Leisure-time physical activity (LTPA) is known to benefit cognition among older adults, but the impact of active travel is unclear. To explore this relationship, data from the 2011–2014 National Health and Nutritional Examination Survey (N = 2,702; mean age = 70) were retrieved on the self-reported frequency and duration of active travel (walking/cycling for transport, >20 min), LTPA engagement (e.g., sport), and three cognitive outcomes. Four groups were created according to physical activity guidelines (600 metabolic equivalent of task/week): inactive (n = 1,790), active travelers (n = 210), engaging in LTPA (n = 579), and engaging in both (n = 123). Analysis of covariance (and follow-up comparisons) revealed a significant main effect for each cognition variable, after adjusting for the covariates, indicating that those engaging in LTPA performed the best. Although correlational, these findings suggest that LTPA engagement may be important for cognition among older adults, but active travel did not provide added benefit.
Madhura Phansikar and Sean P. Mullen
Kelly Cornett, Katherine Bray-Simons, Heather M. Devlin, Sunil Iyengar, Patricia Moore Shaffer and Janet E. Fulton
Artur Direito, Joseph J. Murphy, Matthew Mclaughlin, Jacqueline Mair, Kelly Mackenzie, Masamitsu Kamada, Rachel Sutherland, Shannon Montgomery, Trevor Shilton and on behalf of the ISPAH Early Career Network
Increasing population levels of physical activity (PA) can assist in achieving the United Nations sustainable development goals, benefiting multiple sectors and contributing to global prosperity. Practices and policies to increase PA levels exist at the subnational, national, and international levels. In 2018, the World Health Organization launched the first Global Action Plan on Physical Activity (GAPPA). The GAPPA provides guidance through a framework of effective and feasible policy actions for increasing PA, and requires engagement and advocacy from a wide spectrum of stakeholders for successful implementation of the proposed actions. Early career professionals, including researchers, practitioners, and policymakers, can play a major role with helping “all people being regularly active” by contributing to 4 overarching areas: (1) generation—of evidence, (2) dissemination—of key messages and evidence, (3) implementation—of the evidence-based actions proposed in the GAPPA, and (4) contributing to advocacy for robust national action plans on PA. The contribution of early career professionals can be achieved through 5 pathways: (1) research, (2) workplace/practice, (3) business, (4) policy, and (5) professional and public opinion. Recommendations of how early career professionals can contribute to the generation, dissemination, and implementation of the evidence and actions proposed by the GAPPA are provided.
Nicolas Hobson, Sherry L. Dupuis, Lora M. Giangregorio and Laura E. Middleton
Persons with mild cognitive impairment (MCI) and early dementia are often physically inactive, despite associated benefits. This study explored the barriers, facilitators, and preferences for exercise among persons living with MCI/early dementia. The authors conducted 2 focus groups among persons living with MCI/early dementia (n = 4, 6 participants) and 2 focus groups among care partners (n = 3, 4 participants). The transcripts were analyzed using thematic analysis, guided by the social-ecological model. Three themes emerged, reinforcing motivation to exercise, managing changes to cognitive and physical health, and variable perceptions of dementia, each with influences from individual, care partner, and community levels. Low intrinsic motivation, poor physical/cognitive health, and stigma restricted the exercise among persons living with MCI/early dementia. The care partners motivated their partners and provided company and transportation to exercise. People with MCI/early dementia also indicated poor access to exercise providers and exercise opportunities that met their needs and preferences was a barrier to exercise participation. Knowledge translation research should develop exercise interventions at the individual, social, and community levels.
Jairo H. Migueles, Alex V. Rowlands, Florian Huber, Séverine Sabia and Vincent T. van Hees
Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The R package GGIR has been made available to all as a tool to convermulti-day high resolution raw accelerometer data from wearable movement sensors into meaningful evidence-based outcomes and insightful reports for the study of human daily physical activity and sleep. This paper aims to provide a one-stop overview of GGIR package, the papers underpinning the theory of GGIR, and how research contributes to the continued growth of the GGIR package. The package includes a range of literature-supported methods to clean the data and provide day-by-day, as well as full recording, weekly, weekend, and weekday estimates of physical activity and sleep parameters. In addition, the package also comes with a shell function that enables the user to process a set of input files and produce csv summary reports with a single function call, ideal for users less proficient in R. GGIR has been used in over 90 peer-reviewed scientific publications to date. The evolution of GGIR over time and widespread use across a range of research areas highlights the importance of open source software development for the research community and advancing methods in physical behavior research.
Salomé Aubert, Joel D. Barnes, Megan L. Forse, Evan Turner, Silvia A. González, Jakub Kalinowski, Peter T. Katzmarzyk, Eun-Young Lee, Reginald Ocansey, John J. Reilly, Natasha Schranz, Leigh M. Vanderloo and Mark S. Tremblay
Background: In response to growing concerns over high levels of physical inactivity among young people, the Active Healthy Kids Global Alliance developed a series of national Report Cards on physical activity for children and youth to advocate for the promotion of physical activity. This article provides updated evidence of the impact of the Report Cards on powering the movement to get children and youth moving globally. Methods: This assessment was performed using quantitative and qualitative sources of information, including surveys, peer-reviewed publications, e-mails, gray literature, and other sources. Results: Although it is still too early to observe a positive change in physical activity levels among children and youth, an impact on raising awareness and capacity building in the national and international scientific community, disseminating information to the general population and stakeholders, and on powering the movement to get kids moving has been observed. Conclusions: It is hoped that the Report Card activities will initiate a measurable shift in the physical activity levels of children and contribute to achieving the 4 strategic objectives of the World Health Organization Global Action Plan as follows: creating an active society, creating active environments, creating active lives, and creating active systems.
Pauline M. Genin, Frédéric Dutheil, Benjamin Larras, Yoland Esquirol, Yves Boirie, Angelo Tremblay, Bruno Pereira, Corinne Praznoczy, David Thivel and Martine Duclos
Meera Sreedhara, Karin Valentine Goins, Christine Frisard, Milagros C. Rosal and Stephenie C. Lemon
Background: Local health departments (LHDs) are increasingly involved in Community Health Improvement Plans (CHIPs), a collaborative planning process that represents an opportunity for prioritizing physical activity. We determined the proportion of LHDs reporting active transportation strategies in CHIPs and associations between LHD characteristics and such strategies. Methods: A national probability survey of US LHDs (<500,000 residents; 30.2% response rate) was conducted in 2017 (n = 162). LHDs reported the inclusion of 8 active transportation strategies in a CHIP. We calculated the proportion of LHDs reporting each strategy. Multivariate logistic regression models determined the associations between LHD characteristics and inclusion of strategies in a CHIP. Inverse probability weights were applied for each stratum. Results: 45.6% of US LHDs reported participating in a CHIP with ≥1 active transportation strategy. Proportions for specific strategies ranged from 22.3% (Safe Routes to School) to 4.1% (Transit-Oriented Development). Achieving national accreditation (odds ratio [OR] = 3.67; 95% confidence interval [CI], 1.11–12.05), pursuing accreditation (OR = 3.40; 95% CI, 1.25–9.22), using credible resources (OR = 5.25; 95% CI, 1.77–15.56), and collaborating on a Community Health Assessment (OR = 4.48; 95% CI, 1.23–16.29) were associated with including a strategy in a CHIP after adjusting for covariates. Conclusions: CHIPs are untapped tools, but national accreditation, using credible resources, and Community Health Assessment collaboration may support strategic planning efforts to improve physical activity.
Natalie M. Golaszewski and John B. Bartholomew
Research suggests 5 forms of social support: companionship, emotional, informational, instrumental, and validation. Despite this, existing measures of social support for physical activity are limited to emotional, companionship, and instrumental support. The purpose was to develop the Physical Activity and Social Support Scale (PASSS) with subscales that reflected all 5 forms. Participants (N = 506, mean age = 34.3 yr) who were active at least twice per week completed a 235-item questionnaire assessing physical activity behaviors, social support for physical activity, general social support, and other psychosocial questions. Exploratory and confirmatory factor analyses were used to develop and validate the PASSS. Exploratory factor analysis supported a 5-factor, 20-item model, χ2(100) = 146.22, p < .05, root mean square error of approximation = .05. Confirmatory factor analysis indicated good fit, Satorra–Bentler χ2(143) = 199.57, p < .001, root mean square error of approximation = .04, comparative-fit index = .97, standardized root mean square residual = .06. Findings support the PASSS to measure all 5 forms for physical activity.