Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon.

An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and selecting the one with the closest match to experiment. However, this inefficient process is not guaranteed to succeed. We introduce a general method to combine atomistic machine learning (ML) with experimental observables that produces atomistic structures compatible with experiment by design. We use this approach in combination with grand-canonical Monte Carlo within a modified Hamiltonian formalism, to generate configurations that agree with experimental data and are chemically sound (low in energy). We apply our approach to understand the atomistic structure of oxygenated amorphous carbon (a-COx), an intriguing carbon-based material, to answer the question of how much oxygen can be added to carbon before it fully decomposes into CO and CO2. Utilizing an ML-based X-ray photoelectron spectroscopy (XPS) model trained from GW and density functional theory (DFT) data, in conjunction with an ML interatomic potential, we identify a-COx structures compliant with experimental XPS predictions that are also energetically favorable with respect to DFT. Employing a network analysis, we accurately deconvolve the XPS spectrum into motif contributions, both revealing the inaccuracies inherent to experimental XPS interpretation and granting us atomistic insight into the structure of a-COx. This method generalizes to multiple experimental observables and allows for the elucidation of the atomistic structure of materials directly from experimental data, thereby enabling experiment-driven materials modeling with a degree of realism previously out of reach.

Experimental and Computational Study Toward Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials.

SnOx has received great attention as an electrocatalyst for CO2 reduction reaction (CO2RR), however; it still suffers from low activity. Moreover, the atomic-level SnOx structure and the nature of the active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, CO2RR performance is enhanced by supporting SnO2 nanoparticles on two common supports, vulcan carbon and TiO2. Then, electrolysis of CO2 at various temperatures in a neutral electrolyte reveals that the application window for this catalyst is between 12 and 30 °C. Furthermore, this study introduces a machine learning interatomic potential method for the atomistic simulation to investigate SnO2 reduction and establish a correlation between SnOx structures and their CO2RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of *H and *CO2 -, where both are correlated with the presence of oxygen on the nanoparticle surface. This study offers in-depth insights into the rational design and application of SnOx-based electrocatalysts for CO2RR.

Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide.

Graphene oxide (GO) materials are widely studied, and yet their atomic-scale structures remain to be fully understood. Here we show that the chemical and configurational space of GO can be rapidly explored by advanced machine-learning methods, combining on-the-fly acceleration for first-principles molecular dynamics with message-passing neural-network potentials. The first step allows for the rapid sampling of chemical structures with very little prior knowledge required; the second step affords state-of-the-art accuracy and predictive power. We apply the method to the thermal reduction of GO, which we describe in a realistic (ten-nanometre scale) structural model. Our simulations are consistent with recent experimental findings, including X-ray photoelectron spectroscopy (XPS), and help to rationalise them in atomistic and mechanistic detail. More generally, our work provides a platform for routine, accurate, and predictive simulations of diverse carbonaceous materials.

Tensor-Reduced Atomic Density Representations.

Density-based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modeling, and the visualization and analysis of material datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. By exploiting symmetry, we recast this approach as tensor factorization of the standard neighbour-density-based descriptors and, using a new notation, identify connections to existing compression algorithms. In doing so, we form compact tensor-reduced representation of the local atomic environment whose size does not depend on the number of chemical elements, is systematically convergable, and therefore remains applicable to a wide range of data analysis and regression tasks.

Multimodal mechanisms of human socially reinforced learning across neurodegenerative diseases.

Social feedback can selectively enhance learning in diverse domains. Relevant neurocognitive mechanisms have been studied mainly in healthy persons, yielding correlational findings. Neurodegenerative lesion models, coupled with multimodal brain measures, can complement standard approaches by revealing direct multidimensional correlates of the phenomenon. To this end, we assessed socially reinforced and non-socially reinforced learning in 40 healthy participants as well as persons with behavioural variant frontotemporal dementia (n = 21), Parkinson's disease (n = 31) and Alzheimer's disease (n = 20). These conditions are typified by predominant deficits in social cognition, feedback-based learning and associative learning, respectively, although all three domains may be partly compromised in the other conditions. We combined a validated behavioural task with ongoing EEG signatures of implicit learning (medial frontal negativity) and offline MRI measures (voxel-based morphometry). In healthy participants, learning was facilitated by social feedback relative to non-social feedback. In comparison with controls, this effect was specifically impaired in behavioural variant frontotemporal dementia and Parkinson's disease, while unspecific learning deficits (across social and non-social conditions) were observed in Alzheimer's disease. EEG results showed increased medial frontal negativity in healthy controls during social feedback and learning. Such a modulation was selectively disrupted in behavioural variant frontotemporal dementia. Neuroanatomical results revealed extended temporo-parietal and fronto-limbic correlates of socially reinforced learning, with specific temporo-parietal associations in behavioural variant frontotemporal dementia and predominantly fronto-limbic regions in Alzheimer's disease. In contrast, non-socially reinforced learning was consistently linked to medial temporal/hippocampal regions. No associations with cortical volume were found in Parkinson's disease. Results are consistent with core social deficits in behavioural variant frontotemporal dementia, subtle disruptions in ongoing feedback-mechanisms and social processes in Parkinson's disease and generalized learning alterations in Alzheimer's disease. This multimodal approach highlights the impact of different neurodegenerative profiles on learning and social feedback. Our findings inform a promising theoretical and clinical agenda in the fields of social learning, socially reinforced learning and neurodegeneration.

A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles.

A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on density-functional theory (DFT) data computed for bulk, surfaces, and nanostructured platinum, in particular nanoparticles. Across the range of tested properties, which include bulk elasticity, surface energetics, and nanoparticle stability, this potential shows excellent transferability and agreement with DFT, providing state-of-the-art accuracy at a low computational cost. We showcase the possibilities for modeling of Pt systems enabled by this potential with two examples: the pressure-temperature phase diagram of Pt calculated using nested sampling and a study of the spontaneous crystallization of a large Pt nanoparticle based on classical dynamics simulations over several nanoseconds.

Gaussian approximation potentials: Theory, software implementation and application examples.

Gaussian Approximation Potentials (GAPs) are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation provides the means for both fitting models using ab initio data and using the resulting potentials in atomic simulations. Details of the GAP theory, algorithms and software are presented, together with detailed usage examples to help new and existing users. We review some recent developments to the GAP framework, including Message Passing Interface parallelisation of the fitting code enabling its use on thousands of central processing unit cores and compression of descriptors to eliminate the poor scaling with the number of different chemical elements.

Meeting physical activity guidelines and its association with health-related quality of life throughout pregnancy: the PregnActive project.

Pregnancy is a unique period in women life, characterized by anatomical and metabolic variation that may affect health-related quality of life (HRQoL). Physical activity has the potential to positively influence HRQoL. The aim of this study is to analyze the association between the fulfillment of physical activity guidelines and HRQoL throughout pregnancy. Seventy-eight pregnant women were assessed at two time point through their pregnancy: at mid- and at later-pregnancy. Physical activity was objectively assessed by a multi-sensor monitor and pregnant women were categorized by the fulfillment of the minimum physical activity recommendations: at least 30 minutes/day on at least 5 days/week. Perceived mental health was evaluated by health-related quality of life and by psychological pregnancy symptoms, using the SF-36 and the Pregnancy Symptoms Inventory, respectively. T-Student Test and hierarchical multiple linear regressions analysis was developed. Pregnant women who fulfilled physical activity recommendations reported better mental HRQoL both at mid-pregnancy (p = 0.148) and later-pregnancy (p = 0.007). The number of days meeting minimum physical activity recommendations contributes to better mental HRQoL and together with depression and anxiety symptoms the model explain the 65% of the mental HRQoL at later pregnancy. Meeting the minimum physical activity recommendations is associated with better perceived health at both midpregnancy and later pregnancy. While mental HRQoL is explained by physical activity, physical HRQoL is explained by others factors such as age or pregnancy-related symptoms, but not by meeting the minimum physical activity recommendations.

Interoception Primes Emotional Processing: Multimodal Evidence from Neurodegeneration.

Recent frameworks in cognitive neuroscience and behavioral neurology underscore interoceptive priors as core modulators of negative emotions. However, the field lacks experimental designs manipulating the priming of emotions via interoception and exploring their multimodal signatures in neurodegenerative models. Here, we designed a novel task that involves interoceptive and control-exteroceptive priming conditions followed by post-interoception and post-exteroception facial emotion recognition (FER). We recruited 114 participants, including healthy controls (HCs) as well as patients with behavioral variant frontotemporal dementia (bvFTD), Parkinson's disease (PD), and Alzheimer's disease (AD). We measured online EEG modulations of the heart-evoked potential (HEP), and associations with both brain structural and resting-state functional connectivity patterns. Behaviorally, post-interoception negative FER was enhanced in HCs but selectively disrupted in bvFTD and PD, with AD presenting generalized disruptions across emotion types. Only bvFTD presented impaired interoceptive accuracy. Increased HEP modulations during post-interoception negative FER was observed in HCs and AD, but not in bvFTD or PD patients. Across all groups, post-interoception negative FER correlated with the volume of the insula and the ACC. Also, negative FER was associated with functional connectivity along the (a) salience network in the post-interoception condition, and along the (b) executive network in the post-exteroception condition. These patterns were selectively disrupted in bvFTD (a) and PD (b), respectively. Our approach underscores the multidimensional impact of interoception on emotion, while revealing a specific pathophysiological marker of bvFTD. These findings inform a promising theoretical and clinical agenda in the fields of nteroception, emotion, allostasis, and neurodegeneration.SIGNIFICANCE STATEMENT We examined whether and how emotions are primed by interoceptive states combining multimodal measures in healthy controls and neurodegenerative models. In controls, negative emotion recognition and ongoing HEP modulations were increased after interoception. These patterns were selectively disrupted in patients with atrophy across key interoceptive-emotional regions (e.g., the insula and the cingulate in frontotemporal dementia, frontostriatal networks in Parkinson's disease), whereas persons with Alzheimer's disease presented generalized emotional processing abnormalities with preserved interoceptive mechanisms. The integration of both domains was associated with the volume and connectivity (salience network) of canonical interoceptive-emotional hubs, critically involving the insula and the anterior cingulate. Our study reveals multimodal markers of interoceptive-emotional priming, laying the groundwork for new agendas in cognitive neuroscience and behavioral neurology.

The Neural Blending of Words and Movement: Event-Related Potential Signatures of Semantic and Action Processes during Motor-Language Coupling.

Behavioral embodied research shows that words evoking limb-specific meanings can affect responses performed with the corresponding body part. However, no study has explored this phenomenon's neural dynamics under implicit processing conditions, let alone by disentangling its conceptual and motoric stages. Here, we examined whether the blending of hand actions and manual action verbs, relative to nonmanual action verbs and nonaction verbs, modulates electrophysiological markers of semantic integration (N400) and motor-related cortical potentials during a lexical decision task. Relative to both other categories, manual action verbs involved reduced posterior N400 amplitude and greater modulations of frontal motor-related cortical potentials. Such effects overlapped in a window of ∼380-440 msec after word presentation and ∼180 msec before response execution, revealing the possible time span in which both semantic and action-related stages reach maximal convergence. These results allow refining current models of motor-language coupling while affording new insights on embodied dynamics at large.

Time to Face Language: Embodied Mechanisms Underpin the Inception of Face-Related Meanings in the Human Brain.

In construing meaning, the brain recruits multimodal (conceptual) systems and embodied (modality-specific) mechanisms. Yet, no consensus exists on how crucial the latter are for the inception of semantic distinctions. To address this issue, we combined electroencephalographic (EEG) and intracranial EEG (iEEG) to examine when nouns denoting facial body parts (FBPs) and nonFBPs are discriminated in face-processing and multimodal networks. First, FBP words increased N170 amplitude (a hallmark of early facial processing). Second, they triggered fast (~100 ms) activity boosts within the face-processing network, alongside later (~275 ms) effects in multimodal circuits. Third, iEEG recordings from face-processing hubs allowed decoding ~80% of items before 200 ms, while classification based on multimodal-network activity only surpassed ~70% after 250 ms. Finally, EEG and iEEG connectivity between both networks proved greater in early (0-200 ms) than later (200-400 ms) windows. Collectively, our findings indicate that, at least for some lexico-semantic categories, meaning is construed through fast reenactments of modality-specific experience.

Training volume and amateur cyclists' health: a six-month follow-up from coinciding with a high-demand cycling event.

This study aimed to analyse the longitudinal association of amateur cycling training volume with health by comparing the proximity of participation in a high-demand cycling event. Variations in cycling training volume, behavioural cardiometabolic risk factors, and physical and psychosocial health were examined. Cyclists decreased their training volume by approximately 40% and their total physical activity volumes by approximately 20%, while controls maintained (~5%). A time*group interaction was found for men's physical conditioning, body mass index and anxiety and, independent of gender, for behavioural cardiometabolic risk factors. Variation in cycling training volume was positively correlated with variation in physical conditioning and total physical activity and negatively correlated with variation in body mass index. The high level of cycling training volume developed at the time coinciding with a high demand cycling event predisposes to better physical health and behavioural cardiometabolic risk factors, without negatively affect psychosocial health, compared with six month later.

Task-specific signatures in the expert brain: Differential correlates of translation and reading in professional interpreters.

Insights on the neurocognitive particularities of expert individuals have benefited from language studies on professional simultaneous interpreters (PSIs). Accruing research indicates that behavioral advantages in this population are restricted to those skills that are directly taxed during professional practice (e.g., translation as opposed to reading), but little is known about the neural signatures of such selective effects. To illuminate the issue, we recruited 17 PSIs and 15 non-interpreter bilinguals and compared behavioral and electrophysiological markers of word reading and translation from and into their native and non-native languages (L1 and L2, respectively). PSIs exhibited greater delta-theta (1-8 â€‹Hz) power across all tasks over varying topographies, but these were accompanied by faster performance only in the case of translation conditions. Moreover, neural differences in PSIs were most marked for L2-L1 translation (the dominant interpreting direction in their market), which exhibited maximally widespread modulations that selectively correlated with behavioral outcomes. Taken together, our results suggest that interpreting experience involves distinct neural signatures across reading and translation mechanisms, but that these are systematically related with processing efficiency only in domains that face elevated demands during everyday practice (i.e., L2-L1 translation). These findings can inform models of simultaneous interpreting, in particular, and expert cognitive processing, in general.

Motor-system dynamics during naturalistic reading of action narratives in first and second language.

Do embodied semantic systems play different roles depending on when and how well a given language was learned? Emergent evidence suggests that this is the case for isolated, decontextualized stimuli, but no study has addressed the issue considering naturalistic narratives. Seeking to bridge this gap, we assessed motor-system dynamics in 26 Spanish-English bilinguals as they engaged in free, unconstrained reading of naturalistic action texts (ATs, highlighting the characters' movements) and neutral texts (NTs, featuring low motility) in their first and second language (L1, L2). To explore functional connectivity spread over each reading session, we recorded ongoing high-density electroencephalographic signals and subjected them to functional connectivity analysis via a spatial clustering approach. Results showed that, in L1, AT (relative to NT) reading involved increased connectivity between left and right central electrodes consistently implicated in action-related processes, as well as distinct source-level modulations in motor regions. In L2, despite null group-level effects, enhanced motor-related connectivity during AT reading correlated positively with L2 proficiency and negatively with age of L2 learning. Taken together, these findings suggest that action simulations during unconstrained narrative reading involve neural couplings between motor-sensitive mechanisms, in proportion to how consolidated a language is. More generally, such evidence addresses recent calls to test the ecological validity of motor-resonance effects while offering new insights on their relation with experiential variables.

At the Heart of Neurological Dimensionality: Cross-Nosological and Multimodal Cardiac Interoceptive Deficits.

OBJECTIVE: Neurological nosology, based on categorical systems, has largely ignored dimensional aspects of neurocognitive impairments. Transdiagnostic dimensional approaches of interoception (the sensing of visceral signals) may improve the descriptions of cross-pathological symptoms at behavioral, electrophysiological, and anatomical levels. Alterations of cardiac interoception (encompassing multidimensional variables such as accuracy, learning, sensibility, and awareness) and its neural correlates (electrophysiological markers, imaging-based anatomical and functional connectivity) have been proposed as critical across disparate neurological disorders. However, no study has examined the specific impact of neural (relative to autonomic) disturbances of cardiac interoception or their differential manifestations across neurological conditions. METHODS: Here, we used a computational approach to classify and evaluate which markers of cardiac interoception (behavioral, metacognitive, electrophysiological, volumetric, or functional) offer the best discrimination between neurological conditions and cardiac (hypertensive) disease (model 1), and among neurological conditions (Alzheimer's disease, frontotemporal dementia, multiple sclerosis, and brain stroke; model 2). In total, the study comprised 52 neurological patients (mean [standard deviation] age = 55.1 [17.3] years; 37 women), 25 cardiac patients (age = 66.2 [9.1] years; 13 women), and 72 healthy controls (age = 52.65 [17.1] years; 50 women). RESULTS: Cardiac interoceptive outcomes successfully classified between neurological and cardiac conditions (model 1: >80% accuracy) but not among neurological conditions (model 2: 53% accuracy). Behavioral cardiac interoceptive alterations, although present in all conditions, were powerful in differentiating between neurological and cardiac diseases. However, among neurological conditions, cardiac interoceptive deficits presented more undifferentiated and unspecific disturbances across dimensions. CONCLUSIONS: Our result suggests a diffuse pattern of interoceptive alterations across neurological conditions, highlighting their potential role as dimensional, transdiagnostic markers.

Associations of 24-hours activity composition with adiposity and cardiorespiratory fitness: The PregnActive project.

AIM: This study examined the associations of activity behaviors composition (sleep, sedentary time, light and moderate-to-vigorous physical activity) with adiposity and cardiorespiratory fitness, and how isotemporal reallocations of time between activity behaviors are associated with differences in adiposity and cardiorespiratory fitness. METHODS: A cross-sectional study was conducted in 130 women during midpregnancy. Activity behaviors, conceptualized as a 24-hours composition, were objectively assessed by multi-sensor monitors. Skinfold thickness, fat mass index, and body mass index were calculated as indicators of adiposity. Cardiorespiratory fitness was assessed using a 6-minute walk test. Log-ratio multiple linear regression models and compositional isotemporal substitutions were used to analyze the associations and estimated differences in outcomes. RESULTS: The activity composition was significantly associated with adiposity indicators (all P < .001) and cardiorespiratory fitness (P values from .025 to <.001) during midpregnancy. The isotemporal substitutions were asymmetrical, showing the highest estimated differences in adiposity (8.7%, 0.80 kg/m2 , for fat mass index; 6.0%, 2.65 mm, for the sum of skinfold thickness; and 3.8%, 1.02 kg/m2 , for body mass index) and cardiorespiratory fitness (3.0%, 1.00 mL/kg min) when 30 minutes of moderate-to-vigorous physical activity was reallocated by sedentary time. CONCLUSION: The activity composition was associated with adiposity and the cardiorespiratory fitness levels during midpregnancy, with moderate-to-vigorous physical activity being the leading activity behavior. The most unfavorable differences in adiposity and cardiorespiratory fitness were found when moderate-to-vigorous physical activity was replaced by another behavior, mainly sedentary time, reinforcing the importance of at least maintaining moderate-to-vigorous physical activity during pregnancy.

Amateur endurance cycling practice and adult's physical and psychosocial health: a cross-sectional study of the influence of training volume.

This study aimed to analyse the association between amateur cycling training volume and physical and psychosocial health. A cross-sectional study was developed, via self-reported survey, among 1669 cyclists and 1039 controls, where analysis of variance and hierarchical multiple linear regression test were developed. Independent of gender, high volumes of amateur endurance cycling practice benefited cyclists' body mass index and male cyclists' physical conditioning, while psychosocial health did not differ among the training volume groups. Hierarchical multiple linear regression analysis highlighted the contribution of training volume to lower cyclists' body mass index and better male cyclists' physical conditioning. All cyclist groups presented better physical and psychosocial health than controls. High volumes of amateur endurance cycling training were associated with better physical health without jeopardizing psychosocial health. The practice of amateur endurance cycling, both in low and high volumes, was associated with better physical and psychosocial health compared with inactivity.

Feasibility and effects of an exercise-based intervention in prison inmates with psychiatric disorders: the PsychiActive project randomized controlled trial.

OBJECTIVE: To evaluate the feasibility and effects of a 12-week intervention combining aerobic and strength exercises in prison inmates with psychiatric disorders. DESIGN: Two parallel-group, randomized controlled trials. SETTING: A psychiatric prison hospital. SUBJECTS: Forty-one men prison inmates (mean age ± SD = 38.2 ± 9.2 years, mean prison duration ± SD = 2.6 ± 2.5 years) with psychiatric disorders (primarily personality disorder, n = 27; mean illness duration ± SD = 12.0 ± 10.5 years). INTERVENTIONS: Participants were randomly allocated to intervention group consisted of exercise plus usual care (n = 21) or control group which received usual care (n = 20) for 12 weeks. The exercise programme included three weekly sessions of group-based moderate-to-high intensity combined exercises designed and supervised by exercise professionals. MAIN MEASURES: Fitness and anthropometric measures were assessed using field-based tests (6-minute walk, Incremental Shuttle Walk, Arm-Curl, and Chair-Stand), handgrip dynamometry, bioelectrical impedance, and waist and hip circumferences. RESULTS: There were no adverse events, and 10 intervention participants withdrew. The remaining 11 participants attended a mean of 28 sessions, of which nine met the compliance criteria. Between-group change differences substantially favoured the compliance intervention group for the 6-minute walk (+21.2%), Incremental Shuttle Walk (+33.9%), Arm-Curl (+13.8%), waist (-3.5%), waist/height0.5 (-1.7%) (-2.7%), waist/hip (-3.4%), and Body Shape Index (-3.3%) (-3.5%). Additional analysis showed beneficial effects of exercise participation on handgrip strength. CONCLUSION: The intervention was safe, had a high dropout rate, and seemed to be effective for improving fitness and anthropometric measures in men prison inmates with psychiatric disorders who attended and participated in the exercise sessions.

Growth Mechanism and Origin of High sp

We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-functional theory data. For the first time, the high sp^{3} fractions in excess of 85% observed experimentally are reproduced by means of computational simulation, and the deposition energy dependence of the film's characteristics is also accurately described. High confidence in the potential and direct access to the atomic interactions allow us to infer the microscopic growth mechanism in this material. While the widespread view is that ta-C grows by "subplantation," we show that the so-called "peening" model is actually the dominant mechanism responsible for the high sp^{3} content. We show that pressure waves lead to bond rearrangement away from the impact site of the incident ion, and high sp^{3} fractions arise from a delicate balance of transitions between three- and fourfold coordinated carbon atoms. These results open the door for a microscopic understanding of carbon nanostructure formation with an unprecedented level of predictive power.

Measuring Sedentary Behavior During Pregnancy: Comparison Between Self-reported and Objective Measures.

Objectives To quantify and compare the sedentary time estimated by the Sedentary Behavior Questionnaire (SBQ) and the sedentary time objectively measured by a multi-sensor monitor (SWA) in pregnant women. Methods One hundred eighty-six participants answered the SBQ and wore the SWA at least 7 valid days. The concordance, correlation, agreement and relative activity levels between both measures of sedentary time were examined. Differences of sedentary time between weekday and weekend and between groups stratified by sociodemographic and clinical characteristic were evaluated by one-way analysis of variance. Results Pregnant women were sedentary the 64% of their waking hours. Television viewing is the most prevalent sedentary behavior. The concordance, correlation, and agreement between SBQ and SWA were weak, yet a significant correlation in weekday and average day sedentary time (r = 0.23 and 0.20, P = 0.001 and 0.008, respectively) was observed. A significant linear trend was found for increasing sedentary time between both methods using a relative activity levels analysis. Conclusions for Practice Pregnant women experience high amount of sedentary time, for approximately half of the day. The SBQ shows a low validity and agreement, but strong ability to rank individuals compared with SWA in pregnant women.