Complementary methodology in the analysis of rhythmic data, using examples from a complex situation, the rhythmicity of temperature in night shift workers

The methodology for analyzing a biological rhythm has already been the subject of much investigation. However, many questions still have no answer, for example, questions such as the periods chosen: fixed or determined. Throughout this article, we suggest a somewhat innovative methodology that makes...

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Bibliographic Details
Published inBiological rhythm research Vol. 36; no. 3; pp. 177 - 193
Main Authors Gouthiere, L, Mauvieux, B, Davenne, D, Waterhouse, J
Format Journal Article
LanguageEnglish
Published Taylor & Francis Ltd 01.07.2005
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ISSN0929-1016
1744-4179
DOI10.1080/09291010400026298

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Summary:The methodology for analyzing a biological rhythm has already been the subject of much investigation. However, many questions still have no answer, for example, questions such as the periods chosen: fixed or determined. Throughout this article, we suggest a somewhat innovative methodology that makes it possible to define the steps that seem essential to us in the scientific analysis of rhythms. For some time, this methodology has been put into practice in our laboratory in various studies, some of which have given rise to publications. The notion of quality is a new notion that is present in industry and, when applied to sampling, can improve experimentation. In this way, one may judge the degree to which data samples can be explored as well as the degree of validity of the results of exploration. We provide several methods for achieving this. The search for periods is also important. For this, we have various methods but we must be able to determine those that are the most appropriate and reliable in a particular case. We propose spectral methods, two of which are new and complement 'Cosinor' methodology. On the other hand, modelling uses various methods such as those from, for example, periodic trigonometric functions or more complex chaos functions. We are interested in models from the field of regression (the cosine model) and complementary statistical tests that make it possible to validate the proposed model.
ISSN:0929-1016
1744-4179
DOI:10.1080/09291010400026298