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Meghan Baruth and Sara Wilcox

Background:

Understanding who is most and least likely to remain active after the completion of physical activity (PA) interventions can assist in developing more targeted and effective programs to enhance prolonged behavior change. The purpose of this study was to examine predictors of meeting PA recommendations 6 months postintervention in participants enrolled in Active for Life.

Methods:

Participants from 2 behavioral PA programs [158 Active Choices (AC); 1025 Active Living Every Day (ALED)] completed surveys 6 months after completion of the active intervention. Analyses examined predictors of meeting PA recommendations at follow-up.

Results:

The following were significant predictors: In ALED: self-report health status, satisfaction with body function, and self-efficacy at baseline; PA status at posttest; changes in self-efficacy, perceived stress, and satisfaction with body function and appearance from baseline to posttest. In AC: PA status at posttest.

Conclusions:

The ultimate goal of health promotion programs is to teach the behavioral skills necessary to sustain behavior change once an active intervention is complete. The findings from this study suggest that predicting PA behavior after cessation of PA interventions may not be straightforward, and predictor variables may operate differently in different intervention approaches.

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Pedro C. Hallal, Pitágoras T. Machado, Giovâni F. Del Duca, Inácio C. Silva, Tales C. Amorim, Thiago T. Borges, Airton J. Rombaldi, Mario R. Azevedo and Alan G. Knuth

Purpose:

To evaluate the prevalence of physical activity advice, the source of the information, and the types of recommendation in a population-based sample of adults living in South Brazil.

Methods:

Population-based study including 972 adults living in Pelotas, Brazil. The outcome variable was based on the following question: “Has anyone ever recommended you to practice physical activity”? If the answer was positive, we asked who was responsible for the prescription (an open question, which was categorized later) and which recommendation was done.

Results:

The prevalence of physical activity advice was 56.2% (95% CI 52.3−60.1). Physical activity advice was mostly done by physicians (92.5%). Walking was, by far, the most frequent recommendation. Females were more likely to receive advice for physical activity practice than males (OR 1.74; 95% CI 1.30−2.31). Age, economic level, body mass index and leisure-time physical activity were positively associated with physical activity advice, while self-reported health presented an inverse association with the outcome.

Conclusions:

The prevalence of physical activity advice was high in this sample, suggesting that the Brazilian health system is incorporating physical activity in its routine.

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Sarah Kozey Keadle, Shirley Bluethmann, Charles E. Matthews, Barry I. Graubard and Frank M. Perna

Background:

This paper tested whether a physical activity index (PAI) that integrates PA-related behaviors (ie, moderate-to-vigorous physical activity [MVPA] and TV viewing) and performance measures (ie, cardiorespiratory fitness and muscle strength) improves prediction of health status.

Methods:

Participants were a nationally representative sample of US adults from 2011 to 2012 NHANES. Dependent variables (self-reported health status, multimorbidity, functional limitations, and metabolic syndrome) were dichotomized. Wald-F tests tested whether the model with all PAI components had statistically significantly higher area under the curve (AUC) values than the models with behavior or performance scores alone, adjusting for covariates and complex survey design.

Results:

The AUC (95% CI) for PAI in relation to health status was 0.72 (0.68, 0.76), and PAI-AUC for multimorbidity was 0.72 (0.69, 0.75), which were significantly higher than the behavior or performance scores alone. For functional limitations, the PAI AUC was 0.71 (0.67, 0.74), significantly higher than performance, but not behavior scores, while the PAI AUC for metabolic syndrome was 0.69 (0.66, 0.73), higher than behavior but not performance scores.

Conclusions:

These results provide empirical support that an integrated PAI may improve prediction of health and disease. Future research should examine the clinical utility of a PAI and verify these findings in prospective studies.

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Marlana J. Kohn, Basia Belza, Miruna Petrescu-Prahova, Christina E. Miyawaki and Katherine H. Hohman

This study examined participant demographic and physical function characteristics from EnhanceFitness, an evidence-based physical activity program for older adults. The sample consisted of 19,964 older adults. Participant data included self-reported health and demographic variables, and results for three physical function tests: chair stand, arm curls, and timed up-and-go. Linear regression models compared physical function test results among eight program site types. Participants were, on average, 72 years old, predominantly female, and reported having one chronic condition. Residential site participants’ physical function test results were significantly poorer on chair stand and timed up-and-go measures at baseline, and timed up-and-go at a four-month follow-up compared with the reference group (senior centers) after controlling for demographic variables and site clustering. Evidence-based health-promotion programs offered in community settings should assess demographic, health, and physical function characteristics to best serve participants’ specific needs, and offer classes tailored to participant function and ability while maintaining program fidelity.

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Inger Mechlenburg, Marianne Tjur and Kristian Overgaard

time domains. A secondary aim was to investigate if prolonged sitting is associated with body mass index (BMI) and self-reported health and how these associations vary between working and leisure time. Methods Recruitment Participants were randomly selected from the Danish Civil Registration System if

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Zisko * Ulrik Wisløff * Dorthe Stensvold * 7 2016 24 3 369 375 10.1123/japa.2015-0148 The Effects of Pilates Training on Balance Control and Self-Reported Health Status in Community-Dwelling Older Adults: A Randomized Controlled Trial Hadas Gabizon * Yan Press * Ilia Volkov * Itshak Melzer

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Ilona I. McMullan, Brendan P. Bunting, Lee Smith, Ai Koyanagi and Mark A. Tully

requirement giving a total number of metabolic equivalent for task minutes per week (the ratio of the rate of energy expended during an activity to the rate of energy expended at rest). Covariates Demographic measures of age, sex, marital status, employment, and education were self-reported. Health and

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Nicolas Aguilar-Farias, Wendy J. Brown, Tina L. Skinner and G.M.E.E. (Geeske) Peeters

.84–9.99 7.68–10.51 7.11–10.63 4.45–6.89 Self-reported health, %  Fair 10.0 10.0 0 17.4  Good 45.0 50.0 47.1 39.1  Very good 33.3 40.0 35.3 26.1  Excellent 11.7 0 17.7 17.4 Physical activity level, %  Inactive 25.0 30.0 6.2 13.1  1 h/wk or less 37.5 45.0 31.3 52.2  More than 1 h/wk 37.5 25.0 62.5 34

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Giovanni Mario Pes, Maria Pina Dore, Alessandra Errigo and Michel Poulain

of cognitive performance, owing to the influence of a low educational level. Self-reported health was high in both genders. Curiously, the percentage of subjects never married was significantly higher among the women (29.4 vs 14.8, p  < 0.0001). Table 1 Main Features of Nonagenarians From

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Margaret K. Danilovich, David E. Conroy and T. George Hornby

the five repetition sit-to-stand test ( Bohannon, 1995 ) was used as an assessment of lower extremity strength. Self-reported health Participants completed the PROMIS-global health ( Hays, Bjorner, Revicki, Spritzer, & Cella, 2009 ), which measures overall health in the domains of physical function