The article presents University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD), a publically available data set of movements related to common exercises performed by patients in physical rehabilitation programs. For the data collection, 10 healthy subjects performed 10 repetitions of different physical therapy movements with a Vicon optical tracker and a Microsoft Kinect sensor used for the motion capturing. The data are in a format that includes positions and angles of full-body joints. The objective of the data set is to provide a basis for mathematical modeling of therapy movements, as well as for establishing performance metrics for evaluation of patient consistency in executing the prescribed rehabilitation exercises.

Understanding the relationships between physical activity (PA) and disease has become a major area of research interest. Activity monitors, devices that quantify free-living PA for prolonged periods of time (days or weeks), are increasingly being used to estimate PA. A range of different activity monitors brands are available for investigators to use, but little is known about how they respond to different levels of PA in the field, nor if data conversion between brands is possible.

Activity monitors (AM) are small, electronic devices used to quantify the amount and intensity of physical activity (PA). Unfortunately, it has been demonstrated that data loss that occurs when AMs are not worn by subjects (removals during sleeping and waking hours) tend to result in biased estimates of PA and total energy expenditure (TEE). No study has reported the degree of data loss in a large study of adults, and/or the degree to which the estimates of PA and TEE are affected. Also, no study in adults has proposed a methodology to minimize the effects of AM removals.

Comprehensive school physical activity programs (CSPAPs) are recommended to support physical education (PE) and increase the amount of physical activity (PA) youth receive each day. However, adoption of CSPAPs in the United States is low. PE teachers are well positioned to lead the implementation of CSPAPs, but research is needed to better understand (a) PE teachers’ confidence to assume the multiple roles involved with CSPAP implementation and (b) the factors that are associated with such confidence. This study examined PE teachers’ role breadth self-efficacy (RBSE) as a measure of PE teachers’ CSPAP-related confidence and its association with seminal life experiences as framed within teacher socialization theory. A survey was emailed to a stratified-random sample of 2976 PE teachers and distributed on social media, garnering a total of 259 responses. Exploratory structural equation modeling supported a three-factor solution for teacher socialization variables (acculturation, professional socialization and organizational socialization), in line with the theoretical framework, and a single factor solution for RBSE. Professional socialization and organizational socialization were significant predictors of RBSE, and qualitative data from open-ended survey questions supported these relationships. The results highlight the importance of preservice teacher education and current employment contexts in PE teachers’ CSPAP-related confidence.

Despite evolving evidence of the health and economic benefits of active transportation (AT) to work, few studies have examined the determinants of AT in large organizations with multiple worksites nor how trends in commuting change over time. The data were obtained from the U.S. Department of the Interior Employee Commuting Census of 2010 (n = 23,230), and 2012–2016 (n = 21,725-25,974). The respondents were grouped into four commuting categories: non-active mode, walking, biking, and mixed-mode. Multinomial logistic regression analysis was utilized to examine the correlates of choosing AT to work for the 2010 data. Next, a repeated cross-sectional analysis was completed for all six years of data. In 2010, AT modes were only chosen by approximately 10% of respondents. Employees who lived farther from work and did not have a public transit station within 0.5 miles from home were generally less likely to choose AT. Respondents working in non-metro workplaces were less likely to bike or take mixed-modes to work, but more likely to walk. Men were more likely to choose AT modes, particularly biking. Respondents aged ≤30 yrs. were less likely to bike than those 31 to 40 yrs., but more likely than those ≥61 yrs. In 2010, the number of respondents that walked was higher, and biked and took mixed-modes was lower when compared to 2016, while the choice to take mixed-modes was higher in 2012 and 2013 when compared to 2016. Daily commuting distances in 2016 tended to be lower than 2010 and 2012, and higher than 2013. However, overall AT choice and commute distance remained reasonably stable over time. Respondents who lived close to their workplace and a public transportation station, worked in a metro location, were male and younger were more likely to choose AT modes to work. The results provide insight for the U.S. Department of the Interior and other large organizations to develop intervention strategies that support AT to work. Further research is warranted to understand the concurrent individual, social, and environmental barriers and facilitators for choosing AT to work.

Background: The National Health and Nutrition Examination Survey physical activity questionnaire (PAQ) is used to estimate activity energy expenditure (AEE) and moderate to vigorous physical activity (MVPA). Bias and variance in estimates of AEE and MVPA from the PAQ have not been described, nor the impact of measurement error when utilizing the PAQ to predict biomarkers and categorize individuals. Methods: The PAQ was administered to 385 adults to estimate AEE (AEE:PAQ) and MVPA (MVPA:PAQ), while simultaneously measuring AEE with doubly labeled water (DLW; AEE:DLW) and MVPA with an accelerometer (MVPA:A). Results: Although AEE:PAQ [3.4 (2.2) MJ·d⁻¹] was not significantly different from AEE:DLW [3.6 (1.6) MJ·d⁻¹; P > .14], MVPA:PAQ [36.2 (24.4) min·d⁻¹] was significantly higher than MVPA:A [8.0 (10.4) min·d⁻¹; P < .0001]. AEE:PAQ regressed on AEE:DLW and MVPA:PAQ regressed on MVPA:A yielded not only significant positive relationships but also large residual variances. The relationships between AEE and MVPA, and 10 of the 12 biomarkers were underestimated by the PAQ. When compared with accelerometers, the PAQ overestimated the number of participants who met the Physical Activity Guidelines for Americans. Conclusions: Group-level bias in AEE:PAQ was small, but large for MVPA:PAQ. Poor within-participant estimates of AEE:PAQ and MVPA:PAQ lead to attenuated relationships with biomarkers and misclassifications of participants who met or who did not meet the Physical Activity Guidelines for Americans.