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141 results for “Behavioral assessment”
Data from: A dynamic state model of migratory behavior and physiology to assess the consequences of environmental variation and anthropogenic disturbance on marine vertebrates
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Data from: Assessing the value of novel habitats to snail kites through foraging behavior and nest survival
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Data from: Mechanical conflict system: a novel operant method for the assessment of nociceptive behavior
A new operant test for preclinical pain research, termed the Mechanical Conflict System (MCS), is presented. Rats were given a choice either to remain in a brightly lit compartment or to escape to a dark compartment by crossing an array of height-adjustable nociceptive probes. Latency to escape the light compartment was evaluated with varying probe heights (0, .5, 1, 2, 3, and 4 mm above compartment floor) in rats with neuropathic pain induced by constriction nerve injury (CCI) and in naive control rats. Escape responses in CCI rats were assessed following intraperitoneal administration of pregabalin (10 and 30 mg/kg), morphine (2.5 and 5 mg/kg), and the tachykinin NK1 receptor antagonist, RP 67580 (1 and 10 mg/kg). Results indicate that escape latency increased as a function of probe height in both naive and CCI rats. Pregabalin (10 and 30 mg/kg) and morphine (5 mg/kg), but not RP 67580, decreased latency to escape in CCI rats suggesting an antinociceptive effect. In contrast, morphine (10 mg/kg) but not pregabalin (30 mg/kg) increased escape latency in naive rats suggesting a possible anxiolytic action of morphine in response to light-induced fear. No order effects following multiple test sessions were observed. We conclude that the MCS is a valid method to assess behavioral signs of affective pain in rodents.
Data from: Correlations of behavioral deficits with brain pathology assessed through longitudinal MRI and histopathology in the HDHQ150/Q150 mouse model of Huntington's disease
A variety of mouse models have been developed that express mutant huntingtin (mHTT) leading to aggregates and inclusions that model the molecular pathology observed in Huntington's disease. Here we show that although homozygous HdhQ150 knock-in mice developed motor impairments (rotarod, locomotor activity, grip strength) by 36 weeks of age, cognitive dysfunction (swimming T maze, fear conditioning, odor discrimination, social interaction) was not evident by 94 weeks. Concomitant to behavioral assessments, T2-weighted MRI volume measurements indicated a slower striatal growth with a significant difference between wild type (WT) and HdhQ150 mice being present even at 15 weeks. Indeed, MRI indicated significant volumetric changes prior to the emergence of the "clinical horizon" of motor impairments at 36 weeks of age. A striatal decrease of 27% was observed over 94 weeks with cortex (12%) and hippocampus (21%) also indicating significant atrophy. A hypothesis-free analysis using tensor-based morphometry highlighted further regions undergoing atrophy by contrasting brain growth and regional neurodegeneration. Histology revealed the widespread presence of mHTT aggregates and cellular inclusions. However, there was little evidence of correlations between these outcome measures, potentially indicating that other factors are important in the causal cascade linking the molecular pathology to the emergence of behavioral impairments. In conclusion, the HdhQ150 mouse model replicates many aspects of the human condition, including an extended pre-manifest period prior to the emergence of motor impairments.
Data from: Assessing the relationship between quality of life and behavioral activation using the Japanese behavioral activation for depression scale-short form
Quality of life (QOL) is an important health-related concept. Identifying factors that affect QOL can help develop and improve health-promotion interventions. Previous studies suggest that behavioral activation fosters subjective QOL, including well-being. However, the mechanism by which behavioral activation improves QOL is not clear. Considering that QOL improves when depressive symptoms improve post-treatment and that behavioral activation is an effective treatment for depression, it is possible that behavioral activation affects QOL indirectly rather than directly. To clarify the mechanism of the influence of behavioral activation on QOL, it is necessary to examine the relationships between factors related to behavioral activation, depressive symptoms, and QOL. Therefore, we attempted to examine the relationship between these factors. Participants comprised 221 Japanese undergraduate students who completed questionnaires on behavioral activation, QOL, and depressive symptoms: the Japanese versions of the Behavioral Activation for Depression Scale-Short Form (BADS-SF), WHO Quality of Life-BREF (WHOQOL-26), and Center for Epidemiologic Studies Depression Scale (CES-D). The BADS-SF comprises two subscales, Activation and Avoidance, and the WHOQOL-26 measures overall QOL and four domains, Physical Health, Psychological Health, Social Relationships, and Environment. Mediation analyses were conducted with BADS-SF activation and avoidance as independent variables, CES-D as a mediator variable, and each WHO-QOL as an outcome variable. Results indicated that depression completely mediated the relationship between Avoidance and QOL, and partially mediated the relationship between Activation and QOL. In addition, analyses of each domain of QOL showed that Activation positively affected all aspects of QOL directly and indirectly, but Avoidance had a negative influence on only part of QOL mainly through depression. The present study provides behavioral activation strategies aimed at QOL enhancement.
Data from: Categorizing and assessing comprehensive drivers of provider behavior for optimizing quality of health care
<p>Inadequate quality of care in healthcare facilities is one of the primary causes of patient mortality in low- and middle-income countries, and understanding the behavior of healthcare providers is key to addressing it. Much of the existing research concentrates on improving resource-focused issues, such as staffing or training, but these interventions do not fully close the gaps in quality of care. By contrast, there is a lack of knowledge regarding the full contextual and internal drivers–such as social norms, beliefs, and emotions–that influence the clinical behaviors of healthcare providers. We aimed to provide two conceptual frameworks to identify such drivers, and investigate them in a facility setting where inadequate quality of care is pronounced. Using immersion interviews and a novel decision-making game incorporating concepts from behavioral science, we systematically and qualitatively identified an extensive set of contextual and internal behavioral drivers in staff nurses working in reproductive, maternal, newborn, and child health (RMNCH) in government public health facilities in Uttar Pradesh, India. We found that the nurses operate in an environment of stress, blame, and lack of control, which appears to influence their perception of their role as often significantly different from the RMNCH program's perspective. That context influences their perceptions of risk for themselves and for their patients, as well as self-efficacy beliefs, which could lead to avoidance of responsibility, or incorrect care. A limitation of the study is its use of only qualitative methods, which provide depth, rather than prevalence estimates of findings. This exploratory study identified previously under-researched contextual and internal drivers influencing the care-related behavior of staff nurses in public facilities in Uttar Pradesh. We recommend four types of interventions to close the gap between actual and target behaviors: structural improvements, systemic changes, community-level shifts, and interventions within healthcare facilities.</p>
Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 2(2)
<p><strong>Background</strong></p><p>The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research</p><p> </p><p><strong>Description of the dataset</strong></p><p>This dataset contains data used for individual particle detection, and is a companion of the main dataset (10.5281/zenodo.10083568). The files need to be downloaded and merged into the given folder structure. Put the folder "1Pix" along with the folder "10Pix" to the folder in "210812/Data-FIS/matlab/2_Experiment/exp/".</p><p> </p><p><strong>Disclaimer</strong></p><p>The data and code are provided as is without any warranty.</p><p> </p><p><strong>Funding</strong></p><p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p>
Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 1(2)
<p><strong>Background</strong></p><p>The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research</p><p> </p><p><strong>Description of the dataset</strong></p><p>This dataset is the main dataset used for the analysis. There is a twin archive connected to this one, which contains the dataset which was used for the individual particle detection routines (10.5281/zenodo.10081788). The files have to be downloaded and merged into the folder structure.</p><p>The following data is included</p><ul><li>individual experimental data and results in the folders<ul><li><strong>210812</strong> (10 µm, coarse sand, low-flow)</li><li><strong>220727</strong> (1 µm, coarse sand, low flow)</li><li><strong>220803</strong> (3 µm, coarse sand, low-flow)</li><li><strong>220818</strong> (1 µm, fine sand, low-flow)</li><li><strong>220901</strong> (1 µm, coarse sand, high-flow)</li></ul></li><li><strong>Comparison</strong> (comparing individual results of the experiments)</li><li><strong>Scripts</strong> (contains the individual matlab scripts)</li><li><strong>labbook.xlsx</strong> (contains metadata on the experiments, which are read out in the matlab scripts)</li></ul><p> </p><p><strong>Description of the code</strong></p><p>The matlab scripts *.m contain the code to read and analyse all experimental data. The scripts are divided for the different input file types.</p><ul><li>Main scripts to analyze experimental data<ul><li><strong>Experiment_Main.m </strong>Main routine for individual experiments, reading and analysing Fluorometer, Levelogger, Flowmeter, Ultrasonics PIV</li><li><strong>Experiment_Main_Compare.m </strong>Comparison of individual experiment results</li></ul></li><li>FIS-dataset<ul><li><strong>FIS_Cal_Individual.m: </strong>Realizes individual calibrations of one experiment</li><li><strong>FIS_Cal_Result.m: </strong>Merges individual calibrations of one experiment</li><li><strong>Experiment_FIS.m: </strong>Load data of one experiment, detect interfaces. Followed by<ul><li><strong>Experiment_FIS_1pix</strong>: Individual particle detection (for 10 µm experiment, no binning)</li><li><strong>Experiment_FIS_10pix</strong>: Particle cloud analysis (all experiments, binning 10 Pix * 10 Pix)</li></ul></li><li><strong>Experiment_FIS_10pix_compare.m: </strong>Compare results of particle cloud analysis for all experiments.</li></ul></li><li>Fluo-data<ul><li><strong>Fluo_Cal.m </strong>Realizes calibration for Fluorometer devices</li></ul></li><li>PIV-dataset<ul><li><strong>PIV_individual.m </strong>Individual analysis of Particle Image Velocimetry (in total 9 different subdatasets, from 3 camera positions, and each 3 different illumination positions)</li><li><strong>PIV_merge.m </strong>Merge<strong> </strong>9 individual results of PIV for a result for one experiment</li></ul></li><li>Profiler-dataset<ul><li><strong>Profiler.m </strong>Analyses data from bedform profiling (merging individual measurements after the experiment)</li><li><strong>Profiler_Compare.m </strong>Compares bedform elevations and metrics between the 5 experiments (acquired after the experiment)</li><li><strong>Profiler_Time.m </strong>Analyses temporal change of bedform elevation during the experiment</li></ul></li></ul><p> </p><p><strong>Disclaimer</strong></p><p>The data and code are provided as is without any warranty.</p><p> </p><p><strong>Funding</strong></p><p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p>
Toothbrushing behavior over time: a correlational analysis of repeatedly assessed brushing performance
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Application of health belief model for the assessment of COVID-19 preventive behavior and its determinants among students: A structural equation modeling analysis
<p><strong>Background:</strong> COVID-19 is a new pandemic that poses a threat to people globally. In Ethiopia, where classrooms are limited, students are at higher risk for COVID-19 unless they take consistent preventative actions. However, there is a lack of evidence in the study area regarding student compliance with COVID-19 preventive behavior (CPB) and its predictors.</p> <p><strong>Objective:</strong> This study aimed to assess CPB and its predictors among students based on the perspective of the Health Belief Model (HBM).</p> <p><strong>Method and materials:</strong> A school-based cross-sectional survey was conducted from November to December 2020 to evaluate the determinants of CPB among high school students using a self-administered structured questionnaire. 370 participants were selected using stratified simple random sampling. Descriptive statistics were used to summarize data, and partial least squares structural equation modeling (PLS-SEM) analyses to evaluate the measurement and structural models proposed by the HBM and to identify associations between HBM variables. A T-value of > 1.96 with 95% CI and a P-value of < 0.05 were used to declare the statistical significance of path coefficients.</p> <p><strong> Result:</strong> A total of 370 students participated with a response rate of 92%. The median (interquartile range) age of the participants (51.9% females) was 18 (2) years. Only 97 (26.2%), 121 (32.7%), and 108 (29.2%) of the students had good practice in keeping physical distance, frequent hand washing, and facemask use respectively. The HBM explained 43% of the variance in CPB. Perceived barrier (β= - 0.15, p < 0.001) and self-efficacy (β= 0.51, p <0.001) were significant predictors of student compliance to CPB. Moreover, the measurement model demonstrated that the instrument had acceptable reliability and validity.</p> <p><strong>Conclusion and recommendations:</strong> COVID-19 prevention practice is quite low among students. HBM demonstrated adequate predictive utility in predicting CPBs among students, where perceived barriers and self-efficacy emerged as significant predictors of CPBs. According to the findings of this study, theory-based behavioral change interventions are urgently required for students to improve their prevention practice. Furthermore, these interventions will be effective if they are designed to remove barriers to CPBs and improve students' self-efficacy in taking preventive measures.</p>
Data from: Worth the reward? An experimental assessment of risk-taking behavior along a life history gradient
Life history theory predicts that species with faster life history strategies should be willing to risk their survival more to acquire resources than those with slower life history strategies. Foraging can be a risky behavior and animals generally face a tradeoff between food consumption and predation risk. We predicted that the degree to which animals invest in current vs. future reproduction (i.e., life history strategy) would determine how they approach this tradeoff. We manipulated food abundance in wetlands to assess whether life history theory could explain risk taking among females of five duck species with respect to foraging. We found evidence consistent with our prediction based on life history theory; species with a faster life history strategy were willing to engage in riskier behavior, by feeding more intensively, for a greater food reward. Females from species with faster life history strategies devoted 25 % more time to feeding when in high food density treatment plots vs. control plots. The percentage of time that females from species with slower life history strategies devoted to feeding was not affected by food density. These findings contribute to our understanding of life history theory and represent a possible mechanism to explain differences in life history strategies among species.
PETRA: Pictorial Assessment of Task Occurrence and Upper Limb Avoidance Behaviors
ClinicalTrials.gov study NCT07031791. IPD Sharing: NO. Countries: 1. Publications: 0.
Assessment of the Predictors and Moderators of Behavior Change
ClinicalTrials.gov study NCT03139643. IPD Sharing: NO. Countries: 0. Publications: 1.
A Survey to Assess Participants' and Physicians' Knowledge, Attitudes and Behavior When Using NATPARA
ClinicalTrials.gov study NCT05556629. IPD Sharing: YES. Countries: 1. Publications: 0.
The MObile AssessMENT of Behavioral and Psychological Symptoms of Dementia in Amnestic MCI and AD (MOMENT) Study
ClinicalTrials.gov study NCT04482036. IPD Sharing: NO. Countries: 1. Publications: 0.
Using Neuroimaging and Behavioral Assessments to Understand Late Talking
ClinicalTrials.gov study NCT06156865. IPD Sharing: YES. Countries: 1. Publications: 0.
Mobile Behavioral Ecological Momentary Assessment and Intervention in Rakai, Uganda
ClinicalTrials.gov study NCT04375423. IPD Sharing: NO. Countries: 0. Publications: 1.
Data from: Worth the reward? An experimental assessment of risk-taking behavior along a life history gradient
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Data from: Correlations of behavioral deficits with brain pathology assessed through longitudinal MRI and histopathology in the HDHQ150/Q150 mouse model of Huntington's disease
Open the record for dataset details and reuse information.
Data from: Categorizing and assessing comprehensive drivers of provider behavior for optimizing quality of health care
Open the record for dataset details and reuse information.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.