ArticleFrontiers in neurology2022
Identifying longitudinal patterns of CPAP treatment in OSA using growth mixture modeling: Disease characteristics and psychological determinants.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Annual Versus Patient-Initiated Follow-Up in Sleep Apnoea Care: A Quasi-Experimental Study.Journal of sleep research · 2026Article
- Exploring the treatment journey of obstructive sleep apnoea: a scoping review on the perspectives of Chinese patients.Journal of thoracic disease · 2025Review
- Patterns and stability of long-term adherence in continuous positive airway pressure therapy for obstructive sleep apnea: a cohort study.Sleep & breathing = Schlaf & Atmung · 2025Article
- Article
- Development and Validation of a Nomogram for Predicting Non-Adherence to Continuous Positive Airway Pressure Therapy in Patients with Obstructive Sleep Apnea.Nature and science of sleep · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we aim to identify the distinct subtypes of continuous positive airway pressure (CPAP) user profiles based on the telemedicine management platform and to determine clinical and psychological predictors of various patterns of adherence. A total of 301 patients used auto-CPAP (Autoset 10, Resmed Inc.) during the treatment period. Four categories of potential predictors for CPAP adherence were examined: (1) demographic and clinical characteristics, (2) disease severity and comorbidities, (3) sleep-related health issues, and (4) psychological evaluation. Then, growth mixture modeling was conducted using Mplus 8.0 to identify the unique trajectories of adherence over time. Adherence data were collected from the telemedicine management platform (Airview, Resmed Inc.) during the treatment. Three novel subgroups were identified and labeled "adherers" (53.8% of samples, intercept = 385, slope = -51, high mean value, negative slope and moderate decline), "Improvers" (18.6%, intercept = 256, slope = 50, moderate mean value, positive slope and moderate growth) and "non-adherers" (27.6%, intercept = 176, slope = -31, low mean value, negative slope and slight decline). The comorbidities associated with OSA and the apnea-hypopnea index (AHI), which reflects the objective severity of the disease, did not differ significantly among the subgroups. However, "improvers" showed higher levels of daytime sleepiness (8.1 ± 6.0 vs. 12.1 ± 7.0 vs. 8.0 ± 6.1 in SWIFT,
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