Comparison of actigraphy‐measured and parent‐reported sleep in association with weight status among preschool children

Abstract

SummaryThis study compared weekday and weekend actigraphy‐measured and parent‐reported sleep in relation to weight status among preschool‐aged children. Participants were 3–6 years old preschoolers from the cross‐sectional DAGIS‐study with sleep data for ≥2 weekday and ≥2 weekend nights. Parents‐reported sleep onset and wake‐up times were gathered alongside 24 h hip‐worn actigraphy. An unsupervised Hidden‐Markov Model algorithm provided actigraphy‐measured night time sleep without the guidance of reported sleep times. Waist‐to‐height ratio and age‐and‐sex‐specific body mass index characterised weight status. Comparison of methods were assessed with consistency in quintile divisions and Spearman correlations. Associations between sleep and weight status were assessed with adjusted regression models. Participants included 638 children (49% girls) with a mean ± SD age of 4.76 ± 0.89. On weekdays, 98%–99% of actigraphy‐measured and parent‐reported sleep estimates were classified in the same or adjacent quintile and were strongly correlated (rs = 0.79–0.85, p < 0.001). On weekends, 84%–98% of actigraphy‐measured and parent‐reported sleep estimates were respectively classified and correlations were moderate to strong (rs = 0.62–0.86, p < 0.001). Compared with actigraphy‐measured sleep, parent‐reported sleep had consistently earlier onset, later wake‐up, and greater duration. Earlier actigraphy‐measured weekday sleep onset and midpoint were associated with a higher body mass index (respective β‐estimates: −0.63, p < 0.01 and −0.75, p < 0.01) and waist‐to‐height ratio (−0.004, p = 0.03 and −0.01, p = 0.02). Though the sleep estimation methods were consistent and correlated, actigraphy measures should be favoured as they are more objective and sensitive to identifying associations between sleep timing and weight status compared with parent reports.

Association of the timing of evening eating with BMI Z-score and waist-to-height ratio among preschool-aged children in Finland

“…Moreover, even though several lifestyle factors were adjusted for in the current study, disentangling the independent effects of later eating on the development of overweight/obesity is difficult, since numerous aspects in eating and in other energy balance-related behaviours also have an impact and may in fact have synergistic effects. Furthermore, regarding the present study, it could be speculated whether the association between evening latency and WHtR was driven more by sleep onset than the timing of eating; higher evening latency in this sample may have been more dependent on later sleep onset than the time of the last EO, and later sleep onset on weekdays was associated with lower BMI and WHtR in a previous study using the current sample (34) . Additionally, there was a significant unadjusted association between later chronotype and lower BMI Z-score in this study.…”

“…Specifically, the performance of a hip-worn actigraphy in measuring sleep is still unclear since the assessment of sleep is usually done with a wrist actigraphy. However, at least one study demonstrating good validity in children exists (62) , and the HMM-based algorithm used in the present study appears promising for hip placement as well (34) .…”

“…Children who had data for less than 16 h/d (from a noon-to-noon window) were excluded. The application of the HMM algorithm to the current data has been described in more detail in a previous paper (34) . Regarding wrist-worn actigraphy, the algorithm has been validated against polysomnography in adults (32) .…”

How energy balance-related behaviours, temperament, stress and overweight associate: a cross-sectional study of Finnish preschoolers

“…To identify the sleep and wake states and to infer sleep onset, wake-up time and sleep duration, we obtained sleep estimates from the accelerometers by applying a hidden Markov model algorithm. The accelerometer-derived sleep estimates have been described earlier and are more sensitive at recognising associations between sleep and weight status compared with parental reports ( 35 ) . Sleep duration was defined as the difference between sleep onset and wake-up time (h), and sleep efficiency was calculated as the time spent asleep divided by total sleep duration.…”