characteristics of the Neonatal Sleep Groups.

	Poor sleepers	Good sleepers
	n = 31	n = 34	p *
Neonate
Gender (% F)	41	54	ns
Gestation age (w.)	32.64 ± 0.3	32.20 ± 0.26	ns
Birth weight (g)	1,686.32 ± 59.3	1,572.66 ± 61.8	ns
Days in NICU	33.21 ± 0.8	39.03 ± 2.9	ns
Parental
Maternal age	31.60 ± 0.9	31.93 ± 0.8	ns
Maternal health	0.23 ± 0.07	0.22 ± 0.07	ns
Maternal education*	3.67 ± 0.2	3.90 ± 0.2	ns
Paternal education	3.33 ± 0.2	3.79 ± 0.2	ns

Note. ns = not significant at the criterion of p < .05; NICU = neonatal intensive care unit; Educ = education level (1 = did not complete high school, 2 = completed high school, 3 = completed undergraduate studies, 4 = some graduate studies, 5 = completed graduate-school degree).

p > .05.

The data presented in Tables 2 and 3 show that the groups were different on all neonatal sleep measures. The effect sizes of the various sleep measures ranged from medium to large (Tables 2 and 3).

Table 2.

Neonatal Sleep Characteristics.

Neonatal sleep	M ± SD	Range
Actigraphy gestation age (w.)	37.4 ± 1.5	35-40
Actual awake (%)	4.8 ± 2.3	2.1-15.7
Sleep latency (min.)	06:05 ± 06:18	0-28
Total activity score	423.1 ± 343.3	71-2,473
Sleep efficiency (%)	88.38 ± 7.29	66.5-97.1

Table 3.

Means and Standard Errors of the Neonatal Sleep Characteristics of the Sleep Efficiency Groups.

Neonatal sleep	Poor sleepers	Good sleepers	F(1, 64)	η²	p
Sleep efficiency (%)	82.06 ± 5.7	94.1 ± 1.94	133.86	.680	<.001
Total activity score	603.79 ± 76.5	258.89 ± 16.13	132.67	.678	<.001
Sleep latency (min.)	10:58 ± 01:03	01:44 ± 00:21	73.19	.537	<.001
Wake bouts (M ± SD)	2.90 ± 1.09	1.55 ± .46	43.44	.408	<.001
Fragmentation index	39.93 ± 9.58	25.48 ± 8.17	42.96	.405	<.001
Sleep bouts (M ± SD)	2.81 ± 1.14	1.51 ± .45	37.50	.374	<.001
Actual sleep (%)	93.87 ± 2.7	96.362 ± .6	26.104	.293	<.001
Actual awake (%)	6.13 ± .5	3.64 ± .1	26.11	.293	<.001

Note. Sleep efficiency = the percentage of time within sleep bouts between sleep onset and final awakening spent asleep; Total activity score = the total number of zero crossings of the piezoelectric beam; Sleep latency = the estimated time before falling asleep (Strang-Karlsson et al., 2008); Wake bouts = the mean duration of periods of wakefulness; Fragmentation index = the ratio of the number of phases of 1-min immobility to the total number of immobile phases of all durations multiplied by 100; Sleep bouts = the average length of uninterrupted sleep; Actual sleep = Actual time spent asleep (%); Actual awake = the percentage of time spent awake.

Pearson’s correlation analysis among the neonatal sleep measures indicated two findings: (a) latency to sleep and SE were strongly related (rs > .8), and (b) actual awake and total activity scores were strongly related (rs > .8). The relationship between these two factors was moderate in strength (on the order of r = .5), indicating a significant portion of shared variance among the sleep measures (Figure 1).

Figure 1.

Longitudinal relationships scheme between neonatal sleep, gaze orienting at age 4 months, and distractibility at age 18 months.

Relationship Between Neonatal SE and Gaze Orienting at 4 Months

To explore the hypothesis that neonatal sleep parameters are related to gaze lengths in the VRM procedure at 4 months, we conducted a Pearson correlation analysis to characterize the relationship between neonatal sleep measures and gaze length during familiarization at age 4 months. The analysis showed that neonatal sleep parameters were related to average gaze-orientation lengths during familiarization with the target at age 4 months. These relationship strengths were characterized as moderate (Figure 1); shorter sleep latency and higher SE were related to shorter gaze length during familiarization, and actual percentage awake was positively related to novelty scores at this age.

We then compared the attention orienting measures at 4 months as a function of neonatal sleep grouping (poor vs. good sleepers). The results of independent t tests showed that infants in the poor sleepers group had significantly longer first gaze length than infants in the good sleepers group in the familiarization phase of the orienting task at age 4 months (42.8 ± 5.3 vs. 23.8 ± 5.2 s, F = 5.542, p < .023, η² = .12); the poor and good sleepers had similar first gaze length in the novelty phase of the test and comparable novelty scores (Figure 2). An ANOVA with GA as a covariate showed that the effect of neonatal sleep on first familiarization gaze length was not modulated by prematurity, F(1, 63) = 5.473, p < .024.

Figure 2.

Visual recognition memory characteristics at age 4 months as a function of neonatal sleep.

Relationship Between Neonatal SE and Distractibility Proneness at Age 18 Months

Manipulation check

As expected, descriptive analysis showed that regardless of neonatal sleep, most infants tended to exhibit longer first gaze durations at high-saliency than low-saliency distracting events (Figure 2, p < .01).

To examine the next phase of the hypothesis, we explored the relationship between neonatal sleep latency and attention to distracting sources at age 18 months. There was interplay between neonatal sleep regulation and distractibility. The Pearson correlations between neonatal sleep measures and measures of distractibility gaze length were moderate at 18 months (schematically depicted in Figure 1). Neonatal SE was negatively related to gaze length at bimodal distractors, and neonatal sleep latency was positively related to the latter factor, indicating that infants with poor neonatal sleep had longer distraction episodes.

An ANOVA showed that poor neonatal sleepers maintained longer first gazes at high-saliency bimodal events than did good neonatal sleepers, indicating that they experienced difficulties disengaging from the bimodal distracter to return their focus to the target toy (Figure 3).

Figure 3.

Gaze lengths at distractors at 18 months as a function of saliency and neonatal sleep.

Predictive Models

Finally, to examine the hypothesized relationships between neonatal sleep, attention orienting at age 4 months, and distractibility proneness at 18 months, we ran two comparable regression models predicting the average gaze length and the frequency of missed high-saliency distractors (Table 4).

Table 4.

Regression Models Predicting Distraction Characteristics at 18 Months Using Prematurity, Neonatal Sleep Quality, and Gaze Length at VRM at Age 4 Months.

Predicting model	R ²	Significance F change	β	t test	p
Average gaze length at bimodal distractors
Prematurity (w.)	.27	.001	.312	2.11	.043
Neonatal sleep group	.36	.021	−.441	−3.1	.004
4 months—VRM average first gaze duration in each test phase	.35	ns	−2.93	−0.65	ns
Frequency of misses of bimodal distractors
Prematurity (w.)	.05	ns	−.12	−1.2	ns
Neonatal sleep group	.05	ns	−.203	−0.73	ns
4 months—VRM average first gaze duration in each test phase	.239	.006	−.453	−2.9	.006

Note. VRM = visual recognition memory; ns = nonsignificant.

The following independent measures were entered as predicting variables in the following chronological order: GA (to ensure that the effects were not accounted for solely by prematurity), neonatal sleep grouping, and the average first gaze-orienting length in the familiarity and novelty phases of the VRM.

The results showed dissociation between the two models, indicating two distinct sets of relations: the first model, which accounted for 36% of the variance, showed that average gaze lengths toward bimodal distractors were predicted by the degree of prematurity and neonatal sleep grouping. Gaze length in the VRM did not contribute further to the variance explained in this model. The second model showed that gaze length in the VRM accounted for 24% of the variance in the number of distractors missed, but prematurity and sleep grouping did not contribute further to this model.

Considering these results, a final comparable model was conducted to predict the accumulated gaze length at the target toy in the control trial (absence of distractors) to ensure that the behaviors accounted for were not general stylistic traits but rather referred discretely to distraction proneness. As expected, the model as a whole and the effects of the three variables were not significant at p < .05.

Discussion

Overall, the results supported the hypothesis that neonatal sleep–wake regulation patterns are related to infants’ emerging attention-regulation competence in complex environments. Specifically, it was found that poor neonatal sleepers were less able to regulate their gaze orientation in conditions such as the VRM at 4 months and conditions that require divided attention at 18 months. Moreover, it was found that neonatal sleep–wake organization and early gaze length in the VRM predict infants’ emerging faculties of selective and divided attention during the 2nd year of life. The data seem to be compatible with the role of early maturing brainstem- and midbrain-related networks in regulating higher order behaviors, such as the maintenance of goal-directed attention in distracting environments (Geva & Feldman, 2008b).

Early canonical work recognized the co-occurrence of rhythmicity, orienting, and distractibility in children aged 3 to 12 months (Thomas, Chess, & Birch, 1968). The current findings extend this work in three ways. First, our results extend the relationships to the neonatal period. Second, this study supports the notion of using a prospective double-blind design with objective and parametric data. Third, these findings specify a discrete mapping of the relationships between sleep–wake characteristics, orienting, and distractibility to advance the understanding of the mechanisms involved in these developmental trajectories.

The first finding, that sleep–wake organization at term age collected prior to the infant’s interaction with the home environment predicts distractibility in the 2nd year of life, is intriguing. This result highlights the potential role of very early sleep–wake organization at a time that precedes social-emotional moderation on long-term regulation of gaze behavior outside the womb and suggests that the level of sleep and arousal organization prior to bonding with the parents at home plays a long-term role in attention regulation that lasts at least through the 2nd year of life. This relationship holds even though other factors such as home aspects and socioemotional development, which were not studied in the current research, may very well moderate this relationship from the second half of the 1st year onward (Gertner et al., 2002; A. Scher, 1991).

At age 18 months, as expected, some infants experienced distractibility-related difficulties. There were differences between the groups of poor sleepers and good sleepers in terms of gaze length toward salient events in three specific aspects of attention regulation: (a) shifting attention between targets, resulting in missed distractors; (b) exerting efficient disengagement from a distracter, resulting in longer latency to shift to a distracter and longer gazes at each distracter; and (c) sustaining attention to a target toy, resulting in longer total time spent observing distractors.

These findings support our hypotheses concerning the relationship between neonatal sleep regulation and the later development of attention regulation in complex and dynamic environments. The data are compatible with the notion that neonatal gaze behaviors, such as gaze length, are effective predictors of sustained attention to a meaningful target as the infant matures (Geva et al., 2012; Harel et al., 2011; Stroganova, Posikera, & Pisarevskii, 2005). The current results extend this literature to indicate a particular mechanism that may play a role in this relationship by showing that neonatal sleep may be involved in gating uninterrupted gaze orienting to meaningful targets in complex environments, thereby compromising data processing and possibly impeding social interaction (Dawson et al., 2004).

In this context, it is important to note that given the potential interrelationship between arousal and attention that is already present in the neonatal phase, and given the instability in development of sleep–wake patterns and attention-regulation characteristics, one cannot deduce a one-way causal relationship between sleep and attention regulation; rather, it might be possible to highlight the potential linkage between these important functions in the neonatal and infancy periods.

This interplay between neonatal sleep regulation and distractibility extends previous findings on the relationship between sleep characteristics and distractibility in infants, older children, adolescents (O’Callaghan et al., 2010), and clinical populations (such as children with ADHD; Golan, Shahar, Ravid, & Pillar, 2004; Gruber et al., 2007). Indeed, the predictive regression models of the present study highlighted the discrete relations between neonatal sleep organization and distractibility measures.

The first model showed that the degree of prematurity and neonatal SE predict the length of distracted episodes, but not the likelihood of being distracted (as shown in the second model) or gaze duration at the target toy (as shown in the third model). This finding suggests that neonatal sleep, which in a way reflects the ability to contain “noise” and manage the related arousal response to enable a smooth transition through the sleep–wake states, is expressed later in development as difficulty disengaging from a distracter in the periphery (van de Weijer-Bergsma et al., 2008).

The second model showed that the average first gaze length on the VRM at age 4 months, which were related to neonatal sleep, predicted the likelihood of missing distractors. Integration of the findings between the three models provides a better understanding of the mechanisms involved in the likelihood of being distracted and the length of distracted episodes in the 2nd year of life by using measures of sleep and early attention.

The data show that the degrees of prematurity and sleep predict the length of distracted episodes, whereas first gaze length during VRM at age 4 months predicted the likelihood of being distracted. These data support the notion that measures of neonatal sleep and early attention in the first months of life are related to the likelihood of being distracted and the length of distraction episodes in the 2nd year of life.

In this respect, these data also point to the uniqueness of the first gaze as a sensitive measure in gaze research. It may be that the first gaze reflects an initial orienting response rather than enabling in-depth processing. Given recent notions regarding the temporal dynamics of gaze regulation during task performance (van Stegeren, Roozendaal, Kindt, Wolf, & Jels, 2010) and the notion that the magnocellular-dorsal stream is evoked rapidly in a manner that precedes parvocellular-ventral processing (Colombo, 1995), one may speculate that the first response to an unfamiliar stimulus in the periphery of the visual field is driven by the dorsal stream and acts as a priming mechanism to direct attention toward novel stimuli in space at younger ages (van Stegeren et al., 2010).

Reports point to the general susceptibility of the dorsal stream in a wide range of developmental disorders, including infants born prematurely (Braddick, Atkinson, & Wattam-Bell, 2011; Hammarrenger et al., 2007). Future gaze-tracking studies and a better understanding of the interactions between noradrenergic and corticosteroid systems through development may elucidate the neural activation mechanisms involved in regulating the initial response to an unfamiliar stimulus, thereby enabling a better understanding of the sleep–attention interplay in this priming mechanism.

Overall, these finding suggest that neonatal sleep–wake regulation patterns relate to sleep-modulated attention in infancy and to distraction proneness during the 2nd year of life. Two notes of caution are warranted with regard to this relationship. First, this study was conducted in the NICU, which provided comparable environments for all participants. All participants were exposed to the same routines in the same room. Such a procedure is difficult to attain in studying infants born at term. They are typically hospitalized in the neonatal unit for very short periods, which are insufficient for an actigraph study. Nevertheless, the hypothesis may very well extend to infants born at term. Future studies could compare sleep in infants born preterm and at term in their respective home environments. Such studies would need to estimate the variance between individual environments but may enable the inclusion of at-term infants to test the generalizability of the hypothesis. Moreover, in the present study, we were typically able to collect 48-hr-long actigraph and diary data, but sleep researchers—who typically work with older participants in home environments—advocate the use of at least three consecutive nights. Therefore, further neonatal sleep studies are encouraged.

Second, it is plausible that neonatal sleep patterns affect later emerging sleep–wake patterns, which in turn affect infants’ attention. Further studies of early progression of sleep maturation from the neonatal period through early childhood are warranted to deepen the exploration of sleep–wake regulation and the understanding of the evolving relations between sleep and infant development, as these may play an important role in understanding the mechanisms that underlie attention regulation. Such an endeavor underscores the importance of neonatal sleep regulation to infants’ attention and sociocognitive development, affords very early targeting of infants who may develop attention deficits in the presence of sleep deficits, and encourages advancements in preventative interventions to limit attention deficits and distractibility by directing caregivers’ awareness to the importance of adapting the neonatal environment to limit sleep disruptions.

Footnotes

Appendix A

Appendix B

Acknowledgements

We thank the participating families for their cooperation, the Neonatal Department at Safra Children’s hospital, Sheba Medical Center, Sackler Medical School, Tel-Aviv University; and Tali Swann-Strenberg and the research team at the Developmental Neuropsychology Lab at the Gonda Brain Research Center, Bar-Ilan University.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Israel Science Foundation (#1518-2007).

Author Biographies

Ronny Geva has been a Fulbright scholar during her PhD studies at the City University of New York. She has been chair of the Clinical Child and Adolescence Psychology and the head of the Developmental Neuropsychology laboratory at the Gonda brain research center program at Bar Ilan University. Dr. Geva is author of multiple peer-reviewed papers and book chapters on attention deficits and neuropsychological development of infants at-risk.

Hagit Yaron holds a masters degree in Experimental Psychology from Bar Ilan University, where she served as lab manager of the Developmental Neuropsychology lab. Ms. Yaron has rich experience in evaluating young infants’ cognitive competence.

Jacob Kuint, a Clinical Associate Professor of Pediatrics, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, has directed the Neonatal Department, at Sheba Medical Center and is currently the director of neonatal department at Maccabi Health care services. Prof. Kuint is an expert in pediatrics and neonatology and has authored multiple peer-reviewed papers on these fields.

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