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The Effect of Augmented Feedback and Expertise on Spinal Manipulation Skills: An Experimental Study
The Effect of Augmented Feedback and Expertise on Spinal Manipulation Skills: An Experimental Study Mégane Pasquier, MSc, a Charlène Cheron, DC, PhD, a Claude Dugas, PhD, b
Arnaud Lardon, DC, PhD, a and Martin Descarreaux, DC, PhD b ABSTRACT
Objectives: The purpose of this study was to investigate the combined effect of augmented feedback and expertise on the performance and retention of basic motor learning spinal manipulation skills. Methods: A total of 103 chiropractic students with various training expertise were recruited for the study. Participants were evaluated at baseline, immediately after trials of augmented feedback practice and 1 week later. During all 3 assessments, students were asked to perform several trials of the same spinal manipulation, for which the maximum preload force, onset of thrust, thrust duration, force and peak force, thrust duration, rate of force application, and any drop in preload force were calculated. The constant error, absolute error, and variable error were calculated for the 3 experimental blocks of trials. Results: Results confirmed that augmented feedback training modified several biomechanical parameters such as the rate of force application, the preload force, and the drop in preload force. The study also confirmed that many biomechanical parameters, including thrust duration and rate of force application, are modified with expertise but failed to identify any interaction effect between expertise and augmented feedback training effects. Conclusion: The study determined that expertise did not influence how students performed after a session of augmented feedback training. The study also determined that augmented feedback related to the global performance can yield improvements in several basic components of the spinal manipulation task. These results should be interpreted considering basic motor learning principles and specific learning environments. (J Manipulative Physiol Ther 2017;40:404-410) Key Indexing Terms: Spinal Manipulation; Feedback; Learning; Education; Expertise
INTRODUCTION Spinal manipulation (SM) is a tool commonly used in manual therapy, and it is the typical treatment administrated by chiropractors. 1 In each chiropractic teaching institution, an important part of the curriculum is dedicated to this learning. Spinal manipulation is characterized by a dynamic thrust of high velocity and low amplitude using a specific contact and direction associated with an audible cavitation. It can be described as an action requiring high-speed, low-amplitude precision that has mechanical consequences. 2 From an experimental standpoint, spinal manipulation is usually a
Institut Franco-Européen de Chiropraxie, Toulouse, France. Department of Human Kinetics, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada. Corresponding author: Martin Descarreaux, DC PhD, Université du Québec à Trois-Rivières, 3351 Boul. des Forges, C.P. 500, Trois-Rivières, QC, G9A 5H7. (e-mail: [email protected]). Paper submitted January 11, 2017; in revised form February 17, 2017; accepted March 14, 2017. 0161-4754 Copyright © 2017 by National University of Health Sciences. http://dx.doi.org/10.1016/j.jmpt.2017.03.010 b
described using the basic biomechanical features characterizing its force-time profile, such as peak force, preload force, thrust duration force, and rate of force application. 3 Although the SM force–time profile is rather stable across experienced clinicians, spinal manipulation can prove to be a complex motor skill depending on the technique used and the segment being treated. As in any motor task, the learning of spinal manipulation requires pedagogic strategies and training regimen based on repetition and feedback. 4 In many cases, spinal manipulation involves postural control and timely synchronization of lower limb and trunk body weight transfer during upper limb force transmission. Mastering these complex skills and proper coordination of the multiple body segments involved enable a better regulation of forces that are applied during a treatment. 5 Previous studies comparing levels of expertise and motor performance during learning suggest that these more complex components of SM are not easily mastered during training years. 6 Although scientific evidence is limited, the planning and organization of SM skill learning can affect students’ motor performance and overall SM skill expertise. 7 Indeed, several studies have explored the relevance of feedback training in spinal manipulation education. These studies have explored the effect of feedback in various
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populations ranging from inexperienced students without any clinical training to students and clinicians with significant clinical experience. 8 Various devices, all based on force-sensing technologies, were used to provide feedback to students and to assess performances. 9,10 In a few studies, 11,12 feedback training was provided using force-sensing devices (no patients), whereas performance assessments were conducted on patients (other students). Altogether, these studies suggest that augmented feedback, defined as “information provided about the action that is supplemental to, or that augments, the inherent feedback,” 13 using force-sensing devices during training improves SM skill performance and reduces variability. Interestingly, only 2 studies included short-term learning retention assessments (1 week after training) 9,10 after augmented feedback training. One could therefore argue that these studies mostly describe improvements in performance rather than true motor learning, usually defined as permanent changes in a person’s capability to execute a motor skill. 14 As highlighted in a recent best-evidence synthesis by Stainsby et al. 15 several mechanical or computerized training aids providing extrinsic forms of feedback have been developed and studied over the past 2 decades. The various training aids are usually developed to address the motor learning challenges faced by students and teachers, and the authors of this best-evidence synthesis concluded that such devices are useful to promote skill development, knowledge transfer, and task retention among students. Overall, although there is no doubt that feedback can improve short-term SM performance and consistency, its effect on motor learning assessed trough retention and transfer tasks is less documented. The effect of various types of augmented feedback as well as optimal strategies, including frequency, timing, and accuracy of augmented feedback in SM skill learning remain to be investigated. In this study, 3 groups of chiropractic students with varying expertise were compared on 3 different occasions: (i) baseline evaluation, (ii) after augmented feedback training, and (iii) 1 week after augmented feedback training. The goals of the study were 2-fold. The first goal of the study was to reaffirm, in a large sample of students, that biomechanical parameters of SM are influenced by the level of expertise and are improved after augmented feedback training. The second goal was to assess how expertise modulates the effect of augmented feedback use during SM training. Several models describe the stages of motor learning 16,17 as a process through which most learners evolve from a verbal-cognitive stage to an independent-automated stage characterizing expertise. Based on such models, it was hypothesized that augmented feedback related to the global performance and reinforced by specific feedback on peak force, thrust duration, and preload force using SM the force–time profile would mostly favor early SM learners.
Pasquier et al Learning Spinal Manipulation Strategies
METHODS Design This was an experimental study.
Participants Participants were all recruited among the first-, third-, and fifth-year chiropractic students (group 1, 2 and 3, respectively) using convenience sampling. Based on previous SMT motor learning studies indicating significant expertise effects, it was estimated that a minimum of 20 participants per group was needed to reveal expertise and training effects. 18 The study was approved by the Institut Franco-Européen de Chiropraxie Ethics Committee (2016_02_26) and each participant provided a written informed consent. Participants’ characteristics for each group are presented in Table 1.
Experimental Sessions The study was divided in 3 different assessments over 2 experimental sessions that were preceded by a 15-minute presentation of the project. During this presentation the protocol and the SM task were briefly explained and demonstrated using a video presentation and both novice and expert SM force-time profiles. Typical characteristics of novice force–time profiles, such as lack or drop in preload force and longer thrust duration, were pointed out to participants. During the first experimental session, each participant performed a total of 53 SMs on an instrumented device using a unilateral hypothenar transverse push technique with a posterior-to-anterior force vector. Participants chose their preferred contact hand and table height to perform the SM and were instructed to perform SM using the same hand during the all assessments. The SMs were performed by contacting the target on the device in a fencer position with the caudal hand pisiform. A familiarization period was first offered and consisted of 3 trials per participant, during which they were instructed to perform SMs of 300 N peak force while trying to replicate expertlike force–time profile. Baseline assessment was conducted during the following 10 trials, which were also performed with a target peak force of 300 N but without feedback. During the training period, participants performed 30 SMs and were provided with both verbal and visual feedback. A global performance feedback was provided verbally and reinforced, when necessary, by specific quantified feedback on peak force, thrust duration, and preload force drawn from the previous SM force–time profile. Post-training assessment consisted of a set of 10 trials without any feedback and with a 300-N peak force target. After a 7-day retention period, a third assessment was conducted during which participants also performed 10 trials without feedback using a 300-N peak force target after familiarization trials. All 3 sets of 10 trials collected at baseline, during the post-training, and at the retention assessment periods were used in the data analysis.
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Apparatus SMs were delivered on a computer-connected device developed to simulate a thoracic spine prone manipulation. Complete description of the apparatus has been published in a previous article. 6 Briefly, participants apply forces to the device, which is linked to a strain gauge (model LSB300; FUTEK Inc, Irvine, CA) and a spring that replicates thoracic spine resistance and movement. When a force of 250 N is reached during SM, the spring and the strain gauge drop by 5 mm to simulate vertebral joint cavitation. Data were collected using Labview software (National Instruments, Austin, TX).
force, and peak force. Thrust duration, rate of force application, and preload force drop were then calculated using these values. The constant error (CE), absolute error (AE), and variable error (VE) were calculated for the 3 experimental blocks of SM trials considering a constant 300-N peak force target. The CE was calculated using the positive or negative difference between the peak force reached and the peak force targeted for each trial. The absolute value of the deviation from the target force was used to obtain the AE (accuracy), whereas the VE, which assesses the participants’ consistency during repeated trials, was calculated using the absolute mean difference between the peak force reached during a given trial and the participant’s mean peak force during a given block of trials. 13
DATA ANALYSIS
Statistical Analysis
Data obtained during the 3 assessment blocks (baseline, post-training, and retention) were analyzed to determine for each trial the maximum preload force, onset of thrust, thrust duration,
Normality of data sets was verified by visual inspection and the Shapiro-Wilk test. A 1-way analysis of variance (ANOVA) was first conducted to test for any differences between
0,15
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45
Absolute error (newtons)
406
40 35 30 25 20 15 10
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Fig. 1. Mean (95% confidence interval) group score for each of the following variables: preload, thrust duration, absolute error, variable error. *Significant differences.
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Table 1. Experimental Group Participant Characteristics (Mean ± SD) Age (y) Weight (kg) Height (cm) Female/male (n)
Population (n = 103)
Group 1 (n = 35)
Group 2 (n = 36)
Group 3 (n = 32)
P
21.8 ± 2.18 65.4 ± 11.9 170.5 ± 9.38 69/34
19.9 ± 1.48 66.8 ± 13.2 171.5 ± 10.05 22 /13
22.1 ± 1.96 64 ± 10.75 171 ± 9.73 22/14
23.5 ± 1.40 65.3 ± 12 168.7 ± 8.18 25/7
b.0001 N.05 N.05 N.05 a
SD, standard deviation. a Cochran Q test.
participants’ characteristics of each group, whereas sex proportion was tested using the Cochrane Q tests. Then the 5 biomechanical parameters (peak force, preload force, thrust duration, rate of force application, and drop in preload force) as well as the CE, AE, and VE outcomes were independently subjected to a 2-way mixed-model ANOVA assessing group × time to test for the main effects of expertise and training as well as possible group × time interactions. When required, post hoc analyses were performed using Tukey test. All statistical analyses were computed with Statistica 10 (Statsoft, Tulsa, OK) and the level of significance was set to P = .05.
RESULTS Participants A total of 103 chiropractic students were included in this study, among whom 35 were first-year students, 36 were third- year students, and 32 were fifth-year students. The 1-way ANOVA was used to test group homogeneity and revealed that there was no significant difference for height (mean ± standard deviation [SD] = 170.5 ± 9.38 cm) or weight (65.4 ± 11.9 kg). Not surprisingly, the analysis revealed a significant difference in group age (19.9 ± 1.48 years old; 22.1 ± 1.96 years old; 23.5 ± 1.40 years old) (P b .05). The age increased with the level of expertise.
duration, and peak force (all P N .05). Mean values (±95% confidence interval [CI]) for all study outcomes are presented in Table 2.
Expertise Effects A main effect of expertise was observed for thrust duration (F[2, 100] = 12.816, P b .0001), which decreased with expertise, and for rate of force application (F[2, 100] = 6.3739, P = .00248), which increased with expertise. The Tukey test revealed that thrust duration was higher in group 1 compared with groups 2 and 3. Moreover, the rate of force application was significantly lower in group 1 compared with group 3. A main effect of expertise was also noted for absolute error (F[2, 100] = 7.4152, P = .001). The Tukey test indicated that participants in groups 2 and 3 had higher absolute error values than group 1 participants. Finally, a main effect of expertise was noted for the variable error (F[2, 100] = 3.6291, P = .0301). The Tukey test indicated that group 2 participants had higher variable errors than group 1 participants. Main results are presented in Figure 1. No expertise effect was noted for constant error, preload force, peak force, or drop in preload force (all P N .05). Mean values (±95% CI) for all study outcomes are presented in Table 2.
Feedback Training Effects The analysis revealed a main effect of training for preload force (F[2, 200] = 16.553, P b .0001), drop in preload force (F[2, 200] = 47.781, P b .0001), and absolute error (F[2, 200] = 17.050, P b .0001). The Tukey tests showed that preload force was significantly increased at both the post-training and retention assessments, whereas drop in preload force and absolute error decreased significantly from baseline to post-training and retention assessments. The analysis also revealed a main effect of feedback training for rate of force application (F[2, 200] = 39.240, P b .0001). The Tukey test showed a significant decrease in rate of force application from baseline to post-training assessment and from the post-training to the retention assessments. Finally, a main effect of training was identified for variable error (F[2, 200] = 8.5274, P = .00028). The Tukey test indicated a significant decrease from the baseline to the retention assessment only. No main effect of training was observed for constant error, thrust
Expertise and Training Interactions Overall, the 2-way mixed-model ANOVA did not reveal any significant group × time interaction effect (all P N .05). However, a trend toward a significant group × time interaction effect (P = .057) was noted for preload forces, indicating that group 1 participants were the only ones producing a significant increase in preload forces after training and through retention.
DISCUSSION This study sought to confirm, in a large sample of students, that biomechanical parameters of SM are influenced by both expertise and augmented feedback training and that levels of expertise and augmented feedback use can influence biomechanical parameters of SM. Most importantly, the study sought to explore the
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Table 2. Means (95% CI) of 3 Experimental Group Biomechanical Parameters Throughout Protocol Outcomes
Assessments
Group 1
Group 2
Group 3
Preload force (N)
Baseline Post-training Retention Baseline Post-training Retention Baseline Post-training Retention Baseline Post-training Retention Baseline Post-training Retention Baseline Post-training Retention Baseline Post-training Retention
104.5 (91.9-116.9) 120.6 (111.1-130.1) 132.5 (121.8-143.2) 0.132 (0.117-0.151) 0.125 (0.112-0.139) 0.130 (0.119-0.142) 289.8 (273-306) 291.7 (280-303) 293.0 (281-304) 1963.1 (1700-2225) 1648.3 (1436-1859) 1472.5 (1257-1688) –10.2 (–26.8 to 6.4) –8.26 (–19.5 to 3.01) –6.97 (–18.6 to 4.61) 39.3 (31.4-47.1) 32.0 (26.6-37.4) 29.4 (23.1-35.8) 22.6 (19.1-26.2) 20.8 (17.7-23.9) 18.0 (15.2-20.8)
118.7 (106.4-131) 129.5 (120-138.9) 128.3 (117.7-138.9) 0.097 (0.079-0.116) 0.101 (0.087-0.114) 0.102 (0.091-0.113) 301.6 (285-318) 303.7 (292-315) 287.6 (276-298) 2306.3 (2047-2564) 2020.6 (1812-2229) 1736.6 (1524-1948) 1.64 (–14.8 to 18) 3.67 (–7.44 to 14.8) –12.4 (–23.9 to –1.02) 53.7 (46.0-61.4) 41.7 (36.4-47.1) 37.7 (31.5-44.0) 26.6 (23.1-30.0) 25.4 (22.4-28.4) 22.7 (19.9-25.5)
107.6 (94.5-120.6) 115.6 (105.7-125.6) 118.0 (106.8-129.2) 0.082 (0.062-0.101) 0.100 (0.086-0.114) 0.098 (0.086-0.109) 286.5 (269-303) 288.8 (276-300) 292.4 (277-301) 2495.2 (2221-2769) 2102.9 (1881-2324) 2022.8 (1797-2248) –13.5 (–30.9 to 3.9) –11.22 (–23.0 to 0.6) –7.64 (–19.8 to 4.5) 52.7 (44.5-60. 9) 33.5 (27.9-39.2) 36.1 (29.5-42.7) 25.8 (22.1-29.5) 21.3 (18.1-24.6) 20.8 (17.9-23.8)
Thrust duration (s)
Peak force (N)
Application force rate (N/s)
Constant error (N)
Absolute error (N)
Variable error (N)
CI, confidence interval.
combined effect of these 2 motor learning variables. Overall, this study determined that augmented feedback training modified several biomechanical parameters, such as the rate of force application, the preload force, and the drop in preload force. The study also confirmed that many biomechanical parameters, such as thrust duration and rate of force application, are modified with expertise. Interestingly, our results failed to identify any combined effect of augmented feedback and expertise, suggesting that students, regardless of their experience or training levels, benefited from augmented feedback related to SM. These results, however, should be interpreted considering basic motor learning principles but also in light of the learning environment specificities.
observed in many other motor tasks. 22 Although participants were instructed to perform an SM of 300 N while trying to replicate an expertlike force–time profile, participants with less experience might have directed their attentional focus on the target force at the expense of instructions related to global SM parameters. In early stages of learning, instructions should allow learners to focus on global features of the task but also include relevant guidance to help them identify, through practice, the most pertinent aspects of the task. Providing specific augmented feedback related to the peak force may have hampered the early learning strategies of our less experienced participants.
Feedback Training Expertise The strong expertise effects identified for several variables in this study are similar to those reported previously 4,6,19,20 and confirmed that thrust duration decreases with expertise, whereas peak force remains relatively constant. As previously described in the literature, 4,6,19-21 decreasing thrust duration combined with a constant force application leads to an increasing rate of force application over time. These changes characterize early stages of SM skill motor learning and represent basic requirements that are usually mastered early in the learning process. Interestingly, group 1 participants had lower absolute and variable errors compared with more experienced participants (group 2 and 3). Although such results may seem surprising, novices (group 1) were more accurate but chose to be slower in their SM execution. Such results indicate a typical speed vs accuracy tradeoff (Fitts’ law)
As reported in previous studies investigating the effects of augmented feedback on SM skill learning, the present study identified a strong augmented feedback effect on biomechanical parameters, not only immediately after training 6,8-10,20,23 but also 1 week later during the retention assessment. 9,10 Many of these previous studies, however, investigated the effect of feedback training using instructions mainly focused on SM peak force. Focusing on SM peak force mostly yielded improvements in force accuracy and variability but few changes in other biomechanical parameters of SM. The present study included broader instructions focusing not only on peak target force but also on the global SM force–time profile. Consequently, the study participants had improvements not only on variable and absolute errors but also on the preload force, rate of force application, and drop in preload force. These results highlight the importance of prepractice information (instruction and demonstration). Early work in motor learning
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has indicated that when learning a complex motor skill, directing attentional focus to 1 specific aspect of the task may be detrimental to the overall performance. 24 Wulf et al, 25 in a recent review presenting the influential factors of motor skill learning and performance, highlighted that even the smallest change in the wording of instructions can lead to significant differences in performance and learning.
Can Expertise Modulate the Effect of Augmented Feedback Training? Contrary to our initial hypothesis stating that augmented feedback related to the global performance and reinforced by specific feedback on basic biomechanical parameters would mostly favor early SM learners, all participants, regardless of their expertise, benefited from the augmented feedback training. This result may be explained by the specific learning environment, in which students with more significant experience (group 3 participants) were not provided with relevant augmented feedback related to SM force-time profile throughout their training years. These students, although experienced and quite proficient compared with novices, benefited from the basic information provided by augmented feedback just as the less experienced participants did.
Limitations The present study did not include a control, and one could argue that most reported effects of augmented feedback training might only be the results of repetition and familiarization with the device and the task to be performed. As stated earlier, several studies including a control group have previously reported specific effects of feedback training, and we are therefore confident that the strong and generalized effects reported in this study are mostly the consequence of feedback training. 23 Also, the same device was used during performance assessment and training trials and could therefore overestimate the augmented feedback effects. Assessments performed through a transfer task using other force-sensing technologies such as instrumented tables could help document the generalizability of augmented feedback training conducted using various simulation devices. 26
Practical Applications Motor learning principles and knowledge of the basic features of SM skills should be taught in the early stages of the curriculum in order to match the needs characterizing the verbal-cognitive stage of learning. Learning strategies during this stage should emphasize global features of the SM task to be performed and should avoid providing the learner with multiple specific components and details of task execution. Later stages of learning should include a gradual increase in the quantity and quality of practice sessions. Feedback quality is defined by its relevance for the learner, a progressive attenuation of the information provided, and should trigger automaticity during task execution.
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CONCLUSION The present study has reported that students with various training experiences in SM (from novice to experienced students) can benefit from augmented feedback training. The study also determined that augmented feedback related to the global performance reinforced by specific quantified feedback using SM force–time profiles can yield improvements in several basic components of the SM task. Future studies should investigate short- and long-term retention assessments as well as task performance transferability to “real life” clinical environments.
FUNDING SOURCES
AND
CONFLICTS
OF INTEREST
No funding sources or conflicts of interest were reported for this study.
CONTRIBUTORSHIP INFORMATION Concept development (provided idea for the research): M.P., C.C., M.D. Design (planned the methods to generate the results): M.P., C.C., M.D. Supervision (provided oversight, responsible for organization and implementation, writing of the manuscript): C.C., M.D. Data collection/processing (responsible for experiments, patient management, organization, or reporting data): M.P., C.C. Analysis/interpretation (responsible for statistical analysis, evaluation, and presentation of the results): M.P., C.C., A.L., M.D. Literature search (performed the literature search): M.P., C.C., C.D., M.D. Writing (responsible for writing a substantive part of the manuscript): M.P., C.C., A.L., C.D., M.D. Critical review (revised manuscript for intellectual content, this does not relate to spelling and grammar checking): C.D.
Practical Applications • The study investigated the combined effect of augmented feedback and expertise on the performance and retention of basic motor learning spinal manipulation skills. • Expertise and feedback lead to improves spinal manipulation skills. • The study failed to identify any interaction effect between expertise and augmented feedback training effects. • Feedback related to the global performance reinforced by specific quantified feedback appears to be a promising teaching strategy.
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REFERENCES 1. Clijsters M, Fronzoni F, Jenkins H. Chiropractic treatment approaches for spinal musculoskeletal conditions: a crosssectional survey. Chiropr Man Therap. 2014;22(1):33. 2. Herzog W. The biomechanics of spinal manipulation. J Bodyw Mov Ther. 2010;14(3):280-286. 3. Downie AS, Vemulpad S, Bull PW. Quantifying the highvelocity, low-amplitude spinal manipulative thrust: a systematic review. J Manipulative Physiol Ther. 2010;33(7):542-553. 4. Descarreaux M, Dugas C, Raymond J, Normand MC. Kinetic analysis of expertise in spinal manipulative therapy using an instrumented manikin. J Chiropr Med. 2005;4(2):53-60. 5. Cohen E, Triano JJ, McGregor M, Papakyriakou M. Biomechanical performance of spinal manipulation therapy by newly trained vs. practicing providers: does experience transfer to unfamiliar procedures? J Manipulative Physiol Ther. 1995;18(6):347-352. 6. Descarreaux M, Dugas C. Learning spinal manipulation skills: assessment of biomechanical parameters in a 5-year longitudinal study. J Manipulative Physiol Ther. 2010;33(3): 226-230. 7. Harvey MP, Wynd S, Richardson L, Dugas C, Descarreaux M. Learning spinal manipulation: a comparison of two teaching models. J Chiropr Educ. 2011;25(2):125-131. 8. Lardon A, Cheron C, Pagé I, Dugas C, Descarreaux M. Systematic augmented feedback and dependency in spinal manipulation learning: a randomized comparative study. J Manipulative Physiol Ther. 2016;39(3):185-191. 9. Watson TA, Radwan H. Comparison of three teaching methods for learning spinal manipulation skill: a pilot study. J Man Manip Ther. 2001;9(1):48-52. 10. Scaringe JG, Chen D, Ross D. The effects of augmented sensory feedback precision on the acquisition and retention of a simulated chiropractic task. J Manipulative Physiol Ther. 2002;25(1):34-41. 11. Triano JJ, Rogers CM, Combs S, Potts D, Sorrels K. Developing skilled performance of lumbar spine manipulation. J Manipulative Physiol Ther. 2002;25(6):353-361. 12. Triano JJ, Rogers CM, Combs S, Potts D, Sorrels K. Quantitative feedback versus standard training for cervical and thoracic manipulation. J Manipulative Physiol Ther. 2003;26(3):131-138.
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13. Schmidt RA, Lee TD. Motor control and learning: a behavioral emphasis. 3rd ed. Champaign, IL: Human Kinetics; 1999495 [xvi]. 14. Coker CA. Motor Learning and Control for Practitioners. MacGraw Hill: New York, NY; 2004. 15. Stainsby BE, Clarke MC, Egonia JR. Learning spinal manipulation: a best-evidence synthesis of teaching methods. J Chiropr Educ. 2016;30(2):138-151. 16. Fitts PM, Posner MI. Human Performance. Brooks/Cole: Belmont, CA; 1967. 17. Gentile AM. Implicit and explicit processes during acquisition of functional skills. Occup Ther. 1998;5(1):7-16. 18. Loranger M, Treboz J, Boucher JA, Nougarou F, Dugas C, Descarreaux M. Correlation of expertise with error detection skills of force application during spinal manipulation learning. J Chiropr Educ. 2016;30(1):1-6. 19. Descarreaux M, Dugas C, Treboz J, Cheron C, Nougarou F. Learning spinal manipulation: the effect of expertise on transfer capability. J Manipulative Physiol Ther. 2015;38(4): 269-274. 20. Triano JJ, Descarreaux M, Dugas C. Biomechanics–review of approaches for performance training in spinal manipulation. J Electromyogr Kinesiol. 2012;22(5):732-739. 21. Triano JJ, Gissler T, Forgie M, Milwid D. Maturation in rate of high-velocity, low-amplitude force development. J Manipulative Physiol Ther. 2011;34(3):173-180. 22. Heitz RP. The speed-accuracy tradeoff: history, physiology, methodology, and behavior. Front Neurosci. 2014;8:150. 23. Descarreaux M, Dugas C, Lalanne K, Vincelette M, Normand MC. Learning spinal manipulation: the importance of augmented feedback relating to various kinetic parameters. Spine J. 2006;6(2):138-145. 24. Hodges NJ, Franks IM. Modelling coaching practice: the role of instruction and demonstration. J Sports Sci. 2002;20(10): 793-811. 25. Wulf G, Shea C, Lewthwaite R. Motor skill learning and performance: a review of influential factors. Med Educ. 2010; 44(1):75-84. 26. Starmer DJ, Guist BP, Tuff TR, Warren SC, Williams MG. Changes in manipulative peak force modulation and time to peak thrust among first-year chiropractic students following a 12-week detraining period. J Manipulative Physiol Ther. 2016;39(4):311-317.
Arnaud Lardon, DC, PhD, a and Martin Descarreaux, DC, PhD b ABSTRACT
Objectives: The purpose of this study was to investigate the combined effect of augmented feedback and expertise on the performance and retention of basic motor learning spinal manipulation skills. Methods: A total of 103 chiropractic students with various training expertise were recruited for the study. Participants were evaluated at baseline, immediately after trials of augmented feedback practice and 1 week later. During all 3 assessments, students were asked to perform several trials of the same spinal manipulation, for which the maximum preload force, onset of thrust, thrust duration, force and peak force, thrust duration, rate of force application, and any drop in preload force were calculated. The constant error, absolute error, and variable error were calculated for the 3 experimental blocks of trials. Results: Results confirmed that augmented feedback training modified several biomechanical parameters such as the rate of force application, the preload force, and the drop in preload force. The study also confirmed that many biomechanical parameters, including thrust duration and rate of force application, are modified with expertise but failed to identify any interaction effect between expertise and augmented feedback training effects. Conclusion: The study determined that expertise did not influence how students performed after a session of augmented feedback training. The study also determined that augmented feedback related to the global performance can yield improvements in several basic components of the spinal manipulation task. These results should be interpreted considering basic motor learning principles and specific learning environments. (J Manipulative Physiol Ther 2017;40:404-410) Key Indexing Terms: Spinal Manipulation; Feedback; Learning; Education; Expertise
INTRODUCTION Spinal manipulation (SM) is a tool commonly used in manual therapy, and it is the typical treatment administrated by chiropractors. 1 In each chiropractic teaching institution, an important part of the curriculum is dedicated to this learning. Spinal manipulation is characterized by a dynamic thrust of high velocity and low amplitude using a specific contact and direction associated with an audible cavitation. It can be described as an action requiring high-speed, low-amplitude precision that has mechanical consequences. 2 From an experimental standpoint, spinal manipulation is usually a
Institut Franco-Européen de Chiropraxie, Toulouse, France. Department of Human Kinetics, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada. Corresponding author: Martin Descarreaux, DC PhD, Université du Québec à Trois-Rivières, 3351 Boul. des Forges, C.P. 500, Trois-Rivières, QC, G9A 5H7. (e-mail: [email protected]). Paper submitted January 11, 2017; in revised form February 17, 2017; accepted March 14, 2017. 0161-4754 Copyright © 2017 by National University of Health Sciences. http://dx.doi.org/10.1016/j.jmpt.2017.03.010 b
described using the basic biomechanical features characterizing its force-time profile, such as peak force, preload force, thrust duration force, and rate of force application. 3 Although the SM force–time profile is rather stable across experienced clinicians, spinal manipulation can prove to be a complex motor skill depending on the technique used and the segment being treated. As in any motor task, the learning of spinal manipulation requires pedagogic strategies and training regimen based on repetition and feedback. 4 In many cases, spinal manipulation involves postural control and timely synchronization of lower limb and trunk body weight transfer during upper limb force transmission. Mastering these complex skills and proper coordination of the multiple body segments involved enable a better regulation of forces that are applied during a treatment. 5 Previous studies comparing levels of expertise and motor performance during learning suggest that these more complex components of SM are not easily mastered during training years. 6 Although scientific evidence is limited, the planning and organization of SM skill learning can affect students’ motor performance and overall SM skill expertise. 7 Indeed, several studies have explored the relevance of feedback training in spinal manipulation education. These studies have explored the effect of feedback in various
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populations ranging from inexperienced students without any clinical training to students and clinicians with significant clinical experience. 8 Various devices, all based on force-sensing technologies, were used to provide feedback to students and to assess performances. 9,10 In a few studies, 11,12 feedback training was provided using force-sensing devices (no patients), whereas performance assessments were conducted on patients (other students). Altogether, these studies suggest that augmented feedback, defined as “information provided about the action that is supplemental to, or that augments, the inherent feedback,” 13 using force-sensing devices during training improves SM skill performance and reduces variability. Interestingly, only 2 studies included short-term learning retention assessments (1 week after training) 9,10 after augmented feedback training. One could therefore argue that these studies mostly describe improvements in performance rather than true motor learning, usually defined as permanent changes in a person’s capability to execute a motor skill. 14 As highlighted in a recent best-evidence synthesis by Stainsby et al. 15 several mechanical or computerized training aids providing extrinsic forms of feedback have been developed and studied over the past 2 decades. The various training aids are usually developed to address the motor learning challenges faced by students and teachers, and the authors of this best-evidence synthesis concluded that such devices are useful to promote skill development, knowledge transfer, and task retention among students. Overall, although there is no doubt that feedback can improve short-term SM performance and consistency, its effect on motor learning assessed trough retention and transfer tasks is less documented. The effect of various types of augmented feedback as well as optimal strategies, including frequency, timing, and accuracy of augmented feedback in SM skill learning remain to be investigated. In this study, 3 groups of chiropractic students with varying expertise were compared on 3 different occasions: (i) baseline evaluation, (ii) after augmented feedback training, and (iii) 1 week after augmented feedback training. The goals of the study were 2-fold. The first goal of the study was to reaffirm, in a large sample of students, that biomechanical parameters of SM are influenced by the level of expertise and are improved after augmented feedback training. The second goal was to assess how expertise modulates the effect of augmented feedback use during SM training. Several models describe the stages of motor learning 16,17 as a process through which most learners evolve from a verbal-cognitive stage to an independent-automated stage characterizing expertise. Based on such models, it was hypothesized that augmented feedback related to the global performance and reinforced by specific feedback on peak force, thrust duration, and preload force using SM the force–time profile would mostly favor early SM learners.
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METHODS Design This was an experimental study.
Participants Participants were all recruited among the first-, third-, and fifth-year chiropractic students (group 1, 2 and 3, respectively) using convenience sampling. Based on previous SMT motor learning studies indicating significant expertise effects, it was estimated that a minimum of 20 participants per group was needed to reveal expertise and training effects. 18 The study was approved by the Institut Franco-Européen de Chiropraxie Ethics Committee (2016_02_26) and each participant provided a written informed consent. Participants’ characteristics for each group are presented in Table 1.
Experimental Sessions The study was divided in 3 different assessments over 2 experimental sessions that were preceded by a 15-minute presentation of the project. During this presentation the protocol and the SM task were briefly explained and demonstrated using a video presentation and both novice and expert SM force-time profiles. Typical characteristics of novice force–time profiles, such as lack or drop in preload force and longer thrust duration, were pointed out to participants. During the first experimental session, each participant performed a total of 53 SMs on an instrumented device using a unilateral hypothenar transverse push technique with a posterior-to-anterior force vector. Participants chose their preferred contact hand and table height to perform the SM and were instructed to perform SM using the same hand during the all assessments. The SMs were performed by contacting the target on the device in a fencer position with the caudal hand pisiform. A familiarization period was first offered and consisted of 3 trials per participant, during which they were instructed to perform SMs of 300 N peak force while trying to replicate expertlike force–time profile. Baseline assessment was conducted during the following 10 trials, which were also performed with a target peak force of 300 N but without feedback. During the training period, participants performed 30 SMs and were provided with both verbal and visual feedback. A global performance feedback was provided verbally and reinforced, when necessary, by specific quantified feedback on peak force, thrust duration, and preload force drawn from the previous SM force–time profile. Post-training assessment consisted of a set of 10 trials without any feedback and with a 300-N peak force target. After a 7-day retention period, a third assessment was conducted during which participants also performed 10 trials without feedback using a 300-N peak force target after familiarization trials. All 3 sets of 10 trials collected at baseline, during the post-training, and at the retention assessment periods were used in the data analysis.
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Apparatus SMs were delivered on a computer-connected device developed to simulate a thoracic spine prone manipulation. Complete description of the apparatus has been published in a previous article. 6 Briefly, participants apply forces to the device, which is linked to a strain gauge (model LSB300; FUTEK Inc, Irvine, CA) and a spring that replicates thoracic spine resistance and movement. When a force of 250 N is reached during SM, the spring and the strain gauge drop by 5 mm to simulate vertebral joint cavitation. Data were collected using Labview software (National Instruments, Austin, TX).
force, and peak force. Thrust duration, rate of force application, and preload force drop were then calculated using these values. The constant error (CE), absolute error (AE), and variable error (VE) were calculated for the 3 experimental blocks of SM trials considering a constant 300-N peak force target. The CE was calculated using the positive or negative difference between the peak force reached and the peak force targeted for each trial. The absolute value of the deviation from the target force was used to obtain the AE (accuracy), whereas the VE, which assesses the participants’ consistency during repeated trials, was calculated using the absolute mean difference between the peak force reached during a given trial and the participant’s mean peak force during a given block of trials. 13
DATA ANALYSIS
Statistical Analysis
Data obtained during the 3 assessment blocks (baseline, post-training, and retention) were analyzed to determine for each trial the maximum preload force, onset of thrust, thrust duration,
Normality of data sets was verified by visual inspection and the Shapiro-Wilk test. A 1-way analysis of variance (ANOVA) was first conducted to test for any differences between
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Fig. 1. Mean (95% confidence interval) group score for each of the following variables: preload, thrust duration, absolute error, variable error. *Significant differences.
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Table 1. Experimental Group Participant Characteristics (Mean ± SD) Age (y) Weight (kg) Height (cm) Female/male (n)
Population (n = 103)
Group 1 (n = 35)
Group 2 (n = 36)
Group 3 (n = 32)
P
21.8 ± 2.18 65.4 ± 11.9 170.5 ± 9.38 69/34
19.9 ± 1.48 66.8 ± 13.2 171.5 ± 10.05 22 /13
22.1 ± 1.96 64 ± 10.75 171 ± 9.73 22/14
23.5 ± 1.40 65.3 ± 12 168.7 ± 8.18 25/7
b.0001 N.05 N.05 N.05 a
SD, standard deviation. a Cochran Q test.
participants’ characteristics of each group, whereas sex proportion was tested using the Cochrane Q tests. Then the 5 biomechanical parameters (peak force, preload force, thrust duration, rate of force application, and drop in preload force) as well as the CE, AE, and VE outcomes were independently subjected to a 2-way mixed-model ANOVA assessing group × time to test for the main effects of expertise and training as well as possible group × time interactions. When required, post hoc analyses were performed using Tukey test. All statistical analyses were computed with Statistica 10 (Statsoft, Tulsa, OK) and the level of significance was set to P = .05.
RESULTS Participants A total of 103 chiropractic students were included in this study, among whom 35 were first-year students, 36 were third- year students, and 32 were fifth-year students. The 1-way ANOVA was used to test group homogeneity and revealed that there was no significant difference for height (mean ± standard deviation [SD] = 170.5 ± 9.38 cm) or weight (65.4 ± 11.9 kg). Not surprisingly, the analysis revealed a significant difference in group age (19.9 ± 1.48 years old; 22.1 ± 1.96 years old; 23.5 ± 1.40 years old) (P b .05). The age increased with the level of expertise.
duration, and peak force (all P N .05). Mean values (±95% confidence interval [CI]) for all study outcomes are presented in Table 2.
Expertise Effects A main effect of expertise was observed for thrust duration (F[2, 100] = 12.816, P b .0001), which decreased with expertise, and for rate of force application (F[2, 100] = 6.3739, P = .00248), which increased with expertise. The Tukey test revealed that thrust duration was higher in group 1 compared with groups 2 and 3. Moreover, the rate of force application was significantly lower in group 1 compared with group 3. A main effect of expertise was also noted for absolute error (F[2, 100] = 7.4152, P = .001). The Tukey test indicated that participants in groups 2 and 3 had higher absolute error values than group 1 participants. Finally, a main effect of expertise was noted for the variable error (F[2, 100] = 3.6291, P = .0301). The Tukey test indicated that group 2 participants had higher variable errors than group 1 participants. Main results are presented in Figure 1. No expertise effect was noted for constant error, preload force, peak force, or drop in preload force (all P N .05). Mean values (±95% CI) for all study outcomes are presented in Table 2.
Feedback Training Effects The analysis revealed a main effect of training for preload force (F[2, 200] = 16.553, P b .0001), drop in preload force (F[2, 200] = 47.781, P b .0001), and absolute error (F[2, 200] = 17.050, P b .0001). The Tukey tests showed that preload force was significantly increased at both the post-training and retention assessments, whereas drop in preload force and absolute error decreased significantly from baseline to post-training and retention assessments. The analysis also revealed a main effect of feedback training for rate of force application (F[2, 200] = 39.240, P b .0001). The Tukey test showed a significant decrease in rate of force application from baseline to post-training assessment and from the post-training to the retention assessments. Finally, a main effect of training was identified for variable error (F[2, 200] = 8.5274, P = .00028). The Tukey test indicated a significant decrease from the baseline to the retention assessment only. No main effect of training was observed for constant error, thrust
Expertise and Training Interactions Overall, the 2-way mixed-model ANOVA did not reveal any significant group × time interaction effect (all P N .05). However, a trend toward a significant group × time interaction effect (P = .057) was noted for preload forces, indicating that group 1 participants were the only ones producing a significant increase in preload forces after training and through retention.
DISCUSSION This study sought to confirm, in a large sample of students, that biomechanical parameters of SM are influenced by both expertise and augmented feedback training and that levels of expertise and augmented feedback use can influence biomechanical parameters of SM. Most importantly, the study sought to explore the
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Table 2. Means (95% CI) of 3 Experimental Group Biomechanical Parameters Throughout Protocol Outcomes
Assessments
Group 1
Group 2
Group 3
Preload force (N)
Baseline Post-training Retention Baseline Post-training Retention Baseline Post-training Retention Baseline Post-training Retention Baseline Post-training Retention Baseline Post-training Retention Baseline Post-training Retention
104.5 (91.9-116.9) 120.6 (111.1-130.1) 132.5 (121.8-143.2) 0.132 (0.117-0.151) 0.125 (0.112-0.139) 0.130 (0.119-0.142) 289.8 (273-306) 291.7 (280-303) 293.0 (281-304) 1963.1 (1700-2225) 1648.3 (1436-1859) 1472.5 (1257-1688) –10.2 (–26.8 to 6.4) –8.26 (–19.5 to 3.01) –6.97 (–18.6 to 4.61) 39.3 (31.4-47.1) 32.0 (26.6-37.4) 29.4 (23.1-35.8) 22.6 (19.1-26.2) 20.8 (17.7-23.9) 18.0 (15.2-20.8)
118.7 (106.4-131) 129.5 (120-138.9) 128.3 (117.7-138.9) 0.097 (0.079-0.116) 0.101 (0.087-0.114) 0.102 (0.091-0.113) 301.6 (285-318) 303.7 (292-315) 287.6 (276-298) 2306.3 (2047-2564) 2020.6 (1812-2229) 1736.6 (1524-1948) 1.64 (–14.8 to 18) 3.67 (–7.44 to 14.8) –12.4 (–23.9 to –1.02) 53.7 (46.0-61.4) 41.7 (36.4-47.1) 37.7 (31.5-44.0) 26.6 (23.1-30.0) 25.4 (22.4-28.4) 22.7 (19.9-25.5)
107.6 (94.5-120.6) 115.6 (105.7-125.6) 118.0 (106.8-129.2) 0.082 (0.062-0.101) 0.100 (0.086-0.114) 0.098 (0.086-0.109) 286.5 (269-303) 288.8 (276-300) 292.4 (277-301) 2495.2 (2221-2769) 2102.9 (1881-2324) 2022.8 (1797-2248) –13.5 (–30.9 to 3.9) –11.22 (–23.0 to 0.6) –7.64 (–19.8 to 4.5) 52.7 (44.5-60. 9) 33.5 (27.9-39.2) 36.1 (29.5-42.7) 25.8 (22.1-29.5) 21.3 (18.1-24.6) 20.8 (17.9-23.8)
Thrust duration (s)
Peak force (N)
Application force rate (N/s)
Constant error (N)
Absolute error (N)
Variable error (N)
CI, confidence interval.
combined effect of these 2 motor learning variables. Overall, this study determined that augmented feedback training modified several biomechanical parameters, such as the rate of force application, the preload force, and the drop in preload force. The study also confirmed that many biomechanical parameters, such as thrust duration and rate of force application, are modified with expertise. Interestingly, our results failed to identify any combined effect of augmented feedback and expertise, suggesting that students, regardless of their experience or training levels, benefited from augmented feedback related to SM. These results, however, should be interpreted considering basic motor learning principles but also in light of the learning environment specificities.
observed in many other motor tasks. 22 Although participants were instructed to perform an SM of 300 N while trying to replicate an expertlike force–time profile, participants with less experience might have directed their attentional focus on the target force at the expense of instructions related to global SM parameters. In early stages of learning, instructions should allow learners to focus on global features of the task but also include relevant guidance to help them identify, through practice, the most pertinent aspects of the task. Providing specific augmented feedback related to the peak force may have hampered the early learning strategies of our less experienced participants.
Feedback Training Expertise The strong expertise effects identified for several variables in this study are similar to those reported previously 4,6,19,20 and confirmed that thrust duration decreases with expertise, whereas peak force remains relatively constant. As previously described in the literature, 4,6,19-21 decreasing thrust duration combined with a constant force application leads to an increasing rate of force application over time. These changes characterize early stages of SM skill motor learning and represent basic requirements that are usually mastered early in the learning process. Interestingly, group 1 participants had lower absolute and variable errors compared with more experienced participants (group 2 and 3). Although such results may seem surprising, novices (group 1) were more accurate but chose to be slower in their SM execution. Such results indicate a typical speed vs accuracy tradeoff (Fitts’ law)
As reported in previous studies investigating the effects of augmented feedback on SM skill learning, the present study identified a strong augmented feedback effect on biomechanical parameters, not only immediately after training 6,8-10,20,23 but also 1 week later during the retention assessment. 9,10 Many of these previous studies, however, investigated the effect of feedback training using instructions mainly focused on SM peak force. Focusing on SM peak force mostly yielded improvements in force accuracy and variability but few changes in other biomechanical parameters of SM. The present study included broader instructions focusing not only on peak target force but also on the global SM force–time profile. Consequently, the study participants had improvements not only on variable and absolute errors but also on the preload force, rate of force application, and drop in preload force. These results highlight the importance of prepractice information (instruction and demonstration). Early work in motor learning
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has indicated that when learning a complex motor skill, directing attentional focus to 1 specific aspect of the task may be detrimental to the overall performance. 24 Wulf et al, 25 in a recent review presenting the influential factors of motor skill learning and performance, highlighted that even the smallest change in the wording of instructions can lead to significant differences in performance and learning.
Can Expertise Modulate the Effect of Augmented Feedback Training? Contrary to our initial hypothesis stating that augmented feedback related to the global performance and reinforced by specific feedback on basic biomechanical parameters would mostly favor early SM learners, all participants, regardless of their expertise, benefited from the augmented feedback training. This result may be explained by the specific learning environment, in which students with more significant experience (group 3 participants) were not provided with relevant augmented feedback related to SM force-time profile throughout their training years. These students, although experienced and quite proficient compared with novices, benefited from the basic information provided by augmented feedback just as the less experienced participants did.
Limitations The present study did not include a control, and one could argue that most reported effects of augmented feedback training might only be the results of repetition and familiarization with the device and the task to be performed. As stated earlier, several studies including a control group have previously reported specific effects of feedback training, and we are therefore confident that the strong and generalized effects reported in this study are mostly the consequence of feedback training. 23 Also, the same device was used during performance assessment and training trials and could therefore overestimate the augmented feedback effects. Assessments performed through a transfer task using other force-sensing technologies such as instrumented tables could help document the generalizability of augmented feedback training conducted using various simulation devices. 26
Practical Applications Motor learning principles and knowledge of the basic features of SM skills should be taught in the early stages of the curriculum in order to match the needs characterizing the verbal-cognitive stage of learning. Learning strategies during this stage should emphasize global features of the SM task to be performed and should avoid providing the learner with multiple specific components and details of task execution. Later stages of learning should include a gradual increase in the quantity and quality of practice sessions. Feedback quality is defined by its relevance for the learner, a progressive attenuation of the information provided, and should trigger automaticity during task execution.
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CONCLUSION The present study has reported that students with various training experiences in SM (from novice to experienced students) can benefit from augmented feedback training. The study also determined that augmented feedback related to the global performance reinforced by specific quantified feedback using SM force–time profiles can yield improvements in several basic components of the SM task. Future studies should investigate short- and long-term retention assessments as well as task performance transferability to “real life” clinical environments.
FUNDING SOURCES
AND
CONFLICTS
OF INTEREST
No funding sources or conflicts of interest were reported for this study.
CONTRIBUTORSHIP INFORMATION Concept development (provided idea for the research): M.P., C.C., M.D. Design (planned the methods to generate the results): M.P., C.C., M.D. Supervision (provided oversight, responsible for organization and implementation, writing of the manuscript): C.C., M.D. Data collection/processing (responsible for experiments, patient management, organization, or reporting data): M.P., C.C. Analysis/interpretation (responsible for statistical analysis, evaluation, and presentation of the results): M.P., C.C., A.L., M.D. Literature search (performed the literature search): M.P., C.C., C.D., M.D. Writing (responsible for writing a substantive part of the manuscript): M.P., C.C., A.L., C.D., M.D. Critical review (revised manuscript for intellectual content, this does not relate to spelling and grammar checking): C.D.
Practical Applications • The study investigated the combined effect of augmented feedback and expertise on the performance and retention of basic motor learning spinal manipulation skills. • Expertise and feedback lead to improves spinal manipulation skills. • The study failed to identify any interaction effect between expertise and augmented feedback training effects. • Feedback related to the global performance reinforced by specific quantified feedback appears to be a promising teaching strategy.
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13. Schmidt RA, Lee TD. Motor control and learning: a behavioral emphasis. 3rd ed. Champaign, IL: Human Kinetics; 1999495 [xvi]. 14. Coker CA. Motor Learning and Control for Practitioners. MacGraw Hill: New York, NY; 2004. 15. Stainsby BE, Clarke MC, Egonia JR. Learning spinal manipulation: a best-evidence synthesis of teaching methods. J Chiropr Educ. 2016;30(2):138-151. 16. Fitts PM, Posner MI. Human Performance. Brooks/Cole: Belmont, CA; 1967. 17. Gentile AM. Implicit and explicit processes during acquisition of functional skills. Occup Ther. 1998;5(1):7-16. 18. Loranger M, Treboz J, Boucher JA, Nougarou F, Dugas C, Descarreaux M. Correlation of expertise with error detection skills of force application during spinal manipulation learning. J Chiropr Educ. 2016;30(1):1-6. 19. Descarreaux M, Dugas C, Treboz J, Cheron C, Nougarou F. Learning spinal manipulation: the effect of expertise on transfer capability. J Manipulative Physiol Ther. 2015;38(4): 269-274. 20. Triano JJ, Descarreaux M, Dugas C. Biomechanics–review of approaches for performance training in spinal manipulation. J Electromyogr Kinesiol. 2012;22(5):732-739. 21. Triano JJ, Gissler T, Forgie M, Milwid D. Maturation in rate of high-velocity, low-amplitude force development. J Manipulative Physiol Ther. 2011;34(3):173-180. 22. Heitz RP. The speed-accuracy tradeoff: history, physiology, methodology, and behavior. Front Neurosci. 2014;8:150. 23. Descarreaux M, Dugas C, Lalanne K, Vincelette M, Normand MC. Learning spinal manipulation: the importance of augmented feedback relating to various kinetic parameters. Spine J. 2006;6(2):138-145. 24. Hodges NJ, Franks IM. Modelling coaching practice: the role of instruction and demonstration. J Sports Sci. 2002;20(10): 793-811. 25. Wulf G, Shea C, Lewthwaite R. Motor skill learning and performance: a review of influential factors. Med Educ. 2010; 44(1):75-84. 26. Starmer DJ, Guist BP, Tuff TR, Warren SC, Williams MG. Changes in manipulative peak force modulation and time to peak thrust among first-year chiropractic students following a 12-week detraining period. J Manipulative Physiol Ther. 2016;39(4):311-317.