Cheryl Der Ananian

Sociodemographic Correlates of Meeting US Department of Health and Human Services Muscle Strengthening Recommendations in Middle-Aged and Older Adults

Introduction A growing body of evidence demonstrates the health benefits of muscular strength training. Physical activity recommendations encourage all adults to participate regularly in muscle strengthening activities. The purpose of this study was to examine the prevalence of meeting the US Department of Health and Human Services (DHHS) muscular strengthening recommendations by middle-aged and older adults and the sociodemographic characteristics associated with meeting these recommendations, using data from the 2011 Behavioral Risk Factor Surveillance System (BRFSS). Methods Data from the 2011 BRFSS were used to examine the prevalence of meeting the DHHS muscle strengthening recommendations by adults older than 45. Simple and multiple regression analyses were used to examine the sociodemographic characteristics associated with meeting the recommendations. Results Of respondents to the muscle strengthening question (N = 333,507), 79,029 (23.7%) reported meeting the muscle strengthening recommendations. Respondents who were female (odds ratio [OR] = 0.80; 95% confidence interval [CI] 0.78–0.83), widowed (OR = 0.69; 95% CI, 0.66–0.72), 85 or older (OR = 0.63; 95% CI, 0.58–0.68), Hispanic (OR = 0.73; 95% CI, 0.67–0.78), with a body mass index of 30.0 kg/m2 or higher (OR = 0.47; 95% CI, 0.45–0.49), and with less than a high school education (OR = 0.32, 95% CI, 0.30–0.35) were less likely to meet the recommendations than their counterparts. Conclusion Sociodemographic characteristics such as sex, age, education, and race/ethnicity are significantly associated with meeting the muscle strengthening recommendations, suggesting a need to create tailored interventions and messages to promote participation in strength training.

Differences in active commuting among younger and older adults.

The demonstrated health benefits of active commuting (AC) and low participation rates among older adults indicate a need to examine the socioecological correlates of AC by age category. An online survey of employed U.S. adults examined AC participation and individual, employment-related, community, and environmental variables. Participants were dichotomized by age (younger: 18-49 yr; n = 638, 64% and older: ≥ 50 yr; n = 359, 36%). Logistic-regression analyses examined differences in AC correlates by age. Older adults were less likely to be active commuters (13.4%) than younger adults (27.9%; p < .001) For older adults, analyses yielded a Nagelkerke R2 = .76, with perceived behavioral control, behavioral beliefs, household cars, and walking distance as predictors. Analyses for younger adults resulted in a Nagelkerke R2 = .79, with perceived behavioral control, coworker normative beliefs, parking problems at work, greater employer and community support for AC, and bad weather as predictors. Findings suggest age should be considered when examining and targeting AC behaviors.

An Examination of the Differences Between Two Methods of Estimating Energy Expenditure in Resistance Training Activities

Abstract Vezina, JW, Der Ananian, CA, Campbell, KD, Meckes, N, and Ainsworth, BE. An examination of the differences between two methods of estimating energy expenditure in resistance training activities. J Strength Cond Res 28(4): 1026–1031, 2014—To date, few studies have looked at the energy expenditure (EE) of individual resistance training (RT) exercises. The purpose of this study was to evaluate the EE of 4 modes of RT (push-ups, curl-ups, pull-ups, and lunges) using 2 different calculation methods for estimating EE. Twelve healthy men with a minimum of 1 year of RT experience were randomly assigned to an RT circuit. Each circuit contained the 4 RT exercises in a specified order. The participants completed 3 trials of their assigned circuit during one visit to the laboratory. Oxygen consumption was measured continuously throughout the trial using indirect calorimetry. Two different calculation methods were applied to estimate EE. Using the traditional method (TEC), we estimated EE by calculating the average oxygen consumption recorded during each activity. Using the second, nontraditional method (NEC), we estimated EE by calculating the average oxygen consumption recorded during the recovery period. Independent T-tests were used to evaluate mean EE differences between the 2 methods. Estimates of EE obtained from the NEC were significantly higher for all the 4 activities (p < 0.001). Using the NEC, 3 of the 4 activities were classified as vigorous intensity (push-ups: 6.91 metabolic equivalents (METs); lunges: 7.52 METs; and pull-ups: 8.03 METs), whereas none were classified as vigorous using the TEC. Findings suggest that the methods we use to calculate the EE of anaerobic activities significantly affect EE estimates. Using the TEC may underestimate actual EE of anaerobic activities.