Enriched Environments for Upper Limb Stroke Rehabilitation
Condition: Stroke
MeSH terms: Stroke
Study type: Interventional

Summary: 
      Stroke contributes significantly to the incidence of disabilities, with upper limb (UL) motor
      impairment being especially prevalent. Animal studies suggest that post-stroke motor recovery
      is largely attributable to adaptive plasticity in brain motor areas. While some environmental
      training factors contributing to plastic mechanisms have been identified in animals,
      translation of this knowledge to the clinical setting is insufficient. Optimal recovery may
      be related to both external (e.g., feedback type) and internal factors (e.g., cognitive
      ability, motivation). Clinically feasible methods for training are needed. Use of enriched
      virtual environments (VEs) may provide a way to address these needs. Outcome measures that
      best reflect recovery need to be identified since this is an essential step to evaluate the
      effect of novel training programs for UL motor recovery in stroke.

      The research question is which clinical and kinematic outcome measures best reflect motor
      performance recovery after a targeted upper limb treatment intervention. Aim 1 is to compare
      changes in outcome measures recorded before and after an upper limb intervention in stroke
      subjects to motor performance in healthy subjects. Aim 2 is to determine motor performance
      between-group differences sample size is based on knowledge of expected outcome measure mean
      score differences between groups. Hypothesis. 1: Specific clinical and kinematic outcome
      measures will be sensitive to within-group (pre-post intervention training) changes.
      Hypothesis. 2: Specific clinical and kinematic outcome measures will be sensitive to
      between-group (healthy vs. patients in enriched vs. conventional intervention groups. Sixteen
      chronic stroke survivors and 8 age- and sex-matched healthy controls will participate.
      Patients will be matched on cognitive and motor impairment levels and divided into two
      groups. Using an single subject (A-B-A) research design, kinematics during two pre-tests, 3
      weeks apart, will be recorded for test-retest reliability. Stroke groups will practice varied
      upper limb reaching movements (15 45-minute sessions in 3 weeks) in environments providing
      different motivation/feedback levels. Pre- and post motor performance evaluations will be
      done with clinical tests and a Test Task with specific motor performance requirements. A
      Transfer Task will also be recorded. By comparing data analysis methods (3-Dimensional (3D)
      analysis of different markers or placements), the investigators will identify which kinematic
      outcome measures best reflect motor improvement in post-test and follow-up sessions
      (retention).

      The expected results are identification of two primary and two secondary outcome measures
      that reflect upper limb motor recovery and can distinguish between motor recovery and
      compensation. The results will be used to design a randomized control trial to determine the
      efficacy of VE-based treatment on arm motor recovery. The goal is to determine how extrinsic
      (environmental) and intrinsic (personal) motivational factors affect motor learning in stroke
      survivors with cognitive and physical impairment. Knowledge gained can also be used for
      rehabilitation of other neurological and orthopedic pathologies.
    

Detailed description: 
      A. Scientific Background

      Stroke is the third leading cause of death in Western countries and upper limb (UL)
      sensorimotor dysfunction contributes significantly to the incidence of physical disabilities
      and handicaps (Olsen 1990). One explanation for poor arm recovery is the focus on task
      accomplishment rather than performance quality. This may reinforce alterative (compensatory)
      movement strategies instead of encouraging the reappearance of pre-morbid movement patterns
      (recovery). Although the rehabilitative goal is recovery of function, whether this is
      achieved through true motor recovery or compensation is still under debate. Indeed, for some
      patients with severe impairment, compensation should be encouraged to maximize functional
      ability. Alternatively, for those with good prognosis, motor recovery is emphasized for
      several reasons. First, given appropriate training, recovery can continue well into the
      chronic stage of stroke (e.g., Michaelsen & Levin 2004). Second, while compensation may
      assist immediate performance, it may lead to longer-term problems such as pain and
      contracture (Ada et al. 1994; Levin 1997). Third, permitting compensations could encourage
      learned nonuse limiting the capacity for subsequent motor gains (Allred et al. 2005). While
      performance gains have been documented after repetitive training of isolated movements (e.g.,
      Whitall et al. 2000), few studies have addressed whether patients can use explicit
      information to optimize motor skill acquisition and whether true behavioural recovery occurs.
      Points to consider in developing optimal training programs is that learning occurs when
      participants are motivated, practice a variety of related tasks and are given relevant
      feedback (Nudo & Friel 1999; Winstein et al. 1999). In addition, patients may not benefit
      from variable practice until missing motor elements are recovered (Carr & Shepherd 1987) and
      motor relearning is related to physical and cognitive impairment level (Cirstea et al. 2006).

      To date, most studies have used only clinical measures to evaluate functional change (e.g.,
      Jang et al. 2003) without considering how movement is performed. This study will focus on the
      ability to distinguish between whether functional improvement results from an increase in
      compensation or from true motor recovery. This can only be done by correlating functional
      improvement (clinical measures) with changes in arm motor patterns through detailed movement
      analysis (kinematics). Since there are a large number of possible kinematic indicators of
      improvement, the investigators need to identify which measures are most indicative of change.
      This is a first step in the determination of clinically meaningful outcome measures to be
      used in randomized control trials of the effectiveness of interventions. Clinical salience is
      essential to translate knowledge from research studies to evidence-based practice (van Peppen
      et al. 2007).

      B. Study Design: 1. Detailed Plan of the Study

      Two groups of 8 stroke subjects will participate in an A-B-A design in which data from
      post-intervention (within one week of the completion of the intervention) and follow-up (one
      month after the completion of the intervention)tests will be compared to data from two
      pre-tests (held at beginning and end of the first week of the study just prior to the start
      of the intervention). Multiple clinical and kinematic motor performance outcomes will be
      measured to determine which ones best describe arm motor recovery. This will be done by
      comparing changes in outcomes before and after training to mean scores of 8 age- and
      gender-matched healthy volunteers (recorded in a single session). Stroke survivors will
      practice movement in two different training environments providing different levels of
      motivation and feedback: Training Environment 1 will be created in a 2D video-based VR
      environment that will provide high motivation, Knowledge of Results (KR) about motor outcome
      (i.e., speed, precision) and non-specific Knowledge of Performance (KP) feedback about trunk
      movement. Environment 2 will be created in a physical environment that provides only KP
      feedback but no KR or additional motivation.

      Training Protocol: After baseline evaluations (physical & cognitive), stroke groups will
      practice pointing to 6 standardized targets placed just beyond reach (12 trials per target)
      during an acquisition phase of 15 sessions spaced over three weeks (3 sessions/wk, 72
      trials/session). This practice regimen incorporates elements necessary for optimal motor
      learning: 1) Varied practice: Although the movements are not new motor tasks, they should be
      re-acquired during stroke recovery. Our aim is not teach subjects a novel task but to
      identify how a movement performed sub-optimally may be improved with practice. 2) Intensive
      practice: The number of repetitions per session was chosen according to studies by Cirstea
      and Levin (2000). Thus, 72 trials per session (3 blocks of 24 trials, 5 min rest between
      blocks) are considered as 'intensive' practice, which is a necessary element for a successful
      training program. Following the acquisition phase, evaluations which will be repeated after
      3mos to evaluate retention and transfer of motor skills.

      2. Methods: Subjects Eight stroke subjects per group will be recruited. They will be matched
      for age and for initial arm motor severity (± 5 pts on the Fugl-Meyer Arm Assessment). Eight
      age- and sex-matched healthy subjects will be recruited as a control group.

      Subject Recruitment: Patients will be recruited from Haim Sheba Hospital discharge lists
      using a procedure in place since 2005. Screening will be done by clinical research
      associates. Potential participants meeting inclusion criteria 1-3 and exclusion criteria will
      be sent an explanatory letter inviting them to contact study investigators. Informed consent
      will be obtained from each participant. Control subjects will be recruited from the
      community.

      Training Environments: Motivation and Feedback: Training will be done in the Dept. of
      Occupational Therapy located at the Sheba Medical Center. Training environments will allow us
      to evaluate the effect of combining different degrees of motivation and feedback on motor
      outcome. Environment 1 is highly motivating (novel and fun) since training is presented as a
      game in which the learner tries to beat his own score in subsequent sessions. Targets are
      presented as balls in a video-based VR system (Gesture Xtreme). The patient sits in front of
      a 36 inch display of the 6 target scene. The patient's image is captured by a video camera
      and inserted into the scene displayed on the monitor. The task is to point to each of the
      targets which appear in a random sequence. Feedback will provide pertinent information for
      motor learning (KR, KP, game score). Environment 2 incorporates the same number and
      disposition of targets as Env. 1 but they will be presented in a physical environment on a
      wooden frame in front of the subject. KR and KP feedback will be provided as verbal cues by
      the experimenter as is done usually in the clinic. However, they will be no additional
      motivational information (game score).

      Preliminary Outcome Measures: Clinical Before and after training in both stroke groups,
      blinded evaluators will measure clinical scores of arm motor impairment and function
      (Fugl-Meyer Arm Score, Reaching Performance Scale, Box and Blocks test, Wolf Motor Function
      Test, Motor Activity Log) as well as cognitive function. All tests are valid and reliable and
      are regularly used in clinical practice.

      Kinematic testing: Test Task and Transfer Task:

      The investigators will also record kinematic outcome measures characterizing arm and trunk
      movements during reaching (elbow extension, shoulder horizontal adduction, shoulder flexion,
      scapular movements, trunk displacement).

      Our aim is to focus on segmental and joint kinematics during a Test and a Transfer Task
      consisting of reaching movements. Test Task will be recorded. The Test target will be placed
      in line with the patient's sternum at a distance just beyond the subject's arm length. The
      Test Task is similar to movements made to one of the practiced targets. To assess motor
      learning, the investigators will determine if elements learned in one task transfer to other
      similar tasks. Thus, the investigators will also assess movements made in a Transfer Task, to
      a target placed in front of the ipsilateral shoulder 5 centimeters (cm) higher than the
      topmost row of trained targets. The Transfer Task is therefore a new movement, not practiced
      during the training. Rigid body segmental kinematics will be recorded from sets of 4 passive
      reflective markers (0.5 cm diameter spheres) attached to the trunk, upper arm and forearm
      segments. This will enable the computation of three translational and three rotational
      degrees of freedom per segment. Joint kinematics will be collected from markers fixed on the
      sternum, acromion, elbow and wrist via exo-skeletal frames. Marker motion will be recorded
      with a calibrated 3 camera opto-electronic motion-capture system (ProReflex MCU-240,
      Qualisys) on suitable computer software (Qualisys, Göteborg, Swe). Data collection (100
      Hertz, 2-5 seconds) will be triggered by movement of the hand from a central position by
      release of a mechanical switch. The accuracy of the measurements of each marker is within an
      error of <0.2 cm.

      As a first step, the investigators will determine movement times, accuracy, smoothness,
      segment and joint rotations and the main components of multi-joint coordination. Cartesian
      coordinates (x,y,z) for each segment will be obtained from the segmental marker sets. Raw
      data from at least three markers per segment will be used after interpolation of missing
      markers (5th-order polynomial). For each trial, movement times and peak velocities will be
      determined from endpoint tangential velocity traces. Movement time is the time between the
      point at which the tangential velocity exceeds and remains above 5% of its maximum and then
      returns to and remains below this level. Movement accuracy in terms of constant extent errors
      will be computed as the mean distance (d) between the final endpoint position (x, y, z) and
      the position of the target (x0, y0, z0). Constant and variable (SD) directional errors will
      also be defined. Movement smoothness will be computed from the tangential velocity trace
      using the index of curvature (ratio of endpoint path length to length of a straight line
      joining the initial and final positions) and the number of peaks in the trajectory path. For
      segment and joint rotations, vectors will be calculated within reference coordinate systems
      for the upper and forearm segments.

      From the resulting segment centroid motion and segment rotation data different outcome
      variables will be calculated. The most relevant ones will define changes in performance with
      respect to the endpoint motion and with respect to the whole-arm posture, particularly, the
      evolution of the movement over time (time derivatives) and the fragmentation of the segmental
      path and posture of the arm while moving towards the different targets. Additional aspects of
      movement will be explored in order to find the most appropriate descriptors of arm motor
      performance and changes after the proposed clinical interventions.

      Change scores will be calculated for each subject to determine an Index of Improvement (IP)
      and an Index of Learning (IL). The IP is defined as the change in each variable as a ratio of
      post- to pre-test values. An IP of 1 indicates no change whereas negative and positive values
      indicate that the value decreased or increased respectively. For some variables, positive
      ratios will indicate improvement (endpoint velocity, endpoint smoothness, angle measures,
      phase amplitudes) while for others, improvement will be demonstrated by negative ratios
      (movement time, movement precision). The IL, used to determine if subjects retain
      improvements after training for each follow-up epoch, is defined as the change in the
      variable at retention- compared to post-test. IL values of 1 indicate that the improvement is
      maintained (no change), of negative value that the parameter decreased and of positive value
      that the parameter increased.

      Statistical analysis: For our first aim, changes in outcome measures will be determined by
      comparing movement outcomes before and after the acquisition and retention phases by
      two-repeated measures mixed design ANOVA (MANOVA) and comparing raw means (Time 1, Time 2,
      Time 3, Time 4) and change scores (IP, IL) between groups. The investigators will determine
      which kinematic measures undergo the most change and thus may be most indicative of motor
      recovery (e.g., increase in elbow and/or shoulder movement, decrease in trunk movement) by
      investigating which measures are the most different between training groups. The
      investigators expect changes in the group training in Environment I to be greater than those
      in from the group training in Environment 2.
    

Intervention name(s): conventional occupational therapy; video capture virtual reality
Intervention type(s): Other; Device
Intervention description(s): upper limb exercises; virtual reality delivered uppe limb exercises
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: Single (Participant)
Primary purpose: Treatment
Keywords: stroke; rehabilitation; virtual reality; upper limb