Abstract: Identifying Longitudinal Patterns of Adherence to Treatment for Obstructive Sleep Apnea

Increasing adherence to medical recommendations is crucial for improving health outcomes and reducing costs of health care. To improve adherence, we have to better understand behavior change over time. The focus of this study was adherence to treatment for obstructive sleep apnea (OSA). Adherence to positive airway pressure (PAP), the most common treatment for OSA, is poor. This study involved an international sample of 161 participants, each with approximately 180 nights of data, and had three phases. First, a separate time series analysis was performed for each individual. Time series parameters included the mean (average hours of use per night), level (the intercept), slope (the rate of change over time), variance (variability in use), and autocorrelation (a measure of dependency). Second, a dynamic cluster analysis was performed to find homogenous subgroups of individuals with similar adherence patterns. A four-cluster solution was found, and the subgroups were labeled (see Figure 1 ): Great Users (17.2%; high mean and level, no slope), Good Users (32.8%; moderate mean and level, no slope), Poor Users (22.7%; low mean and level, negative slope), and Slow Decliners (moderate mean and level, negative slope, high variance). Third, participants in the identified subgroups were compared on a number of variables that were not involved in the clustering to establish external validity. Some notable findings at later time points include the following: Great Users reported the most self-efficacy (confidence to use PAP), Poor Users reported the most sleepiness, and Great Users reported the highest quality of sleep. Combining time series analysis and dynamic cluster analysis is a useful way to evaluate adherence patterns at both the individual level and subgroup level. Psychological variables relevant to adherence patterns, such as self-efficacy, could be the focus of interventions to increase PAP usage.