A tablet-based game for the assessment of visual motor skills in autistic children

Visual-Motor Integration (VMI) Is Also Relevant for Computer, Smartphone, and Tablet Use by Adults: Introducing the Brief Box Clicking Test

IMPORTANCE

Visual-motor integration (VMI) is typically examined in children to promote handwriting, but it may also be relevant for adults' capacity for technology use.

OBJECTIVE

To examine the reliability and validity of speed of completion of the box clicking test, a web-based test of VMI.

DESIGN

Participants in the Understanding America Study completed online surveys on a regular basis, including a very brief (less than 30 s) self-administered box clicking test. For validity testing, we examined whether box clicking speed was associated with constructs relevant to visual-perceptual skills and motor coordination, the skills underlying VMI. Test-retest reliability was examined by computation of intraclass correlation coefficients.

PARTICIPANTS

A total of 11,114 adults.

MEASURES

Measures included the completion time for the box clicking task and measures relevant to visual perception (e.g., perceptual speed) and motor coordination (e.g., self-reported functional limitation).

RESULTS

Results suggested that the box clicking test was a VMI task. Slower test performance was associated with lower visual-perceptual speed and a greater likelihood of reporting difficulties with dressing, a motor coordination relevant task. Box clicking tests taken within at least 2 yr of one another had moderate test-retest stability, but future studies are needed to examine test-retest reliabilities over brief (e.g., 2-wk) time intervals.

CONCLUSIONS AND RELEVANCE

The box clicking test may serve both as a tool for research and to clinically observe whether clients have VMI difficulties that interfere with computer, smartphone, or tablet use. Plain-Language Summary: Use of devices such as smartphones and computers is increasingly becoming integral for daily functioning. Visual-motor integration (VMI) has often been addressed by occupational therapists to support handwriting of children, but it may also be important for technology use by adults. Prior literature supports the relevance of VMI to technology use, and adults with various chronic conditions have been found to have decrements in VMI. We tested the psychometric properties of a brief box clicking test of VMI that could be used to examine VMI underlying technology use among adults. Overall, results suggested that the box clicking test was a VMI task. Just as speed of gait has been used as an index of functional mobility, speed on the box clicking task seemed serviceable as an index of VMI ability. The box clicking test may also be used for clinical observation of whether VMI interferes with technology use.

A Systematic Review and Meta-Analysis of the Associations Between Motor Milestone Timing and Motor Development in Neurodevelopmental Conditions

Early motor skills may be important early markers of neurodevelopmental conditions or predictors of their later onset. To explore this, we conducted a systematic review and meta-analysis of infant motor skill assessments in those who go on to gain a clinical diagnosis of autism, attention deficit hyperactivity disorder (ADHD), schizophrenia, language conditions, tic disorders, or developmental coordination disorder (DCD). In total, 65 articles met inclusion criteria. Three three-level meta-analyses were run. Meta-analysis of milestone achievement in N=21354 individuals revealed gross motor milestones were significantly delayed compared to controls (g= 0.53, p< 0.001). Subgroup analyses revealed autism (g= 0.63) and DCD (g= 0.53) had the highest magnitude delays. Specific delays were revealed for holding the head up (g= 0.21), sitting (g= 0.28), standing (g= 0.35), crawling (g=0.19), and walking (g= 0.71). Meta-analyses of standardised motor skill measurements in N=1976 individuals revealed reduced performance compared to controls in autism and language conditions (g= -0.54, p< 0.001). Together, these findings demonstrate delayed milestone attainment and motor impairments in early childhood in neurodevelopmental conditions.

Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification

In recent years, research has been demonstrating that movement analysis, utilizing machine learning methods, can be a promising aid for clinicians in supporting autism diagnostic process. Within this field of research, we aim to explore new models and delve into the detailed observation of certain features that previous literature has identified as prominent in the classification process. Our study employs a game-based tablet application to collect motor data. We use artificial neural networks to analyze raw trajectories in a "drag and drop" task. We compare a two-features model (utilizing only raw coordinates) with a four-features model (including velocities and accelerations). The aim is to assess the effectiveness of raw data analysis and determine the impact of acceleration on autism classification. Our results revealed that both models demonstrate promising accuracy in classifying motor trajectories. The four-features model consistently outperforms the two-features model, as evidenced by accuracy values (0.90 vs. 0.76). However, our findings support the potential of raw data analysis in objectively assessing motor behaviors related to autism. While the four-features model excels, the two-features model still achieves reasonable accuracy. Addressing limitations related to sample size and noise is essential for future research. Our study emphasizes the importance of integrating intelligent solutions to enhance and assist autism traditional diagnostic process and intervention, paving the way for more effective tools in assessing motor skills.

Autism Caregiver Coaching in Africa (ACACIA): Protocol for a type 1-hybrid effectiveness-implementation trial

BACKGROUND

While early autism intervention can significantly improve outcomes, gaps in implementation exist globally. These gaps are clearest in Africa, where forty percent of the world's children will live by 2050. Task-sharing early intervention to non-specialists is a key implementation strategy, given the lack of specialists in Africa. Naturalistic Developmental Behavioral Interventions (NDBI) are a class of early autism intervention that can be delivered by caregivers. As a foundational step to address the early autism intervention gap, we adapted a non-specialist delivered caregiver coaching NDBI for the South African context, and pre-piloted this cascaded task-sharing approach in an existing system of care.

OBJECTIVES

First, we will test the effectiveness of the caregiver coaching NDBI compared to usual care. Second, we will describe coaching implementation factors within the Western Cape Department of Education in South Africa.

METHODS

This is a type 1 effectiveness-implementation hybrid design; assessor-blinded, group randomized controlled trial. Participants include 150 autistic children (18-72 months) and their caregivers who live in Cape Town, South Africa, and those involved in intervention implementation. Early Childhood Development practitioners, employed by the Department of Education, will deliver 12, one hour, coaching sessions to the intervention group. The control group will receive usual care. Distal co-primary outcomes include the Communication Domain Standard Score (Vineland Adaptive Behavior Scales, Third Edition) and the Language and Communication Developmental Quotient (Griffiths Scales of Child Development, Third Edition). Proximal secondary outcome include caregiver strategies measured by the sum of five items from the Joint Engagement Rating Inventory. We will describe key implementation determinants.

RESULTS

Participant enrolment started in April 2023. Estimated primary completion date is March 2027.

CONCLUSION

The ACACIA trial will determine whether a cascaded task-sharing intervention delivered in an educational setting leads to meaningful improvements in communication abilities of autistic children, and identify implementation barriers and facilitators.

TRIAL REGISTRATION

NCT05551728 in Clinical Trial Registry (https://clinicaltrials.gov).

Early detection of autism using digital behavioral phenotyping

Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17-36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning. An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.

Autism Caregiver Coaching in Africa (ACACIA): Protocol for a type 1-hybrid effectiveness-implementation trial

Background

While early autism intervention can significantly improve outcomes, gaps in implementation exist globally. These gaps are clearest in Africa, where forty percent of the world's children will live by 2050. Task-sharing early intervention to non-specialists is a key implementation strategy, given the lack of specialists in Africa. Naturalistic Developmental Behavioral Interventions (NDBI) are a class of early autism intervention that can be delivered by caregivers. As a foundational step to address the early autism intervention gap, we adapted a non-specialist delivered caregiver coaching NDBI for the South African context, and pre-piloted this cascaded task-sharing approach in an existing system of care.

Objectives

First, we will test the effectiveness of the caregiver coaching NDBI compared to usual care. Second, we will describe coaching implementation factors within the Western Cape Department of Education in South Africa.

Methods

This is a type 1 effectiveness-implementation hybrid design; assessor-blinded, group randomized controlled trial. Participants include 150 autistic children (18-72 months) and their caregivers who live in Cape Town, South Africa, and those involved in intervention implementation. Early Childhood Development practitioners, employed by the Department of Education, will deliver 12, one hour, coaching sessions to the intervention group. The control group will receive usual care. Distal co-primary outcomes include the Communication Domain Standard Score (Vineland Adaptive Behavior Scales, Third Edition) and the Language and Communication Developmental Quotient (Griffiths Scales of Child Development, Third Edition). Proximal secondary outcome include caregiver strategies measured by the sum of five items from the Joint Engagement Rating Inventory. We will describe key implementation determinants.

Results

Participant enrolment started in April 2023. Estimated primary completion date is March 2027.

Conclusion

The ACACIA trial will determine whether a cascaded task-sharing intervention delivered in an educational setting leads to meaningful improvements in communication abilities of autistic children, and identify implementation barriers and facilitators.

Relationship between quantitative digital behavioral features and clinical profiles in young autistic children

Early behavioral markers for autism include differences in social attention and orienting in response to one's name when called, and differences in body movements and motor abilities. More efficient, scalable, objective, and reliable measures of these behaviors could improve early screening for autism. This study evaluated whether objective and quantitative measures of autism-related behaviors elicited from an app (SenseToKnow) administered on a smartphone or tablet and measured via computer vision analysis (CVA) are correlated with standardized caregiver-report and clinician administered measures of autism-related behaviors and cognitive, language, and motor abilities. This is an essential step in establishing the concurrent validity of a digital phenotyping approach. In a sample of 485 toddlers, 43 of whom were diagnosed with autism, we found that CVA-based gaze variables related to social attention were associated with the level of autism-related behaviors. Two language-related behaviors measured via the app, attention to people during a conversation and responding to one's name being called, were associated with children's language skills. Finally, performance during a bubble popping game was associated with fine motor skills. These findings provide initial support for the concurrent validity of the SenseToKnow app and its potential utility in identifying clinical profiles associated with autism. Future research is needed to determine whether the app can be used as an autism screening tool, can reliably stratify autism-related behaviors, and measure changes in autism-related behaviors over time.