Comparison of longitudinal diary and actigraphy-assessed sleep in pregnant women

Study objectives: Various methods are employed to assess sleep in pregnant women, including self report, sleep diary, and actigraphy. Unfortunately, the data are often contradictory, and interpretations are often inconsistent. The current aims are to compare subjective and objective sleep data in pregnant women collected longitudinally in early pregnancy. Methods: In this secondary analysis of 104 pregnant women, sleep was collected via diary and actigraphy for 14 days during three separate occasions (10-12 weeks; 14-16 weeks; and 18-20 weeks). Sleep variables included wake after sleep onset (WASO), sleep efficiency (SE), bedtime/lights out, sleep onset latency (SL), and total sleep duration (TST). Repeated measures ANOVAs compared each sleep variable across Time and by Method of data collection, while controlling for parity and daytime naps. Results: Significant differences were noted for only the method of data collection for sleep component studied for WASO, F (1, 98) =147.20, p < 0.001; SE, F (1, 98) =129.41, p < 0.001); bedtime/lights out, F (1, 103) = 5.33, p < 0.05); and sleep duration, F (1, 104) = 182.75, p < 0.001). Significant variation was not seen in any variable across time-period. Conclusions: There are substantial discrepancies between diary and actigraphy-assessed sleep measures in pregnant women which is in alignment with previous literature. These data highlight that these methodologies assess different constructs. We contend that these data may be useful as a reference to compare high-risk women or those with sleep disorders. Using a multi-modal approach to identify sleep disturbance in pregnancy is likely a more clinically useful option. (c) 2021 Published by Elsevier B.V.

Automated summary

The study compared subjective and objective sleep data in pregnant women, finding significant differences in sleep components assessed by diary and actigraphy. However, no significant variations were observed across different time periods for any sleep variable. This highlights the importance of using a multi-modal approach to identify sleep disturbances in pregnancy.