179
Views
7
CrossRef citations to date
0
Altmetric
Research Article

Multi-level modelling of physical activity in nuclear families

, , , , &
Pages 138-144 | Received 14 Apr 2013, Accepted 31 Jul 2013, Published online: 11 Oct 2013
 

Abstract

Background: Few studies focus on the different dyadic relations among family members to study physical activity (PA) levels.

Aim: The aim was to investigate predictors and sources of variance of PA levels in nuclear families using multi-level modelling.

Subjects and methods: The sample consisted of 2661 Portuguese four-member nuclear families (10 644 subjects). PA was measured using a questionnaire and socioeconomic status (SES) was assessed by parental occupation. Height and weight were measured in children, while parents self-reported their values.

Results: The results showed that intra-generational similarities were higher than inter-generational, with correlation values of 0.26 and 0.10, respectively. SES was unrelated to any family members’ PA level. Being male (β = 0.26, t = 21.77), being older (β = −0.36, t = −4.73) and greater BMI for mothers (β = 0.02, t = 2.55) had effects on individuals’ PA.

Conclusion: These results suggest a strong dyadic resemblance in PA, showed different effects of gender, age and BMI on individuals’ PA and demonstrated that multi-level modelling is a useful strategy to study PA in families.

Acknowledgements

This work was supported by the Portuguese Foundation of Science and Technology: PTDC/DES/67569/2006 and FCOMP-01-0124-FEDEB-09608. The authors would like to thank the reviewers for their comments that help improve the manuscript.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access
  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart
* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.