The influence of scaffolding on intrinsic motivation and autonomous adherence to a game-based, unsupervised home rehabilitation program for people with upper extremity hemiparesis due to stroke. A randomized controlled trial.

The influence of scaffolding on intrinsic motivation and autonomous adherence to a game-based, unsupervised home rehabilitation program for people with upper extremity hemiparesis due to stroke. 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A randomized controlled trial. Gerard Fluet, Qinyin Qiu, Amanda Gross, Holly Gorin, Jigna Patel, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4438077/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2024 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted 12 You are reading this latest preprint version Abstract Background: This parallel, randomized controlled trial examines intrinsic motivation, adherence and motor function improvement demonstrated by two groups of subjects that performed a twelve-week, home-based upper extremity rehabilitation program. Seventeen subjects played games presenting eight to twelve discrete levels of increasing difficulty. Sixteen subjects performed the same activities controlled by success algorithms that modify game difficulty incrementally. Methods: 33 persons 20 to 80 years of age, at least six months post stroke with moderate to mild hemiparesis were randomized using a random number generator into the two groups. They were tested using the Action Research Arm Test, Upper Extremity Fugl Meyer Assessment, Stroke Impact Scale and Intrinsic Motivation Inventory pre and post training. Adherence was measured using timestamps generated by the system. Subjects had the Home Virtual Rehabilitation System [1]systems placed in their homes and were taught to perform rehabilitation games using it. Subjects were instructed to train twenty minutes per day but were allowed to train as much as they chose. Subjects trained for twelve weeks without appointments and received intermittent support from study staff. Group outcomes were compared using ANOVA. Correlations between subject demographics and adherence, as well as motor outcome, were evaluated using Pearson Correlation Coefficients. Classification and Regression Tree (CART) models were generated to predict responders using demographics and baseline measures. Results: There were 5 dropouts and no adverse events. The main effect of time was statistically significant for four of the five clinical outcome measures. There were no significant training group by time interactions. Measures of adherence did not differ between groups. 21 subjects from both groups, demonstrated clinically important improvements in UEFMA score of at least 4.25 points. Subjects with pre training UEFMA scores below 53.5 averaged a seven-point UEFMA increase. IMI scores were stable pre to post training. Conclusions: Scaffolding did not have a meaningful impact on adherence or motor function improvement. A sparsely supervised program of game-based treatment in the home was sufficient to elicit meaningful improvements in motor function and activities of daily living. Common factors considered barriers to the utilization of telerehabilitation did not impact adherence or motor outcome. Trial registration: Clinical Trials.gov - NCT03985761, Registered June 14, 2019. serious games rehabilitation hand arm telerehabilitation stroke Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Despite decades of research attempting to remediate upper extremity impairments following stroke, a rehabilitation approach that elicits substantial improvements in function that do not decay over time has not been developed [2]. This points to a need for opportunities for persons with residual impairments following stroke to work on their arm and hand function away from the clinical environment with relative independence [3]. The use of traditional and technology-supported home-based rehabilitation programs has increased steadily in the last two decades and was further accelerated by the COVID – 19 pandemic [4]. Short term and directly supervised telerehabilitation programs produce comparable outcomes to clinic-based treatments [5, 6]. Longer programs and sparsely supervised programs have not been studied as well, and outcomes are less consistent. In general, adherence to programs of activity designed to improve or maintain motor function following a stroke is relatively low [7]. Multiple barriers to consistent performance of motor function training activities exist, including low motivation as well as a lack of interest in, or enjoyment of, training activities [8]. Multiple authors have proposed that game-based rehabilitation activities may help overcome these barriers and provide a solution to low adherence to home based rehabilitation programs [9-11]. This said, the published evidence presents a range of adherence rates to gamified, home based rehabilitation, suggesting that simply presenting a rehabilitation activity as a game might not result in across the board improvements in adherence [9, 12-17]. Multiple factors have been identified as possible causes for varied adherence to technology supported rehabilitation interventions in the home [9, 18, 19]. Various authors have speculated that personal attributes such as computer literacy, age and level of education, as well as socioeconomic factors such as employment status and income, might have an impact on the ability of persons with rehabilitation needs to accept and utilize technology based rehabilitation effectively [20, 21]. However, few studies have evaluated these speculations. This study will evaluate the impact of personal and socioeconomic factors on 1) adherence to a technology supported rehabilitation program and 2) the ability to make motor function improvements after participating in a technology supported rehabilitation program. The gaming industry utilizes a wide variety of gaming mechanics, processes that govern the way a game flows, information is presented, and player success or failure is communicated to influence the frequency players pick up a game and play it, as well as the amount of time they play a game after initiating [22]. This study focused on scaffolding, a very common gaming mechanism that presents a relatively easy version of a game, followed by gradually ascending levels of difficulty as a participant succeeds [23]. This affords the participant immediate initial feelings of self-efficacy and then proceeds to challenge them. Appropriate levels of challenge [24] and feelings of self-efficacy [25] are both associated with higher levels of motivation, as is the clear knowledge of results feedback [24] a participant receives when they are presented with a new challenge after they succeed or they are required to repeat a level if they fail. This study will utilize a parallel randomized clinical trial to examine the adherence levels of subjects with stroke performing a twelve-week, home-based upper extremity rehabilitation program incorporating simulations that used scaffolding to that of a control group of subjects that performed the same activities controlled by success algorithms that increase and decrease game difficulty incrementally and undetectably [26, 27]. We compared these approaches to controlling game difficulty using 1) the Intrinsic Motivation Inventory to measure the impact of the two approaches on motivation, 2) system-collected measurement of actual game play frequency and total training time to measure adherence and 3) clinical measures of upper extremity function to determine the effectiveness of the training programs. Our study focused on autonomous adherence to the training program by setting the subjects up with the system and having them perform their training without direct supervision or appointments in an attempt to approximate a sparsely supervised rehabilitation program conducted by a therapist. Methods Subjects: Inclusion criteria were a) 20-80 years old, b) diagnosis of stroke confirmed from medical records, c) score greater than or equal to 22 on the Montreal Cognitive Assessment [28], d) visual field perception that allowed for attention to an entire 24” computer screen, e) proprioception sufficient to performing training activities without looking at their hand, f) Upper Extremity Fugl-Meyer Assessment (UEFMA) score of 10-60/66 [29] and g) receptive and expressive communication consistent with informed consent. Exclusion criteria were a) upper extremity orthopedic dysfunction that would limit upper extremity activity and b) chronic central nervous system pathology other than stroke. Subjects were recruited via local clinician referral and at stroke support groups. Subjects were screened and consented subjects by a study coordinator. After this they were assigned to one of either the Enhanced Motivation (EM) or Algorithm Controlled (AC) group using a random number generator (https://www.random.org/), following a simple randomization pattern. Subjects were blinded to treatment group allocation and the comparison being examined. Training System The Home Virtual Rehabilitation System (HoVRS) is a computer based rehabilitation system designed to support independent training as well as remotely supervised training in the homes of persons with stroke (please see [1] for a detailed description of the system). HoVRS consists of two subsystems: 1) a patient-based system that presents rehabilitation games and 2) a cloud-based online data pipeline that allows for asynchronous monitoring and remote supervision. The patient-based system utilizes arm, wrist and hand position data collected by a Leap Motion Controller™ (LMC), an infrared camera-based tracking device. Images collected by the cameras are transmitted using the LMC’s tracking software, which transforms the images into three dimensional representations. The LMC’s application programming interface estimates relative wrist and finger positions, allowing the system to train specific motions of the fingers (flexion, extension and individuation) and wrist (flexion, extension, pronation, supination, radial and ulnar deviation). Tracking of hand position in 3d space allows for training of all elbow and shoulder movements as well. Upper extremity movements are used to control game play in a suite of games developed in the Unity 3D™ game engine. A variety of support systems, including mechanical arm supports and tabletop forearm platforms, were utilized as needed to maintain a participant’s hand in the active workspace of the LMC during arm, wrist or finger activities. Software consists of a library of twelve games, designed by our team to train shoulder/elbow, wrist and finger motions. Basic games train movements in isolation, while more advanced games train coordinated combinations of movements. Games are designed to accommodate a wide variety of active movement abilities via a calibration protocol that scales the amount of patient movement required to elicit avatar movement in the games. Game speeds, target / obstacle densities and sensory presentations are also scaled using the approaches described below to accommodate patients with moderate to severe impairments and challenge them as they progress. Treatment Programs Protocol After randomization to one of the two interventions, subjects used the NJIT-HoVRS system to train movement of their shoulder, elbow, wrist, and fingers (Please see a detailed description of the HoVRS system in Qiu et al. 2021 [1]). Study teams consisting of a Physical Therapist and a technologist, who were not blinded to group allocation, set up the apparatus with all subjects in their homes at an initial visit and trained them to set up the system, open their assigned rehabilitation games, and play them. Treatment groups The enhanced motivation (EM) group played two to five of the twelve available rehabilitation games, depending on their goals and the movements they wanted to train. These games provided the user with eight to twelve levels of gradually increasing difficulty and complexity (scaffolding). A screen announced each level change and the graphics for each new level changed substantially. Scoring opportunities increased at each new level as well. The algorithm control (AC) group also played two to five of the same twelve rehabilitation games. Game difficulty was modified using adaptive algorithms based on maintaining an eighty percent success rate over any given period of sixty seconds. Difficulty changes were designed to be incremental with the goal of making them imperceptible to subjects. Scoring opportunities and graphics did not change when the algorithms changed difficulty. Initially, subjects were assigned three simple simulations: one each for the shoulder / elbow, wrist, and fingers. Subjects were assigned games that targeted movements that limited their ability to perform daily functional tasks as determined by the study therapist during pre-testing. At follow up sessions, the study therapist updated the subjects’ training routines. Individual games were adjusted by increasing the amount of movement required to affect game play or increasing game speed, accuracy demands or target / obstacle densities. When simple games were mastered, games that combined wrist and hand movements (e.g. combining hand opening and pronation / supination) or games that combined finger movement with hand transport (e.g. moving the hand across a piano keyboard to press specific keys) were introduced. Subjects played the rehabilitation games in their homes independently, with on-line or in-person support as needed. All subjects were encouraged to play at least twenty minutes daily, but were allowed to play the games as much as they liked. Data Collection All data were collected in subjects’ homes. Demographic Data: Demographic data, including subject age, occupation, employment status, level of education, a self-rating of computer literacy and the median income corresponding to each subject’s zip code, were collected prior to training. Outcome Measures: The impact of scaffolding on motivation was measured using the Intrinsic Motivation Inventory (IMI) [30]. Subjects completed a twelve-item version of the Intrinsic Motivation Interview (See Appendix 1) after the first and last training weeks to evaluate the impact of training game configuration on motivation to play the games, and the impact of extended play of the games (twelve weeks) on motivation as well as the correlation between levels of intrinsic motivation and adherence. Adherence to the training programs was monitored and measured by tracking performance data collected by the system. Total treatment time over the twelve-week training period was estimated for each subject using computer timestamps of the files with performance data saved after each training session. In addition, the number of training sessions over the twelve-week training period was evaluated. To measure the impact of training on changes in upper extremity motor function and examine the relationship between adherence to training on these changes, subjects completed the UEFMA [29], and Action Research Arm Test (ARAT) [31], just prior to and immediately after their participation in training. In addition, subjects completed the Hand, Activities of Daily Living, and Participation sub-scales of the Stroke Impact Scale (SIS) [32]. Tests were administered by a trained therapist blinded to group assignment. Data Analysis Primary and secondary analyses Anderson-Darling normality test was used to check for baseline data normality. Total treatment time, the primary analysis, was not normally distributed and thus analyzed using Mann–Whitney U tests for between group comparisons and Wilcoxon signed‐ranks test for related samples. Secondary outcome measures were IMI, ARAT and SIS scores. A one-between, one-within repeated measures ANOVA was used to examine the effects of the treatment group (Enhanced Motivation, Algorithm Controlled) and testing time (Baseline, Post) on the secondary outcome measures. Ancillary analyses Classification and regression tree (CART) analysis, a machine learning procedure designed to create an optimal decision tree, was used to identify the optimal level of initial impairment for our intervention [33]. CART classification was used to evaluate the 1) ability of baseline clinical demographic factors to predict achieving a clinically important increase in UEFMA score (≥ 4.25 points as per Page [34]). Variables considered in the CART analysis were Training Group (EM or AC), baseline UEFMA score (BaseFM), Baseline ARAT score (BaseARAT), total training time (Minutes), total number of training sessions, (Sessions) median income for the subjects’ zip code (Income), baseline SIS hand subscale score (BaselineSIShand), baseline SIS activity of daily living subscale score (BaseSISADL), baseline SIS activity of participation subscale score (BaseSISPart), baseline IMI score (BaseIMI), age, months since CVA, and sex (M,F). All 28 subjects were used for the CART analysis. We tested the model using ten-fold cross validation. Performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). Correlations between baseline demographics, clinical measures and training adherence were evaluated using Pearson Correlation Coefficients for continuous variables and Spearman Correlation Coefficients for categorical variables. All analyses were performed in Minitab 22. Results Subjects A total of 33 subjects (24 male and 9 female) satisfied the inclusion and exclusion criteria. Subject mean age was 57 (SD = 13). Mean time since stroke was 47 months (SD = 65) and baseline UEFMA was 43 (SD = 13). Subjects were randomized into EM (n = 17) and AC (n = 16) groups after baseline testing. There were five dropouts. There were no adverse events. For the remaining subjects, there were no statistically significant differences in baseline characteristics between EM and AC groups (Please see Fig. 1 and Table 1 ). Table 1 Baseline Demographic and Clinical Test Scores Algorithm Controlled n = 13 Enhanced Motivation n = 15 Baseline t-test Study Mean n = 28 Age 55.87 (14.5) 58.00 (11.1) 0.674 56.86 (12.3) Sex M/F 9/4 13/2 22/6 Months Since CVA 63.00 (84.0) 29.15 (28.8) 0.180 47.29 (64.8) Median ZIP Income 95.00 (32.4) 102.31 (34.7) 0.577 98.39 (33.1) Intrinsic Motivation Inventory 69.27 (6.8) 65.30 (6.0) 0.121 67.42 (6.6) UEFMA 43.07 (12.0) 43.00 (14.3) 0.990 43.04 (13.0) ARAT 32.33 (17.7) 26.08 (20.3) 0.401 29.43 (18.9) Stroke Impact Scale - Hand 14.37 (5.7) 12.71 (4.8) 0.424 13.60 (5.3) Stroke Impact Scale - ADL 37.08 (7.5) 35.10 (5.8) 0.453 36.16 (6.7) Stroke Impact Scale - Participation 27.99 (6.7) 25.60 (8.4) 0.421 26.88 (7.6) Intrinsic Motivation Inventory There were no statistically significant between group differences in IMI scores at baseline or post intervention testing, and there was no statistically significant training group by time interaction (See Table 2 ). The main effect of time was statistically significant (F (1,26) = 7.83, p = 0.007), and positive, suggesting that extended play of the rehabilitation games did not result in a decrease in intrinsic motivation. There were weak to moderate correlations between baseline as well as post intervention IMI and total training minutes, suggesting that there was a relationship between intrinsic motivation related to game play and adherence to the training protocol (See Table 3 ). Table 2 Outcome Measure Scores (Standard Deviation), Δ = Change, Median , [ Interquartile Range ] IMI Minutes Sessions UEFMA ARAT SIS Hand SIS ADL SIS Participation Pre Post Δ Total Total Pre Post Δ Pre Post Δ Pre Post Δ Pre Post Δ Pre Post Δ EM 65.30 (6.0) 70.91 (6.8) 5.62 (4.4) 966 [442–1570] 37.15 (17.6) 43.00 (14.3) 48.23 (11.9) 5.23 (3.2) 26.08 (20.2) 31.69 (20.8) 5.62 (4.9) 12.71 (4.8) 14.46 (5.5) 1.81 (2.5) 35.10 (5.8) 38.49 (6.3) 3.39 (2.2) 25.60 (8.4) 27.46 (6.8) 2.05 (2.5) AC 69.27 (6.8) 73.49 (6.1) 4.22 (5.7) 680 [412–902] 47.67 (16.4) 43.07 (12) 49.47 (11.3) 6.40 (2.5) 32.33 (17.7) 35.93 (17.5) 3.47 (3.9) 14.37 (5.7) 15.40 (5.5) 0.82 (3.7) 37.08 (7.4) 38.85 (6.6) 1.77 (2.9) 27.99 (6.7) 27.57 (10.2) 1.66 (2.7) Group 67.42 (6.6) 72.29 (6.5) 4.87 (5) 765 [440–1071] 42.79 (16.4) 43.04 (12.9) 48.89 (11.4) 5.86 (2.8) 29.43 (18.9) 33.96 (18.9) 4.46 (4.5) 13.60 (5.3) 14.96 (5.4) 1.28 (3.28) 36.16 (6.7) 38.68 (6.4) 2.52 (2.7) 26.88 (7.5) 27.52 (8.6) 1.84 (2.6) (Standard Deviation), Δ = Change, Median, [ Interquartile Range ] Table 3 Correlations between demographics, and adherence measures Minutes Sessions UEFMAΔ ARATΔ SIS Hand Δ Sessions .769* UEFMAΔ − .135 − .099 ARATΔ .163 − .029 .287 SIS Hand Δ .111 .352 − .331 .178 SIS ADL Δ − .328 − .141 − .167 .033 .323 *= p < .05, bold = Spearman Correlation Coefficient Adherence The EM group had two dropouts, and the AC group had three. One of the AC group dropouts did not enjoy the games. The other four dropouts reported difficulties with setup and playing the games as reasons for discontinuing training. There were no adverse events. Subjects that completed the protocol from both groups demonstrated substantial variance in adherence to the training protocol / total training time. EM group subjects' training time ranged between 299 and 2672 minutes of training with a median training time of 966 (IQR = 442–1570) minutes. AC group subjects' training time ranged between 165 and 1208 minutes of training with a median training time of 680 (IQR = 412–902) minutes. The within group variance and between group differences in the number of training sessions were smaller than those of total minutes. EM group subjects performed between 18 and 77 sessions. Mean number of sessions for the EM group was 48 (SD = 16). AC group subjects performed between 6 and 68 sessions. Mean number of sessions for the AC group was 37 (SD = 18) (See Table 2 ). Clinical Outcome Measures (See Table 2 ) Main effect of time was statistically significant for UEFMA (F (1,26) = 112.4, p < 0.001), ARAT (F (1,26) = 29.1, p < 0.001), SIS-ADL (F (1,28) = 26.2, p < 0 .001), and SIS-Hand (F (1,26) = 5.7, p = 0.025). Subjects’ SIS – Participation scores did not change from pre to post-test. There were no statistically significant training group by time interactions for any of the clinical outcome measures. There were no statistically significant correlations between training time and any of the clinical outcome measures. 12 of the 15 subjects in the EM group and 9 of the 14 subjects in the AC group demonstrated improvements in UEFMA score that exceeded the published minimum clinically important difference (MCID) of 4.25 points for persons with chronic stroke [ 34 ]. 4 of the 15 subjects in the EM group and 7 of the 14 subjects in the AC group demonstrated improvements in ARAT score that exceeded the published minimum clinically important difference (MCID) for persons with chronic stroke [ 35 ]. Ancillary analyses To identify subjects best able to benefit from our intervention, we used an exploratory predictive analytics approach using CART classification analysis, a decision tree algorithm that used changes in the UEFMA score from baseline to post-training to partition our subjects into two groups (see Fig. 2 ). Subjects who demonstrated a clinically important improvement greater than or equal to 4.25 points due to HoVRs training (21 subjects, Group1) had a BaselineFM scores of less or equal to 53.5, with a mean (SD) change in the UEFMA score of 7.0 (2.2). The second group (7 subjects, Group 2), defined by the CART algorithm, had initial BaselineFM scores larger than 53.5, with a mean (SD) change of 2.4 (1.49). Note that none of our subjects reached the maximal UEFMA score of 66. CART analysis performance was acceptable to excellent, with the area under the curve (AUC) for the training ROC of 0.905 and for the testing ROC of 0.738 (See Fig. 3 ). Odds ratio for training was 120. Odds ratio for testing was 15. When considering the other factors included in this analysis (See Fig. 2 ), a single demographic factor, median income for the subjects’ zip code, was the next strongest predictor of clinically important improvement (See Fig. 4 ). Relative variable importance (RVI) of income demonstrated 84% of the predictive power of baseline UEFMA score. Subjects living in communities with lower median incomes were more likely to demonstrate clinically important differences. Computer skill level had substantially less predictive power (RVI = 12.6%). Four clinical baseline characteristics measures followed, Baseline ARAT score (RVI = 34.4%), Baseline SIS participation (RVI = 17.4%), Baseline SISADL (RVI = 15.4%) and Baseline SIS Hand (RVI = 11.1%). Subjects with lower baseline scores for these outcome measures were more likely to demonstrate clinically important differences. The two adherence measures, Baseline IMI and IMI Change, all other demographic measures and training group demonstrated trivial predictive power when compared to the baseline UEFMA score (See Fig. 4 ). There were no statistically significant correlations between demographic factors and adherence. There were weak to moderate correlations between baseline as well as post intervention IMI and total training minutes, suggesting that there was a relationship between intrinsic motivation related to game play and adherence to the training protocol. (See Table 3 ) There were no statistically significant correlations between training time or number of training sessions and any clinical outcome measures (See Table 4 ). Table 4 Correlations between demographics and adherence measures Minutes Sessions Age Income Computer Skills Education Baseline IMI Sessions .769* Age .247 .182 Income .187 .156 .358 Computer Skills − .087 − .029 − .325 − .026 Education .028 − .175 .111 .245 .325 Baseline IMI .172 .246 .107 .092 − .195 − .310 Post IMI .459* .223 − .014 .067 − .028 − .176 0.703* *= p < .05, bold = Spearman Correlation Coefficient Discussion This study examined the adherence of a group of persons with upper extremity hemiparesis due to stroke who performed one of two different game-based, autonomous training programs targeting their paretic arms, hands and fingers. The two programs differed in the level of explicit feedback related to success that they were provided during game play. The EM group, which was presented with more explicit feedback, demonstrated similar IMI scores immediately after the first week of training and immediately after the last week of training compared to the AC group that was provided less explicit feedback. Despite this similarity and the fact that there was a moderate correlation between IMI scores and total training time, the EM group demonstrated slightly larger median training times over the 12-week training program than the AC group. This suggests that there was some aspect of the interaction between the two training programs and subjects that differed, which was not captured by the IMI. There were no statistically significant correlations between training time and improvements in clinical outcomes. The lack of a relationship between training time and outcome differs from some studies of the relationship between UE rehabilitation time and outcome [ 36 ] but is similar to other studies that cite a relatively weak relationship between training dosage and clinical outcomes after a minimum training threshold is achieved [ 37 , 38 ]. This said, the number of subjects that demonstrated clinically important improvements in UEFMA and ARAT scores suggests that the training stimulus was strong enough even at lower training volumes to impact hand function. Overall adherence to both training programs was modest. Dropout rates were 11% and 13% for the two groups and total training time was lower than many studies of home-based rehabilitation. It’s likely that this is due to the fact that 1) the intervention was relatively long (twelve weeks), and 2) subjects did not have to train by appointment. When comparing subjects in studies examining sparsely supervised, home based rehabilitation interventions, adherence rates and training time were better than those of subjects in a study by Standen [ 17 ] but not as good as those in a study by Rand [ 9 ]. The relatively high IMI scores and statistically significant increase in total IMI score over time might suggest that both of the training programs were relatively engaging over the course of training. The salience of training stimuli and engagement in training are both cited as factors influencing experience dependent neuroplasticity that underlies motor recovery post-stroke [ 39 ]. This might suggest that high levels of interest and engagement in training might be an important variable related to the consistent improvements in motor function in spite of relatively modest total training volumes. Advanced age, lower levels of technology / computer literacy and lower levels of education have been cited as potential barriers to the use of and ability to benefit from technology supported rehabilitation approaches [ 40 ]. Interestingly, our data did not support these generalizations, with all three of these factors failing to make substantial contributions to the model predicting UEFMA improvement and the lack of correlation between adherence and these variables. We feel that this may be due to a general trend in increasing computer literacy / skill in older persons and/or a concerted effort to design the HoVRS system to be used by persons with minimal computer skills. Another design objective for this system was the ability to accommodate persons with relatively severe upper extremity motor impairments. We feel that the CART analysis, including subjects with baseline UEFMA scores below thirty in the cluster of subjects making more substantial improvements, suggests that this objective was achieved. With these statements made, it is obvious that further study designed to evaluate these assertions prospectively, in a larger group of subjects, is indicated before definitive conclusions can be made. Finally, our finding that higher levels of income predicted lower levels of UEFMA improvement using our system is ripe for further examination as well. Limitations of this study include the lack of retention testing of clinical outcomes. Another limitation is the focus of this study on a single approach to motivation enhancement. This points to the need for continued study of this area of inquiry to investigate the additive effects of an expanded set of enhancement techniques that might include competition, cooperative play and narrative. Our subjects volunteered to participate in a study of technology supported rehabilitation which might limit our findings' generalizability to persons who are highly averse to technology. Conclusions This study examined the impact of scaffolding on adherence to a sparsely supervised home-based training program targeting the paretic upper extremity of persons with stroke. The effect of scaffolding elicited a non-significant difference in training time that had no effect on intrinsic motivation or improvements in motor function due to training. Across the board improvements in upper extremity motor function suggest that a sparsely supervised, game-based training program performed in the home can have meaningful, positive effects on arm, hand and finger function in persons with chronic hemiparesis due to stroke. Abbreviations HoVRS Home Virtual Rehabilitation System CART Correlation Coefficients. Classification and Regression Tree UEFMA Upper Extremity Fugl Meyer Assessment IMI Intrinsic Motivation Inventory EM Enhanced Motivation AC Algorithm Controlled LMC Leap Motion Controller NJIT New Jersey Institute of Technology ARAT Action Research Arm Test SIS Stroke Impact Scale BBT Box and Blocks Test IQR Interquartile Range MCID Minimum Clinically Important Difference Declarations Ethics Approval and Consent to Participate All subjects signed informed consent and agreement to participate in a research study documentation. This protocol was approved by the Institutional Review Boards of the New Jersey Institute of Technology and Rutgers, The State University of New Jersey in accordance with the Declaration of Helsinki. The protocol was registered at Clinical Trails.gov NCT03985761 on June 14, 2019. Availability of Data Data supporting this submission will be furnished upon written request to the corresponding author. Competing Interests Qinyin Qiu, Alma S Merians and Sergei V Adamovich are inventor of the NJIT – HoVRS system. Qinyin Qiu and Amanda Cronce are founders and employees of NeuroTechR3 Inc. a company that is bringing the R3THA system, a downstream iteration of NJIT-HoVRS to market. Funding The authors acknowledge funding support from NIDILRR (90RE5021) and the NIH (R15HD095403, R01HD058301 and R01NS085122). References Qiu, Q., et al., Development of the Home based Virtual Rehabilitation System (HoVRS) to remotely deliver an intense and customized upper extremity training. Journal of neuroengineering and rehabilitation, 2020. 17 : p. 1-10. Huang, J., et al., Effects of physical therapy-based rehabilitation on recovery of upper limb motor function after stroke in adults: a systematic review and meta-analysis of randomized controlled trials. Annals of Palliative Medicine, 2022. 11 (2): p. 52131-52531. 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Physical therapy, 2015. 95 (3): p. 350-359. Threapleton, K., A. Drummond, and P. Standen, Virtual rehabilitation: what are the practical barriers for home-based research? Digit Health. 2016; 2: 2055207616641302. doi: 10.1177/2055207616641302 . Fluet, G.G., et al., Participant Adherence to a Video Game-Based Tele-rehabilitation Program: A Mixed-Methods Case Series , in Virtual Reality in Health and Rehabilitation . 2020, CRC Press. p. 169-184. Tyagi, S., et al., Acceptance of tele-rehabilitation by stroke patients: perceived barriers and facilitators. Archives of physical medicine and rehabilitation, 2018. 99 (12): p. 2472-2477. e2. Lemke, M., et al., Motivators and barriers to using information and communication technology in everyday life following stroke: a qualitative and video observation study. Disability and Rehabilitation, 2020. 42 (14): p. 1954-1962. Whittaker, L., R. Russell‐Bennett, and R. Mulcahy, Reward‐based or meaningful gaming? A field study on game mechanics and serious games for sustainability. Psychology & Marketing, 2021. 38 (6): p. 981-1000. Charles, D., et al., Virtual reality design for stroke rehabilitation. Biomedical Visualisation: Volume 6, 2020: p. 53-87. Oyake, K., et al., Motivational strategies for stroke rehabilitation: a Delphi study. Archives of Physical Medicine and Rehabilitation, 2020. 101 (11): p. 1929-1936. Gangwani, R., et al., Leveraging factors of self-efficacy and motivation to optimize stroke recovery. Frontiers in Neurology, 2022. 13 : p. 823202. Zahabi, M. and A.M. Abdul Razak, Adaptive virtual reality-based training: a systematic literature review and framework. Virtual Reality, 2020. 24 (4): p. 725-752. Fluet, G.G., et al., Motor skill changes and neurophysiologic adaptation to recovery-oriented virtual rehabilitation of hand function in a person with subacute stroke: a case study. Disability and rehabilitation, 2017. 39 (15): p. 1524-1531. Nasreddine, Z.S., et al., The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 2005. 53 (4): p. 695-699. Deakin, A., H. Hill, and V.M. Pomeroy, Rough guide to the Fugl-Meyer Assessment: upper limb section. Physiotherapy, 2003. 89 (12): p. 751-763. McAuley, E., T. Duncan, and V.V. Tammen, Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: A confirmatory factor analysis. Research quarterly for exercise and sport, 1989. 60 (1): p. 48-58. Yozbatiran, N., L. Der-Yeghiaian, and S.C. Cramer, A standardized approach to performing the action research arm test. Neurorehabilitation and neural repair, 2008. 22 (1): p. 78-90. Duncan, P., et al., Stroke Impact Scale-16: A brief assessment of physical function. Neurology, 2003. 60 (2): p. 291-296. Cleophas, T.J., et al., Regression trees: classification and regression tree (CART) models. Regression Analysis in Medical Research: for Starters and 2nd Levelers, 2021: p. 383-391. Page, S.J., G.D. Fulk, and P. Boyne, Clinically important differences for the upper-extremity Fugl-Meyer Scale in people with minimal to moderate impairment due to chronic stroke. Physical therapy, 2012. 92 (6): p. 791-798. Lang, C.E., et al., Estimating minimal clinically important differences of upper-extremity measures early after stroke. Archives of physical medicine and rehabilitation, 2008. 89 (9): p. 1693-1700. Clark, B., et al., The effect of time spent in rehabilitation on activity limitation and impairment after stroke. Cochrane Database of Systematic Reviews, 2021(10). Schneider, E.J., et al., Increasing the amount of usual rehabilitation improves activity after stroke: a systematic review. Journal of physiotherapy, 2016. 62 (4): p. 182-187. Lang, C.E., et al., Dose response of task‐specific upper limb training in people at least 6 months poststroke: a phase II, single‐blind, randomized, controlled trial. Annals of neurology, 2016. 80 (3): p. 342-354. Kleim, J.A. and T.A. Jones, Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. 2008. Lang, S., et al., Do digital interventions increase adherence to home exercise rehabilitation? A systematic review of randomised controlled trials. Archives of physiotherapy, 2022. 12 (1): p. 24. Additional Declarations Competing interest reported. Alma Merians, Qinyin Qiu, Sergei Adamovich and Amanda Gross are inventors of NJIT - HoVRS. Qinyin Qiu and Amanda Gross are founders and employees of NeuroTechR3, a company that is bringing R3THA, a subsequent iteration of NJIT HoVRS to market. Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2024 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted Editorial decision: Revision requested 13 Jun, 2024 Reviews received at journal 13 Jun, 2024 Reviewers agreed at journal 11 Jun, 2024 Reviews received at journal 09 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviewers agreed at journal 03 Jun, 2024 Reviewers agreed at journal 03 Jun, 2024 Reviewers agreed at journal 30 May, 2024 Reviewers invited by journal 28 May, 2024 Editor assigned by journal 27 May, 2024 Submission checks completed at journal 26 May, 2024 First submitted to journal 17 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4438077","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311438371,"identity":"44564694-6e36-4010-b57d-05ee5716edd4","order_by":0,"name":"Gerard Fluet","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBAC9nYILccggSLOhlsLz2EgcYCBwRiqxYB4LYkNxGth5jF8/OGXTfr82b0HmCvb/tj1Sx9/wPCh7DA+LcYGB/vScjfcOZfAeLbNIHlmX44B44xzuLXYM/OYSRzsOZy7QQKoshGoxeAMDwMzbxteW8x/HOz5ny4/A6rF/gz7A+a/+LWYMRz4cSCB4QZEi50BD4MBMyNeLWzFEmcbkg033MhLONhwzjhB4gyPwcGec+m4tbA3b/xQ8cdOXn5G7sGHDWVy9vw97A8f/CizxqkFDBjbwLpB8QOMIAZwRBECfyBaQMCesOpRMApGwSgYaQAAXHJUrswHHB0AAAAASUVORK5CYII=","orcid":"","institution":"Rutgers, The State University of New Jersey","correspondingAuthor":true,"prefix":"","firstName":"Gerard","middleName":"","lastName":"Fluet","suffix":""},{"id":311438372,"identity":"c8106993-c566-41d4-ad54-e0e7cf6567c6","order_by":1,"name":"Qinyin Qiu","email":"","orcid":"","institution":"Rutgers, The State University of New Jersey","correspondingAuthor":false,"prefix":"","firstName":"Qinyin","middleName":"","lastName":"Qiu","suffix":""},{"id":311438373,"identity":"d3f7ec40-d6bc-442a-8d4d-d28e68c2dd8b","order_by":2,"name":"Amanda Gross","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"","lastName":"Gross","suffix":""},{"id":311438374,"identity":"e78b6de9-d3ee-4a25-be0d-a1115f4145ca","order_by":3,"name":"Holly Gorin","email":"","orcid":"","institution":"Rutgers, The State University of New Jersey","correspondingAuthor":false,"prefix":"","firstName":"Holly","middleName":"","lastName":"Gorin","suffix":""},{"id":311438375,"identity":"27bec060-1aac-4a5e-b72f-b921c8b3e9f6","order_by":4,"name":"Jigna Patel","email":"","orcid":"","institution":"Rutgers, The State University of New Jersey","correspondingAuthor":false,"prefix":"","firstName":"Jigna","middleName":"","lastName":"Patel","suffix":""},{"id":311438376,"identity":"f101dc5c-0568-4ccf-bf6a-20256ca0ca68","order_by":5,"name":"Alma Merians","email":"","orcid":"","institution":"Rutgers, The State University of New Jersey","correspondingAuthor":false,"prefix":"","firstName":"Alma","middleName":"","lastName":"Merians","suffix":""},{"id":311438377,"identity":"2550ac62-50e3-470c-9f3c-0a1ca76bf35f","order_by":6,"name":"Sergei Adamovich","email":"","orcid":"","institution":"New Jersey Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sergei","middleName":"","lastName":"Adamovich","suffix":""}],"badges":[],"createdAt":"2024-05-17 17:22:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4438077/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4438077/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12984-024-01441-7","type":"published","date":"2024-08-13T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57897566,"identity":"6feeb005-a626-496f-a73f-777e6306be21","added_by":"auto","created_at":"2024-06-07 08:00:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35597,"visible":true,"origin":"","legend":"\u003cp\u003eCONSORT Diagram\u003c/p\u003e","description":"","filename":"Figure1CONSORTFluet2024.png","url":"https://assets-eu.researchsquare.com/files/rs-4438077/v1/2c8ce953ec86fe1fee27f456.png"},{"id":57897573,"identity":"0f9250a4-c9b0-4a46-9832-0f740a7ba2af","added_by":"auto","created_at":"2024-06-07 08:00:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121576,"visible":true,"origin":"","legend":"\u003cp\u003eOptimal Tree produced by a 2 node CART classification analysis identifying responders (subjects demonstrating at least a 4.25-point increase in UEFMA score). Factors considered were: responders vs. age, months since stroke, baseline UEFMA score, baseline ARAT score, total training minutes, total training sessions, income, baseline SIS hand score, baseline SIS ADL score and baseline SIS participation score.\u003c/p\u003e","description":"","filename":"Figure2Fluet2024.png","url":"https://assets-eu.researchsquare.com/files/rs-4438077/v1/37776049e1934d2b21c38819.png"},{"id":57897568,"identity":"b8a96faa-ec3b-4829-816f-29f36862ee8c","added_by":"auto","created_at":"2024-06-07 08:00:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63603,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves. Training curve was generated using all 28 subjects that completed post testing. Testing curve was generated using ten-fold cross validation.\u003c/p\u003e","description":"","filename":"Figure3Fluet2024.png","url":"https://assets-eu.researchsquare.com/files/rs-4438077/v1/8d07715bfca8e3cd35f08a16.png"},{"id":57898132,"identity":"eccaec0f-6c5f-47e8-9ecb-1ab02cc2c44c","added_by":"auto","created_at":"2024-06-07 08:08:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":128603,"visible":true,"origin":"","legend":"\u003cp\u003eRelative value identification for each of the factors considered. Values below 100% describe the level of classification improvement that could be achieved if at least one node was split using this factor.\u003c/p\u003e","description":"","filename":"Figure4Fluet2024.png","url":"https://assets-eu.researchsquare.com/files/rs-4438077/v1/26572377409a2950498b6284.png"},{"id":63071395,"identity":"c133fe48-fe60-4e81-b531-132abdc8bada","added_by":"auto","created_at":"2024-08-22 20:06:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1032868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4438077/v1/c214c007-e4e0-42a8-a413-b62f5a53ed7d.pdf"}],"financialInterests":"Competing interest reported. Alma Merians, Qinyin Qiu, Sergei Adamovich and Amanda Gross are inventors of NJIT - HoVRS. Qinyin Qiu and Amanda Gross are founders and employees of NeuroTechR3, a company that is bringing R3THA, a subsequent iteration of NJIT HoVRS to market.","formattedTitle":"The influence of scaffolding on intrinsic motivation and autonomous adherence to a game-based, unsupervised home rehabilitation program for people with upper extremity hemiparesis due to stroke. A randomized controlled trial.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite decades of research attempting to remediate upper extremity impairments following stroke, a rehabilitation approach that elicits substantial improvements in function that do not decay over time has not been developed [2]. This points to a need for opportunities for persons with residual impairments following stroke to work on their arm and hand function away from the clinical environment with relative independence [3]. The use of traditional and technology-supported home-based rehabilitation programs has increased steadily in the last two decades and was further accelerated by the COVID – 19 pandemic [4]. Short term and directly supervised telerehabilitation programs produce comparable outcomes to clinic-based treatments [5, 6]. Longer programs and sparsely supervised programs have not been studied as well, and outcomes are less consistent. In general, adherence to programs of activity designed to improve or maintain motor function following a stroke is relatively low [7]. Multiple barriers to consistent performance of motor function training activities exist, including low motivation as well as a lack of interest in, or enjoyment of, training activities [8]. \u0026nbsp;Multiple authors have proposed that game-based rehabilitation activities may help overcome these barriers and provide a solution to low adherence to home based rehabilitation programs [9-11]. This said, the published evidence presents a range of adherence rates to gamified, home based rehabilitation, suggesting that simply presenting a rehabilitation activity as a game might not result in across the board improvements in adherence [9, 12-17]. Multiple factors have been identified as possible causes for varied adherence to technology supported rehabilitation interventions in the home [9, 18, 19]. Various authors have speculated that personal attributes such as computer literacy, age and level of education, as well as socioeconomic factors such as employment status and income, might have an impact on the ability of persons with rehabilitation needs to accept and utilize technology based rehabilitation effectively [20, 21]. However, few studies have evaluated these speculations. \u0026nbsp;This study will evaluate the impact of personal and socioeconomic factors on 1) adherence to a technology supported rehabilitation program and 2) the ability to make motor function improvements after participating in a technology supported rehabilitation program.\u003c/p\u003e\n\u003cp\u003eThe gaming industry utilizes a wide variety of gaming mechanics, processes that govern the way a game flows, information is presented, and player success or failure is communicated to influence the frequency players pick up a game and play it, as well as the amount of time they play a game after initiating [22]. This study focused on scaffolding, a very common gaming mechanism that presents a relatively easy version of a game, followed by gradually ascending levels of difficulty as a participant succeeds [23]. This affords the participant immediate initial feelings of self-efficacy and then proceeds to challenge them. Appropriate levels of challenge [24] and feelings of self-efficacy [25] are both associated with higher levels of motivation, as is the clear knowledge of results feedback [24] a participant receives when they are presented with a new challenge after they succeed or they are required to repeat a level if they fail.\u003c/p\u003e\n\u003cp\u003eThis study will utilize a parallel randomized clinical trial to examine the adherence levels of subjects with stroke performing a twelve-week, home-based upper extremity rehabilitation program incorporating simulations that used scaffolding to that of a control group of subjects that performed the same activities controlled by success algorithms \u0026nbsp;that increase and decrease game difficulty incrementally and undetectably [26, 27]. We compared these approaches to controlling game difficulty using 1) the Intrinsic Motivation Inventory to measure the impact of the two approaches on motivation, 2) system-collected measurement of actual game play frequency and total training time to measure adherence and 3) clinical measures of upper extremity function to determine the effectiveness of the training programs. Our study focused on autonomous adherence to the training program by setting the subjects up with the system and having them perform their training without direct supervision or appointments in an attempt to approximate a sparsely supervised rehabilitation program conducted by a therapist.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSubjects: Inclusion criteria were a) 20-80 years old, b) diagnosis of stroke confirmed from medical records, c) score greater than or equal to 22 on the Montreal Cognitive Assessment [28], d) visual field perception that allowed for attention to an entire 24\u0026rdquo; computer screen, e) proprioception sufficient to performing training activities without looking at their hand, f) Upper Extremity Fugl-Meyer Assessment (UEFMA) score of \u0026nbsp;10-60/66 [29] and g) receptive and expressive communication consistent with informed consent. Exclusion criteria were a) upper extremity orthopedic dysfunction that would limit upper extremity activity and b) chronic central nervous system pathology other than stroke. Subjects were recruited via local clinician referral and at stroke support groups. Subjects were screened and consented subjects by a study coordinator. After this they were assigned to one of either the Enhanced Motivation (EM) or Algorithm Controlled (AC) group using a random number generator (https://www.random.org/), following a simple randomization pattern. Subjects were blinded to treatment group allocation and the comparison being examined.\u003c/p\u003e\n\u003cp\u003eTraining System\u003c/p\u003e\n\u003cp\u003eThe Home Virtual Rehabilitation System (HoVRS) is a computer based rehabilitation system designed to support independent training as well as remotely supervised training in the homes of persons with stroke (please see [1] for a detailed description of the system). HoVRS consists of two subsystems: 1) a patient-based system that presents rehabilitation games and 2) a cloud-based online data pipeline that allows for asynchronous monitoring and remote supervision. The patient-based system utilizes arm, wrist and hand position data collected by a Leap Motion Controller\u0026trade; (LMC), an infrared camera-based tracking device. Images collected by the cameras are transmitted using the LMC\u0026rsquo;s tracking software, which transforms the images into three dimensional representations. The LMC\u0026rsquo;s application programming interface estimates relative wrist and finger positions, allowing the system to train specific motions of the fingers (flexion, extension and individuation) and wrist (flexion, extension, pronation, supination, radial and ulnar deviation). Tracking of hand position in 3d space allows for training of all elbow and shoulder movements as well. \u0026nbsp;Upper extremity movements are used to control game play in a suite of games developed in the Unity 3D\u0026trade; game engine. A variety of support systems, including mechanical arm supports and tabletop forearm platforms, were utilized as needed to maintain a participant\u0026rsquo;s hand in the active workspace of the LMC during arm, wrist or finger activities. Software consists of a library of twelve games, designed by our team to train shoulder/elbow, wrist and finger motions. Basic games train movements in isolation, while more advanced games train coordinated combinations of movements. Games are designed to accommodate a wide variety of active movement abilities via a calibration protocol that scales the amount of patient movement required to elicit avatar movement in the games. Game speeds, target / obstacle densities and sensory presentations are also scaled using the approaches described below to accommodate patients with moderate to severe impairments and challenge them as they progress.\u003c/p\u003e\n\u003cp\u003eTreatment Programs\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProtocol\u0026nbsp;\u003c/em\u003eAfter randomization to one of the two interventions, subjects used the NJIT-HoVRS system to train movement of their shoulder, elbow, wrist, and fingers \u0026nbsp;(Please see a detailed description of the HoVRS system in Qiu et al. 2021 [1]). Study teams consisting of a Physical Therapist and a technologist, who were not blinded to group allocation, set up the apparatus with all subjects in their homes at an initial visit and trained them to set up the system, open their assigned rehabilitation games, and play them.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTreatment groups\u003c/em\u003e The enhanced motivation (EM) group played two to five of the twelve available rehabilitation games, depending on their goals and the movements they wanted to train. These games provided the user with eight to twelve levels of gradually increasing difficulty and complexity (scaffolding). A screen announced each level change and the graphics for each new level changed substantially. Scoring opportunities increased at each new level as well. The algorithm control (AC) group also played two to five of the same twelve rehabilitation games. Game difficulty was modified using adaptive algorithms based on maintaining an eighty percent success rate over any given period of sixty seconds. Difficulty changes were designed to be incremental with the goal of making them imperceptible to subjects. Scoring opportunities and graphics did not change when the algorithms changed difficulty. Initially, subjects were assigned three simple simulations: one each for the shoulder / elbow, wrist, and fingers. Subjects were assigned games that targeted movements that limited their ability to perform daily functional tasks as determined by the study therapist during pre-testing. At follow up sessions, the study therapist updated the subjects\u0026rsquo; training routines. Individual games were adjusted by increasing the amount of movement required to affect game play or increasing game speed, accuracy demands or target / obstacle densities. When simple games were mastered, games that combined wrist and hand movements (e.g. combining hand opening and pronation / supination) or games that combined finger movement with hand transport (e.g. moving the hand across a piano keyboard to press specific keys) were introduced. \u0026nbsp;Subjects played the rehabilitation games in their homes independently, with on-line or in-person support as needed. All subjects were encouraged to play at least twenty minutes daily, but were allowed to play the games as much as they liked.\u003c/p\u003e\n\u003cp\u003eData Collection\u003c/p\u003e\n\u003cp\u003eAll data were collected in subjects\u0026rsquo; homes.\u003c/p\u003e\n\u003cp\u003eDemographic Data: Demographic data, including subject age, occupation, employment status, level of education, a self-rating of computer literacy and the median income corresponding to each subject\u0026rsquo;s zip code, were collected prior to training.\u003c/p\u003e\n\u003cp\u003eOutcome Measures: \u0026nbsp;The impact of scaffolding on motivation was measured using the Intrinsic Motivation Inventory (IMI) [30]. Subjects completed a twelve-item version of the Intrinsic Motivation Interview (See Appendix 1) after the first and last training weeks to evaluate the impact of training game configuration on motivation to play the games, and the impact of extended play of the games (twelve weeks) on motivation as well as the correlation between levels of intrinsic motivation and adherence.\u003c/p\u003e\n\u003cp\u003eAdherence to the training programs was monitored and measured by tracking performance data collected by the system. Total treatment time over the twelve-week training period was estimated for each subject using computer timestamps of the files with performance data saved after each training session. In addition, the number of training sessions over the twelve-week training period was evaluated.\u003c/p\u003e\n\u003cp\u003eTo measure the impact of training on changes in upper extremity motor function and examine the relationship between adherence to training on these changes, subjects completed the UEFMA [29], and Action Research Arm Test (ARAT) [31], just prior to and immediately after their participation in training. In addition, subjects completed the Hand, Activities of Daily Living, and Participation sub-scales of the Stroke Impact Scale (SIS) [32]. Tests were administered by a trained therapist blinded to group assignment.\u003c/p\u003e\n\u003cp\u003eData Analysis\u003c/p\u003e\n\u003cp\u003ePrimary and secondary analyses\u003c/p\u003e\n\u003cp\u003eAnderson-Darling normality test was used to check for baseline data normality. Total treatment time, the primary analysis, was not normally distributed and thus analyzed using Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e tests for between group comparisons and Wilcoxon signed‐ranks test for related samples.\u0026nbsp;Secondary outcome measures were IMI, ARAT and SIS scores.\u0026nbsp;A one-between, one-within repeated measures ANOVA was used to\u0026nbsp;examine the effects of the treatment group (Enhanced Motivation, Algorithm Controlled) and testing time (Baseline, Post) on the secondary outcome measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAncillary analyses\u003c/p\u003e\n\u003cp\u003eClassification and regression tree (CART) analysis, a machine learning procedure designed to create an optimal decision tree, was used to identify\u0026nbsp;the optimal level of initial impairment for our intervention\u0026nbsp;[33]. CART classification was used to evaluate the 1) ability of baseline clinical demographic factors to predict achieving a clinically important increase in UEFMA score (\u0026ge; 4.25 points as per Page\u0026nbsp;[34]).\u0026nbsp;Variables considered in the CART analysis were Training Group (EM or AC), baseline UEFMA score (BaseFM), Baseline ARAT score (BaseARAT), total training time (Minutes), total number of training sessions, (Sessions) median income for the subjects\u0026rsquo; zip code (Income), baseline SIS hand subscale score (BaselineSIShand), baseline SIS activity of daily living subscale score (BaseSISADL), baseline SIS activity of participation subscale score (BaseSISPart), \u0026nbsp;baseline IMI score (BaseIMI), age, months since CVA, and sex (M,F). All 28 subjects were used for the CART analysis. We tested the model using ten-fold cross validation. Performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrelations between baseline demographics, clinical measures and training adherence were evaluated using Pearson Correlation Coefficients for continuous variables and Spearman Correlation Coefficients for categorical variables. All analyses were performed in Minitab 22.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSubjects\u003c/p\u003e\n\u003cp\u003eA total of 33 subjects (24 male and 9 female) satisfied the inclusion and exclusion criteria. Subject mean age was 57 (SD\u0026thinsp;=\u0026thinsp;13). Mean time since stroke was 47 months (SD\u0026thinsp;=\u0026thinsp;65) and baseline UEFMA was 43 (SD\u0026thinsp;=\u0026thinsp;13). Subjects were randomized into EM (n\u0026thinsp;=\u0026thinsp;17) and AC (n\u0026thinsp;=\u0026thinsp;16) groups after baseline testing. There were five dropouts. There were no adverse events. For the remaining subjects, there were no statistically significant differences in baseline characteristics between EM and AC groups (Please see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Demographic and Clinical Test Scores\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlgorithm Controlled\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnhanced\u003c/p\u003e\n \u003cp\u003eMotivation\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;15\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBaseline t-test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy Mean\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;28\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.87 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.00 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.86 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex M/F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22/6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonths Since CVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.00 (84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.15 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.29 (64.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian ZIP Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.00 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.31 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.39 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntrinsic Motivation Inventory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.27 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.30 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.42 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUEFMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.07 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.00 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.04 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.33 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.08 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.43 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke Impact Scale - Hand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.37 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.71 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.60 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke Impact Scale - ADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.08 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.10 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.16 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke Impact Scale - Participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.99 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.60 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.88 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIntrinsic Motivation Inventory\u003c/p\u003e\n\u003cp\u003eThere were no statistically significant between group differences in IMI scores at baseline or post intervention testing, and there was no statistically significant training group by time interaction (See Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The main effect of time was statistically significant (F (1,26)\u0026thinsp;=\u0026thinsp;7.83, p\u0026thinsp;=\u0026thinsp;0.007), and positive, suggesting that extended play of the rehabilitation games did not result in a \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003edecrease\u003c/span\u003e in intrinsic motivation. There were weak to moderate correlations between baseline as well as post intervention IMI and total training minutes, suggesting that there was a relationship between intrinsic motivation related to game play and adherence to the training protocol (See Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOutcome Measure Scores (Standard Deviation), \u0026Delta;\u0026thinsp;=\u0026thinsp;Change, \u003cstrong\u003eMedian\u003c/strong\u003e, [\u003cstrong\u003eInterquartile Range\u003c/strong\u003e]\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"21\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eIMI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinutes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSessions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUEFMA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eARAT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSIS Hand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSIS ADL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSIS Participation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.30\u003c/p\u003e\n \u003cp\u003e(6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.91\u003c/p\u003e\n \u003cp\u003e(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003cp\u003e(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e966\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[442\u0026ndash;1570]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.15\u003c/p\u003e\n \u003cp\u003e(17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.00\u003c/p\u003e\n \u003cp\u003e(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.23\u003c/p\u003e\n \u003cp\u003e(11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003cp\u003e(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.08\u003c/p\u003e\n \u003cp\u003e(20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.69\u003c/p\u003e\n \u003cp\u003e(20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003cp\u003e(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.71\u003c/p\u003e\n \u003cp\u003e(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.46\u003c/p\u003e\n \u003cp\u003e(5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003cp\u003e(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.10\u003c/p\u003e\n \u003cp\u003e(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.49\u003c/p\u003e\n \u003cp\u003e(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003cp\u003e(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.60\u003c/p\u003e\n \u003cp\u003e(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.46\u003c/p\u003e\n \u003cp\u003e(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003cp\u003e(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.27\u003c/p\u003e\n \u003cp\u003e(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.49\u003c/p\u003e\n \u003cp\u003e(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003cp\u003e(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e680\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[412\u0026ndash;902]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.67\u003c/p\u003e\n \u003cp\u003e(16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.07\u003c/p\u003e\n \u003cp\u003e(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.47\u003c/p\u003e\n \u003cp\u003e(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003cp\u003e(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.33\u003c/p\u003e\n \u003cp\u003e(17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.93\u003c/p\u003e\n \u003cp\u003e(17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003cp\u003e(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.37\u003c/p\u003e\n \u003cp\u003e(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.40\u003c/p\u003e\n \u003cp\u003e(5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003cp\u003e(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.08\u003c/p\u003e\n \u003cp\u003e(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.85\u003c/p\u003e\n \u003cp\u003e(6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003cp\u003e(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.99\u003c/p\u003e\n \u003cp\u003e(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.57\u003c/p\u003e\n \u003cp\u003e(10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003cp\u003e(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.42\u003c/p\u003e\n \u003cp\u003e(6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.29\u003c/p\u003e\n \u003cp\u003e(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e765\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[440\u0026ndash;1071]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.79\u003c/p\u003e\n \u003cp\u003e(16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.04\u003c/p\u003e\n \u003cp\u003e(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.89\u003c/p\u003e\n \u003cp\u003e(11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.86\u003c/p\u003e\n \u003cp\u003e(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.43\u003c/p\u003e\n \u003cp\u003e(18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.96\u003c/p\u003e\n \u003cp\u003e(18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.46\u003c/p\u003e\n \u003cp\u003e(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.60\u003c/p\u003e\n \u003cp\u003e(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.96\u003c/p\u003e\n \u003cp\u003e(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003cp\u003e(3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.16\u003c/p\u003e\n \u003cp\u003e(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.68\u003c/p\u003e\n \u003cp\u003e(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003cp\u003e(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.88\u003c/p\u003e\n \u003cp\u003e(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.52\u003c/p\u003e\n \u003cp\u003e(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003cp\u003e(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e(Standard Deviation), \u0026Delta; = Change, \u003cstrong\u003eMedian,\u0026nbsp;\u003c/strong\u003e[\u003cstrong\u003eInterquartile Range\u003c/strong\u003e]\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelations between demographics, and adherence measures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinutes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSessions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUEFMA\u0026Delta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eARAT\u0026Delta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSIS Hand \u0026Delta;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSessions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.769*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUEFMA\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;.099\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARAT\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.287\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIS Hand \u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.352\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;.331\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.178\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIS ADL \u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;.141\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;.167\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.323\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e*= p\u0026thinsp;\u0026lt;\u0026thinsp;.05, \u003cstrong\u003ebold\u0026thinsp;=\u0026thinsp;Spearman Correlation Coefficient\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAdherence\u003c/p\u003e\n\u003cp\u003eThe EM group had two dropouts, and the AC group had three. One of the AC group dropouts did not enjoy the games. The other four dropouts reported difficulties with setup and playing the games as reasons for discontinuing training. There were no adverse events. Subjects that completed the protocol from both groups demonstrated substantial variance in adherence to the training protocol / total training time. EM group subjects\u0026apos; training time ranged between 299 and 2672 minutes of training with a median training time of 966 (IQR\u0026thinsp;=\u0026thinsp;442\u0026ndash;1570) minutes. AC group subjects\u0026apos; training time ranged between 165 and 1208 minutes of training with a median training time of 680 (IQR\u0026thinsp;=\u0026thinsp;412\u0026ndash;902) minutes. The within group variance and between group differences in the number of training sessions were smaller than those of total minutes. EM group subjects performed between 18 and 77 sessions. Mean number of sessions for the EM group was 48 (SD\u0026thinsp;=\u0026thinsp;16). AC group subjects performed between 6 and 68 sessions. Mean number of sessions for the AC group was 37 (SD\u0026thinsp;=\u0026thinsp;18) (See Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eClinical Outcome Measures\u003c/p\u003e\n\u003cp\u003e(See Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) Main effect of time was statistically significant for UEFMA (F (1,26)\u0026thinsp;=\u0026thinsp;112.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ARAT (F (1,26)\u0026thinsp;=\u0026thinsp;29.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SIS-ADL (F (1,28)\u0026thinsp;=\u0026thinsp;26.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0 .001), and SIS-Hand (F (1,26)\u0026thinsp;=\u0026thinsp;5.7, p\u0026thinsp;=\u0026thinsp;0.025). Subjects\u0026rsquo; SIS \u0026ndash; Participation scores did not change from pre to post-test. There were no statistically significant training group by time interactions for any of the clinical outcome measures. There were no statistically significant correlations between training time and any of the clinical outcome measures. 12 of the 15 subjects in the EM group and 9 of the 14 subjects in the AC group demonstrated improvements in UEFMA score that exceeded the published minimum clinically important difference (MCID) of 4.25 points for persons with chronic stroke [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. 4 of the 15 subjects in the EM group and 7 of the 14 subjects in the AC group demonstrated improvements in ARAT score that exceeded the published minimum clinically important difference (MCID) for persons with chronic stroke [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAncillary analyses\u003c/p\u003e\n\u003cp\u003eTo identify subjects best able to benefit from our intervention, we used an exploratory predictive analytics approach using CART classification analysis, a decision tree algorithm that used changes in the UEFMA score from baseline to post-training to partition our subjects into two groups (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Subjects who demonstrated a clinically important improvement greater than or equal to 4.25 points due to HoVRs training (21 subjects, Group1) had a BaselineFM scores of less or equal to 53.5, with a mean (SD) change in the UEFMA score of 7.0 (2.2). The second group (7 subjects, Group 2), defined by the CART algorithm, had initial BaselineFM scores larger than 53.5, with a mean (SD) change of 2.4 (1.49). Note that none of our subjects reached the maximal UEFMA score of 66. CART analysis performance was acceptable to excellent, with the area under the curve (AUC) for the training ROC of 0.905 and for the testing ROC of 0.738 (See Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Odds ratio for training was 120. Odds ratio for testing was 15.\u003c/p\u003e\n\u003cp\u003eWhen considering the other factors included in this analysis (See Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), a single demographic factor, median income for the subjects\u0026rsquo; zip code, was the next strongest predictor of clinically important improvement (See Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Relative variable importance (RVI) of income demonstrated 84% of the predictive power of baseline UEFMA score. Subjects living in communities with lower median incomes were more likely to demonstrate clinically important differences. Computer skill level had substantially less predictive power (RVI\u0026thinsp;=\u0026thinsp;12.6%). Four clinical baseline characteristics measures followed, Baseline ARAT score (RVI\u0026thinsp;=\u0026thinsp;34.4%), Baseline SIS participation (RVI\u0026thinsp;=\u0026thinsp;17.4%), Baseline SISADL (RVI\u0026thinsp;=\u0026thinsp;15.4%) and Baseline SIS Hand (RVI\u0026thinsp;=\u0026thinsp;11.1%). Subjects with lower baseline scores for these outcome measures were more likely to demonstrate clinically important differences. The two adherence measures, Baseline IMI and IMI Change, all other demographic measures and training group demonstrated trivial predictive power when compared to the baseline UEFMA score (See Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThere were no statistically significant correlations between demographic factors and adherence. There were weak to moderate correlations between baseline as well as post intervention IMI and total training minutes, suggesting that there was a relationship between intrinsic motivation related to game play and adherence to the training protocol. (See Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) There were no statistically significant correlations between training time or number of training sessions and any clinical outcome measures (See Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelations between demographics and adherence measures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinutes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSessions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComputer Skills\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBaseline IMI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSessions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.769*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.182\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.156\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.358\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComputer Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline IMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.246\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.107\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.092\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost IMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.459*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.223\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.067\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.703*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e*= p\u0026thinsp;\u0026lt;\u0026thinsp;.05, \u003cstrong\u003ebold\u0026thinsp;=\u0026thinsp;Spearman Correlation Coefficient\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the adherence of a group of persons with upper extremity hemiparesis due to stroke who performed one of two different game-based, autonomous training programs targeting their paretic arms, hands and fingers. The two programs differed in the level of explicit feedback related to success that they were provided during game play. The EM group, which was presented with more explicit feedback, demonstrated similar IMI scores immediately after the first week of training and immediately after the last week of training compared to the AC group that was provided less explicit feedback. Despite this similarity and the fact that there was a moderate correlation between IMI scores and total training time, the EM group demonstrated slightly larger median training times over the 12-week training program than the AC group. This suggests that there was some aspect of the interaction between the two training programs and subjects that differed, which was not captured by the IMI.\u003c/p\u003e \u003cp\u003eThere were no statistically significant correlations between training time and improvements in clinical outcomes. The lack of a relationship between training time and outcome differs from some studies of the relationship between UE rehabilitation time and outcome [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] but is similar to other studies that cite a relatively weak relationship between training dosage and clinical outcomes after a minimum training threshold is achieved [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This said, the number of subjects that demonstrated clinically important improvements in UEFMA and ARAT scores suggests that the training stimulus was strong enough even at lower training volumes to impact hand function.\u003c/p\u003e \u003cp\u003eOverall adherence to both training programs was modest. Dropout rates were 11% and 13% for the two groups and total training time was lower than many studies of home-based rehabilitation. It\u0026rsquo;s likely that this is due to the fact that 1) the intervention was relatively long (twelve weeks), and 2) subjects did not have to train by appointment. When comparing subjects in studies examining sparsely supervised, home based rehabilitation interventions, adherence rates and training time were better than those of subjects in a study by Standen [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] but not as good as those in a study by Rand [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relatively high IMI scores and statistically significant increase in total IMI score over time might suggest that both of the training programs were relatively engaging over the course of training. The salience of training stimuli and engagement in training are both cited as factors influencing experience dependent neuroplasticity that underlies motor recovery post-stroke [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This might suggest that high levels of interest and engagement in training might be an important variable related to the consistent improvements in motor function in spite of relatively modest total training volumes.\u003c/p\u003e \u003cp\u003eAdvanced age, lower levels of technology / computer literacy and lower levels of education have been cited as potential barriers to the use of and ability to benefit from technology supported rehabilitation approaches [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Interestingly, our data did not support these generalizations, with all three of these factors failing to make substantial contributions to the model predicting UEFMA improvement and the lack of correlation between adherence and these variables. We feel that this may be due to a general trend in increasing computer literacy / skill in older persons and/or a concerted effort to design the HoVRS system to be used by persons with minimal computer skills. Another design objective for this system was the ability to accommodate persons with relatively severe upper extremity motor impairments. We feel that the CART analysis, including subjects with baseline UEFMA scores below thirty in the cluster of subjects making more substantial improvements, suggests that this objective was achieved. With these statements made, it is obvious that further study designed to evaluate these assertions prospectively, in a larger group of subjects, is indicated before definitive conclusions can be made. Finally, our finding that higher levels of income predicted lower levels of UEFMA improvement using our system is ripe for further examination as well.\u003c/p\u003e \u003cp\u003eLimitations of this study include the lack of retention testing of clinical outcomes. Another limitation is the focus of this study on a single approach to motivation enhancement. This points to the need for continued study of this area of inquiry to investigate the additive effects of an expanded set of enhancement techniques that might include competition, cooperative play and narrative. Our subjects volunteered to participate in a study of technology supported rehabilitation which might limit our findings' generalizability to persons who are highly averse to technology.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study examined the impact of scaffolding on adherence to a sparsely supervised home-based training program targeting the paretic upper extremity of persons with stroke. The effect of scaffolding elicited a non-significant difference in training time that had no effect on intrinsic motivation or improvements in motor function due to training. Across the board improvements in upper extremity motor function suggest that a sparsely supervised, game-based training program performed in the home can have meaningful, positive effects on arm, hand and finger function in persons with chronic hemiparesis due to stroke.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHoVRS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Home Virtual Rehabilitation System\u003c/p\u003e\n\u003cp\u003eCART \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Correlation Coefficients. Classification and Regression Tree\u003c/p\u003e\n\u003cp\u003eUEFMA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Upper Extremity Fugl Meyer Assessment\u003c/p\u003e\n\u003cp\u003eIMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intrinsic Motivation Inventory\u003c/p\u003e\n\u003cp\u003eEM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Enhanced Motivation\u003c/p\u003e\n\u003cp\u003eAC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Algorithm Controlled\u003c/p\u003e\n\u003cp\u003eLMC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Leap Motion Controller\u003c/p\u003e\n\u003cp\u003eNJIT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;New Jersey Institute of Technology\u003c/p\u003e\n\u003cp\u003eARAT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Action Research Arm Test\u003c/p\u003e\n\u003cp\u003eSIS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Stroke Impact Scale\u003c/p\u003e\n\u003cp\u003eBBT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Box and Blocks Test\u003c/p\u003e\n\u003cp\u003eIQR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interquartile Range\u003c/p\u003e\n\u003cp\u003eMCID \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Minimum Clinically Important Difference\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll subjects signed informed consent and agreement to participate in a research study documentation.\u003c/p\u003e\n\u003cp\u003eThis protocol was approved by the Institutional Review Boards of the New Jersey Institute of Technology and Rutgers, The State University of New Jersey in accordance with the Declaration of Helsinki. The protocol was registered at Clinical Trails.gov NCT03985761 on June 14, 2019.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting this submission will be furnished upon written request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQinyin Qiu, Alma S Merians and Sergei V Adamovich are inventor of the NJIT – HoVRS system.\u003c/p\u003e\n\u003cp\u003eQinyin Qiu and Amanda Cronce are founders and employees of NeuroTechR3 Inc. a company that is bringing the R3THA system, a downstream iteration of NJIT-HoVRS to market.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge funding support from NIDILRR (90RE5021) and the NIH (R15HD095403, R01HD058301 and\u0026nbsp;R01NS085122).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eQiu, Q., et al., \u003cem\u003eDevelopment of the Home based Virtual Rehabilitation System (HoVRS) to remotely deliver an intense and customized upper extremity training.\u003c/em\u003e Journal of neuroengineering and rehabilitation, 2020. \u003cstrong\u003e17\u003c/strong\u003e: p. 1-10.\u003c/li\u003e\n\u003cli\u003eHuang, J., et al., \u003cem\u003eEffects of physical therapy-based rehabilitation on recovery of upper limb motor function after stroke in adults: a systematic review and meta-analysis of randomized controlled trials.\u003c/em\u003e Annals of Palliative Medicine, 2022. \u003cstrong\u003e11\u003c/strong\u003e(2): p. 52131-52531.\u003c/li\u003e\n\u003cli\u003eChi, N.-F., et al., \u003cem\u003eSystematic review and meta-analysis of home-based rehabilitation on improving physical function among home-dwelling patients with a stroke.\u003c/em\u003e Archives of physical medicine and rehabilitation, 2020. \u003cstrong\u003e101\u003c/strong\u003e(2): p. 359-373.\u003c/li\u003e\n\u003cli\u003eTenforde, A.S., et al., \u003cem\u003eEvidence-based physiatry: efficacy of home-based telerehabilitation versus in-clinic therapy for adults after stroke.\u003c/em\u003e American Journal of Physical Medicine \u0026amp; Rehabilitation, 2020. \u003cstrong\u003e99\u003c/strong\u003e(8): p. 764-765.\u003c/li\u003e\n\u003cli\u003eLaver, K.E., et al., \u003cem\u003eTelerehabilitation services for stroke.\u003c/em\u003e Cochrane Database of Systematic Reviews, 2020(1).\u003c/li\u003e\n\u003cli\u003eCramer, S.C., et al., \u003cem\u003eEfficacy of home-based telerehabilitation vs in-clinic therapy for adults after stroke: a randomized clinical trial.\u003c/em\u003e JAMA neurology, 2019. \u003cstrong\u003e76\u003c/strong\u003e(9): p. 1079-1087.\u003c/li\u003e\n\u003cli\u003eDonoso Brown, E.V., et al., \u003cem\u003eHome program practices for supporting and measuring adherence in post-stroke rehabilitation: a scoping review.\u003c/em\u003e Topics in Stroke Rehabilitation, 2020. \u003cstrong\u003e27\u003c/strong\u003e(5): p. 377-400.\u003c/li\u003e\n\u003cli\u003eRimmer, J.H., E. Wang, and D. 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Drummond, and P. Standen, \u003cem\u003eVirtual rehabilitation: what are the practical barriers for home-based research? Digit Health. 2016; 2: 2055207616641302. doi: 10.1177/2055207616641302\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eFluet, G.G., et al., \u003cem\u003eParticipant Adherence to a Video Game-Based Tele-rehabilitation Program: A Mixed-Methods Case Series\u003c/em\u003e, in \u003cem\u003eVirtual Reality in Health and Rehabilitation\u003c/em\u003e. 2020, CRC Press. p. 169-184.\u003c/li\u003e\n\u003cli\u003eTyagi, S., et al., \u003cem\u003eAcceptance of tele-rehabilitation by stroke patients: perceived barriers and facilitators.\u003c/em\u003e Archives of physical medicine and rehabilitation, 2018. \u003cstrong\u003e99\u003c/strong\u003e(12): p. 2472-2477. e2.\u003c/li\u003e\n\u003cli\u003eLemke, M., et al., \u003cem\u003eMotivators and barriers to using information and communication technology in everyday life following stroke: a qualitative and video observation study.\u003c/em\u003e Disability and Rehabilitation, 2020. \u003cstrong\u003e42\u003c/strong\u003e(14): p. 1954-1962.\u003c/li\u003e\n\u003cli\u003eWhittaker, L., R. Russell‐Bennett, and R. Mulcahy, \u003cem\u003eReward‐based or meaningful gaming? A field study on game mechanics and serious games for sustainability.\u003c/em\u003e Psychology \u0026amp; Marketing, 2021. \u003cstrong\u003e38\u003c/strong\u003e(6): p. 981-1000.\u003c/li\u003e\n\u003cli\u003eCharles, D., et al., \u003cem\u003eVirtual reality design for stroke rehabilitation.\u003c/em\u003e Biomedical Visualisation: Volume 6, 2020: p. 53-87.\u003c/li\u003e\n\u003cli\u003eOyake, K., et al., \u003cem\u003eMotivational strategies for stroke rehabilitation: a Delphi study.\u003c/em\u003e Archives of Physical Medicine and Rehabilitation, 2020. \u003cstrong\u003e101\u003c/strong\u003e(11): p. 1929-1936.\u003c/li\u003e\n\u003cli\u003eGangwani, R., et al., \u003cem\u003eLeveraging factors of self-efficacy and motivation to optimize stroke recovery.\u003c/em\u003e Frontiers in Neurology, 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 823202.\u003c/li\u003e\n\u003cli\u003eZahabi, M. and A.M. Abdul Razak, \u003cem\u003eAdaptive virtual reality-based training: a systematic literature review and framework.\u003c/em\u003e Virtual Reality, 2020. \u003cstrong\u003e24\u003c/strong\u003e(4): p. 725-752.\u003c/li\u003e\n\u003cli\u003eFluet, G.G., et al., \u003cem\u003eMotor skill changes and neurophysiologic adaptation to recovery-oriented virtual rehabilitation of hand function in a person with subacute stroke: a case study.\u003c/em\u003e Disability and rehabilitation, 2017. \u003cstrong\u003e39\u003c/strong\u003e(15): p. 1524-1531.\u003c/li\u003e\n\u003cli\u003eNasreddine, Z.S., et al., \u003cem\u003eThe Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.\u003c/em\u003e Journal of the American Geriatrics Society, 2005. \u003cstrong\u003e53\u003c/strong\u003e(4): p. 695-699.\u003c/li\u003e\n\u003cli\u003eDeakin, A., H. Hill, and V.M. Pomeroy, \u003cem\u003eRough guide to the Fugl-Meyer Assessment: upper limb section.\u003c/em\u003e Physiotherapy, 2003. \u003cstrong\u003e89\u003c/strong\u003e(12): p. 751-763.\u003c/li\u003e\n\u003cli\u003eMcAuley, E., T. Duncan, and V.V. Tammen, \u003cem\u003ePsychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: A confirmatory factor analysis.\u003c/em\u003e Research quarterly for exercise and sport, 1989. \u003cstrong\u003e60\u003c/strong\u003e(1): p. 48-58.\u003c/li\u003e\n\u003cli\u003eYozbatiran, N., L. Der-Yeghiaian, and S.C. Cramer, \u003cem\u003eA standardized approach to performing the action research arm test.\u003c/em\u003e Neurorehabilitation and neural repair, 2008. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 78-90.\u003c/li\u003e\n\u003cli\u003eDuncan, P., et al., \u003cem\u003eStroke Impact Scale-16: A brief assessment of physical function.\u003c/em\u003e Neurology, 2003. \u003cstrong\u003e60\u003c/strong\u003e(2): p. 291-296.\u003c/li\u003e\n\u003cli\u003eCleophas, T.J., et al., \u003cem\u003eRegression trees: classification and regression tree (CART) models.\u003c/em\u003e Regression Analysis in Medical Research: for Starters and 2nd Levelers, 2021: p. 383-391.\u003c/li\u003e\n\u003cli\u003ePage, S.J., G.D. Fulk, and P. Boyne, \u003cem\u003eClinically important differences for the upper-extremity Fugl-Meyer Scale in people with minimal to moderate impairment due to chronic stroke.\u003c/em\u003e Physical therapy, 2012. \u003cstrong\u003e92\u003c/strong\u003e(6): p. 791-798.\u003c/li\u003e\n\u003cli\u003eLang, C.E., et al., \u003cem\u003eEstimating minimal clinically important differences of upper-extremity measures early after stroke.\u003c/em\u003e Archives of physical medicine and rehabilitation, 2008. \u003cstrong\u003e89\u003c/strong\u003e(9): p. 1693-1700.\u003c/li\u003e\n\u003cli\u003eClark, B., et al., \u003cem\u003eThe effect of time spent in rehabilitation on activity limitation and impairment after stroke.\u003c/em\u003e Cochrane Database of Systematic Reviews, 2021(10).\u003c/li\u003e\n\u003cli\u003eSchneider, E.J., et al., \u003cem\u003eIncreasing the amount of usual rehabilitation improves activity after stroke: a systematic review.\u003c/em\u003e Journal of physiotherapy, 2016. \u003cstrong\u003e62\u003c/strong\u003e(4): p. 182-187.\u003c/li\u003e\n\u003cli\u003eLang, C.E., et al., \u003cem\u003eDose response of task‐specific upper limb training in people at least 6 months poststroke: a phase II, single‐blind, randomized, controlled trial.\u003c/em\u003e Annals of neurology, 2016. \u003cstrong\u003e80\u003c/strong\u003e(3): p. 342-354.\u003c/li\u003e\n\u003cli\u003eKleim, J.A. and T.A. Jones, \u003cem\u003ePrinciples of experience-dependent neural plasticity: implications for rehabilitation after brain damage.\u003c/em\u003e 2008.\u003c/li\u003e\n\u003cli\u003eLang, S., et al., \u003cem\u003eDo digital interventions increase adherence to home exercise rehabilitation? A systematic review of randomised controlled trials.\u003c/em\u003e Archives of physiotherapy, 2022. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 24.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"serious games, rehabilitation, hand, arm, telerehabilitation, stroke","lastPublishedDoi":"10.21203/rs.3.rs-4438077/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4438077/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/u\u003e\u003cstrong\u003e \u003c/strong\u003eThis parallel, randomized controlled trial examines intrinsic motivation, adherence and motor function improvement demonstrated by two groups of subjects that performed a twelve-week, home-based upper extremity rehabilitation program. Seventeen subjects played games presenting eight to twelve discrete levels of increasing difficulty. Sixteen subjects performed the same activities controlled by success algorithms that modify game difficulty incrementally.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/u\u003e\u003cstrong\u003e \u003c/strong\u003e33 persons 20 to 80 years of age, at least six months post stroke with moderate to mild hemiparesis were randomized using a random number generator into the two groups. They were tested using the Action Research Arm Test, Upper Extremity Fugl Meyer Assessment, Stroke Impact Scale and Intrinsic Motivation Inventory pre and post training. Adherence was measured using timestamps generated by the system. Subjects had the Home Virtual Rehabilitation System [1]systems placed in their homes and were taught to perform rehabilitation games using it. Subjects were instructed to train twenty minutes per day but were allowed to train as much as they chose. Subjects trained for twelve weeks without appointments and received intermittent support from study staff. Group outcomes were compared using ANOVA. Correlations between subject demographics and adherence, as well as motor outcome, were evaluated using Pearson Correlation Coefficients. Classification and Regression Tree (CART) models were generated to predict responders using demographics and baseline measures. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/u\u003e There were 5 dropouts and no adverse events. The main effect of time was statistically significant for four of the five clinical outcome measures. There were no significant training group by time interactions. Measures of adherence did not differ between groups. 21 subjects from both groups, demonstrated clinically important improvements in UEFMA score of at least 4.25 points. Subjects with pre training UEFMA scores below 53.5 averaged a seven-point UEFMA increase. IMI scores were stable pre to post training.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/u\u003e\u003cstrong\u003e \u003c/strong\u003eScaffolding did not have a meaningful impact on adherence or motor function improvement. A sparsely supervised program of game-based treatment in the home was sufficient to elicit meaningful improvements in motor function and activities of daily living. Common factors considered barriers to the utilization of telerehabilitation did not impact adherence or motor outcome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e\u003c/u\u003e Clinical Trials.gov - NCT03985761, Registered June 14, 2019.\u003c/p\u003e","manuscriptTitle":"The influence of scaffolding on intrinsic motivation and autonomous adherence to a game-based, unsupervised home rehabilitation program for people with upper extremity hemiparesis due to stroke. A randomized controlled trial.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 08:00:44","doi":"10.21203/rs.3.rs-4438077/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-13T17:18:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-13T14:59:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253295998514884620980326783954232825042","date":"2024-06-11T20:12:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-09T18:26:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44377694864547044560603415416196334057","date":"2024-06-06T15:42:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250605375948041305789590655102795929705","date":"2024-06-03T17:19:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223770677172072471137204676507021437666","date":"2024-06-03T17:03:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252295492783703125706109195237207453263","date":"2024-05-30T08:37:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-28T11:14:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-27T09:07:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-26T22:50:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2024-05-17T17:20:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4061875a-e6e7-4070-81b9-db6dd112bab8","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T19:37:58+00:00","versionOfRecord":{"articleIdentity":"rs-4438077","link":"https://doi.org/10.1186/s12984-024-01441-7","journal":{"identity":"journal-of-neuroengineering-and-rehabilitation","isVorOnly":false,"title":"Journal of NeuroEngineering and Rehabilitation"},"publishedOn":"2024-08-13 15:58:16","publishedOnDateReadable":"August 13th, 2024"},"versionCreatedAt":"2024-06-07 08:00:44","video":"","vorDoi":"10.1186/s12984-024-01441-7","vorDoiUrl":"https://doi.org/10.1186/s12984-024-01441-7","workflowStages":[]},"version":"v1","identity":"rs-4438077","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4438077","identity":"rs-4438077","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}