Accelerometry-assessed sleep duration and timing in late childhood and adolescence in Scottish schoolchildren: A feasibility study

Abstract

Children and adolescents commonly suffer from sleep and circadian rhythm disturbances, which may contribute to poorer mental health and wellbeing during this critical developmental phase. Many studies however rely on self-reported sleep measures. This study assessed whether accelerometry data collection was feasible within the school setting as a method for investigating the extent of sleep and circadian disruption, and associations with subjective wellbeing, in Scotland. Fourteen days of wrist-worn accelerometry data were collected from 69 pupils, aged 10–14 years. Objective measures of sleep timing, sleep duration and circadian rest-activity patterns were derived. Questionnaires assessed subjective sleep timing, depressive symptoms, and experiences of wearing the accelerometer. Pupils slept on average less than 8 hours per night, failing to meet standard age-specific recommendations. Sleep timing was later and duration longer on weekends compared to weekdays (B = 0.87, 95% confidence interval (CI) 0.70, 1.04; B = 0.49, 95% CI 0.29, 0.69), indicating social jetlag. Lower daytime activity was correlated with higher depressive symptoms (r = -0.84, p = 0.008). Compared to primary school pupils, secondary pupils had shorter sleep window duration and lower circadian relative amplitude. Over half of participants reported some discomfort/inconvenience wearing the accelerometer. These data highlight that inadequate sleep is prevalent in this sample of schoolchildren. Future, larger scale investigations will examine in more detail the associations between sleep, circadian function and physical activity with mental health and wellbeing.

Associations of social jetlag with physical activity and sedentary behaviour in children and adolescents: A systematic review and meta‐analysis

“…Twenty‐one studies assessed physical activity and 14 studies assessed sedentary behaviour exclusively, while 17 studies measured both of them. Device‐based measurement was applied for physical activity in nine studies (Jansen et al, 2020; Johnson et al, 2020; Lyall et al, 2020; Martoni et al, 2016; Merikanto et al, 2020; Mitchell et al, 2017; Moreno et al, 2021; Quante et al, 2019; Skjåkødegård et al, 2021). Most of the studies assessed screen media use using subjective measures, except for two studies which examined the association between accelerometer‐measured overall sedentary behaviour (time) and chronotype (Merikanto et al, 2020; Moreno et al, 2021).…”

“…Eleven studies that examined the relationship between social jetlag and physical activity were eligible for inclusion in meta‐analysis (Aoki et al, 2020; Chandrakar, 2017; de Zwart et al, 2018; Jankovic et al, 2022; Jansen et al, 2020; Johnson et al, 2020; Kohyama, 2020; Liang et al, 2022; Lyall et al, 2020; Máchal et al, 2018; Skjåkødegård et al, 2021). Three of them targeted adolescents only (Chandrakar, 2017; de Zwart et al, 2018; Liang et al, 2022) and the others included both children and adolescents aged 6 years or older.…”

“…Three of them targeted adolescents only (Chandrakar, 2017; de Zwart et al, 2018; Liang et al, 2022) and the others included both children and adolescents aged 6 years or older. A variety of physical activity outcomes were reported, including moderate‐to‐vigorous physical activity (MVPA) (Aoki et al, 2020; Jansen et al, 2020; Johnson et al, 2020; Liang et al, 2022; Skjåkødegård et al, 2021), average activity during the most active 10 h (Lyall et al, 2020), outdoor activity (Chandrakar, 2017), days of having physical activity engagement (Kohyama, 2020), sport participation (Máchal et al, 2018), physical activity score (de Zwart et al, 2018), and physical activity related energy expenditure (Jankovic et al, 2022). Three studies (Chandrakar, 2017; Jankovic et al, 2022; Kohyama, 2020) were rated as low quality of evidence.…”

Evaluating Outdoor Nature-Based Early Learning and Childcare Provision for Children Aged 3 Years: Protocol of a Feasibility and Pilot Quasi-Experimental Design

“…Sleep will be estimated by calculating the sleep period time (SPT) frame, the time window from initial sleep onset and waking up after the last sleep episode of the night, based on z-angle variance [ 65 , 66 ]. Using this, the average time of sleep onset and waking (beginning and end of SPT window) will be calculated.…”

“…Using this, the average time of sleep onset and waking (beginning and end of SPT window) will be calculated. The total period of continuous inactivity periods (no change in z-angle of >5° for a minimum of 5 min) within the SPT-window will be calculated to estimate sleep duration per night, then averaged across available nights [ 66 ]. A minimum wear time of 3 complete days will be required for analysis.…”