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Quantifying Area-Level Physical Activity Offerings in Social Context: A Novel Concept That Goes Beyond Walkability and Access to Open Spaces

Dafna Merom, Drew Meehan, Philayrath Phongsavan, and Ori Gudes

Background: Social support is a crucial factor influencing the sustainability of physical activity (PA). This proof-of-concept study presents the development of a Social Physical Activity Index for Area (SPAIFA) an indicator reflecting opportunities for individuals to actively participate as part of a group within which opportunities for social interaction can be developed. Methods: Six government councils in the state of New South Wales, Australia, were selected encompassing 174 suburbs. Using 2 search engines’ map tools, we identified PA venues for each suburb (eg, park, studios, etc). To enumerate activities per suburb, we used (1) venue websites, (2) New South Wales Office of Sport website, (3) national websites of grassroots PA for nonorganized activity (eg, parkrun, meetup, etc), and (4) social media. The database was linked to the suburb demographic profile, the area disadvantage score, walkability and open space scores, and the proportion of insufficiently active residents. Spatial analysis techniques were used to identify SPAIFA clusters. Results: SPAIFA councils’ average was 9.9 activities per 10,000 people (ranging from 6.6/10,000 to 16.2/10,000). SPAIFA-Old (ie, activities specific to older adults) varied significantly (ranging from 11.7/10,000 to 0.8/10,000 seniors). Disadvantaged areas and a high proportion of insufficiently active residents were associated with being classified as low SPAIFA (P < .01). Three clusters of low SPAIFA were identified, and 17 high-risk areas where low SPAIFA was compounded by poor environmental support. Conclusions: SPAIFA can be used by councils and policymakers as an indicator for monitoring and intervening in areas where natural and/or urban design is not conducive for PA.

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Physical Activity Policies at National and Subnational Levels: A Study in Colombia, Costa Rica, Ecuador, and Mexico

Juliana Mejía Grueso, Michael Pratt, Eugen Resendiz, Deborah Salvo, Gloria Isabel Niño Cruz, Nubia Yaneth Ruiz Gómez, Rafael Alexander Leandro Gómez, Inés Revuelta Sánchez, Gerardo Alonso Araya Vargas, Angélica María Ochoa Avilés, Raúl Francisco Pérez Tasigchana, Alejandra Jáuregui, Pedro C. Hallal, and Andrea Ramírez Varela

Background: National physical activity (PA) policy processes are only beginning to be studied in Latin America, and little attention has focused at the subnational level. This study examined national–subnational relations in the policy process (agenda setting, policy formulation, adoption, implementation, and evaluation) in selected Latin American countries. Methods: The Global Observatory for Physical Activity’s (GoPA!) INTEGRATE-PA-Pol tool was applied in Colombia, Costa Rica, Ecuador, and Mexico. Data were collected in matched pairs of the capital plus one noncapital city among national and subnational policymakers (n = 27), previously identified by the GoPA! Country Contacts. PA policy development and implementation were assessed using descriptive statistics. Results: Twenty-three (response rate = 85.2%) informants provided data, mainly from the health sector (52.2%), followed by the sport (26.1%), transport (13.0%), and education (8.7%) sectors. Most informants reported that their countries had a current PA policy embedded within noncommunicable diseases prevention plans (46.2%), other plans (46.2%), or obesity prevention/management/control plans (7.7%). Respondents at the subnational level rated PA promotion as central (64.3%), while the national-level role was important but not central (75.0%). National and subnational policymakers indicated low-to-little involvement in the other level’s PA policy processes across the 5 policy stages. Conclusions: This study demonstrated that collecting national and subnational PA policy data across countries with the active collaboration of the GoPA! network was feasible. We also successfully identified governmental interactions throughout the PA policy process, suggesting suboptimal engagement between national and subnational levels.

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Artificial Intelligence and Machine Learning—Powerful Yet Underutilized Tools and Algorithms in Physical Activity and Sedentary Behavior Research

Vahid Farrahi and Philip Clare

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A Mobile Health Resistance Training Intervention Using Outdoor Gym Equipment: Process Evaluation of the Ecofit Effectiveness Randomized Controlled Trial

Anna K. Jansson, Mitch J. Duncan, Adrian Bauman, Jordan J. Smith, David R. Lubans, John Attia, and Ronald C. Plotnikoff

Background: Few mobile health resistance-based physical activity interventions have targeted community-dwelling adults. “Ecofit” is a multicomponent intervention that promotes resistance and aerobic activities using smartphone technology, outdoor gyms, and social support. This study evaluated process evaluation outcomes of the ecofit randomized controlled trial: (1) the acceptability and usability of the ecofit smartphone app and app user workouts; (2) perceptions of factors influencing outdoor gym use; and (3) the fidelity, reach, recruitment, and dose received of the ecofit program. Methods: Process data were collected through program evaluation surveys at 3 months, and app usage data were collected via the intervention platform for up to 3 months. Data were analyzed using descriptive statistics. Results: The survey was completed by 57% (n = 69) of eligible participants. The majority (93%) believed the app provided them with sufficient information to perform muscle-strengthening activities. Approximately half (51%) agreed that the goal-setting function encouraged them to complete their workouts, and 42% agreed that the self-assessment helped them monitor progress. “Proximity” to outdoor gyms emerged as the most important factor for choosing locations to workout (mean = 5.5, SD = 1.1). Participants logged a median of 5.5 (interquartile range = 19) workouts and 1 (interquartile range = 1) upper- and lower-body muscular fitness self-assessment. Conclusions: The ecofit app provided participants with sufficient skills to perform unsupervised resistance training exercises using mobile health. Only half of the participants regarded self-assessments and goal setting as useful, suggesting a need for modifications to how these are implemented. Mobile health remains a promising delivery platform to promote unsupervised resistance training, although more research is needed to improve uptake.

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The Physical Activity Advice Continuum—A Guide for Physical Activity Promotion in Health Care

Nicole Freene, Stephen Barrett, Emily R. Cox, Jessica Hill, Roger Lay, Jessica Seymour, Kimberley Szeto, and Sjaan R. Gomersall

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Volume 21 (2024): Issue 2 (Feb 2024)

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The Legacy of Dr. Harold W. (Bill) Kohl III From the Perspective of His Mentees

Gregory Knell, Kathryn Burford, Tammy Calise, Erin E. Dooley, Augusto Ferreira de Moraes, Leigh Ann Ganzar, Ho Han, Natalia Heredia, Alejandra Jáuregui, Ashleigh Johnson, Kevin Lanza, Eileen Nehme, Hildi Nicksic, Abiodun O. Oluyomi, Anna Porter, Andrea Ramirez Varela, Michael Robertson, Deborah Salvo, Timothy Walker, and Yuzi Zhang

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Erratum. Patterns and Correlates of Participation in a Weekly Mass Participation Physical Activity Event, parkrun, in Australia, 2011–2020

Journal of Physical Activity and Health

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Physical Activity Dependence on Relative Temperature and Humidity Characteristics in a Young, Insufficiently Active Population: A Weather Typing Analysis

Daniel J. Vecellio, Constantino M. Lagoa, and David E. Conroy

Background: Physical activity (PA) is an important contributor to one’s physical and mental health both acutely and across the lifespan. Much research has done on the ambient environment’s impact on PA; however, these studies have used absolute values of atmospheric measures such as temperature and humidity, which vary spatiotemporally and make comparisons between studies which differ in location or time of year difficult to square with one another. Methods: Here, we employ the Global Weather Type Classification, Version 2, to determine the combined impact of temperature and humidity on PA in a sample of insufficiently active young adults. We conducted secondary analyses of data from a single-group behavioral intervention trial that varied the number of digital messages sent daily. Young adults (n = 81) wore Fitbit Versa smartwatches for a 6-month period sometime between April 2019 and July 2020, and location was tracked using a custom smartphone application. Results: Mixed linear models indicated that, across 8179 person-days, PA was significantly lower on days with humid conditions and significantly higher on warm dry days, though the latter relationship was no longer significant when controlling for timing in relation to the COVID-19 pandemic declaration. Demographic factors did not affect the relationship between weather and PA. Conclusions: Results are a first step in providing additional guidance for encouraging PA in insufficiently active individuals given forecasted daily weather conditions. Future work should examine seasonal variability in the weather type–PA relationship without the influence of a world-altering event influencing results.

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Associations Between Intersecting Sociodemographic Characteristics and Device-Measured Physical Activity Among Children and Adolescents Living in the United States

Denver M.Y. Brown, Bryce Summerville, Stuart J. Fairclough, Gregore I. Mielke, and Richard Tyler

Background: Despite robust evidence demonstrating sociodemographic characteristics may underly some of the disparities in physical activity observed among children and adolescents, the often-overlooked nexus of potential interactions between these characteristics warrants further exploration. This study explored the intersectionality of gender, race/ethnicity, parental education, and household income in relation to device-measured physical activity volume and intensity in a nationally representative sample of US children and adolescents. Methods: Cross-sectional data from 3 cycles of the US National Health and Nutrition Survey (2011–2012; 2012 National Youth Fitness Survey; and 2013–2014) were used. A total of 6116 participants (49% female) between 3 and 17 years of age wore an accelerometer on their nondominant wrist for 7 days. Monitor-independent movement summary units were used to represent physical activity volume and intensity. A Social Jeopardy Index was created to represent increasing levels of intersecting social disadvantages based on combinations of gender, race/ethnicity, parental education, and household income-to-poverty ratio tertiles. Generalized linear regression models were computed. Results: The results showed social disadvantages become increasingly evident among children and adolescents during the most intense 60 minutes of daily physical activity (B = −48.69 [9.94] SE, P < .001), but disparities in total volume were not observed (B = 34.01 [44.96] SE, P = .45). Conclusions: Findings suggest that patterns of physical activity behavior may differ based on intersecting sociodemographic characteristics—more socially disadvantaged children and adolescents appear to accumulate activity at lighter intensities. Collecting contextual information about device-measured physical activity represents an important next step for gaining insight into these sociodemographic differences.