Search Results (334)

(1) Background: Physical activity (PA) is a key modifiable risk factor for chronic diseases, yet many adults do not meet PA guidelines. Just-in-time adaptive interventions (JITAIs), a type of mobile health (mHealth) intervention, offer tailored support based on an individual’s context to promote PA. Integrating behavior change techniques (BCTs) and theories is critical to the design of effective mHealth interventions. Understanding which BCTs and theories work best can inform future JITAI development. (2) Objective: The objective of this study is to examine how behavior change theories and BCTs are implemented in mHealth PA JITAIs and assess their relationship with PA-related outcomes. (3) Methods: This scoping review followed the PRISMA-ScR guidelines. A total of 29 studies were included. (4) Results: The most commonly used BCTs include prompts/cues (n = 29), goal-setting (behavior) (n = 15), and feedback on behavior (n = 14), while self-determination theory (n = 4) and social cognitive theory (n = 4) are the most commonly used theories. However, there is insufficient evidence as to which theories and BCTs are most effective in eliciting effective PA behavior change. (5) Conclusions: Clearer reporting and integration of BCTs and behavior change theories, along with optimized user interfaces, are needed to improve the intervention quality, replicability, and long-term effectiveness of PA JITAIs. Full article

This study proposes a computational framework that transforms eye-tracking analysis from statistical description to cognitive structure modeling, aiming to reveal the organizational features embedded in the viewing process. Using the designers’ observation of a traditional Chinese landscape painting as an example, the study draws on the goal-oriented nature of design thinking to suggest that such visual exploration may exhibit latent structural tendencies, reflected in patterns of fixation and transition. Rather than focusing on traditional fixation hotspots, our four-dimensional framework (Region, Relation, Weight, Time) treats viewing behavior as structured cognitive networks. To operationalize this framework, we developed a data-driven computational approach that integrates fixation coordinate transformation, K-means clustering, extremum point detection, and linear interpolation. These techniques identify regions of concentrated visual attention and define their spatial boundaries, allowing for the modeling of inter-regional relationships and cognitive organization among visual areas. An adaptive buffer zone method is further employed to quantify the strength of connections between regions and to delineate potential visual nodes and transition pathways. Three design-trained participants were invited to observe the same painting while performing a think-aloud task, with one participant selected for the detailed demonstration of the analytical process. The framework’s applicability across different viewers was validated through consistent structural patterns observed across all three participants, while simultaneously revealing individual differences in their visual exploration strategies. These findings demonstrate that the proposed framework provides a replicable and generalizable method for systematically analyzing viewing behavior across individuals, enabling rapid identification of both common patterns and individual differences in visual exploration. This approach opens new possibilities for discovering structural organization within visual exploration data and analyzing goal-directed viewing behaviors. Although this study focuses on method demonstration, it proposes a preliminary hypothesis that designers’ gaze structures are significantly more clustered and hierarchically organized than those of novices, providing a foundation for future confirmatory testing. Full article

Objective: The present study is a qualitative analysis of participant experiences and perspectives from people who received cognitive remediation therapy for adult obesity (CRT-O). Method: Post-intervention data were generated from an open-ended question requesting the participants to write, in the form of a letter to their therapist, about their experiences and reflections upon taking part in cognitive remediation therapy for adult obesity. Participants’ letters were thematically analyzed. Results: Four themes and nested subthemes emerged from participant responses, including (1) motivation and initial response to CRT-O for the adult obesity study eligibility process with the nested subthemes of initial apprehension pre-intervention and awareness and acknowledgement of one’s problematic eating behaviors; (2) perceived benefits from cognitive remediation therapy for adult obesity with the nested subthemes of the strategies and techniques that were found beneficial and the role of the cognitive remediation therapy for adult obesity therapists in facilitating positive change; (3) perceived outcomes post-intervention with the nested subthemes of changed relationship with food, self-acceptance and gaining control to effect positive lifestyle change; and (4) expectations and beliefs about the longer-term impact of cognitive remediation therapy for adult obesity with the nested subthemes of using the cognitive remediation therapy for adult obesity strategies as a lifestyle routine, apprehension about not having follow-up therapist support, and concern about potential relapse. Conclusion: Our analysis found helpful insights into the consumer perception of this novel intervention and highlighted the clinical utility of implementing cognitive remediation therapy in those living with a higher body weight. Full article

Artificial intelligence (AI)-powered mental health chatbots have evolved quickly as scalable means for psychological support, bringing novel solutions through natural language processing (NLP), mobile accessibility, and generative AI. This systematic literature review (SLR), following PRISMA 2020 guidelines, collates evidence from 25 published, peer-reviewed studies between 2020 and 2025 and reviews therapeutic techniques, cultural adaptation, technical design, system assessment, and ethics. Studies were extracted from seven academic databases, screened against specific inclusion criteria, and thematically analyzed. Cognitive behavioral therapy (CBT) was the most common therapeutic model, featured in 15 systems, frequently being used jointly with journaling, mindfulness, and behavioral activation, followed by emotion-based approaches, which were featured in seven systems. Innovative techniques like GPT-based emotional processing, multimodal interaction (e.g., AR/VR), and LSTM-SVM classification models (greater than 94% accuracy) showed increased conversation flexibility but missed long-term clinical validation. Cultural adaptability was varied, and effective localization was seen in systems like XiaoE, okBot, and Luda Lee, while Western-oriented systems had restricted contextual adaptability. Accessibility and inclusivity are still major challenges, especially within low-resource settings, since digital literacy, support for multiple languages, and infrastructure deficits are still challenges. Ethical aspects—data privacy, explainability, and crisis plans—were under-evidenced for most deployments. This review is different from previous ones since it focuses on cultural adaptability, ethics, and hybrid public health incorporation and proposes a comprehensive approach for deploying AI mental health chatbots safely, effectively, and inclusively. Central to this review, symmetry is emphasized as a fundamental idea incorporated into frameworks for cultural adaptation, decision-making processes, and therapeutic structures. In particular, symmetry ensures equal cultural responsiveness, balanced user–chatbot interactions, and ethically aligned AI systems, all of which enhance the efficacy and dependability of mental health services. Recognizing these benefits, the review further underscores the necessity for more rigorous academic research into the development, deployment, and evaluation of mental health chatbots and apps, particularly to address cultural sensitivity, ethical accountability, and long-term clinical outcomes. Full article

The growing implementation of digital platforms and mobile devices in educational environments has generated the need to explore new approaches for evaluating the learning experience beyond traditional self-reports or instructor presence. In this context, the NPFC-Test dataset was created from an experimental protocol conducted at the Experiential Classroom of the Institute for the Future of Education. The dataset was built by collecting multimodal indicators such as neuronal, physiological, and facial data using a portable EEG headband, a medical-grade biometric bracelet, a high-resolution depth camera, and self-report questionnaires. The participants were exposed to a digital test lasting 20 min, composed of audiovisual stimuli and cognitive challenges, during which synchronized data from all devices were gathered. The dataset includes timestamped records related to emotional valence, arousal, and concentration, offering a valuable resource for multimodal learning analytics (MMLA). The recorded data were processed through calibration procedures, temporal alignment techniques, and emotion recognition models. It is expected that the NPFC-Test dataset will support future studies in human–computer interaction and educational data science by providing structured evidence to analyze cognitive and emotional states in learning processes. In addition, it offers a replicable framework for capturing synchronized biometric and behavioral data in controlled academic settings. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to movement impairment, cognitive decline, and psychiatric symptoms. Key manifestations of PD include bradykinesia (the slowness of movement), changes in voice or speech, and gait disturbances. The quantification of neurological disorders through voice analysis has emerged as a rapidly expanding research domain, offering the potential for non-invasive and large-scale monitoring. This review explores existing research on the application of machine learning (ML) in speech, voice, and language processing for the diagnosis of PD. It comprehensively analyzes current methodologies, highlights key findings and their associated limitations, and proposes strategies to address existing challenges. A systematic review was conducted following PRISMA guidelines. We searched four databases: PubMed, Web of Science, Scopus, and IEEE Xplore. The primary focus was on the diagnosis, detection, or identification of PD through voice, speech, and language characteristics. We included 34 studies that used ML techniques to detect or classify PD based on vocal features. The most used approaches involved free speech and reading-speech tasks. In addition to widely used feature extraction toolkits, several studies implemented custom-built feature sets. Although nearly all studies reported high classification performance, significant limitations were identified, including challenges in comparability and incomplete integration with clinical applications. Emerging trends in this field include the collection of real-world, everyday speech data to facilitate longitudinal tracking and capture participants’ natural behaviors. Another promising direction involves the incorporation of additional modalities alongside voice analysis, which may enhance both analytical performance and clinical applicability. Further research is required to determine optimal methodologies for leveraging speech and voice changes as early biomarkers of PD, thereby enhancing early detection and informing clinical intervention strategies. Full article

Effective postoperative pain management remains a major clinical challenge in spinal surgery, with poorly controlled pain affecting up to 50% of patients and contributing to delayed mobilization, prolonged hospitalization, and risk of chronic postsurgical pain. This review synthesizes current and emerging strategies in postoperative spinal pain management, tracing the evolution from opioid-centric paradigms to individualized, multimodal approaches. Multimodal analgesia (MMA) has become the cornerstone of contemporary care, combining pharmacologic agents, such as non-steroidal anti-inflammatory drugs (NSAIDs), acetaminophen, and gabapentinoids, with regional anesthesia techniques, including erector spinae plane blocks and liposomal bupivacaine. Adjunctive nonpharmacologic modalities like early mobilization, cognitive behavioral therapy, and mindfulness-based interventions further optimize recovery and address the biopsychosocial dimensions of pain. For patients with refractory pain, neuromodulation techniques such as spinal cord and peripheral nerve stimulation offer promising results. Advances in artificial intelligence (AI), biomarker discovery, and nanotechnology are poised to enhance personalized pain protocols through predictive modeling and targeted drug delivery. Enhanced recovery after surgery protocols, which integrate many of these strategies, have been shown to reduce opioid use, hospital length of stay, and complication rates. Nevertheless, variability in implementation and the need for individualized protocols remain key challenges. Future directions include AI-guided analytics, regenerative therapies, and expanded research on long-term functional outcomes. This review provides an evidence-based framework for pain control following spinal surgery, emphasizing integration of multimodal and innovative approaches tailored to diverse patient populations. Full article

This study aims to illustrate the impact of accumulated traumatic experiences in adolescence and to evaluate the potential of brief focal psychotherapy (BFP) as a treatment approach for complex trauma. We present the case of a 14-year-old boy who experienced vicarious gender-based violence, child abuse, early maternal separation without alternative secure attachment figures, and forced sudden migration. The patient exhibited symptoms consistent with post-traumatic stress disorder (PTSD) and complex trauma. The culturally sensitive intervention, delivered at a public child and adolescent mental health center, consisted of twenty weekly individual sessions of 45 min each, complemented by three 45 min psychoeducation sessions with the caregiver. The assessment was conducted using a multitrait and multi-informant approach, systematically gathering information across multiple domains of functioning (emotional–behavioral, physical, cognitive, self-perception, and relational) and from different sources (the adolescent, his mother, and the clinician) through clinical interviews, projective techniques, and parental feedback. The primary therapeutic focus was the establishment of a secure therapeutic alliance to facilitate emotional exploration and trauma processing. Following treatment, the patient demonstrated significant improvements in emotional regulation, family relationships, and school performance, as measured by both self-report and parental observations. This case highlights the potential of BFP in addressing complex trauma in adolescents, particularly during a developmental stage marked by increased vulnerability to the effects of chronic trauma exposure. The findings suggest that BFP can effectively reduce both acute symptomatology and broader psychosocial consequences associated with prolonged and cumulative trauma. Further research, particularly controlled studies and longitudinal follow-ups, is needed to refine and optimize the use of BFP by mental health professionals working with adolescents affected by complex trauma. Full article

Alzheimer’s disease (AD), a predominant neurodegenerative disorder, is clinically characterized by progressive cognitive deterioration and behavioral deficits. An in-depth understanding of the pathogenesis and neuropathology of AD is essential for the development of effective treatments and early diagnosis techniques. The neuropathological signature of AD involves two hallmark lesions: intraneuronal neurofibrillary tangles composed of hyperphosphorylated tau aggregates and extracellular senile plaques containing amyloid-β (Aβ) peptide depositions. Although Aβ-centric research has dominated AD investigations over the past three decades, pharmacological interventions targeting Aβ pathology have failed to demonstrate clinical efficacy. Tau, a microtubule-associated protein predominantly localized to neuronal axons, orchestrates microtubule stabilization and axonal transport through dynamic tubulin interactions under physiological conditions. In AD pathogenesis, however, tau undergoes pathogenic post-translational modifications (PTMs), encompassing hyperphosphorylation, lysine acetylation, methylation, ubiquitination, and glycosylation. These PTM-driven alterations induce microtubule network disintegration, mitochondrial dysfunction, synaptic impairment, and neuroinflammatory cascades, ultimately culminating in irreversible neurodegeneration and progressive cognitive decline. This review synthesizes contemporary advances in tau PTM research and delineates their mechanistic contributions to AD pathogenesis, thereby establishing a framework for biomarker discovery, targeted therapeutic development, and precision medicine approaches in tauopathies. This review synthesizes contemporary advances in tau PTM research and delineates their mechanistic contributions to AD pathogenesis, thereby establishing a solid theoretical and experimental basis for the early diagnosis of neurodegenerative diseases, the discovery of therapeutic targets, and the development of novel therapeutic strategies. Full article

Congenital Rubella Syndrome (CRS) results from maternal infection with the rubella virus during pregnancy, particularly in the first trimester, when the risk of vertical transmission and severe fetal damage is highest. CRS is characterized by a broad spectrum of congenital anomalies, including sensorineural hearing loss, congenital heart defects, cataracts, neurodevelopmental delay, and behavioral disorders. Despite the absence of specific antiviral therapies, active immunization remains the only effective strategy to prevent rubella infection and its congenital consequences. Global immunization efforts, particularly in the Americas, have led to the elimination of rubella and CRS in several countries. However, challenges persist in the post-elimination era, including declining vaccine coverage, vaccine hesitancy, and setbacks caused by the COVID-19 pandemic. Diagnosis relies on maternal serology, fetal imaging, postnatal antibody testing, and molecular techniques. Management requires long-term, multidisciplinary follow-up due to the complex and lifelong sequelae affecting sensory, motor, and cognitive development. This review highlights the clinical, epidemiological, and pathophysiological aspects of CRS, while emphasizing the urgent need to maintain high vaccination coverage and strengthen surveillance systems. Sustained public health commitment is essential to prevent the reemergence of rubella and protect future generations from this preventable syndrome. Full article

Background and Objectives: Breast cancer remains one of the most prevalent malignancies affecting women and one of the most emotionally distressing diagnoses. Emerging evidence suggests that chronic psychological stress may influence cancer progression via neuroendocrine and immune mechanisms. This rapid narrative review explores three key arguments for integrating psycho-oncologists into the multidisciplinary care of breast cancer patients: (1) early detection and reduction of psychological distress, (2) improvement of treatment adherence, and (3) enhancement of quality of life through personalized psychological interventions. Materials and Methods: The review was conducted through comprehensive searches in PubMed, Scopus, and Web of Science for peer-reviewed articles published between 2010 and 2025. Inclusion criteria comprised randomized controlled trials, systematic reviews, meta-analyses, and theoretical papers. Of 246 identified articles, 50 met the inclusion criteria. Results: Selected studies show that psycho-oncological interventions—including cognitive–behavioral therapy, mindfulness-based techniques, narrative therapy, and guided imagery—significantly reduce anxiety, depression, and fear of recurrence. These approaches improve adherence to endocrine therapy and chemotherapy, enhance emotional resilience, and promote overall well-being. Also, recent research concepts emphasize the role of psycho-oncologists in encouraging post-traumatic growth and helping patients redefine cancer as an opportunity for transformation. Conclusions: Integrating psycho-oncologists into standard breast cancer care improves psychological and clinical outcomes. By addressing emotional distress, strengthening coping mechanisms, and supporting existential resilience, psycho-oncologists contribute to a holistic, patient-centered model of oncology care. Wider implementation of psycho-oncological services is warranted as a core component of comprehensive cancer management. Full article

Background: Working memory (WM) is a critical component of cognitive performance. Although transcranial direct current stimulation (tDCS) is a promising neuromodulation technique, its effect on working memory in healthy individuals remains unclear. Methods: In this study, EEG signals were recorded from different groups (working memory group, tDCS group, and sham group) and analyzed using phase–amplitude coupling, power spectral density, and brain network analysis to investigate the effect and mechanism of tDCS on working memory tasks in healthy individuals. Results: The results showed that in the tDCS group, the power spectral density of the EEG was increased in the gamma band and decreased in the delta band, while the delta–gamma phase–amplitude coupling was also decreased during the working memory task. Meanwhile, brain network analysis indicated significant differences in the node degree and clustering coefficient between the active and sham stimuli. Conclusions: Despite the absence of immediate behavioral improvements, these results suggest that tDCS could significantly modulate working memory-related EEG activity. Full article

The use of advanced adaptive algorithms in Augmented Reality (AR) systems works to advance spatial skills with valuable relevance in many professional spheres by providing personalized feedback in an immersive environment. This study combines Bayesian Knowledge Tracing (BKT) and Human Plausible Reasoning (HPR) to design an AR system that can adapt to dynamic simulations with quantitative as well as qualitative cognitive methodologies. The system records a broad range of interactions from users, such as objects being rotated, changes in viewing perspective, and time spent on tasks, which are later analyzed through probabilistic updates with respect to skill building along with rule-based reasoning for determining behavioral patterns. Results from an in-depth case study show that the BKT module properly tracks improvement in spatial skills, while the HPR application highlights suboptimal approaches that hide underlying conceptual understanding. The adaptive system used then provides metacognitive hints that adjust by optimizing task difficulty levels, leading to improved student performance compared to standard non-adaptive AR techniques. Results show that using BKT and HPR in an AR environment not only enables accurate task performance but supports greater insight in approach strategies, leading to better and transferable spatial skills. Full article

Background: Life satisfaction serves as a preventive agent against various emotional, cognitive, and behavioral challenges, making it a crucial cognitive indicator of subjective well-being, particularly during adolescence. Accurately assessing life satisfaction is essential for understanding and promoting adolescent mental health, especially in applied settings such as physical education, which plays a key role in fostering psychological well-being and positive youth development. However, additional investigation is needed to confirm the tools used for this purpose. This study aimed to analyze the psychometric properties, metric invariance, and temporal stability of the Satisfaction with Life Scale (SWLS) in adolescents from a region in southeastern Spain. Thus, the present study sought to answer the following research questions: (1) Does the SWLS demonstrate adequate psychometric properties in an adolescent population? (2) Is the SWLS invariant across gender and residential environments? (3) Does the SWLS show adequate stability over time? Methods: A sample of 400 students was assessed using exploratory and confirmatory factor analyses, multigroup comparisons, and test–retest techniques. Results: The results showed significant differences in scale scores in the sex and demographic location variables. Also, a robust unifactorial model with five items demonstrated good performance in terms of goodness of fit and internal consistency. Furthermore, full metric invariance was observed across genders, while configural invariance was supported for residential environment. Concurrent validity analyses revealed significant associations with another unidimensional well-being measure, and temporal stability was confirmed through the intraclass correlation coefficient. Conclusions: The findings support the SWLS as a potentially valid, reliable, and time-effective tool for assessing adolescent life satisfaction. Its strong psychometric properties make it highly suitable for use in mental health research, longitudinal monitoring, and large-scale studies. Moreover, its ease of administration allows its integration into educational, clinical, community-based, and physical education contexts, offering insightful information for the creation of long-lasting mental health regulations and preventive measures meant to improve the well-being of adolescents. Notwithstanding these encouraging results, some restrictions must be noted. The sample was restricted to a single geographic area, and contextual or cultural factors may have an impact on how satisfied people are with their lives. Furthermore, response biases could have been introduced by using self-report measures. Full article

Background/Objectives: High-altitude environments have a significant detrimental impact on the cognitive functions of the brain. Qi Jing Wan (QJW), a traditional herbal formula composed of Angelica sinensis, Astragalus membranaceus, and Rhizoma Polygonati Odorati, has demonstrated potential efficacy in treating cognitive disorders. However, its effects on cognitive dysfunction in plateau hypoxic environments remain unclear. Methods: In this study, acute and chronic plateau cognitive impairment mouse models were constructed to investigate the preventive and therapeutic effects of QJW and its significant active ingredient, diosgenin (Dio). Behavioral experiments were conducted to assess learning and memory in mice. Morphological changes in hippocampal neurons and synapses were assessed, and microglial activation and inflammatory factor levels were measured to evaluate brain damage. Potential active ingredients capable of crossing the blood–brain barrier were identified through chemical composition analysis and network database screening, followed by validation in animal and brain organoid experiments. Transcriptomics analysis, immunofluorescence staining, and molecular docking techniques were employed to explore the underlying mechanisms. Results: QJW significantly enhanced learning and memory abilities in plateau model mice, reduced structural damage to hippocampal neurons, restored NeuN expression, inhibited inflammatory factor levels and microglial activation, and improved hippocampal synaptic damage. Transcriptomics analysis revealed that Dio alleviated hypoxic brain damage and protected cognitive function by regulating the expression of PDE4C. Conclusions: These findings indicate that QJW and its significant active ingredient Dio effectively mitigate hypoxic brain injury and prevent cognitive impairment in high-altitude environments. Full article