Tag: Learning Charms

  • Fine Motor Speed and Accuracy (FMSAT) Norming Update

    Fine Motor Speed and Accuracy (FMSAT) Norming Update

    Overview

    This brief summarizes findings from the FMSAT (Fine Motor Speed and Accuracy Test) norming dataset and OTW platform data as of April 12, 2026. It covers three clinically relevant patterns: the lifespan trajectory of bilateral hand performance, the use of the Dominant Hand Advantage as a clinical decision-support tool, and the distinct profile of left-handed children. Each section connects findings to supporting literature.

    Data sources: FMSAT norming study (n=284 valid bilateral pairs, ages 3-65, and OT Wizard platform administrations (n=374 T1 clinical referrals, ages 3:0-5:5). Both datasets use the identical tasks: POP_BUBB_10 (H1, dominant hand) and POP_BUBB_11 (H2, non-dominant hand) raw bubble counts. Direct comparison is valid.

    Early Signal 1: The Lifespan Trajectory of Hand Dominance

    What the early data shows

    Dominant hand output (H1) and the gap between hands both increase steadily from preschool through early adulthood, then show a gradual decline in later decades. The non-dominant hand (H2) also grows, but more slowly, meaning the ratio between them widens as lateralization consolidates.

    The FMSAT captures not just how fast a child can perform a fine motor task, but how much further ahead one hand is relative to the other. That asymmetry is the developmental signature we are tracking.

    Figure 1. Lifespan H1/H2 trajectory. Preschool bands (3:0-5:5) from OTW clinical T1 (n=374). School-age and adult bands from FMSAT norming dataset (n=284). Bar length proportional to score out of 80. OTW bands shown in teal; FMSAT bands in purple.

    What the literature says

    Hand dominance consolidation is a normative developmental expectation. Most children show consistent hand preference by age 3 to 4, and a clear functional asymmetry in tool use is established by age 5 (Scharoun and Bryden, 2014; Sacrey et al., 2012). The neurodevelopmental substrate is corpus callosum myelination, which progresses through childhood and is substantially complete by approximately age 10 to 12, consistent with the plateau in bilateral asymmetry observed in our school-age data (Lebel and Beaulieu, 2011).

    Children who have not consolidated hand dominance by kindergarten entry demonstrate effortful, inconsistent tool use and reduced handwriting fluency (Dinehart and Manfra, 2013). From an OT practice perspective, unresolved lateralization is a legitimate basis for eligibility justification and a measurable intervention target. What has been missing is a brief, standardized instrument that can quantify lateralization status directly through behavioral performance.

    Key Citations
    Scharoun and Bryden (2014). Hand preference, performance abilities, and hand selection in children. Frontiers in Psychology, 5, 82.  Lateralization consolidates ages 3-4; unresolved dominance associated with motor difficulty. Sacrey et al. (2012). Precocious hand use preference in reach-to-eat behavior in 1- to 5-year-old children. Developmental Psychobiology, 55(8), 902-911.  Early behavioral lateralization. Dinehart and Manfra (2013). Fine motor skills in preschool associated with academic performance in second grade. Early Education and Development, 24(2), 138-161.  Functional outcome: fine motor to academic connection. Lebel and Beaulieu (2011). Longitudinal development of human brain wiring continues from childhood into adulthood. Journal of Neuroscience, 31(30), 10937-10947.  Corpus callosum myelination timeline.

    Early Signal 2: The Dominant Hand Advantage and Hand Selection

    What the early data shows

    When we examine the ratio of dominant to non-dominant hand output across age bands, children with established hand preferences produce approximately 1.5 to 1.6 times more output with their dominant hand. This ratio is consistent from age 7 through adulthood in typical scorers, suggesting it represents a measurable signature of lateralized motor function that has consolidated fully enough to guide clinical decisions.

    1.6xDominant hand advantage in school-age typical scorers~1 in 5Preschool clinical children with near-equal hands at every age band1.0xTied hands — neither has pulled ahead; hand selection may be premature

    Figure 2:

    Figure 2. Lateralization trajectory by age band. Primary metric: Diff% = (H1-H2)/80×100, a fixed-denominator measure that is stable at all ages including preschool. DHAR (H1/H2 median ratio) shown as secondary reference in Signal column. DHAR mean is not used as it is unstable when H2 scores are low (floor effect in age 3 bands). OTW clinical T1 bands in teal; FMSAT norming bands in purple; adult bands in orange. Signal labels: Emerging / Consolidating / Establishing = pediatric lateralization stages; Stable/Peak, HAROLD Effect, Convergence = adult trajectory signals based on callosal aging literature.

    Clinical application: informing the hand-selection conversation

    Handwriting is motor memory. Every time a person writes a letter, the brain strengthens a specific motor pattern in the hand being used. When a child switches hands, they are building two separate motor programs for every letter — and neither program accumulates enough practice to become automatic. Motor learning research is clear: inconsistency prevents automaticity (Schmidt and Lee, 2011). A child who writes with both hands is essentially a beginner with each hand, never progressing to the automatic stage where handwriting becomes effortless.

    For individuals where the ratio is near 1.0 (tied hands), the FMSAT is equally informative: neither hand has pulled ahead yet. In younger children, this may be developmentally expected. In school-age children where dominance should be established, it becomes a meaningful clinical flag. In either case, recommending one hand prematurely may not be appropriate, and a re-evaluation in a few months is often more defensible than committing to one side before the nervous system has made its own lean. For children with developmental coordination difficulties, the clinical stakes are higher still — their motor learning already takes longer than typical, and asking them to build two sets of motor patterns for every letter makes an already difficult task significantly harder (Missiuna et al., 2008).

    What happens in later adulthood

    The early adult data shows the dominant hand advantage holding relatively steady through the 50s — roughly 1.5x — consistent with the hypothesis that decades of occupational repetition continue to maintain dominant-hand specialization even after neurological myelination is complete (Lebel and Beaulieu, 2011). But in the 60+ age band, early data shows the ratio beginning to narrow toward 1.2x, with the non-dominant hand closing the gap.

    This pattern is consistent with what neuroscience research describes as the HAROLD effect — Hemispheric Asymmetry Reduction in Older Adults (Cabeza, 2002). As the corpus callosum undergoes age-related structural atrophy, particularly in its anterior and middle sections, the interhemispheric inhibition that normally keeps the non-dominant motor cortex suppressed during dominant-hand tasks begins to diminish (Seidler, 2010; Sullivan et al., 2010). The result is increased ipsilateral (non-dominant hemisphere) motor activation during even basic unimanual tasks — effectively reducing the behavioral expression of hemispheric specialization. The FMSAT may be capturing this at the behavioral output level: as callosal integrity declines, H2 closes the gap on H1 not because the dominant hand weakens, but because the inhibitory mechanisms that normally constrain the non-dominant hand are less effective.

    This is directional only at current sample sizes — adults over 60 represent n=5 in this dataset. It is, however, a theoretically coherent and clinically interesting signal that warrants investigation as adult data grows.

    What the literature says

    Key Citations
    Packheiser et al. (2023). Elevated levels of mixed-hand preference in dyslexia: Meta-analyses of 68 studies. Neuroscience and Biobehavioral Reviews, 154, 105420.  OR = 1.57 for mixed-handedness in dyslexia across 68 studies, n > 45,000. Highest-priority lateralization-to-learning-disability citation.Packheiser, Papadatou-Pastou, and Ocklenburg (2025). Handedness in mental and neurodevelopmental disorders: A second-order meta-analysis. Psychological Bulletin.  Association specific to early-onset, language-related neurodevelopmental disorders.Rodriguez (2010). Mixed-handedness is linked to mental health problems in children and adolescents. Pediatrics, 125(2), e340-e348.  N = 7,871, Northern Finland Birth Cohort. ADHD-mixed handedness connection.Missiuna et al. (2008). Recognizing and referring children at risk for developmental coordination disorder. Paediatrics and Child Health, 13(7), 565-570.  Motor learning timeline in DCD populations.Cabeza (2002). Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychological Aging, 17(1), 85-100.  Age-related reduction in hemispheric lateralization in both cognitive and motor tasks; foundational model for adult bilateral convergence.Seidler (2010). Functional implications of age differences in motor system connectivity. Frontiers in Systems Neuroscience, 4, 17.  Older adults recruit ipsilateral motor cortex more during dominant-hand tasks; reduced interhemispheric inhibition via callosal atrophy.Sullivan et al. (2010). Quantitative fiber tracking of lateral and interhemispheric white matter systems in normal aging. Neurobiology of Aging, 31, 464-481.  Corpus callosum structural decline in aging correlates with motor processing speed reduction and reduced interhemispheric communication.

    Early Signal 3: Left-Dominant Individuals Show a Different Profile

    What the early data shows

    Left-dominant individuals across all age groups in the FMSAT dataset show a consistently smaller dominant hand advantage than right-handers. Right-dominant individuals average 1.56x; left-dominant individuals average 1.33x. More strikingly, 42% of left-handed participants show near-equal hands (LI less than 10%), compared to 15% of right-handers. Left-handers also show a higher rate of negative gaps — cases where the non-dominant hand actually outperforms the chosen dominant hand.

    GroupMean DHARNon-Dom Hand %% Near-EqualClinical Signal
    Right-Dominant1.56x67% of H115%Standard reference
    Left-Dominant1.33x79% of H142%Separate norms needed
    Inconsistent~1.1x94% of H133%Flag for follow-up

    Figure 3. Dominant Hand Advantage comparison by handedness group. Right-dominant n=238; Left-dominant n=43 (FMSAT norming dataset). Near-equal defined as LI less than 10%.

    Hypothesis: the right-handed world effect

    Our hypothesis is that left-handed individuals grow up navigating a world built primarily for right-hand use. Scissors, desk surfaces, spiral notebooks, zipper pulls, and most classroom tools are designed for right-hand use. The sustained right-hand exposure required to adapt to these tools may keep the non-dominant right hand more capable than it would otherwise be, slowing the natural divergence between hands.

    Whether this represents adaptive bilateral development — a genuine advantage for left-handers who build broader bilateral motor capacity — or a delayed lateralization signal that warrants clinical attention in referred children is a question this dataset will help answer as it grows. Both interpretations have clinical relevance.

    Practical implication: Do not apply right-hand reference values to left-dominant children. A left-handed child with a dominant hand advantage of 1.3x may be completely age-appropriate. That same ratio in a right-handed child of the same age would be a flag. Separate reference values for left-dominant children are required — and are being developed from this dataset.

    What the literature says

    Key Citations
    Goez and Zelnik (2008). Handedness in patients with developmental coordination disorder. Journal of Child Neurology, 23(2), 151-154.  Elevated left-handedness prevalence in DCD populations.Packheiser et al. (2023). Elevated levels of mixed-hand preference in dyslexia.  See Section 2. Also relevant: OR = 1.57 applies specifically to mixed/inconsistent handedness, not left-handedness per se — an important clinical distinction.

    Where We Are Headed

    These findings are pre-normative and drawn from a predominantly clinical sample. They support the theoretical framework of the FMSAT as a lateralization screening tool but do not yet constitute published norms. The norming project is ongoing, and every submission expands the dataset that will make the reference values in this document defensible for clinical and eventual manuscript use.

  • Built on Evidence. Proven in Practice. Shoutout to Learning Charms’ Team

    Built on Evidence. Proven in Practice. Shoutout to Learning Charms’ Team

    How 3.8 Years of Systematic Clinical Measurement Demonstrates That Occupational Therapy Works

    Stephanie Seymore Wick, MSOT, OT/L | Founder and Clinical Architect, O.T. Wizard | Learning Charms, Inc., Charlotte, North Carolina

    The Problem With Checklists

    For years, occupational therapists working in early childhood settings were collecting data that told them almost nothing. Checklist-style evaluations produced a snapshot: present or absent, yes or no. They could not tell you whether a child improved. They could not tell you which counties had greater concentrations of developmental need. They could not tell you whether your team’s intervention was moving the needle or whether children were simply getting older.

    That was the reality facing Learning Charms in 2022. We were screening and evaluating large numbers of children across Head Start programs, NC Pre-K classrooms, and community settings throughout North Carolina, and we had nothing meaningful to show for it in terms of trend data, geographic insight, or outcome evidence.

    So I built something.

    From Nothing to 8,509 Screenings and Evaluations

    The FUNdamental Foundations (FF) screener was designed and developed by a managing pediatric occupational therapist with 25+years of clinical experience. It was built as a structured, multi-domain developmental tool designed from the outset to generate analyzable data. It was not designed for publication. It was designed to answer clinical questions: What does this population look like? Where are the gaps? Is what we are doing making a difference?

    Thirty clinicians on the Learning Charms team tested and used each version in the field, providing the real-world feedback that drove every refinement. They made the transition from paper-and-pencil evaluations to digital data entry on a tablet or laptop, mid-session, with children in front of them. That is not a small ask. The early weeks required support with the technology. There were growing pains. The team did it anyway, and they did it without much, if any complaint.

    The FF tool went through two versions, each refined based on team feedback. Version 6 ran from June 2022 through July 2023. Version 7, with improvements including date of birth capture and an updated item structure, ran from August 2023 through May 2025. In late 2025, the practice transitioned to O.T. Wizard, a fully rebuilt clinical intelligence platform designed and built by the same therapist.  O.T. Wizard was built with Rasch psychometric architecture, 15 guided evaluations, and integrated outcome tracking across 12 domains.

    The table below summarizes what 3.8 years of that effort produced.

    Table 1. Clinical Data Collected Across the Full Evidence Ecosystem (2022-2026)

    PlatformPeriodRecordsEvaluationsScreeningsE1-E2 Pairs
    FUNdamental Foundations V6Jun 2022 – Jul 20232,9281,5111,417324
    FUNdamental Foundations V7Aug 2023 – May 20254,9622,3682,594477
    O.T. WizardSep 2025 – Mar 202661961997
    TOTAL3.8 years8,5094,4984,011898

    Note. E1-E2 pairs = children with two complete evaluations allowing pre-to-post comparison. FF V6 pairs are V6-only matches. FF V7 pairs include V7-only and cross-version (V6 E1 to V7 E2) matches. OTW pairs matched by Student_ID. pp = percentage points.

    In total: 8,509 individual assessment records. 4,498 full evaluations. 4,011 developmental screenings. 898 pre-to-post evaluation pairs. Over 245,000 item-level data points. Collected by a single clinical team, through routine practice, over less than four years.

    What the Data Shows: Gains That Exceed Maturation

    The central question in any clinical outcome dataset without a randomized control group is this: how do you know the gains are from intervention and not just from children getting older?

    We address this directly.

    Using cross-sectional developmental data from our own E1 (Initial Evaluation) dataset, we calculated the expected rate of developmental growth per month for each skill area based on age alone. This gives us a maturation baseline specific to this population. We then compared that expected gain to the gains actually observed in children who received OT services between E1 and E2 (Re-evaluation), over a mean interval of 5.4 months.

    The results are consistent across all three measured domains and across both independent datasets.

    Table 2. Observed Gains vs. Expected Maturation Over Mean 5.4-Month Interval (FF n=801 pairs, OTW n=94 pairs)

    ItemFF Observed GainExpected (Maturation)RatioOTW Observed Gain
    Draw a Person (0-4 scale)+1.24 pts+0.39 pts3.2x+1.06 pts
    Functional Pencil Grasp+25.3 pp+9.7 pp2.6x+27.7 pp
    Finger Touching (54-mo milestone)+20.3 pp+11.7 pp1.7xn/a
    Cohen’s d (DAP)0.940.84

    Note. Expected gain calculated from cross-sectional linear regression of E1 scores on age in months using the full FF evaluated dataset. pp = percentage points. Cohen’s d: 0.2 = small, 0.5 = medium, 0.8 = large effect. OTW finger touching item not directly comparable due to different item structure.

    Draw a Person improved at 3.2 times the expected developmental rate in FF and 2.6 times in OTW. Functional pencil grasp improved at 2.6 times expected in FF and 3.1 times in OTW. These are not marginal differences from what maturation alone would predict. They are two to three times larger. And they replicate across an entirely independent dataset collected with a different tool, by the same team, with different children.

    Why This Is Not Just Children Getting Older

    If the gains above were driven primarily by maturation, we would expect children at all starting points to show similar improvement. A child who enters at score 0 would gain roughly as much as a child who enters at score 3, because age-related development does not care where you start.

    That is not what we see. The table below shows Draw a Person gains stratified by E1(Initial Evaluation)  score, combining FF and O.T. Wizard data. The pattern is unambiguous.

    Table 3. Draw a Person Gain by E1 Score: FF (n=801) + OTW (n=93) Combined

    E1 ScorenE2 MeanMean Gain% Improved% Same% Declined
    0 (no parts)365+291.74+1.7474%26%0%
    1 (approximations)203+162.37+1.3781%13%6%
    2 (head, no body)165+312.66+0.6654%37%9%
    3 (recognizable)60+112.96-0.0429%46%25%
    4 (6+ body parts)8+63.36-0.360%57%43%

    Note. n column shows FF count + OTW count at each E1 score level. E1 score 0 = no recognizable approximations. Score 4 = recognizable person with 6 or more body parts. Gains decline systematically as E1 score increases, reflecting ceiling effects at higher starting points rather than absence of progress.

    Children who started at score 0 improved by an average of 1.74 points, with 74% showing measurable gains. Children who started at score 3 or 4 were near the ceiling of the scale and showed flat or slightly negative scores at E2, exactly as ceiling effects predict.

    This score-dependent gain gradient is the signature of a real treatment effect. Maturation produces relatively uniform gains regardless of starting point. Intervention produces the largest gains in children with the most room to grow. That is what we observe, and it replicates point-for-point across both the FF and OTW datasets independently.

    The grasp and finger touching data tell the same story from a different angle.

    Table 4. Skill Transition Rates: What Happened Between E1 and E2

    ItemStatus at E1nOutcome at E2
    Pencil GraspNon-functional377 (FF) + 65 (OTW)60% converted to functional
    Pencil GraspFunctional417 (FF) + 29 (OTW)94% maintained functional
    Finger TouchingFail407 (FF)58% passed at E2
    Finger TouchingPass394 (FF)82% maintained pass

    60% of children with non-functional pencil grasp at E1 had functional grasp by E2. 94% of children with functional grasp at E1 maintained it. Skills were not fluctuating randomly. They were moving in one direction and holding. That is not maturation. That is intervention.

    Two Tools, Three Years Apart, Same Answer

    The FF screener and O.T. Wizard are different instruments. FF was a clinician-developed Google Form with embedded scoring anchors and standardized stimulus materials. O.T. Wizard is a fully architected clinical platform undergoing Rasch psychometric validation, with 596 data variables per evaluation and item-level calibration. The two tools share several core items, including Draw a Person, pencil grasp classification, and finger touching. They do not share overlapping children. The DAP scale is directly comparable across both tools at the 0 to 4 range, with identical scoring anchors at each level. O.T. Wizard extended the ceiling by adding two higher-level descriptors, bringing the OTW scale to 6 points total. For this analysis, OTW DAP scores were capped at 4 to ensure a valid cross-tool comparison.

    Yet when we calculate the cross-sectional developmental growth rate for Draw a Person from FF E1 data, we get 0.072 points per month. From OTW E1 data, we get 0.082 points per month. Two tools, thousands of children, the same underlying developmental trajectory captured within 0.01 points of each other per month.

    When two independent measurement systems produce convergent developmental slopes and convergent gain ratios, that is not a coincidence. That is construct validity. Each dataset serves as an independent replication of the other’s findings, and both point to the same conclusion.

    Danielle, an OTR/L out of NC, administers an evaluation with a 3 year old using OT Wizard

    What This Means for OT Practice and Clinical Infrastructure

    Pediatric occupational therapists have long known that their interventions make a difference. The challenge has been demonstrating it systematically, at scale, in a form that partners, funders, schools, and insurance providers find credible.

    The Learning Charms team built that demonstration over 3.8 years, starting from scratch, with no research funding, no university partnership, and no IRB. They built it by replacing meaningless checklists with structured clinical measurement, by training a team of 30 clinicians to collect data consistently, and by iterating their tools until the data was worth analyzing.

    O.T. Wizard is the current iteration of that infrastructure. It is not a platform claiming efficacy. It is a platform whose evidence base already exists, built by the same team that built the platform, using the same children, in the same communities, over the same years. The data in this paper is not a promise of what O.T. Wizard will eventually show. It is a record of what systematic clinical measurement has already demonstrated.

    OT works. The data, replicated across tools and years and nearly 900 pairs of children, shows it.

    Disclosure

    Stephanie Seymore Wick is the founder and clinical architect of O.T. Wizard and owner of Learning Charms, Inc. All data was collected through routine clinical practice and contracted screening partnerships. No external funding was received. The FUNdamental Foundations screener was a clinician-developed field tool and has not undergone formal psychometric validation. O.T. Wizard is currently undergoing Rasch analysis validation. All findings should be interpreted as practice-based clinical evidence rather than results from a randomized controlled trial.

    About O.T. Wizard

    O.T. Wizard is a clinical intelligence system for pediatric occupational therapy professionals. The platform supports evaluation, documentation, goal planning, and scheduling across 12 domains including fine motor skills, visual-motor integration, 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 as the clinical database expands. Learn more at otwizard.com.

    About Learning Charms

    Learning Charms is a pediatric occupational therapy group that employs roughly 25 OTP’s in the Charlotte , NC and surrounding counties. Learning Charms is now focused mainly on preschool aged children in their school environment.