Accelerometry and Heart Rate as a Measure of Physical Fitness: Proof of Concept
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PURPOSE: This study focused on developing a new method to assess VO2max outside laboratory conditions and without the need for maximal exertion. We hypothesized that the combined use of accelerometry and HR monitoring, under daily life conditions, could provide a good estimate of physical fitness. METHODS: Twenty-six healthy subjects (15 women, 11 men), aged 28 +/- 7 yr, performed a maximal incremental test on a bicycle ergometer to determine VO2max. Body composition was measured with underwater weighing and deuterium dilution using a three-compartment model. A triaxial accelerometer (Tracmor) and an HR monitor were worn for seven consecutive days under free-living conditions. The ratio of HR to activity counts per minute (ACM) was used as a fitness index (HR.ACM(-1)). RESULTS: As hypothesized, HR.ACM(-1) was significantly correlated with VO2max. Using fat-free mass (FFM) (P < 0.0001), age (P = 0.025), and HR.ACM(-1) (P = 0.021) as the independent variables, the explained variation in VO2max was 76% (P < 0.0001, SEE = 363 mL x min(-1)). In order to generate a prediction formula that is applicable in the field when no data on body composition are available, the same analysis was done with body mass and gender in the model instead of FFM. HR.ACM(-1) was significantly (P = 0.023) correlated with VO2max. The total explained variation of the model was 71%, with a SEE of 409 mL x min(-1), or 13.7% of the average VO2max. CONCLUSION: After correction for body composition, VO2max was significantly related to HR.ACM(-1). It is, to our knowledge, the first tool that yields a measure of VO2max by monitoring people in their daily life activities without the need for a specific protocol or for maximal exertion, and therefore is applicable to a large variety of subjects.
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Wearable devices, such as activity trackers and smartwatches, increasingly provide opportunities for non-intrusive objective monitoring of biological signals such as HR and movement during free-living, potentially enabling more precise prediction of VO2max without the need to conduct a specific exercise test.
“Wearable devices, such as activity trackers and smartwatches, increasingly provide opportunities for non-intrusive objective monitoring of biological signals such as HR and movement during free-living, potentially enabling more precise prediction of VO2max without the need to conduct a specific exercise test [140].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [140]
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Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
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- Authors
- PLASQUI, G., & WESTERTERP, K. R.
- Affiliations
- Maastricht University (NL)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2005-05
- Volume
- 37
- Issue
- 5
- Pages
- 872-876
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2016065466
- Cited by
- 56
- References
- 17
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 5 · 2020: 2 · 2019: 2 · 2018: 2 · 2017: 5 · 2016: 2 · 2015: 2 · 2014: 5 · 2013: 5 · 2012: 3
- FWCI
- 8.94
- Type
- journal article
- Language
- en
- Open access
- Licence
- other-oa
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Complementary and alternative medicine › Cardiovascular and exercise physiology
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
- Keywords
- VO2max prediction, physical fitness assessment, accelerometry, heart rate monitoring, triaxial accelerometer, free-living conditions, physical fitness index, body composition, fat-free mass, prediction formula, wearable sensors
- MeSH terms
- Activities of Daily Living, Adolescent, Adult, Age Factors, Body Composition, Exercise Test, Female, Heart Rate, Humans, Male, Middle Aged, Oxygen Consumption, Physical Fitness, Regression Analysis, Monitoring, Ambulatory
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