Larson, Cathy A. PhD, PT; Surber-Berro, Moneach R. MPT
“I look different from the other kids at school when I am working at my desk,” explained the young boy. When questioned further, the child specified that his arm looks “funny” when pointing at words or illustrations in his textbook or when writing. Improving the quality of movement while performing a functional task and not appearing different from their peers may be important concerns for children with cerebral palsy (CP), but they may become especially important as children approach adolescence. It is debatable whether the quality of movement can be improved in children with CP.1,2 Therefore, some therapists may question whether improving the quality of movement should be an explicit goal of physical therapy. Many therapists believe that the primary focus of therapy should be the successful performance of functional tasks under a variety of task conditions,3,4 whereas others use manual techniques such as neurodevelopmental treatment to attempt to “normalize” the patient’s underlying movement pattern while concurrently attempting to successfully accomplish the functional task.5–8
In this case study, the major goal for physical therapy intervention was improving the quality of a child’s arm movement while maintaining or improving his or her accuracy during a reach-and-point task. Patient- centered therapy goals, mutually agreed upon by the child, parents, and therapist, should drive the formation of therapy goals.9 In this case, the primary reason the child and parents sought therapy was to improve the quality of the child’s arm movement. The therapists believed that the child, because he had only minor underlying impairments, had the potential to accomplish this goal. The child also appeared to be confined to using one, fixed reach-and-point movement strategy, and the ability to vary this movement pattern is a critical aspect of functional movement.
If the purpose of an intervention is to improve the quality of a child’s movement pattern, therapists need to know the normal kinematic characteristics of a wide variety of movements to be able to recognize a movement that deviates from customary movement patterns. Therapists appear to make a composite judgment of movement quality, which incorporates many attributes.10 For example, the therapist may observe overall spatial trajectory smoothness, joint range of motion, multijoint coordination, trunk alignment and mobility, and target accuracy.11–14 When a child with neuropathology performs a movement judged to be of limited quality, the therapist must often determine which specific aspect of the movement is inappropriate and which movement component the child should attempt to correct. Often, the patient cannot recognize the movement errors with only the therapist’s verbal description.
Frequently, therapists use visual observation to assess quality of movement; however, objectivity, accuracy, and repeatability may be limited.1 Current video and digital technology allow easy and economical recording of movement for both motion analysis and to provide observational feedback. Recording and viewing video clips with or without the use of simple analysis tools (eg, linear distance, joint angle, and/or temporal measurements) can enhance the therapist’s ability to assess the quality of movement. This same technology can be used to provide observational feedback to promote motor learning.
Observational feedback is defined as viewing a model or oneself perform a motor task, usually by videotape playback. After viewing a model’s task performance, the learner acquires an overall “picture” of the skill, learns task expectations, recognizes the spatial and temporal movement pattern, and identifies his/her own performance errors.15 According to social learning theory, motor learning involves the construction of a conceptual movement representation that provides an internal model for movement or task production. When physically performing the task/movement, the learner uses the movement representation to select and program the required response.16 The representation serves as a reference for detecting and correcting errors.17 The learner can build, update, and refine this centrally stored, movement representation aided by observational feedback, knowledge of results (KR), which is defined as augmented feedback related to the movement’s results or the goal of the task18 and/or knowledge of performance (KP), which is defined as augmented feedback related to the nature of the movement produced or the specific movement characteristics.19–22
The most effective method of delivering observational feedback is under debate.23–25 For example, is it better to view a videotape of a model’s or one’s own performance? Subjects who observed a model performing an upper- extremity sequential barrier task performed the same task with fewer errors than subjects who viewed videotaped feedback of their own performance or subjects who physically practiced the task.26 Is it better to view a correct model who consistently displays flawless performance or a learning model who experiences difficulties, demonstrates problem-solving strategies and, over time, gradually improves skill performance? Viewing either a correct or a learning model was reported by some to be equally effective in the acquisition and retention of a sport skill.27,28 Others reported the superiority of viewing a learning model.18,29
Repeated viewing of a model’s performance also appears to be critical.16 Upon repeated viewing, the learner identifies details of the movement that were missed upon initial observation and can focus his/her attention on more “troublesome” portions of the movement to solidify the cognitive conceptual representation of the movement. In addition, verbal cues are needed to direct the learner’s attention to the crucial features of the model’s performance, or the learner may not extract the necessary information to construct the internal cognitive representation. For example, subjects who viewed a videotape of a soccer pass and received visual and verbal cues highlighting task-relevant information displayed fewer errors and more-appropriate soccer pass form than subjects receiving reduced forms of cueing.29 Thus, research on healthy subjects suggests that when constructing an intervention session using observational feedback, one should employ repeated observations of either a correct or learning model while providing verbal cues that direct the learner’s attention to the crucial features of the model’s performance.
In a review of the literature, we were unable to identify studies that examined the effect of viewing a split-screen or side-by-side comparison of a model’s and one’s own performance of a task. In the current study, the child viewed repeated videotaped playback recordings of a model and himself reaching and pointing to a target (same target size, distance, and reach direction) displayed side-by-side on a computer monitor. It seems reasonable that viewing a side-by-side comparison would enable the child to better recognize spatial and temporal movement incongruence between the model’s and his own performance. We believed the child would be better able to extract information concerning the effective movement strategies and/or the task requirements while viewing the split-screen comparison that would be more difficult when viewing the model alone. On the basis of social learning theory, the child’s recognition of the incongruence between the model’s and his own physical performance and the child’s internal, cognitive movement representation should promote better estimates of performance error and, ultimately, build a more correct internal cognitive model of the reach-and-point movement. In addition, the compare-and-contrast cognitive process inherent in the split-screen comparison-learning situation may result in a greater level of cognitive effort. Cognitive effort can be conceptualized as “the mental work involved in making decisions” or the intensity of the cognitive, decision-making processes underlying skilled behavior.30,31 Practice manipulations that require more cognitive effort are predicted to be more effective for motor learning.
This case study was designed to compare the performance of an aimed reach-and-point task before and after the child viewed two forms of observational feedback while simultaneously receiving verbal cues concerning his reaching performance. The two forms of observational feedback included (1) viewing a model’s reach and point performance and (2) viewing a split-screen comparison of the model’s and child’s performance while in both phases simultaneously receiving verbal cues to attend to specific movement characteristics. The parameters measured in this case included spatial trajectory linearity, shoulder, elbow and wrist joint angle excursion, target accuracy, and movement time while the child performed a multijoint reach-and-point task.
The subject was a 13-year-old boy who was left-handed and diagnosed with spastic CP. He had no known visual, auditory, or cognitive impairments. The child was observed to use one consistent movement pattern while performing the reach-and-point task, specifically maintaining a relatively fixed elbow and wrist flexed posture while reaching and using varying amounts of shoulder flexion, but the child’s final end-reach target location (accuracy) was somewhat variable. Physical therapy examination revealed mild bilateral upper-extremity involvement with minimal-to-moderate resistance to passive elongation of the elbow, wrist and finger flexor muscles, full passive range of motion, and mild coordination impairments.
The model was a 22-year-old woman with no known pathologies who was selected primarily because of her left-hand dominant reaching pattern and as a matter of convenience. We considered the age difference between the child subject and model inconsequential because transport-and-grasp kinematics and accuracy in children older than nine to 11 years have been reported to be similar to that of adults.32,33
The PT Vision system (PT Vision Digital Motion Analysis System, version 3.2, 1999, Interactive Frontiers, Inc., Livonia, MI) is a video-based, computerized motion analysis system that may be used to record and analyze movement and provide learners with observational feedback. Two Canon Optura Digital cameras (Canon Optura Digital camera, Canon U.S.A., Inc., Itasca, IL) captured sagittal and overhead views of the child and the PTVision Motion Analysis System was used to digitally analyze the videotaped performance of both the model’s and the child’s reaching movements. A standard ruler was used to calibrate the PTVision System before measuring individual reach movements. Accuracy (using a known measure in nine areas in the field of view) of the motion analysis system was reported as ±1.8 mm (horizontal plane) and ±3.5 mm (vertical plane) with a reliability of 1.0 (intraclass correlation coefficient) when calibration occurs in the same plane as the measurements.34
After receiving a complete explanation of the procedures, the child and his parent read and signed an Institutional Review Board-approved assent and consent form, respectively. This study used a single-subject, A1-B-C-A2 design. During the first session (phase A1), baseline measurements of the child’s reach-and-point performance were recorded. The child performed the aimed reach-and-point task with his left arm while sitting in a chair facing a table on which a starting location (located at the child’s midline) and three target locations were arranged. Three six-mm circular targets were placed 25.4 cm (10 inches) at 45-, 90-, and 135-degree angles from the start location (Fig. 1). All targets were located within a distance less than the child’s left arm length to minimize trunk movement and were intentionally located at three different angles from the start location to engage a variety of upper limb, multijoint movement patterns. For our left-handed subject, reaching to target one (45 degrees) was an ipsilateral reach primarily requiring elbow/wrist movement with little shoulder movement. Reaching to targets two (90 degrees) and three (135 degrees) required a midline and a contralateral reach, respectively, requiring more shoulder or elbow movement and less wrist movement. Markers were placed on the child’s acromion process, lateral epicondyle, ulnar styloid process, and fifth metacarpal process. While holding an ink gel pen, the child performed 15 reach-and-point movements from the start location to one of the three target locations in a random order at a “comfortable,” self-selected speed recorded at 60 Hz.
Three days later, during session two (phase B), the child observed a videotaped-playback of the model performing 10 reach-and-point movements to target three and then target one in a blocked practice format played at normal speed (60 frames per second) and then at slow speed (seven frames per second); reaches to target two were not viewed because of time constraints. While observing the video-playback reaching movements, the child concurrently received verbal cues (100% KP frequency) encouraging him to observe the model’s erect posture and use of a relaxed, fluid wrist movement when the model reached for the targets. The reason for directing the child’s attention to erect posture was based upon the neurodevelopmental treatment theory that providing a well-aligned musculoskeletal base and encouraging proximal trunk control improves selective upper-limb motor control.5 The reason for directing the child’s attention to the relaxed, fluid wrist movement was to encourage wrist extension rather than a flexed wrist because wrist extension is a common characteristic of reach-and-point movements.35 The child then completed a practice session consisting of 45 reach-and-point movements to the three targets in a random order without verbal cues (0% KP; total repetitions = 135). Subsequently, the child completed 15 reach-and-point movements to each of the three targets presented in a random order at a self-selected speed during which the child’s reach performance was recorded using the motion analysis system.
In phase C, which occurred four days later, the child observed a split-screen comparison of his own and the model’s reach-and-point performance using the same procedures as in phase B, ie, 10 reaches to target three and then target one in a blocked format (video-playback at normal and slow speeds). Reaches to target two were not viewed because of time constraints and to be consistent with phase B. Reach movements chosen for the child’s observation were the model’s and the child’s reaches obtained in phase A1 matched for target location and, as close as possible, movement speed. While observing the split screen comparisons, the child received verbal cues (100% KP frequency) to direct his attention to the model’s erect posture and fluid wrist movement. Then the child completed 45 practice reach-and-point movements to all three targets without verbal cues (0% KP, total repetitions = 135). Then reach parameters were recorded at 60 Hz using the PTVision system, while the child completed 15 reach-and-point movements at a self-selected speed to each of the three targets presented in a random order. Phase A2 occurred three days later and consisted of recording the child’s performance of 15 reaches to all three targets in a random order at a self-selected movement speed to measure retention.
All reach-and-point movements performed by the model and the child in phases A1, B, C, and A2 were analyzed using the PTVision System. Reach characteristics examined included spatial trajectory curvilinearity, target accuracy, upper-extremity joint angles, and total movement time. Multijoint upper-extremity reaching movements are characterized by essentially straight movement trajectories from start to target locations14,36,37; therefore, we determined the amount of spatial trajectory curvature for all reaches to the three targets. Spatial trajectory curvilinearity or the maximum deviation of the actual trajectory from a straight line was operationally defined as the distance from the point of maximum curvature of the actual trajectory to a point on a straight line (drawn from start to target locations) at a 90-degree angle (Fig. 2).
An accurate reach-and-point movement was defined at the end of the reach-and-point movement as the pen tip within or on the edge of the circular target and a “miss” was defined as the pen tip outside of the circular target. Miss frequency, ie, the number of “missed” reaches divided by the total number of reaches × 100%, was determined for each of the three targets.
Total movement time (TMT) was determined as end minus initial position/time data based on frame identification. Four upper-extremity joint angles were measured at mid- and end-reach, as determined by one-half and total movement time from the start to target locations, respectively, by manual-digitization using the angle tool supplied by the PTVision software. Shoulder, elbow, and wrist flexion/extension angles were determined from lines drawn from trunk bisection, acromion, lateral epicondyle, ulnar styloid, and fith metacarpal markers. Wrist radial and ulnar deviation angles were obtained by measuring the angle between lines bisecting the third metacarpal and the forearm.
To identify changes between each adjacent experimental phase (phases A1, B, C, and A2) joint angle, spatial trajectory maximum deviation, and TMT were analyzed using visual graphic analysis, the two standard deviation (2SD) band method, and/or the split middle method.38 If a trend existed between two adjacent phases, the split middle method was used and, if there was no trend, the 2SD band method was used; both methods were used to support and verify visual analysis. Target accuracy was analyzed using visual analysis solely because mean miss frequency was compared between phases rather than by using individual trial data. Visual analysis was used when making comparisons between the model’s and child’s performances during phase A1 (before intervention).
Comparison of the Child’s and Model’s Performances before Intervention
Before intervention (phase A1) the child, as compared with the model, performed the reach-and-point movement with more curved spatial trajectories (greater maximum deviations) to all targets (Fig. 3). Mean absolute maximum deviation from a straight trajectory was 1.9 ± 1.2 cm for the child as compared with 1.0 ± 0.7 cm for the model. Both the child and model produced more curvilinear spatial trajectories when reaching to targets one and three as compared with target two.
Comparisons of upper-extremity joint angles at mid- and end-reach were made between the model and child in phase A1 (before intervention). The child as compared to the model, consistently used greater and more variable shoulder flexion, similar elbow extension, and similar wrist radial deviation when reaching to targets one and three. Most notably, he maintained his wrist in flexion (mean = –22.64 ± 11.2 degrees) whereas the model’s wrist was in extension (mean = 25.3 ± 6.7 degrees) at mid- and end-reach. Figure 4 displays shoulder, elbow, and wrist flexion/extension and wrist radial/ulnar deviation joint angles at mid-reach to target one for both the model’s and child’s (phase A1) reach and point movements.
The target miss frequency was greater for reach and point movements performed by the child in phase A1 as compared to the model for all three targets (Fig. 5). Average miss frequency was 42.2 ± 7.7% for the child (phase A1-before intervention) compared with 15.6 ± 3.9% for the model pooled for all target locations. Target location did not have an effect on target accuracy for reaches either completed by the model or the child (phase A1).
Total Movement Time
The child (phase A1) performed the reach and point movement slower than the model for all three target locations (Fig. 6). TMT for the model’s reach and point movements was 653 ± 115 ms and was 785 ± 117 ms by the child pooled for all three target locations (phase A1).
Child’s Motor Learning over Phases A1, B, C, and A2
Comparisons were made between consecutive phases A1, B, C, and A2 for the child with CP to characterize changes in reach-and-point kinematic characteristics caused by the observational feedback and verbal cue intervention.
When examining reach-and-point spatial trajectory as measured by maximum deviation from a straight line (Fig. 7), the major changes noted for the child with CP were between phases B and C. The split-screen comparison with verbal cues intervention session (phase C) resulted in an increase in spatial trajectory curvilinearity for targets one, two and three. There were no consistent changes in maximum deviation noted between baseline (phase A1) and phase B or between phases C and A2 (retention phase).
The child’s target miss frequency increased between phases A1 and B for reach and point movements to targets one and two (Fig. 5). Therefore, after viewing a model’s reaching performance, the child was less accurate when reaching and pointing. On the other hand, there was a significant decrease in the child’s target miss frequency between phases B and C for reach and point movements to targets one and two. Thus, the child became more accurate after viewing a split screen comparison between the model’s and the child’s reaching movements. There were no consistent findings between phases C and A2 (retention phase) for reach and point movements to targets one and two. No changes in miss frequency were observed between all phases for reaches to target three.
Total Movement Time
Significant increases in TMT were noted only between phases B and C for all three target locations (Fig. 6); TMT did not change between phases A1 and B or phases C and A2 for all targets. Target location did not affect TMT in any of the phases.
Figure 8 and 9 illustrate shoulder, elbow and wrist flexion/extension, and wrist radial/ulnar deviation joint angles at mid- and end-reach for phases A1, B, C, and A2 for the child’s left upper extremity as the child completed reaches to targets one (Fig. 8) and three (Fig. 9). An asterisk on the figure indicates when a change in joint angle level occurred between consecutive phases as determined by visual analysis, the split middle method, and/or the 2SD Band method (see Table 1). The child used progressively less shoulder flexion across all phases (negative trend) with a significant change in level at mid-reach between phases C and A2 (target one) and at end-reach between phases B and C (target three). The elbow joint angle essentially stayed the same between phases A1-B-C and then had significant changes in level between phases C and A2 (retention phase) at mid- and end-reach for target one and end-reach for target three. The child had a significant change in wrist joint angle between phases B and C (after viewing the split screen comparison), specifically using greater wrist extension (target one) or a relatively-neutral wrist (target three) at both mid- and end-reach. However, two changes were observed between phase A1 and B: (1) there was a greater positive slope in phase A1 and smaller positive in phase B (after viewing the model) at mid-reach to target one and (2) a change in level between phases A1 and B (use of a relatively-neutral wrist) in end-reach to target three. In addition, there appeared to be an inconsistent trend for progressive wrist ulnar deviation across phases A1 to B and a consistent change in level from a neutral wrist to wrist ulnar deviation between phases B and C at end-reach to target one and mid- and end-reach to target three. Overall, the child performed the reach and point movement with significantly more wrist extension (range of mean change = 9.4–21.9°) and significantly more wrist ulnar deviation (range of mean change = 14.6–23.7°), specifically after viewing the split-screen comparison (phase B to C).
Current technology such as computer-generated observational feedback and virtual reality39–41 make it possible for persons with disabilities to work at computer-based stations producing the large number of repetitions over the long practice durations needed to improve performance in functional tasks and/or the quality of movement. In the current study, the child with CP, as compared with the model, produced slower, less-accurate reach-and-point movements characterized by more curved spatial trajectories, which is typical for children with CP,2 using greater and more variable shoulder flexion with the wrist held in a fixed flexed position. Therefore, it was appropriate to attempt to improve the ability to vary this movement pattern and improve the reach and point movement quality and accuracy through use of computer-aided observational feedback.
A number of important motor learning issues must be critically and methodically explored when implementing this technology. For example, what types of observational feedback promote motor learning most effectively and what aspects of the movement or task should the learner’s attention be directed? In the current study, observational feedback via a side-by-side, spilt-screen comparison between a model’s and one’s own performance with simultaneous verbal cues appeared to have had the greatest impact upon reach and point performance. Specifically, exposing this child with CP to the split-screen observational feedback intervention was associated with increased target accuracy, slower movement speed, increased spatial trajectory curvilinearity, and use of a reaching pattern characterized by a more neutral/extended, yet ulnar-deviated, wrist position. These motor learning outcomes were judged to be both beneficial and detrimental. The increased target accuracy was most likely attributable to the fact that the child also reduced his movement speed. Speed-accuracy trade-off is a well-known and widely accepted motor control principle. When moving slowly, one is more accurate and vice versa.42 Or perhaps, the improved accuracy was primarily due to accumulated practice since target error has been shown to decrease with practice.11,43
On the other hand, the spatial trajectory became more curved after the child viewed the split-screen comparison, which was judged as a decline in motor performance because subjects usually perform the reach-and-point movement with essentially straight trajectories with decreasing variability with practice.44,45 Using a more neutral or slightly extended wrist position at mid- and end-reach, rather than maintaining wrist flexion, was considered a positive outcome of the observational feedback intervention since wrist extension is a typical kinematic characteristic of functional reaching.46,47 Verbal cues were given to encourage the child to selectively attend to the model’s erect posture and fluid wrist movement while reaching for the target. It is unclear whether the aligned trunk facilitated selective upper-extremity control3,5,48 or if the verbal cues to reach with a more fluid wrist were more influential.
Regardless, this child with CP was able to correct his wrist flexion posture both at mid- and end-reach with observational feedback and verbal cueing. This outcome seems remarkable because patients often are thought to require therapist manual facilitation and/or skillful task design to promote changes in movement kinematics.1 On the other hand, it is unclear why the child assumed a position of exaggerated wrist ulnar deviation during the reach-and-point movement. This outcome was judged as a negative effect of intervention because a near-neutral wrist position is usually observed during reaching.47 It is possible that the child may have misinterpreted the “fluid wrist out toward the target” verbal cues or visually observed some unidentified aspect of the behavior exhibited by the model leading to this wrist position. Ultimately, viewing the split-screen comparison appeared to enable the child to recognize the spatial and temporal movement incongruence between the model’s and his own motor performance, which promoted both beneficial and detrimental kinematic changes.
It is a matter of speculation as to why both beneficial and detrimental effects were observed when using an observational feedback intervention. It seems reasonable that as the child focused his attention on changing the kinematics of his reach-and-point movement after both types of observational feedback intervention; he had less attention reserves to simultaneously produce straight, fast, and accurate movements. Attention has a limited or maximum capacity and, as attention is focused on one activity, a decrement in performance often is observed in a second concurrent activity,18,49 particularly in children.50 In addition, McNevin51 reviewed a number of studies that suggested that instructing learners to direct their attentional focus to the coordination of their body movements (internal focus) was detrimental to motor learning, whereas motor learning was enhanced when learners directed their attentional focus to the effects of their movements (external focus). For example, subjects without experience in golf were asked to perform a pitch shot directed toward a circular target.52 One group focused on the swing of their arms (internal focus) and the other group focused on the pendulum motion of the club head (external focus). Both groups became more accurate with practice; however, the subjects who received the external-focus instructions improved pitch accuracy performance at a greater rate than subjects who received the internal-focus instructions.
The benefits of an external focus seems undeniable53–55; however, it seems reasonable that therapist–patient interactions should be a dynamic process, perhaps serially or simultaneously directing the child’s attention to internal and external movement characteristics. If directed to only an external focus (target accuracy), the child, in this study, may have never attempted to change the quality of his reach movement. In the clinic, initially, we suggest that the therapist should observe and analyze several movement repetitions, use his/her best judgment to determine the most significant movement problem, and then provide verbal instructions to direct the child’s attention to the problem component. In this study, the child was verbally cued to attend to both the model’s and his own posture and wrist movement; however, as the child performed the reach with more wrist extension, he also assumed an uncharacteristic wrist ulnar deviation position at mid- and end-reach. Ideally, the therapist would observe this emerging motor behavior and provide subsequent verbal cues to direct the child’s attention to correct future reach and point performance. Throughout this process, we would expect target accuracy to deteriorate and/or movement speed to decrease; however, as practice continued, the child could possibly attend to wrist position, target accuracy, movement speed, and/or spatial trajectory simultaneously. If the child has the cognitive ability, the child may alternate between an internal and external attentional focus and then progress to simultaneously attending to both.
A number of other motor learning issues need to be addressed when using observational feedback. In this case study, the child first viewed the model and then the split-screen comparison, in that order. Neither residual learning effects from initially viewing the model nor cumulative practice effects can be ruled out as an influence on subsequent phase performance. In addition, the child received both forms of observational feedback in a blocked format while receiving 100% KP for both posture and wrist corrections. The blocked schedule was imposed by the limitations of the PTVision equipment, specifically because of the time required to synchronize the two side-by-side video-clip comparisons. A random schedule has been shown to be superior to blocked-scheduled observational learning in promoting skill retention31 and reduced KR frequency during observational learning promoted motor learning of a complex upper extremity task.56
Further study of many aspects of observational feedback is definitely warranted. In particular, a randomized, multigroup, experimental study is needed to more definitively compare model vs split-screen-comparison observational feedback. Another form of split-screen, observational feedback, also seems very promising; specifically, viewing a model’s videotaped performance while simultaneously viewing the learner’s real-time physical performance projected in a side-by-side comparison. Another promising idea is self-controlled observational feedback or giving the learner control over the observation schedule.57 If the child could self-select the frequency and timing of the observational feedback, the child may acquire information about a confusing or unclear aspect of the movement when specifically needed. In addition, error estimation procedures28 should be incorporated into observational learning. Learners create hypotheses about their own performance using sensory (or observational) feedback and then use KR to verify their hypotheses; thus learners may develop more effective error detection mechanisms.31
The retention period was only three days; longer retention periods should be explored in the future. Finally, after practicing reach-and-point movements using observational feedback, the transfer of learning to other functional tasks, such as reach and grasp, should be explored. Ultimately, many important motor learning issues must be critically and methodically explored when incorporating technology into physical therapy intervention.
If the technology is available, observational feedback via a videotaped-playback, split-screen comparison between a model and a learner’s performance with simultaneous, appropriate verbal cues appears to promote motor learning. Clearly, one cannot come to any definitive conclusions based upon a single case study; however, viewing a split-screen comparison may enable the learner to recognize spatial and temporal movement incongruence between the model’s and the learner’s motor performance and thus alter the learner’s internal conceptual movement representation. Ultimately, when using technology to augment therapy, the intervention should be designed considering the most current motor learning principles, and a therapist must periodically monitor the effects of the intervention.
We would like to thank Interactive Frontiers, Inc. for donating the PTVision motion analysis system for use in this study.
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