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Fitting curves to physiological data.

S S Verma, R K Gupta, R M Rai

    Indian Journal of Physiology and Pharmacology
    |January 1, 1983
    PubMed
    Summary
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    Parabolic and logistic curves accurately model human heat output and finger temperature responses to ambient temperature changes. These models explain nearly 98% of the variation, offering precise physiological insights.

    Area of Science:

    • Physiology
    • Biophysics
    • Environmental Health

    Background:

    • Human physiological responses to ambient temperature are complex.
    • Understanding thermoregulation is crucial for health and comfort.
    • Accurate modeling of physiological parameters aids in predicting responses.

    Purpose of the Study:

    • To fit mathematical curves relating heat output and finger temperature to ambient temperature.
    • To evaluate the precision of parabolic and three-parameter logistic models.
    • To compare the efficacy of these models against other curve-fitting methods.

    Main Methods:

    • Fitting a parabola and a three-parameter logistic curve.
    • Relating heat output and initial index finger temperature with ambient temperature.

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  • Calculating coefficients of determination (R2%) to assess model fit.

    • Main Results:

    • Coefficients of determination (R2%) close to 98% were achieved for both models.
    • The fitted curves demonstrated a high percentage of variation explained.
    • The superiority of the fitted parabola and logistic curves was statistically proven.

    Conclusions:

    • Parabolic and three-parameter logistic curves precisely explain the relationship between heat output, finger temperature, and ambient temperature.
    • These models offer a more accurate representation compared to other curve-fitting approaches.
    • The findings provide valuable quantitative insights into human thermoregulation.