CPAP Adherence Improvement for OSA Patients Through Integrated Feedback Systems
This study presents a groundbreaking strategy for the homecare management of Obstructive Sleep Apnea (OSA). With an emphasis on patient empowerment through the integration of feedback management systems into sleep treatment, this study offers an innovative approach to the homecare management of obstructive sleep apnea (OSA). Fundamentally the research attempts to significantly increase, via tailored interventions, adherence to Continuous Positive Airway Pressure (CPAP) therapy. It accomplishes this by combining qualitative patient feedback with quantitative CPAP machine monitoring data to improve patient clustering and in turn treatment outcomes. The research methodology is comprehensive, encompassing various stages that include advanced patient grouping, continuous incorporation of new patient data, integration of feedback from surveys on both intervention and medical sleep, and a thorough cycle of interventions and evaluations. This iterative refinement process is essential as it allows for the dynamic updating of patient profiles and clustering based on evolving data and treatment responses. All these efforts are focused on fostering a more tailored approach to patient care. The creation of patient-centered treatment plans that maximize treatment efficacy by utilizing intervention repositories and data analytics is at the heart of this research. The study also explores how personalized care can improve CPAP adherence and underscores the need to tailor interventions and content based on patient feedback. This multi-layered approach aims at improving patient treatment adherence. It creates a more efficient patient-centered care model for individuals with OSA by continuously adapting and personalizing the interventions provided to each patient, thereby fostering a stronger relationship between patients and their treatments.
Authors
- Néjib Moalla (ORCID: https://orcid.org/0000-0003-4806-0320)
- Aïcha Sekhari (ORCID: https://orcid.org/0000-0002-1184-1433)
- Tao Wang (ORCID: https://orcid.org/0000-0002-4765-9377)
- Olivier Grasset (ORCID: https://orcid.org/0009-0001-9233-1231)
- Yasaman Kakaei Siahkal
Institutions
- Université Claude Bernard Lyon 1 (FR)
- Université Lumière Lyon 2 (FR)
- Vinci (France) (FR)
- Decision & Information Sciences for Production Systems (FR)
- Institut National des Sciences Appliquées de Lyon (FR)
- Université Jean Monnet (FR)
Publication Details
- Published
- 2024-11-10
- DOI
- https://doi.org/10.1109/bhi62660.2024.10913908
- Citations
- 2
- Primary Topic
- Obstructive Sleep Apnea Research
- Type
- article
- Field-Weighted Citation Impact
- 0.65