Markov models for treatment adherence in obstructive sleep apnea

Yuncheol Kang, Vittaldas V. Prabhu, Amy M. Sawyer, Paul M. Griffin

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations


Nonadherence to continuous positive airway pressure therapy (CPAP) in patients with obstructive sleep apnea (OSA) hinders effective treatment of this type of sleep disorder. Markov models can be a useful tool to analyze such behavior by revealing the underlying dynamics. In this paper, we build Markov models based on data obtained from CPAP devices and examine how such dynamics of adherence change over time. Specifically, we define CPAP usage level as a state, and estimate transition probabilities among the states from the CPAP usage data to build a Markov chain. For comparison purposes, we also classify subjects into subgroups in terms of descriptive information or clustering results, and build a Markov chain for each group. By revealing such dynamics, we hope to obtain insights for developing strategies for improving CPAP adherence.

Original languageEnglish
Number of pages8
StatePublished - 2013
EventIIE Annual Conference and Expo 2013 - San Juan, Puerto Rico
Duration: 18 May 201322 May 2013


ConferenceIIE Annual Conference and Expo 2013
Country/TerritoryPuerto Rico
CitySan Juan


  • Continuous positive airway pressure therapy (CPAP)
  • Markov models
  • Obstructive sleep apnea (OSA)


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