Sleep is vital for healthy development of young children; however, it is not understood how the quality and quantity vary between the weekends and weekdays (WE–WD). Research focused on older children has demonstrated that there is significant WE–WD variability and that this is associated with adiposity. It is unclear how this is experienced among preschoolers. This study explored: (a) the accuracy of WE–WD sleep as reported in parental logbooks compared with accelerometers; (b) the difference between WE and WD total sleep time, sleep efficiency, and timing, as assessed by accelerometers; and (c) the association between the variability of these metrics and adiposity. Eighty-seven preschoolers (M = 46; 4.48 ± 0.89 years) wore an accelerometer on their right hip for 7 days. Parents were given logbooks to track “lights out” times (sleep onset) and out of bed time (sleep offset). Compared with accelerometers, parental logbook reports indicated earlier sleep onset and later sleep offset times on both WEs and WDs. Accelerometer-derived total sleep time, sleep efficiency, and onset/offset were not significantly different on the WEs and WDs; however, a sex effect was observed, with males going to bed and waking up earlier than females. Correlation analyses revealed that variability of sleep onset times throughout the week was positively correlated with percentage of fat mass in children. Results suggest that variability of sleep onset may be associated with increased adiposity in preschool children. Additional research with larger and more socioeconomically and racially diverse samples is needed to confirm these findings.
Bridget Coyle-Asbil, Hannah J. Coyle-Asbil, David W.L. Ma, Jess Haines, and Lori Ann Vallis
Becky Breau, Hannah J. Coyle-Asbil, and Lori Ann Vallis
The purpose of this scoping review was to examine publications using accelerometers in children aged 6 months to <6 years and report on current methodologies used for data collection and analyses. We examined device make and model, device placement, sampling frequency, data collection protocol, definition of nonwear time, inclusion criteria, epoch duration, and cut points. Five online databases and three gray literature databases were searched. Studies were included if they were published in English between January 2009 and March 2021. A total of 627 articles were included for descriptive analyses. Of the reviewed articles, 75% used ActiGraph devices. The most common device placement was hip or waist. More than 80% of articles did not report a sampling frequency, and 7-day protocols during only waking hours were the most frequently reported. Fifteen-second epoch durations and the cut points developed by Pate et al. in 2006 were the most common. A total of 203 articles did not report which definition of nonwear time was used; when reported, “20 minutes of consecutive zeros” was the most frequently used. Finally, the most common inclusion criteria were “greater or equal to 10 hr/day for at least 3 days” for studies conducted in free-living environments and “greater than 50% of the school day” for studies conducted in preschool or childcare environments. Results demonstrated a major lack of reporting of methods used to analyze accelerometer data from young children. A list of recommended reporting practices was developed to encourage increased reporting of key methodological details for research in this area.
Hannah J. Coyle-Asbil, Anuj Bhatia, Andrew Lim, and Mandeep Singh
Individuals suffering from neuropathic pain commonly report issues associated with sleep. To measure sleep in this population, researchers have used actigraphy. Historically, actigraphy data have been analyzed in the form of counts; however, due to the proprietary nature, many opt to quantify data in its raw form. Various processing techniques exist to accomplish this; however, it remains unclear how they compare to one another. This study sought to compare sleep measures derived using the GGIR R package versus the GENEActiv (GA) R Markdown tool in a neuropathic pain population. It was hypothesized that the processing techniques would yield significantly different sleep outcomes. One hundred and twelve individuals (mean age = 52.72 ± 13.01 years; 60 M) with neuropathic pain in their back and/or lower limbs were included. While simultaneously undergoing spinal cord stimulation, actigraphy devices were worn on the wrist for a minimum of 7 days (GA; 50 Hz). Upon completing the protocol, sleep outcome measures were calculated using (a) the GGIR R package and (b) the GA R Markdown tool. To compare these algorithms, paired-samples t tests and Bland–Altman plots were used to compare the total sleep time, sleep efficiency, wake after sleep onset, sleep onset time, and rise times. According to the paired-samples t test, the GA R Markdown yielded lower total sleep time and sleep efficiency and a greater wake after sleep onset, compared with the GGIR package. Furthermore, later sleep onset times and earlier rise times were reported by the GGIR package compared with the GA R Markdown.
Becky Breau, Hannah J. Coyle-Asbil, Jess Haines, David W.L. Ma, Lori Ann Vallis, and on behalf of the Guelph Family Health Study
Purpose: Examine the effect of cutpoint selection on physical activity (PA) metrics calculated from young children’s accelerometer data and on the proportion of children meeting PA guidelines. Methods: A total of 262 children (3.6 ± 1.4 years, 126 males) wore ActiGraph wGT3X-BT accelerometers on their right hip for 7 days, 24 hr/day. Ten cutpoint sets were applied to the sample categorized by age, based on populations of the original cutpoint calibration studies using ActiLife software. Resulting sedentary behavior, light PA, moderate to vigorous PA, and total PA were compared using repeated-measures analysis of variance. Proportion of children meeting age-appropriate PA guidelines based on each cutpoint set was assessed using Cochran’s q tests. Results: Children wore the accelerometer for an average of 7.6 ± 1.2 days for an average of 11.9 ± 1.2 hr/day. Significant differences in time spent in each intensity were found across all cutpoints except for sedentary, and total PA for three comparisons (Trost vs. Butte Vertical Axis [VA], Pate vs. Puyau, and Costa VA vs. Evenson) and moderate to vigorous PA for four comparisons (Trost vs. Pate, Trost vs. Pate and Pfeiffer, Pate vs. Pate and Pfeiffer, and van Cauwenberghe vs. Evenson). When examined within age-appropriate groups, all sets of cutpoints resulted in significant differences across all intensities and in the number of children meeting PA guidelines. Conclusion: Choice of cutpoints applied to data from young children significantly affects times calculated for different movement intensities, which in turn impacts the proportion of children meeting guidelines. Thus, comparisons of movement intensities should not be made across studies using different sets of cutpoints.