JMIR FORMATIVE RESEARCH

Gehringer et al

Case ReportFeasibility of At-Home Hand Arm Bimanual Intensive Training in
Virtual Reality: Case StudyJames E Gehringer1,2, PhD; Anne Woodruff Jameson2, DPT; Hailey Boyer2, DPT, PCS; Jennifer Konieczny3, OTR/L,
MS; Ryan Thomas1,2, BS; James Pierce III1,2, MS; Andrea B Cunha2, PT, PhD; Sandra Willett2,4, PT, PCS, PhD
1

Virtual Reality Laboratory, Munroe-Meyer Insitute, University of Nebraska Medical Center, Omaha, NE, United States

2

Department of Physical Therapy, Munroe-Meyer Insitute, University of Nebraska Medical Center, Omaha, NE, United States

3

Department of Occupational Therapy, Munroe-Meyer Insitute, University of Nebraska Medical Center, Omaha, NE, United States4

Physical Therapy Program, Department of Kinesiology, Colorado Mesa University, Grand Junction, CO, United States

Corresponding Author:
James E Gehringer, PhD
Virtual Reality Laboratory
Munroe-Meyer Insitute
University of Nebraska Medical Center
985450 Nebraska Medical Center
Omaha, NE, 68198
United States
Phone: 1 4025592562
Email: james.gehringer@unmc.edu

Abstract
This single-participant case study examines the feasibility of using custom virtual reality (VR) gaming software in the homeenvironment for low-dose Hand Arm Bimanual Intensive Training (HABIT). A 10-year-old with right unilateral cerebral palsy
participated in this trial. Fine and gross motor skills as well as personal goals for motor outcomes were assessed before and after
the intervention using the Box and Blocks Test, Nine-Hole Peg Test, and Canadian Occupational Performance Measure. Movement
intensities collected via the VR hardware accelerometers, VR game scores, and task accuracy were recorded via the HABIT-VR
software as indices of motor performance. The child and family were instructed to use the HABIT-VR games twice daily for 30
minutes over a 14-day period and asked to record when they used the system. The child used the system and completed the
14-hour, low-dose HABIT-VR intervention across 22 days. There was no change in Box and Blocks Test and Nine-Hole Peg
Test scores before and after the intervention. Canadian Occupational Performance Measure scores increased but did not reachthe clinically relevant threshold, due to high scores at baseline. Changes in motor task intensities during the use of VR and masteryof the VR bimanual tasks suggested improved motor efficiency. This case study provides preliminary evidence that HABIT-VR
is useful for promoting adherence to HABIT activities and for the maintenance of upper extremity motor skills in the homesetting.
(JMIR Form Res 2024;8:e57588) doi: 10.2196/57588
KEYWORDS
cerebral palsy; HABIT; home intervention; virtual reality; rehabilitation; VR; case study; hand; hands; arm; arms; intensive
training; feasibility; game; games; gaming; hand arm bimanual intensive training; motor; movement; home setting; home-based;child; children; male; males; men; quasi-experimental; parent; parents; intervention; interventions

Introduction
Unilateral cerebral palsy (CP) is a neurological condition thatis present from birth or acquired within the first year of a child’slife [1]. It is characterized by abnormalities of tone, muscle
weakness, sensory or perceptual deficits, and motor
incoordination of the affected side, which can lead to significant
impairments of motor function result. Physical and occupational
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therapy focuses on motor learning tasks that incorporate theintensive, repetitive, and progressively challenging practice ofgoal-oriented functional tasks that improve upper extremity
motor function for children with unilateral CP [2,3].
Hand Arm Bimanual Intensive Training (HABIT) is atherapeutic approach for unilateral CP that focuses on
coordinated, bimanual, and functional movements of the upper
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extremities [4-14]. As a therapy with a robust foundation ofevidence, HABIT uses principles of neuroplasticity and motor
learning or control to enhance hand and arm function in children
with unilateral CP [15]. A high-intensity block of HABIT
therapy, or 30-90 hours within a 2-week period of time, is the
documented “dose” necessary to enhance bimanual ability,
unilateral dexterity, self-care function, and achievement offunctional motor goals post intervention [4-6,11-13,16-20].
Despite its effectiveness, implementation of HABIT is primarily
confined to short-term, intensive camp-like settings [20]. The
structure of these intensive camps presents challenges in theongoing implementation of HABIT due to financial constraints,
therapists’ availability, scheduling time with children or families,
and transportation barriers [8-10]. Further, the positive effects
of HABIT, particularly in improving the functional use of theaffected hand and arm, persist only 3 months postintervention,then decline by 6 months post intervention [21]. Given the
decline in functional abilities post-HABIT intensives, innovative
home-based strategies are essential to help children withunilateral CP access interventions like HABIT and maintainfunctional motor gains.
Virtual reality (VR) is a therapeutic tool poised to transform
interventions like HABIT. Offering an immersive, engaging
environment that replicates real-life scenarios for functional
movement, VR provides an interactive and motivating “gaming”
environment that potentially enhances adherence and access totherapeutic interventions. Multiple studies demonstrate theeffectiveness of VR games in improving gross motor function,balance, gait, and reaching kinematics in children with CP[22-27]. Our interdisciplinary team developed HABIT-VR, atherapeutic VR software that incorporates motor learning
principles and gamifies bimanual upper extremity HABIT tasks
into portable environments that are scalable to individual ability
levels [12].
This innovation has several advantages over traditional HABIT
camp: (1) it gamifies therapy thus potentially enhancing client
motivation and interest; (2) it addresses potential frustration or
fatigue through automated task scaling, for example, making
the task harder or easier based on the participant’s ongoing
successes or failures; and (3) it is readily available for use in
the home environment. The ultimate goal of this home-based
HABIT-VR is to create an optimal motor challenge.
Modifications to task-specific parameters, such as accuracy or
speed, precisely scale the challenge point, and, in theory, foster
both increased motor learning and task engagement [28-31].
While early investigations suggest HABIT-VR’s success when
integrated with traditional HABIT treatment, further explorationis crucial to determine its feasibility as a remote therapy tool
[12].
Past attempts at therapy gamification have used commercialized
video games [32-36]. Such games do not have the customizationneeded to scale the game to the child’s unique motor ability.

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Gehringer et al
Accessibility options change difficulty levels globally, when it
may be more appropriate to scale a specific challenging aspect.
This leads to games that are either too difficult or too simple topromote the child’s interest [37]. Motor learning principles
essential to the success of HABIT, such as scaling of taskdifficulty, intrinsic and extrinsic motivators, and variable motor
practice, are important for positive therapeutic outcomes. Most
gamified therapies to date have not integrated these features[37]. Further, for a study examining the feasibility of use athome, these motor learning features need to be automaticallyscaled based on performance, to allow for the maintenance ofan optimal challenge point, sustained participant use, anddynamic task constraint variations [37]. HABIT-VR aims toaddress these issues through patient performance tracking, allwithout requiring in-person intervention.
The purpose of this case study is to present the outcomes of a
10-year-old child with right unilateral CP who participated in
low-dose, one-game HABIT-VR in their home following
discharge from a conventional physical therapy plan of care.
We hypothesized that the use of HABIT-VR in the homeenvironment would lead to improvements in upper-extremity
motor performance and an individual’s perceived occupational
performance. Secondarily, this investigation explores ways toanalyze upper extremity activity using actigraphy data (ie, awearable device used to measure physical activity levels andrest cycles over time) to better understand changes in upper
extremity motor control while using VR.

Methods
Ethical Considerations
The Institutional Review Board at the University of Nebraska
Medical Center reviewed and approved the protocol(0491-20-EP) for this investigation. The procedures followed
were in accordance with the ethical standards of the responsible
committee on human experimentation and with the Helsinki
Declaration of 1975, as revised in 2000. The participant and his
guardian provided informed assent and consent, respectively.

Participant
The child (“M”) in this study was a 10-year-old male withunilateral CP, left hand dominant, who was classified as Gross
Motor Function Classification System level 1, Manual Ability
Classification System level 1. “M” had just been discharged
from an episode of outpatient PT services. “M” had prior video
game experience and tried HABIT-VR games periodically toprovide developers with end user perceptions and suggestions
for games.

HABIT-VR Development for Home UseThis investigation strategically automated various game features
to align with critical motor learning principles and enhance thehome-based use of a single HABIT-VR game (Figure 1).

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Figure 1. Hand Arm Bimanual Intensive Training virtual reality (HABIT-VR) single game. (A) The participants played a single HABIT-VR gamethat tasked them with using rockets to fire at robots. (B) Rockets were activated by touching the back of the rocket while holding the rocket with theother hand. Rockets would only fire when one hand was holding the rocket and the other was touching the back. (C) Power-ups would show up
occasionally, in this image the green orbs with stars in them, which were designed to encourage the user. (D) Once the user beat their high score, theboss battle would begin.

The HABIT approach provides intensive, child-focused
repetitive practice by offering a structured practice embeddedin play and functional activities requiring the use of both hands
while maintaining a child-friendly approach [6]. It uses key
principles of motor learning and neuroplasticity such as practicespecificity, types of practice, feedback, repetition, increasing
movement complexity, and motivation [11]. These key
principles were integrated into the HABIT-VR game. The gamegradually increases in complexity, requiring children to use
both hands repetitively and in progressively challenging ways.
By incorporating tasks such as retrieving rockets and hitting
targets, we ensure that the activities are engaging and needbimanual use. The game was also designed to create a fun andimmersive experience that would keep children motivated andengaged while practicing repetitive bimanual activities
Specifically, in this single HABIT-VR game, the player is tasked
with shooting robots with rockets (Figure 1A). The player holds
the Meta Quest controller to move the VR hands in 3D space.
The position and rotation of the controller are tracked via
infrared cameras embedded in the Meta Quest VR headset. The
player must pick up a rocket, by moving one hand and controller
over the rocket’s location and pressing a controller trigger. Oncethe trigger is pressed and held, an activation point appears at
the back of the rocket (Figure 1B). Using the hand and controller
pair that is not holding the rocket, the player can move over thelocation of the activation point to launch the rocket. The rocket
then flies linearly in the forward direction. If the rocket makes
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contact with a robot after it is fired, a point is added to the score.
The long-term task in this game is to beat a target score, whichis the player’s high score plus 5%. If the target score is beaten,
then the boss battle is activated, which is a single larger robotthat needs to be hit multiple times in quick succession (Figure1D). All these activities are to be performed within 30 minutes,
and if 30 minutes pass, the game exits automatically.
Task demands were graded to allow for success, and difficulty
was progressed with the child’s performance improved byrequiring greater speed or accuracy and based on our previous
experience [12]. By incorporating automated difficulty scaling,
motivators, and variable practice elements, the investigation
aimed to maintain optimal challenge points, sustained participant
use, and diversified task constraints without necessitatingin-person intervention [37]. This approach not only ensures amore engaging and effective home-based intervention but also
holds promise for broader applications in motor learning
contexts. Finally, remote data monitoring of hand and arm
function through 3D accelerometers in the VR hardware wasadded to explore the use of actigraphy as a measure of
movement characteristics. Actigraphy uses accelerometer-based
devices to monitor and quantify physical activity levels andmovement patterns over time. By wearing an actigraph device,
individuals can track their daily activity levels, including
intensity of movement, duration of physical activity, and periodsof rest or sedentary behavior. The device records movement
data continuously providing a detailed overview of activity
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patterns throughout the day and night. Overall, actigraphy inthe context of movement analysis offers a noninvasive andobjective method for monitoring physical activity and movement
patterns, which can be valuable for various research, clinical,
and rehabilitation purposes.
The automation of difficulty scaling was achieved throughreal-time participant performance tracking. Each attempt at thedesignated task, launching a rocket, was recorded by thesoftware. Successful rocket launches, resulting in hits on target
robots, were categorized as successes, whereas instances where
rockets were fired but targets were not hit were considered
failures. The software then analyzed the success rate based on
the last set of attempts within a specific time frame. If theparticipant’s success rate exceeded a predefined threshold(60%), a global increase in difficulty level was initiated.
Consequently, the speed of the target was increased, and its size
was reduced, thereby enhancing the demands on visual-motor
tracking, timing, and motor skills. Conversely, if the successrate fell below another predefined threshold (30%), the global
difficulty level was decreased, resulting in slower targets withlarger sizes. The game encompassed 5 distinct difficulty levels,with each session commencing at level 1. The adjustments intarget speed and size between difficulty levels were governedby predefined percentages (25% for speed and 10% for size).
It is noteworthy that these difficulty levels were designed toensure that targets remained within the realm of achievability,
thus mitigating the potential for participant discouragement
arising from unachievable challenges [38-41].
Simple motivators were incorporated into the game in the formof a high-score tracker, a target score mechanism, and rocket
power-ups. The high-score tracker and target score served adual purpose: providing participants with specific performance
objectives and triggering in-game events. The game perpetually
tracked the participant’s high score and set the target score at alevel 5% higher than the current high score. This design strategy
consistently encouraged participants to strive for improved
performance. To further incentivize participants to attain thetarget score, the culmination of the game, symbolized by the“boss battle,” was unlocked exclusively upon reaching the target
score. The ensuing boss battle will be discussed in greater detaillater. Additionally, rocket power-ups were introduced to thegame, offering 4 unique enhancements: bigger rockets, target
freeze, rapid-fire, and triple rockets. These power-ups were
integrated to align with motor learning and neuroplasticity
principles by supporting the value of novel or surprise-driven
learning experiences [42,43]. Power-ups were randomized
within the game environment, requiring the participant to hit
them with rockets to activate their benefits. In addition to theirunexpected nature, these power-ups were designed to encourage
curiosity and exploration, thereby serving as additional
motivational factors during the early phases of the game.
Variable practice was instilled within the game through the useof the “boss battle” scenario and the associated power-ups.
While the standard targets necessitated a single rocket hit andmoved along all 3 dimensions, the “boss battle” featured asolitary, sizable target that exclusively moved horizontallywithin the participant’s field of view. However, this boss target
demanded a rapid succession of 7 rocket hits for successful
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Gehringer et alcompletion. Maintaining momentum was crucial; a rocket hitting
the boss within a 3-second interval of the preceding hit preserved
progress, but intervals exceeding 3 seconds necessitated a restart.
This design shift emphasized the speed of rocket firing, diverting
focus from target accuracy and precision. Additionally, thepower-ups, denoted by stars, introduced variable practice as
they were activated by hitting smaller stationary targets.
Although hitting these stationary targets required higher
accuracy, the absence of movement obviated the extensive
reliance on visuo-motor tracking skills.
HABIT-VR may also hold significant potential for providing
meaningful and quantifiable feedback on changes in the user’s
hand or arm motor function, both to the user and remotely tothe clinician. Recent endeavors have used actigraphy toobjectively quantify motor changes [44-46] and have been used
to measure changes in upper extremity use in children withunilateral CP [16]. Importantly, these measures could be
integrated within HABIT-VR, as the data necessary for theircalculation can be collected through the sensors in the VR
headset. The integration of these measures presents a valuable
opportunity for objective evaluation of changes in hand andarm function, as these measures could be reported back totreatment teams remotely to quantify changes, enhancing boththe clinical relevance and applicability of HABIT-VR intherapeutic home settings.

Motor Function Assessment
A case study design was used. The participant attended 3
baseline (Phase A) assessment sessions, each spaced a week
apart, starting 3 weeks before the onset of intervention. The
posttraining assessment took place 1 week after the 14-hourtreatment threshold was reached. Pre- and posttraining
assessments included the box and blocks test (BBT), and theNine-hole peg test (NHPT). These clinical assessments werechosen to mirror assessments commonly used to evaluate theeffects of HABIT [3,8,16,30,31]. The Canadian Occupational
Performance Measure (COPM) is a semistructured interview
designed to set goals and capture a client’s self-perception ofoccupational performance in the areas of self-care, productivity,and leisure. It has been used to measure self-efficacy in children
with CP, which is an individual’s belief that they can perform
a task and that they have the capacity to change how they
perform to succeed [47]. This has been used to measureself-efficacy in the motor domain in children with CP and in
VR environments VR training is thought to improveself-efficacy in children with CP as it may provide a greater
sense of accomplishment [47].
The BBT was administered as standard, with 2 trials per arm.
The BBT is a tool to measure unilateral gross manual dexterityin children with CP. It has a documented, intraclass correlation
coefficient (ICC) of 0.98, a minimal detectable change of 5.95,
and a minimal clinically important difference ranging from 5.29
to 6.46 [48]. The NHPT was performed with a 50-second time
limit, as defined by the Shirley Ryan Ability Lab [49]. The
NHPT is considered a valid and reliable tool to measure finemanual dexterity in children with CP, with high intrarater
reliability (ICC of 0.94 for the nonaffected side and ICC of 0.96

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for the affected side), a minimal detectable change of 4 s forthe dominant side and 12 s for the nondominant side [50].

Task Performance Measures
Motor learning measures captured through the VR software
included the following: the number of targets hit, which is thegame score, and task accuracy. The software added 1 point to
the game score every time a target was hit. This score wasrecorded and visible to the participant in the game as a motivator
for the participant, to assess skill performance and progression,and for goal setting to maintain a correct challenge point. The
participant was tasked with matching a target score every
session, which was set to 105% of their high score. In addition,
each rocket launched was tracked, so that task accuracy could
be calculated.

Actigraphy Measures
The 3D movement accelerometry data were collected at 120
Hz using the accelerometers in the Meta Quest controllers used
to control the game. These data were collected during thepreassessments, the at-home intervention phase, and the
postassessments. Activity counts through actigraphy wereselected to explore the use of accelerometry measures. These
data were then down-sampled to 30 Hz and reformatted into
the correct CSV format to be processed via Actilife (version
6.13.5; ActiGraph Corporation) and transformed into movement
intensities (activity level per session: light physical activity[LPA] vs moderate to vigorous physical activity [MVPA]). In
Actilife, the standard activity count processing pipeline used
Evenson youth cutoffs. While Evenson youth cutoffs were
established based on neurotypical adults, these cutoffs havebeen validated for children with CP [51,52].

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Intervention
During the intervention phase (Phase B), “M” used the VR
device and played one of the HABIT-VR games at home overa 3 weeks’ time frame. The participant and their parents were
instructed to play the single HABIT-VR game twice a day, 30
minutes per session, for a total of 1 hour per day. They wereasked to do this for 14 consecutive days, for a total of 14 hoursof treatment. This is considered the minimum amount of timeneeded to see motor learning changes [53]. Adherence torecommended dosage was tracked in two ways, (1) with a family
log of dates or times used and (2) with the VR software.

Data Analysis
Analyses were conducted using R (R Foundation for StatisticalComputing), using the Single-Case Data Analyses for Single
and Multiple Baseline Designs (scan) package [54]. Data wereanalyzed according to 3 phases: preintervention, during theintervention, and postintervention. Cohen d was used to quantify
the effect size of changes within game scores, firing rates,
accuracy rates, and movement intensities. Cohen suggested thatd of 0.2 be considered a “small” effect size, 0.5 represents a“medium” effect size, and 0.8 a “large” effect size [55]. The
standard deviation considers variations within individual
participants and can be used to derive an estimate across cases
for a standardized change in level [56].

Results
Adherence to Recommended Intervention Parameters
Total dose was 14 hours across 22 days, with 11 days adheringto the intervention recommendations, 5 days with a half dosage,
and 6 days skipped (Figure 2). These data were reported by boththe HABIT-VR software and the log the participant maintained.

Figure 2. Calendar representation of Hand Arm Bimanual Intensive Training virtual reality (HABIT-VR) use. Each number represents the number ofdays since the participant was given HABIT-VR to use at home. Green days represent days that the participant used HABIT-VR for 1 hour per day.
Yellow days represent days that HABIT-VR was used for half an hour per day. White days represent days that HABIT-VR was not used.

Clinical Measures
COPM goals were established and focused on goals regarding
walking skills and school participation that include
upper-extremity skills. Goal 1 was regarding pain when running,goal 2 focused on participation on their basketball team, goal
3 focused on participation in a physical education class, goal 4

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focused on walking at home without an assistive device, andgoal 5 focused on stair climbing speed. Preintervention, theaverage COPM performance and satisfaction scores were 7.8(SD 1.2) and 7.4 (SD 1.4), respectively (Table 1).
Postintervention, the average COPM performance andsatisfaction scores were 9.4 (SD 0.8) and 9 (SD 1.3)
respectively.

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Table 1. Canadian Occupational Performance Measure scores. Each row is an individual skill the participant wanted to work on. Scores in the firstcolumns are preintervention, while scores in the second columns are postintervention scores.
Performance

Perception

Preintervention

Postintervention

Preintervention

Postintervention

Goal 1

8

10

7

10

Goal 26

8

5

7

Goal 379

8

8

Goal 4

9

10

9

10

Goal 5

9

10

8

10

Box and Blocks
For the dominant hand, the average box and blocks score during
preassessment was 60.5 (SD 2.6) blocks; postassessment, 65

blocks (Table 2). For the nondominant hand, the preassessment
average was 50.8 (SD 2.1) blocks; postassessment, 51.5 blocks(Table 2).

Table 2. Box and Blocks scores pre- and postintervention scores. The preintervention data represent the average of the 3 preintervention scores. The
postintervention score was the 1 postsession score. Both tests were performed with the dominant and nondominant hand. The measurement is the numberof blocks moved.
Preintervention, mean (SD)

Postintervention

Dominant

60.5 (2.6)

65

Nondominant

50.8 (2.1)

51.5

Nine Hole Peg Test
For the dominant hand, the average 9-hole peg test score forthe preassessment was 0.91 (SD 0.03) pegs/second;

postassessment, 0.92 pegs/second (Table 3). For the
nondominant hand, the preassessment average was 0.57 (SD0.05) pegs/second; postassessment, 0.62 pegs/second (Table 3).

Table 3. Nine Hole Peg test scores before and after the intervention. The preintervention data represent the average of the 3 preintervention scores.
The postintervention score was the 1 postsession score. Both tests were performed with the dominant and nondominant hand. The measurement is thenumber of pegs placed per second.
Preintervention, mean (SD)

Postintervention

Dominant

0.91 (0.03)

0.92

Nondominant

0.57 (0.05)

0.62

Task Performancethe postassessment success rate was 87.6%, with an effect size(d) of 1.17; large effect (Figure 3).

The average preintervention task success rate was 67.3% (SD
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Figure 3. Task accuracy when using Hand Arm Bimanual Intensive Training virtual reality (HABIT-VR). This graph represents the percentage ofrockets fired when using HABIT-VR that hit a target. The y-axis represents the percentage of rockets fired that hit a target. The x-axis represents each
day the participant used HABIT-VR including the pre- and postintervention assessments. Days circled in red represent the days where the participant
only used HABIT-VR for half an hour. Blank days are the days the participant did not use HABIT-VR. The red line represents the trend of the values
for the respective phase.

The average preintervention number of attempts was 728.3 (SD82.1) shots, the intervention average was 837.0 (SD 74.0) shots,

and the postassessment number of attempts was 1020.0 shots,with an effect size (d) 1.39; large effect (Figure 4).

Figure 4. Task attempts when using Hand Arm Bimanual Intensive Training virtual reality (HABIT-VR). This graph represents the number of rockets
fired when using HABIT-VR. The y-axis represents the absolute number of rockets fired. The x-axis represents each day the participant used HABIT-VR
including the pre- and postintervention assessments. Days circled in red represent the days where the participant only used HABIT-VR for half an hour.
Blank days are the days the participant did not use HABIT-VR. The red line represents the trend of the values for the respective phase.

The average preintervention firing rate was 25.1 (SD 3.2) shots
per minute, the intervention average was 29.0 (SD 2.8) shots
per minute, and the postassessment firing rate was 34.0 shots

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per minute, with an effect size of d 1.31; large effect (Figure
5).

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Figure 5. Firing rate when using Hand Arm Bimanual Intensive Training virtual reality (HABIT-VR). This graph represents the rockets fired per minutewhen using HABIT-VR. The y-axis represents the rockets per minute fired. The x-axis represents each day the participant used HABIT-VR including
the pre- and postintervention assessments. Days circled in red represent the days where the participant only used HABIT-VR for half an hour. Blank
days are the days the participant did not use HABIT-VR. The red line represents the trend of the values for the respective phase.

Actigraphyintervention, and 66.1% time in post (Figure 6A). This had aneffect size (d) of 0.649; medium effect.

For the dominant hand, it spent 69.2% (SD 16.7%) time in
MVPA in baseline, 53.1% (SD 27.7%) time in MVPA in
Figure 6. Dominant hand movement intensities when using Hand Arm Bimanual Intensive Training virtual reality (HABIT-VR). This graph represents
the percentage of time the dominant hand spent in (A) moderate to vigorous physical activity and (B) low physical activity when using HABIT-VR.
The y-axis represents the percentage of time in the respective activity level. The x-axis represents each day the participant used HABIT-VR including
the pre- and postintervention assessments. Days circled in red represent the days where the participant only used HABIT-VR for half an hour. Blank
days are the days the participant did not use HABIT-VR. The red line represents the trend of the values for the respective phase. MVPA: moderate tovigorous physical activity.

For the dominant hand, it spent 30.6% (SD 16.6%) time in lightphysical activity at baseline, 46.1% (SD 27.4%) time in lightphysical activity in intervention, and 33.9% time in post (Figure
6B). This had an effect size (d) 0.629; medium effect.

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For the nondominant hand, it spent 14.2% (SD 4.8%) time in
MVPA in baseline, 25.5% (SD 18.5%) time in MVPA inintervention, and 18.8% time in post (Figure 7A). This had aneffect size (d) 0.801; large effect.

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Figure 7. Nondominant hand movement intensities when using Hand Arm Bimanual Intensive Training virtual reality (HABIT-VR). This graphrepresents the percentage of time the nondominant hand spent in (A) moderate to vigorous physical activity and (B) low physical activity when using
HABIT-VR. The y-axis represents the percentage of time in the respective activity level. The x-axis represents each day the participant used HABIT-VR
including the pre- and postintervention assessments. Days circled in red represent the days where the participant only used HABIT-VR for half an hour.
Blank days are the days the participant did not use HABIT-VR. The red line represents the trend of the values for the respective phase.

For the nondominant hand, it spent 85.5% (SD 4.8%) time inlight physical activity at baseline, 74.2% (SD 18.3%) time inlight physical activity in intervention, and 81% time in post
(Figure 7B). This had an effect size (d) 0.812; large effect.

Discussion
This study examined the feasibility of using a single, customized
HABIT-VR game in the home. The main outcomes wereadherence to the protocol, changes in upper-extremity gross andfine motor clinical measures, self-perception of occupational
performance, and VR task performance. Additionally, thisinvestigation explored the use of actigraphy data recorded fromthe VR hardware to quantify and understand changes in motor
activity of the upper extremity. These results suggest that
HABIT-VR may be a therapeutic option in the homeenvironment that may maintain upper extremity motor function
over time and between episodes of care. Collectively, results
underscore the potential impact of HABIT-VR at home on bothclinical measures and objective kinematic assessments.
Adherence to home programs is one way of understanding theusefulness of a therapeutic program [57]. Adherence to therecommended VR gaming protocol exhibited variations overthis study’s duration, extending beyond the initially planned 14
days to conclude after 22 days. The participant attributed theabsence of therapy in the early phase to reported illness andpersonal events, while a period of waning interest, due to playing
the same game repeatedly, during the middle phase contributed
to the days with a single 30-minute session. However, theparticipant emphasized that the incorporation of a high-scorefunction motivated interaction with the game. Notably, theparticipant suggested that the inclusion of additional games orenvironments within the single game could have enhanced theirexperience. Future at-home studies should explore the use of
the complete suite of HABIT-VR games to determine long-term
effects on both adherence and motor outcomes.
The maintenance of clinically relevant upper extremity motor
outcome measures following a low-dosage home treatment inour study is noteworthy. This finding holds particularsignificance considering the potential for children with unilateral
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CP to experience a decline in functional abilities post discharge.
The ability of low-dosage, at-home HABIT-VR gaming tosustain performance over a 1-month period is noteworthy. This
study provides preliminary evidence suggesting that HABIT-VR
may serve as a valuable intervention tool between in-clinic
sessions, potentially bridging the therapeutic gap. Recognizingthat 60% of children aged 8 to 18 years engage in approximately
2.5 hours of video game activity daily [58], the integration of
low-dose HABIT-VR into leisure time is a feasible strategy tohelp maintain the functional gains achieved through in-clinic
intensives.
The COPM ratings increased in our participant, a positive
outcome considering the prevalent issue of self-efficacy inchildren with CP. Self-efficacy, an individual’s assessment of
their ability to learn and perform skills in specific situations,has been a focal point in previous studies using therapeutic
video game systems with children with CP [47]. These studies
report improvements in COPM scores, emphasizing thesignificance of a VR play environment in providing children
with CP with enjoyable, age-appropriate activities. Ourparticipant reported enhanced performance and satisfaction.
While the average COPM score did not exhibit a clinically
relevant 2-point change, this may be attributed to a ceiling effectwith initial scores being high. The average of both performanceand perception scores improved by 1.6 points, with 3 skills
showing at least a 2-point increase in perceived performanceand satisfaction. Also, of note for the COPM: the 2-point
minimal clinically important difference was originallyestablished based on data from adults in a mental health facility[59] and recent works suggest the relevant change may bedifferent for different populations and contexts [60-62]. Indeed,
the change observed in this trial may highlight that technologicalinterventions like HABIT-VR can positively impact
self-perception among individuals with CP.
Skill performance, assessed through task success rates, task
attempts, and firing rate, demonstrated an improvement.
Specifically, the task success rate improved from an averagepreintervention success rate of 67.3% (SD 19.8%) to 87.6% at
postassessment. Task attempts and firing rate follow similar
trends, with a 40% and 35.5% increase from the pre- to
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JMIR FORMATIVE RESEARCH
postintervention phase, respectively. These data reflect typicalmotor learning patterns, with a sharp increase early on, andincremental increases in later learning [63-66]. The sharp
increase is typically indicative of the fast motor learning stage[65,67], while the incremental increases seen during intervention
represent slow motor learning [64,68,69]. This suggests thatmotor learning occurs while using HABIT-VR. Overall, theparticipant increased their performance of the task by over 30%
for all performance metrics, demonstrating a high level ofmastery of the single HABIT-VR game.
Actigraphy measures revealed high levels of movement intensity
initially, followed by a decrease in the second half of the study.
This reduction in movement intensity may indicate increased
boredom with the single-game HABIT-VR. The participant
reported decreased interest in the game as the study progressed,
and in turn, movement intensities may have decreased. However,when combined with the number of attempts and firing rate
while playing the game, it seems that the participant was playing
the game at a higher performance level with reduced physicalexertion. This suggests improved movement efficiency with apracticed motor task. With either interpretation, actigraphymeasurements serve as a useful measure for automated detectionof therapeutic gaming use and motor function. Integrating
open-source options directly into therapeutic software provides
clinicians with objective, measurable data for decision-making[70]. Further, integrating these measures into the VR hardware
device eliminates the need for an additional device to record
these data, reducing research overhead, reducing participant
burden, and allowing for remote monitoring of home programs.
Despite insights gained from this study, certain limitations are
acknowledged. The use of a case study design, particularly
within the context of the Manual Ability Classification Systemlevel I, restricts the generalizability of our findings to a broader
population. While the detailed exploration of a single case

Gehringer et alprovides in-depth insights, caution must be exercised in
extrapolating these results to individuals with varying levels of
manual ability. Additionally, it is crucial to note that the analysts
were not blinded, potentially introducing bias into theassessment process. Future studies using larger sample sizes
and incorporating blinded analyses would enhance the validity
and reliability of the findings. Another limitation is the singularpostintervention data collection. There is potential for variation
between data collection sessions, as demonstrated in thepreintervention data, so a singular data collection
postintervention may not capture the full change in outcome
measures. Future studies should include additional
postintervention data collections to ensure a more robust
postintervention measure. Further, this investigation did notinclude a direct measure of bimanual function. While we wereable to measure the function of both hands independently, futurestudies should include measures of bimanual function, such as
the Assisting Hand Assessment. Finally, this investigation onlyused a single HABIT-VR game. This single-game setup has theparticipant focusing on a single set of movements, and not thewhole array of movements that HABIT-VR or traditional
HABIT would have a child working on. It may have affected
the participant’s level of interest. Future investigations should
explore how these outcomes change when additional HABIT-VR
games are included.
This investigation provides preliminary evidence supporting
the feasibility of HABIT-VR treatment as an at-home therapeutic
option. These results suggest that HABIT-VR may be an
innovative approach to bridge the gap between intensive
in-clinic sessions, such as traditional HABIT, or between active
treatment phases. Moreover, the utility of actigraphy as ameasure for both performance and use emerges as a valuable
contribution, providing clinicians with objective data aboutadherence and motor changes that could be monitored remotely.

Acknowledgments
The project described is supported by the National Institute of General Medical Sciences (U54 GM115458), which funds theGreat Plains IDeA-CTR Network. The content is solely the responsibility of the authors and does not necessarily represent theofficial views of the NIH.

Conflicts of Interest
None declared.

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Abbreviations
BBT: Box and Blocks Test
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JMIR FORMATIVE RESEARCH

Gehringer et al

COPM: Canadian Occupational Performance Measure
CP: cerebral palsy
HABIT: Hand Arm Bimanual Intensive Training
MVPA: moderate to vigorous physical activity
NHPT: Nine-Hole Peg Test
VR: virtual reality

Edited by A Mavragani; submitted 20.02.24; peer-reviewed by F Noveletto, YJM Janssen-Potten; comments to author 13.05.24;
revised version received 12.07.24; accepted 16.07.24; published 06.09.24
Please cite as:Gehringer JE, Woodruff Jameson A, Boyer H, Konieczny J, Thomas R, Pierce III J, Cunha AB, Willett S
Feasibility of At-Home Hand Arm Bimanual Intensive Training in Virtual Reality: Case StudyJMIR Form Res 2024;8:e57588
URL: https://formative.jmir.org/2024/1/e57588
doi: 10.2196/57588
PMID:©James E Gehringer, Anne Woodruff Jameson, Hailey Boyer, Jennifer Konieczny, Ryan Thomas, James Pierce III, Andrea B
Cunha, Sandra Willett. Originally published in JMIR Formative Research (https://formative.jmir.org), 06.09.2024. This is an
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License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information,a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

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