Sofia de la Fuente Garcia

Background: While interventions have been designed which use extended reality (XR) technology in promoting physical, mental and social well-being through cultural heritage experiences, well-defined methodologies for the assessment of such interventions is lacking. Objectives: We present a protocol for evaluating the usability and effectiveness of an XR system that mediates and facilitates access to intangible cultural heritage experiences. We aim to assess the effects of these experiences on user well-being and attitudes across four case studies: ageing societies, sustainable tourism, disappearing communities, and immigration and multiculturalism. Methods and analysis: Participants will be randomly assigned to control or intervention groups. The effects of the XR intervention on well-being will be assessed through statistical analysis of the participants’ salivary cortisol and cortisone levels, physiological signals, and subjective ratings, both pre- and post-intervention and between control and intervention groups. Usability will be measured through a system usability scale. Speech will be recorded for qualitative and natural language processing analysis. Machine learning models will be developed for prediction of affect and well-being on multimodal data. Discussion: This is one of the first international and multidisciplinary studies to explore the effects of XR-mediated intangible cultural heritage experiences on well-being and attitudes towards issues of societal importance. One of the main strengths of this study is the range of data modalities it collects, and the range of methods it employs to analyse these data in a complementary manner, including qualitative, statistical and advanced machine learning methods. Conclusion: This protocol offers a method and four case studies to assess the potential of immersive XR experiences and interventions of intangible cultural heritage as contributors to increased well-being and as actors of societal change. It stands as a reference model for further similar interventions in the field.

Morality is deeply rooted in the context of relationships. Yet the influence of relational factors, such as attachment, on moral concerns remains largely unexplored. Nonetheless, recent scholarship has shed some light on the connection between attachment and moral considerations. To extend this understanding, the current study seeks to investigate whether relationship-specific attachment dimensions, specifically anxiety and avoidance, predict Haidt’s moral foundations—care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, and purity/degradation. After receiving ethics approval, a convenience sample of 468 individuals was recruited via the research team's social media networks. Participants completed an online survey, comprising questionnaires covering demographics, morality, personality, and attachment. Following data cleaning, 254 participants (61% female, 38% male) ranging from 18 to 74 years of age (Mean = 33.9, SD = 13.9), were included in the final analysis. First, correlational analysis was conducted, followed by several hierarchical multiple regression analyses, yielding significant results. Specifically, higher attachment anxiety with mother predicted greater moral concerns about authority, while higher attachment anxiety with father predicted greater concerns about harm and lower concerns about fairness. In contrast, higher attachment avoidance with mother predicted lower concerns about authority, whereas higher attachment avoidance with father predicted greater concerns about fairness. Friendship and romantic attachment dimensions did not predict moral foundations. Overall, our findings suggest that parental attachment may influence moral considerations, hinting at the potential origins of moral diversity in early attachment relationships.

Consensual non-monogamy (CNM)—a relational model in which individuals engagein multiple romantic or sexual partnerships with mutual consent—challenges traditionalmonogamy-based frameworks in psychological research, particularly in the study of adultattachment. This systematic review aimed to identify methodological trends, limitations,and future directions in attachment research on CNM populations. Following PRISMAguidelines, 13 studies published between 1970 and 2025 were identified through comprehensivedatabase searches and reference screening. Most studies employed cross-sectional,quantitative designs and used monogamy-oriented attachment instruments such as the Experiencesin Close Relationships-Revised (ECR-R) and its short form (ECR-S). Whilethese tools often demonstrated strong internal reliability, few studies assessed their validityin CNM contexts. Sampling was predominantly convenience-based and skewed towardWhite, Western, and LGBTQ+ participants, limiting generalizability. Findings alsohighlighted a lack of longitudinal and mixed-methods approaches, with only one qualitativestudy offering deeper insight into relational processes. This review concludes thatfuture research should prioritize developing CNM-specific attachment measures, expanddemographic diversity, and adopt longitudinal and qualitative designs to better capture thecomplexity of attachment in diverse relationship structures.

The COVID-19 pandemic has led to unprecedented restrictions in people’s lifestyle which have affected their psychological wellbeing. In this context, this paper investigates the use of social signal processing techniques for remote assessment of emotions. It presents a machine learning method for affect recognition applied to recordings taken during the COVID-19 winter lockdown in Scotland (UK). This method is exclusively based on acoustic features extracted from voice recordings collected through home and mobile devices (i.e. phones, tablets), thus providing insight into the feasibility of monitoring people’s psychological wellbeing remotely, automatically and at scale. The proposed model is able to predict affect with a concordance correlation coefficient of 0.4230 (using Random Forest) and 0.3354 (using Decision Trees) for arousal and valence respectively. Clinical relevance In 2018/2019, 12% and 14% of Scottish adults reported depression and anxiety symptoms. Remote emotion recognition through home devices would support the detection of these difficulties, which are often underdiagnosed and, if untreated, may lead to temporal or chronic disability.

Building on the success of the ADReSS Challenge at Interspeech 2020, which attracted the participation of 34 teams from across the world, the ADReSSo Challenge targets three difficult automatic prediction problems of societal and medical relevance, namely: detection of Alzheimer's Dementia, inference of cognitive testing scores, and prediction of cognitive decline. This paper presents these prediction tasks in detail, describes the datasets used, and reports the results of the baseline classification and regression models we developed for each task. A combination of acoustic and linguistic features extracted directly from audio recordings, without human intervention, yielded a baseline accuracy of 78.87% for the AD classification task, an MMSE prediction root mean squared (RMSE) error of 5.28, and 68.75% accuracy for the cognitive decline prediction task.

Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

Language is a valuable source of clinical information in Alzheimer's Disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. This paper summarises current findings on the use of artificial intelligence, speech and language processing to predict cognitive decline in the context of Alzheimer's Disease, detailing current research procedures, highlighting their limitations and suggesting strategies to address them. We conducted a systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase) and Web of Science. Bibliographies of relevant papers were screened until December 2019. From 3,654 search results 51 articles were selected against the eligibility criteria. Four tables summarise their findings: study details (aim, population, interventions, comparisons, methods and outcomes), data details (size, type, modalities, annotation, balance, availability and language of study), methodology (pre-processing, feature generation, machine learning, evaluation and results) and clinical applicability (research implications, clinical potential, risk of bias and strengths/limitations). While promising results are reported across nearly all 51 studies, very few have been implemented in clinical research or practice. We concluded that the main limitations of the field are poor standardisation, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Attempts to close these gaps should support translation of future research into clinical practice.

The ADReSS Challenge at INTERSPEECH 2020 defines a shared task through which different approaches to the automated recognition of Alzheimer's dementia based on spontaneous speech can be compared. ADReSS provides researchers with a benchmark speech dataset which has been acoustically pre-processed and balanced in terms of age and gender, defining two cognitive assessment tasks, namely: the Alzheimer's speech classification task and the neuropsychological score regression task. In the Alzheimer's speech classification task, ADReSS challenge participants create models for classifying speech as dementia or healthy control speech. In the the neuropsychological score regression task, participants create models to predict mini-mental state examination scores. This paper describes the ADReSS Challenge in detail and presents a baseline for both tasks, including feature extraction procedures and results for classification and regression models. ADReSS aims to provide the speech and language Alzheimer's research community with a platform for comprehensive methodological comparisons. This will hopefully contribute to addressing the lack of standardisation that currently affects the field and shed light on avenues for future research and clinical applicability.