CC BY-NC-ND 4.0 · Sports Med Int Open 2018; 02(01): E9-E15
DOI: 10.1055/s-0043-122081
Clinical Sciences
Eigentümer und Copyright ©Georg Thieme Verlag KG 2018

Inter-Day Reliability of Finapres® Cardiovascular Measurements During Rest and Exercise

Mark Waldron
1   St Mary's University Twickenham London, School of Sport, Health and Applied Sci, London, United Kingdom of Great Britain and Northern Ireland
Stephen David Patterson
1   St Mary's University Twickenham London, School of Sport, Health and Applied Sci, London, United Kingdom of Great Britain and Northern Ireland
Owen Jeffries
1   St Mary's University Twickenham London, School of Sport, Health and Applied Sci, London, United Kingdom of Great Britain and Northern Ireland
› Author Affiliations
Further Information

Publication History

received 03 August 2017
revised 02 October 2017

accepted 13 October 2017

Publication Date:
17 November 2017 (online)


This study evaluated the inter-day test-retest reliability of the Finapres® finger pulse pressure measuring device during rest and exercise. Eight male participants visited the laboratory twice for evaluation of the inter-day reliability of the Finapres® finger-pulse pressure device to measure: heart rate (HR), stroke volume (SV), cardiac output (Q̇) and mean arterial pressure (MAP) at rest, and treadmill walking at 3 km/h on 1% and 5% inclines. There were no systematic biases for any of the variables between days. The coefficient of variation (CV%) and 95% limits of agreement (95% LoA) was smallest for MAP (CV%=1.6–3.2%; LoA total error=4.6–12 mmHg) and HR (CV%=3.2–3.9%; LoA total error=6.8–11.9 b/min), increasing with exercise intensity (gradient). The pattern of error was different for Q̇, with decreasing CV% (4.8–3.8%) and LoA (4.2–5.7 L/min) from rest to 5% gradient, with the larger errors occurring for resting SV (CV=7.4%; LoA total error=21.5 ml). The device measures MAP and HR reliably between days; however, error increases at higher intensities. The measurement of SV is less reliable, probably owing to underlying algorithmic assumptions.

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