Motivational models of alcohol use emphasize that drinking motives (enhancement, coping, social, and conformity) drive alcohol consumption. However, few studies have directly tested how drinking motives relate to acute responses to alcohol, and existing studies typically omit social motives and expectancy effects and rely on solitary drinking paradigms with limited ecological validity. This preregistered study addresses these gaps by testing associations between drinking motives and alcohol's acute effects on emotions and social outcomes in a large, multiperson alcohol administration study with placebo control. Heavy-drinking young adults (N = 393; 50% female) completed the Drinking Motives Questionnaire-Revised and were grouped into 131 triads of unacquainted individuals. Groups consumed either alcohol (males: 0.82 g/kg; females: 0.74 g/kg) or placebo over 36 min. Interactions were video-recorded and coded for facial expressions (e.g., using Ekman's Facial Action Coding System), speech, and laughter. Postdrink, participants reported affect, stimulation, and social bonding. Multilevel structural equation modeling showed that many associations between drinking motives and acute alcohol responses, particularly enhanced stimulation and positive affect linked to enhancement, coping, and social motives, occurred similarly in both alcohol and placebo conditions, highlighting the significant role of expectancy effects. However, coping motives uniquely predicted greater perceived relief of negative affect specifically in the alcohol condition, even after accounting for other motives, despite no evidence of actual reductions in negative affect from pre- to postdrinking. Findings underscore the value of considering expectancy effects and ecologically valid contexts when examining how motives shape alcohol responses. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Abstract Researchers suspect large unsystematic variation (noise) in criminal sentencing, but past attempts to quantify it have used short hypothetical vignettes administered in low‐stakes settings to small, heterogeneous samples of judges. Such vignettes are deficient in detail and ecological validity. Sample heterogeneity also makes it difficult to separate noise from systematic variation (e.g., experience effects). In the current study, these concerns were mitigated with a naturally occurring noise audit: an early‐career judicial exam in Poland, which included a 390‐min, high‐stakes sentencing task, wherein performance affected career prospects. Based on a uniform court file of over 100 pages, each judicial candidate ( N = 232) composed a sentencing judgement in a criminal case and then justified their decision at length. The proposed sentences exhibited substantial disparities in the type and amount of punishment; different judges gave the same defendant a small fine or a long prison term. Continuous sentencing outcomes (e.g., fine amounts and imprisonment terms) were widely dispersed. The implication is that statutory sentencing ranges, which are intended to allow adjustments for aggravating and mitigating circumstances, also give judges room to treat identical defendants differently.

ABSTRACT Subthreshold depression (StD), a subclinical depression state, exhibits high prevalence and elevates the risk of developing major depressive disorder. Previous studies have found that individuals with StD were impaired in facial emotional expression recognition, and yet these studies primarily used static rather than dynamical facial emotional expressions with relatively highly ecological validity. It remains unclear whether StD could be associated with impaired recognition of dynamic facial emotional expressions and whether the abnormalities could be stable over time. Forty-six individuals with StD and forty-five non-depressed individuals performed a dynamic and a static facial emotional expression recognition task, and they also performed a follow-up assessment with the same tasks as the initial assessment after a 4-month interval. In the dynamic task, StD individuals showed lower recognition thresholds only for the angry emotional expression at both the initial and follow-up assessments, compared to the non-depressed individuals. In the static task, the StD group demonstrated significantly higher accuracy only for angry expressions at the initial assessment but did not at the follow-up assessment. These results indicate that the dynamic facial expression recognition task, recruiting higher ecological validity relative to the static task, may be a potential tool as an auxiliary objective marker for depression.

With the rapid integration of artificial intelligence technology into the medical field, this study focuses on dental surgery and systematically examines the complex impact mechanisms of AI on patients' treatment anxiety and postoperative satisfaction through three progressive experiments involving a total of 470 participants. Guided by a TAM-informed, trust-augmented framework, we examine the mediating role of trust in AI and the moderating effects of gender and information transparency. Key findings indicate: (1) AI technology significantly reduces patient treatment anxiety levels (p < 0.001) and enhances postoperative satisfaction (p < 0.001); (2) Across the three studies, trust in AI functioned as a partial mediator overall. The mediation did not emerge in the text-based simulation (Study 1), emerged in the video simulation (Study 2), and became robust in the clinical field study (Study 3; indirect effect 95% CI [- 0.89, - 0.21]) while the direct paths remained significant; (3) Gender differences significantly moderate the effects of AI technology, with female patients showing lower levels of technology trust; (4) Technology transparency positively moderates the therapeutic effect of AI application. Under high transparency conditions, the anxiety relief effect of AI technology (ΔM = 3.04) is significantly stronger than that under low transparency conditions (ΔM = 2.06). Collectively, the findings indicate that trust in AI operates as a partial, context-dependent mediator whose magnitude increases with ecological validity and transparency, clarifying when and how AI use relates to patient anxiety and satisfaction.

Remote photoplethysmography (rPPG) offers a non-invasive means of estimating heart rate in telemedicine settings. Yet its reliability remains uncertain due to the limited diversity and ecological validity of existing benchmark datasets. In this work, we systematically investigate the robustness of rPPG methods under challenging conditions, specifically low illumination and elevated heart rates. We introduce the CHILL dataset, which comprises video and PPG signals collected from 45 participants across two lighting conditions (bright and dark) and exercise-induced heart rates ranging from 54 to 141 beats per minute. We assessed eight rPPG algorithms, including four signal processing-based and four deep learning-based approaches, across three datasets: the newly collected CHILL dataset and two widely used public benchmarks, PURE and COHFACE. Within-dataset analysis on the CHILL dataset revealed that many existing rPPG methods struggle under challenging conditions. Five of the eight methods experienced a statistically significant decline in performance at elevated heart rates. In contrast, low illumination had a comparatively smaller impact. Cross-dataset analysis further revealed that several deep learning models failed to generalize effectively to the CHILL dataset. Among the models that did generalize, many still showed a significant decline in performance under elevated heart rate conditions, regardless of the training dataset. These findings highlight a critical limitation in current rPPG algorithms, namely their susceptibility to high heart rates. Our evaluation of rPPG methods on the CHILL dataset underscores the need for more robust approaches to enable accurate, non-invasive physiological monitoring in real-world digital health environments.

Background Advanced footwear technology (AFT) is reported to elicit an approximate 4% average improvement in running economy (RE). However, a large inter-individual variability remains unexplained, and limited research examined the impact of AFT during outdoor running. The aim was to compare the physiological, biomechanical and perceptual responses of 36 well-trained athletes to running outdoors using three different AFT and a traditional racing shoe. Methods Thirty-six well-trained athletes (19 males and 17 females) had their maximal aerobic capacity ( V ˙ O 2 max) and anaerobic threshold (AT) determined in laboratory conditions and were familiarised to the different shoe running conditions. Within 7 days, athletes ran 4 × 6 min running bouts, paced outdoors at 95% of their individual AT with 10 min recovery, in three different AFT conditions and a traditional flat. Oxygen uptake ( V ˙ O 2 ), heart rate, rating of perceived exertion (RPE), lactate, shoe perception, and biomechanical responses were compared between the four running trials. Results No differences were observed in the RE between shoe conditions, with a great inter-individual variability (range: 12% impairment to 14% improvement in RE). This variability was accompanied by a significant V ˙ O 2 order effect across exercise bouts (bout 2 lower than one [-1.1 mL/kg/min, p = 0.002]; bout 3 lower than 2 [-0.8 mL/kg/min, p = 0.027]; no differences between bouts 3 and 4). This variability was likely due to methodological issues such as one squared-wave RE measurement per shoe condition or the lack of a mirrored experimental design, among others. There was no order effect in other physiological or biomechanical variables. No significant differences were found in lactate, heart rate or rate of perceived exertion between running trials. Biomechanical responses to the different shoe conditions were also highly variable. One of the advanced AFT shoes showed a greater strike angle (+2.07°; p = 0.001), with no other significant differences between shoes conditions. Conclusion The large variability in the physiological response to AFT may be explained by methodological considerations. A minimum of two-bout RE measurements, the use of a mirrored order, a sufficient familiarisation with shoes and experimental designs, among other considerations, seem crucial to enhance the ecological validity and reduce data variability.

Wearable sensors provide a promising approach for monitoring physiological parameters, offering valuable insights into mental health conditions, including Social Anxiety Disorder (SAD). Early detection of SAD using physiological data can facilitate timely interventions, yet the development of robust anxiety detection models requires high-quality datasets. While real-world studies provide ecological validity, controlled studies ensure structured, high-quality data with minimal missing values, making them ideal for developing generalized and personalized anxiety detection models.; AB@Existing publicly available datasets related to anxiety research are limited to developed nations and focus on one or two anxiety-inducing activities. However, cultural differences significantly influence how anxiety is experienced and expressed, highlighting the need for datasets from diverse populations. This work presents MAD, a novel dataset collected in a low-to-middle-income country that addresses this gap. Our study involved participants engaging in three anxiety-inducing activities—speech, group discussion, and interview—each structured into three phases: anticipation, performance, and reflection. Physiological data were collected using wearable sensors, including electrocardiogram, electrodermal activity, and photoplethysmography, along with self-reported anxiety levels.; AB@Our dataset (N = 97) is unique in its inclusion of multiple anxiety-inducing activities, comprehensive phase-wise assessment, and representation of an underrepresented population. It provides a valuable resource for developing generalizable anxiety detection models, designing personalized interventions, and studying cultural variations in anxiety responses. By making MAD available, we aim to facilitate future research in machine learning-based mental health analysis, cross-cultural studies, and privacy-preserving anxiety detection approaches.

Objective For research on dyadic coping—how partners in intimate relationships deal with stress together—video-recorded behavioral observations in a lab are considered to be the best way to study actual coping behavior interactions. To date, little is known about the ecological validity of these laboratory situations. The current study aimed to ask couples directly what they considered to be typical when looking back at a videotaped behavioral interaction sequence in the lab. Methods A qualitative video recall approach was used in which interview data were collected. N = 117 couples participated in a videotaped stress communication in a lab setting and were subsequently separately interviewed in a video-review task on the typicality of the lab situation and their partner's behavior. Interviews were audiotaped and analyzed using qualitative content analyses. Results Participants mostly viewed their partner's behavior as typical. However, the setting and the course of the conversation were viewed as less typical. There were gender and age differences with regards to the ecological validity of lab settings. Conclusions and implications Results indicate that the ecological validity of dyadic coping in the laboratory is multidimensional. The lab setting seems comparable to situations where both partners have time and are not stressed.

Background Current vestibular assessments typically focus on isolated reflex pathways, failing to reflect the integrative nature of balance control. Consequently, clinical results often do not align with patient-reported symptoms or functional limitations in daily life. Objective To develop and present a comprehensive multimodal protocol for assessing functional impairments in patients with unilateral vestibulopathy (UV) and bilateral vestibulopathy (BV) using wearable sensors and ecologically valid daily-life tasks. Methods We designed a protocol combining nine inertial measurement units (IMUs), eye-tracking glasses, and plantar pressure insoles to assess participants during 15 standardized tasks reflecting daily activities. Tasks were selected through literature review, validated questionnaires (DHI, VADL), and patient interviews. The protocol is conducted in a semi-naturalistic rehabilitation facility environment to maximize ecological validity while maintaining standardization. We tested feasibility with 60 participants (20 UV, 20 BV, 20 healthy controls). Protocol outcomes The protocol successfully demonstrates feasibility across all sensor modalities and task categories. In this paper we describe the methodology used for task selection, the results of task performance in people with unilateral and bilateral vestibulopathy and healthy controls, and the sensor methodology (inertial measurement units, eye-tracking glasses, plantar pressure insoles). Analysis of sensor data will be presented in future papers. Conclusion This protocol provides a patient-centered, ecologically valid framework for quantifying vestibular-related functional impairments beyond traditional laboratory settings. The methodology bridges the gap between clinical vestibular testing and lived patient experiences, enabling objective assessment of real-world mobility challenges for personalized rehabilitation and treatment monitoring.

The team agility plus test: A novel three-dimensional approach for assessing agility in multidirectional sports.

Research note: a novel framework for investigating chronic intestinal stressors in commercial broiler chickens.

This study aimed to provide preliminary information on the clinical usability of a set of Italian telephone-based cognitive screening (TBCS) tests in patients with neurodegenerative MCI and dementia. Eighty-one patients with MCI (N = 32) and dementia (N = 49) due to Alzheimer's disease, frontotemporal lobar degeneration and Lewy body disease and 100 healthy controls (HCs) were administered a battery of TBCS tests assessing global cognition (Telephone Interview for Cognitive Status), executive functioning (Telephone-based Frontal Assessment Battery), verbal fluency (Telephone-based Phonemic and Semantic Verbal Fluency), working memory (Telephone-based Backward Digit Span) and language (Telephone Language Screener). For each test, we assessed their (1) applicability, (2) construct validity against in-person first- and second-level cognitive measures, (3) ecological validity against the Amsterdam IADL Questionnaire-Short Version (A-IADL-Q-SV), (4) capability to discriminate MCI and dementia patients from HCs and (5) to discriminate MCI from dementia. TBCS tests could be completed by the majority of patients (75-96%), with applicability rates being higher in MCI than in dementia. Moderate-to-strong correlations were detected between TBCS tests and both in-person cognitive measures and the A-IADL-Q-SV. All TBCS tests - except for the Backward Digit Span - optimally-to-excellently discriminated HCs from both MCI and dementia, by nevertheless being less accurate in discriminating between these two entities. Italian TBCS tests are valid and accurate measures for the detection of MCI and dementia due to neurodegenerative etiologies, thus prompting further research on the topic and their use in clinical practice and research.

Machine learning in ecotoxicology: Pollutant exposure levels and detection, biotoxicity and environmental behavior prediction.

This study was designed to validate the factor structure of the Startle and Surprise Inventories using multilevel confirmatory factor analysis in an ecologically valid flightdeck setting. The Startle and Surprise Inventories were developed to assess self-report startle and surprise to target stimuli. As their use expands in operational settings, construct validity should be further examined in contexts with ecological validity. 208 observations were collected from 26 professional pilots exposed to eight scenarios with varied levels of startle and surprise in a motion-based simulator. After each scenario, pilots completed the Startle and Surprise Inventories. A two-factor model, comprising the constructs Startle and Surprise, demonstrated superior and acceptable fit over a one-factor model. All items demonstrated significant factor loadings at both within- and between-scenario levels in the two-factor solution. McDonald’s ω ranged from ω = 0.88 to ω = 0.96 for the Startle Inventory, and ω = 0.77 to ω = 0.96 for the Surprise Inventory, indicating acceptable to excellent internal consistency. The findings offer empirical support for the construct validity and reliability of the Startle and Surprise Inventories in a highly ecologically-valid setting. The validated and reliable measures can inform evidence-based safety training protocols and interventions in aviation and other safety-critical domains.

ABSTRACT Markerless (ML) motion capture has emerged as a viable option to marker-based (MB) motion capture in estimating movement biomechanics, but limited data exists on the accuracy of ML systems during high-speed throwing. This study evaluated the accuracy and reliability of an in-stadium (Hawk-Eye) and a portable (Theia3D) ML motion-capture system in quantifying baseball pitching kinematics and kinetics relative to an MB reference. Eighteen collegiate pitchers were simultaneously recorded using all three systems. Mean per-joint position error (MPJPE), statistical parametric mapping (SPM), root mean square error (RMSE), Bland-Altman analysis, and concordance correlation coefficients (CCC) were used to assess agreement. Both ML systems demonstrated measurable discrepancies across variables, with MPJPE values of 56.6 ± 9.4 mm (Hawk-Eye) and 52.0 ± 12.3 mm (Theia3D). Stride length exhibited the strongest agreement with MB in both systems (CCC > 0.85), whereas shoulder rotational variables showed greater variability. Error magnitudes in joint positions and kinematic waveforms were comparable to those reported for other ML systems during dynamic movements. These results highlight the influence of system configuration, camera deployment, and pose-estimation models on biomechanical accuracy. Overall, both configurations showed potential for estimating pitching biomechanics, underscoring the trade-offs between criterion and ecological validity in markerless motion capture.

Cardiovascular diseases (CVDs) are a leading cause of death and disability worldwide. For people living with CVD, clinical guidelines recommend ongoing self-care such as symptom monitoring, medication adherence, and lifestyle modifications. However, many people struggle to engage in this due to the complexity of disease management, limited understanding, and a lack of cultural support. Conversational agents (CAs) offer a solution by providing artificial intelligence-driven, voice-based support that enables human-like communication. While many CAs and digital interventions are good for people with CVDs, they are for mainstream populations and overlook culturally and linguistically diverse communities. This study outlines the protocol for pilot testing the feasibility and usability of Diaspora Engaged Self-Care Intervention and Heart (DESI-Heart) program, to support self-care management among Indian diaspora populations with CVDs in Australia over an 8-week intervention period. The formative development of DESI-Heart is also described. We integrated the Double Diamond Model and the ecological validity model to develop our DESI-Heart program. First, we co-designed the program with end users, who identified 4 key goals for engagement with self-care through culturally and linguistically appropriate approaches. Based on these priorities and ideas, we developed specific goals, including (1) medication reminders, (2) daily exercise guidance, (3) diet buddy, and (4) guided meditation. Participants will access the DESI-Heart program through a web-based CA, available on smartphones, laptops, or PCs. Based on their preferred timing, individuals will receive links to access specific components of the program corresponding to each goal. These links will be sent to participants via SMS or email, depending on their preference. A single-arm prepost pilot trial (N=28) will be conducted to evaluate the feasibility and usability of the DESI-Heart program among Indian adults living in Australia with CVDs. The primary outcome will assess feasibility indicators, including recruitment, engagement, and usability, while secondary outcomes will examine changes in self-care behaviors and quality of life. The DESI-Heart program received ethics approval in July 2024. Recruitment for the pilot trial is scheduled to begin in June 2025 and conclude by September 2025, with participant follow-up expected to be completed by the end of December 2025. All 28 participants have been recruited, and data analysis will be conducted once follow-up is finalized. We have co-designed and developed the DESI-Heart program, a culturally and linguistically appropriate self-care intervention aimed at supporting Indian adults with CVD living in Australia. The next step is to conduct a pilot study to assess the feasibility and usability of DESI-Heart, which will inform the design of a larger evaluation trial. DESI-Heart has the potential to complement existing health services by helping individuals with CVD manage their condition within the community, while acknowledging their cultural backgrounds and language preferences. PRR1-10.2196/78923.

Fairness is a fundamental social norm guiding human decision-making. Yet, much of our empirical understanding of fairness derives from controlled laboratory studies with homogeneous student samples, raising concerns about the ecological validity of experimental findings. Here, we tackle this challenge by introducing a citizen science, lab-in-the-field approach, embedding a classic fairness paradigm, the Ultimatum Game (UG), in a well-visited public space within a community: a museum. Over the course of 13 months, we recorded >18,672 decisions from a heterogeneous sample of volunteer members of the public. Each participant responded to four allocation offers from anonymous proposers (two generous, two selfish), with the option to view proposers’ past behaviour (previously generous vs. selfish), before deciding whether to accept or reject each offer. Results closely replicated classic UG effects, with unfair offers frequently rejected, confirming the presence of inequality aversion beyond the laboratory. Notably, the majority of participants chose to sample proposer-history information, and those who did showed heightened sensitivity to fairness violations. Specifically, selfish offers from a proposer who had previously acted generously to others elicited the strongest rejection rates, demonstrating that judgements of unfairness are shaped by expectations which emerge from voluntary information sampling. Furthermore, the ecologically enriched design helped uncover temporal and demographic patterns, namely an association between time-of-day and information-seeking behaviour, and an increased willingness to accept unfairness across age. Methodologically, by situating a foundational experimental paradigm in a community venue, our approach aims to provide a scalable model for studying decision-making in ecologically enhanced contexts and a framework for research seeking to examine authentic behaviours beyond the laboratory, ultimately helping to deepen our understanding of the crucial norms that shape society.

Non-Suicidal Self-Injury (NSSI) is a prevalent and complex behavior among adolescents, often linked to negative emotions such as loneliness, anxiety, and emptiness. Traditional self-report and experimental methods rely on autobiographical recall and are therefore vulnerable to bias and low ecological validity. Accordingly, approaches that repeatedly sample NSSI-related feelings and contexts in daily life such as Ecological Momentary Assessment (EMA) are needed. This study aimed to identify emotional predictors of NSSI thoughts among adolescents using machine learning and multilevel logistic regression. The study included 42 adolescents (aged 12–15 years) who had engaged in NSSI in the past year. Participants reported their mood and NSSI behaviors three times daily over a 14-day EMA period via a smartphone application. Predictor variables included depression, anxiety, loneliness, self-anger, anger towards others, shame, and emptiness. A random forest model identified loneliness (feature importance: 0.40), anxiety (0.18), and emptiness (0.14) as the most significant predictors of NSSI thoughts. Multilevel logistic regression confirmed these findings, showing that each one-unit increase in anxiety, loneliness, and emptiness corresponded to a 24%, 19%, and 24% increase in the odds of experiencing NSSI thoughts, respectively. The ICC value of 0.26 indicated substantial between-individual variance, justifying multilevel modeling. However, random effects analysis revealed no significant individual differences, suggesting uniform effects across participants. These findings highlight loneliness as the most influential predictor, emphasizing the need to address social connections in interventions. Combining machine learning with traditional statistical methods enhanced interpretability, providing practical insights for developing tailored, emotion-focused interventions for adolescents engaging in NSSI.

Against the backdrop of globalization driving a growing number of trilingual speakers, understanding the mechanisms of trilingual semantic access has emerged as a pivotal research direction in cognitive linguistics. Existing studies predominantly focus on Indo-European language combinations and explicit memory paradigms, leaving gaps in the exploration of mechanisms for "cross-family + multi-script" language combinations (e.g., Chinese-English-Korean) and comparative analyses across third language (L3) proficiency levels. To address this gap, the present study employed a cross-language repetition priming paradigm—characterized by high ecological validity—using 48 sets of Chinese-English-Korean translation equivalents as stimuli. Sixty-one native Chinese speakers, with English as their second language (L2) and Korean as their third language (L3), participated in semantic categorization and lexical decision tasks. The study aimed to investigate the roles of the first language (L1, Chinese) and L2 in L3 semantic access, as well as differences in access pathways across varying L3 proficiency levels. Results revealed three key findings: (1) L1 Chinese played a dynamic mediating role in L3 Korean semantic access; (2) L2 English exerted a stable mediating role in L3 Korean semantic access; (3) High-proficiency L3 learners accessed L3 semantics directly through the connection between L2 and concepts, whereas low-proficiency learners relied on dual mediation of both L1 and L2.These findings enrich research on trilingual semantic representation, extend bilingual theories to cross-family language contexts, and provide support for differentiated pedagogical strategies in third language instruction.

Rapid global urbanization has intensified the demand for sensing solutions that can capture the complex interactions between urban environments and their impact on human physical and mental health. Conventional laboratory-based approaches, while offering high experimental control, often lack ecological validity and fail to represent real-world exposures. To address this gap, we present the eMOTIONAL Cities Walker—a portable multimodal sensing platform designed as a wearable backpack unit developed for the synchronous collecting of multimodal data in either indoor or outdoor settings. The system integrates a suite of environmental sensors (covering microclimate, air pollution and acoustic monitoring) with physiological sensing technologies, including electroencephalography (EEG), mobile eye-tracking and wrist-based physiological monitoring. This configuration enables real-time acquisition of environmental and physiological signals in dynamic, naturalistic settings. Here, we describe the system’s technical architecture, sensor specifications, and field deployment across selected Lisbon locations, demonstrating its feasibility and robustness in urban environments. By bridging controlled laboratory paradigms with ecologically valid real-world sensing, this platform provides a novel tool to advance translational research at the intersection of sensor technology, human experience, and urban health.