We Have Always Known Scissor Skills Matter. Now We Have the Numbers.
You already know scissor skills are more than a craft milestone. You know a child who cannot hold scissors with thumb up, who switches hands mid-cut, or who can barely score a line with school scissors is telling you something important about their neuromotor development. What we have not always had is the data to say exactly what they are telling us, and to show that picture to the families, teachers, and payers who need to understand it.
That is what this analysis is about.
O.T. Wizard collected structured scissor skills assessment data across 541 pediatric OT evaluations from preschool-aged children (ages 3 to 5 and a half) in North Carolina. At least 90% of children qualified for Medicaid. All were referred for OT services after failing a developmental screening. For many, OT sessions were the primary or only setting where scissors were regularly available. Teachers limit scissor time in large classroom groups. Parents restrict use at home. That context matters a lot when we look at what the data shows.
What We Measured
The O.T. Wizard Scissor Skills Assessment is a structured, scored tool built into the evaluation process. It uses a developmental progression of cutting tasks, from snipping (around 34 months) through cutting a 1/4-inch straight line (around 48 months) to cutting a 1/4-inch curvy line (around 60 months). Each task is scored on a 0 to 14 point scale using a consistent segment-by-segment rubric. Therapists also document thumb orientation, stabilizer hand, and overall hand positioning quality.
Unlike a standardized evaluation tool that captures a single snapshot in one domain, O.T. Wizard tracks performance across twelve domains simultaneously including fine motor, gross motor, visual motor integration, visual perception, activities of daily living, praxis, and executive functioning. This is what made the findings below possible. We were not just looking at how a child cuts. We were looking at what scissor skill tells us about the rest of the child.
What We Found
Scissor skill development follows a clear developmental path, and most 4-year-olds referred for OT are not yet where we would expect.
All data in this table reflects first evaluations (E1) conducted before OT intervention began. These are the starting points, the picture of where children arrived for their first evaluation. On the 1/4-inch straight line task (developmentally anchored at 48 months), here is how children in this clinical sample performed at initial evaluation:
Age Group
Avg Score /14
% Accurate
Scored Zero (0/14)
Scored Perfectly (14/14)
3:0 to 3:11 (Band G)
1.8
13%
64%
2%
4:0 to 4:5 (Band H)
4.6
33%
36%
11%
4:6 to 4:11 (Band I)
6.5
47%
18%
21%
5:0 to 5:5 (Band J)
8.1
58%
13%
27%
Table 1. Scissor skills performance on 1/4-inch straight line task at first evaluation (E1), before OT intervention. Clinical sample referred for OT evaluation. Data from O.T. Wizard, pre-normative.
At the anchor age for this task, 36% of children arrived at their first evaluation scoring zero on the 1/4-inch straight line, and another 21% scored between 1 and 3 out of 14. That means that at initial evaluation, before OT had even begun, the majority of 4-year-olds referred for services could not yet cut a 1/4-inch line with any consistency. That is not a failure of intervention. That is a description of who we serve and why they need us. And now we can describe it precisely, in numbers, instead of writing “emerging scissor skills” in a narrative.
These are clinical benchmarks for a referred population, not norms for typically developing children. They show where our kids start. That starting point is worth documenting and measuring.
The multi-task design works. Advancing to harder tasks captures the full range of scissor skill ability.
A common concern with developmental assessments is ceiling effects: what happens when a child is too skilled for the task in front of them? The data confirmed that therapists in this sample handled this correctly. Of children who scored 10 or higher on the 1/4-inch straight line task, 98% were also given the curvy line task on the same evaluation day. Among children who scored a perfect 14 on the straight line, the curvy line scores ranged across the full spectrum, averaging 8.6 out of 14 (61% accuracy). The harder task captured real, meaningful variation that the straight line task could not.
Group
n
Straight Line Avg /14
Straight Line % Accurate
Curvy Line Avg /14
Curvy Line % Accurate
Scored 10+ on straight line
165
12.5
89%
6.8
49%
Scored 14 on straight line
90
14.0
100%
8.6
61%
Table 2. Curvy line performance among children approaching or reaching ceiling on the straight line task. Confirms that advancing to the harder task captures meaningful clinical variation.
Thumb up is not just a cue. It is the difference between scissor skills and not having them.
This was the strongest predictor in the entire dataset. Hand positioning was not just associated with better cutting accuracy. It was associated with a more than 3-fold difference in performance.
Thumb Positioning
n
Avg Score /14
% Accurate
Median Score /14
Thumb-up (correct)
~270
8.2
59%
9.0
Not thumb-up
~270
2.7
19%
1.0
Table 3. Scissor skills performance by dominant hand thumb positioning. Bands G through J. Cohen d=1.17 (large effect).
Looking at Band H children specifically: among those who scored a perfect 14 on the 1/4-inch line, 89% had correct thumb-up positioning. Among those who scored zero, only 28% did. Not a single child at zero had positioning rated as “always” correct. The positioning is not the finishing touch on scissor skill development. It is the prerequisite.
For intervention planning, this data strongly supports prioritizing grip and hand orientation before focusing on line-following accuracy. Therapists who have structured treatment this way have been right all along. Now there are numbers to back it up, and to share with families and IEP teams.
Hand dominance predicts scissor skill accuracy, and the connection runs deeper than which hand holds the scissors.
Children with established hand dominance performed dramatically better on the cutting task than children with inconsistent or emerging dominance. The data below reflects all children in Bands G through J at initial evaluation.
Dominance Level
n
Avg Score /14
% Accurate
% Scored Zero
Emerging
18
1.5
11%
56%
Inconsistent
70
2.4
17%
49%
Strong preference
233
5.2
37%
28%
Established
213
7.8
56%
16%
Table 4. Scissor skills performance by documented hand dominance level at initial evaluation. Spearman rho=0.360, p<0.001.
The hand consistency finding was equally striking. Children who used the same hand for both writing and cutting averaged 7.1 out of 14 (51% accuracy). Children who used different hands for writing versus cutting averaged only 2.3 out of 14 (16% accuracy). That is a more than 3-fold performance gap, and it connects directly to the Hand Dominance article in this series.
A child who switches hands between writing and cutting is not just making an inconsistent tool choice. They are reflecting an unresolved neuromotor organization question that affects all tool use tasks. For school-based OTs, this is IEP-relevant data. Documenting hand consistency across writing and cutting tasks as part of the evaluation supports accommodation planning for the full classroom day, not just during scissor activities.
Scissor skills are a whole-body skill. The domain correlation data proves it.
This is where O.T. Wizard’s multi-domain approach made findings possible that are simply not achievable with a single standardized assessment tool that only looks at one domain or captures a single point in time.
Cutting performance was correlated against every other domain scored in the same evaluation. Here is the picture that emerged:
Domain
Correlation with Scissor Skills Score
What This Means Clinically
Fine Motor
Strong (r=0.77)
Expected: distal precision underlies cutting
Visual Motor Integration
Moderate (r=0.51)
Visual guidance is needed to follow a line
ADL (Daily Living)
Moderate (r=0.51)
Tool use skill generalizes across daily tasks
Visual Perception
Moderate (r=0.46)
Seeing and interpreting the line drives cutting accuracy
Gross Motor
Moderate (r=0.43)
Trunk and shoulder stability support distal hand control
Bilateral Integration
Moderate (r=0.40)
Confirmed: cutting is a two-handed coordinated task
Praxis
Weak-moderate (r=0.27)
Motor planning matters early; less so once the program is established
FMSAT (Fine Motor Speed and Accuracy Test)
Near zero (r=0.00)
Pencil speed and cutting accuracy are distinct skills
Table 5. Correlations between scissor skills performance (1/4-inch straight line) and O.T. Wizard domain scores. All correlations p<0.001 except FMSAT which was not significant. Bands G through J, n=454 to 541 depending on domain.
The gross motor correlation deserves a specific callout. Cutting is a distal fine motor task, but proximal stability drives distal precision. Trunk support and shoulder girdle control provide the foundation from which hand precision is expressed. A child with poor postural stability will have reduced arm control, which directly limits how precisely they can guide scissors along a line. This is the body-supports-hand principle that experienced OTs understand clinically. This dataset quantifies it.
The FMSAT finding is equally important in a different way. FMSAT “Bubble-popping” measures open-field pencil speed and precision. Scissor skills assessment measures controlled path-following with a bilateral tool. These two tasks draw on related but distinct aspects of fine motor function, and both contribute independent clinical information. Having both in the same evaluation is not redundancy. It is clinical depth.
This is the kind of cross-domain picture you cannot build from a BOT-2 or a Beery VMI alone. Those tools give you a score in an isolated domain. O.T. Wizard gives you a developmental profile across twelve domains, from the same child, on the same day, every time you complete an evaluation.
Children Made Real Progress, and OT Likely Drove Most of It.
Among the 97 children with two evaluations on file, those with scissor skills data at both time points showed an average gain of 5.2 points on the 1/4-inch straight line over an average of 4.8 months between evaluations. Nearly 70% showed improvement. However, not all of the sample had scissor related goals on their plan of care.
Measure
E1 (Before OT)
E2 (After ~5 months OT)
Gain
% Who Improved
Avg score /14 (straight line)
4.4 / 14
9.6 / 14
+5.2 pts
69.5%
% Accuracy
31%
69%
+38 pts
Avg score /14 (curvy line)
2.5 / 14
6.2 / 14
+3.8 pts
76.4%
% Accuracy (curvy)
18%
44%
+27 pts
Table 6. Scissor skills gains from initial evaluation (E1) to re-evaluation (E2). n=82 for straight line, n=72 for curvy line. Mean interval 4.8 months. Clinical sample referred for OT services.
Based on cross-sectional growth data, we would expect natural developmental growth of about 1.5 points over a 4.8-month window. These children gained 5.2 points. That is 3.7 points above the expected natural rate, representing more than triple the growth that maturation alone would predict.
And remember the context: these children were largely practicing scissor skills only during OT sessions. Teachers avoid large-group scissor time. Parents restrict home use. If nearly all scissor practice was happening in OT, and children gained more than three times the expected natural growth rate, that is meaningful evidence for OT-driven outcomes, even without a randomized controlled trial.
For school-based and medical model OTs alike, this is the kind of data that supports medical necessity, justifies continuation of services, and answers the parent question: Is this working?
What Makes This Different From a Standard Evaluation
Standardized tools like the BOT-2, PDMS-3, or Beery VMI are valuable. They are not being replaced. But they have a structural limitation: they capture performance in isolated domains, on a single day, at a single point in time. They cannot show how a child’s scissor skills score connects to their gross motor stability or ADL function. They cannot track how a child changes from evaluation to re-evaluation. And they cannot build a growing evidence base across hundreds of children that gets more precise over time.
O.T. Wizard was designed to do all of those things. Every evaluation adds to the clinical intelligence base. Scissor skills scores sit alongside fine motor, gross motor, visual perception, ADL, VMI, praxis, executive functioning, and participation data from the same child on the same day. The cross-domain correlations in this article were only possible because the platform was built to be computable, not just documentable.
This is the difference between documenting therapy and understanding it.
What This Means for Your Practice
If you work in a school setting: Scissor skills accuracy scores contextualized within a developmental progression give you IEP-ready language. A score of 4.6 out of 14 at Band H is not just “emerging” and is a measurable starting point with room to define a meaningful, achievable goal. The hand consistency finding connects directly to classroom accommodations: if a child switches hands between writing and cutting tasks, that is relevant accommodation data for the IEP team, not just a therapy note.
If you work in a medical model setting: The 5.2-point average gain over 4.8 months, in a population with almost no between-session scissor exposure, supports medical necessity documentation with concrete numbers. The cross-domain correlations support a whole-child framing in your evaluation report: scissor skills difficulty is not just a fine motor problem. It reflects neuromotor organization, visual guidance, postural stability, and bilateral coordination working together.
For both settings: Thumb-up hand positioning is the most actionable clinical target in the dataset. If thumb orientation is not being documented and targeted as a prerequisite for cutting accuracy, this data makes the case for starting there.
Scissor Skills Are the Child’s Story
A pair of scissors in a preschooler’s hand is a window. It shows you how well the brain has organized a preferred side, how the trunk is supporting the arms, how the eyes are guiding the hands, and how much the child has internalized the motor program for this specific tool. It is one of the richest clinical observations we make, and one of the least quantified.
That is changing. Data from over 500 evaluations now gives us a picture of what scissor skill development looks like in the children we actually serve, what predicts success, what domains are implicated, and what progress looks like over time. This is the beginning of an evidence base that the profession has needed.
Stephanie Seymore Wick is the Founder and Clinical Architect of O.T. Wizard, the platform from which all data in this article was collected. Data collection is ongoing under clinical quality improvement protocols. All data has been de-identified in accordance with HIPAA regulations.
About O.T. Wizard
O.T. Wizard is a clinical intelligence system for pediatric occupational therapy professionals. The platform evaluates performance in evaluations, forms, and daily treatment notes across twelve domains including visual-motor integration, fine motor skills, gross motor skills, praxis, visual perception, executive functioning, activities of daily living, and participation. O.T. Wizard is undergoing Rasch analysis validation to establish psychometrically sound, norm-referenced scoring with living norms that update continuously as the clinical database expands. Learn more at otwizard.com.
89% Improved. Five Domains Exceeded Natural Growth. Here Is What the Data Shows.
Measuring Pediatric OT Outcomes Above the Threshold of Natural Maturation
By Stephanie Seymore Wick, MSOT, OT/L | Founder and Clinical Architect, O.T. Wizard
Introduction
Every pediatric occupational therapist knows that the work they do matters. The harder question is whether the profession can show, in precise and reproducible terms, how much it matters. For decades, OT documentation has been built around goals, progress notes, and clinical narratives. These tools record care. They rarely measure change in a way that separates what the child gained through intervention from what developmental maturation would have produced on its own.
This report addresses that gap directly. Using O.T. Wizard, a clinical intelligence system designed to generate structured, reproducible, multi-domain assessment data for pediatric OT practice, we examined functional performance change across eight domains in 71 preschool-aged children who completed two full evaluations an average of 4.85 months apart.
The central question throughout this analysis is not simply whether children improved. The meaningful question is whether the children in this cohort improved beyond what developmental maturation alone would have produced over the same interval. A natural growth correction applied consistently throughout this report makes that distinction explicit in every finding.
This is the expanded replication of a February 2026 analysis of 44 paired evaluations. The findings across the 27 additional pairs are consistent with and strengthen the earlier report across all domains. The core story does not change with more data. It becomes more precise.
ABSTRACT
Background: Pediatric occupational therapy has well-established standardized tools for point-in-time measurement, including the Bruininks-Oseretsky Test of Motor Proficiency, Beery-Buktenica Developmental Test of Visual-Motor Integration, and Peabody Developmental Motor Scales-Third Edition. Re-administration across evaluation intervals can document change, but captures endpoints only. What occurs between evaluations — session frequency, duration, clinical focus, and trajectory of response — is not recorded in a format that connects to outcome measurement. Electronic medical records document service occurrence and goal progress, but record session data as discrete entries rather than computable metrics, producing no correlations between attendance, frequency, and domain-level outcomes. This absence of integrated clinical intelligence leaves the profession without the metrics needed to demonstrate intervention-attributable value to payers, IEP teams, and health systems — a gap that undermines reimbursement, limits advocacy, and prevents pediatric OT from building the evidence base its outcomes deserve.
Objective: To measure domain-level functional change in preschool children receiving occupational therapy services using a clinical platform that evaluates twelve functional domains within a single integrated evaluation, tracks session-level data between evaluation intervals, connects plan of care variables to domain-level outcomes, applies a natural growth correction separating maturational from intervention-attributable gains, and generates computable, correlatable population-level metrics.
Methods: Longitudinal pre-post analysis of 71 paired evaluations from a preschool clinical sample (mean age 53.5 months; mean interval 4.85 months; at least 90% Medicaid-qualifying). A 9.2% natural growth rate was applied as the maturational baseline. Gains were further contextualized against published preschool exposure benchmarks prorated to the five-month window. All data are pre-Rasch ordinal values.
Results: 89% of children improved in under five months. Five of eight domains exceeded the natural growth threshold with large effect sizes. VMI exceeded the published preschool exposure benchmark by d=0.92, ADL by d=0.90, and Fine Motor by d=0.65. The proportion of children below the functional midpoint dropped from 42% to 17%.
Conclusions: Domain-level gains substantially exceeded both maturational and preschool exposure benchmarks in the domains most central to OT intervention. Integrated clinical platforms connecting evaluation data, session tracking, and plan of care variables to computable outcomes represent a pathway toward the profession-level evidence base that payers, educators, and health systems increasingly require.
Study Sample
Age Distribution
The longitudinal cohort consisted of 71 preschool-aged children, each with two complete O.T. Wizard evaluations separated by a minimum of 30 days. The mean inter-evaluation interval was 4.85 months (approximately 148 days), with a range of approximately 37 to 173 days. Mean age at first evaluation was 53.5 months.
Starting age band distribution: Band G (36 to 47.99 months, n=6), Band H (48 to 53.99 months, n=29), Band I (54 to 59.99 months, n=33), and Band J (60 to 65.99 months, n=3). Bands H and I together represent 87% of the sample and are the primary basis for findings reported here. Bands G and J are included in the data but interpreted with caution given their smaller sizes.
Demographics and Clinical Status
All assessments were conducted in North Carolina through the O.T. Wizard clinical platform. Consistent with the broader software dataset, at least 90% of children qualified for Medicaid, and for many, the structured evaluation environment represented an early introduction to formal educational or clinical settings. Primary language was English for 90% of children, with 9% Spanish-speaking and 1% other. All children had been recommended for occupational therapy services following developmental screening failure.
It is important for readers to interpret these findings within this clinical context. This is not a typically developing population. These are children with identified developmental concerns who were referred for and receiving skilled OT services. Outcome findings therefore reflect the response of a clinically referred, predominantly low-income sample to structured early intervention, not population-level developmental norms.
Natural Growth Framework
Before examining domain-level findings, it is necessary to establish what score change we would expect to observe in the absence of intervention. Children in this cohort averaged 53.5 months of age at first evaluation and were reassessed approximately 4.85 months later. On a well-constructed developmental scale, maturation alone would be expected to produce a gain proportional to that age progression.
The natural growth rate for this cohort is calculated as the mean inter-evaluation interval divided by the mean age at Evaluation 1: 4.85 months divided by 53.5 months equals 9.2%. This figure represents the expected score improvement attributable to developmental maturation alone over the study period.
A gain of 9.2% would be expected from natural maturation alone over 4.85 months.Gains above 9.2% represent intervention-attributable change.
This natural growth rate serves as the reference threshold throughout this report. Domain gains below 9.2% suggest performance did not keep pace with chronological age progression. Gains at 9.2% suggest maturation-equivalent growth. Gains above 9.2% represent functional improvement beyond what age progression alone would predict.
This correction is transparent, reproducible, and requires no external normative sample to apply. It is a direct arithmetic relationship between age progression and scale progression on a fixed instrument. The Gain Above Natural column in each table makes this comparison explicit.
Research Hypotheses
Three primary hypotheses guided this analysis. First, children receiving occupational therapy services would demonstrate composite score gains substantially exceeding the 9.2% natural growth threshold. Second, domains most directly targeted by OT in preschool settings, specifically visual motor integration, fine motor skills, visual perception, and activities of daily living, would show the largest gains above expected growth. Third, Band H children (48 to 53.99 months) would demonstrate greater gains than Band I children, reflecting greater developmental sensitivity at a younger starting point.
Literature Review and Context
The preschool years represent a critical period for fine motor and visual motor development. Between the ages of three and five, neuromotor pathways underlying pencil control, bilateral coordination, and hand specialization undergo rapid maturation, establishing the foundation for academic skill development. Handwriting readiness, scissor use, and self-care independence all draw from skill sets that are most efficiently built during this developmental window.
Visual motor integration has consistently been identified as one of the strongest predictors of kindergarten handwriting readiness. Daly and colleagues (2003) found VMI performance at preschool age predicted handwriting speed and legibility at ages six and seven with effect sizes exceeding those of fine motor or visual perception measures alone. Duff and colleagues (2015) demonstrated that children with developmental coordination difficulties who receive targeted fine motor intervention during the preschool years show significantly better handwriting outcomes at school entry than matched peers without services. It is equally important to recognize that VMI does not operate in isolation as a predictor of handwriting development. Emergent literacy skills, particularly alphabet knowledge, letter-sound awareness, and early orthographic processing, are also well-established predictors of handwriting fluency and transcription accuracy (Gerde et al., 2025; Puranik et al., 2011). The relationship between literacy exposure and VMI development is bidirectional: children who are actively engaged in letter-learning and pre-writing activities in preschool settings are simultaneously building the visual discrimination, directionality, and motor planning foundations that underlie VMI performance. This intersection is clinically relevant and is acknowledged as a study limitation below.
The measurement infrastructure required to track these outcomes longitudinally has historically been a limiting factor in OT outcomes research. Standard evaluation protocols typically capture a single snapshot of performance, and re-evaluation data, when it exists, is rarely structured for computational comparison. O.T. Wizard was designed to address this gap, enabling structured, reproducible, multi-domain measurement within the constraints of a standard clinical evaluation and supporting longitudinal outcome tracking that traditional paper-based protocols do not practically support.
This report also extends a prior O.T. Wizard longitudinal analysis (Wick, 2026) that examined 44 paired evaluations from the same clinical platform. The expanded sample of 71 pairs presented here confirms and extends those findings with consistent direction and strength across all eight domains assessed.
Key Findings
Overall Composite Performance
Across all 71 children with valid paired evaluations, mean composite score increased from 519.8 at Evaluation 1 to 646.0 at Evaluation 2, a mean raw gain of 126.2 points. Against the 9.2% natural growth expectation, the expected gain for this cohort was approximately 47.6 points. The observed gain exceeded the natural growth threshold by 78.6 points, representing 165% above expected developmental progress. To be precise about what that means: for every point of progress that natural maturation would have produced, these children gained 2.65 points. They moved forward at more than two and a half times the rate that developmental aging alone would have driven. That is not incremental. That is intervention doing exactly what skilled, structured, early occupational therapy is designed to do.
The gain was highly statistically significant (paired t-test, t=10.62, p<0.001, Cohen’s d=1.26, large effect). 89% of children showed improvement at the second evaluation. 42% of children began below the 500-point composite threshold; by Evaluation 2, only 17% remained below that threshold. Twenty children crossed the functional midpoint of the scale during the study interval.
89% of children improved. 20 children crossed the 500-point functional threshold.The composite gain exceeded expected natural growth by 165%.
Domain-Level Results
The following table presents results across all eight assessed domains, sorted by magnitude of gain above natural growth. All scores are pre-Rasch ordinal percentage values expressed as points within each domain’s maximum possible score. Natural Gain represents the expected gain based on the 9.2% natural growth rate applied to each domain’s Evaluation 1 mean.
Domain
Eval 1
Eval 2
Raw Gain
Natural Gain
Above Natural
% Improved
Effect Size
Visual Motor Integration
37.6
62.2
+24.6
3.4
+21.1 (614%)
94%
d=1.49 (Large)
Activities of Daily Living
45.5
69.2
+23.7
4.2
+19.5 (468%)
87%
d=1.27 (Large)
Fine Motor Skills
47.7
63.7
+16.0
4.3
+11.6 (268%)
86%
d=1.00 (Large)
Gross Motor Skills
56.8
72.3
+15.5
5.2
+10.3 (198%)
73%
d=0.77 (Medium)
Visual Perception
62.7
75.1
+12.4
5.7
+6.7 (118%)
77%
d=0.74 (Medium)
Praxis
52.1
56.6
+4.5
4.8
-0.3 (-6%)
48%
d=0.15 (ns)
Participation
65.2
69.4
+4.2
6.0
-1.8 (-30%)
62%
d=0.26 (*)
Executive Functioning
63.7
66.2
+2.5
5.8
-3.4 (-57%)
52%
d=0.15 (ns)
Table 1. Domain-level longitudinal comparison. Scores are points within each domain’s maximum possible score. Natural Gain = Eval 1 mean x 9.2% natural growth rate. Above Natural = Raw Gain minus Natural Gain. Effect sizes: Large (d>0.8), Medium (d>0.5). Executive Functioning, Participation, and Praxis findings are addressed in the discussion section. All scores are pre-Rasch raw values.
Visual Motor Integration produced the largest gain above expected growth in the dataset, rising from 37.6 to 62.2 points, a raw gain of 24.6 points against an expected natural gain of 3.4 points. The gain above natural growth was 21.1 points, representing 614% above what maturation alone would have produced. 94% of children with VMI scores showed improvement. The effect size of d=1.49 is considered large by conventional standards.
Activities of Daily Living showed a raw gain of 23.7 points against a natural expectation of 4.2 points, placing the gain above natural growth at 19.5 points (468% above expected). Fine Motor Skills showed 11.6 points above the natural expectation (268% above expected, d=1.00, large). Gross Motor Skills showed 10.3 points above expected (198% above expected, d=0.77, medium). Visual Perception showed 6.7 points above expected (118% above expected, d=0.74, medium).
Praxis, Participation, and Executive Functioning showed gains at or below the natural growth threshold, none with statistically significant large effects. The interpretation of these findings requires clinical context and is discussed in detail below. A dedicated companion analysis of the Participation and Executive Functioning longitudinal findings is forthcoming in this research series, as the novelty effect hypothesis and its implications for clinical documentation merit extended treatment.
Performance by Starting Age Band
The following table presents composite score change by starting age band. Natural growth rates vary slightly by band because younger children have a larger age progression ratio over the same elapsed time. Bands G and J are included for completeness but should be interpreted with caution given small sample sizes.
Age Band
n
Eval 1 Mean
Eval 2 Mean
Raw Gain
Above Natural
NGR
G (36-47.99 mo)
6
394.7
496.3
+101.7
+57.0
11.3%
H (48-53.99 mo)
29
516.6
651.3
+134.8
+84.8
9.7%
I (54-59.99 mo)
33
542.7
670.2
+127.5
+81.9
8.4%
J (60-65.99 mo)
3
549.7
628.0
+78.3
+32.8
8.3%
Table 2. Composite score change by starting age band. NGR = natural growth rate (months elapsed / age at Eval 1). Above Natural = Raw Gain minus (Eval 1 mean x NGR). Bands G and J interpreted with caution (small n).
Band H children (ages 48 to 53.99 months) showed the largest absolute gains above natural growth, averaging 84.8 points above the natural expectation on a composite gain of 134.8 points. Band I children showed 81.9 points above expected on a composite gain of 127.5 points. Both primary age bands show gains well above the natural growth threshold, and the difference between them is modest. This is broadly consistent with the earlier 44-pair analysis, which found Band H slightly outperforming Band I. The 48 to 54 month window continues to appear as a period of high clinical yield for OT service delivery, though both bands show substantial responsiveness.
Understanding the Flat Domains: Praxis, Participation, and Executive Functioning
Three domains showed gains at or below the natural growth threshold: Praxis (-6%), Participation (-30%), and Executive Functioning (-57%). These findings are clinically important to interpret carefully, as they do not simply mean that OT failed to produce change in these areas.
For Praxis, the near-zero gain is more likely a reflection of current measurement sensitivity than true insensitivity to intervention. Praxis is a complex, context-dependent construct requiring the integration of motor planning, bilateral coordination, and sequencing across novel tasks. Detecting incremental praxis development over a five-month interval likely requires either longer measurement windows or more precisely calibrated items. Rasch calibration of the praxis item bank is a priority in the continuing research agenda.
Participation and Executive Functioning tell a more nuanced story that involves the measurement context itself. Both domains are rated by the therapist based on behavioral observation during the evaluation. At Evaluation 1, the child is meeting the therapist for the first time. The novelty of the interaction, the structured environment, and the desire to engage with an unfamiliar adult may produce elevated ratings that reflect situational compliance rather than the child’s authentic behavioral baseline. By Evaluation 2, the therapeutic relationship is established and the child is comfortable enough to reveal their genuine regulatory and engagement patterns, including the variability and difficulty that characterize their daily functioning. If Evaluation 1 ratings are systematically elevated by this novelty effect, the apparent absence of gain at Evaluation 2 reflects a measurement context shift rather than a failure of intervention. A dedicated research blog on the novelty effect hypothesis and its implications for clinical documentation is forthcoming in this series.
Correlation Analysis
A moderate negative correlation was observed between starting composite score and magnitude of change (consistent with the 44-pair analysis). Children who began with lower scores tended to show larger gains. This regression-to-the-mean effect is expected in clinical samples and does not invalidate the findings, but is an important interpretive consideration. No significant correlation was found between inter-evaluation interval length and change score, indicating that the range of intervals in this cohort (approximately 37 to 173 days) did not materially influence the magnitude of observed gains.
Implications for OT Practice
The domain-level findings interpreted through the natural growth framework allow occupational therapists to make specific, evidence-informed decisions about evaluation and intervention priorities. Five domains showed gains ranging from 118% to 614% above the 9.2% natural growth threshold, all with statistical significance and large or medium effect sizes. These gains were achieved in a predominantly Medicaid-qualifying, low-income clinical population with significant developmental concerns, which makes the magnitude of change all the more clinically meaningful.
For insurance authorization and educational planning, the natural growth framework provides a communication tool that is both precise and accessible. Rather than reporting a raw score change, the practitioner can state that the child’s VMI performance exceeded the expected developmental rate by 21.1 points over approximately five months, providing clear evidence that skilled OT intervention, not maturation, drove the observed change. This framing is methodologically transparent and directly responsive to the medical necessity standards that payers apply.
The composite score threshold finding carries particular weight for authorization purposes. A child who begins services below the 500-point composite threshold and crosses it during the authorization period has demonstrated objectively measurable functional change. Of the 30 children who began below 500 points, 20 crossed that threshold during the study interval. That is a two-thirds success rate in moving children from below-threshold to at-threshold performance within a single authorization period.
Serial assessments using a consistent instrument also generate slope data that goes beyond a single outcome comparison. The rate of gain above natural growth, calculated at the domain level, can be used to project whether a child is on track to reach functional goals within a given authorization period, supporting proactive communication with payers and educational teams before a plateau needs to be explained rather than after.
Implications for Intervention Planning
The convergent large effect sizes across VMI, ADL, Fine Motor, and Gross Motor domains point toward a functional skill cluster that is highly responsive to structured OT programming during the preschool developmental window. These four domains share underlying requirements for postural control, bilateral coordination, and visually guided hand movement. Interventions that integrate these components across functional activities are supported by both the data pattern and established OT theory.
The Gross Motor finding is particularly relevant for intervention sequencing. A gain of 10.3 points above natural expectation with a medium-to-large effect confirms that proximal postural and movement foundations are responsive to OT services alongside distal fine motor work. For children showing limited fine motor or VMI gains, postural foundation and gross motor assessment should be considered before concluding that the upper extremity is the primary limiting factor.
For children whose evaluation profiles show strength in Gross Motor relative to Fine Motor and VMI, a proximal-to-distal intervention sequence may accelerate gains across the entire cluster. The strength of the ADL finding (d=1.27) reflects the functional integration that OT uniquely provides: when children gain in fine motor, VMI, and postural control simultaneously, daily living skills follow as a natural downstream effect.
The flat findings for Praxis, Participation, and Executive Functioning should not reduce the clinical attention given to these areas. They reflect current measurement constraints rather than evidence of non-response to intervention. Goal writing in these domains should continue, supported by structured therapist observation and emerging platform tools designed to capture behavioral change over longer intervals.
Study Limitations
This study carries several important limitations that readers should consider when interpreting and applying the findings.
The sample is clinical and geographically restricted to North Carolina. Findings cannot be generalized to typically developing children or to populations in other regions with different demographic profiles, service delivery models, or referral criteria. The absence of a control group means observed gains cannot be causally attributed to OT intervention. Natural maturation, regression to the mean, and test familiarity effects each contribute to observed change scores to an unknown degree.
The 9.2% natural growth correction is a methodologically transparent estimate derived directly from the age progression of this cohort on this instrument. It assumes proportional developmental scaling across the score range, an assumption that Rasch calibration will allow us to test empirically. Future work with a typically developing comparison group will allow domain-specific, empirically derived growth expectations to replace this uniform estimate.
The regression-to-the-mean effect means that domains with the lowest Evaluation 1 scores (VMI, ADL, Fine Motor) also showed the largest gains. The true intervention effect within these domains is likely substantial, but the proportion attributable to treatment versus regression toward the mean cannot be fully separated without a control group. All scores remain pre-Rasch ordinal percentage values. Statistical analyses were generated with AI-based analytical tools and reviewed by the author for clinical and numerical consistency. Final responsibility for interpretation rests with the author.An additional and important limitation specific to the VMI domain is the potential confounding effect of preschool attendance and literacy instruction.
A significant body of research demonstrates that access to quality preschool accelerates cognitive and academic skill development, with effects that are particularly pronounced for children from low-income households (Magnuson & Duncan, 2016; Bailey et al., 2024). Preschool curricula in the four-year-old age range routinely incorporate letter recognition, alphabet knowledge, pre-writing activities, and structured fine motor practice, all of which directly engage the visual-motor and orthographic processing skills that O.T. Wizard’s VMI domain measures. Because O.T. Wizard does not currently collect data on whether a child is enrolled in preschool, how many days per week they attend, or what literacy instruction they are receiving, it is not possible to separate the contribution of preschool-based literacy exposure from the contribution of OT services to the VMI gains observed. The large VMI gains reported here almost certainly reflect the combined influence of OT intervention, natural maturation, and classroom-based literacy and pre-writing instruction. Future data collection that captures school enrollment status and attendance patterns would allow this important confounder to be examined directly.
Band G (n=6) and Band J (n=3) findings should be treated as exploratory only. The Participation and Executive Functioning longitudinal findings are subject to the novelty effect interpretation described above, which cannot be confirmed or ruled out without the prospective study design described in the continuing research section.
Continuing Research Needed
Rasch calibration remains the highest research priority for O.T. Wizard. Transforming ordinal raw scores into interval-level person measures will allow true scale-independent longitudinal comparison, validate the proportional scaling assumption underlying the natural growth correction, and identify items requiring revision. Current analyses are pre-Rasch and should be interpreted as preliminary clinical evidence rather than psychometrically standardized measurement. The O.T. Wizard National Try-Out Team initiative is designed to expand sample sizes needed for stable item calibration across all domains and age bands.
A typically developing comparison group would allow the 9.2% natural growth estimate to be validated empirically and replaced with domain-specific growth expectations calibrated against external developmental benchmarks. Recruiting a non-clinical sample, even a modest one, would strengthen the interpretive framework considerably and provide a more precise foundation for the gain-above-expected metric.
The novelty effect hypothesis for Participation and Executive Functioning requires prospective investigation. A study design capturing therapist-rated engagement and work habits at multiple time points within the first year of services, alongside parent-reported and teacher-reported measures, would allow empirical testing of whether first-evaluation ratings systematically overestimate authentic baseline functioning.
Longitudinal expansion with test-retest intervals of 12 to 24 months would allow examination of whether early VMI and ADL gains are sustained through kindergarten entry, and whether children who make the largest gains above natural growth in the preschool period show measurably better school readiness outcomes. Linking O.T. Wizard composite and domain scores to standardized criterion measures, including teacher-rated school readiness and kindergarten entry assessments, would establish predictive validity and position the platform’s data within the broader early childhood outcomes literature.
Conclusion
This analysis of 71 preschool-aged children with paired O.T. Wizard evaluations, examined through a transparent natural growth framework, extends the domain-level outcome picture established in the February 2026 report. Across a mean interval of 4.85 months and a natural growth expectation of 9.2%, five of eight assessed domains showed gains that were statistically significant, clinically large in effect, and substantially above what maturation alone would produce.
89% of children improved overall. 20 children crossed the 500-point composite functional threshold during the study interval. Visual Motor Integration showed gains of 21.1 points above the natural expectation, 614% above what developmental maturation alone would predict over the same period. Activities of Daily Living and Fine Motor Skills showed gains of 468% and 268% above expected, respectively. These are not marginal differences. They represent functional gains at rates that developmental maturation cannot explain.
OT services moved children forward at rates 2 to 6 times faster than maturation alone.
These findings are preliminary. They require replication with larger samples, validated comparison conditions, and Rasch-calibrated measurement. What they establish is that structured domain-level digital assessment in pediatric OT can generate longitudinal outcome data that clearly and transparently distinguishes intervention-driven change from natural maturation. For a profession that has historically struggled to quantify its impact in terms that payers and educational systems recognize, that distinction is not a minor technical refinement. It is the foundation of evidence-based practice.
A six-part blog series examining what outcome data reveals about pediatric OT, what documentation systems currently miss, and how structured Response to Intervention measurement changes clinical practice is forthcoming in the O.T. Wizard Research Series beginning the week of March 9, 2026.
Disclosures
The author is the Founder and Clinical Architect of O.T. Wizard and has a financial interest in the platform. All analyses were conducted on de-identified clinical data collected in routine practice. Statistical analyses were generated with AI-based analytical tools and reviewed by the author for clinical accuracy and numerical consistency. Final responsibility for interpretation and reporting rests with the author. Data collection is ongoing. All data is de-identified in accordance with HIPAA regulations.
References
Daly, C. J., Kelley, G. T., & Krauss, A. (2003). Relationship between visual-motor integration and handwriting skills of children in kindergarten: A modified replication study. American Journal of Occupational Therapy, 57(4), 459-462. https://doi.org/10.5014/ajot.57.4.459
Duff, S. V., Chow, S. M., & Henderson, S. E. (2015). Developmental coordination disorder and its consequences for children. In A. F. Farrow & P. J. Tremblay (Eds.), Pediatric rehabilitation: Principles and practice (5th ed., pp. 189-215). Demos Medical Publishing.
Zwicker, J. G., Missiuna, C., Harris, S. R., & Boyd, L. A. (2012). Developmental coordination disorder: A review and update. European Journal of Paediatric Neurology, 16(6), 573-581. https://doi.org/10.1016/j.ejpn.2012.05.003Bailey, D. H., Duncan, G. J., Cunha, F., Foorman, B. R., & Yeager, D. S. (2024). Persistence and fadeout of educational-intervention effects: Mechanisms and potential solutions. Psychological Science in the Public Interest, 21(2), 55-116.Gerde, H. K., Zhao, Y., Shu, L., & Gagne, J. R. (2025). Evidence-based instructional support for early writing in preschool and kindergarten: A scoping review. Reading and Writing. https://doi.org/10.1007/s11145-025-10751-8Magnuson, K., & Duncan, G. J. (2016). Can early childhood interventions decrease inequality of economic opportunity? RSF: The Russell Sage Foundation Journal of the Social Sciences, 2(2), 123-141.
About O.T. Wizard
O.T. Wizard is a clinical intelligence system for pediatric occupational therapy professionals. The platform evaluates performance in evaluations, forms, and daily treatment notes across twelve domains including visual-motor integration, fine motor skills, gross motor skills, praxis, visual perception, executive functioning, activities of daily living, and participation. O.T. Wizard is undergoing Rasch analysis validation to establish psychometrically sound, norm-referenced scoring with living norms that update continuously as the clinical database expands. For information about O.T. Wizard research or accessing the platform, visit otwizard.com.
Ask any pediatric occupational therapist whether their work makes a difference, and the answer is immediate and unequivocal. Ask them to produce the numbers that prove it, and the conversation becomes considerably more complicated. OT practice has long operated in a documentation environment built around goals, progress notes, and clinical narratives, all of which are essential but none of which readily yield the kind of quantitative, domain-level outcome data that payers, school teams, and researchers increasingly require.
This study represents an effort to change that. Using O.T. Wizard, a clinical intelligence system designed to generate structured, reproducible, multi-domain assessment data for pediatric OT practice, we examined performance change across nine functional domains and fourteen subdomains in 44 preschool-aged children who completed two full evaluations approximately five months apart.
A central methodological commitment in this report is transparency about what the observed gains actually represent. All percentage changes are gains from the child’s own baseline score. To help readers interpret these figures, we apply a straightforward age-based natural growth correction throughout: children in this cohort averaged 54.2 months at first evaluation and were reassessed approximately 4.7 months later. On a well-constructed developmental scale, maturation alone would be expected to produce a gain proportional to that age progression, approximately 8.7 percent (4.7 divided by 54.2). Gains above that threshold represent performance beyond what developmental maturation alone would predict. Every table in this report includes both the total observed gain and the gain above this expected growth rate, allowing readers to evaluate the data with appropriate context.
Study Sample
Age Distribution
The longitudinal cohort consisted of 44 preschool-aged children, each with two complete O.T. Wizard evaluations separated by a minimum of 28 days. Three additional pairs were excluded due to inter-evaluation intervals of fewer than 28 days. The mean inter-evaluation interval was 141.7 days, approximately 4.7 months, with a range of 56 to 169 days. Mean age at first evaluation was 54.2 months, range 44 to 60 months. Mean age at second evaluation was 58.9 months.
Starting age band distribution: Band G (36 to 47.99 months, n=2), Band H (48 to 53.99 months, n=18), Band I (54 to 59.99 months, n=22), and Band J (60 to 65.99 months, n=2). Bands H and I together accounted for 91 percent of the sample.
Demographics and Clinical Status
The sample was evenly distributed by gender with 22 females and 22 males. All assessments were conducted in North Carolina through O.T. Wizard’s clinical network. Consistent with the broader platform dataset, at least 90 percent of children qualified for Medicaid. The primary referring diagnosis was Specific Developmental Disorder of Motor Function (ICD-10: F82). All children had been recommended for occupational therapy services following developmental screening failure.
A critical methodological strength of this dataset is rater consistency: 43 of 44 pairs, or 98 percent, were evaluated by the same therapist at both time points, using identical items, response scales, and scoring weights. This same-instrument, same-rater design substantially reduces the risk that observed score changes reflect measurement variability rather than true functional change.
Natural Growth Framework
Expected natural growth over the 4.7-month study interval: 8.7% (calculated as 4.7 months / 54.2 months mean age at Evaluation 1)
Before presenting findings, it is important to establish what we would expect to observe even without intervention. A child who matures from 54.2 months to 58.9 months of age has progressed approximately 8.7 percent through the developmental continuum captured by the instrument. This expected gain is attributable to natural maturation and applies regardless of clinical status. It requires no external literature to defend: it is a direct arithmetic relationship between age progression and scale progression on a fixed instrument.
This 8.7 percent figure serves as the reference threshold throughout this report. Domain gains below 8.7 percent suggest development did not keep pace with chronological age progression. Gains equal to 8.7 percent suggest maturation-equivalent growth. Gains above 8.7 percent represent functional improvement beyond what age progression alone would predict, and are therefore the most meaningful indicator of intervention impact. The Gain Above Expected column in each table makes this comparison explicit for every domain and subdomain assessed.
Research Hypotheses
Three primary hypotheses guided this analysis. First, children receiving occupational therapy services would demonstrate composite score gains substantially exceeding the 8.7 percent expected natural growth threshold. Second, domains most directly targeted by OT in preschool settings, specifically visual motor integration, fine motor skills, visual perception, and activities of daily living, would show the largest gains above expected growth. Third, Band H children (48 to 53.99 months) would demonstrate greater gains than Band I children, reflecting greater developmental sensitivity at a younger starting point.
Brief Literature Review and Context
The preschool years represent a critical window for fine motor and visual motor development. Prerequisite skills for handwriting, scissor use, and self-care begin consolidating between ages three and five, with neuromotor pathways underlying pencil control and bilateral coordination undergoing rapid maturation during this period. Interventions delivered during this window have the potential to alter developmental trajectories in ways that become substantially more difficult to achieve once children enter formal schooling.
Visual motor integration has been identified as one of the strongest predictors of kindergarten handwriting readiness. Daly and colleagues (2003) found that VMI performance at preschool age predicted handwriting speed and legibility at ages six to seven, with effect sizes exceeding those of fine motor or visual perception measures alone. Duff and colleagues (2015) demonstrated that children with developmental coordination disorder who receive targeted fine motor intervention during the preschool years show significantly better handwriting outcomes at school entry than matched peers without services, underscoring the importance of early, precisely measured intervention.
The measurement infrastructure required to track these outcomes longitudinally has historically been a limiting factor in OT outcomes research. O.T. Wizard was designed to address this gap, enabling structured, reproducible multi-domain measurement within the constraints of a standard clinical evaluation and supporting the kind of longitudinal outcome tracking that traditional paper-based protocols make impractical.
Key Findings
Composite Score: Overall Outcome
Across all 44 children, mean composite score increased from 502.9 at Evaluation 1 to 609.7 at Evaluation 2, a mean gain of 106.8 points representing 21.2 percent growth from baseline. Against the expected natural growth rate of 8.7 percent, this represents a gain of 12.5 percent above what maturation alone would predict. The difference was highly statistically significant (paired t-test, p < 0.001, Cohen’s d = 0.98). Thirty-six of 44 children (82 percent) showed improvement at the second evaluation.
Total composite gain: +21.2% | Expected natural growth: +8.7% | Gain above expected: +12.5%
Domain-Level Findings
The following table presents results across all seven skill-based assessed domains. Participation and Executive Functioning are addressed separately below, as their measurement properties during first-time evaluations warrant distinct interpretive considerations.
Domain
n
Eval 1
Eval 2
Total Gain
Gain Above Expected (8.7%)
p-value
Cohen’s d
Visual Motor Integration
43
36.0
61.3
+70.1%
+61.4%
< 0.001
1.38 (Large)
Activities of Daily Living
37
44.4
64.2
+44.8%
+36.1%
< 0.001
1.17 (Large)
Fine Motor Skills
39
45.6
58.8
+28.8%
+20.1%
< 0.001
0.85 (Large)
Gross Motor Skills
43
55.9
72.6
+30.0%
+21.3%
< 0.001
0.82 (Large)
Visual Perception
41
61.4
72.9
+18.8%
+10.1%
< 0.001
0.69 (Medium)
Praxis
44
51.7
51.9
+0.4%
-8.3%
0.964
0.01 (Negligible)
Table 1. Domain-level longitudinal comparison. Scores are percentage of maximum possible. Gain Above Expected subtracts the 8.7% natural growth threshold from total observed gain. Shaded rows reached statistical significance. All scores are pre-Rasch raw percentage values. Participation and Executive Functioning appear in dedicated sections below.
Visual Motor Integration produced the largest gain in the dataset, rising from 36.0 to 61.3, a total gain of 70.1 percent from baseline representing 61.4 percent above expected natural growth. Children who began with VMI performance at barely more than one-third of expected capacity exited the study interval having crossed the functional midpoint of the scale. Activities of Daily Living showed 36.1 percent above expected growth (d=1.17). Fine Motor Skills and Gross Motor Skills each exceeded 20 percent above expected, with large effect sizes. Visual Perception showed 10.1 percent above expected growth with a medium-to-large effect.
Praxis fell below the expected natural growth threshold with a negative Gain Above Expected value. This is interpreted as a measurement sensitivity question as much as a treatment response question: Praxis is a complex, context-dependent construct that may require longer intervention timelines, different assessment approaches, or Rasch-calibrated items to detect meaningful change over five months.
Subdomain-Level Findings
Subdomain-level analysis reveals where within each domain gains were concentrated and adds clinical texture to the domain-level picture.
Subdomain
n
Eval 1
Eval 2
Total Gain
Gain Above Expected (8.7%)
p-value
Cohen’s d
WAND-PreK Handwriting
40
37.3
63.5
+70.1%
+61.4%
< 0.001
1.18 (Large)
Dressing
43
44.8
63.1
+40.8%
+32.1%
< 0.001
1.06 (Large)
Hand Use
44
53.1
68.8
+29.6%
+20.9%
< 0.001
1.03 (Large)
Trunk Stability
44
56.1
73.2
+30.4%
+21.7%
< 0.001
0.84 (Large)
Scissor Use
44
36.0
59.5
+65.5%
+56.8%
< 0.001
0.84 (Large)
Complex Visual Motor Representation
44
30.9
52.3
+69.1%
+60.4%
< 0.001
0.80 (Large)
Bilateral Integration
43
49.6
63.6
+28.1%
+19.4%
< 0.001
0.71 (Medium)
Visual Discrimination
44
66.6
80.6
+21.0%
+12.3%
< 0.001
0.59 (Medium)
Visual Figure Ground
43
69.3
82.6
+19.1%
+10.4%
0.006
0.44 (Small)
Visual Memory
42
56.1
65.5
+16.8%
+8.1%
0.054
0.31 (Small, ns)
Visual Spatial Relations
42
53.1
61.6
+16.1%
+7.4%
0.143
0.23 (Small, ns)
Sequencing Praxis
44
51.7
51.9
+0.4%
-8.3%
0.964
0.01 (Negligible)
Table 2. Subdomain-level longitudinal comparison. Sorted by effect size. Gain Above Expected subtracts the 8.7% natural growth threshold. Shaded rows reached statistical significance (p < 0.05). All scores are pre-Rasch raw percentage values.
WAND-PreK Handwriting showed the largest subdomain effect size (d=1.18, p<0.001), with 61.4 percent above expected growth. Children who began with pre-writing performance at 37.3 percent of expected reached 63.5 percent after approximately five months of OT services. Scissor Use showed 56.8 percent above expected growth, rising from a mean of 36.0 to 59.5. Complex Visual Motor Representation showed 60.4 percent above expected. These three findings converge on a single clinical message: the VMI domain gain is not abstract. It is translating directly into the functional pre-academic tasks that define preschool readiness.
Hand Use (20.9% above expected, d=1.03), Dressing (32.1% above expected, d=1.06), and Trunk Stability (21.7% above expected, d=0.84) all showed large effect sizes with direct functional implications for classroom participation and daily independence. Bilateral Integration showed 19.4 percent above expected growth, consistent with bilateral coordination emerging as a measurable downstream benefit of fine motor and VMI development.
Visual Memory and Visual Spatial Relations showed gains of 8.1 and 7.4 percent above expected growth respectively, neither reaching statistical significance. These findings are consistent with prior O.T. Wizard analyses suggesting these constructs require longer measurement intervals or refined item calibration.
Performance by Starting Age Band
Starting Band
n
Eval 1 Mean
Eval 2 Mean
Point Change
Composite % Gain
G (36-47.99 mo)
2
408.0
421.0
+13.0
+3.2%
H (48-53.99 mo)
18
461.4
591.2
+129.7
+28.1%
I (54-59.99 mo)
22
531.2
639.1
+108.0
+20.3%
J (60-65.99 mo)
2
659.5
642.0
-17.5
-2.7%
Table 3. Composite score change by starting age band. Bands G and J should be interpreted with caution due to small sample sizes (n=2 each).
Band H children (ages 48 to 53.99 months) showed the largest absolute gains, averaging 129.7 points or 28.1 percent composite growth, more than three times the 8.7 percent natural growth expectation. Band I children averaged 20.3 percent composite growth, also substantially above expectation. These findings support Hypothesis 3 and suggest that the 48 to 54 month window may represent a particularly high-yield period for OT service delivery in this population.
Correlation Analysis
A significant negative correlation was observed between starting composite score and magnitude of change (r=-0.32, p=0.032), consistent with a regression-to-the-mean effect. Children who began with lower scores tended to show larger gains. This is expected in any clinical sample and does not invalidate the findings, but it is an important consideration when interpreting the highest-gain domains, which also tended to have the lowest Evaluation 1 starting scores. No significant correlation was found between inter-evaluation interval length and change score (r=0.05, p=0.742), nor between starting age and change score (r=-0.10, p=0.518).
A Closer Look: Bilateral Fine Motor Speed and Hand Dominance Development
The Fine Motor Speed and Accuracy task, referred to in O.T. Wizard, as the FMSAT (Fine Motor Speed and Accuracy) “Bubble Popping” assessment, requires children to use a pencil to puncture as many small circles as possible in 30 seconds, first with their preferred hand and then with the other hand. The two bubble counts are recorded separately, capturing not just fine motor speed in isolation but the functional relationship between dominant and non-dominant hand performance.
This bilateral structure means the Speed and Accuracy results cannot be interpreted as a simple pre-post longitudinal measure in the same way as other subdomains. Instead, the raw hand-level bubble counts offer a window into two parallel developmental processes: absolute fine motor speed improvement in each hand, and the widening of the performance gap between hands as hand dominance consolidates.
Measure
n
Eval 1
Eval 2
Change
% Change
p-value
Cohen’s d
Hand 1 (Dominant) – bubbles popped
39
15.3
18.8
+3.4
+22.4%
0.002
0.52 (Medium)
Hand 2 (Non-dominant) – bubbles popped
39
10.4
12.4
+2.0
+18.9%
0.100
0.27 (Trending)
Dominance gap (H1 minus H2)
39
4.90
6.36
+1.46
—
0.324
0.16 (Hypothesis-generating)
Table 4. Bilateral fine motor speed longitudinal analysis. Bubble counts reflect circles punctured in 30 seconds per hand. Dominance gap = Hand 1 minus Hand 2. Natural growth expectation: 8.7%.
The dominant hand showed a statistically significant gain of 3.44 bubbles (p=0.002, d=0.52), representing 22.4 percent improvement from baseline, well above the 8.7 percent natural growth expectation. The non-dominant hand showed a trending gain of 1.97 bubbles (p=0.10, d=0.27), representing 18.9 percent improvement, also above the natural growth threshold. Both hands therefore showed above-expected growth, with the dominant hand improving at a meaningfully faster rate.
The performance gap between hands widened directionally from 4.90 bubbles at Evaluation 1 to 6.36 bubbles at Evaluation 2, a gap change of 1.46 bubbles. This did not reach statistical significance at n=39 (p=0.324), which is expected: the O.T. Wizard hand dominance study required 348 children to detect a gap change of 0.73 bubbles over six months. At n=39, detecting a 1.46 bubble gap change requires larger samples than this longitudinal cohort provides. The finding is directionally consistent and hypothesis-generating rather than confirmatory.
Contextualizing these findings against the O.T. Wizard hand dominance study adds clinical depth. That analysis of 459 children found that children with established hand dominance showed a mean inter-hand gap of 4.55 bubbles, while children with no clear dominance showed only a 0.73 bubble gap. The current longitudinal cohort entered with a gap of 4.90 bubbles, already at the established-dominance level, and exited with a gap of 6.36 bubbles. This suggests that OT services may be supporting continued hand specialization beyond initial dominance establishment, driving the dominant hand to further advantage through targeted fine motor programming. Confirmation requires larger samples but the pattern is coherent with motor learning theory and with the hand dominance findings published separately in this research series.
Participation: Domain-Level Engagement Ratings
O.T. Wizard captures participation separately from skill performance through nine domain-specific engagement ratings, each scored on a five-point scale from No Engagement to Excellent Engagement. These ratings ask the evaluating therapist to rate how the child engaged during that domain’s evaluation tasks, creating a domain-matched engagement record that parallels the skill score data.
Rather than treating participation as a single aggregated outcome domain comparable to VMI or Fine Motor Skills, this report presents participation data as a construct validity and clinical context measure. The central question is whether children who perform better in a given skill domain also engage more fully during that domain’s assessment tasks. The answer, consistently across all four matched domains analyzed, is yes.
Skill Domain
Participation Matched Rating
Pearson r
Spearman rho
p-value
n
Visual Motor Integration
PART_VM
0.317
0.301
< 0.001
465
Visual Perception
PART_VP
0.481
0.464
< 0.001
444
Activities of Daily Living
PART_ADL
0.497
0.506
< 0.001
445
Gross Motor Skills
PART_GM
0.465
0.459
< 0.001
468
Table 5. Domain-matched skill score versus domain-specific participation rating correlations. All correlations significant at p < 0.001.
The overall composite skill score correlates with composite participation ratings at r=0.762 (p<0.001, n=469), a strong relationship that confirms participation ratings are not arbitrary. Children with higher functional skill levels are rated as more engaged during assessment tasks, and this relationship holds across domains. For VMI specifically, children in the lowest skill tertile received a mean VM participation rating of 3.42, while children in the highest skill tertile averaged 4.06. The proportion rated Good or Excellent engagement rose from 46.7 percent in the lowest VMI group to 82.9 percent in the highest.
These correlations serve as an important construct validity signal for the platform. A child’s competence in a domain predicts their engagement during that domain’s evaluation tasks, which is precisely what developmental and occupational therapy theory would predict. Skill and participation are not independent; they reinforce each other, and O.T. Wizard’s measurement structure captures that relationship.
The Novelty Effect Hypothesis and Longitudinal Participation
The longitudinal cohort of 44 children showed a composite participation gain from 61.0 to 63.1 over five months, a change that was not statistically significant (p=0.394, d=0.13). This result warrants careful interpretation rather than the conclusion that participation did not improve with OT services.
A clinically grounded alternative explanation is the novelty effect. At Evaluation 1, the child is meeting the therapist for the first time. The environment is new, the tasks are novel, and the structured one-on-one interaction may naturally elicit high cooperative behavior. Participation ratings at Eval 1 may therefore reflect novelty-driven engagement rather than the child’s true baseline functional participation. By Evaluation 2, the therapeutic relationship is established, the child is comfortable enough to reveal authentic behavioral and engagement patterns, and the therapist has sufficient rapport to observe the child’s genuine participation profile rather than their best performance.
If this hypothesis holds, Evaluation 1 participation ratings are systematically inflated relative to what they would show in a familiar context, meaning the scale at Evaluation 2 is measuring a meaningfully different construct than at Evaluation 1. This measurement context shift would explain the apparent lack of longitudinal gain without implying that participation failed to respond to intervention. It also opens a question with direct clinical and psychometric implications: are participation ratings most informative when collected after the therapeutic relationship is established, and should baseline participation norms account for the evaluative context?
This remains a hypothesis requiring empirical testing with larger longitudinal samples. It is noted here as both a study limitation and a direction for continuing research.
Executive Functioning: Work Habits Observed During Evaluation
The Executive Functioning domain in O.T. Wizard is assessed through the Work Habits in a 1:1 Therapy Setting subdomain, comprising eight items: Attention, Cooperation, Task Initiation, Task Persistence, Task Completion, Transition, Impulse Control, and Carryover of Skills. Each item is rated on a five-point scale from Not Present/Unable to Proficient, with ratings reflecting the child’s behavior as observed during the evaluation itself.
Across the full cross-sectional sample of 471 children, Work Habits items showed mean ratings ranging from 3.32 for Attention to 4.10 for Task Completion, indicating that the majority of children in this clinical population demonstrated adequate to proficient work habits during the evaluation. The EF Work Habits composite score correlates with overall composite skill performance at r=0.759 (p<0.001), a relationship comparable in strength to the participation-skill correlation. Higher-functioning children, as measured by composite skill scores, are observed to demonstrate better work habits during assessment.
Item-level correlations with specific skill domains add clinical texture. Attention correlates with VMI performance at r=0.324 and with Fine Motor at r=0.413. Task Initiation shows r=0.351 with VMI and r=0.456 with Fine Motor. Task Persistence correlates at r=0.447 with Fine Motor. These moderate relationships are consistent with the theoretical link between executive function and fine motor learning: children who can attend, initiate, and persist through structured tasks acquire fine motor skills more efficiently, and children with stronger fine motor programs may experience less frustration-driven task avoidance.
The Novelty Effect in Executive Functioning
The longitudinal cohort showed near-zero change in Executive Functioning from Evaluation 1 to Evaluation 2 (+0.5%, p=0.916, d=0.02). The same novelty effect hypothesis that applies to participation applies here with equal force, and arguably with stronger clinical support.
A child encountering a new therapist in a structured evaluation setting has strong situational motivators for compliance: the novelty of the interaction, the desire to please an unfamiliar adult, and the absence of habituated behavioral patterns in that specific context. Cooperation, Impulse Control, and Task Persistence at Evaluation 1 may reflect the child’s best-case executive behavior under novel conditions rather than their typical regulatory profile. By Evaluation 2, these situational scaffolds have diminished. The child knows the therapist, has formed expectations about the session, and is more likely to exhibit their authentic regulatory patterns, including the attentional variability, transition difficulty, or impulse control challenges that characterize their daily functioning.
If Evaluation 1 Work Habits ratings are inflated by novelty effects and Evaluation 2 ratings reflect more authentic behavioral observation, then the absence of longitudinal gain is not evidence that OT failed to improve executive functioning. It may instead reflect the instrument now measuring what it was designed to measure. This interpretation has a meaningful implication for clinical documentation: therapist-observed Work Habits ratings collected after the therapeutic relationship is established may provide more valid clinical and baseline data than those collected at first encounter.
As with participation, this hypothesis requires prospective testing with larger samples and ideally with parent-reported or teacher-reported behavioral measures to establish convergent validity across contexts. It is presented here as a clinically grounded interpretive framework and a research priority.
Implications for OT Practice
The domain-level findings, interpreted through the natural growth framework, allow occupational therapists to make specific, evidence-informed decisions about evaluation and intervention priorities. Five domains showed gains ranging from 10.1 to 61.4 percent above the 8.7 percent natural growth rate, all with statistical significance and large or medium effect sizes. These gains were achieved in a predominantly Medicaid-qualifying, low-income clinical population with significant developmental concerns, which makes the magnitude of change all the more clinically meaningful.
The VMI finding demands particular attention in practice planning. A gain of 61.4 percent above expected natural growth indicates that children receiving OT services made VMI gains at roughly eight times the rate that maturation alone would predict over the same interval. Given VMI’s established role as a predictor of kindergarten handwriting readiness, prioritizing VMI-targeted activities including complex copying tasks, directed drawing, and structured pre-writing programs is strongly supported by this data.
For insurance authorization and educational planning, the natural growth framework provides a uniquely effective communication tool. Rather than reporting a raw score gain, the practitioner can state that the child’s VMI performance exceeded the expected developmental rate by 61 percentage points over five months, providing clear evidence that skilled OT intervention, not maturation, drove the observed change. This framing is defensible, transparent in its methodology, and directly responsive to the kind of medical necessity standard that payers apply.
The bilateral fine motor speed findings support continued OT intervention targeting hand specialization in children whose dominance is still consolidating. The dominant hand’s significant gain above natural growth, combined with the directional widening of the inter-hand gap, is consistent with OT services accelerating hand specialization. For children presenting with inter-hand gaps below two bubbles at preschool age, more intensive hand-specific programming may be clinically warranted.
The participation and work habits findings carry a practical message for therapist documentation practice. Domain-specific participation ratings that are collected after the therapeutic relationship is established are likely to be more clinically informative than those captured at first evaluation. Therapists should be aware that novelty effects at initial evaluation may produce elevated engagement and compliance ratings that do not generalize to the child’s authentic daily functioning profile.
Implications for Intervention Planning
The convergent gains across WAND-PreK Handwriting, Scissor Use, Complex VMI, Hand Use, Bilateral Integration, and Trunk Stability point toward a functional fine motor and VMI cluster that is highly responsive to structured OT programming in this developmental window. Gains in this cluster ranged from 19.4 to 61.4 percent above expected growth. Interventions that integrate tool use, bilateral coordination, and visual guidance of hand movements across this cluster are supported by both the data pattern and established OT theory.
The Trunk Stability findings reinforce a foundational clinical principle: proximal postural control precedes and supports distal fine motor development. Children who gained in trunk stability tended to gain across the fine motor and VMI cluster. For children showing limited fine motor or VMI gains, postural foundation assessment and intervention should be considered before assuming the upper extremity is the primary limiting factor.
Scissor use showed 56.8 percent above expected growth with a large effect size. Scissors require bilateral coordination, hand differentiation, VMI guidance, and sustained attention simultaneously, making scissor-based activities a naturally integrative target across multiple domains. The large effect size and high gain above expected growth suggest this subdomain is both highly responsive to OT intervention and highly sensitive to measurement, making it a valuable progress-monitoring target.
For Praxis, intervention targeting should not be abandoned based on the longitudinal findings reported here. The near-zero gain more likely reflects current measurement limitations than true insensitivity to OT services. As O.T. Wizard’s item bank is refined through Rasch calibration, this domain may demonstrate the sensitivity needed to capture incremental change that OT practitioners observe clinically.
The domain-matched participation correlations support incorporating participation-focused goal setting alongside skill-based goals, particularly for children showing low engagement in specific domains. A child rated at Minimal Engagement in VMI activities is likely showing competence-related avoidance rather than willful noncompliance, and addressing the underlying skill deficit is the most direct route to improved participation in that domain.
Study Limitations
This study carries several important limitations. The sample is clinical and geographically restricted to North Carolina, and findings cannot be generalized to typically developing children or populations in other regions. The absence of a control group means observed gains cannot be causally attributed to OT intervention.
The 8.7 percent natural growth correction applied throughout this report is a methodologically transparent and defensible estimate derived directly from the age progression of this cohort on this instrument. It assumes proportional developmental scaling across the score range, an assumption that Rasch calibration will allow us to test empirically. Future work with a typically developing comparison group will allow domain-specific, empirically derived growth expectations to replace this uniform estimate, strengthening the interpretive framework considerably.
The regression-to-the-mean effect (r=-0.32) means children with lower starting scores showed larger gains on average. Visual Motor Integration, WAND-PreK Handwriting, Scissor Use, and Complex VMI all had the lowest Evaluation 1 scores and the largest gains above expected growth. The true intervention effect within these domains is likely substantial, but the proportion attributable to treatment versus regression toward the mean cannot be fully separated without a control group.
Participation and Executive Functioning longitudinal findings are subject to a novelty effect interpretation that cannot be confirmed or ruled out with the current dataset. The possibility that Evaluation 1 ratings in these domains reflect novelty-driven behavior rather than true baseline functioning represents a meaningful threat to the internal validity of longitudinal comparisons in these areas specifically. This does not affect the skill-domain findings but warrants dedicated investigation in future work.
The bilateral fine motor speed gap-widening finding is hypothesis-generating at n=39 and requires larger samples to reach significance. Bands G and J contained only two children each, precluding any band-specific inference for those age ranges. All scores remain pre-Rasch ordinal percentage values. Statistical analyses were generated with the assistance of an AI-based analytical tool and reviewed by the author for clinical and numerical consistency. Final responsibility for interpretation and reporting rests with the author.
Continuing Research Needed
Rasch calibration remains the highest research priority. Transforming ordinal raw scores into interval-level person measures will allow true scale-independent longitudinal comparison, validate the proportional scaling assumption underlying the natural growth correction, and identify items requiring revision. The O.T. Wizard National Try-Out Team initiative is designed to accelerate this process by expanding the sample sizes needed for stable item calibration across all domains and age bands.
A comparison group of typically developing children will allow the 8.7 percent natural growth estimate to be validated empirically and replaced with domain-specific growth expectations calibrated against external developmental benchmarks. This will strengthen the natural growth framework applied in this report and make the gain-above-expected metric more precise for insurance and educational reporting contexts.
The novelty effect hypothesis for participation and executive functioning requires prospective investigation. A study design that captures therapist-rated participation and work habits at multiple time points within the first year of services, alongside parent-reported and teacher-reported behavioral measures, would allow empirical testing of whether first-evaluation ratings systematically overestimate engagement and compliance relative to ratings collected after therapeutic relationship establishment. Confirming or disconfirming this hypothesis has implications for how O.T. Wizard instructs therapists to interpret and apply participation and EF data in clinical documentation.
The domain-matched participation correlations reported here establish a foundation for construct validity research. Future work linking participation ratings to standardized adaptive behavior measures and teacher-rated classroom engagement would extend these findings and position the participation ratings as clinically actionable data rather than descriptive context.
The bilateral fine motor speed findings warrant dedicated follow-up with larger samples. Testing whether inter-hand gap widening reaches significance at n=100 or greater, whether gap widening correlates with therapist-documented hand dominance establishment, and whether children who begin with gaps below two bubbles show different intervention trajectories would substantially extend the hand dominance research program established in the O.T. Wizard FMSAT study.
Linking O.T. Wizard performance data to standardized criterion measures including the Beery VMI, BOT-2, and teacher-rated school readiness indicators would establish concurrent and predictive validity, and would allow the natural growth framework applied here to be calibrated against externally validated developmental benchmarks.
Conclusion
This analysis of 44 preschool-aged children with paired O.T. Wizard evaluations, examined through a transparent natural growth framework, provides the most complete domain-level longitudinal outcome picture generated by the platform to date. The expected natural growth rate of 8.7 percent over the 4.7-month study interval serves as a consistent interpretive reference throughout, allowing readers to distinguish maturation from intervention-attributable change across every domain reported.
The findings are striking. Five of seven skill domains showed gains substantially exceeding the 8.7 percent natural growth threshold, with effect sizes ranging from medium to large. Visual Motor Integration showed gains 61.4 percent above expected. WAND-PreK Handwriting showed gains 61.4 percent above expected. Scissor Use showed gains 56.8 percent above expected. These are not marginal differences from expectation. They represent functional gains at rates six to eight times what chronological maturation alone would produce over the same interval.
The domain-matched participation analysis adds a construct validity dimension to these findings. Children who perform better in a skill domain engage more fully during that domain’s assessment, with domain-matched correlations ranging from r=0.317 to r=0.497 and an overall composite skill-participation correlation of r=0.762. Participation and executive functioning longitudinal data are reframed here through the novelty effect hypothesis, which proposes that first-evaluation ratings in these behavioral domains may reflect situational compliance rather than authentic baseline functioning, with more valid observational data emerging after the therapeutic relationship is established.
The FMSAT (“Bubble Popping”) extends these findings into hand dominance development, showing that the dominant hand improved significantly above the natural growth rate and that the inter-hand performance gap widened directionally, consistent with progressive hand specialization during OT services.
These findings are preliminary. They require replication with larger samples, validated comparison conditions, and Rasch-calibrated measurement. What they establish is that structured domain-level digital assessment in pediatric OT can generate longitudinal outcome data that clearly and transparently distinguishes intervention-driven change from natural maturation. For a profession that has historically struggled to quantify its impact in terms that payers and educational systems recognize, that distinction is not a minor technical refinement. It is the foundation of evidence-based practice. O.T. Wizard is building that foundation, one evaluation at a time.
Disclosures
The author is the founder of O.T. Wizard and has a financial interest in the platform. All analyses were conducted on de-identified clinical data collected in routine practice.
References
Daly, C. J., Kelley, G. T., & Krauss, A. (2003). Relationship between visual-motor integration and handwriting skills of children in kindergarten: A modified replication study. American Journal of Occupational Therapy, 57(4), 459-462. https://doi.org/10.5014/ajot.57.4.459
Duff, S. V., Chow, S. M., & Henderson, S. E. (2015). Developmental coordination disorder and its consequences for children. In A. F. Farrow & P. J. Tremblay (Eds.), Pediatric rehabilitation: Principles and practice (5th ed., pp. 189-215). Demos Medical Publishing.
Zwicker, J. G., Missiuna, C., Harris, S. R., & Boyd, L. A. (2012). Developmental coordination disorder: A review and update. European Journal of Paediatric Neurology, 16(6), 573-581. https://doi.org/10.1016/j.ejpn.2012.05.005
Polatajko, H. J., & Cantin, N. (2010). Exploring the effectiveness of occupational therapy interventions, other than the sensory integration approach, with children and adolescents experiencing difficulty processing and integrating sensory information. American Journal of Occupational Therapy, 64(3), 415-429. https://doi.org/10.5014/ajot.2010.09073
About O.T. Wizard
O.T. Wizard is a clinical intelligence system for pediatric occupational therapy professionals. The platform evaluates performance in evaluations, forms, and daily treatment notes across twelve domains including visual-motor integration, fine motor skills, gross motor skills, praxis, visual perception, executive functioning, activities of daily living, and participation. OT Wizard is undergoing Rasch analysis validation to establish psychometrically sound, norm-referenced scoring with living norms that update continuously as the clinical database expands.
Data collection is ongoing. All data is de-identified in accordance with HIPAA and FERPA regulations.For information about O.T. Wizard research or accessing the platform, visit otwizard.com
From time to time, we invite occupational therapy practitioners whose use of OT Wizard reflects strong clinical reasoning, real-world application, and thoughtful feedback to participate in a spotlight.
User spotlight with Angie Bowman, COTA/L
Can you tell us a little about your background as an OTP, and your work setting(s) and typical patient ages ?
I have always worked in pediatrics in various settings, primarily schools and clinics but some in-home early intervention as well for the past 30 years!
Do you have a favorite domain or type of challenge you enjoy treating most?
Sensory processing is probably the most interesting area that OT’s address to me. There is so much new research coming out that is validating what we as OTP’s have known for years and I feel like it is something commonly misunderstood.
What does a “normal” day look like for you?
I am working with only preschool children so really busy in the mornings and less so in the afternoons. I work until the little ones nap, then go home for paperwork and planning.
Would you rather make your own OT supplies or buy them?
A combination of both I think although some of my best and most used toys are the homemade ones.
If you could only bring 5 therapy items to a session, what would they be?
1) Theraputty or some type of resistive media because I really like starting off the session with it. Great for hand strengthening but also for providing the proprioceptive input they need before fine motor work.
2) A ball! You can do a lot with them besides catch and toss! Rolling up and down the wall or across tracks on the wall with tape are great motor planning and bilateral hand skills activities.
3) Crayons and paper, broken ones preferably taped to a wall.
4) A pickle picker! This is my favorite fine motor tool
5) Scooter board, you can use them in any setting and do so much with them.
When you were told that your therapy group would be using OT Wizard, how did you initially feel about adding on a new process for evaluations?
I was intrigued. As a COTA/L I have assisted with a lot of standardized evaluations but I loved that this one is performed online and that it encompasses so many areas.
Was there a moment where OT Wizard really “clicked” for you?
I liked it right away but as I began to read the reports more and more, and I saw how good they were, it really sold me.
Has OT Wizard changed your approach to evaluations or reports now, even in small ways?
Absolutely. I love generating a therapist report on my children. It is generated under the abbreviated report with therapist focused as the target audience. It gives you a clinical snapshot with strengths and weaknesses and clinical takeaways. THEN, evidenced-based interventions with different phases for implementation. It gets you thinking about what you have done that has or has not worked and helps you to generate new ideas and/or affirm your thought process. Sometimes we get into ruts when working with a child for a long time or that is particularly challenging in response to therapy and this is a great tool to use.
What is your favorite feature in OT Wizard?
Definitely the report as stated above.
What upcoming feature are you most excited about?
Daily notes and progress reports for parents via the integrated messaging.
What would you say to another OTP who’s feeling hesitant or skeptical about trying OT Wizard?
The time you save in report writing alone is unbelievable and in a lot of ways has made me a better therapist!
What’s one thing you don’t miss doing since using OT Wizard?
Extra paperwork
Angie is on the Leadership Team at Learning Charms, which is the company that created O.T. Wizard. Angie is always excited to try new features, and makes great suggestions for changes and upcoming features. As Angie has been practicing for 30 years, she understands which features are most meaningful to OTP’s. Angie works in preschools that cater to at risk children and loves collaboration with teachers and getting hugs from her kiddos. For these reasons, this OT Wizard hat is well deserved.
A descriptive snapshot from structured pediatric OT evaluation data (pre-Rasch)
Occupational therapists are trained to interpret evaluations one child at a time. What we rarely get to see is how our observations look in aggregate, across hundreds of evaluations completed using the same structure.
This article summarizes descriptive patterns from the PHI free data of OT Wizard’s preschool OT evaluation data collected since September 2025 using a consistent, structured evaluation framework. The goal is not to draw diagnostic or psychometric conclusions, but to surface real patterns that emerge when data is viewed collectively.
Sample Overview
Number of evaluations: 404
Approximate number of students: 404
Gender: Male: 56%, Female: 44%
Student Age range (two bands): 4:0-4:11.99
Primary Language Spoken: English: 90%, Spanish: 9%, Other: 1%
Primary Diagnosis: F82(Specific Dev Disorder of Motor Function): 95%, Autism: 5%
Sample is from preschoolers referred to OT evaluation; not a normative population sample
Total item-level data points: 19,062 normalized responses
Therapist Experience in this dataset
Average therapist experience: 10.3 years
Range of therapist experience: 3 to 30 years
Composite Scores (raw, non-Rasch)
Sample average composite score:559.7 /1000 (or 55.9%: Emerging Band)
Observed range:137 – 873 /1000 (or 13.7%-87.3%)
This wide range reflects substantial variability within a relatively narrow age span, reinforcing the importance of looking beyond single summary numbers.
Methods (brief and explicit)
Item-level results are based on normalized scores (0–1) , averaged across evaluations and excluding missing values. Domain and subdomain scores reflect the mean of available scores only; missing data were not treated as zero. No Rasch or item-response modeling has been applied at this stage.
Domain Scores (out of 100 points)
Ranked highest → lowest
Participation, 71
Executive Functioning, 67
Visual Perception, 63
Gross Motor Skills, 63
Praxis, 55
Fine Motor Skills. 53
Activities of Daily Living (ADL), 50
Visual Motor Integration, 42
Even when age bands are combined, the ordering remains stable. Participation and executive functioning consistently rank highest, while visual motor integration anchors the lower end of the distribution. However, even the highest score (Participation) is at the low range of Proficient.
Subdomain Scores (out of 100 points)
Ranked highest → lowest
Visual Discrimination, 74
Visual Figure–Ground, 71
Participation, 71
Work Habits (1:1 Therapy Setting), 67
Trunk Stability, 62
Hand Use, 62
Visual Memory, 56
Bilateral Integration, 61
Visual Spatial Relations, 55
Sequencing Praxis, 55
Dressing, 50
Scissor Use, 47
Pre Handwriting ,43
Speed and Accuracy, 43
Complex Visual Motor Representation & Integration, 38
Looking at the full ordering — not just the top or bottom — helps clarify where variation clusters across perceptual, motor, and participation-based constructs.
Item-Level Results (out of 100 points)
Normalized scores, ranked highest → lowest
Highest-ranking items
Match the shape (VP_VD_35), 100
Find the same shape (VP_VD_40), 100
What color is each bear? (VP_VD_47), 91
Dons slip-on shoes (ADL_DRESS_36), 100
Balance on one foot for 5 seconds (GM_TS_40), 86
Snip paper in at least one spot (FM_SCIS_6), 82
Dons simple front-opening coat (ADL_DRESS_33), 87
Transition performance during evaluation (EF_WORKHAB_5), 81
Several of these items show average normalized scores approaching or reaching 1.0.
Lowest-ranking items
Sequencing praxis: 4 steps with symbolic action (PR_SP_72)
Visual Memory (multiple VP_VM items)
Hop on one foot repeatedly (GM_TS_44)
Skip for multiple cycles (GM_TS_60, GM_TS_72)
Draw-A-Person task (VM_DAP)
Letters written legibly (VM_WANDPK4)
Pencil bubble popping (FMSAT) – non-dominant hand (POP_BUBB_11)
Latch a separated zipper (ADL_DRESS_54)
These items consistently fall at the lower end of the distribution across the combined preschool sample.
A necessary note about very high average scores
Items with average normalized scores near 1.0 may reflect several possibilities:
Developmentally easy tasks for much of the sample
Ceiling effects
Limited discrimination at this age range
Potential item misfit
Which explanation applies cannot be determined without psychometric modeling. These patterns are signals to investigate, not conclusions — and they help inform future Rasch analysis decisions.
Why this snapshot matters
None of these findings are surprising in isolation. What is meaningful is seeing how the entire evaluation system orders itself when applied consistently across hundreds of children.
This kind of ranking:
Makes implicit patterns visible
Highlights where variability concentrates
Provides a grounded baseline for future validation work
Most importantly, it reflects how structured OT evaluation data actually behaves — not how we assume it does when cases are viewed one at a time.
As this dataset grows and Rasch analysis is completed, these descriptive patterns will be tested, refined, and in some cases challenged. For now, they offer a clear, honest snapshot of the current data — and a strong foundation for what comes next.
About O.T. Wizard
Data for this analysis was collected through OT Wizard, a clinical intelligence system for pediatric occupational therapy assessment. The platform evaluates performance across up to twelve domains including visual-motor integration, fine motor skills, gross motor skills, praxis, visual perception, visual motor integration, executive functioning, activities of daily living, and participation. OT Wizard is undergoing Rasch analysis validation to establish psychometrically sound, norm-referenced scoring with living norms that update continuously as the clinical database expands.
Unlike traditional checklist-based assessments, OT Wizard converts all observations to continuous metrics that enable progress tracking, cross-domain comparison, and comprehensive reporting. The platform captures all six factors identified in this research as predictive of handwriting success: fine motor skills, visual perception (with subdomain specificity), praxis, cooperation, attention, and task participation. Behavioral regulation is assessed within the context of actual task performance rather than as an isolated rating, providing clinically relevant data about how attention and cooperation affect functional skill demonstration.
For handwriting readiness assessment specifically, OT Wizard provides quantified performance across visual discrimination, visual-motor integration, fine motor control, motor planning, and behavioral engagement during writing tasks. This comprehensive approach addresses the multifactorial nature of handwriting development identified in this research. As the platform undergoes Rasch analysis validation and accumulates longitudinal outcome data, it will establish whether comprehensive baseline assessment across all six predictors improves identification of children at risk for handwriting difficulty and informs more effective intervention planning.
OT Wizard is committed to advancing the occupational therapy profession by collecting de-identified clinical data from real therapist users, building the largest developmental database in pediatric occupational therapy history. This continuous data collection enables research on developmental trends, intervention effectiveness, and response to intervention patterns that elevate practice from perception-based to data-driven decision making and strengthen the evidence base for the entire profession
For OT professionals interested in data-driven assessment tools, visit otwizard.com to learn more about evidence-based pediatric evaluation.
Every occupational therapist has witnessed it: a child struggling to cut along a line, gripping their pencil awkwardly, or switching hands mid-task. While we understand hand dominance is foundational to fine motor skill development, we’ve historically relied more on clinical observation than quantitative data to assess when hand preference becomes truly established.
How much motor output difference between a child’s dominant and non-dominant hand is “normal”? Does this gap widen as children mature? And critically, can we measure hand dominance in a way that’s both clinically meaningful and statistically sound?
These questions drove me to analyze data from O.T. Wizard’s Fine Motor and Accuracy Test (FMSAT) —a simple, 60-second paper-pencil task that measures fine motor speed, precision, and coordination. The results from 459 pediatric assessments offer compelling insights into how hand dominance manifests in functional performance, and what this means for OT practice.
THE ASSESSMENT: FINE MOTOR SPEED AND ACCURACY TEST (FMSAT)
The FMSAT task is straightforward: children use a sharpened pencil to “pop” (puncture) as many small circles “bubbles” as possible in 30 seconds, with the worksheet placed over craft foam. After completing one hand, they switch and repeat with the opposite hand. The task measures fine motor coordination and control, speed and precision, finger strength, and hand preference and dominance patterns.
Children are allowed to choose which hand to use first—a critical design feature that lets us observe natural hand preference rather than imposing it. Some kids switch hands mid-task, for those , the therapist selects RL or LR (meaning started R and switched to L or vice versa).
STUDY SAMPLE
This analysis draws from 459 clinical assessments collected through O.T. Wizard during our soft launch phase. All data comes from pediatric occupational therapy evaluations conducted in North Carolina. “n” is the sample size.
Age Distribution:
Band G (ages 36-47 months): n=53 (12%);
Band H (ages 48-53 months): n=185 (40%);
Band I (ages 54-59 months): n=221 (48%)
Demographics:
57% male, 43% female.
Language: 90% English primary, 9% Spanish, 1% other.
Clinical status: 86% of children were ultimately recommended for OT services, with the primary diagnosis being Specific Developmental Disorder of Motor Function (F82, ICD-10). *The FMSAT was included in the evaluation; recommendation to OT services was based on the full evaluation results.
Important Note: Due to limited sample size in Band G (only 19 children completed both hands of the assessment), primary statistical analyses focus on Bands H and I (n=406 total, n=348 with complete bilateral data). This is consistent with psychometric best practices, which recommend minimum sample sizes of 30-50 per category for stable estimates.
RESEARCH HYPOTHESES
Based on developmental occupational therapy theory and clinical observation, I hypothesized:
Hypothesis 1: Children will preferentially choose their preferred or dominant hand first (Hand 1), resulting in significantly better performance with Hand 1 compared to Hand 2.
Hypothesis 2: The performance gap between dominant and non-dominant hands should increase with age, as hand dominance becomes more established through the preschool years.
Hypothesis 3: Children with consistent, established hand dominance (right or left) will show larger performance differences between hands compared to children with inconsistent or unclear hand preferences.
BRIEF LITERATURE CONTEXT
Hand dominance emerges gradually during early childhood, with most children showing consistent hand preference by age 3-4 years and full establishment by age 6. This developmental process is crucial for skill refinement—as one hand becomes increasingly specialized for fine motor tasks, the other develops complementary stabilization and assist functions.
Research consistently links established hand dominance to improved academic skills, particularly handwriting fluency and speed. Conversely, delayed or inconsistent hand preference has been associated with developmental coordination difficulties and may signal underlying neurological immaturity.
However, most studies examine hand preference categorically (right vs. left) rather than quantifying the degree of functional difference between hands. This gap motivated our analysis: can we measure not just which hand a child prefers, but how much better that hand actually performs?
KEY FINDINGS
Overall Performance Patterns (Bands H and I Combined)
Across 348 children with complete bilateral data,
Hand 1 (First Hand ) averaged 16.15 bubbles popped
Hand 2 (Second Hand) averaged 11.74 bubbles popped, with a mean difference of 4.41 bubbles
Notably, 79.3% of children performed better with Hand 1 than Hand 2.
These findings strongly support Hypothesis 1—children naturally select their more proficient hand first, and this difference is both statistically significant and functionally meaningful.
Hand Dominance Categories and Performance
Children were categorized based on therapist-documented hand dominance:
Right Hand Dominant (n=285, 82%):
Hand 1 averaged 16.65 bubbles,
Hand 2 averaged 11.90 bubbles, with a difference of 4.74 bubbles (28.5% advantage).
80.7% showed Hand 1 greater than Hand 2, and only 27.4% showed similar performance (less than or equal to 2 bubble difference).
Left Hand Dominant (n=33, 9%):
Hand 1 averaged 14.67 bubbles,
Hand 2 averaged 11.82 bubbles, with a difference of 2.85 bubbles (19.4% advantage).
75.8% showed Hand 1 greater than Hand 2, and 45.5% showed similar performance.
No Clear Dominance (n=30, 9%):
Hand 1 averaged 12.20 bubbles,
Hand 2 averaged 11.47 bubbles, with a difference of 0.73 bubbles (6.0% advantage).
46.7% showed Hand 1 greater than Hand 2, with a median difference of 0 bubbles.
Statistical Comparison: Children with consistent dominance (right or left) showed a 4.55 bubble difference. Children with no clear dominance showed a 0.73 bubble difference. This represents a 6.2-fold larger performance gap in children with established dominance, strongly confirming Hypothesis 3.
Developmental Progression: Ages 4:00-4:11
One of the most clinically significant findings emerged when examining how hand dominance evolves across just six months of development:
Age Band H (4:00-4:05, n=157): Mean difference of 3.82 bubbles (25.4%), with 75.8% showing Hand 1 greater than Hand 2.
Age Band I (4:06-4:11, n=191): Mean difference of 4.55 bubbles (26.8%), with 78.5% showing Hand 1 greater than Hand 2.
Change from H to I: plus 0.73 bubbles (plus 1.4 percentage points)
The pattern is clear: hand dominance differences increase with age, supporting Hypothesis 2. While the effect size is small, the consistent directional trend across this brief developmental window suggests progressive hand specialization.
Critically, this increase is driven by differential skill development. The dominant hand (Hand 1) improved by 1.96 bubbles (H to I), while the non-dominant hand (Hand 2) improved by 1.22 bubbles. The dominant hand gained 0.73 more bubbles than the non-dominant hand.
This pattern exemplifies the developmental principle of differentiation and specialization—the dominant hand isn’t just maintaining its advantage; it’s actively pulling further ahead as neuromotor pathways become increasingly refined.
Correlation Analysis
Hand 1 vs. Hand 2 Performance showed a strong positive correlation, indicating that while children vary in overall fine motor ability, bilateral coordination remains relatively consistent within individuals. Children with higher Hand 1 scores also tend to have higher Hand 2 scores, but the gap between them widens with established dominance.
IMPLICATIONS FOR OT PRACTICE
Assessment and Evaluation
Rather than simply documenting “right” or “left” hand preference, OT practitioners may now quantify the degree of dominance. A 4-5 bubble difference may suggest well-established dominance, while differences under 2 bubbles may indicate emerging or inconsistent preference.
Children with no clear hand dominance showed near-equal performance between hands (0.73 bubble difference), lower overall performance (12.20 vs. 16.65 bubbles for right-dominant peers), and only 46.7% consistency in hand choice. These children warrant closer developmental monitoring and may benefit from interventions targeting hand specialization alongside general fine motor development. Of course, this would not be the case in younger children than our sample when laterality is still in development.
For children ages 4:00-4:11, expect a 3-5 bubble advantage for the dominant hand, 75-80% consistency in using the dominant hand for skilled tasks, and progressive widening of the performance gap over 6-month intervals.
Intervention Planning
Children showing less than 2 bubbles difference and inconsistent hand use at age 4 or older may benefit from activities that encourage hand preference establishment, not just bilateral coordination practice.
The strong correlation between Hand 1 and Hand 2 performance suggests that improving overall fine motor skills benefits both hands. However, children with established dominance show specialized development—interventions should support both general skill building AND hand-specific refinement.
Documentation for Insurance and Educational Teams
Performance data showing significantly reduced fine motor speed and precision provides concrete evidence for accommodations such as extended time for written work, reduced copying requirements, and assistive technology considerations.
For insurance authorization, demonstrating that a child’s hand dominance pattern deviates from age-expected norms (e.g., less than 2 bubble difference at age 4 or older) may help support medical necessity for skilled OT intervention.
Serial assessments can document functional improvement in hand dominance establishment, not just overall fine motor gains.
IDENTIFYING THE WRITING HAND IN OLDER CHILDREN
This simple assessment becomes invaluable when working with older children who appear ambidextrous or who have been switching hands for years. Many parents proudly report “my child is ambidextrous!” when their 7-year-old writes with both hands. However, upon closer examination, the handwriting is often slow, effortful, and illegible with both hands.
The clinical dilemma: how do you choose which hand to train for handwriting when a child has been switching for years?
Handwriting is motor memory. Every time you write the letter “a,” your brain strengthens a specific motor pattern in the hand you’re using. When a child switches hands, they’re essentially learning two different motor programs for the same letter—and neither program gets enough practice to become automatic.
Motor learning research is clear: inconsistency prevents automaticity. A child who writes with both hands is essentially a beginner with each hand, never progressing to the automatic stage where handwriting becomes effortless.
The FMSAT provides objective data in just 60 seconds. A 3-bubble or greater difference may suggest a neurologically preferred hand—even if the child has been switching hands for years due to habit, environmental factors, or well-meaning adults who thought “using both hands” was beneficial. For a four year old, equal performance (less than 2-bubble difference) tells you either hand could work, so consider other factors like which side shows better pencil grip, less fatigue, or more consistent letter formation.
Once you’ve identified the better hand based on speed and accuracy data, you’re committed. I explain to parents and teachers: “We’re going to consistently use the right hand (or left hand) for ALL writing/drawing/coloring tasks from now on. This gives the brain a chance to build the motor memory it needs for fluent handwriting. ”
This is especially critical for children with developmental coordination difficulties. Their motor learning already takes longer than typical—asking them to learn two sets of motor patterns for every letter makes an already difficult task nearly impossible.
Strategies: I’ve found that putting a “stamp” on the dominant hand or a soft bracelet on the writing hand helps a child remember which hand to use so that verbal cues aren’t as necessary. To explain to a preschooler, I tell them “This is your boss hand. When you color or draw or write, this one holds the pencil because its the boss. Your other hand is the helper. “
STUDY LIMITATIONS
Several limitations should be considered when interpreting these findings:
Geographic and Cultural Homogeneity: All data comes from North Carolina, with 90% English-speaking children. Hand dominance patterns may vary across cultures with different tool use expectations or writing systems.
Clinical Population: About 86% of the children were recommended for OT services, meaning this sample represents children with developmental concerns rather than typically developing peers. The hand dominance differences observed may differ from the general population.
Cross-Sectional Design: We examined different children at different ages rather than following the same children over time. Longitudinal studies would provide stronger evidence of developmental trajectories.
Age Band G Underpowered: Only 19 children in Band G (ages 3:06-3:11) completed both hands, limiting our ability to examine younger developmental patterns.
Single Assessment Task: While FMSAT measures important fine motor components, hand dominance manifests across many functional tasks. Triangulating with other assessments would strengthen findings.
CONTINUING RESEARCH NEEDED
Expanded Age Bands: Critical questions remain about hand preference emergence in younger children (ages 2:00-3:05) and whether the hand dominance gap continues widening through elementary years (ages 5:00-7:11) or plateaus. Establishing adult norms would provide developmental endpoints for clinical interpretation.
Longitudinal Studies: Following individual children over 12-24 months would reveal individual variation in dominance establishment timelines, whether intervention can accelerate hand preference development, and predictive validity: do FMSAT differences at age 4 predict handwriting fluency at age 6?
Typically Developing Comparison: Recruiting a non-clinical sample would establish true normative data, determine if clinical populations show delayed or atypical dominance patterns, and support differential diagnosis and intervention planning.
Academic Outcome Correlations: Linking FMSAT performance to standardized handwriting assessments, teacher-reported classroom performance, and academic achievement in writing-heavy subjects.
Expanded Diversity: Collecting data across multiple geographic regions, diverse cultural backgrounds, various language groups, and different diagnostic categories.
CONCLUSION
This analysis of 459 clinical assessments provides compelling evidence that hand dominance can be quantified in a simple, time-efficient assessment that holds clinical meaning for OT practitioners. In our data, the FMSAT task successfully discriminates between children with established versus unclear hand preferences, captures expected developmental progression across the preschool years, and generates data precise enough for goal-setting, progress monitoring, and outcomes research.
Three key findings stand out:
First, children with consistent hand dominance show 6.2 times larger performance differences between hands compared to children with unclear preferences (4.55 vs. 0.73 bubbles).
Second, hand dominance strengthens measurably over just six months in the preschool period (ages 4:00-4:11), with the dominant hand pulling 0.73 bubbles further ahead.
Third, the majority of 4-year-olds (91%) demonstrate clear hand dominance, making this a critical developmental window for identification and intervention.
As O.T. Wizard continues collecting data and expanding age bands, we’re building an evidence base that moves pediatric OT practice from subjective observation to quantifiable, research-informed assessment. Hand dominance isn’t just a checkbox on an evaluation form—it’s a measurable developmental milestone with implications for every fine motor task a child will encounter in school and daily life.
For the 9% of children in our sample who showed no clear hand dominance at age 4 or older, this data validates what we see clinically: these children need support. Not just general fine motor therapy, but targeted intervention to establish the hand specialization that underlies skilled tool use, handwriting, and bilateral coordination.
Most importantly for clinical practice, this 60-second assessment provides objective data to confidently identify the neurologically preferred hand in older children who have been switching—ending the ambidexterity myth and establishing the consistency needed for motor learning to progress to automaticity.
REFERENCES
Kushki, A., Chau, T., & Anagnostou, E. (2011). Handwriting difficulties in children with autism spectrum disorders: A scoping review. Journal of Autism and Developmental Disorders, 41(12), 1706-1716.
Marschik, P. B., Einspieler, C., Guzzetta, A., et al. (2008). Behavioral patterns of exploration and approach in children with and without developmental delay. Developmental Medicine & Child Neurology, 50(9), 664-669.
Sacrey, L. A., Arnold, B., Whishaw, I. Q., & Gonzalez, C. L. (2012). Precocious hand use preference in reach-to-eat behavior versus manual construction in 1- to 5-year-old children. Developmental Psychobiology, 55(8), 902-911.
Scharoun, S. M., & Bryden, P. J. (2014). Hand preference, performance abilities, and hand selection in children. Frontiers in Psychology, 5, 82.
About O.T. Wizard
Data for this analysis was collected through OT Wizard, a clinical intelligence system for pediatric occupational therapy assessment. The platform evaluates performance across up to twelve domains including visual-motor integration, fine motor skills, gross motor skills, praxis, visual perception, visual motor integration, executive functioning, activities of daily living, and participation. OT Wizard is undergoing Rasch analysis validation to establish psychometrically sound, norm-referenced scoring with living norms that update continuously as the clinical database expands.
Unlike traditional checklist-based assessments, OT Wizard converts all observations to continuous metrics that enable progress tracking, cross-domain comparison, and comprehensive reporting. The platform captures all six factors identified in this research as predictive of handwriting success: fine motor skills, visual perception (with subdomain specificity), praxis, cooperation, attention, and task participation. Behavioral regulation is assessed within the context of actual task performance rather than as an isolated rating, providing clinically relevant data about how attention and cooperation affect functional skill demonstration.
For handwriting readiness assessment specifically, OT Wizard provides quantified performance across visual discrimination, visual-motor integration, fine motor control, motor planning, and behavioral engagement during writing tasks. This comprehensive approach addresses the multifactorial nature of handwriting development identified in this research. As the platform undergoes Rasch analysis validation and accumulates longitudinal outcome data, it will establish whether comprehensive baseline assessment across all six predictors improves identification of children at risk for handwriting difficulty and informs more effective intervention planning.
OT Wizard is committed to advancing the occupational therapy profession by collecting de-identified clinical data from real therapist users, building the largest developmental database in pediatric occupational therapy history. This continuous data collection enables research on developmental trends, intervention effectiveness, and response to intervention patterns that elevate practice from perception-based to data-driven decision making and strengthen the evidence base for the entire profession
For OT professionals interested in data-driven assessment tools, visit otwizard.com to learn more about evidence-based pediatric evaluation.
All data de-identified in accordance with HIPAA regulations.
A Data-Driven Investigation into How Letter Recognition and Copying Skills Actually Develop
The Question That Started It All
For decades, occupational therapists and educators have debated a fundamental question: Do children need to identify letters before they can copy them?
Traditional developmental hierarchies suggest a clear sequence: first comes letter recognition (knowing that the shape “A” is called “A”), then comes the ability to reproduce that letter through writing or copying. This assumption underlies countless kindergarten readiness checklists and early intervention programs.
But what if this assumption is wrong?
What the Research Literature Says
Recent research paints an interesting picture about the relationship between letter knowledge and handwriting. Multiple studies from 2005-2025 have established that handwriting practice enhances letter recognition. Most occupational therapists agree with that. Children who learn letters through handwriting (copying or tracing) demonstrate better letter identification on post-tests compared to those who learn by typing (Neuroscience NewsScienceDirect), and fMRI studies show that handwriting activates visual letter-processing regions in the brain more effectively than other methods(PubMed Central).
Educational researchers recommend that when students are learning letter identification, they should simultaneously engage in learning how to form the letter ( Uiowa). Writing readiness prerequisites identified in the literature include both alphabet letter recognition and basic stroke formation( Illinois) – presented as co-occurring skills rather than sequential steps.
However, here’s what’s largely missing from the research: Does letter identification actually predict copying ability? Most studies examine whether handwriting improves recognition (it does), but few investigate whether recognition is necessary for copying success.
We analyzed assessment pre-Rasch data from 471 preschool children (ages 3-5) of a clinical sample who completed a letter copying assessment. Each child was asked to:
Identify 10 uppercase letters (L, F, R, S, X, N, C, K, V, A)
Copy 10 letters (L, F, R, S, X, N, C, K, V, A) from a visual model
For each copied letter, we scored (binary, yes/no):
Formation: Did they use a mostly top-to-bottom stroke?
Legibility: Was the copied letter recognizable as such?
Directionality: Was the letter copied with correct spatial orientation?
We then calculated correlations – a statistical measure of how strongly two skills relate to each other. A correlation of 1.0 means they’re perfectly linked; 0.0 means they’re completely independent; and anything below 0.3 is considered very weak.
The Ah-Ha Moment #1: Letter ID Barely Predicts Copying
Here’s what shocked us: The correlation between letter identification and copying skills ranged from 0.05 to 0.29 across all age groups and all copying tasks.
What does this mean ?
At r = 0.29, r² = 0.08, Letter Identification explains about 8% of variance in copying performance. That’s like saying knowing someone’s height tells you almost nothing about their shoe size – the two things just aren’t that related. The graphic below shows how letter identification skills correlate to the other variables and within each age band.
r value range
relationship
0.0 – 0.1
No meaningful relationship
0.1 – 0.3
Weak relationship
0.3 – 0.5
Moderate relationship
0.5 – 0.7
Strong relationship
0.7 – 1.0
Very strong relationship
Age Group (years)
ID →Top to Bottom Formation
ID → Legibility
ID → Directionality
Youngest (3:5-4:0)
r = -0.05
r = 0.20
r = 0.20
Middle (4:0-4:5)
r = 0.28
r = 0.19
r = 0.14
Oldest (4:5-5:0)
r = 0.23
r = 0.29
r = 0.17
Translation: Whether a child can identify a letter tells you almost nothing about whether they can copy it successfully. These are developing as largely independent skills.
The Ah-Ha Moment #2: The “Letter A Paradox”
The clearest example came from the letter A in our youngest group:
36.4% could identify the letter A (highest recognition rate and likely because its at the beginning of the alphabet)
Only 5.6% could copy it with correct formation
Only 3.7% could copy it legibly
That’s a 30-point gap. Kids knew it was an A, but couldn’t reproduce the complex diagonal strokes needed to draw one.
Meanwhile, for the letter L:
23.6% could identify L
20.4% could copy it with correct formation
Only a 3-point gap – nearly equal performance.
Why? Letter A requires two diagonal strokes meeting at a precise point with a horizontal crossbar (developmentally complex) -therefore recognition and production appear to rely on different skill demands. Letter L is just a vertical line with a horizontal base – simple enough to copy through visual matching alone, even without knowing it’s called an “L.”
The Ah-Ha Moment #3: By Age Band 4:5-5:0, Copying Can Be Easier Than Identifying
Here’s where it gets really interesting. By the oldest age group, we started seeing positive gaps – children who could copy letters they couldn’t identify:
Letter L: 44.5% could identify it, but 63.4% could copy it correctly (+19 points)
Letter V: 24.7% could identify it, but 44.2% could copy it correctly (+20 points)
Directionality overall: Children performed 3.1 percentage points better at copying with correct spatial orientation than at identifying letters
This pattern defies the traditional hierarchy. Children were using visual matching – copying the shapes they saw – without needing to know the letter names.
What Does Predict Copying Success?
If letter identification doesn’t predict copying, what does?
We found that top-to-bottom formation strongly predicts legibility (correlation of 0.61-0.89, explaining 37-79% of variance).
But top to bottom formation itself correlates most strongly with:
Visual-Motor Integration: r = 0.76 (explains 58% of variance)
Visual Perception: r = 0.42
Fine Motor Skills: r = 0.44
But NOT with general motor planning (Praxis): r = 0.24 (only 5.7% variance)
The takeaway: Letter copying is primarily a visual-motor integration task – the ability to coordinate what you see with what your hand does. It’s not about general motor planning, and it’s certainly not dependent on knowing letter names.
How This Compares to Existing Research
Our findings complement rather than contradict current research:
Current research says: “Handwriting practice improves letter recognition” ✓ Our data adds: “But copying ability develops independently from letter knowledge”
Current research says: “Teach letter ID and handwriting together” ✓ Our data explains WHY: They support each other but develop through different pathways – one verbal-visual (naming), one visual-motor (copying)
The key distinction: Most research examines writing from memory (where letter knowledge clearly helps), while our study examined copying from a visual model (where visual-motor integration dominates).
Implications for Occupational Therapy Practice
1. Don’t Wait for Letter Mastery to Start Copying Practice
The weak correlations (r < 0.3) mean you can’t predict copying readiness from letter identification scores. A child who struggles to name letters might still succeed at copying them.
Action: Include copying tasks in early intervention even when letter knowledge is limited. You’re building visual-motor skills that develop on a parallel track.
2. Motor Control Develops Independently
Our data showed boundary control (staying within lines) actually performed better than letter identification at all ages – evidence that fine motor control for pencil management is a separate developmental pathway.
Action: Work on “staying in the lines” without requiring letter identification first. These are independent skills.
3. Use Simple Geometric Letters as Confidence Builders
Letters L and F showed the smallest gaps between ID and copying (-3 points) because their simple vertical/horizontal geometry enables visual matching. This is consistent with programs such as Handwriting Without Tears, as they order uppercase letter instruction by geometric easy to hard.
Action: Start with L, F, T, I for early success. Save A, K, R, S (complex diagonal/curve letters) for later, regardless of which letters the child can name.
4. Target the Critical Window: Middle Preschool
Our biggest developmental gains happened between the youngest and middle age groups (Ages G→H), not between middle and oldest (H→I):
Formation gap improved 4.6 points (G→H) vs. 4.8 points (H→I)
Legibility gap improved 6.1 points (G→H) vs. 5.1 points (H→I)
Directionality gap improved 8.5 points (G→H) vs. 8.4 points (H→I)
Action: Middle preschool (roughly ages 4-5) is your prime intervention window for copying skills. Don’t wait until kindergarten.
Implications for Education and Parents
1. Parallel Practice, Not Sequential Prerequisites
Old thinking: “My child needs to know their ABCs before we practice writing” Data-driven approach: “We’ll teach letter names AND practice copying simultaneously – they support each other through different pathways”
2. Copying Success ≠ Letter Knowledge
Don’t assume that because your child can copy a letter, they know what it’s called. And don’t assume that because they can name it, they can reproduce it.
Letter A showed this clearly: High recognition, low reproduction. These are different skills.
3. The “Legibility Integration Challenge”
Legibility showed the largest negative gaps at all ages (copying legibly is 6-18 points harder than identifying letters).
Parenting insight: Be patient with legibility. It’s the most complex integration task and develops last. Celebrate formation accuracy and directionality before expecting neat, legible letters.
4. Use Letter Copying as a Window into Visual-Motor Skills
Since copying correlates strongly with visual-motor integration (r = 0.76) but weakly with letter knowledge (r < 0.3), copying tasks reveal visual-motor development more than academic readiness.
For educators: A child who struggles to copy letters might need visual-motor support, not more letter drills.
The Bottom Line
After analyzing 471 children’s performance on letter identification and copying tasks, the data tells a clear story: Letter identification is not a meaningful prerequisite for letter copying.
These skills develop as parallel pathways:
Letter Identification pathway: Verbal-visual learning (naming, recognizing)
Letter Copying pathway: Visual-motor integration (seeing, matching, executing)
Both are valuable. Both support eventual handwriting fluency. But one doesn’t have to come before the other.
For practitioners and parents: Stop waiting. Introduce copying practice early. Use simple geometric letters (L, F, E, D, P) for confidence. Target middle preschool for maximum gains. And remember – a child who can name every letter might still struggle to draw an A, while a child who can’t name any letters might successfully copy an L.
The question isn’t “ID before copying?” but rather “How do we support BOTH simultaneously to maximize letter learning through every available pathway?”
About This Research
This analysis drew from assessment data collected through OT Wizard (otwizard.com), a pediatric clinical intelligence tool. The study included 471 evaluations across three age bands (Ages G, H, I, representing 30-36 months through 60-72 months). Assessment included the preschool version of Magic WAND™ , a letter copying task with 10 uppercase letters (L, F, R, S, X, N, C, K, V, A) scored for formation accuracy, legibility, and directionality. Statistical analyses examined correlations between letter identification scores and multiple copying performance measures.
About O.T. Wizard
Data for this analysis was collected through OT Wizard, a clinical intelligence system for pediatric occupational therapy assessment. The platform evaluates performance across up to twelve domains including visual-motor integration, fine motor skills, gross motor skills, praxis, visual perception, visual motor integration, executive functioning, activities of daily living, and participation. OT Wizard is undergoing Rasch analysis validation to establish psychometrically sound, norm-referenced scoring with living norms that update continuously as the clinical database expands.
Unlike traditional checklist-based assessments, OT Wizard converts all observations to continuous metrics that enable progress tracking, cross-domain comparison, and comprehensive reporting. The platform captures all six factors identified in this research as predictive of handwriting success: fine motor skills, visual perception (with subdomain specificity), praxis, cooperation, attention, and task participation. Behavioral regulation is assessed within the context of actual task performance rather than as an isolated rating, providing clinically relevant data about how attention and cooperation affect functional skill demonstration.
For handwriting readiness assessment specifically, OT Wizard provides quantified performance across visual discrimination, visual-motor integration, fine motor control, motor planning, and behavioral engagement during writing tasks. This comprehensive approach addresses the multifactorial nature of handwriting development identified in this research. As the platform undergoes Rasch analysis validation and accumulates longitudinal outcome data, it will establish whether comprehensive baseline assessment across all six predictors improves identification of children at risk for handwriting difficulty and informs more effective intervention planning.
OT Wizard is committed to advancing the occupational therapy profession by collecting de-identified clinical data from real therapist users, building the largest developmental database in pediatric occupational therapy history. This continuous data collection enables research on developmental trends, intervention effectiveness, and response to intervention patterns that elevate practice from perception-based to data-driven decision making and strengthen the evidence base for the entire profession
For OT professionals interested in data-driven assessment tools, visit otwizard.com to learn more about evidence-based pediatric evaluation.
You spent 90 minutes conducting a thorough pediatric occupational therapy evaluation. Another hour and a half writing a detailed report. You submitted it to insurance with confidence. Then the denial letter arrives: “Medical necessity not established.”
Sound familiar? You’re not alone. Insurance denials for occupational therapy evaluations are frustrating, time-consuming, and costly. But here’s the good news: most denials happen because of how the report is written, not whether the child actually needs services.
Let’s fix that.
The Insurance Approval Formula: Medical Necessity + Functional Impact + Skilled Service
Insurance companies don’t deny services because they don’t believe children need help. They deny because the documentation doesn’t prove three critical elements:
1. Medical Necessity: A documented diagnosis or condition that requires intervention 2. Functional Impact: Clear evidence that the condition limits daily functioning 3. Skilled Service: Proof that an occupational therapist’s expertise is required (not just supervision or general instruction)
Your evaluation report must explicitly address all three. If even one is missing or unclear, expect a denial.
What Insurance Reviewers Actually Read (And What They Skip)
Here’s a secret: the person reviewing your report spends about 90 seconds on it. They’re not reading every word. They’re scanning for specific elements. Most likely they are being read by their AI Bot.
The takeaway: Front-load your report with the information they need. Don’t bury medical necessity in paragraph seven.
The 7 Elements Every Insurance-Approved Report Contains
1. Clear Diagnosis at the Top
Wrong: “Johnny is a 5-year-old male referred for fine motor concerns.”
Right: “Johnny is a 5-year-old male with a diagnosis of Developmental Coordination Disorder (ICD-10: F82) referred for occupational therapy evaluation secondary to significant fine motor and visual-motor integration deficits impacting activities of daily living.”
Notice the difference? The second version includes diagnosis code, specific deficit areas, and functional impact in the first sentence.
2. Functional Limitations Stated Explicitly
Insurance doesn’t care that a child scores in the 5th percentile on the Beery VMI. They care that this score means the child cannot complete age appropriate functional activities, such as independently buttoning their shirt, writing their name legibly, or using utensils safely.
For every test score, include the “so what” statement:
Test Result: Visual perception skills measured at 2 standard deviations below age expectations on TVPS-4 (standard score: 70).
Functional Impact: This significant deficit prevents Johnny from independently locating items in his backpack, finding his desk in the classroom, and distinguishing similar letters (b/d, p/q) during early literacy tasks. Parent reports Johnny requires maximum assistance with dressing due to inability to orient clothing correctly.
3. Objective Measurements and Standardized Scores
Clinical observations alone don’t prove medical necessity. You need numbers.
Include :
Standardized test scores with percentiles or standard scores (like Peabody, Beery VMI) or Rasch-calibrated /Criterion referenced assessments (like PEDI-CAT, OT Wizard, or HELP)
Timed performance measures (e.g., “completed pegboard task in 145 seconds; age expectation is 45 seconds”)
Quantifiable observations (e.g., “grasped pencil in fisted grasp 100% of observed writing attempts”)
Measurable functional deficits (e.g., “required 4 verbal cues and 2 physical assists to don shirt”)
Research shows: Reports with comprehensive domain coverage across 8 areas (ADL, Executive Functioning, Fine Motor, Gross Motor, Visual Perception, Visual Motor Integration, Praxis, and Participation) have significantly higher approval rates because they provide objective evidence across multiple functional areas.
4. Medical Necessity Language (Not Educational Language)
If you primarily treat in schools, but are a medical based provider (meaning you aren’t an IEP provider), this is critical. Insurance reviewers don’t understand educational terminology.
Educational Language (Don’t Use): “Johnny requires OT services to access his educational curriculum and participate in classroom activities per his IEP.”
Medical Necessity Language (Use This): “Johnny requires skilled occupational therapy intervention to develop functional grasp patterns, visual-motor integration skills, and bilateral coordination necessary for age-appropriate self-care tasks including dressing, feeding, and personal hygiene.”
Key Differences:
Educational
Medical
Student
Patient
Classroom participation
Functional independence
IEP goals
Treatment goals
Educational benefit
Medical necessity
School activities
Activities of daily living
5. Safety Concerns (When Present)
Safety issues fast-track approvals. If present, state them clearly.
Examples:
“Child demonstrates impulsive behavior and poor body awareness, resulting in 3 falls from playground equipment in past month per parent report. Requires skilled OT intervention to develop safety awareness and motor planning.”
“Significant oral-motor deficits result in choking incidents during meals 2-3 times per week. Skilled feeding therapy required to establish safe swallowing patterns.”
“Decreased proximal stability and postural control result in frequent loss of balance during mobility, with 2 documented injuries requiring medical attention in past 6 months.”
6. Why Skilled OT is Required (Not Just Caregiver Training)
Insurance will deny if they think a parent or teacher could provide the same intervention. You must prove why your clinical expertise is necessary.
Not Skilled: “Child will benefit from practice with buttoning and zipping.”
Skilled Service: “Child requires skilled occupational therapy to analyze specific motor planning deficits preventing successful fastener manipulation, develop individualized strategies to compensate for bilateral coordination limitations, and systematically grade activity complexity while addressing underlying sensory processing difficulties that interfere with tactile discrimination necessary for fastener manipulation.”
See the difference? The second version demonstrates clinical reasoning, assessment expertise, and therapeutic skill that cannot be provided by non-therapists.
7. Concrete Frequency and Duration Recommendations
Vague recommendations get denied. Be specific.
Too Vague: “Recommend outpatient OT services.”
Specific and Justified: “Patient requires skilled occupational therapy 2x/week for 8 weeks (16 sessions) to address bilateral coordination deficits, visual-motor integration delays, and ADL skill development. Frequency based on severity of deficits (2+ standard deviations below age expectations across 4 domains) and need for motor learning repetition to establish new movement patterns. Re-evaluation recommended after 8-week intervention period to assess progress and determine ongoing needs.”
Common Denial Reasons and How to Avoid Them
Denial Reason #1: “Diagnosis not covered”
Prevention: Check the insurance company’s covered diagnosis list before evaluating. If the primary diagnosis isn’t covered, lead with a secondary diagnosis that is covered but still supports the need for OT.
Example: Autism (F84.0) might not be covered for outpatient OT, but Developmental Coordination Disorder (F82) or Sensory Processing Disorder coded as Other Specified Developmental Disorders (F88) often are.
Denial Reason #2: “Educational, not medical”
Prevention: Even if you’re a school-based therapist, emphasize ADL and home function impacts, not just classroom performance.
Include:
Dressing difficulties
Feeding/utensil use challenges
Hygiene and self-care limitations
Safety concerns at home
Community participation barriers
Denial Reason #3: “Not medically necessary”
Prevention: State explicitly in your report: “Skilled occupational therapy is medically necessary to address [diagnosis] which significantly impacts patient’s ability to [specific functional tasks], resulting in dependence on caregivers for age-appropriate self-care and safety concerns during daily activities.”
Denial Reason #4: “Insufficient objective data”
Prevention: Use standardized assessments. Clinical observations alone aren’t enough. Data from 404 evaluations shows that assessments with zero missing data and comprehensive domain coverage provide the objective evidence insurance requires.
The Report Structure Insurance Prefers
Section 1: Demographics and Diagnosis (Top of Page)
Name, DOB, date of evaluation
Primary diagnosis with ICD-10 code
Referring physician
Section 2: Medical Necessity Statement (First Paragraph) One clear paragraph stating diagnosis, functional limitations, and why skilled OT is required.
Section 3: Assessment Results
Standardized test scores
Functional performance observations
Quantifiable data
Each with functional impact statement
Section 4: Clinical Impressions
Summary of findings
How deficits impact daily function
Safety concerns (if applicable)
Section 5: Recommendations
Specific frequency (2x/week)
Specific duration (8 weeks)
Justification for both
Explicit medical necessity statement
Keep it concise: 2-3 pages maximum. Remember, they spend 90 seconds reading it.
Real Example: Before and After
Before (Gets Denied):
“Johnny is a pleasant 5-year-old boy who was referred for OT evaluation. He has difficulty with handwriting and gets frustrated during fine motor tasks at school. During testing, Johnny had trouble copying shapes and his pencil grasp looked immature. He would benefit from OT to work on these skills. Recommend weekly OT.”
Problems: No diagnosis code, no standardized scores, educational focus, vague recommendations, no medical necessity statement.
After (Gets Approved):
“Johnny is a 5-year-old male with Developmental Coordination Disorder (F82) referred for occupational therapy evaluation secondary to significant visual-motor and fine motor deficits impacting activities of daily living and self-care independence.
O.T. Wizard: Composite 550/1000, ADL 50/100, Fine Motor 72/100, Gross Motor 42/100, Sequencing Praxis 27/100, Visual Motor Integration 72/100
Functional grasp assessment: Fisted grasp pattern 90% of observed attempts
Functional Impact: Visual-motor integration and fine motor deficits prevent Johnny from independently managing fasteners (buttons, zippers, snaps), requiring maximum assistance for dressing. Unable to use utensils safely, resulting in frequent spills and parent reports of choking incidents 1-2x weekly. Cannot complete age-appropriate self-care tasks including tooth brushing and hair combing without hand-over-hand assistance.
Medical Necessity: Johnny requires skilled occupational therapy to develop functional grasp patterns, bilateral coordination, sequencing praxis, gross motor, and visual-motor integration skills necessary for age-appropriate self-care independence. Deficits 2 standard deviations below age expectations indicate significant impairment requiring therapeutic intervention. Safety concerns related to feeding and frequent falls during mobility necessitate skilled assessment and intervention.
Recommendations: Skilled occupational therapy 2x/week for 12 weeks to address bilateral coordination, visual-motor integration, and ADL skill development. Frequency based on severity of deficits and need for repetition to establish motor learning. Re-evaluation after 12 weeks to assess progress.”
Why it works: Diagnosis code in first sentence, standardized scores with functional impact, medical necessity explicitly stated, safety concerns noted, specific recommendations with justification.
Special Considerations for Different Settings
School-Based Therapists Seeking Medical Insurance Coverage
You can write reports that work for both IEP teams and insurance, but you need two versions:
IEP Version: Focus on educational impact and access to curriculum Insurance Version: Same data, different framing focused on ADL and medical necessity
Pro Tip: Complete your evaluation once, but generate two reports with different emphasis. Your assessment data doesn’t change, just how you present it.
Outpatient Clinic Therapists
You have an advantage because you’re already documenting medical necessity. Just ensure you’re:
Using covered diagnosis codes
Quantifying functional limitations
Stating skilled service needs explicitly
Providing specific frequency/duration with rationale
Early Intervention Providers
Insurance approval for 0-3 age range requires extra emphasis on:
Developmental delay severity (how far behind age expectations)
Impact on parent-child interaction
Safety concerns
Risk of further delay without intervention
The Bottom Line
Insurance approval isn’t about luck. It’s about documentation. Every denied evaluation report is missing at least one of these elements:
✓ Diagnosis code in first paragraph ✓ Standardized assessment scores ✓ Functional impact statements for every deficit area ✓ Medical necessity language (not educational) ✓ Explicit statement of why skilled OT is required ✓ Specific frequency and duration with justification ✓ Safety concerns (when applicable)
Master these seven elements, and your approval rate will skyrocket.
Stop spending hours appealing denials. Write it right the first time.
Streamline Insurance-Compliant Documentation
Writing insurance-approved reports doesn’t have to take hours. OT Wizard generates comprehensive evaluation reports with all required elements automatically included: diagnosis codes, standardized scores across 8 domains, functional impact statements, and medical necessity /educational eligibility. Choose medical or educational report tone with one click. Stop rewriting reports for insurance appeals.
Every pediatric occupational therapist has encountered this scenario: A 4-year-old with excellent fine motor skills, good visual perception scores, and established hand dominance still cannot write letters legibly. Meanwhile, another child with weaker motor skills and inconsistent grip produces surprisingly readable work.
What makes the difference?
New data from 185 preschool-age children reveals why handwriting success is so unpredictable and why our traditional assessment approaches may be missing critical pieces of the puzzle.
CURRENT HANDWRITING ASSESSMENT PRACTICES
Occupational therapists typically evaluate handwriting readiness through standardized assessments focusing on visual-motor integration and fine motor skills:
Beery VMI (Visual-Motor Integration), 6th Edition measures the ability to copy geometric forms of increasing complexity. Children progress from simple lines to complex shapes, with performance compared to age-based norms. The assessment assumes that shape copying ability predicts letter formation success.
PDMS-3 (Peabody Developmental Motor Scales, 3rd Edition) assesses fine and gross motor development through grasping and visual-motor integration subtests. The fine motor composite includes tasks similar to letter copying and provides age-based standard scores. While more comprehensive than the Beery VMI alone, it focuses primarily on motor execution.
BOT-2 (Bruininks-Oseretsky Test of Motor Proficiency, 2nd Edition) evaluates fine and gross motor proficiency including precision, integration, and manual dexterity tasks. Many subtests emphasize speed and accuracy under timed conditions, making it useful for identifying motor delays but less specific to handwriting readiness.
The Print Tool evaluates actual letter and number formation in children ages 3 to 7, rating legibility, size, spacing, and alignment. While more functional than shape copying, it requires children to already have some writing exposure.
Developmental Test of Visual Perception (DTVP-3) assesses visual-perceptual and visual-motor skills through tasks including copying, form constancy, and figure-ground discrimination. Performance on these isolated visual tasks is presumed to indicate readiness for integrated writing tasks.
Minnesota Handwriting Assessment evaluates speed, legibility, and form in school-age children who already write, making it less useful for identifying preschool readiness factors.
THE RESEARCH
We analyzed 185 children ages 4 to 4.5 years who received occupational therapy evaluations in North Carolina. This clinical sample consisted of children referred for developmental concerns, with 95 percent qualifying for Medicaid services. Many had limited exposure to structured preschool settings.
The children were given a comprehensive evaluation using O.T. Wizard and included 8-10 domains per child. During evaluation, children completed a letter copying task: 10 uppercase letters arranged from developmentally simple (L, F, R) to complex (S, X, N). Children copied each letter into a defined box below the model. Occupational therapists rated both the quality of letter production and the child’s behavior during the task.
The use of uppercase letter copying rather than geometric shapes in preschool assessment warrants clarification. For children lacking letter recognition, uppercase letters serve as geometric forms with the added benefit of providing functional, longitudinal work samples. Unlike abstract shapes that become irrelevant once writing instruction begins, letter samples document the progression from letters-as-shapes to letters-as-symbols, capturing both motor and cognitive development across the transition to formal writing.
We then examined how well various factors predicted performance on this functional handwriting task. Rather than assuming certain skills matter most, we calculated correlations to let the data reveal which factors actually related to success.
UNDERSTANDING CORRELATION: THE “r” VALUE
Before presenting findings, it helps to understand what correlation means and how to interpret the numbers.
Correlation measures the strength of the relationship between two variables. The correlation coefficient, represented as r, ranges from 0 to 1.0:
r = 0.0 to 0.1: No meaningful relationship
r = 0.1 to 0.3: Weak relationship
r = 0.3 to 0.5: Moderate relationship
r = 0.5 to 0.7: Strong relationship
r = 0.7 to 1.0: Very strong relationship
A simple example: Height and shoe size have a strong correlation (r = approximately 0.7). Taller people tend to wear larger shoes, though exceptions exist. The relationship is strong but not perfect.
In contrast, height and intelligence have essentially no correlation (r = approximately 0.0). Knowing someone’s height tells you nothing about their cognitive ability.
For our study, correlation indicates how well each skill predicts letter copying success. A high correlation means children with strong skills in that area tend to perform better on writing tasks. A low correlation means the skill does not reliably predict writing performance.
THE FINDINGS
Six factors showed moderate correlations with handwriting (visual motor integration) performance, all clustering tightly between r = 0.31 and r = 0.39:
Fine Motor Skills: r = 0.393
Cooperation (during evaluation): r = 0.365
Visual Perception: r = 0.340
Attention (during evaluation):r = 0.314
Praxis (Motor Planning): r = 0.314
Participation (during writing task): r = 0.310
The most striking finding is not which factor ranked highest, but rather that all six fell within an 8-point range. Fine Motor scored highest at 0.393, but Participation scored 0.310, a difference of only 0.083.
Statistical interpretation: All six predictors are moderate in strength, and none dominates. The child with the highest fine motor score has only a slightly better chance of writing success than the child with the highest cooperation score.
VISUAL PERCEPTION SUBDOMAINS: TASK DEMANDS MATTER
An interesting pattern emerged when examining visual perception subdomains separately. Not all visual skills predicted copying performance equally:
Visual Discrimination: r = 0.379
Visual Figure Ground: r = 0.304
Visual Spatial Relations: r = 0.158
Visual Memory: r = 0.137
Visual Discrimination, the ability to see small differences between similar forms, predicted letter copying better than the overall Visual Perception domain score. This makes perfect sense given the task demands. Copying letters requires discriminating between similar features: Is this a C or an O? Does this letter have a diagonal line or a curve? Are these two vertical lines parallel or converging?
In contrast, Visual Memory showed the weakest correlation at r = 0.137, barely above no relationship at all. This finding initially seems surprising given that handwriting literature often emphasizes visual memory as critical for letter formation. However, the weak correlation makes complete sense when we consider the actual task. Children were asked to copy letters with the model remaining visible throughout. They could look back and forth between the stimulus letter and their work as many times as needed. Visual memory is irrelevant when the visual information stays available.
Visual memory would matter for different handwriting tasks: Writing letters from dictation (hear the letter name, recall what it looks like) Writing spelling words independently (recall the letter in memory) Reproducing letters after brief exposure (look once, then write from memory)
But for direct copying with continuous visual access to the model, discrimination ability predicts success while memory does not.
This finding has important implications for assessment practices. If we evaluate visual memory but not visual discrimination, we may be measuring the wrong visual skill for near point copying tasks. Comprehensive assessment requires matching the skills tested to the actual task demands the child will face in the classroom.
In preschool and early kindergarten, children primarily engage in near point copying: copying letters from a worksheet placed directly in front of them, tracing over models, and reproducing shapes from a stimulus card on the table. These near point tasks allow continuous visual reference, making discrimination critical and memory less important.
As children progress through elementary school, task demands shift to far point copying: copying from the board, reproducing teacher demonstrations, writing from dictation. These tasks require visual memory because the model is not continuously accessible. A child must look at the board, hold the letter image in memory while looking down at paper, then reproduce from that mental representation.
For the preschool population in this study engaged in near point copying tasks, visual discrimination predicted success while visual memory did not. This relationship may change for older children performing far point copying or writing from dictation.
WHAT THE NUMBERS MEAN IN PRACTICE
Consider what these moderate correlations reveal:
If fine motor skills were the primary driver of handwriting, we would expect r = 0.6 or higher. Instead, r = 0.393 means fine motor capability explains only about 15 percent of handwriting performance. The remaining 85 percent depends on other factors.
Similarly, visual perception (r = 0.340) explains about 12 percent. Praxis explains about 10 percent. Cooperation explains about 13 percent.
No single factor accounts for even 20 percent of performance. Handwriting emerges from complex interactions among multiple systems, not mastery of any single prerequisite.
THE BEHAVIORAL FACTOR SURPRISE
Perhaps most notable: Behavioral factors predicted success as well as skill factors.
Cooperation (r = 0.365) nearly matched fine motor skills (r = 0.393). A child who cooperates with feedback and accepts correction has almost the same probability of writing success as a child with superior hand strength and coordination.
Attention during evaluation (r = 0.314) predicted exactly as well as motor planning ability (r = 0.314). The child who can focus for the duration of the task performs comparably to the child with better movement sequencing skills.
Participation during the actual writing task (r = 0.310) predicted nearly as well as any other factor. Willingness to engage with the challenge matters almost as much as capability.
This explains common clinical observations:
The child with excellent fine motor skills who gives up after one attempt struggles more than the child with weaker skills who persists through frustration.
The child who resists feedback and insists on doing it “my way” fails to improve despite adequate motor capability.
The child who cannot sustain attention long enough to complete three letters never accumulates the practice necessary for skill development.
CONTEXT MATTERS: THE 1-ON-1 EVALUATION PROBLEM
An important limitation: Cooperation, attention, and participation were rated during one-on-one evaluation sessions with an occupational therapist providing full support and individualized pacing.
This context differs dramatically from classroom writing instruction, where:
One teacher manages 15 to 20 students simultaneously Individual feedback is limited and delayed Pacing is group-determined rather than individualized Distractions are constant Tasks continue for extended periods without breaks
A child rated as having “adequate cooperation” in a quiet therapy room with undivided therapist attention may demonstrate very different behavior in a busy kindergarten classroom during 15-minute writing periods.
This suggests our correlations may actually underestimate the importance of behavioral factors. If cooperation, attention, and participation predict success even in optimal conditions, they likely matter even more in typical educational settings.
IMPLICATIONS FOR ASSESSMENT PRACTICES
Current handwriting readiness assessments focus heavily on visual-motor integration and fine motor skills while largely ignoring behavioral factors. The Beery VMI, for instance, requires sustained attention and task persistence to complete 30 forms, but these behavioral requirements are not scored or interpreted. A child may fail due to attention limitations rather than visual-motor deficits, yet both receive the same low score.
More critically, the Beery VMI is frequently used in isolation to qualify children for occupational therapy services for handwriting concerns. Given our findings, this practice is problematic. Visual-perception represents only one of six factors that predict handwriting success in preschoolers, and it predicts moderately (r = 0.340), not strongly. A child may score low on the Beery VMI yet succeed at functional handwriting due to strong cooperation, attention, and participation. Conversely, a child may pass the Beery VMI but struggle with classroom writing due to behavioral regulation challenges that the assessment does not capture.
Using the Beery VMI as a sole qualifying criterion systematically misidentifies which children need services. Comprehensive evaluation across all six predictive factors provides more accurate identification of handwriting risk.
Additionally, visual perception assessments for preschool populations should emphasize visual discrimination for near point copying tasks. Our findings demonstrate that for preschoolers copying letters with the model continuously visible, discrimination ability (r = 0.379) predicts substantially better than memory (r = 0.137). This does not suggest visual memory is unimportant for handwriting development overall. Rather, it indicates that the specific skills required depend on task type and developmental stage. Visual memory likely becomes increasingly important as children transition to far point copying and writing from dictation in elementary grades.
Ratings of current assessments used by OT’s and how they capture handwriting prediction
These assessments share common limitations. Based on our findings, we can evaluate how well each captures the six factors that actually predict handwriting success:.
Beery VMI (with supplemental tests): Rating 5/10 IF subtests administered. 3/10 if only the VMI section is administered. Captures visual-motor integration (r=0.340) through the primary copying task. Supplemental Visual Perception and Motor Coordination subtests add assessment of visual discrimination and fine motor control, bringing total coverage to 2-3 of 6 predictive factors. However, the Visual Perception subtest does not distinguish between visual discrimination (r=0.379, highly relevant) and visual memory (r=0.137, less relevant for near point copying). Completely misses cooperation, attention, praxis, and task participation. When administered with all three subtests, it provides more comprehensive data than VMI alone, but therapists often use only the primary VMI subtest for qualification decisions.
PDMS-3: Rating 6/10 Captures fine motor skills (r=0.393) through grasping subtests and visual-motor integration (r=0.340) through copying tasks. Provides 2 of 6 critical factors. Misses cooperation, attention, praxis, and task participation entirely. No assessment of behavioral regulation during tasks or visual discrimination as distinct from visual-motor integration.
BOT-2: Rating 4/10 Primarily assesses motor proficiency with fine motor precision and integration subtests capturing fine motor skills (r=0.393). However, heavy emphasis on timed performance may penalize slow-but-accurate children. Completely misses visual perception, cooperation, attention, and task participation. Designed for motor proficiency screening rather than handwriting-specific readiness. Captures only 1 of 6 predictive factors.
DTVP-3: Rating 5/10 Assesses visual perception (r=0.340) across multiple subdomains but does not distinguish between visual discrimination (r=0.379, highly relevant for copying) and visual memory (r=0.137, less relevant for near point tasks). Misses fine motor execution, cooperation, attention, praxis, and task participation. Provides visual skills assessment but in isolation from functional writing context.
The fundamental issue: These assessments emphasize isolated skill measurement (motor proficiency, visual perception, visual-motor integration) while ignoring behavioral regulation factors that predict equally well. Additionally, they provide scores but often rely on checklist observations that cannot be converted to continuous metrics for tracking progress or comparing across domains.
Comprehensive assessment should include:
Fine motor capability: Strength, coordination, precision, tool control Visual-perceptual skills with task-appropriate emphasis: Visual discrimination (critical for copying) Visual figure ground (moderate importance) Visual spatial relations (less critical for copying) Visual memory (only relevant for tasks without visible models) Motor planning: Ability to sequence multi-step actions, organize approach Cooperation: Willingness to accept feedback, modify approach when unsuccessful Attention: Capacity to sustain focus through multi-step tasks Task participation: Engagement level, persistence through challenge, frustration tolerance
Single-domain screening (testing only visual skills or only motor skills) will systematically miss children at risk. A child may pass fine motor screening with flying colors but struggle with writing due to attention deficits, poor cooperation, or low task engagement.
Similarly, a child may score well on visual memory subtests but fail at letter copying due to poor visual discrimination. Matching assessed skills to actual task demands is essential.
Conversely, a child with borderline fine motor scores but strong behavioral regulation may achieve functional writing through persistence and acceptance of instruction.
IMPLICATIONS FOR INTERVENTION
Traditional intervention models often follow a sequential approach: establish attention, then build fine motor skills, then introduce visual tasks, then combine into writing. Our data suggests this may be inefficient.
If multiple factors contribute equally and simultaneously, intervention should address them concurrently rather than sequentially. Children need practice integrating behavioral regulation, motor control, visual processing, and motor planning from the start.
Effective intervention might include:
Brief, varied tasks that build attention capacity while practicing motor skills (address both simultaneously) Immediate feedback on both motor execution and behavioral approach (cooperation, persistence) Functional writing activities that require visual processing, motor planning, and sustained attention in authentic context Explicit instruction in self-regulation during challenging tasks (managing frustration, accepting correction)
Isolated prerequisite activities (strengthening exercises, shape sorting, sequencing games) practiced separately from writing context may not transfer effectively. The child builds attention during tabletop games but cannot apply it during writing. The child demonstrates fine motor control during bead threading but not during letter formation.
Integration practice appears more efficient: Work on attention, motor control, visual processing, and cooperation simultaneously within functional writing activities.
WHY SOME CHILDREN SUCCEED DESPITE LIMITATIONS
These findings explain puzzling clinical observations.
The child with weak fine motor skills who succeeds likely compensates through: Strong visual perception (carefully observes letter features) High persistence (keeps trying despite motor difficulty) Good cooperation (accepts feedback, modifies approach) Strong attention (focuses carefully on each stroke)
The combination of strengths in four areas compensates for weakness in one.
The child with excellent fine motor skills who fails likely struggles with: Poor attention (loses focus mid-letter) Low persistence (gives up when first attempt is imperfect) Resistance to feedback (insists on incorrect approach) Low task engagement (avoids writing activities)
Motor capability alone cannot overcome behavioral limitations.
THE CLINICAL SAMPLE CONTEXT
These findings emerge from a specific population: low-income preschoolers referred for occupational therapy evaluation. Many had limited exposure to structured educational settings or formal writing instruction.
This context matters for interpretation:
Children with school experience might show different patterns, as they have had more opportunity to develop attention and cooperation within structured tasks.
Higher-income samples with more educational exposure might demonstrate stronger correlations for skill factors and weaker correlations for behavioral factors.
Typically developing children (not referred for therapy) might show different relationships among variables.
However, this clinical sample represents the population occupational therapists actually serve. Understanding what predicts success in children with developmental concerns and limited educational exposure has direct clinical relevance.
RESEARCH CONTEXT: HOW OUR FINDINGS COMPARE
Our findings align with and extend existing research on handwriting development while revealing some important differences.
Feder and Majnemer (2007) conducted a systematic review identifying visual-motor integration, fine motor skills, and in-hand manipulation as significant predictors of handwriting performance in school-age children. Their meta-analysis found moderate correlations (r = 0.3-0.5) between these factors and handwriting, consistent with our fine motor (r = 0.393) and visual perception (r = 0.340) findings. However, their review focused on older children already engaged in writing instruction, while our sample examined preschoolers in pre-handwriting stages.
Reference: Feder, K. P., & Majnemer, A. (2007). Handwriting development, competency, and intervention. Developmental Medicine & Child Neurology, 49(4), 312-317.
Volman, van Schendel, and Jongmans (2006) examined handwriting readiness in kindergarten children and found that visual-motor integration was a significant predictor but explained only a modest portion of variance. This supports our finding that visual-motor skills predict moderately (r = 0.340) but do not dominate. Importantly, they also identified attention and behavioral regulation as contributing factors, aligning with our cooperation (r = 0.365) and attention (r = 0.314) findings.
Reference: Volman, M. J., van Schendel, B. M., & Jongmans, M. J. (2006). Handwriting difficulties in primary school children: A search for underlying mechanisms. The American Journal of Occupational Therapy, 60(4), 451-460.
Kaiser, Albaret, and Doudin (2009) investigated the relationship between handwriting quality and various factors in first graders. They found visual perception, fine motor skills, and graphomotor skills all contributed, but no single factor was sufficient. Their findings that multiple factors contribute equally strongly support our multifactorial model. However, they did not examine behavioral factors like cooperation or task-specific participation, which our data suggests are equally important.
Reference: Kaiser, M. L., Albaret, J. M., & Doudin, P. A. (2009). Relationship between visual-motor integration, eye-hand coordination, and quality of handwriting. Journal of Occupational Therapy, Schools, & Early Intervention, 2(2), 87-95.
Notably absent from existing literature: Studies examining task-specific participation and cooperation as predictors of handwriting success in preschool populations. Most handwriting research focuses on school-age children who have already received writing instruction and emphasizes motor and perceptual factors while treating behavioral factors as confounding variables rather than legitimate predictors.
Our finding that cooperation predicts nearly as well as fine motor skills (r = 0.365 vs r = 0.393) extends the literature by demonstrating that behavioral regulation deserves equal consideration in handwriting readiness assessment. The clinical sample context (children referred for evaluation, limited school exposure, 95 percent low-income) may explain why behavioral factors emerged as stronger predictors than in general population studies.
Additionally, our visual perception subdomain analysis revealing that visual discrimination (r = 0.379) predicts substantially better than visual memory (r = 0.137) for near point copying tasks provides specificity often missing in broader visual perception assessments. This has practical implications for selecting which visual subtests to administer when evaluating preschool handwriting readiness.
ABOUT OT WIZARD
Data for this analysis was collected through OT Wizard, a clinical intelligence system for pediatric occupational therapy assessment. The platform evaluates performance across up to twelve domains including visual-motor integration, fine motor skills, gross motor skills, praxis, visual perception, executive functioning, activities of daily living, and participation. OT Wizard is undergoing Rasch analysis validation to establish psychometrically sound, norm-referenced scoring with living norms that update continuously as the clinical database expands.
Unlike traditional checklist-based assessments, OT Wizard converts all observations to continuous metrics that enable progress tracking, cross-domain comparison, and comprehensive reporting. The platform captures all six factors identified in this research as predictive of handwriting success: fine motor skills, visual perception (with subdomain specificity), praxis, cooperation, attention, and task participation. Behavioral regulation is assessed within the context of actual task performance rather than as an isolated rating, providing clinically relevant data about how attention and cooperation affect functional skill demonstration.
For handwriting readiness assessment specifically, OT Wizard provides quantified performance across visual discrimination, visual-motor integration, fine motor control, motor planning, and behavioral engagement during writing tasks. This comprehensive approach addresses the multifactorial nature of handwriting development identified in this research. As the platform undergoes Rasch analysis validation and accumulates longitudinal outcome data, it will establish whether comprehensive baseline assessment across all six predictors improves identification of children at risk for handwriting difficulty and informs more effective intervention planning.
OT Wizard is committed to advancing the occupational therapy profession by collecting de-identified clinical data from real therapist users, building the largest developmental database in pediatric occupational therapy history. This continuous data collection enables research on developmental trends, intervention effectiveness, and response to intervention patterns that elevate practice from perception-based to data-driven decision making and strengthen the evidence base for the entire profession
A data-based look at executive functioning during OT evaluations
Occupational therapy practitioners often hear some version of the same question: “They seemed to do fine with you. Why are they struggling so much in class?”
To explore this, we examined therapist-rated Executive Functioning collected during one-on-one OT evaluations with preschoolers. These ratings reflect how children worked with the therapist during the evaluation itself, not how they function in classrooms, group settings, or home environments.
This article focuses on what the data shows and how therapists can interpret these observations responsibly.
Important context about this sample
Children were referred for OT after failing an occupational therapy screening
400+ preschoolers aged 4:0-4:11 , with about equal % of males and females
This is a clinically referred preschool sample, not a normative group
Ratings reflect performance during a one-on-one OT evaluation
Work habits were rated using descriptive anchors:
Not present or Beginning
Inconsistent or Emerging
Developing
Proficient
Mastered
What the work habit data shows overall
Across all work habit areas, most children in this referred preschool sample were rated in the Developing to Adequate range when working one-on-one with an occupational therapist.
This suggests that many children who struggle in classrooms or group environments are able to participate meaningfully when demands are reduced, expectations are clear, and adult support is individualized.
How children performed across specific work habit areas
Transitions and cooperation
Most children were rated as Developing or Proficient in their ability to transition between activities and cooperate with the therapist during the evaluation.
This indicates that many preschoolers can adapt well to structured tasks when the environment is predictable and supportive, even if transitions are difficult in other settings.
Task initiation and task completion
Task initiation and task completion were commonly rated in the Developing to Proficient range.
Many children required prompting or encouragement to begin tasks, but were generally able to complete them once engaged. This reflects emerging executive functioning skills rather than refusal or lack of effort.
Impulse control and task persistence
Impulse control and task persistence tended to fall in the Developing range.
Children often attempted tasks and remained engaged for short periods but had difficulty sustaining effort across multiple activities. This pattern is typical in preschoolers and becomes more pronounced when task demands increase.
Attention during the evaluation
Attention was the most commonly impacted work habit, even in a one-on-one setting.
Many children were rated between Emerging and Developing for attention. This is an important clinical signal.
When a child struggles to attend during a one-on-one evaluation, they will generally struggle even more in larger, less structured settings such as classrooms, group activities, or busy home environments. Difficulty sustaining attention in a low-demand context often predicts greater challenges when environmental demands increase.
When poor evaluation performance reflects anxiety, not ability
It is also important to acknowledge that not all low work habit ratings reflect true functional ability.
Some preschoolers perform poorly during evaluations because they are:
Nervous
Anxious
Overwhelmed by unfamiliar adults or environments
Uncertain about expectations
In these cases, performance may underestimate a child’s true skills.
Occupational therapists play a critical role in distinguishing between skill limitations and emotional readiness. Establishing rapport is not optional. It is a clinical necessity.
For some children, establishing rapport may include:
Spending additional time building trust
Allowing the child to observe before participating
Using play or preferred activities to reduce anxiety
Delaying formal evaluation tasks until the child appears comfortable with the therapist
Evaluation data is only as meaningful as the context in which it is collected.
What this tells us about one-on-one OT evaluations in our 400+ preschool sample
Taken together, these findings suggest:
Most preschoolers in this referred sample can demonstrate Developing to Adequate work habits in a one-on-one OT evaluation
Stronger performance in this setting does not negate difficulties in classrooms or group environments
Attention difficulties observed in one-on-one contexts often signal more significant challenges in larger settings
Anxiety and lack of rapport can temporarily suppress performance and must be considered during interpretation
Why this matters for interpretation and communication
These data support a critical clinical message:
Performance in a one-on-one OT evaluation reflects what a child can do with individualized support, not what they are expected to manage independently in more complex environments.
When communicating results to families, teachers, and teams, it is essential to clarify the difference between supported performance and real-world participation demands.
Key takeaway for therapists
Based on therapist-rated work habit descriptors:
Most preschoolers in this referred sample demonstrated Developing to Adequate work habits during a one-on-one OT evaluation, while attention and self-regulation remained vulnerable areas, particularly when demands increase or anxiety is present.
This reinforces the importance of careful interpretation, thoughtful rapport building, and contextualized clinical judgment.
📣OTPs : Does this match your experience when evaluating preschoolers? Leave us a comment and let us know.
