The School of Kinesiology at Auburn University is using Movband Technology to support online learning in their physical activity program. Active Auburn is a 2-hr credit course that encourages students (n = 2,000/year) to become physically active through online instruction and tracking physical activity using Movband technology. Movband technology allows for uploading and monitoring group physical activity data. The implementation of this technology has allowed the School of Kinesiology to: (a) promote physical activity on our campus, (b) serve a large number of students, (c) reduce demand on classroom/physical activity space, and (d) promote our research and outreach scholarship as well, by collecting physical activity profiles for students enrolled in the course. Students report they enjoy the course and that they appreciate the “freedom to exercise” when it best fits into their schedule. This course generates considerable revenue to support course instruction and much more for the School of Kinesiology.
Sheri J. Brock, Danielle Wadsworth, Nikki Hollett and Mary E. Rudisill
Alexander H.K. Montoye, John Vusich, John Mitrzyk and Matt Wiersma
Background: Consumer-based activity monitors use accelerometers to estimate Calories (kcals), but it is unknown if monitors measuring heart rate (HR) use HR in kcal prediction. Purpose: Determine if there is a difference in kcal estimations in Fitbits measuring HR compared to those not measuring HR. Methods: Participants (n = 23) wore five Fitbits and performed nine activities for five minutes each, split into four groupings (G1: sitting, standing, cycling 50–150W; G2: level (0%) and inclined (10%) walking at 1.1 m/s; G3: level (0%) and inclined (10%) walking at 1.4 m/s; G4: level (0%) and inclined (3%) jogging at 2.2–4.5 m/s) in the laboratory. Three Fitbits (Blaze, Charge HR, Alta HR) assessed steps, HR, and kcals, and two Fitbits (Alta, Flex2) assessed steps and kcals. Steps, HR, and kcals data from the Fitbits were compared to criterion measures and between Fitbits measuring HR and Fitbits without HR. Results: Fitbits with HR had significantly higher kcal predictions (10.5–23.8% higher, p < .05) during inclined compared to level activities in G2–G4, whereas Fitbits without HR had similar kcal estimates between level and inclined activities. Mean absolute percent errors for kcal predictions were similar for Fitbits measuring HR (33.7–38.3%) and Fitbits without HR (32.4–36.6%). Conclusion: Fitbits measuring HR appear to use HR when predicting kcals. However, kcal prediction accuracies were similarly poor compared to Fitbits without HR compared to criterion measures.
David R. Bassett, Patty S. Freedson and Dinesh John
( International Data Corp., 2017 ). The growth in the number of research publications about Fitbit is increasing exponentially (Figure 1 ). The global wearable-technology market, including activity trackers, is projected to grow from over $30 billion in 2016 to over $150 billion in 2026 ( CISION PR Newswire
Adam Douglas, Michael A. Rotondi, Joseph Baker, Veronica K. Jamnik and Alison K. Macpherson
shown to be a valid and reliable measure to count sport-based explosive actions in female athletes. 16 , 17 With the increased use of wearable technology to measure the work being performed in other sports, there is no research applying this technology in the sport of ice hockey. The purpose of this
Paul G. Montgomery and Brendan D. Maloney
to offence. Shots made from beyond the traditional 3-point line are counted as 2 points. If a team reaches 21 points within the 10 minutes, they are deemed the winner. All other rules are consistent with traditional basketball. Wearable technology has become commonplace in many team sports
Paul G. Montgomery and Brendan D. Maloney
, allowing teams to implement recovery strategies. The 3×3 basketball tournament competition requires several games a day over several days; hence, the perceived demands are considerably different. The objective of this study was to utilize wearable technology to assess the physical and physiological changes
Louis Passfield and James G. Hopker
This paper explores the notion that the availability and analysis of large data sets have the capacity to improve practice and change the nature of science in the sport and exercise setting. The increasing use of data and information technology in sport is giving rise to this change. Web sites hold large data repositories, and the development of wearable technology, mobile phone applications, and related instruments for monitoring physical activity, training, and competition provide large data sets of extensive and detailed measurements. Innovative approaches conceived to more fully exploit these large data sets could provide a basis for more objective evaluation of coaching strategies and new approaches to how science is conducted. An emerging discipline, sports analytics, could help overcome some of the challenges involved in obtaining knowledge and wisdom from these large data sets. Examples of where large data sets have been analyzed, to evaluate the career development of elite cyclists and to characterize and optimize the training load of well-trained runners, are discussed. Careful verification of large data sets is time consuming and imperative before useful conclusions can be drawn. Consequently, it is recommended that prospective studies be preferred over retrospective analyses of data. It is concluded that rigorous analysis of large data sets could enhance our knowledge in the sport and exercise sciences, inform competitive strategies, and allow innovative new research and findings.
Roel De Ridder, Julien Lebleu, Tine Willems, Cedric De Blaiser, Christine Detrembleur and Philip Roosen
collection. The authors have no conflicts of interest to disclose. References 1. Bonato P . Advances in wearable technology and applications in physical medicine and rehabilitation . J Neuroeng Rehabil . 2005 ; 2 ( 1 ): 2 . PubMed ID: 15733322 doi:10.1186/1743-0003-2-2 15733322 10.1186/1743-0003-2-2 2
Shona L. Halson, Alan G. Hahn and Aaron J. Coutts
high reliability but low ecological validity, while field assessments may have lower reliability but strong ecological validity. With the advent of wearable technologies, markerless motion-analysis systems, and sophisticated competition-analysis tools, there has been a rapid expansion of the ability to
Blake D. McLean, Donald Strack, Jennifer Russell and Aaron J. Coutts
-related research. This section explores rules directly affecting the implementation of technologies and research related to understanding the physical demands of the NBA. Approved Technologies The NBA CBA states that teams may request players use the following wearable technologies: Adidas miCoach Elite systems