A latent-class heteroskedastic hurdle trajectory model: patterns of adherence in obstructive sleep apnea patients on CPAP therapy

Personality traits and pre-treatment beliefs and cognitions predicting patient adherence to continuous positive airway pressure: A systematic review

Adherence to Continuous Positive Airway Pressure (CPAP) for obstructive sleep apnoea (OSA) can be improved by behavioural interventions which modify patients' beliefs and cognitions about OSA, CPAP, and themselves. We have conducted the first systematic review of the literature on beliefs and cognitions held before starting treatment, and personality (which influences the former) that predict the decision to purchase or start CPAP, or CPAP adherence one month or more after CPAP initiation. A systematic search and screen of articles identified 21 eligible publications from an initial 1317. Quality assessment performed using an adapted Newcastle-Ottawa Scale demonstrated that 13 (62%) studies were poor quality and only seven (33%) were high quality. Eighteen factors, such as self-efficacy (confidence) in using CPAP and value placed on health predicted CPAP adherence; however, for only six (33%), utility as an intervention target is known, from calculation of individual predictive power. Studies did not use new behavioural frameworks effective at explaining adherence behaviours, nor did they interview patients to collect in-depth data on barriers and facilitators of CPAP use. Future studies cannot have these limitations if high quality evidence is to be generated for intervention development, which is currently sparse as highlighted by this review.

Remote Monitoring of Positive Airway Pressure Data: Challenges, Pitfalls, and Strategies to Consider for Optimal Data Science Applications

Over recent years, positive airway pressure (PAP) remote monitoring has transformed the management of OSA and produced a large amount of data. Accumulated PAP data provide valuable and objective information regarding patient treatment adherence and efficiency. However, the majority of studies that have analyzed longitudinal PAP remote monitoring have summarized data trajectories in static and simplistic metrics for PAP adherence and the residual apnea-hypopnea index by the use of mean or median values. The aims of this article are to suggest directions for improving data cleaning and processing and to address major concerns for the following data science applications: (1) conditions for residual apnea-hypopnea index reliability, (2) lack of standardization of indicators provided by different PAP models, (3) missing values, and (4) consideration of treatment interruptions. To allow fair comparison among studies and to avoid biases in computation, PAP data processing and management should be conducted rigorously with these points in mind. PAP remote monitoring data contain a wealth of information that currently is underused in the field of sleep research. Improving the quality and standardizing data handling could facilitate data sharing among specialists worldwide and enable artificial intelligence strategies to be applied in the field of sleep apnea.