Study Results
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Basic Information
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TERMINATED
NA
6 participants
INTERVENTIONAL
2021-05-03
2023-08-31
Brief Summary
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Detailed Description
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Power analysis was performed with the SIMR package in R which estimates power for generalized linear mixed models using Monte Carlo simulations. The main analysis will compare the evolution through time of the primary outcome variable between the two within-subject conditions placebo and active intervention. Power was estimated as a function of the number of patients who complete the entire study protocol and as a function of the number of assessment moments per patient. In addition, the power analysis was run under the assumption that the measurement error (residual variance) would be equal to 0.20 SDs. The latter implies that the outcome variable must have a reliability of at least .80. The power analysis revealed that 8 patients need to complete the entire study protocol (per-protocol sample size) - when the study protocol involves a 1-day in-between assessment schedule - to detect a moderate effect size (SD = 0.5) with a type I error rate of 1% and a power of 80%. Thus, for each counterbalancing group a minimum of 4 patients is needed. Assuming that 50% of all patients allocated to a counterbalancing group drop-out at some time point during the study, a total of 16 patients will be recruited to obtain a large enough per-protocol sample size.
MISSING DATA HANDLING:
Missing data can occur when patients do not take part in one or more visits throughout the study protocol (non-monotonous missing data) or when patients drop-out from the study and there is no data available of a patient after drop-out (monotonous missing data). The frequency of occurrence of these two types of missing data will be reported. If inconsistent data occurs on an individual level this will not be considered to be missing data. Out-of-range results for most behavioral outcomes are not likely to occur because computerized assessment tasks guarantee accurate data acquisition. For the behavioral observation scale the inter-rater reliability will be evaluated as a quality check. If the inter-rater reliability across all assessments made in the context of the study is lower than .70 this measure will be reported as insufficiently reliable to be used as a meaningful outcome variable. Only eye tracking data that was sufficiently accurately measured will be considered to be used as an outcome measure. Meaning that, if eye tracker calibration is not good to excellent according to the software delivered with the eye tracker after 5 repeated calibrations the eye tracking data for that assessment will be considered as missing data.
STATISTICAL ANALYSIS:
1. MAIN ANALYSIS: The data will be analyzed using Bayesian mixed models in R. Mixed models are the recommended approach to combine data of single cases and are increasingly acknowledged as a more powerful data analysis approach for clinical trials compared to classic ANCOVAs since mixed models can accurately model time-unstructured data. A Bayesian approach to analyze data is preferred above a classic null hypothesis significance testing because the Bayesian approach allows to quantify the strength of evidence in favor of the null hypothesis. The latter is a valuable attribute in the context of clinical trials as these studies often require proof for no difference between groups on covariates that can be assumed to affect response to treatment.
The main analysis of interest will compare the effect of the within-subject conditions placebo and active intervention. The model to estimate this effect will include the main effect of time since start of intervention condition, intervention and the counterbalancing group. In addition, the pairwise and three-way interactions of these predictors will be included. A random intercept and random slope for time will be included in the model. This model will be used to predict the primary outcome variable and the secondary outcome variables.
In addition, the association between the different outcome variables will be reported as a means to estimate to what extent treatment effects may have affected 1 specific outcome or to what extent symptom evolution across different outcome variables was associated.
2. EXPLORATORY ANALYSES: In addition to these analyses, the experience of patients with the VR game based intervention will also be reported. The vocal responses made by patients during gameplay will be rated by two independent raters as expressions of negative or positive emotions. The number of negative and the number of positive expressions relative to the total number of expressions will be compared to each other. If the proportion of positive expressions is higher than the proportion of negative expressions this is taken as evidence suggesting that the patients had a positive experience with the game and vice versa. In addition, since not all patients will spontaneously make vocal responses during gameplay, the mean score of patients on the questionnaire that gauges their experience with the VR-game based intervention will be reported. Given the exploratory nature of this part of the study descriptive statistics will be reported, but no statistical analysis on this outcome variable will be performed. The results of the safety checklist will also be reported. These data are valuable since it can inform other researchers on whether VR is safe to use within the stroke population. These data will be reported in the form of descriptive statistics. All exploratory analyses will be performed on the intention-to-treat sample.
3. SIGNFICANCE LEVEL: The Bayes Factors will be interpreted according to the following interpretation rule: a Bayes Factor of larger than 3.2 suggests substantial evidence in favor of the alternative model, a Bayes Factor larger than 10 suggests strong evidence in favor of the alternative model and a Bayes Factor larger than 100 is decisive for the alternative model. All effects will be evaluated against a threshold of a Bayes Factor of 10. Bayes Factors that are in between 1/10 and 10 will be interpreted as inconclusive evidence. Evaluating effects at a threshold of a Bayes Factor of 10 is comparable to the approach of evaluating effects at a significance level of .01. The primary outcome variable and 5 secondary outcome variables should lead to a maximum Type I error rate of 6% in a worst-case scenario where all 6 outcome variables are completely uncorrelated. This type I error rate is obtained through the formula: 100 \[1- (1- α)\^k \] where α stands for the significance level and k stands for the number of independent measures.
Conditions
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Study Design
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RANDOMIZED
CROSSOVER
TREATMENT
DOUBLE
Study Groups
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Group A
In period 1 group A will receive the active intervention and in period 2 they will receive the placebo intervention.
Active Intervention
An audiovisual expanding (looming) stimulus is presented repeatedly to patients during the intervention (Dent \& Humphreys, 2011). During the game a disk is presented to the player. This disk expands and contracts in size. The presentation of the disk coincides with the presentation of a sound that matches in frequency. The disk predicts the location where the next target will be presented. The player must discriminate between two types of target stimuli that are presented at the center of the disk. To discriminate between the two targets, the player receives a limited time window. The location of the disk and target stimuli are adjusted in real-time as a function of the player's performance. The primary goal of this algorithm is to present the multisensory looming stimuli more frequently in the contralesional field than in the ipsilesional field.
Placebo Intervention
The active and placebo intervention are identical in all aspects except for the fact that stimulus presentation will be located in the center of the visual field.
Group B
In period 1 group B will receive the placebo intervention and in period 2 they will receive the active intervention.
Active Intervention
An audiovisual expanding (looming) stimulus is presented repeatedly to patients during the intervention (Dent \& Humphreys, 2011). During the game a disk is presented to the player. This disk expands and contracts in size. The presentation of the disk coincides with the presentation of a sound that matches in frequency. The disk predicts the location where the next target will be presented. The player must discriminate between two types of target stimuli that are presented at the center of the disk. To discriminate between the two targets, the player receives a limited time window. The location of the disk and target stimuli are adjusted in real-time as a function of the player's performance. The primary goal of this algorithm is to present the multisensory looming stimuli more frequently in the contralesional field than in the ipsilesional field.
Placebo Intervention
The active and placebo intervention are identical in all aspects except for the fact that stimulus presentation will be located in the center of the visual field.
Interventions
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Active Intervention
An audiovisual expanding (looming) stimulus is presented repeatedly to patients during the intervention (Dent \& Humphreys, 2011). During the game a disk is presented to the player. This disk expands and contracts in size. The presentation of the disk coincides with the presentation of a sound that matches in frequency. The disk predicts the location where the next target will be presented. The player must discriminate between two types of target stimuli that are presented at the center of the disk. To discriminate between the two targets, the player receives a limited time window. The location of the disk and target stimuli are adjusted in real-time as a function of the player's performance. The primary goal of this algorithm is to present the multisensory looming stimuli more frequently in the contralesional field than in the ipsilesional field.
Placebo Intervention
The active and placebo intervention are identical in all aspects except for the fact that stimulus presentation will be located in the center of the visual field.
Eligibility Criteria
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Inclusion Criteria
* They have had a stroke.
Exclusion Criteria
* They have a severe comorbid psychiatric (E.g. psychotic symptoms) disorder.
* They have a premorbid neurodegenerative disease (E.g. Alzheimer's dementia, vascular dementia).
* They have severe written language comprehension deficits.
* They have a medical implant, such as a cochlear implant or a pacemaker.
* They have a severe visual or auditory impairment that cannot be corrected for by wearing glasses or a hearing aid while wearing the Oculus Rift headset.
* They are unable to concentrate on a task for more than 15 minutes or are unable to complete a task according to simple task instructions.
* They have a history of epileptic seizures.
* They do not show signs of a spatial asymmetry in performance on a battery of screening tasks.
* The expected discharge of patients is in a period shorter than 7 weeks.
18 Years
ALL
No
Sponsors
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KU Leuven
OTHER
Responsible Party
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Céline Gillebert
Prof. Dr.
Principal Investigators
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Céline Gillebert, Prof. Dr.
Role: PRINCIPAL_INVESTIGATOR
KU Leuven
Locations
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RevArte
Edegem, Antwerp, Belgium
University Hospital Leuven Pellenberg
Leuven, Vlaams Brabant, Belgium
Countries
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References
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Andersen SW, Millen BA. On the practical application of mixed effects models for repeated measures to clinical trial data. Pharm Stat. 2013 Jan-Feb;12(1):7-16. doi: 10.1002/pst.1548. Epub 2012 Dec 13.
Azouvi P, Olivier S, de Montety G, Samuel C, Louis-Dreyfus A, Tesio L. Behavioral assessment of unilateral neglect: study of the psychometric properties of the Catherine Bergego Scale. Arch Phys Med Rehabil. 2003 Jan;84(1):51-7. doi: 10.1053/apmr.2003.50062.
Dent K, Humphreys GW. Neuropsychological evidence for a competitive bias against contracting stimuli. Neurocase. 2011;17(2):112-21. doi: 10.1080/13554794.2010.498381. Epub 2010 Sep 1.
Green, P., MacLeod, CJ. SIMR: an R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4): 493-498, 2016.
Kass, RE., Raftery, AE. Bayes Factors. Journal of the Americal Statistical Association, 90(430): 773-795, 1995.
Nijboer TC, Kollen BJ, Kwakkel G. Time course of visuospatial neglect early after stroke: a longitudinal cohort study. Cortex. 2013 Sep;49(8):2021-7. doi: 10.1016/j.cortex.2012.11.006. Epub 2012 Dec 19.
O'Connell RG, Bellgrove MA, Dockree PM, Lau A, Fitzgerald M, Robertson IH. Self-Alert Training: volitional modulation of autonomic arousal improves sustained attention. Neuropsychologia. 2008 Apr;46(5):1379-90. doi: 10.1016/j.neuropsychologia.2007.12.018. Epub 2007 Dec 27.
Rouder JN, Speckman PL, Sun D, Morey RD, Iverson G. Bayesian t tests for accepting and rejecting the null hypothesis. Psychon Bull Rev. 2009 Apr;16(2):225-37. doi: 10.3758/PBR.16.2.225.
Schwamm LH, Koroshetz WJ, Sorensen AG, Wang B, Copen WA, Budzik R, Rordorf G, Buonanno FS, Schaefer PW, Gonzalez RG. Time course of lesion development in patients with acute stroke: serial diffusion- and hemodynamic-weighted magnetic resonance imaging. Stroke. 1998 Nov;29(11):2268-76. doi: 10.1161/01.str.29.11.2268.
Van den Noortgate, W., Onghena, P. Combining Single-Case Experimental Data Using Hierarchical Linear Models. School Psychology Quarterly Fall 2003, 18(3): 325-346, 2003.
Longley V, Hazelton C, Heal C, Pollock A, Woodward-Nutt K, Mitchell C, Pobric G, Vail A, Bowen A. Non-pharmacological interventions for spatial neglect or inattention following stroke and other non-progressive brain injury. Cochrane Database Syst Rev. 2021 Jul 1;7(7):CD003586. doi: 10.1002/14651858.CD003586.pub4.
Provided Documents
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Document Type: Study Protocol and Statistical Analysis Plan
Other Identifiers
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VRAT001
Identifier Type: -
Identifier Source: org_study_id
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