Functional Behavior Assessments for Challenging Behavior in Autism

Healy, Olive; Brett, Denise

doi:10.1007/978-1-4614-4788-7_191

Cited by 5 publications

(2 citation statements)

References 65 publications

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“…Our proposed method of information extraction may also be used to assist in behavioral management techniques like the "functional behavioral analysis." This type of clinical assessment has been proven effective in analyzing and designing treatment plans for problem behaviors but can require as many as 40-50 clinician observation sessions per child [26,27]. Video analysis and telecommunication have already been implemented to aid teachers in functional analyses at school with some success [28,29], and the same strategy could be applied using home video.…”

Section: Clinical Workmentioning

confidence: 99%

Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning

O'Donovan¹,

Sezgın²,

Bambach³

et al. 2020

JMIR Form Res

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Background Qualitative self- or parent-reports used in assessing children’s behavioral disorders are often inconvenient to collect and can be misleading due to missing information, rater biases, and limited validity. A data-driven approach to quantify behavioral disorders could alleviate these concerns. This study proposes a machine learning approach to identify screams in voice recordings that avoids the need to gather large amounts of clinical data for model training. Objective The goal of this study is to evaluate if a machine learning model trained only on publicly available audio data sets could be used to detect screaming sounds in audio streams captured in an at-home setting. Methods Two sets of audio samples were prepared to evaluate the model: a subset of the publicly available AudioSet data set and a set of audio data extracted from the TV show Supernanny, which was chosen for its similarity to clinical data. Scream events were manually annotated for the Supernanny data, and existing annotations were refined for the AudioSet data. Audio feature extraction was performed with a convolutional neural network pretrained on AudioSet. A gradient-boosted tree model was trained and cross-validated for scream classification on the AudioSet data and then validated independently on the Supernanny audio. Results On the held-out AudioSet clips, the model achieved a receiver operating characteristic (ROC)–area under the curve (AUC) of 0.86. The same model applied to three full episodes of Supernanny audio achieved an ROC-AUC of 0.95 and an average precision (positive predictive value) of 42% despite screams only making up 1.3% (n=92/7166 seconds) of the total run time. Conclusions These results suggest that a scream-detection model trained with publicly available data could be valuable for monitoring clinical recordings and identifying tantrums as opposed to depending on collecting costly privacy-protected clinical data for model training.

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Section: Clinical Workmentioning

confidence: 99%

Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning

O'Donovan¹,

Sezgın²,

Bambach³

et al. 2020

JMIR Form Res

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“…Each patient was assessed at baseline with a Vineland-3 that was subsequently used by the BCBA in creating their TP. BCBAs conducted functional behavior assessments to inform highly individualized strategies to mitigate these IBs, which identify the IB's function (e.g., the behavior of elopement could function to avoid an undesirable task) and promote constructive replacement behaviors [47]. TPs additionally included a treatment dosage recommendation made by the BCBA.…”

Section: Intervention Deliverymentioning

confidence: 99%

Family-Centric Applied Behavior Analysis Facilitates Improved Treatment Utilization and Outcomes

Adelson,

Ciobanu,

Garikipati

et al. 2024

JCM

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Background/Objective: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by lifelong impacts on functional social and daily living skills, and restricted, repetitive behaviors (RRBs). Applied behavior analysis (ABA), the gold-standard treatment for ASD, has been extensively validated. ABA access is hindered by limited availability of qualified professionals and logistical and financial barriers. Scientifically validated, parent-led ABA can fill the accessibility gap by overcoming treatment barriers. This retrospective cohort study examines how our ABA treatment model, utilizing parent behavior technicians (pBTs) to deliver ABA, impacts adaptive behaviors and interfering behaviors (IBs) in a cohort of children on the autism spectrum with varying ASD severity levels, and with or without clinically significant IBs. Methods: Clinical outcomes of 36 patients ages 3–15 years were assessed using longitudinal changes in Vineland-3 after 3+ months of pBT-delivered ABA treatment. Results: Within the pBT model, our patients demonstrated clinically significant improvements in Vineland-3 Composite, domain, and subdomain scores, and utilization was higher in severe ASD. pBTs utilized more prescribed ABA when children initiated treatment with clinically significant IBs, and these children also showed greater gains in their Composite scores. Study limitations include sample size, inter-rater reliability, potential assessment metric bias and schedule variability, and confounding intrinsic or extrinsic factors. Conclusion: Overall, our pBT model facilitated high treatment utilization and showed robust effectiveness, achieving improved adaptive behaviors and reduced IBs when compared to conventional ABA delivery. The pBT model is a strong contender to fill the widening treatment accessibility gap and represents a powerful tool for addressing systemic problems in ABA treatment delivery.

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