ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2025
Novel assessment of CPAP adherence data reveals distinct diurnal patterns.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
STUDY
objectivesObstructive 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.
methodsMachine 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.
resultsEach 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.
conclusionsThis 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.
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