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A nonlinear mixed model approach to predict energy expenditure from heart rate (2021)

Kortelainen, L., Helske, J., Finni, T., Mehtätalo, L., Tikkanen, O., & Kärkkäinen, S. (2021). A nonlinear mixed model approach to predict energy expenditure from heart rate. Physiological Measurement, 42(3), Article 035001. https://doi.org/10.1088/1361-6579/abea25

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Kortelainen, Lauri; Helske, Jouni; Finni, Taija; Mehtätalo, Lauri; Tikkanen, Olli; Kärkkäinen, Salme

Lehti tai sarja: Physiological Measurement

ISSN: 0967-3334

eISSN: 1361-6579

Julkaisuvuosi: 2021

Volyymi: 42

Lehden numero: 3

Artikkelinumero: 035001

Kustantaja: Institute of Physics

Julkaisumaa: Britannia

Julkaisun kieli: englanti

DOI: https://doi.org/10.1088/1361-6579/abea25

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/77613


Objective: Heart rate (HR) monitoring provides a convenient and inexpensive way to predict energy expenditure (EE) during physical activity. However, there is a lot of variation among individuals in the EE-HR relationship, which should be taken into account in predictions. The objective is to develop a model that allows the prediction of EE based on HR as accurately as possible and allows an improvement of the prediction using calibration measurements from the target individual.

Approach: We propose a nonlinear (logistic) mixed model for EE and HR measurements and an approach to calibrate the model for a new person who does not belong to the data set used to estimate the model. The calibration utilizes the estimated model parameters and calibration measurements of HR and EE from the person in question. We compare the results of the logistic mixed model with a simpler linear mixed model for which the calibration is easier to perform.

Main results: We show that the calibration is beneficial already with only one pair of measurements on HR and EE. That is an important benefit over an individual-level model fitting which requires a larger number of measurements. Moreover, we present an algorithm for calculating the confidence and prediction intervals of the calibrated predictions. The analysis was based on up to eleven pairs of EE and HR measurements from each of 54 individuals of a heterogeneous group of people, who performed a maximal treadmill test.

Significance: The proposed method allows accurate energy expenditure predictions based on only a few calibration measurements from a new individual without access to the original dataset, thus making the approach viable for example on wearable computers.

YSO-asiasanat: energiankulutus (aineenvaihdunta); fyysinen aktiivisuus; mittausmenetelmät; syke; sykemittarit; kalibrointi; tilastolliset mallit

Vapaat asiasanat: energiankulutus: sykemittaus; kalibrointi; logistinen sekamalli; fyysinen aktiivisuus.

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

Raportointivuosi: 2021

JUFO-taso: 1

Viimeisin päivitys 2022-20-09 klo 13:54