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Relationship between Health Risk Behaviors and Physical Inactivity in Montana Adults

Peter D. Hart
Journal of Physical Activity Research. 2021, 6(2), 126-129. DOI: 10.12691/jpar-6-2-10
Received August 20, 2021; Revised September 24, 2021; Accepted October 08, 2021

Abstract

Background: Evidence supports the interrelationships between modifiable health risk behaviors (HRBs) in adults. However, few studies have specifically examined the extent to which HRBs relate to physical inactivity (PIA). The aim of this research was to fill this aforementioned gap. Methods: Data for this study came from the 2020 Montana Behavioral Risk Factor Surveillance System (BRFSS). Six different binary (yes/no) HRB variables were created and included overweight (OVERWEIGHT), obese (OBESE), current smoking (SMOKING), heavy drinking (DRINKING), not always using seatbelt (SEATBELT), and driven after drinking too much (DDRIVING). PIA status was assessed from a question asking adults if they participated in any physical activities or exercises during the past month. Logistic regression was used to examine the relationship between each HRB and PIA. Results: Bivariate analyses indicated a significantly (ps < .001) greater prevalence of PIA for those at high risk for all HRBs, except DRINKING. Fully adjusted regression models showed increased odds of PIA for adults at high risk for OVERWEIGHT (OR = 1.30, 95% CI: 1.06 – 1.60), OBESE (OR = 1.78, 95% CI: 1.47 – 2.16), SMOKING (OR = 1.58, 95% CI: 1.25 – 2.00), SEATBELT (OR = 1.32, 95% CI: 1.08 – 1.61), and DDRIVING (OR = 1.97, 95% CI: 1.09 – 3.55). Additionally, the OBESE × DDRIVING interaction was significant (p = .046) and indicated substantially greater odds of PIA for those considered high risk for OBESE and DDRIVING (OR = 5.98, 95% CI: 2.08 – 17.18), as compared to their OBESE counterparts who are not high risk DDRIVING (OR = 1.78, 95% CI: 1.40 – 2.27). Conclusion: This study found that several HRBs relate to PIA in adults from Montana. Health promotion specialists concerned with increasing physical activity should consider interventions that target multiple HRBs.

1. Introduction

Health risk behaviors (HRBs) are considered modifiable activities that increase the likelihood of a negative outcome 1. Example HRBs include smoking tobacco, excessive alcohol consumption, lack of fruit and vegetable intake, overweightness, and inadequate sleep 2, 3. HRBs can lead to premature morbidity and mortality from chronic diseases like heart disease, cancer, stroke, and lung disease, as well as from acute situations such as serious injury 4, 5. Physical activity (PA) is a behavior recommended for all adults because of its health benefits and its ability to reduce chronic disease risk 6. Thus, physical inactivity (PIA) is another HRB of major concern. Studies have shown interrelationships between many different HRBs 7. Such relationships have public health implications by impacting policy change and health promotion intervention strategies 8. However, few studies have examined the extent to which HRBs relate to PIA in United States (U.S.) adult populations. Therefore, the aim of this study was to examine the association between several different HRBs and PIA among adults.

2. Materials & Methods

Data for this study came from the 2020 Behavioral Risk Factor Surveillance System (BRFSS) and methodological details can be found elsewhere 9, 10. Briefly, the BRFSS is a state-based annual telephone survey designed to collect data on HRBs and health status in noninstitutionalized U.S. adults 18 years of age and older. The Montana BRFSS data only were used for this study.

Six different binary (yes/no) HRB variables were created and used as separate independent variables. An overweight (OVERWEIGHT) variable was created from body mass index (BMI) (computed from self-reported height and weight) where participants were considered “high risk” for OVERWEIGHT if their BMI exceeded 25.0 kg/m2. An obese (OBESE) variable was also created from BMI where participants were considered “high risk” for OBESE if their BMI equaled or exceeded 30.0 kg/m2. A current smoking (SMOKING) variable was created from questions asking participants if they smoked at least 100 cigarettes in their entire life and if they currently smoke every day, some days, or not at all. Participants reporting having ever smoked 100+ cigarettes and reporting currently smoking every day or some days were considered “high risk” for SMOKING. A heavy drinking (DRINKING) variable was created from questions asking participants how many days per week they consumed alcohol and how many drinks per occasion they consumed alcohol on average (in the previous 30 days). Participants who reported having more than 14 drinks per week (males) or who reported having more than 7 drinks per week (females) were considered “high risk” for DRINKING.

A seatbelt use (SEATBELT) variable was created from a question asking participants how often they use seatbelts when in a car. Participants who reported anything less than “always” (“nearly always” to “never”) were considered “high risk” for SEATBELT. A drinking and driving (DDRIVING) variable was created from a question asking participants how many times they had driven after drinking too much (in the previous 30 days). Participants who reported anything more than “none” were considered “high risk” for DDRIVING. The physical inactivity (PIA) outcome variable in this study was created from a question asking participants if they did any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise. Participants who reported “no” were considered “high risk” for PIA. Study covariates included sex, age, race/ethnicity, income, education, marital status, and rural/urban status.

Statistical analyses included prevalence estimates (%) with standard errors (SEs) and 95% confidence intervals (CIs) for PIA overall and across each HRB. Test for difference in proportions were employed using the Rao-Scott chi-square statistic (χ2RS). Logistic regression was used to estimate the odds of PIA (compared to not) for those who have high risk HRB over the odds of PIA (compared to not) for those who do not have high risk HRB. Analyses were weighted to produce generalizations representative of noninstitutionalized adults in Montana. SAS version 9.4 and SPSS version 27 were used for all analyses 11, 12, 13, 14.

3. Results

A total of N = 6,315 participants had complete physical activity data with a loss of 471, 471, 218, 311, 236, and 2,729 for OVERWEIGHT, OBESE, SMOKING, DRINKING, SEATBELT, and DDRIVING analyses, respectively. Table 1 contains prevalence of PIA overall and by HRB. Results showed greater prevalence of PIA for those at high risk for OVERWEIGHT (20.7% vs. 14.8%, p < .0001), OBESE (26.1% vs. 15.6%, p < .0001), SMOKING (29.4% vs. 16.9%, p < .0001), SEATBELT (22.9% vs. 17.8%, p = .0003), and DDRIVING (26.5% vs. 14.2%, p = .0016). No significant prevalence difference was seen in PIA for DRINKING (20.2% vs. 19.0%, p = .5980).

  • Table 2. Multiple regression analyses examining the association between each health risk behavior (HRB) and physical inactivity (PIA) in Montana adults, 2020

  • Figure 1. Prevalence of PIA by OBESE and DDRIVING status in Montana adults, 2020 (Note. N = 3,424. OR (95% CI) is odds ratio defined as odds of PIA (compared to not) for those who are obese over the odds of PIA (compared to not) for those not obese. CI is confidence interval. ORs are by driven after drinking status and adjusted for age and sex. OBESE × DDRIVING interaction was significant (p = .046). χ2RS is Rao-Scott chi-square statistic for difference in proportions)

Table 2 contains the multiple regression analyses examining the association between each HRB and PIA. Each of the three sets of models (unadjusted, age and sex adjusted, and fully adjusted for age, sex, race, income, education, marital status, and rural status) indicated similar trends. Fully adjusted regression models showed increased odds of PIA for adults at high risk for OVERWEIGHT (OR = 1.30, 95% CI: 1.06 – 1.60), OBESE (OR = 1.78, 95% CI: 1.47 – 2.16), SMOKING (OR = 1.58, 95% CI: 1.25 – 2.00), SEATBELT (OR = 1.32, 95% CI: 1.08 – 1.61), and DDRIVING (OR = 1.97, 95% CI: 1.09 – 3.55). No significant association was seen for PIA and DRINKING (p = . 7756). Figure 1 displays simple effects analyses due to a significant (p = .046) OBESE × DDRIVING interaction. The graph shows substantially greater odds of PIA for those at high risk for OBESE and DDRIVING (OR = 5.98, 95% CI: 2.08 – 17.18), as compared to their high risk OBESE counterparts who are not high risk for DDRIVING (OR = 1.78, 95% CI: 1.40 – 2.27).

4. Discussion

Some of these findings have been confirmed by studies from other countries. Specifically, PIA has been found related to obesity and smoking in adults from Chile and Finland, respectively 15, 16. On the other hand, not much evidence supports the current findings relating PIA to seatbelt use or driving after drinking too much. The current null findings for PIA and heavy alcohol consumption may in part make sense since data from other studies reveal conflicting results. That is, some research indicate a positive relationship between PA and alcohol consumption 17. Whereas other data support a negative PA and alcohol relationship 18. Thus, more research is needed to corroborate the relationships between PIA and seatbelt use, drinking and driving, and alcohol consumption.

The major strength of this study is its use of a current and representative sample of noninstitutionalized adults in Montana. Additionally, the large number of HRBs assessed by the BRFSS allowed for greater coverage of risky behavior as compared to other studies. The most pressing limitation of the BRFSS is its cross-sectional nature. This limitation should highlight the fact that cause-and-effect associations are not possible in this paper. As well, all variables were assessed via self-report mechanism by trained interviewers. Therefore, misclassification and measurement error cannot be ruled out. Therefore, findings from this study should be considered with caution.

5. Conclusions

Results from this study found that high risk of overweight, obesity, smoking, seatbelt use, and drinking and driving increases the likelihood of PIA among adults in Montana. Health promotion specialists concerned with increasing physical activity should consider interventions that target multiple HRBs.

References

[1]  Peltzer K, Pengpid S, Yung TK, Aounallah‐Skhiri H, Rehman R. Comparison of health risk behavior, awareness, and health benefit beliefs of health science and non‐health science students: An international study. Nursing & health sciences. 2016 Jun; 18(2): 180-7.
In article      View Article  PubMed
 
[2]  Mudryj AN, Riediger ND, Bombak AE. The relationships between health-related behaviours in the Canadian adult population. BMC public health. 2019 Dec; 19(1): 1-9.
In article      View Article  PubMed
 
[3]  Svendsen MT, Bak CK, Sørensen K, Pelikan J, Riddersholm SJ, Skals RK, Mortensen RN, Maindal HT, Bøggild H, Nielsen G, Torp-Pedersen C. Associations of health literacy with socioeconomic position, health risk behavior, and health status: a large national population-based survey among Danish adults. BMC Public Health. 2020 Dec; 20(1): 1-2.
In article      View Article  PubMed
 
[4]  Jackson SE, Brown J, Ussher M, Shahab L, Steptoe A, Smith L. Combined health risks of cigarette smoking and low levels of physical activity: a prospective cohort study in England with 12-year follow-up. BMJ open. 2019 Nov 1; 9(11): e032852.
In article      View Article  PubMed
 
[5]  Kouvonen A, Kivimäki M, Oksanen T, Pentti J, De Vogli R, Virtanen M, Vahtera J. Obesity and occupational injury: a prospective cohort study of 69,515 public sector employees. PloS one. 2013 Oct 16; 8(10): e77178.
In article      View Article  PubMed
 
[6]  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.
In article      View Article  PubMed
 
[7]  Birch J, Petty R, Hooper L, Bauld L, Rosenberg G, Vohra J. Clustering of behavioural risk factors for health in UK adults in 2016: a cross-sectional survey. Journal of Public Health. 2019 Sep 30; 41(3): e226-36.
In article      View Article  PubMed
 
[8]  Zabaleta-del-Olmo E, Pombo H, Pons-Vigués M, Casajuana-Closas M, Pujol-Ribera E, López-Jiménez T, Cabezas-Peña C, Martín-Borràs C, Serrano-Blanco A, Rubio-Valera M, Llobera J. Complex multiple risk intervention to promote healthy behaviours in people between 45 to 75 years attended in primary health care (EIRA study): study protocol for a hybrid trial. BMC Public Health. 2018 Dec; 18(1): 1-5.
In article      View Article  PubMed
 
[9]  Centers for Disease Control and Prevention. The BRFSS data user guide. August 15, 2013.
In article      
 
[10]  Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Overview: BRFSS 2019. July 26, 2019.
In article      
 
[11]  SAS Institute Inc. 2015. SAS/STAT® 14.1 User’s Guide. Cary, NC: SAS Institute Inc.
In article      
 
[12]  IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp.
In article      
 
[13]  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.
In article      View Article
 
[14]  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).
In article      
 
[15]  Díaz-Martínez X, Petermann F, Leiva AM, Garrido-Méndez A, Salas-Bravo C, Martínez MA, Labraña AM, Duran E, Valdivia-Moral P, Zagalaz ML, Poblete-Valderrama F. Association of physical inactivity with obesity, diabetes, hypertension and metabolic syndrome in the Chilean population. Revista medica de Chile. 2018 May 1; 146(5): 585-95.
In article      View Article  PubMed
 
[16]  Piirtola M, Kaprio J, Silventoinen K, Svedberg P, Korhonen T, Ropponen A. Association between long-term smoking and leisure-time physical inactivity: a cohort study among Finnish twins with a 35-year follow-up. International journal of public health. 2017 Sep; 62(7): 819-29.
In article      View Article  PubMed
 
[17]  Gilchrist JD, Conroy DE, Sabiston CM. Associations between alcohol consumption and physical activity in breast cancer survivors. Journal of behavioral medicine. 2020 Apr; 43(2): 166-73.
In article      View Article  PubMed
 
[18]  Vancampfort D, Vandael H, Hallgren M, Probst M, Hagemann N, Bouckaert F, Van Damme T. Physical fitness and physical activity levels in people with alcohol use disorder versus matched healthy controls: a pilot study. Alcohol. 2019 May 1; 76: 73-9.
In article      View Article  PubMed
 

Published with license by Science and Education Publishing, Copyright © 2021 Peter D. Hart

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/

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Peter D. Hart. Relationship between Health Risk Behaviors and Physical Inactivity in Montana Adults. Journal of Physical Activity Research. Vol. 6, No. 2, 2021, pp 126-129. http://pubs.sciepub.com/jpar/6/2/10
MLA Style
Hart, Peter D.. "Relationship between Health Risk Behaviors and Physical Inactivity in Montana Adults." Journal of Physical Activity Research 6.2 (2021): 126-129.
APA Style
Hart, P. D. (2021). Relationship between Health Risk Behaviors and Physical Inactivity in Montana Adults. Journal of Physical Activity Research, 6(2), 126-129.
Chicago Style
Hart, Peter D.. "Relationship between Health Risk Behaviors and Physical Inactivity in Montana Adults." Journal of Physical Activity Research 6, no. 2 (2021): 126-129.
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  • Figure 1. Prevalence of PIA by OBESE and DDRIVING status in Montana adults, 2020 (Note. N = 3,424. OR (95% CI) is odds ratio defined as odds of PIA (compared to not) for those who are obese over the odds of PIA (compared to not) for those not obese. CI is confidence interval. ORs are by driven after drinking status and adjusted for age and sex. OBESE × DDRIVING interaction was significant (p = .046). χ2RS is Rao-Scott chi-square statistic for difference in proportions)
  • Table 2. Multiple regression analyses examining the association between each health risk behavior (HRB) and physical inactivity (PIA) in Montana adults, 2020
[1]  Peltzer K, Pengpid S, Yung TK, Aounallah‐Skhiri H, Rehman R. Comparison of health risk behavior, awareness, and health benefit beliefs of health science and non‐health science students: An international study. Nursing & health sciences. 2016 Jun; 18(2): 180-7.
In article      View Article  PubMed
 
[2]  Mudryj AN, Riediger ND, Bombak AE. The relationships between health-related behaviours in the Canadian adult population. BMC public health. 2019 Dec; 19(1): 1-9.
In article      View Article  PubMed
 
[3]  Svendsen MT, Bak CK, Sørensen K, Pelikan J, Riddersholm SJ, Skals RK, Mortensen RN, Maindal HT, Bøggild H, Nielsen G, Torp-Pedersen C. Associations of health literacy with socioeconomic position, health risk behavior, and health status: a large national population-based survey among Danish adults. BMC Public Health. 2020 Dec; 20(1): 1-2.
In article      View Article  PubMed
 
[4]  Jackson SE, Brown J, Ussher M, Shahab L, Steptoe A, Smith L. Combined health risks of cigarette smoking and low levels of physical activity: a prospective cohort study in England with 12-year follow-up. BMJ open. 2019 Nov 1; 9(11): e032852.
In article      View Article  PubMed
 
[5]  Kouvonen A, Kivimäki M, Oksanen T, Pentti J, De Vogli R, Virtanen M, Vahtera J. Obesity and occupational injury: a prospective cohort study of 69,515 public sector employees. PloS one. 2013 Oct 16; 8(10): e77178.
In article      View Article  PubMed
 
[6]  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.
In article      View Article  PubMed
 
[7]  Birch J, Petty R, Hooper L, Bauld L, Rosenberg G, Vohra J. Clustering of behavioural risk factors for health in UK adults in 2016: a cross-sectional survey. Journal of Public Health. 2019 Sep 30; 41(3): e226-36.
In article      View Article  PubMed
 
[8]  Zabaleta-del-Olmo E, Pombo H, Pons-Vigués M, Casajuana-Closas M, Pujol-Ribera E, López-Jiménez T, Cabezas-Peña C, Martín-Borràs C, Serrano-Blanco A, Rubio-Valera M, Llobera J. Complex multiple risk intervention to promote healthy behaviours in people between 45 to 75 years attended in primary health care (EIRA study): study protocol for a hybrid trial. BMC Public Health. 2018 Dec; 18(1): 1-5.
In article      View Article  PubMed
 
[9]  Centers for Disease Control and Prevention. The BRFSS data user guide. August 15, 2013.
In article      
 
[10]  Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Overview: BRFSS 2019. July 26, 2019.
In article      
 
[11]  SAS Institute Inc. 2015. SAS/STAT® 14.1 User’s Guide. Cary, NC: SAS Institute Inc.
In article      
 
[12]  IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp.
In article      
 
[13]  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.
In article      View Article
 
[14]  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).
In article      
 
[15]  Díaz-Martínez X, Petermann F, Leiva AM, Garrido-Méndez A, Salas-Bravo C, Martínez MA, Labraña AM, Duran E, Valdivia-Moral P, Zagalaz ML, Poblete-Valderrama F. Association of physical inactivity with obesity, diabetes, hypertension and metabolic syndrome in the Chilean population. Revista medica de Chile. 2018 May 1; 146(5): 585-95.
In article      View Article  PubMed
 
[16]  Piirtola M, Kaprio J, Silventoinen K, Svedberg P, Korhonen T, Ropponen A. Association between long-term smoking and leisure-time physical inactivity: a cohort study among Finnish twins with a 35-year follow-up. International journal of public health. 2017 Sep; 62(7): 819-29.
In article      View Article  PubMed
 
[17]  Gilchrist JD, Conroy DE, Sabiston CM. Associations between alcohol consumption and physical activity in breast cancer survivors. Journal of behavioral medicine. 2020 Apr; 43(2): 166-73.
In article      View Article  PubMed
 
[18]  Vancampfort D, Vandael H, Hallgren M, Probst M, Hagemann N, Bouckaert F, Van Damme T. Physical fitness and physical activity levels in people with alcohol use disorder versus matched healthy controls: a pilot study. Alcohol. 2019 May 1; 76: 73-9.
In article      View Article  PubMed