Search Results (34)

Background/Objectives: Traditional models like ordinary least squares (OLS) struggle to capture non-linear relationships in children’s subjective well-being (SWB), which is associated with physical activity. This study evaluated machine learning (ML) for predicting SWB, focusing on sports participation, and explored theoretical prediction limits using a global dataset. It addresses a gap in understanding complex patterns across diverse cultural contexts. Methods: We analyzed 128,184 records from the ISCWeB survey (ages 6–14, 35 countries), with self-reported data on sports frequency, emotional states, and family support. To ensure cross-country generalizability, we used GroupKFold CV (grouped by country) and leave-one-country-out (LOCO) validation, yielding mean R² = 0.45 ± 0.05, confirming robustness beyond cultural patterns, SHAP for interpretability, and bootstrapping for error estimation. No pre-registration was required for this secondary analysis. Results: XGBoost and LightGBM outperformed OLS, achieving R² up to 0.504 in restricted datasets (sensitivity excluding affective leakage: R² = 0.35), with sports-related variables (e.g., exercise frequency) associated positively with SWB predictions (SHAP values: +0.15–0.25; incremental ΔR² = 0.06 over demographics/family/school base). Using test–retest reliability from literature (r = 0.74), the estimated irreducible RMSE reached 0.941; XGBoost achieved RMSE = 1.323, approaching the predictability bound with 68.1% of explainable variance captured (after noise adjustment). Partial dependence plots showed linear associations with exercise without satiation and slight age decline. Conclusions: ML improves SWB prediction in children, highlighting associations with sports participation, and approaches predictable variance bounds. These findings suggest potential for data-driven tools to identify patterns, such as through physical literacy pathways, informing physical activity interventions. However, longitudinal studies are needed to explore causality and address cultural biases in self-reports. Full article

Flourishing in children is an indicator of positive development in the areas of emotional, social, and cognitive development. Using a recent dataset from the US National Survey of Children’s Health, this study investigates the association between access to a quality healthcare system and flourishing indicators in school-aged children with autism. The outcome variable describes the proportion of children aged 6–17 with autism meeting the flourishing criteria, which include measures related to learning, resilience, and self-regulation. The main independent variable includes six core indicators for school-aged children and adolescents, which assess whether the family feels like a partner in their child’s care, the child has a medical home, receives regular medical and dental preventive care, has adequate insurance, has no unmet needs or barriers to accessing services, and has prepared for transition to adult healthcare. Multivariable logistic regression models were used to examine the relationships between various independent variables and the outcome of interest. The results show that children with autism who receive comprehensive and coordinated care are more likely to flourish compared to those without such care for five of these six indicators, while controlling for sex, race, parental education, household income, self-reported autism severity, general health status, and the number of adverse childhood experiences. Children with autism are more likely to flourish when their families and healthcare providers work together effectively. Addressing gaps in the quality care system is essential for developing holistic approaches that empower children with autism to thrive and reach their full potential. Supportive health policies and effective coordination between families and healthcare providers are crucial for fostering the flourishing of children with autism, ensuring comprehensive, individualized, and continuous care. Full article

Background: Sexual violence against adolescents and young people (AYP) remains a public health concern. This study explores patterns of sexual violence and help-seeking behaviour as well as their associated risk/protective factors with guidance of a technical package (INSPIRE) designed to reduce sexual violence in low-resource settings. Methods: The 2019 Violence Against Children Survey (VACS) dataset comprises 788 males and 1344 females. After describing the prevalence and patterns of sexual violence and help-seeking behaviour (informal disclosure, knowledge of where to seek formal help, seeking formal help, and receipt of formal help) among 13- to 24-year-old AYP, logistic regression models were then fitted to predict past-year sexual violence and informal disclosure among adolescent girls and young women (AGYW). Results: More young women than young men informally disclosed sexual violence experience (46% versus 23%). Gender inequitable attitudes [AOR 3.07 (1.10–8.56); p = 0.03], experiencing emotional violence at home [AOR 2.11 (1.17–3.81); p = 0.01] and cyberbullying [AOR 5.90 (2.83–12.29); p = 0.00] were identified as risk factors for sexual violence among AGYW. Life skills training [AOR 0.22 (0.07–0.73); p = 0.01] and positive parental monitoring [AOR 0.31 (0.10–0.99); p = 0.05] were found to be protective against sexual violence among AGYW. Positive parental monitoring [AOR 3.85 (1.56–9.46); p = 0.00] was associated with an increased likelihood of informal disclosure among AGYW. Conclusions: As Kenya intensifies efforts towards sexual violence prevention, this study underscores the need to develop and strengthen policies and programs on life skills training, cultural norms, and positive parenting, as well as improve awareness and access to post-violence response and support services. Full article

The emergence of AI companion robots is transforming the landscape of elderly care, offering numerous conveniences to senior citizens when their children are not around. This trend is particularly pertinent in ageing societies such as China. Against this backdrop, the present study aims to explore the acceptance of AI companion robots among the elderly from a user-centric perspective. By leveraging insights from existing studies in the literature, we identified three individual characteristic variables—technology optimism, innovativeness, and familiarity—to extend the Artificial Intelligence Device Use Acceptance (AIDUA) model. Subsequently, we developed a conceptual model which was empirically tested through structural equation modelling (SEM) analysis. Our dataset comprised responses from 452 elderly individuals in China. The results revealed that technology optimism and innovativeness were positively associated with performance expectancy and effort expectancy, whereas familiarity inversely predicted perceived risk. Furthermore, emotion was found to be positively influenced by performance expectancy and effort expectancy but negatively impacted by perceived risk. This research extends the AIDUA model within the context of AI companion robots by integrating individual characteristic variables. These findings offer valuable insights for the design and development of companion robots and enrich the domain of Human–Robot Interaction (HRI). Full article

Urbanisation and reduced natural spaces pose increasing challenges to children’s holistic development in early learning environments. This study investigates how four biophilic design elements—water, plants, animals, and ecosystems—affect the physical, mental, and social well-being of kindergarten children in Henan Province, China. A quantitative questionnaire survey was conducted with children, parents, and teachers from four selected kindergartens. The questionnaire consisted of three parts: demographic information, preferences toward biophilic design elements, and perceived impacts of these elements on children’s development. Considering young children’s limited ability to self-report psychological and emotional states, children’s preferences were statistically compared to those of parents and teachers using IBM SPSS Statistics Version 26. Results showed no significant differences; thus, data from parents and teachers were retained for further analysis. Subsequently, Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to explore relationships between biophilic elements and children’s developmental outcomes. Results indicated that water and animal elements were associated with higher levels of physical activity and psychological resilience, plants were linked to greater social adaptability, and ecosystem landscapes were related to overall indicators of child development. Because the dataset is geographically limited, these quantitative results should be interpreted as exploratory evidence. Importantly, these interventions can be feasibly incorporated into existing facilities, offering practical avenues for swift implementation. To better facilitate such practical implementation, this study synthesises key findings into a comprehensive framework, explicitly outlining how these biophilic elements can be prioritised and effectively integrated into kindergarten designs. Future research is recommended to examine long-term effects and cultural adaptability. Full article

Background and Objectives: Adolescent smoking is influenced by sociodemographic and psychological factors, including emotional and conduct problems. Understanding how sample size impacts the interpretation of these associations is critical for improving study design and public health interventions. This study examines the relationships between smoking behaviours, sociodemographic factors, and emotional and conduct problems, focusing on how sample size affects statistical significance and effect size interpretation. Materials and Methods: Data from the Latvian Health Behaviour in School-aged Children study was analysed. Chi-square tests and logistic regression were conducted to evaluate associations between smoking behaviours, sociodemographic factors, and emotional and conduct problems. Analyses were performed on both a large general sample and ten smaller generated subsamples to compare the impact of sample size on statistical outcomes. Results: Age and conduct problems emerged as the most consistent predictors of adolescent smoking behaviours across large and small samples, while other predictors, such as family affluence and family structure, showed weaker and less consistent associations. A large sample produced significant results even for weak predictors. Conclusions: This study highlights the importance of integrating effect size interpretation with statistical significance, particularly in large datasets, to avoid overstating findings. By leveraging real-world data, it provides practical recommendations for improving study design and interpretation in behavioural, medical, and public health research, contributing to more effective interventions targeting adolescent smoking. Full article

With advances in digital technology, including deep learning and big data analytics, new methods have been developed for autism diagnosis and intervention. Emotion recognition and the detection of autism in children are prominent subjects in autism research. Typically using single-modal data to analyze the emotional states of children with autism, previous research has found that the accuracy of recognition algorithms must be improved. Our study creates datasets on the facial and speech emotions of children with autism in their natural states. A convolutional vision transformer-based emotion recognition model is constructed for the two distinct datasets. The findings indicate that the model achieves accuracies of 79.12% and 83.47% for facial expression recognition and Mel spectrogram recognition, respectively. Consequently, we propose a multimodal data fusion strategy for emotion recognition and construct a feature fusion model based on an attention mechanism, which attains a recognition accuracy of 90.73%. Ultimately, by using gradient-weighted class activation mapping, a prediction heat map is produced to visualize facial expressions and speech features under four emotional states. This study offers a technical direction for the use of intelligent perception technology in the realm of special education and enriches the theory of emotional intelligence perception of children with autism. Full article

Children with severe motor and communication impairments (SMCIs) face significant challenges in expressing emotions, often leading to unmet needs and social isolation. This study investigated the potential of machine learning to identify emotions in children with SMCIs through the analysis of physiological signals. A model was created based on the data from the DEAP online dataset to identify the emotions of typically developing (TD) participants. The DEAP model was then adapted for use by participants with SMCIs using data collected within the Building and Designing Assistive Technology Lab (BDAT). Key adaptations to the DEAP model resulted in the exclusion of respiratory signals, a reduction in wavelet levels, and the analysis of shorter-duration data segments to enhance the model’s applicability. The adapted SMCI model demonstrated an accuracy comparable to the DEAP model, performing better than chance in TD populations and showing promise for adaptation to SMCI contexts. The models were not reliable for the effective identification of emotions; however, these findings highlight the feasibility of using machine learning to bridge communication gaps for children with SMCIs, enabling better emotional understanding. Future efforts should focus on expanding the data collection of physiological signals for diverse populations and developing personalized models to account for individual differences. This study underscores the importance of collecting data from populations with SMCIs for the development of inclusive technologies to promote empathetic care and enhance the quality of life of children with communication difficulties. Full article

This paper investigates the integration of affective computing techniques using biophysical data to advance emotionally aware machines and enhance child–robot interaction (CRI). By leveraging interdisciplinary insights from neuroscience, psychology, and artificial intelligence, the study focuses on creating adaptive, emotion-aware systems capable of dynamically recognizing and responding to human emotional states. Through a real-world CRI pilot study involving the NAO robot, this research demonstrates how facial expression analysis and speech emotion recognition can be employed to detect and address negative emotions in real time, fostering positive emotional engagement. The emotion recognition system combines handcrafted and deep learning features for facial expressions, achieving an 85% classification accuracy during real-time CRI, while speech emotions are analyzed using acoustic features processed through machine learning models with an 83% accuracy rate. Offline evaluation of the combined emotion dataset using a Dynamic Bayesian Mixture Model (DBMM) achieved a 92% accuracy for facial expressions, and the multilingual speech dataset yielded 98% accuracy for speech emotions using the DBMM ensemble. Observations from psychological and technological aspects, coupled with statistical analysis, reveal the robot’s ability to transition negative emotions into neutral or positive states in most cases, contributing to emotional regulation in children. This work underscores the potential of emotion-aware robots to support therapeutic and educational interventions, particularly for pediatric populations, while setting a foundation for developing personalized and empathetic human–machine interactions. These findings demonstrate the transformative role of affective computing in bridging the gap between technological functionality and emotional intelligence across diverse domains. Full article

This study explores children’s emotions through a novel approach of Generative Artificial Intelligence (GenAI) and Facial Muscle Activation (FMA). It examines GenAI’s effectiveness in creating facial images that produce genuine emotional responses in children, alongside FMA’s analysis of muscular activation during these expressions. The aim is to determine if AI can realistically generate and recognize emotions similar to human experiences. The study involves generating a database of 280 images (40 per emotion) of children expressing various emotions. For real children’s faces from public databases (DEFSS and NIMH-CHEFS), five emotions were considered: happiness, angry, fear, sadness, and neutral. In contrast, for AI-generated images, seven emotions were analyzed, including the previous five plus surprise and disgust. A feature vector is extracted from these images, indicating lengths between reference points on the face that contract or expand based on the expressed emotion. This vector is then input into an artificial neural network for emotion recognition and classification, achieving accuracies of up to 99% in certain cases. This approach offers new avenues for training and validating AI algorithms, enabling models to be trained with artificial and real-world data interchangeably. The integration of both datasets during training and validation phases enhances model performance and adaptability. Full article

When a guardian is diagnosed with cancer, the emotional and psychological toll they endure can have a profound impact on their children’s mental health. Understanding the factors that contribute to heightened anxiety in these children is crucial for identifying mental health disorders early. This cross-sectional study explored the relationship between having a guardian with cancer and elevated anxiety levels in adolescents, accounting for confounding variables such as sex, age, and socioeconomic status. Data were obtained from the 2022 National Health Interview Survey (NHIS) using the Sample Adult Interview (27,651 participants) and Sample Child Interview (7464 participants) datasets. The independent variable (guardian’s cancer diagnosis) was derived from the Sample Adult Interview, while the dependent variable (childhood anxiety) and confounders were derived from the Sample Child Interview. Using Stata 16.0, the datasets were merged based on household index variables, yielding a final sample of 4563 participants. Logistic regression analyses assessed the correlation between a guardian’s cancer diagnosis and anxiety levels in children. The results show that children with a guardian battling cancer are significantly more likely to develop anxiety (through the use of odds ratio), with the effect size varying based on factors such as sex, income, and environment. Girls, children from lower-income families, and those with a guardian experiencing depression were at particularly high risk. These findings highlight the strong link between familial health challenges and adolescent anxiety, emphasizing the need for early intervention and mental health support in families affected by cancer. Full article

This research ventures into the critical public health challenge of childhood obesity by exploring the dynamic interplay between psychological well-being and Body Mass Index (BMI) throughout various developmental stages of childhood. It delves into how emotional regulation, attachment dynamics, and social relationships correlate with obesity from early childhood to adolescence. Highlighting key findings, such as the negative correlation between psychological resilience and higher BMI in young children, the impact of social relationships on obesity risk during pre-adolescence, and the link between adaptive emotional strategies and higher BMI in adolescents, this study brings to the fore the nuanced relationship between psychological factors and obesity. Psychological metrics in this study were obtained via referenced questionnaires, leading up to the utilization of the interdisciplinary process of bioinformatics. Utilizing the interdisciplinary process of bioinformatics, this research synergizes psychometric and biomedical data to unearth psychological markers critical for crafting targeted, age-appropriate interventions. This study advocates for a holistic healthcare approach, emphasizing the integration of psychological support within obesity prevention and management strategies, thereby underscoring the indispensable role of psychological factors in the fight against childhood obesity. The application of bioinformatics methods to analyze complex datasets demonstrates how collaboration across medical specialties can enrich our understanding and response to childhood obesity, contributing significantly to the development of comprehensive, bioinformatics-enhanced healthcare solutions. Full article

Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child’s growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedures can assist in the process. This study leverages the current advances in artificial intelligence to address the prediction of neurodevelopmental disorders through a comprehensive machine learning approach. A novel and recently developed serious game dataset, collecting various data on children’s speech and linguistic responses, was used. The initial dataset comprised 520 instances, reduced to 473 participants after rigorous data preprocessing. Cluster analysis revealed distinct patterns and structures in the data, while reliability analysis ensured measurement consistency. A robust prediction model was developed using logistic regression. Applied to a subset of 184 participants with an average age of 7 years, the model demonstrated high accuracy, precision, recall, and F1-score, effectively distinguishing between instances with and without neurodevelopmental disorders. In conclusion, this research highlights the effectiveness of the machine learning approach in diagnosing neurodevelopmental disorders based on cognitive features, and offers new opportunities for decision making, classification, and clinical assessment, paving the way for early and personalized interventions for at-risk individuals. Full article

Detecting and understanding emotions are critical for our daily activities. As emotion recognition (ER) systems develop, we start looking at more difficult cases than just acted adult audio–visual speech. In this work, we investigate the automatic classification of the audio–visual emotional speech of children, which presents several challenges including the lack of publicly available annotated datasets and the low performance of the state-of-the art audio–visual ER systems. In this paper, we present a new corpus of children’s audio–visual emotional speech that we collected. Then, we propose a neural network solution that improves the utilization of the temporal relationships between audio and video modalities in the cross-modal fusion for children’s audio–visual emotion recognition. We select a state-of-the-art neural network architecture as a baseline and present several modifications focused on a deeper learning of the cross-modal temporal relationships using attention. By conducting experiments with our proposed approach and the selected baseline model, we observe a relative improvement in performance by 2%. Finally, we conclude that focusing more on the cross-modal temporal relationships may be beneficial for building ER systems for child–machine communications and environments where qualified professionals work with children. Full article

In line with United Nations Sustainable Development Goal (SDG) 4.2, South Africa’s National Development Plan commits to providing high-quality early childhood education to all children by 2030 to drive improved child outcomes. Prior to 2016, South Africa lacked reliable, locally standardised, valid, and cross-culturally fair assessment tools for measuring preschool quality and child outcomes, suitable for use at scale within a resource-constrained context. In this paper we detail the development and evolution of a suite of early learning measurement (ELOM) tools designed to address this measurement gap. The development process included reviews of literature and other relevant assessment tools; a review of local curriculum standards and expected child outcomes; extensive consultation with government officials, child development experts, and early learning practitioners, iterative user testing; and assessment of linguistic, cultural, functional, and metric equivalence across all 11 official South African languages. To support use of the ELOM tools at scale, and by users with varying levels of research expertise, administration is digitised and embedded within an end-to-end data value chain. ELOM data collected since 2016 quantify the striking socio-economic gradient in early childhood development in South Africa, demonstrate the relationship between physical stunting, socio-emotional functioning and learning outcomes, and provide evidence of the positive impact of high-quality early learning programmes on preschool child outcomes. To promote secondary analyses, data from multiple studies are regularly collated into a shared dataset, which is made open access via an online data portal. We describe the services and support that make up the ELOM data value chain, noting several key challenges and enablers of data-driven change within this context. These include deep technical expertise within a multidisciplinary and collaborative team, patient and flexible capital from mission-aligned investors, a fit-for-purpose institutional home, the appropriate use of technology, a user-centred approach to development and testing, sensitivity to children’s diverse linguistic and socio-economic circumstances, careful consideration of requirements for scale, appropriate training and support for a non-professional assessor base, and a commitment to ongoing learning and continuous enhancement. Practical examples are provided of ways in which the ELOM tools and data are used for programme monitoring and enhancement purposes, to evaluate the relative effectiveness of early learning interventions, to motivate for greater budget and inform more effective resource allocation, to support the development of enabling Government systems, and to track progress towards the attainment of national and global development goals. We share lessons learnt during the development of the tools and discuss the factors that have driven their uptake in South Africa. Full article