A combined heart rate and movement sensor: proof of concept and preliminary testing study
Abstract
Objective Heart rate monitoring has previously been used as a technique for measuring energy expenditure (EE) in field studies. However, the combination of heart rate monitoring with movement sensoring could have theoretical advantages compared to either method used alone. Therefore, this study was undertaken to develop and validate a new combined heart rate monitor and movement sensor instrument (HR+M) for measuring EE.Methods The HR+M instrument is a single-piece instrument worn around the chest which records minute-by-minute heart rate and movement. Eight subjects underwent an individual calibration in which EE and heart rate were measured at rest and during a sub-maximal bicycle ergometer test. They then wore the HR+M for 24 hours in a whole-body calorimeter and underwent a standard protocol including periods of physical activity and inactivity. Minute-by-minute heart rate was converted to EE using individual calibration curves with the motion data discriminating between periods of inactivity and activity at low heart rate levels. EE was also calculated using the HRFlex method which relies on heart rate alone. Both estimates of EE were compared to EE measured in the whole-body calorimeter.Results The mean percentage error of the HR+M method calculating TEE compared with the gold standard of the calorimeter measurement was 0.00% (95% CI of the mean error -0.25, 1. 25). The HRFlex method using the heart rate information alone resulted in a mean percentage error of 16.5% (95% CI of the mean error -0.57, 1.76).Conclusions This preliminary test of HR+M demonstrates its ability to estimate EE and the pattern of EE and activity throughout the day. Further validation studies in free-living individuals are necessary.Sponsorship NJW is an MRC Clinician Scientist Fellow. KLR holds an MRC PhD scholarship.
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Numerous studies have shown that it is possible to estimate energy expenditure from heart rate using multivariate predictive equations derived from group data in adults and children.
“Numerous studies have shown that it is possible to estimate EE from HR using multivariate predictive equations derived from group data in adults [85,[88][89][90][91] and children [92].”
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References 22 · Cited by 83
- Authors
- Rennie, K., Rowsell, T., Jebb, S., Holburn, D., & Wareham, N.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Wareham, NJ
- ORCID
- Rennie, K
- Venue
- European Journal of Clinical Nutrition
- Published
- 2000-05-01
- Volume
- 54
- Issue
- 5
- Pages
- 409-414
- ISSN
- 0954-3007 (ISSN-L)
- 1476-5640
- OpenAlex
- W1982548616
- Cited by
- 83
- References
- 22
- Citations by year
- 2023: 1 · 2018: 2 · 2017: 3 · 2016: 5 · 2015: 3 · 2014: 8 · 2013: 5 · 2012: 3
- FWCI
- 1.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Journal of Clinical Nutrition (publishedVersion)
- PubMed (publishedVersion)
- University of Hertfordshire Research Archive (University of Hertfordshire) (submittedVersion)
- Funders
- Medical Research Council
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- heart rate monitoring, motion sensors, energy expenditure estimation, whole-body calorimetry, physical activity detection, total energy expenditure
- MeSH terms
- Adult, Calorimetry, Energy Metabolism, Exercise Test, Female, Heart Rate, Humans, Male, Middle Aged, Monitoring, Physiologic, Movement, Body Mass Index, Monitoring, Ambulatory
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