Mapping features and patterns of accelerometry data on human movement in different age groups and associated health...

Human movement is considered one of the important factors for maintaining an independent life. Individuals in different age groups have different characteristics of locomotion patterns and some health conditions can affect or be affected by mobility changes. Few studies clarify or present data about the influence of different ages and biopsychosocial factors on accelerometry features. The aim of this study was to identify characteristics and variables in the frequency signals for different age groups and their relationship with associated health conditions in raw accelerometry data obtained from the use of a triaxial accelerometer during 7 days of activities of daily living.

A cross-sectional study was conducted based on the database of the first evaluations of the Epidemiological Study of Movement (EPIMOV) cohort. Frequency, signal amplitude, and entropy accelerometry features of EPIMOV participants who used a triaxial accelerometer for 7 days were extracted. Sociodemographic, clinical, anthropometric and physical activity assessments were also performed. Two-way ANOVA was performed to compare accelerometry features within different age groups. A series of stepwise multiple regressions were performed on accelerometry variables to analyze their relationships with demographic, anthropometric and cardiovascular risk variables.

The sample consisted mostly of female, white, and high school graduates. The most prevalent cardiovascular risk factors were sedentary behavior and obesity. When analyzing the accelerometry variables, it was possible to observe that the entropy feature, and the counts, decrease in the group of older adults, while the feature of harmonic components of gait (frequency × amplitude) increases in the group of older adults. Regarding the amplitude feature, there were no significant differences between the groups. Through stepwise multiple linear regression, it was possible to observe that demographic, anthropometric and cardiovascular risk factors are associated with most accelerometry variables.

The results confirm that human movement can be influenced by different ages, sex, demographic, anthropometric and cardiovascular risk factors. Future studies and clinical analyzes can use the methods proposed in this research to adjust movement patterns for sex and different age groups, thus obtaining new interpretations about human movement.