Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

Physical Activity and Body Mass Index (BMI) as Predictors of Health-related Quality of Life in Montana Adults

Peter D. Hart

Health Promotion Research, Havre, MT 59501

Abstract: Background: Health-related quality of life (HRQOL) is an important concept related to how health status affects a person’s life. Engaging in physical activity (PA) and maintaining healthy body weight are each linked to favorable HRQOL. However, the extent to which PA and body weight independently influence HRQOL is less known. The aim of this research was to examine how meeting PA guidelines and body mass index (BMI) affect a measure of HRQOL in adults. Methods: The Montana Behavioral Risk Factor Surveillance System (BRFSS, 2019) was used for this study. Three different PA guideline variables were used and included a two-level aerobic PA (APA) (met APA or did not meet APA) measure, a two-level muscle strengthening activity (MSA) (met MSA or did not meet MSA) measure, and a two-level PA guidelines (APA/MSA) (met both APA and MSA or did not meet both) measure. BMI was calculated from self-reported height and weight (kg/m²). HRQOL was assessed from a question asking participants to rate their general health with the following response options: “excellent”, “very good”, “good”, “fair” or “poor”. Multinomial logistic regression was used to examine the independent effects of PA and BMI on each HRQOL rating (relative to excellent) while controlling for sociodemographic variables. Results: Differences in HRQOL prevalence was seen within all sociodemographic variables except sex. Additionally, BMI was significantly (p < .05) greater in adults reporting fair or poor health (Mean = 30.30, SE = 0.32) compared to those reporting excellent, very good or good health (Mean = 27.28, SE = 0.09), with a similar trend seen across all sociodemographic groups. The fully adjusted regression model including APA/MSA showed decreased odds of very good (OR = 0.75, 95% CI: 0.60 – 0.92), good (OR = 0.61, 95% CI: 0.49 – 0.78), fair (OR = 0.56, 95% CI: 0.40 – 0.78), and poor health (OR = 0.44, 95% CI: 0.28 – 0.69) (relative to excellent health) for adults meeting both APA and MSA. In the same model, increased odds was seen for very good (OR = 1.08, 95% CI: 1.06 – 1.10), good (OR = 1.15, 95% CI: 1.13 – 1.18), fair (OR = 1.19, 95% CI: 1.16 – 1.23), and poor health (OR = 1.16, 95% CI: 1.12 – 1.21) (relative to excellent health) for each 1-unit increase in BMI (1.00 kg/m²). Similar findings were seen in the separate APA model but not the MSA model. Conclusion: This study found that meeting PA guidelines and BMI were both independently related to HRQOL in adults. However, meeting MSA showed lower effects and inconsistent effects on HRQOL. Health promotion specialists concerned with improving HRQOL should promote both APA and MSA guidelines along with healthy body weight behavior. Physical activity programming should consider APA a priority over MSA for improving HRQOL in Montana adults.

Keywords: body mass index (BMI), physical activity (PA), muscle strengthening activity (MSA), health-related quality of life (HRQOL)

Received September 07, 2021; Revised October 11, 2021; Accepted October 19, 2021

Citation: Peter D. Hart, Physical Activity and Body Mass Index (BMI) as Predictors of Health-related Quality of Life in Montana Adults. Journal of Physical Activity Research, 6 (2). 135-141. January 2021. doi:10.12691/jpar-6-2-12

1. Introduction

Health-related quality of life (HRQOL) is a commonly used outcome measure in both population-based and clinical-based research [1,2,3,4,5,6]. From January 2020 to October 2021, over 9,500 articles using the phrase “health-related quality of life” have been indexed in the PUBMED database, accounting for almost 20% of the total articles on the topic since 1982. More specifically, HRQOL is a subjective multidimensional concept that extends beyond objective measures of health-status by measuring the extent to which health can affect the quality of a person’s life [7]. Physical activity (PA) is a health behavior known to positively influence HRQIL in adults [8,9]. Additionally, muscle strengthening activity (MSA), a specific form of PA, is known to improve HRQOL when used as an intervention component in adults [10]. Obesity is a major health problem worldwide and is strongly associated with the modifiable behaviors of poor diet and physical inactivity [11]. As expected, HRQOL is also associated with obesity with obese populations more likely reporting poor health than normal weight counterparts [12,13]. Given these clear associations linking both PA and obesity to HRQOL, it is unclear the extent to which PA and obesity measures independently influence HRQOL in adult populations. Therefore, the aim of this research was to examine how meeting PA guidelines and body mass index (BMI) affect a measure of HRQOL in adults.

2. Materials & Methods

Data for this study came from the 2019 Montana Behavioral Risk Factor Surveillance System (BRFSS). BRFSS methodological details can be found elsewhere [14,15]. The BRFSS is a state-based annual telephone survey aimed at collecting data about health factors related to the leading causes of premature morbidity and mortality. The BRFSS samples noninstitutionalized U.S. adults 18+ years of age. The Montana BRFSS data were extracted from the larger dataset for this study.

Three different PA guideline variables were used in this study. A two-level aerobic PA (APA) variable was constructed and participants were classified as either those that “met APA” or “did not meet APA” guidelines. A two-level muscle strengthening activity (MSA) variable was constructed and participants were classified as either those that “met MSA” or “did not meet MSA” guidelines. A final two-level PA variable was constructed and participants were classified as either those that “met both APA and MSA” or “met neither” guideline. Meeting APA was determined from a series of questions asking participants about their PA and exercise during the previous month. After reporting the types of activities, the usual frequency, and usual duration, a total minutes of PA per week was computed for each respondent. Those reporting 150+ minutes of total PA per week were considered those that “met APA” guidelines. Those reporting less than 150 minutes of total PA per week were considered those that “did not meet APA” guidelines [16].

Meeting MSA was determined from a series of questions asking participants about their muscle strengthening PA and exercise during the previous month. Participants reporting 2+ days per week of MSA were considered those that “met MSA” guidelines and participants reporting less than 2 days per week were considered those that “did not meet MSA” guidelines [17]. BMI was calculated from self-reported height and weight (kg/m²). HRQOL was assessed from a question asking participants to rate their general health with the following response options: “excellent”, “very good”, “good”, “fair” or “poor”. Study covariates included sex, age, race/ethnicity, income, education, and rural/urban status.

Statistical analyses included prevalence estimates (%) and standard errors (SEs) for HRQOL groups across sociodemographic characteristics. Also, means and SEs were computed for BMI (kg/m²) by HRQOL groups and across sociodemographic variables. APA and MSA parameter status. Test for difference in prevalence estimates and means were performed using the Rao-Scott chi-square (χ²RS) test of independence and regression analysis t statistic, respectively. The proportional odds assumption was violated (χ² = 11,831.8, p < .0001) for HRQOL which lead to a generalized logit model. Multinomial logistic regression was then used to examine the independent effects of PA and BMI on each HRQOL rating relative to excellent health. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported to evaluate predictor variables. Fully adjusted regression models were controlled for sex, age, race/ethnicity, income, education, and rural/urban status. 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 [18,19,20,21].

3. Results

Overall, a total of N = 6,477 participants had complete HRQOL data with an 84.8% (95% CI: 83.7% – 85.8%) prevalence of good HRQOL (excellent or very good, or good) and a 15.2% (95% CI: 14.2% – 16.3%) prevalence of poor HRQOL (fair or poor). Table 1 contains these prevalence estimates across pertinent sociodemographic variables. Most notably, sex difference in HRQOL estimates were not significant (p = .7502). However, older adult, American Indian and Multiracial, lower income, less educated, and rural groups all trended toward higher rates of poor health, compared to their respective counterparts (all ps < .05). Table 2 contains BMI comparison by HRQOL status across the same sociodemographic variables. Overall, BMI was significantly (p < .0001) lower in good HRQOL (Mean = 27.28, SE = 0.09) as compared to poor HRQOL (Mean = 30.30, SE = 0.32). Similar significant differences were seen across all sociodemographic groups with the largest HRQOL-related BMI differences seen in young adulthood (25 to 34 yr) and middle age (45 to 54 yr) and Hispanic and Multiracial. Table 3 contains unadjusted multinomial logistic regression models for PA and BMI predicting HRQOL. PA and BMI was significantly related to all general health categories for APA, MSA, and both APA/MSA models.

Table 4 contains the adjusted multinomial logistic regression models for PA and BMI predicting HRQOL. These results indicate that PA and BMI significantly relate to all general health categories in APA and APA/MSA models only. For example, the fully adjusted regression model including APA/MSA showed decreased odds of very good (OR = 0.75, 95% CI: 0.60 – 0.92), good (OR = 0.61, 95% CI: 0.49 – 0.78), fair (OR = 0.56, 95% CI: 0.40 – 0.78), and poor health (OR = 0.44, 95% CI: 0.28 – 0.69) (relative to excellent health) for adults meeting both APA and MSA. In the same model, increased odds was seen for very good (OR = 1.08, 95% CI: 1.06 – 1.10), good (OR = 1.15, 95% CI: 1.13 – 1.18), fair (OR = 1.19, 95% CI: 1.16 – 1.23), and poor health (OR = 1.16, 95% CI: 1.12 – 1.21) (relative to excellent health) for each 1-unit increase in BMI (1.00 kg/m²). In the MSA model, odds of very good health (relative to excellent health), odds of fair health (relative to excellent health), and odds of poor health (relative to excellent health) were not significantly different for those meeting MSA guidelines. This left a decreased odds of good health (OR = 0.68, 95% CI: 0.55 – 0.85), relative to excellent health, for adults meeting MSA.

Table 1. Distribution of general health status by sociodemographic characteristic in Montana adults, 2019

Table 2. Body mass index (BMI) comparison by general health status across sociodemographic characteristic in Montana adults, 2019

Table 3. Unadjusted multinomial logistic regression models for physical activity (PA) and body mass index (BMI) predicting general health in Montana adults, 2019

Table 4. Adjusted multinomial logistic regression models for physical activity (PA) and body mass index (BMI) predicting general health in Montana adults, 2019

Figure 1. Mosaic plot displaying the distribution of general health by BMI categories in Montana adults, 2019 (Note. N = 6,028. χ²RS = 395.3, p < .0001. Body mass index (BMI) computed from self-reported height and weight (kg/m²))

Figure 2. Mosaic plot displaying the distribution of general health by PA guidelines in Montana adults, 2019 (Note. N = 5,810. χ²RS = 172.8, p < .0001)

Figure 1 is a mosaic plot displaying the distribution of general health by BMI categories in Montana adults. Visually, the plot indicates that general health status is dependent on BMI category. This visual pattern is consistent with the significant chi-square test, χ²RS = 395.3, p < .0001. Figure 2 is a different mosaic plot displaying the distribution of general health by PA guideline status. Similarly, the plot indicates that general health status is dependent on PA guideline status. This visual pattern is consistent with the significant chi-square test, χ²RS = 172.8, p < .0001.

4. Discussion

The purpose of this study was to examine how meeting PA guidelines and BMI affect a measure of HRQOL in adults. Results indicate that meeting PA guidelines and BMI independently predict different levels of HRQOL in adults. Specifically, meeting APA was associated with decreased odds of reporting all lower general health categories, relative to excellent health, controlling for BMI and sociodemographic variables. Similar results were seen for the meeting both APA/MSA analyses. The noteworthy finding was that the MSA predictor was not robustly related to HRQOL. In fact, meeting MSA decreased the odds of reporting only good health, relative to excellent health, controlling for BMI and sociodemographic variables. In other words, MSA could not predict varying levels of reported general health. This finding may also suggest that the PA and HRQOL relationship seen in the APA/MSA analyses was due primarily to the APA effect on HRQOL and not MSA. Another interesting discussion point from this study is the fact that BMI was significantly related to HRQOL in all analyses. Specifically, odds of reporting all lower general health categories increased, relative to excellent health, as BMI increased – and did so consistently in APA, MSA, and APA/MSA models, independent of PA and all sociodemographic variables.

There are some limitations worth mentioning about these findings. One such limitation is the cross-sectional nature of the BRFSS. Cross-sectional research is limited to correlational generalizations and not cause-and-effect inferences that can be identified using experimental designs. Regardless of this limitation, the findings of this study are consistent with those of other studies. For example, a large study of over 1,900 participants found that adults meeting 150+ minutes of accelerometer-determined moderate-to-vigorous PA per week had significantly greater HRQOL scores than counterparts not meeting the same PA guideline [22]. Another limitation of this study is that the data were collected via telephone. This becomes a limitation if certain segments of the population are less likely to have access to a phone. Moreover, these subpopulations may be less likely to meet PA guidelines, more likely to report poor health status, and more likely to be obese. A final limitation of this study is the use of self-reported assessments of PA, BMI, and HRQOL. Given these limitations, findings from this study should be interpreted with caution.

5. Conclusions

This study found that meeting PA guidelines and BMI were both independently related to HRQOL in adults. However, meeting MSA showed lower effects and inconsistent effects on HRQOL. Health promotion specialists concerned with improving HRQOL should promote both APA and MSA guidelines along with healthy body weight status. Physical activity programming should consider APA a priority over MSA for improving HRQOL in Montana adults.

References

[1]   | Julian V, Thivel D, Miguet M, Pereira B, Lambert C, Costes F, Richard R, Duclos M. Eccentric Cycling Training Improves Health-Related Quality of Life in Adolescents with Obesity. Obesity Facts. 2020; 13(6): 548-59.

[2]  
MacDonald CS, Nielsen SM, Bjørner J, Johansen MY, Christensen R, Vaag A, Lieberman DE, Pedersen BK, Langberg H, Ried-Larsen M, Midtgaard J. One-year intensive lifestyle intervention and improvements in health-related quality of life and mental health in persons with type 2 diabetes: a secondary analysis of the U-TURN randomized controlled trial. BMJ Open Diabetes Research and Care. 2021 Jan 1; 9(1): e001840.

[3]  
Tous-Espelosín M, Gorostegi-Anduaga I, Corres P, Martinez Aguirre-Betolaza A, Maldonado-Martín S. Impact on Health-Related Quality of Life after Different Aerobic Exercise Programs in Physically Inactive Adults with Overweight/Obesity and Primary Hypertension: Data from the EXERDIET-HTA Study. International Journal of Environmental Research and Public Health. 2020 Jan; 17(24): 9349.

[4]  
Ryu M, Lee S, Kim H, Baek WC, Kimm H. Effect of Aerobic Physical Activity on Health-Related Quality of Life in Middle Aged Women with Osteoarthritis: Korea National Health and Nutrition Examination Survey (2016–2017). International journal of environmental research and public health. 2020 Jan; 17(2): 527.

[5]  
Xu F, Cohen SA, Lofgren IE, Greene GW, Delmonico MJ, Greaney ML. Relationship between diet quality, physical activity and health-related quality of life in older adults: Findings from 2007–2014 national health and nutrition examination survey. The journal of nutrition, health & aging. 2018 Nov; 22(9): 1072-9.

[6]  
Marcos-Delgado A, Fernández-Villa T, Martínez-González MÁ, Salas-Salvadó J, Corella D, Castañer O, Martínez JA, Alonso-Gómez ÁM, Wärnberg J, Vioque J, Romaguera D. The effect of physical activity and high body mass index on health-related quality of life in individuals with metabolic syndrome. International Journal of Environmental Research and Public Health. 2020 Jan; 17(10): 3728.

[7]   | Office of Disease Prevention and Health Promotion. (n.d.). Health-Related Quality of Life & Well-Being. Healthy People 2020. U.S. Department of Health and Human Services. https://www.healthypeople.gov/2020/topics-objectives/topic/health-related-quality-of-life-well-being.
[8]   | Hart PD. Meeting recommended levels of physical activity and health-related quality of life in rural adults. Journal of lifestyle medicine. 2016 Mar; 6(1): 1.
[9]   | Hart PD. Sex differences in the physical inactivity and health-related quality of life relationship among rural adults. Health promotion perspectives. 2016; 6(4): 185.
[10]   | 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.
[11]   | McArdle WD, Katch FI, Katch VL. Exercise physiology: nutrition, energy, and human performance. Lippincott Williams & Wilkins; 2010.
[12]   | Wilkins J, Ghosh P, Vivar J, Chakraborty B, Ghosh S. Exploring the associations between systemic inflammation, obesity and healthy days: a health related quality of life (HRQOL) analysis of NHANES 2005–2008. BMC obesity. 2018 Dec; 5(1): 1-2.
[13]   | Amiri P, Jalali-Farahani S, Rezaei M, Cheraghi L, Hosseinpanah F, Azizi F. Which obesity phenotypes predict poor health-related quality of life in adult men and women? Tehran Lipid and Glucose Study. PloS one. 2018 Sep 12; 13(9): e0203028.
[14]   | Piercy KL, Troiano RP. Physical activity guidelines for Americans from the US department of health and human services: Cardiovascular benefits and recommendations. Circulation: Cardiovascular Quality and Outcomes. 2018 Nov; 11(11): e005263.
[15]   | 2018 Physical Activity Guidelines Advisory Committee. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC: U.S. Department of Health and Human Services, 2018.
[16]   | Centers for Disease Control and Prevention. The BRFSS data user guide. August 15, 2013.
[17]   | Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Overview: BRFSS 2019. July 26, 2019.
[18]   | SAS Institute Inc. 2015. SAS/STAT® 14.1 User’s Guide. Cary, NC: SAS Institute Inc.
[19]   | IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp.
[20]   | 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.
[21]   | 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).
[22]   | Sun K, Song J, Lee J, Chang RW, Eaton CB, Ehrlich-Jones L, Kwoh KC, Manheim LM, Semanik PA, Sharma L, Sohn MW. Relationship of meeting physical activity guidelines with health-related utility. Arthritis care & research. 2014 Jul; 66(7): 1041-7.