Open AccessMultiple imputation to deal with missing objectively-measured physical activity data: findings from two cohorts
Author(s) -
Rafaela Costa Martins,
Bruna Gonçalves C da Silva,
Cauane Blumenberg,
Luiza Isnardi Cardoso Ricardo,
Shana Ginar da Silva,
João Ribeiro,
Alícia Matijasevich,
Ana Maria Baptista Menezes,
Helen Gonçalves,
Fernando C. Wehrmeister,
Iná dos Santos,
Inácio CrochemoreSilva,
Aluísio J. D. Barros
Publication year - 2021
Publication title -
revista brasileira de atividade física e saúde
Language(s) - English
Resource type - Journals
eISSN - 2317-1634
pISSN - 1413-3482
DOI - 10.12820/rbafs.26e0209
Subject(s) - imputation (statistics) , missing data , physical activity , cohort , statistics , socioeconomic status , demography , medicine , mathematics , physical therapy , population , sociology
The objective of this article was to describe patterns of losses of information regarding accelerometer data and to assess the use of multiple imputation to generate physical activity estimates for individuals without accelerometry data. Two birth cohort studies from Pelotas (Brazil) with participants aged 22 and 11-years old assessed objectively measured physical activity differences between complete and imputed cases. Mean values of overall physical activity for complete cases (n1993 = 2,985 and n2004 = 3,348) and for complete cases plus imputed cases (n1993 = 760 and n2004 = 79) were described according to predictors. Male individuals, participants with black skin color, and less schooled individuals presented higher averages of overall physical activity than their counterparts. Almost all imputed estimates were comparable to the complete cases, and the highest difference found was 0.7 mg for the first quintile of socioeconomic status of the 1993 birth cohort. Multiple imputation is a positive technique to deal with missing data from objectively measured physical activity. It provides a set of relevant variables to be used in order to efficiently predict accelerometer data.