Barbara E. Ainsworth’s research while affiliated with Shanghai University of Sport and other places

Highlights •At least 59 min/week of moderate-to-vigorous intensity physical activity (MVPA) lowers the risks of incident diabetic vascular events (macrovascular, microvascular, and heart failure) in people with diabetes and prediabetes. •Engaging in light-intensity PA (LPA) at moderate or high volumes did not reduce vascular events in people with diabetes and prediabetes. •Spending more than 12 h/day in sedentary behaviors increases the risk of developing vascular events in people with diabetes and prediabetes. •Replacing 10 min and 30 min of sedentary and LPA with MVPA reduces the risk for vascular events in people with diabetes and prediabetes.

Highlights •Short sleep duration (<6 h/day) increased the risk for incident type 2 diabetes (T2D). •Short sleep duration coupled with high total physical activity (TPA), light-intensity PA (LPA), or meeting moderate-to-vigorous PA (MVPA) guidelines nullified the risk for incident T2D. •Persons with socioeconomic and health disparities had an increased risk for incident T2D if they were sedentary or had low TPA, LPA, and MVPA. •Accumulating ∼5 h/day of LPA or ∼15 min/day of MVPA reduces the risk for incident type 2 diabetes (T2D).

Background: Energy expenditure may be difficult to assess when hiking difficult trails. Case presentation: We measured physical activity exercise energy expenditure (PAEE) directly from oxygen uptake using a mobile device (cardiopulmonary exercise testing, CPET), and by using a formula based on heart rate (HR), or metabolic equivalent values from the Compendium of Physical Activity, and other physiological outcomes. Outcomes and implications: Total PAEE (1342 kcal) using CPET showed a two-fold difference between ascending and descending (887 vs. 455 kcal) during a 124-min hike. For HR, PAEE was 1893kcal (+551 kcal overreporting), while compendium-based scenarios ranged from 1179 to 1446 kcal, which was in closer range (−163 to +104 kcal/min) compared to the CPET data. Fluid consumption was 1300 mL/hour, with 1.1% bodyweight loss, peak skin temperature of 35.2°C and core body temperature of 39.2°C. Recommendations: Tables reasonably predict energy expenditure while not precisely reflecting the actual situation.

The aim of the present study is to determine the associations between lower-extremity muscle strength qualities and change of direction (CoD) performance. Three databases were used to perform a systematic literature search, and up to September 30 2022. From the studies that met the inclusion criteria, the Pearson’s r correlation coefficient to examine relationships muscle strength qualities and COD performance was extracted. The quality of included studies was evaluated by the modified version of the Downs and Black Quality Index tool. Heterogeneity was testified via the Q statistic and I2, and Egger’s test was used to assess small study bias. The results revealed that lower-extremity maximal strength (pooled: r=-0.54, dynamic: r=-0.60, static: r=-0.41), joint strength (pooled: r=-0.59, EXT-ecc: r=-0.63, FLEX-ecc: r=-0.59), reactive strength (r=-0.42) and power (pooled: r=-0.45, jump height: r=-0.41, jump distance: r=-0.60, peak power: r=-0.41) were negatively and moderately related to CoD performance. To conclude, the results highlight that a number of muscle strength qualities are associated with CoD performance, that are pertinent to specific phases of a directional change. It should be noted that the conclusions of this study do not establish causality and further research is needed to better understand their training effects and underlying mechanisms.

Since 2008, the American College of Sports Medicine (ACSM) American Fitness Index program has provided data, materials, resources, and connections to help city officials, public health professionals, and community-based organizations assess the fitness of their residents, identify opportunities to improve health outcomes, and enable physically active lifestyles. The purpose of this article was to describe the development and methods used to create the annual scores and rankings for the Fitness Index. Fitness Index indicators were originally selected by ACSM content experts and other nationally recognized fitness experts who understood the importance of community assets to improve health behaviors and outcomes. Indicator values for the 100 largest US cities were gathered from reputable, regularly updated, publicly available data sources. Indicator values for the cities were ranked, weighted, and combined into a personal health subscore, a community/environment subscore, and a total score that were then ranked from the most fit (rank 1) to least fit (rank 100) cities. Fitness Index indicators are reviewed by ACSM content experts and updated annually to ensure the Fitness Index stays true to its goal. Strengths of the Fitness Index include the use of multiple indicators related to the health and behaviors of residents as well as community assets and policies. Some data limitations for the Fitness Index include sample variability from year to year, use of self-reported survey data with well-known biases and variability, and lack of availability of important data indicators. The annual summary report suggests how these rankings and data can be translated into action within cities. Additional resources for the American Fitness Index, including an interactive tool that allows comparison of indicator values among cities, are available online.