William K Wohlgemuth1, Diana A Chirinos2, Samantha Domingo3, Douglas M Wallace4. 1. Sleep Disorders Center, Bruce W. Carter VA Medical Center, Miami, FL, USA. Electronic address: william.wohlgemuth@va.gov. 2. Psychology, University of Miami, Coral Gables, FL, USA. 3. Psychology, Nova Southeastern University, Ft Lauderdale, FL, USA. 4. Sleep Disorders Center, Bruce W. Carter VA Medical Center, Miami, FL, USA; Neurology, University of Miami, Miami, FL, USA.
Abstract
STUDY OBJECTIVES: To examine whether subtypes of continuous positive airway pressure (CPAP) user profiles could be identified, and to determine predictors of CPAP subgroup membership. DESIGN: A retrospective, correlational approach was used. Subjects attended clinic where a CPAP download was performed and questionnaires were completed. Additional information was obtained from the electronic medical record. SETTING: Miami VA Sleep Clinic. PARTICIPANTS: Obstructive sleep apnea patients (N = 207). MEASUREMENTS: Three adherence variables comprised the profile: % of nights of CPAP use, % of nights of CPAP use > 4 hours and average nightly use in minutes. Predictors included age, AHI, time since CPAP therapy was initiated, CPAP pressure, residual AHI, BMI, social-cognitive variables, insomnia, sleepiness, and psychiatric and medical comorbidities. RESULTS: Latent profile analysis was used to identify CPAP user profiles. Three subgroups were identified and labeled "Non-Adherers," "Attempters," and "Adherers". Non-Adherers (37.6% of the sample) used CPAP for an average of 37 minutes nightly, used CPAP 18.2% of nights and used CPAP > 4 hour 6.2 % of nights. Attempters (32.9%) used CPAP for 156 minutes on average, used CPAP 68.2% of nights and used CPAP > 4 hour 29.3% of nights. Adherers (29.5%) used CPAP for 392 minutes, used CPAP 95.4% of nights and used CPAP >4 hour 86.2% of nights. Self-efficacy, insomnia, AHI, time since CPAP was initiated, and CPAP pressure predicted CPAP subgroup membership. CONCLUSION: Sixty-seven percent of users (Non-Adherers, Attempters) had suboptimal adherence. Understanding CPAP use profiles and their predictors enable identification of those who may require additional intervention to improve adherence. Published by Elsevier B.V.
STUDY OBJECTIVES: To examine whether subtypes of continuous positive airway pressure (CPAP) user profiles could be identified, and to determine predictors of CPAP subgroup membership. DESIGN: A retrospective, correlational approach was used. Subjects attended clinic where a CPAP download was performed and questionnaires were completed. Additional information was obtained from the electronic medical record. SETTING: Miami VA Sleep Clinic. PARTICIPANTS: Obstructive sleep apneapatients (N = 207). MEASUREMENTS: Three adherence variables comprised the profile: % of nights of CPAP use, % of nights of CPAP use > 4 hours and average nightly use in minutes. Predictors included age, AHI, time since CPAP therapy was initiated, CPAP pressure, residual AHI, BMI, social-cognitive variables, insomnia, sleepiness, and psychiatric and medical comorbidities. RESULTS: Latent profile analysis was used to identify CPAP user profiles. Three subgroups were identified and labeled "Non-Adherers," "Attempters," and "Adherers". Non-Adherers (37.6% of the sample) used CPAP for an average of 37 minutes nightly, used CPAP 18.2% of nights and used CPAP > 4 hour 6.2 % of nights. Attempters (32.9%) used CPAP for 156 minutes on average, used CPAP 68.2% of nights and used CPAP > 4 hour 29.3% of nights. Adherers (29.5%) used CPAP for 392 minutes, used CPAP 95.4% of nights and used CPAP >4 hour 86.2% of nights. Self-efficacy, insomnia, AHI, time since CPAP was initiated, and CPAP pressure predicted CPAP subgroup membership. CONCLUSION: Sixty-seven percent of users (Non-Adherers, Attempters) had suboptimal adherence. Understanding CPAP use profiles and their predictors enable identification of those who may require additional intervention to improve adherence. Published by Elsevier B.V.
Authors: Bjorg Eysteinsdottir; Thorarinn Gislason; Allan I Pack; Bryndís Benediktsdottir; Erna S Arnardottir; Samuel T Kuna; Erla Björnsdottir Journal: J Sleep Res Date: 2016-12-15 Impact factor: 3.981