Measuring the impact of apnea and obesity on circadian activity patterns using functional linear modeling of actigraphy data

Abstract

BackgroundActigraphy provides a way to objectively measure activity in human subjects. This paper describes a novel family of statistical methods that can be used to analyze this data in a more comprehensive way.MethodsA statistical method for testing differences in activity patterns measured by actigraphy across subgroups using functional data analysis is described. For illustration this method is used to statistically assess the impact of apnea-hypopnea index (apnea) and body mass index (BMI) on circadian activity patterns measured using actigraphy in 395 participants from 18 to 80 years old, referred to the Washington University Sleep Medicine Center for general sleep medicine care. Mathematical descriptions of the methods and results from their application to real data are presented.ResultsActivity patterns were recorded by an Actical device (Philips Respironics Inc.) every minute for at least seven days. Functional linear modeling was used to detect the association between circadian activity patterns and apnea and BMI. Results indicate that participants in high apnea group have statistically lower activity during the day, and that BMI in our study population does not significantly impact circadian patterns.ConclusionsCompared with analysis using summary measures (e.g., average activity over 24 hours, total sleep time), Functional Data Analysis (FDA) is a novel statistical framework that more efficiently analyzes information from actigraphy data. FDA has the potential to reposition the focus of actigraphy data from general sleep assessment to rigorous analyses of circadian activity rhythms.

The Relationship Between Affective State and the Rhythmicity of Activity in Bipolar Disorder

“…For example, the use of functional principal components analysis may allow for a more refined quantification of the actigraph data and the flexibility to search for functions representing activity patterns that may distinguish between subgroups and between-subject variability in bipolar patlents. 24 …”

Genetic contributions to circadian activity rhythm and sleep pattern phenotypes in pedigrees segregating for severe bipolar disorder

“…S2), we analyzed activity data from 558 individuals, including 136 BP-I individuals and 422 of their non-BP-I relatives (Table 1). A series of algorithms obtained from published sources (14)(15)(16)(17) were then applied to the activity data to obtain 116 quantitative sleep and activity phenotypes. These phenotypes can be classified into six broad domains that quantified patterns of activity and sleep during the major rest period of the day (i) and during the awake period (ii), the fragmentation or consolidation of activity (iii), overall activity levels (iv), and the fit of daily activity patterns to curves based on sine and cosine functions (using two different approaches) (v and vi).…”

“…S5 and S6). We analyzed sleep parameters using a script written in R, based on published Respironics definitions and algorithms (14)(15)(16)(17). R scripts are available upon request.…”

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“…Functional linear modeling (FLM) was used to characterize and illustrate 24‐hr sleep‐wake patterns. This approach, specifically designed for actigraphy time‐series data analysis, measures raw activity counts within and between samples, and avoids summary statistics that can mask differences across groups (Wang et al, ). FLM was used to compare activity patterns between the Hadza and an equatorial small‐scale, natural‐light, agricultural society in Madagascar that has previously been assessed as having nocturnally bi‐phasic sleep (Samson, Manus, et al, ).…”