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Background: Coronary heart disease (CHD) is the number 1 cause of death in United States (U.S.) with stroke also a leading killer at number 5. Aside from decreased quality of life and increased years of potential life lost (YPLL), cardiovascular disease (CVD) has a major impact on health care costs. While many CVD events can be prevented by adopting a healthy lifestyle, data quantifying this are sparse. The aim of this study was to assess the proportion of CVD cases that can be attributed to not meeting physical activity (PA) guidelines in a specific adult population. Methods: The Montana Behavioral Risk Factor Surveillance System (BRFSS, 2019) was used for this study. A dichotomous PA variable was created indicating whether or not a participant met current PA guidelines. Seven other healthy lifestyle variables were created and included smoking, alcohol consumption, overweight status, vegetable consumption, fruit consumption, and, health coverage. Three different outcome variables were used and included CHD, stroke, and CVD (CHD or stroke). Logistic regression was used to examine the relationship between PA and each CVD outcome while controlling for healthy lifestyle factors and sociodemographic variables. Population attributable risk (PAR) percentages were computed substituting PA odds ratio (OR) values into Levin's formula. Results: Prevalence of reported CHD, stroke, and CVD were 6.7%, 3.1%, and 8.7%, respectively. Additionally, prevalence of each CVD outcome was significantly (ps < .05) higher in adult groups not meeting PA guidelines, as compared to their more active counterparts. PA was significantly related to CHD (OR = 1.36, 95% CI: 1.04 – 1.79) and CVD (OR = 1.32, 95% CI: 1.02 – 1.71) in fully adjusted models and suggestively related to stroke (OR = 1.24, 95% CI: 0.81 – 1.91). PAR analyses indicated that approximately 12.1%, 8.4%, and 10.8% of CHD, stroke, and CVD cases (respectively) could be prevented in Montana with PA intervention. Conclusion: This study found that after controlling for several healthy lifestyle factors, not meeting PA guidelines independently contributes to a sizable number of CVD cases in Montana. Health promotion programming leaders should use such PAR statistics to drive the needs assessment process and plan for PA interventions.

Background: With growing concerns for obesity and health comes the need to better understand factors that may affect body mass index (BMI). The aim of this research was to examine the influence of healthy lifestyle factors and health status indicators on BMI in adults. Methods: The Montana Behavioral Risk Factor Surveillance System (BRFSS, 2020) was used for this study. Seven healthy lifestyle variables were created indicating “high risk” and included physical activity, smoking, alcohol consumption, seatbelt use, visiting a dentist, health insurance, and sleep quantity. Nine health status variables were created indicating “poor” health and included self-rated health, heart disease, stroke, cancer, lung disease, depression, arthritis, kidney disease, and diabetes. Multiple linear regression was used to examine the effect of healthy lifestyle factors and health status indicators on BMI while controlling for sociodemographic variables. Results: The fully adjusted healthy lifestyles model showed high risk of physical activity (slope (b) = 1.72 kg/m2), seatbelt use (b = 1.04 kg/m2), and sleep quantity (b = 0.92 kg/m2) directly related and smoking (b = -2.14 kg/m2) and alcohol consumption (b = -0.81 kg/m2) indirectly related to BMI (all ps < .05). The healthy lifestyle factors of visiting a dentist and health insurance did not independently influence BMI. The fully adjusted health status model showed poor health status for self-rated health (b = 1.84 kg/m2), depression (b = 1.54 kg/m2), arthritis (b = 1.02 kg/m2), and diabetes (b = 3.28 kg/m2) directly related to BMI (all ps < .05). The health status indicators of heart disease, stroke, cancer, and lung disease did not independently influence BMI. Furthermore, the physical activity × diabetes status interaction was significant (p = .031) and indicated substantially greater mean BMI for those high risk for physical activity (b = 2.57 kg/m2, p = .003) among those with poor health status for diabetes, as compared to those high risk for physical activity (b = 1.33 kg/m2, p < .0001) among those with good health status for diabetes. Conclusion: This study found that several healthy lifestyle factors and health status indicators influence BMI in adults. Health promotion specialists concerned with obesity should understand the influence that each healthy lifestyle factor has on relative body weight. Physical activity programming should in particular target those who have poor health status for diabetes in Montana.

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.

Background: Physical activity (PA) and risk of obesity are inversely related in adult populations. However, the extent to which meeting different PA guidelines influence body mass index (BMI) is less known. The aim of this research was to examine how different PA guideline parameters affect BMI in adults. Methods: The Montana Behavioral Risk Factor Surveillance System (BRFSS, 2019) was used for this study. Four different PA guideline variables were used and included 1) 2-level aerobic PA (APA) (met APA or did not meet APA), 2) 2-level muscle strengthening activity (MSA) (met MSA or did not meet MSA), 3) 4-level PA (met both APA and MSA, met APA only, met MSA only, or met neither), and 4) 3-level APA categories (active, insufficiently active, or inactive). BMI was calculated from self-reported height and weight (kg/m2). Multiple linear regression was used to examine the effect of each PA guideline parameter on BMI while controlling for sociodemographic variables. Results: Findings showed that meeting the 2-level APA (slope (b) = -0.74 kg/m2, p < .0001) or the 2-level MSA (b = -0.75 kg/m2, p < .0001) guideline was associated with lower mean BMI. In the 4-level PA model, as compared to meeting neither guideline, meeting APA only (b = -0.58 kg/m2, p = .0119) or meeting both APA and MSA (b = -1.32 kg/m2, p < .0001) was associated with lower mean BMI. Meeting MSA only did not show significantly (p = .1748) different BMI from those meeting neither guideline. In the 3-level APA model, as compared to those categorized as inactive, those categorized as active (b = -0.91 kg/m2, p < .0001) had lower mean BMI. Those categorized as insufficiently active did not have significantly (p = .1748) different BMI from those categorized as inactive. Furthermore, the 4-level PA guidelines × sex interaction was significant (p = .030) and indicated lower mean BMI for females meeting either MSA only (b = -1.05 kg/m2, p = .0215), APA only (b = -1.14 kg/m2, p = .0014), or both APA and MSA (b = -1.84 kg/m2, p < .0001) guideline (p for trend < .0001). Whereas for males, only meeting both APA and MSA was associated with lower mean BMI (b = -0.77 kg/m2, p = .0077). Conclusion: This study found that meeting either APA or MSA guideline is associated with lower BMI in adults. However, sex-specific analyses indicate that this is true for females only and in dose-response fashion. Lower BMI in males is only associated with meeting both APA and MSA guidelines. Health promotion specialists concerned with obesity should understand the influence that each PA guideline has on relative body weight. Physical activity programming should promote both APA and MSA among adults in Montana.

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 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.

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 p for trend p for trend p for trend 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.

Background: There is much debate surrounding the fat but fit paradox, namely, questioning the extent to which physical fitness protects obese/overweight populations from premature mortality. The purpose of this study was to examine the link between joint body weight (BW) profile and physical activity (PA) (BW.PA) and cardiovascular disease (CVD) risk in adults. Methods: Data for this study came from the 2019 Montana Behavioral Risk Factor Surveillance System (BRFSS). Participants were firstly assigned to one of three BW groups (normal weight, overweight, obese) using CDC BMI criteria, secondly assigned to one of three PA groups (active, insufficiently active, inactive) using self-reported moderate-to-vigorous PA (MVPA), and finally assigned to one of the resulting nine joint BW.PA groups. Three main CVD risk factors (hypertension, hypercholesterolemia, diabetes) were used as separate and then combined outcomes. Logistic regression was employed to estimate the odds of having a CVD risk factor for each BW.PA group, relative to “normal weight and active”. Results: A direct linear trend in odds ratios (ORs) was observed across BW.PA groups for each CVD risk factor, from “normal weight and active” to “obese and inactive”. Fully adjusted regression models showed increased odds of having any of the three CVD risk factors (versus having none) for adults “overweight and active” (OR = 2.22, 95% CI: 1.73 – 2.85) and adults “obese and active” (OR = 4.03, 95% CI: 3.08 – 5.27), compared to those “normal weight and active”. Odds were also significantly (p < .05) greater for the “obese and active” as compared to the “overweight and active”. Additionally, PA did not significantly alter odds of having any CVD risk factor in obese populations. Conclusion: This study found that BW profile was a strong and consistent predictor of three primary CVD risk factors. Obese adults were much more likely to have CVD risk, regardless of PA participation. Hence, fat but fit may not be a paradox among adults in Montana.

Background: Much research supports the physical activity (PA) and health-related quality of life (HRQOL) relationship. However, less is known about this association when advanced measurement techniques are used to assess HRQOL. The aim of this study was to use item response theory (IRT) to score HRQOL items and subsequently examine the extent to which PA relates to the new measure. Methods: Data from N = 6,245 adults 18+ years of age participating in the 2019 Montana Behavioral Risk Factor Surveillance System (BRFSS) were used. Four different 3-category HRQOL items were created from survey questions regarding general, physical, mental, and activity health. Two PA variables were created from survey questions regarding PA guidelines and PA quantity. A graded response IRT model (GRM) was used to assess the HRQOL scale and output ability scores. Multinomial logistic regression was used to examine the relationship between PA and HRQOL tertile membership while adjusting for sex, age, race, income, education, marital status, BMI, smoking status, and alcohol consumption. Results: Factor analysis retained a single HRQOL factor (loadings > .68) with ordinal alpha indicating acceptable internal consistency (αordinal = .83). The GRM analysis confirmed all HRQOL items fit a unidimensional construct with adequate discrimination (as: 1.17 to 5.13) and difficulty (bs: -2.052 to -0.07). Fully adjusted regression models showed increased odds of high HRQOL tertile membership (versus low tertile) for adults meeting aerobic PA (APA) (OR = 1.64, 95% CI: 1.28 – 2.09) and adults meeting both APA and muscle strengthening activity (MSA) (OR = 1.73, 95% CI: 1.34 – 2.23) guidelines, compared to those meeting neither APA nor MSA guidelines. Similarly, there was increased odds of high HRQOL tertile membership (versus low tertile) for adults considered sufficiently active (OR = 1.59, 95% CI: 1.16 – 2.19) and adults considered highly active (OR = 2.03, 95% CI: 1.58 – 2.61), compared to those considered inactive. Adults meeting MSA guidelines only and adults insufficiently active were no more likely to see high HRQOL tertile membership than their less active counterparts. Conclusion: The IRT-derived HRQOL score is a novel outcome measure and found to be associated with both PA guidelines and PA quantity among adults in Montana.

Background: Few studies have used a multi-item approach to measure diet when examining its relationship with physical activity (PA) in youth. Design and Methods: Data came from the 2019 Youth Risk Behavior Survey (YRBS). Item response theory (IRT) was applied to develop the Poor Diet Scale (PDS). Ten items were dichotomized to indicate poor diet: fruit juice, fruit, green salad, potatoes, carrots, other vegetables, milk, water, breakfast, and soda. Six variables were dichotomized to indicate PA risk: active for 5+ days (PA5d), active all 7 days (PA7d), 3+ days of muscle strengthening exercise (MSE3d), physical education all 5 days (PE5d), participation in 1+ sport teams (ST1), and combined PA.MSE. Logistic regression was used to model PA using the IRT-derived PDS scores (theta) while controlling for age, sex, race, BMI percentile, and sedentary time. Results: All items significantly fit (ps < .001) the IRT model except for soda, which was dropped from the scale. In fully adjusted regression models, odds of PA risk increased significantly (ps < .05) with every 1 z-score (theta) increase in poor diet for PA5d (OR = 1.74), PA7d (OR = 1.53), MSE3d (OR = 1.85), PE5d (OR = 1.15), ST1 (OR = 1.73), and PA.MSE (OR = 1.81). Additionally, students not trying to lose weight had significantly (p = .004) greater odds as theta increased for PA7d risk (OR = 1.68), as compared to their counterparts (OR = 1.34). Conclusions: Results highlight the utility of using multiple items to measure a complex health risk behavior.

Background: Abdominal obesity is a cardiometabolic risk factor and shown to be related to cardiorespiratory fitness (CRF). The purpose of this study was to examine the moderating effects of health insurance (HI) on the waist circumference (WC) and CRF relationship in U.S. adolescents. Methods: Data from youths 12 to 15 years of age participating in the 2012 NHANES National Youth Fitness Survey (NNYFS) were used. Both WC (cm) and CRF (ml/kg/min) were measured by trained medical personnel with HI status assessed via questionnaire by proxy. Binary variables were created at the median for WC and CRF. Multivariate logistic regression was used to examine the relationship between WC, HI and CRF while controlling for confounding variables. Results: The overall prevalence of no HI was 6.5% (95% CI: 2.78 - 10.20). Findings from the fully adjusted model predicting low CRF showed a significant (p = .020) WC-by-HI interaction. Slicing the model by HI status showed a significant decreased risk of low CRF for adolescents with HI and low WC (OR = 0.25, 95% CI: 0.19 - 0.33). Additionally, a significant decreased risk of low CRF was seen among those with no HI and low WC (OR = 0.50, 95% CI: 0.28 - 0.88). Slicing the model by WC status showed a significant increased risk of low CRF for adolescents with low WC and no HI (OR = 2.44, 95% CI: 1.22 - 5.32). However, no significant HI and CRF relationship was observed among adolescents with high WC. Conclusion: This study showed that HI moderates the WC and CRF relationship in U.S. adolescents. Specifically, having low WC appears to protect adolescents against low CRF. Whereas having HI looks as if to protect against low CRF only for those with low WC.

Background: A simple prediction equation that accurately predicts an individual’s percent body fat (PBF) with easy to obtain inputs could benefit health and exercise science professionals. The purpose of this study was to develop and cross-validate a set of regression equations predicting PBF in young adults. Methods: A subset of N=684 participants from a national health survey between the ages of 18 and 24 years was used in this study. Criterion values of PBF (PBF.DXA) were obtained using dual energy X-ray absorptiometry (DXA). Predictor variables included age, sex, body mass index (BMI), and waist circumference (WC). The sample was split equally and randomly into training and validation samples. Two sets of equations were evaluated in the training sample, one set using BMI and the other using WC. Both sets were tested to determine if age was a useful predictor of PBF.DXA. Cross-validation of selected model coefficients using the validation sample was evaluated using Pearson correlation coefficients, Bland and Altman limits of agreement (LOA) plots and Kappa statistics for obesity classification. Results: The selected models were both two-predictor equations: PBF.BMI2 = 10.47827 + BMI*0.98342 – 12.50670; R2 = .872 and PBF.WC2 = 2.51020 + WC*0.38914 – 13.34843; R2 = .866. Cross-validation correlation coefficients were large for both PBF.BMI2 (r = .91) and PBF.WC2 (r = .92) equations. LOA plots indicated small bias of -0.49 ± 7.5% and -0.25 ± 6.8% in PBF.BMI2 and PBF.WC2 analyses, respectively. Kappa coefficients for agreement between the two obesity classification methods were considered “substantial” for PBF.BMI2 (κ = .64) and PBF.WC2 (κ = .70) models. Conclusion: This study provides validation evidence supporting the use of BMI- and WC-based PBF prediction equations in young adult populations.

Background: Grip strength is a measure of muscular fitness and is related to many health problems in women. The primary purpose of this study was to examine the relationship between grip strength and HRQOL in U.S. women. A secondary purpose was to examine the extent to which physical activity (PA), obesity, and smoking moderate the grip strength and HRQOL relationship. Methods: Data for this research came from women 20 years of age and older participating in the 2013-2014 National Health and Nutrition Examination Survey (NHANES). Grip strength (kg) was measured in both hands using a handgrip dynamometer and the largest reading across all trials served as the participant’s score. HRQOL was assessed by a single question asking participants to rate their general health. Additionally, measures of body mass index (BMI), waist circumference (WC), moderate-to-vigorous physical activity (PA) (MVPA), TV time, sedentary time, and smoking were assessed. Multiple linear regression was used to model the relationship between HRQOL and grip strength while controlling for confounding variables. Results: Grip strength decreased proportionately in women with increasing age (ppslope=2.04 kg, SE=0.26, pConclusion: Results from this study indicate that grip strength and HRQOL are related in U.S. women. Furthermore, the grip strength and HRQOL relationship appears to remain in women who are current smokers.

Background: With the growing need to promote muscle strengthening activity (MSA) for improved health-related quality of life (HRQOL) comes the growing need for proper measurement of MSA behavior. The purpose of this study was to examine test and item functioning of the MSA scale (MSAS) using item response theory (IRT). Methods: The current research fit data from a sample of N = 400 respondents to two different graded response models (GRMs), a three-item muscular strength scale and a three-item muscular endurance scale. For each GRM, model-data fit was examined and IRT assumptions assessed. Results: An unconstrained GRM was found to fit the data better than the constrained model (Δ G2Strength = 10.3, p = .006, RMSEA = .043 & Δ G2Endurance = 7.0, p = .031, RMSEA = .021). GRM boundary location parameters covered the latent trait scale well for both strength (bs: -4.26 to 2.58) and endurance (bs: -3.86 to 1.79) scales with each item showing adequate fit to the data (all RMSEAs strength) and 93.5% (endurance). Only 2.3 and 1.5% of persons misfit the strength and endurance GRMs, respectively. Conclusion: The MSAS has shown to be a valid tool for measuring MSA behavior in adults using modern psychometric theory.

Background: Few national-level studies have examined the effects of both physical activity (PA) and health-related quality of life (HRQOL) on mortality. The purpose of this study was to examine the ability of PA and HRQOL to predict all-cause mortality in U.S. adults. Methods: Data for this research came from the 2003-2004 National Health and Nutrition Examination Survey (NHANES) and its 2011 mortality-linked National Death Index (NDI) file. Moderate-to-vigorous PA (MVPA, min/day) was objectively determined by accelerometry and participants were categorized into low or high groups using the median. HRQOL was assessed by a single question asking participants to rate their general health. Participants rating their health as “good”, “very good”, or “excellent” were considered to have good HRQOL whereas those rating it “fair” or “poor” were considered to have poor HRQOL. Cox proportional hazard regression was used to determine the independent effects of MVPA and HRQOL on all-cause mortality while controlling for covariates. Results: In the fully adjusted females model, participants were at greater risk of mortality if they engaged in low amounts of MVPA (Hazard Ratio (HR)=2.82, 95% CI: 1.02-7.85) and had poor HRQOL (HR=7.19, 95% CI: 2.30-22.51) with no significant (p=.133) MVPA-by-HRQOL interaction. Conversely, the equivalent males model saw a significant (p=.002) MVPA-by-HRQOL interaction, resulting in an analysis of simple effects. In the fully adjusted males with good HRQOL model, participants were at greater risk of mortality if they engaged in low amounts of MVPA (HR=2.92, 95% CI: 1.56-5.49). However, in the males with poor HRQOL model, MVPA could not significantly predict mortality (HR=1.04, 95% CI: 0.49-2.24). Conclusion: Results from this study indicate that MVPA and HRQOL independently predict all-cause mortality in U.S. female adults. In U.S. male adults, MVPA predicts all-cause mortality only in those with good HRQOL.

Background: The 2018 (2nd edition) Physical Activity Guidelines for Americans states that adults should participate in muscle strengthening activity (MSA) of at least moderate intensity using all major muscle groups on two or more days a week. However, these guidelines do not promote specific types of MSA such as muscular strength training or muscular endurance training. This ambiguity, in part, is due to the lack of evidence linking specific types of MSA to health outcomes. And this lack of evidence, in part, is due to the inability to measure varying MSA behavior. This study reports the construct validity evidence for the MSA Scale (MSAS). Methods: The following research consists of a second development stage presenting validity evidence for the MSAS. Previous research indicates that seven items can measure three MSA dimensions: a three-item muscular strength dimension, a three-item muscular endurance dimension, and a single-item body weight exercise dimension. The current research used both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to examine the MSAS construct validity. Results: EFA indicated a two-factor structure explained 100% of the common variance among the 6 strength and endurance items (3 items per factor with all loadings > .52). The first factor was defined as strength and the second endurance. CFA indicated the two-factor MSAS measurement model had adequate fit (χ2/df = 4.24, GFI = 0.97, CFI = 0.92, and RMSEA = 0.09) with strength and endurance significantly (p strength scores were strongly correlated with strength sum scale scores and weakly correlated with endurance and body sum scale scores. Similarly, factor endurance scores were strongly correlated with endurance sum scale scores and weakly correlated with strength and body sum scale scores. Conclusion: The seven-item MSAS is a simple and valid tool for measuring MSA behavior in adults. Two additional items are included in the MSAS to quantify MSA participation.

Background A need exists for a population-based evaluation of muscular strength in terms of its association with health-related quality of life (HRQOL) in males. Therefore, the purpose of this study was to examine the relationship between grip strength and HRQOL in a representative sample of U.S. men. Methods This study used data from adult males 20+ years of age participating in the 2013–2014 National Health and Nutrition Examination Survey. Grip strength (kg) was measured in both hands using a handgrip dynamometer. HRQOL was assessed by a single question asking participants to rate their general health. Additionally, measures of moderate-to-vigorous physical activity (PA), body mass index, waist circumference, TV time, sedentary time, and smoking were assessed. Multiple linear regression modeling for complex samples was used to examine the effect of HRQOL on grip strength while controlling for confounding variables. Results Overall, males with good HRQOL (Mean = 47.5 kg, SE = 0.31) had significantly greater grip strength than males with poor HRQOL (Mean = 44.5 kg, SE = 0.51, p < 0.001). In fully adjusted models, males with good HRQOL had greater grip strength (slope = 2.5 kg, SE = 0.57, p = 0.001) than their poor HRQOL counterparts. Additionally, HRQOL was a significant predictor of grip strength in male adults who did not meet PA guidelines but not in those who did meet PA guidelines. Conclusion Results from this study indicate that muscular strength and HRQOL are related in U.S. men. Furthermore, the muscular strength and HRQOL relationship appears to remain in adult males who do not meet PA guidelines.

Background: The fitness professional may often benefit from the use of a simple equation in determining a health outcome for an individual in lieu of a more complicated or expensive procedure. Therefore, the purpose of this study was to examine the reliability and validity of several standard prediction equations for percent body fat (PBF). Methods: Data used for this study came from a body composition assessment of N = 131 college students. Five different PBF prediction equations were used, with body mass index (BMI), age, and sex as inputs for each (PBFEQ1 thru PBFEQ5). Additionally, PBF using a bioelectric impedance (BIA) handheld device (PBFHH) was measured for each participant. Equivalence reliability was examined across the five PBF prediction equations using different analysis of variance (ANOVA) models of the intraclass correlation coefficient (ICC). Convergent validity between the prediction equations and PBFHH was determined by examining Pearson correlation coefficients and Bland and Altman limits of agreement (LOA). Reliability and validity was also examined for obesity classification using the Kappa statistic. Results: Reliability across the five PBF prediction equations was excellent for all ICC models in both female (ICCs > .985) and male (ICCs > .976) analyses. PBFHH scores adequately converged with scores from each prediction equation in both female (rs > .913) and male (rs > .817) analyses. LOA between PBFHH and PBFEQ5 indicate small to moderate bias of 4.0 ± 5.1% and 4.7 ± 7.9% in female and male analyses, respectively. Finally, reliability and validity of the prediction equations to classify participants into obese and non-obese categories ranged from moderate to almost perfect. Conclusion: This study provides psychometric evidence supporting the use of PBF prediction equations in a college student population.

This BriefTrends first describes the prevalence of meeting physical activity (PA) guidelines (MPAG) by education in Montana adults and second examines the relationship between MPAG and education.

PurposeEarly identification of a potentially deteriorating clinical course in hospitalized COVID-19 patients is critical since there exists a resource-demand gap for the ventilators.MaterialsWe aimed to develop and validate a deep learning-based approach to predict the need for mechanical ventilation as early as at the time of initial radiographic evaluation. We exploited the well-established DenseNet121 deep learning architecture for this purpose on 663 X-ray images derived from 528 hospitalized COVID-19 patients. Two Pulmonary and Critical Care experts blindly and independently evaluated the same X-ray images for purpose of validation.ResultsWe found that our deep learning model predicted the need for ventilation with a high accuracy, sensitivity and specificity (90.06%, 86.34% and 84.38%, respectively). This prediction was done approximately three days ahead of the actual intubation event. Our model also outperformed two Pulmonary and Critical Care experts who evaluated the same X-ray images and provided an incremental accuracy of 7.24–13.25%.ConclusionOur deep learning model accurately predicted the need for mechanical ventilation early during hospitalization of COVID-19 patients. Until effective preventive or treatment measures become widely available for COVID-19 patients, prognostic stratification as provided by our model is likely to be highly valuable.

Background: The five components of health-related fitness are cardiorespiratory endurance, muscular strength, muscular endurance, body composition, and flexibility. To assess an individual on all five components can be time consuming. Thus, it would be useful to fitness specialists if a simpler and valid fitness assessment was available to measure overall health-related fitness. The purpose of this study was to employ honest assessment predictive modeling to find a parsimonious set of variables that can predict overall health-related fitness. Methods: Data used for this study came from college students who completed a fitness test battery. An overall health-related fitness score (T-score) was constructed using maximal oxygen consumption (VO2, ml/kg/min), 1RM bench press (BP, lb), maximal push-up repetition (PU, #), and percent body fat (PBF, %). The set of possible predictor variables consisted of participant age (yr), sex (male/female), body mass index (BMI, kg/m2), waist circumference (WC, cm), 1RM leg press (LP, lb), countermovement vertical jump (VJ, in), flexed arm hang (FAH, sec), physical activity rating (PAR, 0 thru 10), and sit-and-reach (SNR, cm). The honest assessment predictive modeling procedure comprised three steps: 1) development of competing models using a TRAINING dataset, 2) selecting an optimal model using a separate VALIDATION dataset, and 3) assessing fitness score construct validity using a final SCORING dataset. Results: Stepwise model selection with Schwarz Bayesian criterion (SBC) on the TRAINING data resulted in five possible models including sex, VJ, PAR, and WC. Results on the VALIDATION data indicated a three-variable model had the lowest average squared error (ASE) and consisted of sex, VJ, and PAR (F=107.8, pR2=.82, SEE=3.09). Finally, predicted values from the SCORING data showed that athletes (Mean=54.9, SD=5.1) had a significantly (p.001) greater mean fitness score than non-athletes (Mean=39.8, SD=4.8). Conclusion: This study presents a valid equation that can simply predict overall health-related fitness in college students.

Background: Health-related fitness tests measure one of five different traits: cardiorespiratory endurance, muscular strength, muscular endurance, body composition, and flexibility. To assess an individual on all five traits can be costly and time consuming. Thus, it would be useful to the fitness practitioner if one single test could be used as a proxy for overall fitness. Therefore, the purpose of this study was to employ multivariate data analyses to examine the ability of the vertical jump (VJ) to predict health-related fitness performance. Methods: This study used data from college students who completed both ten different health-related fitness tests and the VJ assessment. Three body composition measures were used: percent body fat (PBF, %), body mass index (BMI, kg/m2), and waist circumference (WC, cm). Four muscular fitness measures were used: 1RM bench press (BP, lb), 1RM leg press (LP, lb), maximal push-up repetition (PU, #), and flexed arm hang time (FAH, sec). Two cardiorespiratory endurance measures were used: maximal oxygen consumption (VO2, ml/kg/min) and physical activity rating score (PAR, 0 thru 10). One flexibility measure was used: sit-and-reach (SNR, cm). The countermovement vertical jump (VJ, in) was used as the single predictor variable and participants were categorized into high or low VJ groups using the sex-specific median. Results: Male-specific multivariate analysis of variance (MANOVA) results showed that VJ significantly predicts the linear combination of body composition (λ=0.85, F=4.8, p=.004), muscular fitness (λ=0.66, F=10.4, pConclusion: Results from this study indicate that VJ is a valid predictor of health-related fitness performance in college students.

Background: Physical fitness tests are often administered as a battery, allowing for several different fitness scores. Furthermore, fitness scores are generally considered outcome variables, with no one test causing the performance of another. Therefore, using loglinear models to examine the relationship between fitness test performances is appropriate. Methods: This study used data from a campus-based fitness study of college students. Four physical fitness measures were used in the analysis. Cardiorespiratory fitness (CRF) was assessed by a multi-stage running test (VO2max, ml/kg/min). Lower-body power was assessed by a countermovement vertical jump (VJ) test (inches). Muscular endurance was assessed by a maximal push-up (PU) test (repetitions). Muscular strength was assessed by a 1RM bench press (BP) test. Participants were categorized into high or low fitness groups using each sex-specific fitness score median. Results: Results of the initial hierarchical loglinear models indicated no significant four-way or three-way interactions. A simplified loglinear model with all two-factor interactions provided adequate fit to the data. The odds of high BP performance were much greater with high PU (OR=3.2) and VJ (OR=2.3) performance. The odds of high CRF performance were also greater with high PU (OR=2.4) and VJ (OR=2.7) performance. Finally, odds of high PU performance were much greater with high VJ (OR=3.6) performance. CRF and BP were statistically independent. Conclusion: Results from this study indicate that the performance on a fitness test can be related to at least one other fitness test performance.

Background: Physical inactivity and sedentary behavior are known factors related to the growing obesity rates in US adults. However, most population-based physical activity research primarily use a single measure of obesity. Therefore, the purpose of this study was to examine the relationship between television (TV) time and two different measures of obesity in US adults. Methods: This study used data from adults 20+ years of age participating in the 2015-2016 National Health and Nutrition Examination Survey (NHANES). Using body mass index (BMI), participants were categorized as obese if their values were 30 kg/m2 or greater. Using waist circumference (WC), participants were categorized as obese if their values were greater than 88 (females) or 102 cm (males). TV time was assessed from a survey question and adults were categorized into one of four different groups. Multinomial logistic regression was used to model the relationship between TV time and three different obese status categories. Results: In fully adjusted models, odds of being BMI obese (OR=1.98; 95% CI: 1.32-2.98) and WC obese (OR=2.76; 95% CI: 1.88-4.05) were significantly greater in adults with 5+ hours of TV time as compared to those with Conclusion: Results from this study indicate that TV time is clearly related to both overall and abdominal obesity in US adults. Furthermore, this relationship remains in light of MVPA and appears stronger for adults with both types of obesity.

Background: Few population-based studies have examined the relationship of both physical activity (PA) and cardiorespiratory fitness (CRF) on the growing health problem of obesity. The purpose of this study was to examine the concurrent relationship of PA and CRF on two different measures of obesity. Methods: This study used data from adults 20-49 years of age participating in the 2003-2004 National Health and Nutrition Examination Survey (NHANES). Moderate-to-vigorous PA (MVPA, min/day) was objectively determined by use of accelerometer and participants were categorized into low or high groups using the median. CRF (ml/kg/min) was assessed using a submaximal treadmill test and measures were categorized into low or high values by applying age- and sex-specific standards. Using body mass index (BMI), participants were categorized as obese if their values were 30 kg/m2 or greater. Using waist circumference (WC), participants were categorized as obese if their values were greater than 88 (females) or 102 cm (males). Linear regression was used to test for mean CRF differences in study variables. Logistic regression was used to model the relationship between MVPA, CRF and obesity. Results: Participants in the high CRF group had significantly (psConclusion: Results from this study indicate that PA is only related to obesity when CRF is low. Furthermore, high CRF may protect less physically active adults from both overall and abdominal obesity.

Background: Physical inactivity (PIA) is a major risk factor linked to many chronic diseases as well as premature mortality. Waist circumference (WC) is a measure of abdominal obesity and is also associated with many health problems. The purpose of this study was to examine both PIA and WC as predictors of all-cause mortality in adults. Methods: Data for this research came from the 2001-02 National Health and Nutrition Examination Survey (NHANES) and linked mortality file. Only participants who were 18+ years of age and eligible for mortality linkage were used in the analysis. PIA status was determined from the answers to two questions that asked subjects if they participated in moderate and then vigorous physical activity. WC was assessed by a trained health professional. Cox proportional hazards regression was used to model the effects of PIA and WC on mortality while controlling for age, sex, race, and income. Results: Approximately 33% (SE=1.12) of adults were physically inactive at interview date with mean WC of 95.6 (SE=0.21) centimeters (cm). A total of 55,288 person-years of follow-up was observed with 965 deaths. In the unadjusted model, physically inactive adults were at greater risk of mortality (Hazard Ratio (HR) =2.42, 95% CI: 2.006, 2.928) as compared to their more active counterparts. A 2% increase in mortality (HR=1.02, 95% CI: 1.016, 1.025) was seen for each 1-cm increase in WC. The fully adjusted model showed a significant increase in mortality (HR=1.40, 95% CI: 1.130, 1.727) among those who were physically inactive independent of a 1% increase in mortality (HR=1.01, 95% CI: 1.002, 1.016) for each 1-cm increase in WC. Conclusion: Results from this study indicate that PIA and WC are independent predictors of mortality in adults. Health promotion programs should consider both physical activity as well as abdominal obesity in their programming objectives.

Body composition (BC) assessment is often conducted using one of several different field techniques, which individually are considered valid tests. Anecdotal evidence has suggested, however, that some individuals may rank relatively high when assessed by one method and relatively low when assessed by another method. This inconsistency would indicate that BC assessments have poor convergent validity. The purpose of this study was to examine the convergent validity of common BC assessments using a norm-referenced approach. A total of 67 college students participated in this measurement study and had their BC assessed by each of three different tests: percent body fat (PBF) by skinfold technique (PBFSF), waist circumference (WC), and body mass index (BMI). Two different statistical procedures were used to evaluate convergent validity of the three BC assessments. First, Cohen’s weighted kappas were calculated using quartiles of each BC measure. This analysis utilized three different 4 x 4 tables from all BC measure pairs. Second, Bland and Altman limits of agreement (LOA) plots were constructed on all pairs after T-score transformation of each measure. Mean (SD) values of PBFSF (%), WC (cm), and BMI (kg/m2) were 12.3 (5.0), 87.0 (8.3), 26.8 (3.5) and 23.3 (7.0), 77.1 (8.8), 24.8 (3.2) for males and females, respectively. Simple kappas showed poor agreement across the three pairs of BC assessments and ranged from .14 to .17. The weighted kappas improved to fair agreement and ranged from .32 to .38. None of the three LOA plots showed systematic bias toward a method. However, 95% LOA were wide for PBFSF vs. WC (± 28.9), BMI vs. PBFSF (± 25.9), and BMI vs. WC (± 12.3). Results of this measurement study indicate that common BC assessments have poor convergent validity among college students. These results further indicate that BC may be a multidimensional trait, requiring a specific test depending on the specific trait of interest.

Purpose: The purpose of this study was to examine the protective effects of different modes of physical activity (PA) on all-cause mortality in adults. Methods: Data for this research came from the 2001-2002 National Health and Nutrition Examination Survey (NHANES). Participants 18+ years of age who were eligible for mortality linkage were used in the analysis. Different modes of PA were determined from a series of questions asking respondents if they participated in transportation (TPA), home/yard (HPA), moderate recreational (MPA), vigorous recreational (VPA), or muscle strengthening (MSPA) physical activity. Those respondents answering “yes” to either question were considered participating in that PA mode. Cox proportional hazards regression was used to model the effects of PA mode on mortality while controlling for age, sex, race, and income. Results: Adults were at less risk of mortality if they participated in TPA (Hazard Ratio (HR) =0.72, 95% CI: 0.57, 0.90), HPA (HR=0.43, 95% CI: 0.33-0.55), VPA (HR=0.30, 95% CI: 0.23-0.38), MPA (HR=0.53, 95% CI: 0.45-0.62), and MSPA (HR=0.44, 95% CI: 0.32-0.60). The adjusted model showed a 24.0% decrease in mortality (HR=0.76, 95% CI: 0.67-0.85) for each additional PA mode adopted. Conclusions: Results from this study indicate that various types of PA protect adults from all-cause mortality. Additionally, a dose-response relationship exists between the number of PA modes adopted and risk of mortality.

Purpose: The purpose of this study was to examine the relationship between Physical activity (PA) and health-related quality of life (HRQOL) in a large-scale population of rural adults with chronic disease. Methods: Data for this study came from the CDC’s 2015 Behavioral Risk Factor Surveillance System (BRFSS). A total of N=65,492 rural adults 20+ years of age were included in the analysis. The main outcome variable was HRQOL as assessed by the CDC Healthy Days Index. Adults were categorized as “meeting” PA guidelines if they reported 150+ minutes of moderate-intensity PA per week and “not meeting” PA guidelines if they reported less than 150 minutes. Multiple logistic regression was used to compute odds ratios (ORs) and 95% confidence intervals (CIs) while adjusting for age, sex, race, and income. Results: Rural adults with at least one chronic disease and meeting recommended levels of PA were significantly more likely to report good HRQOL (72.5%; 70.7-74.3) than their less active counterparts (58.1%; 56.4-59.7, pConclusions: Results from this study indicate that meeting recommended levels of PA is strongly associated with optimal levels of HRQOL among rural adults with chronic disease.

Objectives The purpose of this study was to examine the relationship of meeting the recommended levels of physical activity (PA) with health status and preventive health behavior in adults. Methods A total of 5630 adults 18 years of age or older were included in this study. PA was assessed using a series of questions that categorized activities based on their metabolic equivalent values and then categorized individuals based on the reported frequency and duration of such activities. Participants reporting 150 minutes or more of moderate-intensity PA per week were considered to have met the PA guidelines. Multiple logistic regression was used to model the relationships between meeting PA guidelines and health status and preventive health behavior, while controlling for confounding variables. Results Overall, 53.9% (95% confidence interval [CI], 51.9 to 55.9%) of adults reported meeting the recommended levels of PA. Among adults with good general health, 56.9% (95% CI, 54.7 to 59.1%) reported meeting the recommended levels of PA versus 43.1% (95% CI, 40.9 to 45.3%) who did not. Adults who met the PA guidelines were significantly more likely not to report high cholesterol, diabetes, chronic obstructive pulmonary disease, arthritis, asthma, depression, or overweight. Furthermore, adults meeting the PA guidelines were significantly more likely to report having health insurance, consuming fruits daily, consuming vegetables daily, and not being a current cigarette smoker. Conclusions In this study, we found meeting the current guidelines for PA to have a protective relationship with both health status and health behavior in adults. Health promotion programs should focus on strategies that help individuals meet the current guidelines of at least 150 minutes per week of moderate-intensity PA.

Background: The purpose of this study was to investigate the relationship between physical inactivity (PIA) and health-related quality of life (HRQOL) in rural adults and examine the extent to which sex differences exist in this relationship. Methods: A total of 5617 adults 18 years of age and older who indicated residing in a rural county was included in this analysis. PIA status was assessed by questions regarding recreational physical activity during the previous month. Five HRQOL measures (physical health, mental health, inactivity health, general health, & unhealthy days) were used as primary outcome variables. PIA and HRQOL prevalence estimates were computed with 95% CIs. Multiple logistic regression was used to obtain odds ratios (ORs) and 95% CIs adjusted for age, ethnicity, and income. Results: Physically inactive rural adults were significantly more likely to report poor HRQOL in all overall crude models with ORs ranging from 1.59 to 2.16. Additionally, sex-by-PIA interactions were significant across all crude HRQOL models with ORs ranging from 2.27 to 3.08 and 1.56 to 2.42 for women and men, respectively. Sex differences were maintained in fully adjusted models, except for mental health and inactivity health with ORs ranging from 1.80 to 2.58 and 1.41 to 1.79 for women and men, respectively. Conclusion: Results from this study show that PIA is a strong predictor of poor HRQOL even after controlling for confounding variables. Furthermore, physically inactive rural women appear more likely to report poor levels of HRQOL than physically inactive rural men.

Background: Resistance training (RT) is recommended as part of our national physical activity guidelines which includes working all major muscle groups on two or more days a week.Older adults can gain many health benefits from RT, such as increased muscle strength,increased muscle mass, and maintenance of bone density. Additionally, certain dimensions of health-related quality of life (HRQOL) have been shown to improve in older adults due to RT intervention. The purpose of this study was to use systematic review and meta-analytic techniques to examine the effect of RT on HRQOL in older adults. Methods: A systematic review of current studies (2008 thru 2017) was conducted using PubMed. Studies were included if they used a randomized controlled design, had RT as an intervention, measured HRQOL using the SF-36/12 assessment, and included adults 50+ years of age. Eight dimension scores (physical functioning, bodily pain, physical role function, general health, mental health, emotional role function, social function, and vitality) and two summary scores (physical component and mental component) were extracted. Ten meta-analyses were performed using standardized mean effect sizes and random effects models. Study quality,moderator and sensitivity analyses were conducted. Results: A total of 16 studies were included in the analyses with a mean Physiotherapy Evidence Database (PEDro) score of 4.9 (SD=1.0). Among the mental health measures, RT had the greatest effect on mental health (Effect size [ES]=0.64, 95% CI: 0.30-0.99, I2=79.7). Among the physical health measures, RT had the largest effect on body pain (ES=0.81, 95% CI: 0.26-1.35, I2=85.9).Initially, RT did not significantly affect measures of emotional role function, social function or physical role function. However, after removing a single study, RT significantly increased all HRQOL measures. Conclusion: The meta-analytic evidence presented in this research clearly supports the promotion of RT in improving HRQOL in older adults.