ArticleBiosensors2024
Evaluation of Photoplethysmography-Based Monitoring of Respiration Rate During High-Intensity Interval Training: Implications for Healthcare Monitoring.
Article in Biosensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Respiratory Monitoring in Motion: An Overview of Wearable Methods and Algorithmic Approaches for Reliable Assessment.Biosensors · 2026Review
- Continuous monitoring of critically ill patients using photoplethysmography-the road to a less invasive ICU monitoring.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Monitoring respiration rate (RR) is crucial in various healthcare settings, particularly during demanding (physical) activities where respiratory dynamics are critical indicators of health status. This study aimed to evaluate the accuracy of photoplethysmography (PPG)-based monitoring of RR during high-intensity interval training (HIIT) and its potential applications in healthcare. Between January and March 2024, healthy volunteers participated in a cycling HIIT session with increasing resistance levels. The RR measurements obtained using the PPG-based CardioWatch 287-2 (Corsano Health) were compared with an ECG patch-derived (Vivalink) reference. Subgroup analyses were conducted based on skin type and sex. A total of 35 participants contributed 1794 paired RR measurements. The PPG algorithm for RR monitoring showed an average root mean square (Arms) error of 2.13 breaths per minute (brpm), a bias of -0.09 brpm, and limits of agreement (LoA) from -4.28 to 4.09 brpm. Results were consistent across the different demographic subgroups. The CardioWatch 287-2 therefore demonstrated reliable RR monitoring during HIIT, supporting its potential use in healthcare settings for continuous, non-invasive respiratory monitoring, particularly in physical rehabilitation and chronic respiratory condition management.
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Registered trials
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