TY  - JOUR
AU  - Turino, Cecilia
AU  - Benítez, Ivan D
AU  - Rafael-Palou, Xavier
AU  - Mayoral, Ana
AU  - Lopera, Alejandro
AU  - Pascual, Lydia
AU  - Vaca, Rafaela
AU  - Cortijo, Anunciación
AU  - Moncusí-Moix, Anna
AU  - Dalmases, Mireia
AU  - Vargiu, Eloisa
AU  - Blanco, Jordi
AU  - Barbé, Ferran
AU  - de Batlle, Jordi
PY  - 2021
DA  - 2021/10/18
TI  - Management and Treatment of Patients With Obstructive Sleep Apnea Using an Intelligent Monitoring System Based on Machine Learning Aiming to Improve Continuous Positive Airway Pressure Treatment Compliance: Randomized Controlled Trial
JO  - J Med Internet Res
SP  - e24072
VL  - 23
IS  - 10
KW  - obstructive sleep apnea
KW  - continuous positive airway pressure
KW  - patient compliance
KW  - remote monitoring
KW  - machine learning
AB  - Background: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but treatment compliance is often unsatisfactory. Objective: The aim of this study was to assess the effectiveness and cost-effectiveness of an intelligent monitoring system for improving CPAP compliance. Methods: This is a prospective, open label, parallel, randomized controlled trial including 60 newly diagnosed patients with OSA requiring CPAP (Apnea–Hypopnea Index [AHI] >15) from Lleida, Spain. Participants were randomized (1:1) to standard management or the MiSAOS intelligent monitoring system, involving (1) early compliance detection, thus providing measures of patient’s CPAP compliance from the very first days of usage; (2) machine learning–based prediction of midterm future CPAP compliance; and (3) rule-based recommendations for the patient (app) and care team. Clinical and anthropometric variables, daytime sleepiness, and quality of life were recorded at baseline and after 6 months, together with patient’s compliance, satisfaction, and health care costs. Results: Randomized patients had a mean age of 57 (SD 11) years, mean AHI of 50 (SD 27), and 13% (8/60) were women. Patients in the intervention arm had a mean (95% CI) of 1.14 (0.04-2.23) hours/day higher adjusted CPAP compliance than controls (P=.047). Patients’ satisfaction was excellent in both arms, and up to 88% (15/17) of intervention patients reported willingness to keep using the MiSAOS app in the future. No significant differences were found in costs (control: mean €90.2 (SD 53.14) (US $105.76 [SD 62.31]); intervention: mean €96.2 (SD 62.13) (US $112.70 [SD 72.85]); P=.70; €1=US $1.17 was considered throughout). Overall costs combined with results on compliance demonstrated cost-effectiveness in a bootstrap-based simulation analysis. Conclusions: A machine learning–based intelligent monitoring system increased daily compliance, reported excellent patient satisfaction similar to that reported in usual care, and did not incur in a substantial increase in costs, thus proving cost-effectiveness. This study supports the implementation of intelligent eHealth frameworks for the management of patients with CPAP-treated OSA and confirms the value of patients’ empowerment in the management of chronic diseases. Trial Registration: ClinicalTrials.gov NCT03116958; https://clinicaltrials.gov/ct2/show/NCT03116958 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/10/e24072
UR  - https://doi.org/10.2196/24072
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34661550
DO  - 10.2196/24072
ID  - info:doi/10.2196/24072
ER  -