Skip to main content
Top
Published in: BMC Public Health 1/2019

Open Access 01-12-2019 | Research article

Evaluation of lifestyle of female adolescents through latent class analysis approach

Authors: Valter Paulo Neves Miranda, Paulo Roberto dos Santos Amorim, Ronaldo Rocha Bastos, Vitor Gabriel Barra Souza, Eliane Rodrigues de Faria, Sylvia do Carmo Castro Franceschini, Silvia Eloiza Priore

Published in: BMC Public Health | Issue 1/2019

Login to get access

Abstract

Background

Lack of regular physical activity, high sedentary behavior and presence of unbalanced alimentary practices are attitudes associated with an inadequate lifestyle among female adolescents.

Objective

to assess the lifestyle of female adolescents based on measurements of behavioral variables.

Methods

Cross-sectional study with 405 female adolescents between 14 and 19 years old, resident and attending public schools in Viçosa (state of Minas Gerais). Their lifestyle was analyzed by the Physical Activity Recall, number of steps, screen time (ST), cellphone time (CT), sitting time, food frequency questionnaire (FFQ), and alcohol and tobacco consumption. With multiple correspondence analysis it was possible to observe dispersion and approximation of the variables’ categories. Latent class analysis (LCA) was used for modeling the “lifestyle” variable, having been conducted in the poLCA (Polychromous Variable Latent Class Analysis) package of the R statistical software.

Results

The mean age was 15.92 ± 1.27 years. Most of the adolescents were considered physically inactive (78%) and with low number of steps (82.57%); 41.45% reported not performing Moderate to Vigorous Physical Activities (MVPA) adequately. Sedentary behavior was found high when assessing ST (72.90%) and CT (65.31%). It was found the best fitted latent class model for the lifestyle (p-G2 = 0.055, p-χ2 = 0.066) featured three latent classes and one covariate (alcohol): Class 1, ‘Inactive and Sedentary’ (γ = 77.5%); Class 2, ‘Inactive and Non-sedentary lifestyle (γ=16.31%); and Class 3, ‘Active and sedentary’ (γ=6.19%). Female adolescents that had ‘never consumed alcohol’ were 2.26 times as likely (log OR = 0.8174; p = 0.033) to belong to class 3 (Active & Sedentary lifestyle) than to class 1 (Inactive & Sedentary lifestyle).

Conclusion

Latent class analysis model with five manifest variable (MVPA, number of steps, ST, sitting time and number of meals) and alcohol consumption like covariate showed itself to be an accurate and objective method in the assessment of female adolescents’ lifestyle. Female adolescents that had ‘never consumed alcohol’ were more as likely to belong to class ‘Active & Sedentary lifestyle’ than to class Inactive & Sedentary lifestyle. An inactive and sedentary lifestyle is coupled to other unhealthy behaviors during adolescence, possibly carrying over into adult life.
Literature
5.
go back to reference Moreno LA, Gottrand F, Huybrechts I, Ruiz JR, González-Gross M, Dehenauw S, et al. Nutrition and Lifestyle in European Adolescents: The HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) Study. American Society for Nutrition. Adv Nut. 2014;5:615S–23S https://doi.org/10.3945/an.113.005678.CrossRef Moreno LA, Gottrand F, Huybrechts I, Ruiz JR, González-Gross M, Dehenauw S, et al. Nutrition and Lifestyle in European Adolescents: The HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) Study. American Society for Nutrition. Adv Nut. 2014;5:615S–23S https://​doi.​org/​10.​3945/​an.​113.​005678.CrossRef
6.
go back to reference Klinker CD, Schipperijn J, Christian H, Kerr J, Ersbøll AK, Troelsen J. Using accelerometers and global positioning system devices to assess gender and age differences in children’s school, transport, leisure and home based physical activity. Int J Behav Nutr Phys Act. 2014;11(8):1–8. Klinker CD, Schipperijn J, Christian H, Kerr J, Ersbøll AK, Troelsen J. Using accelerometers and global positioning system devices to assess gender and age differences in children’s school, transport, leisure and home based physical activity. Int J Behav Nutr Phys Act. 2014;11(8):1–8.
12.
go back to reference Falbe J, Willett C, Rosner B, Field AE. Body mass index, new modes of TV viewing and active video games. Pediatr Obes. 2017;12(5):406–13 doi: 10.1111/ijpo.12158.PubMedCrossRef Falbe J, Willett C, Rosner B, Field AE. Body mass index, new modes of TV viewing and active video games. Pediatr Obes. 2017;12(5):406–13 doi: 10.1111/ijpo.12158.PubMedCrossRef
14.
go back to reference Cochran WG. Sampling Techniques. 3ª ed. New York: Wiley; 1977. p. 77. Cochran WG. Sampling Techniques. 3ª ed. New York: Wiley; 1977. p. 77.
19.
go back to reference Bratteby LE, Sandhagen BO, Fan H, Samuelson G. A 7-day activity diary for assessment of daily energy expenditure validated by the doubly labelled water method in adolescents. Eur J Clin Nutr. 1997a;51:585–91.PubMedCrossRef Bratteby LE, Sandhagen BO, Fan H, Samuelson G. A 7-day activity diary for assessment of daily energy expenditure validated by the doubly labelled water method in adolescents. Eur J Clin Nutr. 1997a;51:585–91.PubMedCrossRef
20.
go back to reference World Health Organization (WHO). Global recommendations on physical activity for health. Geneva: World Health Organization; 2010. World Health Organization (WHO). Global recommendations on physical activity for health. Geneva: World Health Organization; 2010.
21.
go back to reference Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum. Nutr Clin Nutr. 1985;39(Suppl 1):5–41.PubMed Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum. Nutr Clin Nutr. 1985;39(Suppl 1):5–41.PubMed
22.
go back to reference Brooks GA, Butte NF, Rand WM, Flatt JP, Caballero B. Chronicle of the Institute of medicine physical activity recommendation: how a physical activity recommendation came to be among dietary recommendations. Am J Clin Nutr. 2004;79(suppl):921S–30.PubMedCrossRef Brooks GA, Butte NF, Rand WM, Flatt JP, Caballero B. Chronicle of the Institute of medicine physical activity recommendation: how a physical activity recommendation came to be among dietary recommendations. Am J Clin Nutr. 2004;79(suppl):921S–30.PubMedCrossRef
26.
go back to reference Jun L, Qingmin L, Yanjun R, Gong T, Shengfeng W, Liming L. Socio-demographic association of multiple modifiable lifestyle risk factors and their clustering in a representative urban population of adults: a cross-sectional study in Hangzhou, China. Int J Behav Nutr Phys Act. 2011;8(40):1–13 https://doi.org/10.1186/1479-5868-8-40. Jun L, Qingmin L, Yanjun R, Gong T, Shengfeng W, Liming L. Socio-demographic association of multiple modifiable lifestyle risk factors and their clustering in a representative urban population of adults: a cross-sectional study in Hangzhou, China. Int J Behav Nutr Phys Act. 2011;8(40):1–13 https://​doi.​org/​10.​1186/​1479-5868-8-40.
37.
go back to reference CGI.br/NIC.br, Regional Center for Studies for the Development of the Information Society (Cetic.br), Research on Internet Use by Children and Adolescents in Brazil - ICT Kids Online Brazil 2016. Disponible in: https://cetic.br/pesquisa/kids-online/. Accessed 2 Oct 2016. CGI.br/NIC.br, Regional Center for Studies for the Development of the Information Society (Cetic.br), Research on Internet Use by Children and Adolescents in Brazil - ICT Kids Online Brazil 2016. Disponible in: https://​cetic.​br/​pesquisa/​kids-online/​. Accessed 2 Oct 2016.
Metadata
Title
Evaluation of lifestyle of female adolescents through latent class analysis approach
Authors
Valter Paulo Neves Miranda
Paulo Roberto dos Santos Amorim
Ronaldo Rocha Bastos
Vitor Gabriel Barra Souza
Eliane Rodrigues de Faria
Sylvia do Carmo Castro Franceschini
Silvia Eloiza Priore
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2019
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-019-6488-8

Other articles of this Issue 1/2019

BMC Public Health 1/2019 Go to the issue