0159 Sleep-wake stability and variability in the middle-aged adult population: a UK Biobank study

Abstract

Abstract Introduction Given the increasing use of consumer, wrist-worn devices with triaxial accelerometry (actigraphy), understanding whether 24-hr activity patterns are associated with specific mental and physical health deficits is of paramount importance. The UK Biobank, a community-based sample of adults in the United Kingdom, provides an opportunity to examine this question given its size (more than 100,000 actigraphy records) and scope (association health records). As a first step in understanding these relationships, understanding the impact of missing data, ways to interpolate missing data, and the general characteristics of 24-hr patterns is necessary. Methods A subset (n=70) of complete (i.e., no missing data) actigraphy records from individuals participating in the UK Biobank were used to examine the impact of missing data on intradaily variability (IV; rhythm fragmentation) and interdaily stability (IS; rhythm regularity). Data were intentionally removed and imputed and the impact of the manipulations was examined with Bland-Altman statistics. After determining the best imputation method and the limits of the method, it was applied to missing data in the full cohort, resulting in 82,840 actigraphy records. These were examined for IV and IS, as well as relative amplitude and the timing of L5 and M10. Data were categorized by age, gender, ethnicity, BMI and material deprivation. ANOVA with eta² effect sizes were used for comparisons. Data shown as median (interquartile range). Results Relative to IV and IS, median and mean imputation methods corrected for missing data of up to 24 hours. Linear imputation of long (>3 hours) missing data worsened IV and IS scores. Non-parametric patterns in the general population were within expected range, with IV=0.89(0.74-1.05), IS=0.55(0.46-0.63), RA=0.90(0.85-0.99), L5-timing=00:52 (00:07-01:37), and M10-timing=08:25(07:40-09:20). Stratifying by gender, age, ethnicity, BMI and material deprivation resulted in significant differences within all groups (p<0.0001), but small effect sizes (0.0019-0.00052). Conclusion Median imputation is useful for correcting for missing data (<24 hours) when examining IV and IS. Non-parametric analysis of the general population indicated values within the expected range. Even though there were significant differences in non-parametric outcomes between genders, age groups, ethnicities, body-mass index and material deprivation, none of these had significant effect sizes. Support (If Any) This research has been conducted using the UK Biobank Resource