Journal of Physical Activity Research
ISSN (Print): 2576-1919 ISSN (Online): 2574-4437 Website: http://www.sciepub.com/journal/jpar Editor-in-chief: Peter Hart
Open Access
Journal Browser
Go
Journal of Physical Activity Research. 2021, 6(2), 122-125
DOI: 10.12691/jpar-6-2-9
Open AccessArticle

Sociodemographic Predictors of Physical Inactivity in Montana Adults

Peter D. Hart1,

1Health Promotion Research, Havre, MT 59501

Pub. Date: October 08, 2021

Cite this paper:
Peter D. Hart. Sociodemographic Predictors of Physical Inactivity in Montana Adults. Journal of Physical Activity Research. 2021; 6(2):122-125. doi: 10.12691/jpar-6-2-9

Abstract

Background: Physical activity (PA) intervention strategies that target specific priority populations can achieve greater public health impact. The aim of this research was to find sociodemographic predictors of physical inactivity (PIA) using multivariate analyses. Methods: Data for this study came from the 2020 Montana Behavioral Risk Factor Surveillance System (BRFSS). Seven different categorical sociodemographic characteristics (SDCs) were used and included age, sex, race/ethnicity, income, education, marital status, and rural/urban status. PIA was assessed from a question asking adults if they participated in any physical activities or exercises during the past month. Multiple logistic regression was employed to examine the relationship between the SDCs and PIA. Results: Bivariate analyses showed significant (ps < .0001) relationships between PIA and age, income, education, marital status, and rural/urban status. Whereas, sex and race/ethnicity were not significantly related to PIA. Fully adjusted regression models showed increasing odds of PIA as age increased from reference group 18 to 24 years (ORs: 1.84 to 3.87, p for trend < .0001), as income decreased from reference group $50,000+ (ORs: 1.42 to 2.73, p for trend < .0001), and as formal education decreased from reference group college graduate (ORs: 2.08 to 4.60, p for trend < .0001). Marital status and rural/urban status both lost predictive ability in light of the other SDCs. Additionally, analyses stratified by race/ethnicity indicated considerably greater odds (OR = 5.13, 95% CI: 1.58 – 16.74) of PIA for Hispanic females (compared to males), with no other race/ethnicity sex differences seen. Conclusion: This study found that several SDCs relate to PIA in adults. Health promotion specialists concerned with increasing PA should consider independently targeting lower income, less educated, and older individuals. Hispanic females may be a priority population for PIA intervention in the state of Montana.

Keywords:
Physical activity (PA) Sociodemographic characteristics (SDCs) health promotion

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, George SM, Olson RD. The physical activity guidelines for Americans. Jama. 2018 Nov 20; 320(19): 2020-8.
 
[2]  Hart PD, Benavidez G, Erickson J. Meeting recommended levels of physical activity in relation to preventive health behavior and health status among adults. Journal of preventive medicine and public health. 2017 Jan; 50(1):10.
 
[3]  Hart PD, Buck DJ. The effect of resistance training on health-related quality of life in older adults: Systematic review and meta-analysis. Health promotion perspectives. 2019; 9(1): 1.
 
[4]  Office of Disease Prevention and Health Promotion. (n.d.). Physical Activity. Healthy People 2030. U.S. Department of Health and Human Services. https://health.gov/healthypeople/objectives-and-data/browse-objectives/ physical-activity.
 
[5]  McKenzie, James F, Brad L. Neiger, and Rosemary Thackeray. Planning, Implementing, and Evaluating Health Promotion Programs: A Primer. , 2017. Print.
 
[6]  Nyenhuis SM, Shah N, Kim H, Marquez DX, Wilbur J, Sharp LK. The Feasibility of a Lifestyle Physical Activity Intervention for Black Women with Asthma. The Journal of Allergy and Clinical Immunology: In Practice. 2021 Jul 29.
 
[7]  Schulz AJ, Israel BA, Mentz GB, Bernal C, Caver D, DeMajo R, Diaz G, Gamboa C, Gaines C, Hoston B, Opperman A. Effectiveness of a walking group intervention to promote physical activity and cardiovascular health in predominantly non-Hispanic black and Hispanic urban neighborhoods: findings from the walk your heart to health intervention. Health Education & Behavior. 2015 Jun; 42(3): 380-92.
 
[8]  Kleinke F, Ulbricht S, Dörr M, Penndorf P, Hoffmann W, van den Berg N. A low-threshold intervention to increase physical activity and reduce physical inactivity in a group of healthy elderly people in Germany: Results of the randomized controlled MOVING study. PloS one. 2021 Sep 16; 16(9): e0257326.
 
[9]  Wilczynska M, Jansson AK, Lubans DR, Smith JJ, Robards SL, Plotnikoff RC. Physical activity intervention for rural middle-aged and older Australian adults: a pilot implementation study of the ecofit program delivered in a real-world setting. Pilot and Feasibility Studies. 2021 Dec; 7(1): 1-7.
 
[10]  Centers for Disease Control and Prevention. The BRFSS data user guide. August 15, 2013.
 
[11]  Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Overview: BRFSS 2019. July 26, 2019.
 
[12]  SAS Institute Inc. 2015. SAS/STAT® 14.1 User’s Guide. Cary, NC: SAS Institute Inc.
 
[13]  IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp.
 
[14]  Zou D, Lloyd JE, Baumbusch JL. Using SPSS to analyze complex survey data: a primer. Journal of Modern Applied Statistical Methods. 2020; 18(1): 16.
 
[15]  Siller AB, Tompkins L. The big four: Analyzing complex sample survey data using SAS, SPSS, STATA, and SUDAAN. Inproceedings of the thirty-first annual SAS® Users Group international conference 2006 Mar 27 (pp. 26-29).