Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
JMIR research protocols
Telehealth Versus In-Person Caregiver-Mediated Behavioral Treatment for Challenging Behaviors in Children With Autism Spectrum Disorder: Protocol for COACH (Caregiver Outreach for Autism Coaching at Home) Randomized Controlled Trial
BACKGROUND: Approximately 40%-60% of children diagnosed with autism spectrum disorder (ASD) exhibit challenging behaviors such as aggression, elopement, self-injury, and property destruction that can endanger the health and safety of the child or others, often cause significant distress for families, and hinder the child's developmental progress. Although behavioral treatments grounded in the principles of applied behavior analysis have been shown to effectively reduce or eliminate these behaviors, significant gaps remain in how to deliver these interventions equitably. OBJECTIVE: This study aims to compare the efficacy of delivering in-person versus telehealth caregiver-mediated behavioral treatment for challenging behaviors in children with ASD. METHODS: The Caregiver Outreach for Autism Coaching at Home trial is a single-site, multiarm, parallel-group randomized controlled trial to evaluate the efficacy of delivering caregiver-mediated behavioral treatment for children with ASD who exhibit challenging behaviors. We aim to recruit 90 caregiver-child dyads with children with ASD aged 2 years, 0 months to 7 years, 11 months from metropolitan, suburban, and rural areas across New Mexico and the neighboring El Paso, Texas region. Following a practical functional assessment to identify the potential social-environmental variables maintaining the child's challenging behavior, caregiver-child dyads will be randomized into one of three parallel treatment arms: (1) caregiver-mediated behavioral treatment delivered via telehealth, (2) caregiver-mediated behavioral treatment delivered in person, or (3) caregiver online psychoeducation. Interventions will be delivered by clinicians in weekly 1-hour sessions in the home setting over 12 weeks. RESULTS: The study was funded in May 2022, and recruitment began in January 2024. As of March 2026, 81 caregiver-child dyads have been enrolled, with equal allocation across the 3 study groups (27 per group). Recruitment is expected to conclude in October 2026, with data analysis planned for Winter 2026 through Spring 2027. CONCLUSIONS: This study will address a critical gap in the scientific literature on scalable, family-centered approaches to safely and effectively reduce challenging behavior in children with ASD and decrease caregiver stress. Findings may inform best practices and payer policy for telehealth-delivered behavioral interventions targeting child safety and caregiver well-being.
29 May 2026
Read appraisal →PloS one
A systematic review and meta-analysis on achievement emotions, working memory and student-teacher relationship during second language learning in primary school
Learning is a multidimensional process resulting from the interaction between cognitive and emotional factors within the learning context; in this respect the quality of the student-teacher relationship plays a significant role. Although the literature suggests that cognitive processes and emotions experienced during learning and task performing play a central role in academic achievement, it remains unclear how these factors interact with socio-affective factors in explaining academic performance, particularly in second language (L2) learning from primary school. This systematic review and meta-analysis focused on the studies that jointly or individually investigated the role of emotional factors (achievement emotions), socio-affective factors (student-teacher relationship) and cognitive factors (working memory) in L2 learning during primary school. Our sample contained 19 primary studies with 5,340 participants involved in at least one of the factors of our interest. 16 out of 19 studies were included in the meta-analysis. Our results showed a positive correlation between working memory and L2 learning, differentiated effects of achievement emotions, with a significant negative association with anxiety, and a small but positive association with enjoyment. The student-teacher relationship was supported only by qualitative evidence, however, showing a protective effect of emotional closeness to the teacher in the learning process in the presence of negative emotions such as anxiety. Findings support the importance of integrating cognitive, emotional, and relational factors to understand L2 learning in primary school. Further empirical research focusing on positive emotions and relational dynamics in different educational contexts is needed.
28 May 2026
Read appraisal →Brain topography
rTMS Modulates Static and Dynamic Brain Functional Networks in Children with Autism Spectrum Disorder: An EEG Microstate Study
Repetitive Transcranial Magnetic Stimulation (rTMS) shows promise for treating Autism Spectrum Disorder (ASD), but its impact on the temporal dynamics of large-scale brain networks remains unclear. This study investigated the modulatory effects of rTMS on static and dynamic brain functional networks in children with ASD. Thirty-two children were randomized into an active rTMS group (1 Hz over the dorsolateral prefrontal cortex) and a sham control group. Resting-state EEG and behavioral assessments were conducted before and after a 9-week intervention. We employed a multi-dimensional analysis approach, combining microstate temporal parameters, static functional connectivity based on the weighted Phase Lag Index (wPLI), and dynamic complexity measured by Fuzzy Entropy. Results indicated that intrinsic features of Microstate B were significantly correlated with social relating deficits. Although rTMS did not induce significant interaction effects in standard microstate temporal parameters, it significantly enhanced static functional connectivity strength and increased the dynamic complexity of brain networks across all microstates. These findings suggest that rTMS exerts its therapeutic effects by strengthening network integration and restoring neural flexibility rather than simply altering the duration of brain states. The study underscores the value of network-based EEG metrics in elucidating the neuroplastic changes induced by neuromodulation in ASD.
12 May 2026
Read appraisal →Behavior research methods
FaceTrack-AOI: An AI-driven tool for automated dynamic AOI placement and eye movement analysis in face perception studies
Face perception is fundamental to human social interaction, relying on the dynamic allocation of visual attention to facial features across natural variations in poses, expressions, lighting and context-critical factors for real-world face perception. While eye-tracking studies have advanced our understanding of this process, analyzing gaze data in naturalistic settings presents challenges due to the labor-intensive and often subjective process of manually defining areas of interest (AOI) in dynamic stimuli. We introduce FaceTrack-AOI, an open-source, artificial intelligence (AI)-driven tool that automates dynamic AOI construction using robust 68-point facial landmark detection. Featuring a user-friendly graphical interface, the tool allows researchers to define and export eye movement metrics without programming expertise. It supports both rectangular and polygon-based AOIs, with customizable buffers, blank zones, and hierarchical configurations, accommodating diverse theoretical and methodological needs. Validation on multi-ethnic naturalistic video frames, images from a standardized face dataset (Chinese Face and Body Dataset [CFBD]), and benchmarks against a published study revealed strong agreement with expert-defined manual AOIs, achieving a mean spatial overlap of 95.73%. A proof-of-concept validation confirmed high fixation capture: over 98.8% of dwell time was correctly directed toward key AOIs. In two experiments with infants (6-18 months) and adults viewing 28,800 dynamic video frames, and children (2-8 years), both neurotypical and with autism spectrum disorder (ASD), viewing 80 static faces, FaceTrack-AOI demonstrated enhanced sensitivity in detecting developmental and group differences in naturalistic face recognition. By integrating AI-powered tracking with an intuitive interface, FaceTrack-AOI streamlines gaze analysis for dynamic facial stimuli, offering researchers an efficient and scalable solution to advance face perception research in ecologically valid contexts.
26 Apr 2026
Read appraisal →PloS one
Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is primarily characterized by deficits in social communication and restricted or repetitive behavioral patterns. Although psychologists contribute significantly to the understanding of ASD, offering insights into its cognitive, emotional, and behavioral dimensions through assessments, diagnoses, therapeutic approaches, and family support, the diagnostic process remains complex. This complexity arises from the diverse manifestations of the disorder and the challenges associated with data sharing. In addition, conventional machine learning approaches for ASD detection may struggle with high-dimensional neuroimaging data and may require careful feature engineering. Consequently, this motivated us to enhance ASD diagnosis by incorporating deep learning (DL) techniques for feature extraction alongside a modified exponential-trigonometric optimization (ETO) algorithm as a feature selection (FS) technique. The modified ETO integrates the Arithmetic Optimization Algorithm (AOA) and the Guided Learning Strategy (GLS) to improve diagnostic performance. To evaluate the effectiveness of the proposed model, we utilized resting-state functional MRI (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE I). Furthermore, the performance of the proposed model was compared with that of established models. The results indicate that the proposed model achieves competitive and, in most cases, superior performance compared with the benchmark methods, demonstrating superior accuracy, sensitivity, and AUC in diagnosing ASD. On average across the three atlas-based feature sets, the proposed model has an accuracy, sensitivity, and AUC of 73%, 78%, and 79%, respectively.
9 Apr 2026
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