Physical behaviors (e.g., sleep, sedentary behavior, and physical activity) often occur in sustained bouts that are punctuated with brief interruptions. To detect and classify these interrupted bouts, researchers commonly use wearable devices and specialized algorithms. Most algorithms examine the data in chronological order, initiating and terminating bouts whenever specific criteria are met. Consequently, the bouts may encapsulate or overlap with later periods that also meet the activation and termination criteria (i.e., alternative bout solutions). In some cases, it is desirable to compare these alternative bout solutions before making a final classification. Thus, comparison-focused algorithms are needed, which can be used in isolation or in concert with their chronology-focused counterparts. In this technical note, we present a comparison-focused algorithm called CRIB (Clustered Recognition of Interrupted Bouts). It uses agglomerative hierarchical clustering to facilitate the comparison of different bout solutions, with the final classification being made in favor of the smallest number of bouts that comply with user-specified criteria (i.e., limits on the number, individual duration, and cumulative duration of interruptions). For demonstration, we use CRIB to assess bouts of moderate to vigorous physical activity in accelerometer data from the National Health and Nutrition Examination Survey, and we include a comparison against results from two established chronology-focused algorithms. Our discussion explores strengths and limitations of CRIB, as well as potential considerations and applications for using it in future studies. An online vignette (https://github.com/paulhibbing/PBpatterns/blob/main/vignettes/CRIB.pdf) is available to assist users with implementing CRIB in R.
Paul R. Hibbing, Seth A. Creasy, and Jordan A. Carlson
Jonas Bjärehed and Marlene Bjärehed
Competitive racing through virtual cycling has established itself as an entirely new discipline within cycling. This study explores what equipment racers use and examines important power metrics for racing. Data were collected from three different races from the current ranking of the most highly regulated and professionally organized race series on the virtual cycling platform Zwift. Power output data from 116 race participants, over five power durations (5 s–20 min), and two separate power measuring sources were collected and analyzed using the Bland–Altman method. The findings indicate that the physiological efforts of these races are comparable to those found in traditional competitive cycling. Furthermore, findings also support that the equipment typically used produces similar power outputs with good agreement between different power meters for most measurement points. Finally, the implications of these results for the status of virtual racing are discussed.
John Bellettiere, Supun Nakandala, Fatima Tuz-Zahra, Elisabeth A.H. Winkler, Paul R. Hibbing, Genevieve N. Healy, David W. Dunstan, Neville Owen, Mikael Anne Greenwood-Hickman, Dori E. Rosenberg, Jingjing Zou, Jordan A. Carlson, Chongzhi Di, Lindsay W. Dillon, Marta M. Jankowska, Andrea Z. LaCroix, Nicola D. Ridgers, Rong Zablocki, Arun Kumar, and Loki Natarajan
Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35–99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL − CHAP-Adult) and 95% limits of agreement were: sedentary time −10.5 (−63.0, 42.0) min/day, breaks in sedentary time 1.9 (−9.2, 12.9) breaks/day, mean bout duration −0.6 (−4.0, 2.7) min, usual bout duration −1.4 (−8.3, 5.4) min, alpha .00 (−.04, .04), and time in ≥30-min bouts −15.1 (−84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: −2.0% (4.0%), −4.7% (12.2%), 4.1% (11.6%), −4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson’s correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.
Lori A. Gano-Overway
Ruth F. Hunter and Ione Avila-Palencia
Cindy H.P. Sit, Wendy Y.J. Huang, Stephen H.S. Wong, Martin C.S. Wong, Raymond K.W. Sum, and Venus M.H. Li
Background: Following the 2019 Hong Kong Para Report Card, the 2022 Hong Kong Para Report Card aimed to provide an updated and evidence-based assessment for nine indicators related to physical activity in children and adolescents with special educational needs and to assess the results using a SWOT (strengths, weaknesses, opportunities, and threats) analysis. Methods: Using a systematic process, the best available data on nine indicators were searched from the past 10 years and were assessed by a research work group. Letter grades were assigned and considered by stakeholders and auditors. Results: Four indicators were assigned a letter grade (overall physical activity: F [mixed device-measured and self-reported data]; sedentary behaviors: D [device-measured data]; active transportation: D−; government strategies & investment: C+). SWOT analysis highlighted opportunities for facilitating children and adolescents with special educational needs to achieve health recommendations. Conclusion: There were deteriorating trends in physical activity and sedentary behaviors. Effective, multilevel, and cross-sector interventions are recommended to promote active behavior in children and adolescents with special educational needs.
This article is a critical celebration of Title IX. Fifty years of this landmark civil rights legislation has brought tremendous progress for girls and women in all areas of the U.S. educational system—including sport. However, Title IX has yet to achieve its full potential. For this to happen, I propose nine pressing issues that must be addressed: enforcing compliance; roster management and other dubious compliance tactics; more opportunities for women of color; the leadership gap; sex-segregated sport; the inclusion of transgender athletes; name, image, and likeness opportunities; the athletic arms race; and sexual harassment and violence. Based on current, scholarship, published data, and contemporary examples, this “nine for IX” approach is not a critique of the law but rather a critique of those aspects of American interscholastic and intercollegiate sport that continually hamstring Title IX’s power.
Susann Arnell, Kajsa Jerlinder, and Lars-Olov Lundqvist
Background: Participation in physical activity among adolescents with autism is often conditional. However, there is a lack of methods for identifying these specific conditions. Therefore, the purpose of this study was to develop and investigate the feasibility of a Q-sort tool to map individual-specific conditions for participation in physical activity among adolescents with autism and to identify different viewpoints regarding conditions for such participation. Method: An exploratory mixed-methods design was employed to investigate the feasibility of using Q methodology and the Q-sort procedure to identify what individual-specific conditions are important for participation in physical activity for adolescents with autism. Results: The adolescents ranked the statements with varying levels of ease. Two viewpoints were identified: Autonomous participation without surprises and Enjoyment of activity in a safe social context. Conclusion: Q-sort is a feasible method for mapping conditions for participation, which can guide the development of tailored physical activity interventions.
Ellen J. Staurowsky, Courtney L. Flowers, Erin Buzuvis, Lindsay Darvin, and Natalie Welch
In 2022, the Women’s Sports Foundation published a report addressing the current status of Title IX compliance in U.S school-based sports, examining the limitations of Title IX as a single axis law that addresses gender but not other areas of intersectionality including race, gender identity, sexual orientation, and ability. What is presented here is the executive summary and policy recommendations from the report.