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Roberto A. González-Fimbres, German Hernández-Cruz, and Andrew A. Flatt

Purpose: To assess heart rate (HR) variability responses to various markers of training load, quantify associations between HR variability and fitness, and compare responses and associations between 1-minute ultrashort and 5-minute criterion measures among a girls’ field hockey team. Methods: A total of 11 players (16.8 [1.1] y) recorded the logarithm of the root mean square of successive differences (LnRMSSD) daily throughout a 4-week training camp. The weekly mean (LnRMSSDM) and coefficient of variation (LnRMSSDCV) were analyzed. The internal training load (ITL) and external training load (ETL) were acquired with session HR and accelerometry, respectively. Speed, agility, repeated sprint ability, and intermittent fitness were assessed precamp and postcamp. Results: Similar increases in the ultrashort and criterion LnRMSSDM were observed in week 3 versus week 1 (P < .05–.06, effect size [ES] = 0.28 to 0.36). The ultrashort and criterion LnRMSSDCV showed small ES reductions in week 2 (ES = −0.40 to −0.50), moderate reductions in week 3 (ES = −0.61 to −0.72), and small reductions in week 4 (ES = −0.42 to −0.51) versus week 1 (P > .05). Strong agreement was observed between the ultrashort and criterion values (intraclass correlation coefficient = .979). The ITL:ETL ratio peaked in week 1 (P < .05 vs weeks 2–4), displaying a weekly pattern similar to LnRMSSDCV, and inversely similar to LnRMSSDM. Changes in the ultrashort and criterion LnRMSSDCV from week 1 to 4 were associated with ITL (P < .01). The ultrashort and criterion LnRMSSDCV in week 4 were associated (P < .05) with postcamp fitness. Conclusions: The ultrashort HR variability parameters paralleled the criterion responses, and the associations with ITL and fitness were similar in magnitude.

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Thomas L. Schmid, Janet E. Fulton, Jean M. McMahon, Heather M. Devlin, Kenneth M. Rose, and Ruth Petersen

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Øyvind Sandbakk, Thomas Haugen, and Gertjan Ettema

Purpose: To provide novel insight regarding the influence of exercise modality on training load management by (1) providing a theoretical framework for the impact of physiological and biomechanical mechanisms associated with different exercise modalities on training load management in endurance exercise and (2) comparing effort-matched low-intensity training sessions performed by top-level athletes in endurance sports with similar energy demands. Practical Applications and Conclusions: The ability to perform endurance training with manageable muscular loads and low injury risks in different exercise modalities is influenced both by mechanical factors and by muscular state and coordination, which interrelate in optimizing power production while reducing friction and/or drag. Consequently, the choice of exercise modality in endurance training influences effort beyond commonly used external and internal load measurements and should be considered alongside duration, frequency, and intensity when managing training load. By comparing effort-matched low- to moderate-intensity sessions performed by top-level athletes in endurance sports, this study exemplifies how endurance exercise with varying modalities leads to different tolerable volumes. For example, the weight-bearing exercise and high-impact forces in long-distance running put high loads on muscles and tendons, leading to relatively low training volume tolerance. In speed skating, the flexed knee and hip position required for effective speed skating leads to occlusion of thighs and low volume tolerance. In contrast, the non-weight-bearing, low-contraction exercises in cycling or swimming allow for large volumes in the specific exercise modalities. Overall, these differences have major implications on training load management in sports.

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Arnaud Hays, Caroline Nicol, Denis Bertin, Romain Hardouin, and Jeanick Brisswalter

Objectives: To identify relevant physiological, mechanical, and strength indices to improve the evaluation of elite mountain bike riders competing in the current Cross-Country Olympic (XCO) format. Methods: Considering the evolution of the XCO race format over the last decade, the present testing protocol adopted a battery of complementary laboratory cycling tests: a maximal aerobic consumption, a force–velocity test, and a multi-short-sprint test. A group of 33 elite-level XCO riders completed the entire testing protocol and at least 5 international competitions. Results: Very large correlations were found between the XCO performance and maximal aerobic power output (r = .78; P < .05), power at the second ventilation threshold (r = .83; P < .05), maximal pedaling force (r = .77; P < .05), and maximum power in the sixth sprint (r = .87; P < .05) of the multi-short-sprint test. A multiple regression model revealed that the normalized XCO performance was predicted at 89.2% (F 3,29 = 89.507; r = .95; P < .001) by maximum power in the sixth sprint (β = 0.602; P < .001), maximal pedaling rate (β = 0.309; P < .001), and relative maximal aerobic power output (β = 0.329; P < .001). Discussion: Confirming our expectations, the current XCO performance was highly correlated with a series of physiological and mechanical parameters reflecting the high level of acyclic and intermittent solicitation of both aerobic and anaerobic metabolic pathways and the required qualities of maximal force and velocity. Conclusion: The combination of physiological, mechanical, and strength characteristics may thus improve the prediction of elite XCO cyclists’ performance. It seems of interest to evaluate the ability to repeatedly produce brief intensive efforts with short active recovery periods.

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Sarah Deck, Brianna DeSantis, Despina Kouali, and Craig Hall

In team sports, it has been found that team mistakes were reported as a stressor by both males and females, and at every playing level (e.g., club, university, national). The purpose of this study was to examine the impact of partners’ play on performance, emotions, and coping of doubles racquet sport athletes. Seventeen one-on-one semistructured interviews were conducted over the course of 6 months. Inductive and deductive analysis produced the main themes of overall impact on performance (i.e., positive, negative, or no impact), negative emotions (i.e., anger), positive emotions (i.e., excitement), emotion-focused coping (i.e., acceptance), and problem-focused coping (i.e., team strategy). These athletes acknowledge that how their partner plays significantly affects not only their emotions but also their own play and their choice of coping strategies. Future research should try to understand which forms of coping reduce the impact of partners’ play.

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Pedro L. Valenzuela, Guillermo Sánchez-Martínez, Elaia Torrontegi, Javier Vázquez-Carrión, Zigor Montalvo, and G. Gregory Haff

Purpose: To analyze the differences in the force–velocity (F–v) profile assessed under unconstrained (ie, using free weights) and constrained (ie, on a Smith machine) vertical jumps, as well as to determine the between-day reliability. Methods: A total of 23 trained participants (18 [1] y) performed an incremental load squat jump test (with ∼35%, 45%, 60%, and 70% of the subjects’ body mass) on 2 different days using free weights and a Smith machine. Nine of these participants repeated the tests on 2 other days for an exploratory analysis of between-day reliability. F–v variables (ie, maximum theoretical force [F 0], velocity [v 0], and power, and the imbalance between the actual and the theoretically optimal F–v profile) were computed from jump height. Results: A poor agreement was observed between the F–v variables assessed under constrained and unconstrained conditions (intraclass correlation coefficient [ICC] < .50 for all). The height attained during each single jump performed under both constrained and unconstrained conditions showed an acceptable reliability (coefficient of variation < 10%, ICC > .70). The F–v variables computed under constrained conditions showed an overall good agreement (ICC = .75–.95 for all variables) and no significant differences between days (P > .05), but a high variability for v 0, the imbalance between the actual and the theoretically optimal F–v profile, and maximal theoretical power (coefficient of variation = 17.0%–27.4%). No between-day differences were observed for any F–v variable assessed under unconstrained conditions (P > .05), but all of the variables presented a low between-day reliability (coefficient of variation > 10% and ICC < .70 for all). Conclusions: F–v variables differed meaningfully when obtained from constrained and unconstrained loaded jumps, and most importantly seemed to present a low between-day reliability.

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Scott J. Strath, Taylor W. Rowley, Chi C. Cho, Allison Hyngstrom, Ann M. Swartz, Kevin G. Keenan, Julian Martinez, and John W. Staudenmayer

Purpose: To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions. Methods: 118 adults self-identifying as healthy (n = 44) and those with arthritis (n = 23), multiple sclerosis (n = 18), Parkinson’s disease (n = 17), and stroke (n = 18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit. Results: All activities’ accelerometer counts per minute (CPM) were 29.5–72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, −0.49 (−0.71, −0.27) for arthritis, −0.38 (−0.53, −0.22) for healthy, −0.44 (−0.68, −0.20) for MS, −0.34 (−0.58, −0.09) for Parkinson’s, and −0.30 (−0.54, −0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, −0.37 METs (−0.52, −0.23); Group 2, −0.44 METs (−0.60, −0.28); and Group 3, −0.33 METs (−0.55, −0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition. Conclusion: Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.

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Supun Nakandala, Marta M. Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan A. Carlson, Andrea Z. LaCroix, Sheri J. Hartman, Dori E. Rosenberg, Jingjing Zou, Arun Kumar, and Loki Natarajan

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

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Stacey Alvarez-Alvarado and Gershon Tenenbaum

Inquiry of the psychological states during the exercise experience failed to fully account for the role of motivation to adhere and the disposition of exertion tolerance (ET). The current study expands the scope of the integrated cognitive–perceptual–affective framework by measuring the motivation to sustain effort in two physical tasks and accounting for ET. Thirty male participants performed cycling and isometric handgrip tasks to assess the progression of the rating of perceived exertion, attentional focus, affective responses, and motivation to adhere, along with an incremental workload. The ET was determined by a handgrip task time to voluntary exhaustion. The findings indicated significant time effects and linear trends for perceived exertion, attentional focus, affect, and perceived arousal but not motivation to adhere during the handgrip and cycling tasks. The ET played a key role in the integrity of the model, particularly in perceptual, attentional, and affective responses. The intended model serves to stimulate new research into adaptation mechanisms.

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Bronwyn Clark, Elisabeth Winker, Matthew Ahmadi, and Stewart Trost

Accurate measurement of time spent sitting, standing, and stepping is important in studies seeking to evaluate interventions to reduce sedentary behavior. In this study, the authors evaluated the agreement in classification of these activities from three algorithms applied to thigh-worn ActiGraph accelerometers using predictions from the widely used activPAL device as a criterion. Participants (n = 29, 72% female, age 23–68 years) wore the activPAL3 micro (processed by PAL software, version 7.2.32) and the ActiGraph GT9X accelerometer on the right front thigh concurrently for working hours on one full workday (7.2 ± 1.2 hr). ActiGraph output was classified via the three test algorithms: ActiGraph’s ActiLife software (inclinometer); an open source method; and, a machine-learning algorithm reported in the literature (Acti4). Performance at an instance level was evaluated by computing classification accuracy (F scores) for 15-s windows. The F scores showed high accuracy relative to the criterion for identifying sitting (96.7–97.1) and were 84.7–85.1 for identifying standing and 78.1–80.6 for identifying stepping. The four methods agreed strongly in total time spent sitting, standing, and stepping, with intraclass correlation coefficients of .96 (95% confidence interval [.92, .96]), .92 (95% confidence interval [.81, .96]), and .87 (95% confidence interval [.53, .95]) but sometimes overestimated sitting time and underestimated standing time relative to activPAL. These algorithms for identifying sitting, standing, and stepping from thigh-worn accelerometers provide estimates that are very similar to those obtained using the activPAL.