There has undoubtedly been a spike in technology use across various industries, leading to the current “big data” revolution. Advancements in creating inexpensive hardware have enabled developers to create software that has made previously unfeasible applications a reality. For example, strength and conditioning professionals have started implementing inertial measurement units (IMUs), which were previously limited to lab-based applications, to their daily practice. Smartphones and tablets enable coaches to leverage machine learning to perform markerless motion tracking to obtain coordination data. Virtual and augmented reality are also bound to make their way into mainstream coaching in the future.
There are two main approaches that coaches and professionals can take when adopting new technologies to collect data that informs their practice:
1. “Data science” approach, whereby large volumes of data are collected and monitored over time to elucidate previously unseen patterns. For example, machine learning algorithms can provide new insights about recovery or injury patterns.
2. “Movement science” approach (which will be the focus of this article) where the periodization process is directly informed using the collected data. For example, quantifying coordination patterns during movement assessments can highlight movement behaviours that should be improved upon to improve performance or reduce the risk of injury.
Coaches can leverage different data in different ways depending on whether they wish to inform macrocycles, mesocycles, microcycles, or individual training sessions. For example, coordination data can inform the general structure of training (i.e., a macrocycle or mesocycle), while other recovery data, such as heart rate variability, can regulate individual training sessions. However, given the previous technological hurdles, less attention has been given to how data can guide instructions and feedback during a training session to change athletes’ movement behaviours. Since coaches now have easier access to technologies that can influence their instructions and feedback, it is crucial to ground their implementation with the existing literature.
This article will briefly outline some considerations for implementing data within a training session to change athletes’ movement behaviours. The first part of the article is a review that will briefly encompass the focus of attention literature and the constraints-led approach (CLA) (highlighted in a previous CSCA post). The second part will provide a specific example of applying this literature using velocity data.
Brief Review of Instructions and Feedback in the Motor Learning Literature
Verbal instructions and feedback are some of the most commonly used tools for strength and conditioning practitioners to alter movement patterns. They are also perhaps the most commonly used forms of feedback with technology at present as coaches “translate” the collected data into actions for the athlete. From a CLA perspective, verbal instructions act as informational constraints that direct athletes’ search strategies in the perceptual-motor landscape. Feedback provides opportunities to direct athletes’ search strategies when task-related goal feedback is not naturally available (e.g., outcome goals such as movement velocity). Verbal instructions and feedback have been dichotomized based on where they direct the learner’s focus of attention. An internal focus of attention directs focus to a learner’s body parts and movements (e.g., “extend your knees as fast as possible”). Conversely, an external focus of attention directs the focus to movement outcomes (e.g., “reach for a target above you”) or the effects of movements (e.g., “push away from the floor”).
Although the blanket statement that an “external focus of attention is better than an internal focus of attention” is somewhat familiar in both research and practice, there is much more nuance to the discussion. Mainly, the appropriateness of directing athletes’ focus of attention either internally or externally is dependent on the importance of proprioceptive and visual information for successful task execution (4) and the relative difficulty of the task (17). For example, Gottwald et al. (2020) (4) argue that since “successful task execution” for weightlifting is less dependent on visual information and more dependent on proprioceptive information that an internal focus of attention would be more appropriate. Similarly, other researchers have argued that an internal focus of attention may be helpful for modifying technique (2,14). Conversely, since success in a task such as a golf putt is relatively less dependent on proprioceptive information and more dependent on visual information, an external focus of attention would be more appropriate. Furthermore, Wulf et al. (2007) (17) argue that the potential benefits of adopting an external focus of attention only emerge when the relative task difficulty (e.g., stability demands) increases.
Though the general idea of ensuring that the focus of attention is selected based on the task goals and difficulty is important, the definition of “successful task execution” is vague. In practice, it is highly dependent on the goals of training. Therefore, coaches and practitioners should not rigidly apply the recommendations from the previous paragraph. For example, in cases where practitioners are selecting exercises to promote the acquisition of specific movement behaviours, such as maintaining a straight spine during squatting tasks with an array of different demands, promoting an internal focus of attention may be more successful. However, when practitioners want to promote faster movement velocities during a squat that transfers to high-velocity specific activities, an external focus of attention may be more appropriate. Finally, if the priority in training is to instead promote maximum hypertrophy, an internal focus of attention may be beneficial relative to an external focus of attention (13).
However, this is still a relatively simplistic way of viewing the focus of attention dichotomy since a coach can prompt either an external or internal focus of attention in several ways. For example, the current data suggest that driving attention to the motion effects rather than external information sources (15) is more effective for learning. Furthermore, promoting discovery and implicit-style learning rather than prescriptive and explicit-style learning also seems more effective for learning (5). As was highlighted in a previous CSCA article about applying the CLA for Olympic Weightlifting CLICK HERE , instructions and feedback strategies, regardless of whether they prompt an internal or external focus of attention, should not prescribe correct technique to the athletes. Instead, they should guide athletes to explore the perceptual-motor landscape to find a solution that achieves the desired outcome based on the interaction between the individual, task, and environmental constraints. An example of the differences between prescribing and guiding instructions can be illustrated in a study by Gray (2018) comparing different cueing and constraints for increasing launch angle in baseball batting (5). In this study, participants were instructed to “get the bat on the same plane as the incoming pitch”, “drive the ball over the infield”, and “contact the bottom half of the ball” in the external focus of attention group. However, in the CLA group participants were instructed to hit the ball over a barrier that required a specific launch angle to clear (the distance and height of the barrier were adjusted to be challenging for the participant). With the external focus of attention instruction in this study, how to move was explicitly prescribed, even though the instruction was focused on the effects of the movement. However, participants in the CLA group were guided in an exploratory process to find coordination strategies that satisfied the task constraints. The differences between guiding and prescribing instructions in strength and conditioning contexts will be further highlighted in the next section.
The current article has only outlined the two main types of instructions and feedback. However, coaches must also consider the frequency at which they use these instructions and feedback to inform training. For example, an inverted-U function between task performance and amount of instruction/feedback that induces an internal focus of attention has been proposed (Figure 1), whereby too little or too much may impair the learning process in individuals (1,8).
This left tail for the internal focus of attention may result from too little information to guide athletes towards an initial search space for finding an appropriate movement solution. Alternatively, the right tail may result from too much information, whereby athletes do not learn to perceive and navigate the perceptual-motor landscape effectively by themselves. However, when the feedback provided induces an external focus of attention, these decrements are not noted for high feedback frequencies (16). Currently, it is unclear how robust these findings may be in practice. For example, if coaches provide feedback after every repetition, it is possible that athletes eventually start to ignore these instructions or feedback due to boredom. A more appropriate implementation may leverage self-controlled learning environments that allow the athlete to dictate when to receive feedback instead of using a fixed frequency schedule (3,7,9).
To summarize, coaches must consider the overarching goals of training when considering the type and frequency of instructions and feedback with technology. In cases where coaches want to promote training adaptations that are less reliant on proprioceptive information, athletes may benefit more from instructions and feedback that promote an external focus. In contrast, adaptations that rely more on proprioceptive information may benefit more from instructions and feedback that promote an internal focus. In settings where the task difficulty is relatively low, a combination of an internal and external focus of attention may be beneficial. Regardless of whether an internal or external focus of attention is selected, the instructions and feedback should guide athletes to a movement solution rather than explicitly state the movement solution. Depending on the focus of attention induced, the feedback frequency should be modulated accordingly (lower for internal, higher for external). Coaches should also allow athletes to determine when they receive feedback from the technology rather than using a fixed schedule created solely by the coach or software.
Applying the Motor Learning Literature with Velocity Data
Given advancements in IMU technology, coaches can now track barbell velocity cheaply and easily. As a result, there has been an increased emphasis in the literature exploring ways to leverage this velocity data. For example, one use for this data is to provide new types of instructions and feedback to enhance training outcomes. This section outlines a potential use case for modifying instructions and feedback during training when barbell velocity data is available to the practitioner.
Current literature has shown velocity feedback prompts faster movement velocities consistently during training (10,12), which has resulted in superior high-velocity specific training improvements (jumping and sprinting) following six weeks of training with professional rugby players (11). Research from our lab expanded upon these findings by assessing the impact of instructions that use challenging velocities targets (6). We recruited 13 experienced powerlifters to participate in two testing sessions. Participants received an instruction to “move as fast as possible” or “attain a target velocity of 1m/s” with a 45% 1RM load in each session. The order of instructions was randomized between participants. Participants received feedback on their movement velocity immediately following each repetition during both sessions. We found a statistically significant increase in barbell velocity when we instructed athletes using a velocity target. Furthermore, 11/13 participants demonstrated faster mean barbell velocities with the target velocity instruction versus the instruction to move as fast as possible.
In addition to the external focus of attention (and motivating properties) that the velocity target instruction and feedback may have provided in the previous study, it can also promote the exploration of new movement solutions during training. If the athlete did not reach the target, they must attempt to reorganize themselves during the next repetition to satisfy the instructions. When simply instructing people to move as fast as possible, there is no prompt for athletes to reorganize their coordination strategies as their success is defined subjectively rather than objectively. Therefore, it could be said that a target velocity is relatively more of a guiding instruction while moving as fast as possible is relatively more of a prescribing instruction. Given these considerations, coaches should expect further improvements in transfer tasks, such as running and jumping, if challenging velocity targets are used instead of the instruction to move as fast as possible during training.
It is now essential to highlight a limitation common in velocity feedback studies. Currently, researchers have used a fixed frequency with no input from the participant. Although this has been important for maintaining internal validity in research, in practice, it may be more appropriate to instead afford the athletes an opportunity to self-determine when they receive feedback of whether they achieved the velocity target or not. Allowing the athletes to play a more active role in their training would likely promote the acquisition of coordinative structures that are more likely to transfer beyond training. Although future research must address this contention specifically, the general motor learning literature suggests that athletes should be more active in determining when they want to receive velocity feedback.
In addition to creating predictive models, implementing data in (near) real-time to change movement behaviours is critical for coaches to extract maximum value from technology. Grounding technology’s use with motor learning theories and research is a way to ensure that coaches are best positioned to take full advantage of the “big data” revolution that will inevitably continue to change the strength and conditioning industry. Although this article only outlined one specific use case with velocity data, the general principles can be adapted to guide many potential applications.
Steven Hirsch, MSc, CSCS, is currently a Ph.D. Student at the University of Toronto in the Biomechanics and Sports Medicine Lab. His current research interests include using (non)linear human movement analyses to inform (re)training programs and developing tools and technologies for practitioners to be more data-driven. Steven has experience working as a Strength and Conditioning coach for athletes ranging from amateur to professional levels and as an externally contracted Sports Biomechanist with the Canadian Sport Institute Ontario. He has also worked as a consultant for multiple tech companies to assist with quantitative analyses of human movement. In addition, Steven has competed in and coached athletes for both Olympic Weightlifting and Powerlifting.
1. Badets, A and Blandin, Y. Feedback schedules for motor-skill learning: the similarities and differences between physical and observational practice. J Mot Behav 42: 257–68, 2010.
2. Carson, HJ and Collins, D. Refining and regaining skills in fixation/diversification stage performers: the Five-A Model. Int Rev Sport Exerc Psychol 4: 146–167, 2011.
3. Chiviacowsky, S and Wulf, G. Self-Controlled Feedback Is Effective if It Is Based on the Learner’s Performance. Res Q Exerc Sport 76: 42–48, 2005.
4. Gottwald, Owen, Lawrence, and McNevin. An internal focus of attention is optimal when congruent with afferent proprioceptive task information. Psychol Sport Exerc 47: 101634–101634, 2020.
5. Gray, R. Comparing cueing and constraints interventions for increasing launch angle in baseball batting. Sport Exerc Perform Psychol 7: 318–332, 2018.
6. Hirsch, SM and Frost, DM. Considerations for Velocity-Based Training: The Instruction to Move “As Fast As Possible” Is Less Effective Than a Target Velocity. J Strength Cond Res , 2019.Available from: http://www.ncbi.nlm.nih.gov/pubmed/31268998
7. Janelle, CM, Barba, DA, Frehlich, SG, Tennant, LK, and Cauraugh, JH. Maximizing Performance Feedback Effectiveness through Videotape Replay and a Self-Controlled Learning Environment. Res Q Exerc Sport 68: 269–279, 1997.
8. Lam, CF, DeRue, DS, Karam, EP, and Hollenbeck, JR. The impact of feedback frequency on learning and task performance: Challenging the “more is better” assumption. Organ Behav Hum Decis Process 116: 217–228, 2011.
9. Lim, S, Ali, A, Kim, W, Kim, J, Choi, S, and Radlo, SJ. Influence of Self-Controlled Feedback on Learning a Serial Motor Skill. Percept Mot Skills 120: 462–474, 2015.
10. Nagata, A, Doma, K, Yamashita, D, Hasegawa, H, and Mori, S. The Effect of Augmented Feedback Type and Frequency on Velocity-Based Training-Induced Adaptation and Retention. J Strength Cond Res , 2018.Available from: http://www.ncbi.nlm.nih.gov/pubmed/29461412
11. Randell, AD, Cronin, JB, Keogh, JWL, Gill, ND, and Pedersen, MC. Effect of instantaneous performance feedback during 6 weeks of velocity-based resistance training on sport-specific performance tests. J Strength Cond Res 25: 87–93, 2011.
12. Randell, AD, Cronin, JB, Keogh, JWL, Gill, ND, and Pedersen, MC. Reliability of performance velocity for jump squats under feedback and nonfeedback conditions. J Strength Cond Res 25: 3514–8, 2011.
13. Schoenfeld, BJ, Vigotsky, A, Contreras, B, Golden, S, Alto, A, Larson, R, et al. Differential effects of attentional focus strategies during long-term resistance training. Eur J Sport Sci 18: 705–712, 2018.
14. Toner, J and Moran, A. Enhancing performance proficiency at the expert level: Considering the role of ‘somaesthetic awareness.’ Psychol Sport Exerc 16: 110–117, 2015.
15. Wulf, G, McNevin, NH, Fuchs, T, Ritter, F, and Toole, T. Attentional Focus in Complex Skill Learning. Res Q Exerc Sport 71: 229–239, 2000.
16. Wulf, G and Prinz, W. Directing attention to movement effects enhances learning: A review. Psychon Bull Rev 8: 648–660, 2001.
17. Wulf, G, Töllner, T, and Shea, CH. Attentional focus effects as a function of task difficulty. Res Q Exerc Sport 78: 257–64, 2007.