Occupational therapy and Beery VMI scores of children with autism spectrum disorder, brachial plexus injury, and cerebral palsy

Blair Carsone; Katherine Green; William Torrence; Bridgett Henry

doi:10.12688/f1000research.52435.1

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Data Note

Occupational therapy and Beery VMI scores of children with autism spectrum disorder, brachial plexus injury, and cerebral palsy

[version 1; peer review: 2 approved with reservations]

Blair Carsone ¹, Katherine Green², William Torrence², Bridgett Henry²

PUBLISHED 29 Jun 2021

Author details Author details

¹ Gannon University, Ruskin, FL, USA
² Concordia University, River Forest, IL, USA

Blair Carsone
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Katherine Green
Roles: Project Administration, Supervision, Writing – Review & Editing

William Torrence
Roles: Methodology, Supervision, Writing – Review & Editing

Bridgett Henry
Roles: Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Data of 107 children (84 males and 23 females) who received occupational therapy services were collected. Data collected included age, gender, prescribed frequency of occupational therapy, number of sessions attended within the six-month timeframe, pre and post Beery Visual Motor Integration scores.

Keywords

Autism Spectrum Disorder, Brachial Plexus Injury, Cerebral Palsy, Occupational Therapy, Beery-Buktenica Developmental Test of Visual Motor Integration

Corresponding author: Blair Carsone

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2021 Carsone B et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Carsone B, Green K, Torrence W and Henry B. Occupational therapy and Beery VMI scores of children with autism spectrum disorder, brachial plexus injury, and cerebral palsy [version 1; peer review: 2 approved with reservations]. F1000Research 2021, 10:515 (https://doi.org/10.12688/f1000research.52435.1) First published: 29 Jun 2021, 10:515 (https://doi.org/10.12688/f1000research.52435.1) Latest published: 29 Jun 2021, 10:515 (https://doi.org/10.12688/f1000research.52435.1)

Introduction

The existing research is at a consensus that autism spectrum disorder (ASD), brachial plexus injury (BPI), and cerebral palsy (CP) affect visual motor integration (VMI) and that VMI may be positively impacted by occupational therapy intervention (Bumin & Kavak, 2008; Cho et al., 2015; Desai & Rege, 2005; Duff & DeMatteo, 2015; Green et al., 2016; Lu et al., 2016; Miller et al., 2014; Wadsworth et al., 2017). However, the research does not adequately address the potential relationship between occupational therapy intervention and the Beery VMI scores of children with ASD, BPI, and CP. This secondary data analysis cohort study addressed the current gap in knowledge by providing data that can be used to investigate the relationship between occupational therapy (prescribed frequency and total sessions attended), age, gender, and pre-Beery VMI scores on post-Beery VMI scores of children with ASD, BPI, and CP.

Methods

Participants

Patients with a formal diagnosis of ASD, BPI, and CP designated in the electronic medical record (EMR) who were between the ages of two years, zero months, and five years 11 months during the initial assessment (occupational therapy evaluation or re-evaluation) were considered for inclusion in the study. Subjects with pre- and post- Beery VMI scores who attended at least one therapy session (excluding the initial assessment and reassessment) were included in the study. Subjects’ identifying information was not reported, meeting Human Subject regulations and standards (Breault, 2006). Table 1 below provides the information of each subject included.

Table 1. Subject information (underlying data).

Gender	Diagnosis	Age at assessment (months)	Pre- Beery	Post- Beery	Sessions attended	Prescribed frequency
M	ASD	36	1	4	40	2
F	ASD	44	9	13	18	2
M	ASD	45	0	9	42	2
M	ASD	40	9	13	37	2
M	ASD	32	0	0	28	1
M	ASD	37	5	8	17	1
M	ASD	37	1	7	17	2
M	ASD	57	10	8	17	2
M	ASD	38	2	2	20	2
M	ASD	68	12	15	40	2
F	ASD	67	9	14	15	1
M	ASD	49	1	5	27	2
M	ASD	60	7	9	15	2
M	ASD	56	13	13	15	1
M	ASD	43	4	3	29	2
M	ASD	46	3	0	21	2
M	ASD	52	0	0	4	2
M	ASD	48	10	11	13	2
F	ASD	34	4	6	14	2
M	ASD	39	4	5	27	2
M	ASD	47	5	6	17	2
F	ASD	31	2	2	24	2
M	ASD	61	11	15	0	2
M	ASD	50	1	3	37	2
M	ASD	38	0	0	26	2
M	ASD	64	9	10	28	2
M	ASD	63	11	15	28	2
M	ASD	41	2	3	16	2
M	ASD	43	9	9	9	1
M	ASD	42	3	3	10	2
F	ASD	42	9	12	6	1
F	ASD	43	6	6	8	2
M	ASD	48	6	8	12	1
M	ASD	58	4	13	37	2
M	ASD	67	9	14	22	2
F	ASD	61	1	6	22	2
M	ASD	31	1	2	32	2
F	ASD	45	6	6	3	2
M	ASD	70	14	20	12	1
M	ASD	30	0	4	35	2
M	ASD	58	6	2	18	1
M	ASD	34	3	9	36	2
M	ASD	55	3	0	26	2
M	CP	69	15	15	22	1
M	ASD	58	0	0	36	2
M	ASD	45	0	9	16	2
F	ASD	46	9	10	21	1
M	ASD	39	0	1	31	2
M	ASD	59	0	0	25	2
M	ASD	49	0	1	44	2
M	ASD	55	15	16	26	2
M	ASD	26	2	2	26	2
M	ASD	34	4	9	15	2
M	ASD	59	6	7	35	2
M	ASD	36	0	1	17	2
M	ASD	65	4	4	43	2
M	ASD	50	1	1	39	2
M	ASD	55	10	15	12	1
M	ASD	47	9	10	39	2
F	ASD	50	1	13	21	2
M	ASD	45	5	6	18	2
M	ASD	35	7	4	30	2
M	ASD	44	3	4	26	2
F	ASD	69	14	15	39	2
M	ASD	62	7	9	36	2
M	ASD	67	13	15	35	2
M	ASD	57	7	9	44	2
F	ASD	61	15	14	8	2
F	ASD	61	10	11	24	1
M	ASD	37	10	9	9	2
M	ASD	66	14	17	10	1
F	ASD	68	16	19	42	2
M	ASD	36	9	8	40	2
M	ASD	52	1	2	28	2
M	ASD	53	9	12	23	2
M	ASD	54	8	10	15	2
F	ASD	35	4	6	25	2
M	ASD	39	7	9	14	2
F	ASD	46	2	2	16	2
F	ASD	50	10	12	45	2
M	ASD	32	3	4	38	2
F	ASD	65	12	14	35	2
M	ASD	42	2	3	21	2
M	ASD	42	9	13	40	2
M	ASD	51	5	5	23	2
F	ASD	42	0	7	9	1
F	ASD	43	6	8	41	2
F	ASD	66	11	13	10	2
M	ASD	42	7	13	18	1
M	ASD	71	5	4	44	2
M	ASD	39	4	11	25	2
M	ASD	43	8	12	20	1
M	ASD	49	2	7	22	2
F	BPI	66	13	13	3	2
M	ASD	64	15	20	15	2
M	ASD	65	10	13	4	2
M	ASD	69	15	18	7	1
M	ASD	45	1	5	12	2
M	ASD	32	1	1	15	2
M	ASD	25	1	2	39	2
M	ASD	61	3	3	35	2
M	ASD	32	4	5	17	1
M	ASD	64	0	1	19	2
F	CP	51	9	12	46	2
M	ASD	38	3	4	26	2
M	ASD	48	7	13	45	2
M	ASD	42	0	0	14	2
M	ASD	68	13	15	17	2

The inclusion criteria for the study was designed based on the findings from the literature. Patients with the diagnosis of ASD, BPI, and CP were selected as a focus for this study because these three conditions represent populations commonly affected by VMI deficits (Bonifacci, 2004; Dowd et al., 2011; Englund et al., 2014; Green et al., 2016). The age range accounted for the potential peak in VMI scores, while the six-month intervention period accounted for the confounding variable of maturation (Ercan et al., 2011; Fang et al., 2017; Heiz & Barisnikov, 2016). Limiting the study to one county accounted for the potential effects of location while focusing on one specific clinic increased the feasibility of the study (Cui et al., 2012; Ng et al., 2015; Visser et al., 2017).

Data collection and analysis

Data for both questions were obtained from one pediatric outpatient clinic in South Florida. Subject data were collected from the pediatric outpatient electronic medical record system, RainTree, based on the inclusion and exclusion criteria. The data reviewed spanned from June 1, 2017 to March 13, 2020. Limiters of age (two years zero months to five years 11 months) and diagnoses (ASD, BPI, or CP) were then employed. After the limiters were applied each individual chart was analyzed for pre- and six-month post- Beery VMI scores. Patient charts with pre- and six-month post Beery VMI scores who attended at least one therapy treatment session were included in the study. Prescribed frequency, therapy attendance, and changes in Beery VMI scores were recorded with relevant demographic information (e.g., age and gender). A summary of this process can be seen in Figure 1.

Figure 1. Data collection procedures.

Gender (male = 1, female = 0), age at initial assessment (24 months to 71 months), prescribed frequency (1 time per week =1, 2 times per week = 0), sessions attended (0 to 52 sessions), pre-Beery VMI raw score (0 to 30 points), post-Beery VMI raw score (0 to 30 points), and change in Beery VMI raw score (0 to 30 points).

Ethical considerations

This data set is part of a publicly defended dissertation and the study was approved by Concordia University Chicago’s IRB (study number 1698245-1). The study was exempt from review as it was determined to be a secondary data analysis. The research involves only information collection and analysis involving the investigator’s use of identifiable health information when that use is regulated under 45 CFR parts 160 and 164, subparts A and E, for the purposes of “health care operations” or “research” as those terms are defined at 45 CFR 164.501 or for “public health activities and purposes” as described under 45 CFR 164.512(b). Consent from participants was therefore not required.

Dataset description

Overwhelmingly, 97% of subjects were diagnosed with ASD while 1% were diagnosed with BPI and 2% were diagnosed with CP. The majority of subjects were male (N = 84, 78.5%; female N = 23, 21.5%). The subjects’ mean age was 49.21 months or approximately 4 years of age. Table 2 below provides an overview of the descriptive statistics.

Table 2. Research question descriptive statistics.

Variable	Mean	Standard deviation
Post Beery	7.89	5.317
Gender	1.79	0.413
Age (Months)	49.21	11.983
Pre-Beery	5.89	4.645
Prescribed frequency	1.82	0.384
Sessions attended	24.07	11.588

Data availability

Underlying data

All data underlying the results are available as part of the article (Table 1) and no additional source data are required.

References

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Version 1

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Author details Author details

¹ Gannon University, Ruskin, FL, USA
² Concordia University, River Forest, IL, USA

Blair Carsone
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Katherine Green
Roles: Project Administration, Supervision, Writing – Review & Editing

William Torrence
Roles: Methodology, Supervision, Writing – Review & Editing

Bridgett Henry
Roles: Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (1)

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Published: 29 Jun 2021, 10:515

https://doi.org/10.12688/f1000research.52435.1

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© 2021 Carsone B et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Carsone B, Green K, Torrence W and Henry B. Occupational therapy and Beery VMI scores of children with autism spectrum disorder, brachial plexus injury, and cerebral palsy [version 1; peer review: 2 approved with reservations]. F1000Research 2021, 10:515 (https://doi.org/10.12688/f1000research.52435.1)

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Reviewer Report 28 Nov 2023

Livia Taverna, Faculty of Education, Free University of Bozen-Bolzano, Bozen, Italy

Approved with Reservations

https://doi.org/10.5256/f1000research.55712.r120500

Research question: Formulate the research hypotheses: for example that you want to see if there are differences in the response to occupational therapy treatment in the various disorders, taking into account age, gender, and the duration of the

Research question: Formulate the research hypotheses: for example that you want to see if there are differences in the response to occupational therapy treatment in the various disorders, taking into account age, gender, and the duration of the sessions.
Participants:
- You can maybe include some socio-demographic information: do these children attend any formal educational institution (kindergarten, pre-kindergarten)? I assume that those attending to any form of school will be exposed to eye-hand coordination activities.
- Socio-demographic information about families: parent's age, education and economic status.
Method:
- Instrument: A brief description of the VMI would be appreciated. Even if it is a well known instrument, I think that for an article it is necessary to have a description. Include also information about scoring; scales and scores.
- Data Analysis:
  - Describe the statistical analysis you will run, which variables are the independent and the dependent and for what purpose you choose this statistical analysis.
  - These are some suggestion to information that would be interesting.
  - You can provide some further analysis comparing children with respect to diagnosis accounting for age, gender or for treatment duration; qualitative analysis giving information about the target figures that were more difficult to realize (obtaining 0 to the VMI).
  - You can also provide detailed information about VMI scores (VMI; Visual Perception and Motor Coordination) and analyze in which areas the improvement occurs more frequently accounting for treatment, gender, age and type of diagnosis.
Results: Report the results and related tables.
Discussion Discuss the abovementioned results.

Is the rationale for creating the dataset(s) clearly described?

Yes
Are the protocols appropriate and is the work technically sound?

Yes
Are sufficient details of methods and materials provided to allow replication by others?

No
Are the datasets clearly presented in a useable and accessible format?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Developmental Psychology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Reviewer Report 28 Oct 2022

Caroline J. Mills, Occupational Therapy, School of Health Sciences, Western Sydney University, Penrith, NSW, Australia

Approved with Reservations

https://doi.org/10.5256/f1000research.55712.r153646

This is an interesting data note which explores VMI scores pre and post for children who attended occupational therapy. The VMI is a commonly used tool in children’s occupational therapy and publication of this dataset may assist in collaboration and ... Continue reading

This is an interesting data note which explores VMI scores pre and post for children who attended occupational therapy. The VMI is a commonly used tool in children’s occupational therapy and publication of this dataset may assist in collaboration and production of high quality research. There is however, some more context required in order to ensure that the data set is used to its maximum potential.

Introduction:

The term ‘autism spectrum disorder (ASD)' is not preferred by the autistic community. Consider saying ‘autism spectrum disorder’, hereafter ‘autism’ rather than ASD.

Methods: Participants:

Beery VMI- this data note may be improved by providing more information about the VMI- you mentioned ‘peak scores’. It’s not clear what this is if the reader does not have the context of the VMI. You should offer more context about where the score came from. Was it from the whole VMI assessment (including visual perception and motor
coordination) or just the main VMI test?

Dataset description:

You should include actual numbers of participants here. How many in total, how many had autism, CP, BPI etc.

The data is presented clearly. I see that majority of the children are autistic. There’s only 1 child with BPI and 2 children with CP. It may appear that these children are outliers. It would not be possible for any researcher to use this data to ‘determine a relationship’ between OT attendance, VMI scores based on these low numbers. I suggest that these outliers are excluded from the dataset unless you can present a more firm justification for why they should be left in there.

It is not clear whether any of these children had any co-occurring diagnoses (in particular global developmental delay or intellectual disability). These may impact a child’s participation in occupational therapy and their VMI scores. Consider including this information or stating if/why it is absent.

Context is needed around what the children worked on in therapy. Were they focused on visual motor integration/visual perception/hand skills in therapy sessions? If a researcher were to use this data to determine a relationship between OT attendance and VMI scores, they would need to context around what type of OT. With a dataset like this, this could be achieved through a process of categorisation, where therapy activities are grouped into categories (fine motor, play etc.). If this data is not available- you should provide a justification as to why.

Table 2:

Don’t use Mean and SD for nominal variables like Gender- use number and percentage of total. You could also offer some more descriptive information such as score range (min-max).

Is the rationale for creating the dataset(s) clearly described?

Yes
Are the protocols appropriate and is the work technically sound?

Yes
Are sufficient details of methods and materials provided to allow replication by others?

Partly
Are the datasets clearly presented in a useable and accessible format?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Children's occupational therapy, autism

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Caroline J. Mills, Western Sydney University, Penrith, Australia
Livia Taverna, Free University of Bozen-Bolzano, Bozen, Italy

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Livia Taverna, Faculty of Education, Free University of Bozen-Bolzano, Bozen, Italy

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Approved With Reservations

Research question: Formulate the research hypotheses: for example that you want to see if there are differences in the response to occupational therapy treatment in the various disorders, taking into account age, gender, and the duration of the sessions.
Participants:
- You can maybe include some socio-demographic information: do these children attend any formal educational institution (kindergarten, pre-kindergarten)? I assume that those attending to any form of school will be exposed to eye-hand coordination activities.
- Socio-demographic information about families: parent's age, education and economic status.
Method:
- Instrument: A brief description of the VMI would be appreciated. Even if it is a well known instrument, I think that for an article it is necessary to have a description. Include also information about scoring; scales and scores.
- Data Analysis:
  - Describe the statistical analysis you will run, which variables are the independent and the dependent and for what purpose you choose this statistical analysis.
  - These are some suggestion to information that would be interesting.
  - You can provide some further analysis comparing children with respect to diagnosis accounting for age, gender or for treatment duration; qualitative analysis giving information about the target figures that were more difficult to realize (obtaining 0 to the VMI).
  - You can also provide detailed information about VMI scores (VMI; Visual Perception and Motor Coordination) and analyze in which areas the improvement occurs more frequently accounting for treatment, gender, age and type of diagnosis.
Results: Report the results and related tables.
Discussion Discuss the abovementioned results.

Is the rationale for creating the dataset(s) clearly described?

Yes
Are the protocols appropriate and is the work technically sound?

Yes
Are sufficient details of methods and materials provided to allow replication by others?

No
Are the datasets clearly presented in a useable and accessible format?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Developmental Psychology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Reviewer Report

7 Views

28 Oct 2022 | for Version 1

Caroline J. Mills, Occupational Therapy, School of Health Sciences, Western Sydney University, Penrith, NSW, Australia

7 Views Cite this report Responses(0)

Approved With Reservations

This is an interesting data note which explores VMI scores pre and post for children who attended occupational therapy. The VMI is a commonly used tool in children’s occupational therapy and publication of this dataset may assist in collaboration and production of high quality research. There is however, some more context required in order to ensure that the data set is used to its maximum potential.

Introduction:

The term ‘autism spectrum disorder (ASD)' is not preferred by the autistic community. Consider saying ‘autism spectrum disorder’, hereafter ‘autism’ rather than ASD.

Methods: Participants:

Beery VMI- this data note may be improved by providing more information about the VMI- you mentioned ‘peak scores’. It’s not clear what this is if the reader does not have the context of the VMI. You should offer more context about where the score came from. Was it from the whole VMI assessment (including visual perception and motor
coordination) or just the main VMI test?

Dataset description:

You should include actual numbers of participants here. How many in total, how many had autism, CP, BPI etc.

The data is presented clearly. I see that majority of the children are autistic. There’s only 1 child with BPI and 2 children with CP. It may appear that these children are outliers. It would not be possible for any researcher to use this data to ‘determine a relationship’ between OT attendance, VMI scores based on these low numbers. I suggest that these outliers are excluded from the dataset unless you can present a more firm justification for why they should be left in there.

It is not clear whether any of these children had any co-occurring diagnoses (in particular global developmental delay or intellectual disability). These may impact a child’s participation in occupational therapy and their VMI scores. Consider including this information or stating if/why it is absent.

Context is needed around what the children worked on in therapy. Were they focused on visual motor integration/visual perception/hand skills in therapy sessions? If a researcher were to use this data to determine a relationship between OT attendance and VMI scores, they would need to context around what type of OT. With a dataset like this, this could be achieved through a process of categorisation, where therapy activities are grouped into categories (fine motor, play etc.). If this data is not available- you should provide a justification as to why.

Table 2:

Don’t use Mean and SD for nominal variables like Gender- use number and percentage of total. You could also offer some more descriptive information such as score range (min-max).

Is the rationale for creating the dataset(s) clearly described?

Yes
Are the protocols appropriate and is the work technically sound?

Yes
Are sufficient details of methods and materials provided to allow replication by others?

Partly
Are the datasets clearly presented in a useable and accessible format?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Children's occupational therapy, autism

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Gender	Diagnosis	Age at assessment (months)	Pre- Beery	Post- Beery	Sessions attended	Prescribed frequency
M	ASD	36	1	4	40	2
F	ASD	44	9	13	18	2
M	ASD	45	0	9	42	2
M	ASD	40	9	13	37	2
M	ASD	32	0	0	28	1
M	ASD	37	5	8	17	1
M	ASD	37	1	7	17	2
M	ASD	57	10	8	17	2
M	ASD	38	2	2	20	2
M	ASD	68	12	15	40	2
F	ASD	67	9	14	15	1
M	ASD	49	1	5	27	2
M	ASD	60	7	9	15	2
M	ASD	56	13	13	15	1
M	ASD	43	4	3	29	2
M	ASD	46	3	0	21	2
M	ASD	52	0	0	4	2
M	ASD	48	10	11	13	2
F	ASD	34	4	6	14	2
M	ASD	39	4	5	27	2
M	ASD	47	5	6	17	2
F	ASD	31	2	2	24	2
M	ASD	61	11	15	0	2
M	ASD	50	1	3	37	2
M	ASD	38	0	0	26	2
M	ASD	64	9	10	28	2
M	ASD	63	11	15	28	2
M	ASD	41	2	3	16	2
M	ASD	43	9	9	9	1
M	ASD	42	3	3	10	2
F	ASD	42	9	12	6	1
F	ASD	43	6	6	8	2
M	ASD	48	6	8	12	1
M	ASD	58	4	13	37	2
M	ASD	67	9	14	22	2
F	ASD	61	1	6	22	2
M	ASD	31	1	2	32	2
F	ASD	45	6	6	3	2
M	ASD	70	14	20	12	1
M	ASD	30	0	4	35	2
M	ASD	58	6	2	18	1
M	ASD	34	3	9	36	2
M	ASD	55	3	0	26	2
M	CP	69	15	15	22	1
M	ASD	58	0	0	36	2
M	ASD	45	0	9	16	2
F	ASD	46	9	10	21	1
M	ASD	39	0	1	31	2
M	ASD	59	0	0	25	2
M	ASD	49	0	1	44	2
M	ASD	55	15	16	26	2
M	ASD	26	2	2	26	2
M	ASD	34	4	9	15	2
M	ASD	59	6	7	35	2
M	ASD	36	0	1	17	2
M	ASD	65	4	4	43	2
M	ASD	50	1	1	39	2
M	ASD	55	10	15	12	1
M	ASD	47	9	10	39	2
F	ASD	50	1	13	21	2
M	ASD	45	5	6	18	2
M	ASD	35	7	4	30	2
M	ASD	44	3	4	26	2
F	ASD	69	14	15	39	2
M	ASD	62	7	9	36	2
M	ASD	67	13	15	35	2
M	ASD	57	7	9	44	2
F	ASD	61	15	14	8	2
F	ASD	61	10	11	24	1
M	ASD	37	10	9	9	2
M	ASD	66	14	17	10	1
F	ASD	68	16	19	42	2
M	ASD	36	9	8	40	2
M	ASD	52	1	2	28	2
M	ASD	53	9	12	23	2
M	ASD	54	8	10	15	2
F	ASD	35	4	6	25	2
M	ASD	39	7	9	14	2
F	ASD	46	2	2	16	2
F	ASD	50	10	12	45	2
M	ASD	32	3	4	38	2
F	ASD	65	12	14	35	2
M	ASD	42	2	3	21	2
M	ASD	42	9	13	40	2
M	ASD	51	5	5	23	2
F	ASD	42	0	7	9	1
F	ASD	43	6	8	41	2
F	ASD	66	11	13	10	2
M	ASD	42	7	13	18	1
M	ASD	71	5	4	44	2
M	ASD	39	4	11	25	2
M	ASD	43	8	12	20	1
M	ASD	49	2	7	22	2
F	BPI	66	13	13	3	2
M	ASD	64	15	20	15	2
M	ASD	65	10	13	4	2
M	ASD	69	15	18	7	1
M	ASD	45	1	5	12	2
M	ASD	32	1	1	15	2
M	ASD	25	1	2	39	2
M	ASD	61	3	3	35	2
M	ASD	32	4	5	17	1
M	ASD	64	0	1	19	2
F	CP	51	9	12	46	2
M	ASD	38	3	4	26	2
M	ASD	48	7	13	45	2
M	ASD	42	0	0	14	2
M	ASD	68	13	15	17	2

Gender	Diagnosis	Age at assessment (months)	Pre- Beery	Post- Beery	Sessions attended	Prescribed frequency
M	ASD	36	1	4	40	2
F	ASD	44	9	13	18	2
M	ASD	45	0	9	42	2
M	ASD	40	9	13	37	2
M	ASD	32	0	0	28	1
M	ASD	37	5	8	17	1
M	ASD	37	1	7	17	2
M	ASD	57	10	8	17	2
M	ASD	38	2	2	20	2
M	ASD	68	12	15	40	2
F	ASD	67	9	14	15	1
M	ASD	49	1	5	27	2
M	ASD	60	7	9	15	2
M	ASD	56	13	13	15	1
M	ASD	43	4	3	29	2
M	ASD	46	3	0	21	2
M	ASD	52	0	0	4	2
M	ASD	48	10	11	13	2
F	ASD	34	4	6	14	2
M	ASD	39	4	5	27	2
M	ASD	47	5	6	17	2
F	ASD	31	2	2	24	2
M	ASD	61	11	15	0	2
M	ASD	50	1	3	37	2
M	ASD	38	0	0	26	2
M	ASD	64	9	10	28	2
M	ASD	63	11	15	28	2
M	ASD	41	2	3	16	2
M	ASD	43	9	9	9	1
M	ASD	42	3	3	10	2
F	ASD	42	9	12	6	1
F	ASD	43	6	6	8	2
M	ASD	48	6	8	12	1
M	ASD	58	4	13	37	2
M	ASD	67	9	14	22	2
F	ASD	61	1	6	22	2
M	ASD	31	1	2	32	2
F	ASD	45	6	6	3	2
M	ASD	70	14	20	12	1
M	ASD	30	0	4	35	2
M	ASD	58	6	2	18	1
M	ASD	34	3	9	36	2
M	ASD	55	3	0	26	2
M	CP	69	15	15	22	1
M	ASD	58	0	0	36	2
M	ASD	45	0	9	16	2
F	ASD	46	9	10	21	1
M	ASD	39	0	1	31	2
M	ASD	59	0	0	25	2
M	ASD	49	0	1	44	2
M	ASD	55	15	16	26	2
M	ASD	26	2	2	26	2
M	ASD	34	4	9	15	2
M	ASD	59	6	7	35	2
M	ASD	36	0	1	17	2
M	ASD	65	4	4	43	2
M	ASD	50	1	1	39	2
M	ASD	55	10	15	12	1
M	ASD	47	9	10	39	2
F	ASD	50	1	13	21	2
M	ASD	45	5	6	18	2
M	ASD	35	7	4	30	2
M	ASD	44	3	4	26	2
F	ASD	69	14	15	39	2
M	ASD	62	7	9	36	2
M	ASD	67	13	15	35	2
M	ASD	57	7	9	44	2
F	ASD	61	15	14	8	2
F	ASD	61	10	11	24	1
M	ASD	37	10	9	9	2
M	ASD	66	14	17	10	1
F	ASD	68	16	19	42	2
M	ASD	36	9	8	40	2
M	ASD	52	1	2	28	2
M	ASD	53	9	12	23	2
M	ASD	54	8	10	15	2
F	ASD	35	4	6	25	2
M	ASD	39	7	9	14	2
F	ASD	46	2	2	16	2
F	ASD	50	10	12	45	2
M	ASD	32	3	4	38	2
F	ASD	65	12	14	35	2
M	ASD	42	2	3	21	2
M	ASD	42	9	13	40	2
M	ASD	51	5	5	23	2
F	ASD	42	0	7	9	1
F	ASD	43	6	8	41	2
F	ASD	66	11	13	10	2
M	ASD	42	7	13	18	1
M	ASD	71	5	4	44	2
M	ASD	39	4	11	25	2
M	ASD	43	8	12	20	1
M	ASD	49	2	7	22	2
F	BPI	66	13	13	3	2
M	ASD	64	15	20	15	2
M	ASD	65	10	13	4	2
M	ASD	69	15	18	7	1
M	ASD	45	1	5	12	2
M	ASD	32	1	1	15	2
M	ASD	25	1	2	39	2
M	ASD	61	3	3	35	2
M	ASD	32	4	5	17	1
M	ASD	64	0	1	19	2
F	CP	51	9	12	46	2
M	ASD	38	3	4	26	2
M	ASD	48	7	13	45	2
M	ASD	42	0	0	14	2
M	ASD	68	13	15	17	2

Occupational therapy and Beery VMI scores of children with autism spectrum disorder, brachial plexus injury, and cerebral palsy

Abstract

Keywords

Introduction

Methods

Participants

Table 1. Subject information (underlying data).

Data collection and analysis

Figure 1. Data collection procedures.

Ethical considerations

Dataset description

Table 2. Research question descriptive statistics.

Data availability

Underlying data

References

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Gender	Diagnosis	Age at assessment (months)	Pre- Beery	Post- Beery	Sessions attended	Prescribed frequency
M	ASD	36	1	4	40	2
F	ASD	44	9	13	18	2
M	ASD	45	0	9	42	2
M	ASD	40	9	13	37	2
M	ASD	32	0	0	28	1
M	ASD	37	5	8	17	1
M	ASD	37	1	7	17	2
M	ASD	57	10	8	17	2
M	ASD	38	2	2	20	2
M	ASD	68	12	15	40	2
F	ASD	67	9	14	15	1
M	ASD	49	1	5	27	2
M	ASD	60	7	9	15	2
M	ASD	56	13	13	15	1
M	ASD	43	4	3	29	2
M	ASD	46	3	0	21	2
M	ASD	52	0	0	4	2
M	ASD	48	10	11	13	2
F	ASD	34	4	6	14	2
M	ASD	39	4	5	27	2
M	ASD	47	5	6	17	2
F	ASD	31	2	2	24	2
M	ASD	61	11	15	0	2
M	ASD	50	1	3	37	2
M	ASD	38	0	0	26	2
M	ASD	64	9	10	28	2
M	ASD	63	11	15	28	2
M	ASD	41	2	3	16	2
M	ASD	43	9	9	9	1
M	ASD	42	3	3	10	2
F	ASD	42	9	12	6	1
F	ASD	43	6	6	8	2
M	ASD	48	6	8	12	1
M	ASD	58	4	13	37	2
M	ASD	67	9	14	22	2
F	ASD	61	1	6	22	2
M	ASD	31	1	2	32	2
F	ASD	45	6	6	3	2
M	ASD	70	14	20	12	1
M	ASD	30	0	4	35	2
M	ASD	58	6	2	18	1
M	ASD	34	3	9	36	2
M	ASD	55	3	0	26	2
M	CP	69	15	15	22	1
M	ASD	58	0	0	36	2
M	ASD	45	0	9	16	2
F	ASD	46	9	10	21	1
M	ASD	39	0	1	31	2
M	ASD	59	0	0	25	2
M	ASD	49	0	1	44	2
M	ASD	55	15	16	26	2
M	ASD	26	2	2	26	2
M	ASD	34	4	9	15	2
M	ASD	59	6	7	35	2
M	ASD	36	0	1	17	2
M	ASD	65	4	4	43	2
M	ASD	50	1	1	39	2
M	ASD	55	10	15	12	1
M	ASD	47	9	10	39	2
F	ASD	50	1	13	21	2
M	ASD	45	5	6	18	2
M	ASD	35	7	4	30	2
M	ASD	44	3	4	26	2
F	ASD	69	14	15	39	2
M	ASD	62	7	9	36	2
M	ASD	67	13	15	35	2
M	ASD	57	7	9	44	2
F	ASD	61	15	14	8	2
F	ASD	61	10	11	24	1
M	ASD	37	10	9	9	2
M	ASD	66	14	17	10	1
F	ASD	68	16	19	42	2
M	ASD	36	9	8	40	2
M	ASD	52	1	2	28	2
M	ASD	53	9	12	23	2
M	ASD	54	8	10	15	2
F	ASD	35	4	6	25	2
M	ASD	39	7	9	14	2
F	ASD	46	2	2	16	2
F	ASD	50	10	12	45	2
M	ASD	32	3	4	38	2
F	ASD	65	12	14	35	2
M	ASD	42	2	3	21	2
M	ASD	42	9	13	40	2
M	ASD	51	5	5	23	2
F	ASD	42	0	7	9	1
F	ASD	43	6	8	41	2
F	ASD	66	11	13	10	2
M	ASD	42	7	13	18	1
M	ASD	71	5	4	44	2
M	ASD	39	4	11	25	2
M	ASD	43	8	12	20	1
M	ASD	49	2	7	22	2
F	BPI	66	13	13	3	2
M	ASD	64	15	20	15	2
M	ASD	65	10	13	4	2
M	ASD	69	15	18	7	1
M	ASD	45	1	5	12	2
M	ASD	32	1	1	15	2
M	ASD	25	1	2	39	2
M	ASD	61	3	3	35	2
M	ASD	32	4	5	17	1
M	ASD	64	0	1	19	2
F	CP	51	9	12	46	2
M	ASD	38	3	4	26	2
M	ASD	48	7	13	45	2
M	ASD	42	0	0	14	2
M	ASD	68	13	15	17	2