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Sleep Apnea

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Updated: Apr 9, 2026

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Rethinking CPAP Adherence with Artificial Intelligence.

Umaer Hanif^1,2,3, Jeanne Vitrac⁴, Pedro Marinho¹

¹Research and Development Department, Data Science Unit, SOS Oxygene, Nice, France.

|April 8, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) can personalize continuous positive airway pressure (CPAP) therapy for obstructive sleep apnea. AI improves patient adherence by predicting needs and enabling tailored interventions, moving beyond one-size-fits-all approaches.

Keywords:

Adherence Artificial Intelligence CPAP OSA Personalized Medicine

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Area of Science:

Sleep Medicine
Artificial Intelligence in Healthcare
Digital Health

Background:

Obstructive sleep apnea (OSA) significantly impacts quality of life and increases mortality risk.
Continuous positive airway pressure (CPAP) is the primary treatment, but poor patient adherence limits its effectiveness.
Current CPAP management lacks personalization, leading to suboptimal outcomes and increased costs.

Purpose of the Study:

To review the application of artificial intelligence (AI) in managing CPAP adherence for OSA patients.
To synthesize evidence on AI-driven approaches for individualized CPAP therapy.
To explore AI's potential in improving patient adherence and clinical outcomes.

Main Methods:

A narrative review of 60 peer-reviewed studies (2017-2025) focusing on AI and CPAP adherence.
Analysis of AI applications in patient phenotyping, adherence prediction, and digital intervention tools.
Synthesis of findings related to unsupervised learning, supervised models, and AI-enabled monitoring.

Main Results:

AI approaches demonstrate consistent predictability in identifying adherence patterns.
Early identification of adherence trends supports targeted, proactive interventions.
AI facilitates personalized, behaviorally informed CPAP care.

Conclusions:

AI offers a promising strategy to overcome limitations in current CPAP management.
A shift towards individualized, AI-driven CPAP care is recommended.
Future research should focus on clinical validation, interpretability, and real-world integration of AI tools.