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Christian A. Clermont, Lauren C. Benson, W. Brent Edwards, Blayne A. Hettinga and Reed Ferber

patterns during prolonged running. 10 Therefore, the first purpose of this study was to quantify subject-specific alterations in running patterns, using wearable technology data, throughout a marathon race. The second purpose of this study was to determine if runners could be clustered into separate

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Catrine Tudor-Locke and Elroy J. Aguiar

in physical activity research, and step-based physical activity goals are increasingly popularized, for example, 10,000 steps/day ( Bassett, Toth, LaMunion, & Crouter, 2017 ). The growth and adoption of wearable technologies (including research-grade accelerometers, consumer-grade wearable devices

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James W. Navalta, Jeffrey Montes, Nathaniel G. Bodell, Charli D. Aguilar, Ana Lujan, Gabriela Guzman, Brandi K. Kam, Jacob W. Manning and Mark DeBeliso

, Sattar, & Lean, 2017 ). In order for individuals to truly attain their step goals, the ability to accurately determine step count becomes important. Wearable technology was rated as the top fitness trend the past two years ( Thompson, 2015 , 2016 ), and this tendency is expected to grow as the use of

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Shona L Halson, Jonathan M. Peake and John P. Sullivan

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Marco Cardinale and Matthew C. Varley

The need to quantify aspects of training to improve training prescription has been the holy grail of sport scientists and coaches for many years. Recently, there has been an increase in scientific interest, possibly due to technological advancements and better equipment to quantify training activities. Over the last few years there has been an increase in the number of studies assessing training load in various athletic cohorts with a bias toward subjective reports and/or quantifications of external load. There is an evident lack of extensive longitudinal studies employing objective internal-load measurements, possibly due to the cost-effectiveness and invasiveness of measures necessary to quantify objective internal loads. Advances in technology might help in developing better wearable tools able to ease the difficulties and costs associated with conducting longitudinal observational studies in athletic cohorts and possibly provide better information on the biological implications of specific external-load patterns. Considering the recent technological developments for monitoring training load and the extensive use of various tools for research and applied work, the aim of this work was to review applications, challenges, and opportunities of various wearable technologies.

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Charlotte L. Edwardson, Melanie Davies, Kamlesh Khunti, Thomas Yates and Alex V. Rowlands

Global sales of wearable technology are increasing substantially year on year, with 32 million units sold in 2014, 72 million in 2015, and over 100 million in 2016 (Statista Website [Internet], 2017a ). Health and fitness trackers make up more than one-third of this wearable technology market

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Emma E. Sypes, Genevieve Newton and Zakkoyya H. Lewis

and enhanced, and in doing so, these devices contribute to the billion-dollar market of wearable technologies. 5 Given the substantial evidence concluding that EAMSs are valid tools for measuring activity levels, 6 , 7 the next step is to determine how they may influence user’s PA levels and promote

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Joseph M. Stock, Ryan T. Pohlig, Matthew J. Botieri, David G. Edwards and Gregory M. Dominick

to modify existing algorithms and make changes to the measurement properties and features to any Fitbit device without warning. Thus, the rapid pace in which wearable technology is advancing, combined with a persistent lack of measurement transparency continues to hinder the ability to disseminate

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Kayla J. Nuss, Joseph L. Sanford, Lucas J. Archambault, Ethan J. Schlemer, Sophie Blake, Jimikaye Beck Courtney, Nicholas A. Hulett and Kaigang Li

.A. , Navalta , J.W. , Fountaine , C.J. , & Reece , J.D. ( 2018 ). Current State of Commercial Wearable Technology in Physical Activity Monitoring 2015–2017 . Retrieved from 29541338 Burke , L.E. , Wang , J. , & Sevick , M

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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.