Original ArticleA longitudinal study of sleep-wake patterns during early infancy using proposed scoring guidelines for actigraphy
Introduction
Infant sleep patterns develop rapidly throughout the first year of life. Shortly after birth, sleep is fragmented and less predictable, as the central circadian pacemaker takes several months to establish the connection from the retina and for light signals to synchronize to the external light and dark cycle [1]. As infants age, sleep consolidation emerges with a more prolonged sleep at night and more sustained wakefulness during the day. Healthy sleep patterns have important implications on later health outcomes including physical growth and the development of a healthy body mass index (BMI) [2], [3]. Therefore, accurate measurements of these infant sleep-wake patterns are essential for evaluating intervention outcomes, providing guidance in clinical care, and understanding how sleep impacts other behaviors such as feeding.
When quantifying infant sleep patterns, much of the existing literature has relied on parent-reported measures, such as questionnaires or sleep diaries [4], [5]. These are biased by subjective report and limited to what parents are able to recall. Instead, actigraphy is a measure in which motor activity is continuously quantified over time using a wrist-worn wearable device. This noninvasive method uses activity counts to infer states of sleep and wake. Actigraphy may be more accurate than parent-report given its objective nature, while also more feasible than polysomnography (PSG) for natural settings like the home environment. Actigraphy has been used widely in sleep research among adults and older children, yet less in infants [6]. Of the studies that include infants, actigraphy has been shown to be a valid measure with agreement rates ranging 72–95% when compared to PSG or observer-identified states of sleep and wake [7], [8], [9].
Like all forms of measurement, actigraphy is not without limitations. The wide variety of devices, scoring algorithms, device placement sites, and definitions of key variables pose a challenge to interpreting results across studies [10]. In particular, there is little guidance available for researchers and clinicians on how to score and interpret infant actigraphy data. This lack of uniformity and clear scoring rules are discussed in a review by Meltzer et al. [6], and Galland et al. [10], with a call for research that clearly describes actigraphy scoring rules and definitions. Therefore, in an effort to advance the evidence base for scoring actigraphy data and improve the rigor and reproducibility of research using these devices for infants, we describe in detail our data scoring rules in our methods.
With the increasing use of actigraphy in pediatric populations [6], it is important to know how sleep values differ when quantified using actigraphy versus mother-reported measures. A few studies have found that mothers report infant sleep schedules (eg, sleep onset/offset) fairly accurately when compared to actigraphy; yet, mothers' perception of infant nighttime sleep duration is less accurate, due to their underestimation of nighttime wakings [11], [12], [13], [14]. One limitation of these studies is that they focus on infants' nighttime sleep behaviors, with little-to-no research that has included infants' daytime sleep behaviors. To explore these relationships, more research is needed that compares actigraphy vs. mother-reported values for both daytime and nighttime sleep durations throughout infancy. Furthermore, the day-to-night and night-to-day temporal patterns of infant sleep have not been studied. On nights when infants sleep longer or more efficient than usual, their next-day nap sleep duration may be affected, and on days when infants' nap sleep duration is longer than usual, that nights’ sleep onset, interval duration, and/or maintenance efficiency (ie, quality) may be affected. These empirical questions remain unanswered and warrant further investigation.
The primary aim of this study is to describe infants' sleep-wake patterns across the first six months of life. To do so, we describe the developmental patterns from 6 to 24 weeks of age and the daily temporal patterns between subsequent night-to-day and day-to-night sleep onset, interval duration, and maintenance efficiency using actigraphy. As a secondary aim, we compare actigraphy and mother-reported values for infants’ daytime nap and nighttime sleep patterns and describe our infant-specific actigraphy scoring process in the methods.
Section snippets
Study design and participants
This observational, pilot study was a micro-longitudinal burst design. There were three, one-week bursts of data collection when infants were 6, 15, and 24 weeks of age. This design allowed us to quantify the developmental patterns (across bursts) and the day-to-day patterns (within bursts) of infant sleep characteristics.
Mothers were recruited during pregnancy from Obstetrics and Gynecology clinics and from Labor and Delivery at a local hospital in Central Pennsylvania. Nurses at these sites
Results
Infants were mostly White, while mothers were mostly married, and college educated (Table 2). The majority (75%) of families earned a household income ≥ $50,000/year (Table 2). There was high compliance with infants’ wearing the actigraphy device. The average percentage of off-ankle time (eg, when infants did not wear the device, per device impedance signal) was minimal at 1 ± 2.5% (range: 0–18.3%) of time on valid recording days. The average number of valid days within a requested one-week
Discussion
Using actigraphy, we characterized patterns of infant sleep-wake states across the first six months of life. From 6 to 24 weeks of age, total daytime nap sleep duration decreased, with a marginal significance for nighttime sleep interval increasing. Total 24-h sleep duration was time invariant. Daily temporal patterns indicated that changes to infants' usual nighttime sleep interval did not influence next-day sleep; yet, at 24 weeks, changes to infants' usual daytime nap sleep duration
Disclosure
Outside of the current work, Orfeu M. Buxton discloses that he received two subcontract grants to Penn State from Mobile Sleep Technologies (NSF/STTR #1622766, NIH/NIA SBIR R43AG056250). Other authors report no conflicts of interest.
Funding source
This work was supported by the Childhood Obesity Prevention Training doctoral program from the National Institute for Food and Agriculture, USDA (Grant #2011–67001-30117). Programming and data visualization costs were supported by startup funds to Dr. Buxton from the Pennsylvania State University College of Health and Human Development and Social Sciences Research Institute.
Acknowledgements
The authors would like to acknowledge Donald Miller at the Penn State Population Research Institute for his efforts on data programming and visualization, and Sally Eagleton and Katie McKnitt at the Center for Childhood Obesity Research at Penn State for their efforts in data collection.
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