Repeated Measures Models Applied to Cancer Patients Treated with Exergames

Miranda, Isabela Pagani Heringer De; Nogueira, Denismar Alves; Martins, Natalia Da Silva; Alves, Ricardo Da Silva; Carvalho, Leonardo

doi:10.22034/APJCP.2018.19.8.2171

Repeated Measures Models Applied to Cancer Patients Treated with Exergames

Document Type : Research Articles

Authors

¹ Applied Statistics and Biometrics Program, Federal University of Alfenas, Alfenas, Brazil.

² Department of Statistic, Federal University of Alfenas, Alfenas, Brazil.

³ Physical Therapy Course, Federal University of Alfenas, Alfenas, Brazil.

⁴ Bioscience Program, Federal University of Alfenas, Alfenas, Brazil.

10.22034/APJCP.2018.19.8.2171

Abstract

Objective: The objective of this study was to define an appropriate linear model to analyse data on muscular fatigue
in cancer patients over time through repeated measures techniques. Methods: Using the split plot in time system and
linear mixed models, three groups of individuals were compared as to the methods used to reduce muscle fatigue.
Group Cancer consisted of individuals who had already been treated; group Control consisted of healthy individuals
and group Chemo / radio-therapy consisted of individuals diagnosed with cancer undergoing chemo and radiation
therapy. Sessions were tested with exergames. A series of muscle strength data for each of the six muscles studied, in the
pre-treatment, mid-treatment and final sessions. Result: The structure that best fit the covariance matrix was ARMA
(1,1), according to AIC and BIC. There were significant differences and tendencies in the data series, especially for
the left tibial muscle, in which the interactions between group and session and between group and time were significant,
showing that exergames treatment increased muscle strength in debilitated patients and, with 20 sessions, the groups
equalled in muscle strength. Conclusion: The linear mixed model proved to be efficient in modelling plots subdivided
in time. Identifying the best structure of the covariance matrix allowed us to better estimate the effects, using tests
appropriately to verify differences between factors that were not detected when using the median frequency of strength.

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