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Prenatal alcohol-exposed (AE) infants and children often demonstrate disrupted sleep patterns, including more frequent awakenings, reduced total sleep time, and more night-to-night sleep variability. Despite the strong connection between sleep patterns and circadian rhythmicity, relatively little is known about circadian rhythm disruptions in individuals with AE. Recently, several reports demonstrated that evaluating the expression patterns of human clock genes in biological fluids could reveal an individual's circadian phenotype. Human saliva offers an emerging and easily available physiological sample that can be collected non-invasively for core-clock gene transcript analyses. We compared the expression patterns of core-clock genes and their regulatory genes in salivary samples of children aged 6-10 years-old with and without AE during the light cycle between ZT0-ZT11. We isolated the RNA from the samples and measured the expression patterns of core clock genes and clock regulating genes using the human specific primers with quantitative real-time PCR. Analysis of core clock genes expression levels in saliva samples from AE children indicates significantly altered levels in expression of core-clock BMAL1, CLOCK, PER1-3 and CRY1,2, as compared to those in age-matched control children. We did not find any sex difference in levels of clock genes in AE and control groups. Cosinor analysis was used to evaluate the rhythmic pattern of these clock genes, which identified circadian patterns in the levels of core clock genes in the control group but absent in the AE group. The gene expression profile of a salivary circadian biomarker ARRB1 was rhythmic in saliva of control children but was arhythmic in AE children. Altered expression patterns were also observed in clock regulatory genes: NPAS2, NFL3, NR1D1, DEC1, DEC2, and DBP, as well as chromatin modifiers: MLL1, P300, SIRT1, EZH2, HDAC3, and ZR1D1, known to maintain rhythmic expression of core-clock genes. Overall, these findings provide the first evidence that AE disturbs the circadian patten expression of core clock genes and clock-regulatory chromatin modifiers in saliva., (© 2024. The Author(s).)

Purpose: This study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children., Methods: 59 healthy boys and girls aged 9-11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory- and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis., Results: Fitbit Charge HR underestimated steps (~11.8 steps per minute), heart rate (~3.58 bpm), and metabolic equivalents (~0.55 METs per minute) and overestimated energy expenditure (~0.34 kcal per minute) relative to research-grade measures (p< 0.05). The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) (sensitivity MVPA: 85.4%; specificity SLPA: 83.1%). Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8% sensitivity in classifying sleep and 56.3% specificity in classifying wake epochs., Conclusions: Fitbit Charge HR had adequate sensitivity in classifying moderate and vigorous intensity physical activity and sleep, but had limitations in detecting wake, and was more accurate in detecting heart rate during sleep than during exercise, in healthy children. Further research is needed to understand potential challenges and limitations of these consumer devices., Competing Interests: The authors have declared that no competing interests exist.

Wearable fitness-tracker devices are becoming increasingly available. We evaluated the agreement between Jawbone UP and polysomnography (PSG) in assessing sleep in a sample of 28 midlife women. As shown previously, for standard actigraphy, Jawbone UP had high sensitivity in detecting sleep (0.97) and low specificity in detecting wake (0.37). However, it showed good overall agreement with PSG with a maximum of two women falling outside Bland-Altman plot agreement limits. Jawbone UP overestimated PSG total sleep time (26.6 ± 35.3 min) and sleep onset latency (5.2 ± 9.6 min), and underestimated wake after sleep onset (31.2 ± 32.3 min) (p's < 0.05), with greater discrepancies in nights with more disrupted sleep. The low-cost and wide-availability of these fitness-tracker devices may make them an attractive alternative to standard actigraphy in monitoring daily sleep-wake rhythms over several days.