Stroke is one of the major causes of adult disability, leading to dependence in activities of daily living, with more than 800 000 incidences per year.1,2 A stroke event causes a number of impairments that contribute toward poor balance,3 leading to a decreased functional status and increased disability.4 Forty percent to 70% of community-dwelling stroke survivors experience detrimental falls each year5 and tend to have 1.5 to 4 times higher risk of hip fracture than their healthy counterparts; with only less than 40% of those individuals regaining independent mobility. Falls thus not only affect activities of daily living but also hamper community reintegration.6
Recent studies7 demonstrate that virtual reality (VR) rehabilitation, in comparison with conventional methods, provides enhanced sensory feedback about movement characteristics and improves both motor task learning and execution.8 Virtual reality rehabilitation methods offer highly customizable, controllable, multimodal simulations that give the subject high levels of motivation and adherence and a strong sense of presence in the virtual environment.9,10 These methods could, potentially, allow more engaging forms of interventions to be accessed under reduced supervision, along with increased functional recovery.11 Despite the advantages, because of high costs, VR systems are still unavailable in many rehabilitation settings.12 Off-the-shelf, low-cost video gaming systems such as Wii Fit (Nintendo Co, Ltd. Kyoto, Japan) and Kinect (Microsoft, Inc., Redmond, WA, U.S.A) provide the stroke survivor a similar environment and effectiveness in balance rehabilitation compared with VR systems13 across various rehabilitation settings.9,14 For example, balance training using Wii Fit has been shown to result in significant improvements in static and dynamic balance control, which corresponds with functional improvements.13
Although balance control rehabilitation is fundamental, recent findings indicate that impairments in cognitive function poststroke may interfere with community mobility and reintegration. In persons with stroke performing a motor and cognitive task concurrently, if both the tasks share the same attentional resources (due to an overlap in structural cortical networks15) or if they are limited (due to age-related deterioration or pathologically compromised central nervous system16), performance on either one or both of the tasks is reduced (cognitive-motor interference). Because of the stroke-induced cortical lesion, there is a decrease in capacity of processing resources and the cognitive-motor interference is significantly greater,17 especially while performing the physical task concurrently with a cognitive task that challenges working memory or executive abilities.18 These findings suggest that integrating cognitive-motor rehabilitation might be crucial in addressing balance recovery among chronic stroke survivors.
Although dual-task (DT) training (training in performance of motor task with a concurrent cognitive task) in various populations such as older adults,19 persons with Parkinson disease,18 and persons with traumatic brain injury21 has been very promising in improving balance control, there is little research on effects of such rehabilitation in stroke survivors. Recent research demonstrates that rehabilitation strategies with DT might produce an efficient integration of the 2 tasks22 and “automate” the performance of the primary motor task,23 providing resources to focus on other tasks.24,25
In this study, we examined a novel and cost-effective cognitive-motor training paradigm, comprising Wii Fit gaming performed in conjunction with various higher-order cognitive tasks, to lay the foundation for a multidimensional treatment paradigm. This could subsequently be translated into a home therapy program, contributing to economical health care management. The purpose of this study was to investigate the adherence and intervention-induced effects of such training in improving intentional balance control under DT conditions. We hypothesized that there would be a significant decrease, in both balance and cognitive costs, when comparing post- with pretraining values, along with significantly greater scores on Intrinsic Motivation Inventory (IMI) postintervention.
Eight ambulatory adults with self-reported chronic hemiparetic stroke were recruited after obtaining informed consent, approved by institutional review board of the University of Illinois. Demographics of the stroke subjects are shown in Table 1.
Subjects with hemiparetic stroke (>6 months, without any presence of aphasia) as confirmed by participants' physician were included. They were required to have the ability to stand independently for at least 5 minutes without the use of an assistive device and no incidence of falls in last 6 months. Participants with cognitive deficits, as measured by the Short Orientation-Memory-Concentration test of cognitive impairment (ie, >8), were excluded.26 Short Orientation-Memory-Concentration is also positively correlated with screening tests for aphasia, suggesting that individuals with higher score on the Short Orientation-Memory-Concentration test would show worse language functioning.27 Subjects' mean ± SD disability status, quantified using the Modified Rankin Scale, ranged from mild to moderate disability (2.62 ± 0.51) (Table 1). Three of the subjects reported having slipped during their activities of daily living but were able to regain balance with external support. Participants with other neurological (eg, Parkinson disease, vestibular deficits, peripheral neuropathy, or unstable epilepsy) and musculoskeletal disorders were excluded. Individuals with cardiovascular disorders as assessed by resting heart rate (>85% of age-predicted maximal) and resting oxygen saturation (<95) were also excluded.
The cognitive-motor training consisted of VR balance training (using the Nintendo Wii Fit console) performed in conjunction with cognitive training for 5 consecutive days of approximately 110 minutes per session. Training consisted of 4 balance board games performed in randomized order: Table tilt, Tightrope, Soccer, and Balance bubble. These games were to be played while performing cognitive tasks, which included memory tasks such as word list generation, letter-number sequencing, and question-answer and memory recall games. In the word list generation task, subjects were asked to recite as many words as they can, from a given category, as quickly as possible for a given duration (eg, participants were asked to say as many names of “animals,” “fruits,” or words from a given alphabet letter, such as “S”). In the letter-number sequencing task, subjects were given a paired letter and number (eg, B2) and asked to recite subsequent letter-number pairings in ascending or descending order (eg, C3, D4, E5). In the question-answer task, subjects were asked to answer a set of questions (eg, what is the date today, what is your favorite season, what is the fastest mode of transportation for you to go home, etc). In the memory recall game, subjects were periodically given a set of 3 simple words (eg, watch, summer, and book) to recite and which they were asked to recall after about 1 minute. During the initial session, participants underwent a brief orientation on how to use the Wii Fit gaming system and the objective of each game (Table tilt: getting the marble in the hole; Tightrope: walk across a rope; Soccer: heading soccer balls; and Balance bubble: navigating through the maze without popping the bubble), which a research assistant then demonstrated. Subjects learned the mechanisms of the gaming system with practice, verbal feedback from the therapist leading the session, and visual feedback from the gaming system, as seen on the television. Training began once they were able to demonstrate ability to play the game by achieving a score of more than zero on each game of the 4 games, without any verbal feedback or physical assistance from the investigator. All subjects were able to achieve this goal within 10 minutes. Subjects participated in all 4 games (23 minutes each) with 5 minutes rest in between games, totaling up to approximately 110 minutes of balance training time per session. Subjects wore a gait belt and a researcher provided external assistance (contact guard support) and supervised the subjects so that no falls occurred during the intervention period.
Primary Outcome Measurements
All the subjects completed assessments pre- and postintervention, which were performed by a research assistant, who was blinded to prevent balance evaluation bias. The details of each test are described in the following text.
Balance Control Task. Balance control was assessed using the limits of stability test,28 a protocol of the EquiTest (Computerized Dynamic Posturography),29,30 with evidence of its reliability31 and validity.32,33 Limitations in the ability to perform the test strongly correlated with other performance-based functional balance measures.32,35 Subjects, secured with a safety harness system, stood on the EquiTest balance platform and were asked to lean their body in either forward or backward directions to move their “avatar” from the center (center of pressure vector projected on the screen in front of them) to the respective target box. Subjects were asked to do this without losing balance, stepping, or reaching for assistance. All subjects performed 2 familiarization trials, after which data for one trial, in both the directions, were collected.
The outcome measures to quantify balance performance recorded by the software included reaction time, movement velocity, maximum excursion, and directional control. Reaction time is the time, in seconds, between the command to move and the onset of the user's movement. Movement velocity is the average speed of center-of-gravity movement in degrees per second. Maximum excursion is the maximum excursion amplitude, in percentage, of center of mass achieved during the trial. Directional control is the comparison, in percentage, of the amount of movement of the center of mass in the intended direction (toward the target) to the amount of extraneous movement (away from the target).
Cognitive Task. While seated, subjects performed a counting backward task, which is a test of working memory function. This is a relatively complex task that requires maintenance, retrieval, and reorganization of information.36 In this cognitive task, subjects were given predetermined numbers and asked to perform serial subtraction (eg, subtract 7 from 40 as many times as you can until stopped). After 2 trials of familiarization, another 2 trials were conducted using 2 new number sets. These trials served as the ST cognitive condition. The number of accurate responses recited within the given time period of each trial was recorded.
Under DT conditions, subjects performed the limits of stability test in conjunction with the counting backward task. Upon cue, subjects were required move their avatar into the target by shifting their weight in the intended direction while simultaneously reciting as many numbers as possible through the given serial subtraction. To allow for accurate comparison of the cognitive performance between ST and DT conditions, the same 2 number sets (start number and subtracting digit) from the seated cognitive task were utilized for the DT task. During testing, subjects were not specifically instructed to prioritize either the cognitive task or the balance task and the numbers for each trial remained same for all the subjects.
Trial Order. Subjects first performed the cognitive task (ST condition). They were then given a 30-minute break as a washout period. The washout period was followed by the limits of stability test. Each trial for the limits of stability test was coded on the basis of the task condition (ST vs DT) and direction (forward vs backward). The order for each trial was randomized using a random number generator computer algorithm and followed for each subject. To avoid fatigue, a 1- to 2-minute rest period was provided between trials.
Secondary Outcome Measurements
In addition to the primary outcome measures, adherence to therapeutic activities37 was measured with the IMI scale. It has been used in several experiments related to intrinsic motivation and self-regulation38,39 and has good evidence of being reliable.40 We also recorded the number of missed training days. Standardized clinical outcome measures such as the Berg Balance Scale and Timed Up and Go were also examined pre- and postintervention. The gaming scores were recorded from the Wii Fit software after each game (during training). Schematic of the study design, demonstrating the chronological sequence of events, consisting of the pretest, intervention, and posttest, is represented in Figure 1.
To determine any change in performance between DT and ST (cognitive-motor interference) conditions, we calculated DT costs for each of the balance outcome measures (reaction time, movement velocity, maximum excursion, and directional control) and the cognitive measure (number of words recited) pre- and postintervention. Dual-task cost equals [(ST − DT)/DT] × 100.41 Positive cost (higher cost) indicated lower performance for all outcome measures except reaction time. Reaction time was expected to increase more under DT conditions than under ST conditions; a higher cost (lower performance) would be indicated by increased negative values. It was postulated that after training DT cost would be lower, indicating significantly greater performance under DT conditions for balance and cognitive measures. Paired t tests were performed on these computed costs, along with changes in the gaming scores, Berg Balance Scale, Timed Up and Go, and IMI scores, to analyze the changes in postintervention compared with preintervention values. A significance level (α) of 0.05 was chosen for statistical comparisons performed using SPSS software version 17.0 for analysis. Also the magnitude of effect between the pre and post intervention were calculated with Cohen's d formula where: d = Pre-intervention mean - Post-intervention mean/SD pooled and = d/√ (d2 + 4). To adjust for changes in intervention Cohen's benchmarks were used to indicate small (≤0.20), medium (≤0.50), and large (≥0.80) effect sizes.
All subjects were present for all 5 days of training. Each subject's raw scores on the balance outcome measures (from the limits of stability test) are presented in Figure 2. All subjects were present for all 5 days of training. Each subject's raw scores on the balance outcome measures (from the limits of stability test) are presented in Figure 2A-H.
Between pre- and postintervention scores, the reaction time cost decreased significantly for forward (P < 0.01; η2 = −0.83; 95% confidence interval [CI], −30.96 to −13.76) and backward (P < 0.01; η2 = −0.92; 95% CI, −68.13 to −45.23) directions (Figure 3A and B). There was a significant decrease postintervention in movement velocity cost for forward (P < 0.01; η2 = 0.71; 95% CI, 9.66-35.62) and backward (P < 0.01; η2 = 0.98; 95% CI, 36.34-46.96) directions (Figure 3C and D). There was a similar decrease in maximum excursion cost for forward (P < 0.05; η2 = 0.93; 95% CI, 24.39-39.91) and backward (P < 0.05; η2 = 0.93; 95% CI, 28.27-42.41) directions (Figure 3E and F) and directional control cost for forward (P < 0.05; η2 = 0.92; 95% CI, 19.66-29.75) and backward (P < 0.05; η2 = 0.81; 95% CI, 8.85-25.30) directions (Figure 3G and H). The cognitive cost was significantly lower for forward (P < 0.05; η2 = 0.71; 95% CI, 10.41-44.1) and backward (P < 0.05; η2 = 0.98; 95% CI, 2.30-40.61) directions (Figure 4).
For the gaming scores between pre- and postintervention (first day and last day), there was a significant increase in Soccer (P < 0.01; η2 = −0.62; 95% CI, (−5.87 to −1.87), Table tilt (P < 0.01; η2 = −0.70; 95% CI, −18.39 to −5.1), Balance bubble (P < 0.01; η2 = −0.78; 95% CI, −90.40 to −34.01), and Tightrope (P < 0.01; η2 = −0.87; 95% CI, −20.24 to −12.75) scores (Table 2). There was a significant improvement in both the Berg Balance Scale (P < 0.05; η2 = −0.36; 95% CI, −3.71 to −1.53) and Timed Up and Go scores postintervention (P < 0.05; η2 = 60; 95% CI, 1.63-4.07). Postintervention scores on the IMI were significantly greater than preintervention scores (P < 0.01; η2 = −0.92; 95% CI, −7.91 to −5.58) (Table 3). For all the outcome variables, the values of resulted in >50% of the variance due to intervention, indicating large practical significance.
The results supported our hypothesis that there would be a significant decrease in both balance and cognitive costs posttraining, suggesting an improvement in these functions and corresponding decrease in cognitive-motor interference. Furthermore, as hypothesized, subjects' adherence to intervention was maintained and postintervention motivation was significantly greater.
There was a significant improvement in training-induced performance on the Wii Fit game scores, suggesting that subjects acquired the ability to successfully execute the balance-related requirements of the gaming tasks: weight shifting, symmetric foot stepping, controlled movements near the limits of stability, and adequate attention, memory, and decision-making skills. In addition, the game scores (results) on each trial provided performance feedback to the subjects, which may have facilitated their decision making for improving performance on subsequent trials.42 This reinforcement could be particularly important for adherence to therapeutic activities in community-dwelling stroke survivors with reduced motivation levels for rehabilitation programs.9,14 Significantly improved scores on the IMI postintervention further lend support to the aforementioned findings.
Better performance on the games indeed translated to an improvement in balance control under DT conditions. Postintervention, subjects decreased DT cost in temporal (reaction time), spatial (movement velocity, maximum excursion, and directional control), and cognitive abilities, indicating improved performance in these functions. These improvements could have been a result of better allocation of cognitive resources, which might have further facilitated attentional control when performing cognitive tasks concurrently with balance activities.
Recent research recommends VR training to promote improved integration of motor and cognitive skills for improving physical function.43 However, other evidence indicates that although VR interfaces allow for improved body awareness, movement processing, and overall motor skill acquisition, they do not provide sufficient cognitive stimulation for addressing higher-order functions such as working memory and executive abilities.44,45 In fact, some studies have discussed the difficulties in transferring cognitive skills trained by video games to real tasks.46,47 This study thus explored a combined cognitive-motor training paradigm, consisting of behavioral training focused on improving higher-order cognitive skills along with balance skills.
Until recently, cognitive training and its influence on motor behavior received little attention. A decline in cognitive function was related to irreversible structural changes associated with aging or pathology, rather than a clinical symptom that could benefit from rehabilitation.48,49 A recent study by Chapman et al50 has demonstrated significant plasticity-induced improvements in cortical activations after a 12-week, 1 h/wk, cognitive training intervention in a group of older adults. The study demonstrated significant gains in resting state activation and increased connectivity and structural integrity in the default mode network (a brain system that corresponds to task introspection and active in the absence of focused task performance) and the central executive network (regions of the brain active while performing executive functions). We believe that DT training utilized in this study challenged higher-order functions including working memory and semantic memory, which could have led to greater provision of attentional resources toward improving both balance control and cognitive performance while performing these tasks concurrently. Given the gradual and progressive decline in working memory, especially in the chronic stages poststroke, such training could have significant impacts to slow this decline.
Recovering from stroke involves relearning complex balance tasks requiring stability and mobility in the presence of motor and cognitive deficits. Individuals with stroke therefore use greater attentional resources to perform activities previously performed skillfully. In other words, these activities will be performed at an associative or cognitive stage as opposed to autonomous stage.14 When the benefits of movement automation are lost, balance control can be expected to be more vulnerable to cognitive distractions (cognitive-motor interference), subsequently increasing the risk of falls.51,52 Consequently, the central element of successful cognitive-motor rehabilitation for stroke survivors should be designed to compensate for damaged cortical regions through the activation of compensatory reserves. Also, as balance control centers in the brainstem are postulated to be influenced by descending cortical inputs, addressing practice-induced plasticity changes in these networks may decrease cognitive-motor interference and improve DT capacity.18,24,53 Interventions should therefore focus on higher-order cognitive functions such as working memory and executive function,18 and provide physical activities with decision-making opportunities to facilitate the complex cognitive processing required for community living.24 Improved performance posttraining, as indicated by a decrease in both balance and cognitive costs, suggests that DT rehabilitation challenging higher-order functions could promote automaticity of the DT performance.
Although the entire intervention was conducted over a week, the overall dosage of approximately 9 hours of training is similar to many other protocols that are spread over longer durations. Also, recent rehabilitation research has shown the importance of high-intensity, short-duration training in improving functional recovery in people who have had a stroke.14,53,54 The post-intervention improvement of 2.6 for the Berg Balance Scale and 2.9s for the Timed up and Go test, were greater or equal to the minimal detectable change of 2.5 for Berg Balance Scale and 2.9s for Timed Up and Go test respectively established for people with chronic stroke. This suggests that the post-intervention change in outcome measures could indeed be a meaningful clinical change that can lower fall-risk in this population. These results further support to shorter-term effects of such high-intensity training protocols, lend further support to shorter-term effects of such high-intensity training protocols.55 While most current literature on adherence to participation includes intervention durations ranging from a few months to years, our study duration was only for a week. To have a wider application of this approach in stroke survivors, future studies should explore dosage requirements for individuals at different stages of recovery, specifically addressing long-term adherence to the protocol and retention of training-induced changes.
Overall, the effect sizes in our study resulted in >50% of the variance due to intervention, indicating medium to large improvement in both balance and cognitive costs. Similar effects were also seen in the gaming scores, Berg Balance Scale, Timed Up and Go, and Intrinsic Motivation Inventory scores post-training. This indicates relevant clinical significance and promotes the importance of integrating this paradigm into a clinical treatment program and assessing it's efficacy for translation into a home therapy program.
The present study design did not provide any estimation of learning or maturation trends during pretest due to the lack of interrupted time-series design. However, previous literature on reliability studies for the limits of stability test demonstrates no learning effects when the test is used for assessment once the patient is sufficiently familiarized with the procedure prior to data collection.29,56,57 Thus, as part of our study design, we provided 2 familiarization trials before the actual testing, ensuring that the test was highly reliable and did not have any maturation trends during the balance testing. Furthermore, baseline tests were done on day 1, followed by the intervention phase from day 4 to day 8, and postintervention assessment performed on day 11 to ensure a sufficient time interval between preassessment, intervention, and postassessment.
The lack of control group in the study might pose a threat to its internal validity. However, the study protocol was designed to address this limitation. It was ensured that the training games selected for intervention used a wide range of balance control mechanisms, such as lateral weight shifting (Soccer and Tightrope game), and controlling the displacement and variability of their center of pressure within their existing base of support (Table tilt and balance bubble). Further to minimize the threat to internal validity, the pre- and postassessments were, however, conducted on the limits of stability test specifically in the forward and backward directions. Similarly, the cognitive task used for the DT assessment was different than the tasks used for training. Furthermore, subjects did not participate in any other physical activity or therapeutic interventions other than carrying on their typical activities of daily living. The pre- and postassessment sessions were performed at the same time of the day. Nonetheless, future studies should also compare balance improvements between equal doses of Wii Fit training with and without dual tasking to determine whether the improvement in balance control is purely due to the DT gaming paradigm or due to the high-intensity Wii Fit training session.
The dual-task training paradigm proposed in this study could effectively promote the ability to maintain optimal function on balance and cognitive tasks under challenging ‘real life’ circumstances without compromising compliance to the practice schedule. Further studies with larger sample sizes are needed to assess efficacy of this intervention for potential translation into a clinical treatment program.
The authors thank the American Heart Association, National Affiliate, for the Scientific Development Grant (PI, Dr Bhatt). The authors also thank Kaitlyn Reinwald for editing the manuscript, Tejal Kajrolkar for assisting with the data collection, and Prakruti Patel for her thoughtful comments on the manuscript.
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