Time series analysis of treatment adherence patterns in individuals with obstructive sleep apnea.
Adherence to medical recommendations is often suboptimal, making examination of adherence data an important scientific concern. Studies that attempt to predict or modify adherence often face the problem that adherence as a dependent variable is complex and non-normally distributed. Traditional statistical approaches to adherence data may mask individual variability that may guide clinician and researcher's development of adherence interventions. In this study, we employ time series analysis to examine adherence patterns objectively in patients with obstructive sleep apnea (OSA). Although treatment adherence is poor in OSA, state-of-the-art adherence monitoring allows a comprehensive examination of objective data. The purpose of the study is to determine the number and types of adherence patterns seen in a sample of patients with OSA receiving positive airway pressure (PAP). Seventy-one moderate to severe OSA participants with 365 days of treatment data were studied. Adherence patterns could be classified into seven categories: (1) Good Users (24%), (2) Slow Improvers (13%), (3) Slow Decliners (14%), (4) Variable Users (17%), (5) Occasional Attempters (8%), (6) Early Drop-outs (13%), and (7) Non-Users (11%). Time series analysis provides a useful method for examining adherence while maintaining a focus on individual differences. Implications for future research are discussed.