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Human one-on-one coaching involves complex multimodal interactions. Successful coaching requires teachers to closely monitor students' cognitive-affective states and provide support of optimal type, timing, and amount. However, most of the existing human tutoring studies focus primarily on verbal interactions and have yet to incorporate the rich aspects of multimodal cognitive-affective experiences. Meanwhile, the research community lacks principled methods to fully exploit complex multimodal data to uncover the causal relationships between coaching supports, students' cognitive-affective experiences, and their stable individual factors. We explore an analytical framework that is explainable and amenable to incorporating domain knowledge. The proposed framework combines statistical approaches in Sparse Multiple Canonical Correlation, causal discovery, and inference methods for observations. We demonstrate this framework using a multimodal one-on-one math problem-solving coaching dataset collected in naturalistic home environments involving parents and young children. The insights derived from our analyses may inform the design of effective technology-inspired interventions that are personalized and adaptive.

Purpose of Review: This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data—including clinical, imaging, and physiological inputs—to identify intricate non-linear relationships that traditional methods might overlook. Recent Findings: ML algorithms of clinical features, neuroimaging, and metrics from the autonomic nervous system enhance the early detection of clinical deterioration and improve outcome prediction. Challenges persist, including issues of data variability, model interpretability, and overfitting. However, advancements in model standardization and validation are key to enhancing their clinical applicability. Summary: ML-based, multimodal approaches offer transformative potential for personalized treatment planning and patient management. Future directions include integrating digital twins and real-time continuous data analysis, reinforcing the idea that comprehensive data amalgamation is essential for precise, adaptive prognostication and decision-making in neurocritical care, ultimately leading to better patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Nursing assistants working in long‐term care (LTC) often report that their job is stressful. To reduce their work stress, a better understanding of their stress profile is needed. Objective: We aimed to pilot test methods to identify and understand stressors that LTC nursing assistants experience. Methods: We asked each participant to provide wearable sensor/watch data, ecological momentary assessment (EMA) surveys and end of day review data over two eight‐hour working shifts. Results: Eight nursing assistants participated. All participants worked in a common continuing care retirement community in Maryland, United States of America. Our stress assessment method revealed 83 stressful events that were classified under 10 categories. Most of the reported events were rated as having a mild to low‐moderate intensity. The three most common causes of stress were work demands and pressure (28.35%), heavy workload and staffing (19.69%), and safety issues and COVID‐19 concerns (17.32%). We also explored the difference between stress events and intensity among different shifts. Disrespect from residents (22.73%) was the most commonly reported stressor during day shifts. Feeling rushed was the most commonly reported stressor during the evening (22.47%) and the night (38.46%) shifts. Conclusions: We found stress was commonly reported. Stress intensity conflicted with prior literature, and we explored possible explanations. Implications for practice: We discuss potential implications for these findings, modification of our methods to increase feasibility, the utility of these data collection methods for future work and suggest next steps. [ABSTRACT FROM AUTHOR]

Background: Paroxysmal Sympathetic Hyperactivity (PSH) occurs with high prevalence among critically ill Traumatic Brain Injury (TBI) patients and is associated with worse outcomes. The PSH-Assessment Measure (PSH-AM) consists of a Clinical Features Scale (CFS) and a Diagnosis Likelihood Tool (DLT), intended to quantify the severity of sympathetically-mediated symptoms and likelihood that they are due to PSH, respectively, on a daily basis. Here, we aim to identify and explore the value of dynamic trends in the evolution of sympathetic hyperactivity following acute TBI using elements of the PSH-AM., Methods: We performed an observational cohort study of 221 acute critically ill TBI patients for whom daily PSH-AM scores were calculated over the first 14 days of hospitalization. A principled group-based trajectory modeling approach using unsupervised K-means clustering was used to identify distinct patterns of CFS evolution within the cohort. We also evaluated the relationships between trajectory group membership and PSH diagnosis, as well as PSH DLT score, hospital discharge GCS, ICU and hospital length of stay, duration of mechanical ventilation, and mortality. Baseline clinical and demographic features predictive of trajectory group membership were analyzed using univariate screening and multivariate multinomial logistic regression., Results: We identified four distinct trajectory groups. Trajectory group membership was significantly associated with clinical outcomes including PSH diagnosis and DLT score, ICU length of stay, and duration of mechanical ventilation. Baseline features independently predictive of trajectory group membership included age and post-resuscitation motor GCS., Conclusions: This study adds to the sparse research characterizing the heterogeneous temporal trends of sympathetic nervous system activation during the acute phase following TBI. This may open avenues for early identification of at-risk patients to receive tailored interventions to limit secondary brain injury associated with autonomic dysfunction and thereby improve TBI patient outcomes.