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. 2025 Mar 1;21(3):493-502.
doi: 10.5664/jcsm.11446.

Novel assessment of CPAP adherence data reveals distinct diurnal patterns

Matthew T Scharf  1   2 , Ioannis P Androulakis  3   4   5
Affiliations

Novel assessment of CPAP adherence data reveals distinct diurnal patterns

Matthew T Scharf et al. J Clin Sleep Med. .

Abstract

Study objectives: Obstructive sleep apnea is a prevalent condition effectively treated by continuous positive airway pressure (CPAP) therapy. CPAP adherence data, routinely gathered in clinical practice, include detailed information regarding both duration and timing of use. The purpose of the present study was to develop a systematic way to measure the diurnal pattern of CPAP adherence data and to see if distinct patterns exist in a clinical cohort.

Methods: Machine learning techniques were employed to analyze CPAP adherence data. A cohort of 200 unselected patients was assessed and a cluster analysis was subsequently performed. Application of this methodology to 17 patients with different visually noted patterns was carried out to further assess performance.

Results: Each 30-day period of CPAP use for each patient was characterized by 4 variables describing the time of day of initiation and discontinuation of CPAP use, as well as the consistency of use during those times. Further analysis identified 6 distinct clusters, reflecting different timing and adherence patterns. Specifically, clusters with relatively normal timing vs delayed timing were identified. Finally, application of this methodology showed generally good performance with limitations in the ability to characterize shift worker and non-24 rhythms.

Conclusions: This study demonstrates a methodology for analysis of diurnal patterns from CPAP adherence data. Furthermore, distinct timing and adherence patterns are demonstrated. The potential impact of these patterns on the beneficial effects of CPAP requires elucidation.

Citation: Scharf MT, Androulakis IP. Novel assessment of CPAP adherence data reveals distinct diurnal patterns. J Clin Sleep Med. 2025;21(3):493-502.

Keywords: CPAP; circadian; diurnal; machine learning; obstructive sleep apnea.

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Conflict of interest statement

All authors have seen and approved the manuscript. This work was performed at Rutgers University. The authors report no conflicts of interest.

References

    1. Senaratna CV , Perret JL , Lodge CJ , et al. . Prevalence of obstructive sleep apnea in the general population: a systematic review . Sleep Med Rev. 2017. ; 34 : 70 – 81 . - PubMed
    1. Wickwire EM . Value-based sleep and breathing: health economic aspects of obstructive sleep apnea . Fac Rev. 2021. ; 10 : 40 . - PMC - PubMed
    1. Johns MW . A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale . Sleep. 1991. ; 14 ( 6 ): 540 – 545 . - PubMed
    1. Stranks EK , Crowe SF . The cognitive effects of obstructive sleep apnea: an updated meta-analysis . Arch Clin Neuropsychol. 2016. ; 31 ( 2 ): 186 – 193 . - PubMed
    1. Young T , Blustein J , Finn L , Palta M . Sleep-disordered breathing and motor vehicle accidents in a population-based sample of employed adults . Sleep. 1997. ; 20 ( 8 ): 608 – 613 . - PubMed

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