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Modelling adherence behaviour for the treatment of obstructive sleep apnoea

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

Abstract

Continuous positive airway pressure therapy (CPAP) is known to be the most efficacious treatment for obstructive sleep apnoea (OSA). Unfortunately, poor adherence behaviour in using CPAP reduces its effectiveness and thereby also limits beneficial outcomes. In this paper, we model the dynamics and patterns of patient adherence behaviour as a basis for designing effective and economical interventions. Specifically, we define patient CPAP usage behaviour as a state and develop Markov models for diverse patient cohorts in order to examine the stochastic dynamics of CPAP usage behaviours. We also examine the impact of behavioural intervention scenarios using a Markov decision process (MDP), and suggest a guideline for designing interventions to improve CPAP adherence behaviour. Behavioural intervention policy that addresses economic aspects of treatment is imperative for translation to clinical practice, particularly in resource-constrained environments that are clinically engaged in the chronic care of OSA.

Suggested Citation

  • Kang, Yuncheol & Sawyer, Amy M. & Griffin, Paul M. & Prabhu, Vittaldas V., 2016. "Modelling adherence behaviour for the treatment of obstructive sleep apnoea," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1005-1013.
    • Handle: RePEc:eee:ejores:v:249:y:2016:i:3:p:1005-1013
    DOI: 10.1016/j.ejor.2015.07.038
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    Cited by:

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    2. Saligrama Agnihothri & Leon Cui & Mohammad Delasay & Balaraman Rajan, 2020. "The value of mHealth for managing chronic conditions," Health Care Management Science, Springer, vol. 23(2), pages 185-202, June.

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