Dataset Information

Investigating Flow State and Cardiac Pre-ejection Period During Electronic Gaming Machine Use.

ABSTRACT: Flow activities (e.g. sports and gaming) have been associated with positive affect and prolonged engagement. In the gambling field, modern electronic gaming machines (EGMs, including modern slot machines) have drawn concern as a potentially flow-inducing activity that may be associated with gambling-related harms. Current research has heavily relied on self-reported flow, and further insights may be afforded by physiological methods. We present data from three separate experiments in which self-reported gambling flow and cardiac pre-ejection period (PEP; a measure of sympathetic nervous system arousal) were examined. Male undergraduate participants gambled on a genuine EGM in a laboratory setting for a period of at least 15 min, and completed the Flow subscale of the game experience questionnaire (GEQ). Aggregated data were analyzed using multilevel regression. Although EGM gambling was not associated with significant changes in PEP across participants, we found that self-reported flow states were associated with significant decreases in PEP during the first five minutes of EGM use. Thus, participants who experienced flow showed a greater sympathetic nervous system response to the onset of gambling. Though these effects were consistent in experiments 1 and 2, in experiment 3 the effect was inverted during the same time window. We conclude that flow during EGM gambling appears to be associated with early changes in sympathetic nervous system activity, but stress that more research is needed to characterize boundary conditions and moderating factors.

SUBMITTER: Murch WS

PROVIDER: S-EPMC7055358 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Investigating Flow State and Cardiac Pre-ejection Period During Electronic Gaming Machine Use.

Murch W Spencer WS Ferrari Mario A MA McDonald Brooke M BM Clark Luke L

Frontiers in psychology 20200226

Flow activities (e.g. sports and gaming) have been associated with positive affect and prolonged engagement. In the gambling field, modern electronic gaming machines (EGMs, including modern slot machines) have drawn concern as a potentially flow-inducing activity that may be associated with gambling-related harms. Current research has heavily relied on self-reported flow, and further insights may be afforded by physiological methods. We present data from three separate experiments in which self- ...[more]

PMID: 32174868

Similar Datasets

Project description:BackgroundHeart failure (HF) hospitalization is a dominant contributor of morbidity and healthcare expenditures in patients with systolic HF. Cardiovascular magnetic resonance (CMR) imaging is increasingly employed for the evaluation of HF given capacity to provide highly reproducible phenotypic markers of disease. The combined value of CMR phenotypic markers and patient health information to deliver predictions of future HF events has not been explored. We sought to develop and validate a novel risk model for the patient-specific prediction of time to HF hospitalization using routinely reported CMR variables, patient-reported health status, and electronic health information.MethodsStandardized data capture was performed for 1,775 consecutive patients with chronic systolic HF referred for CMR imaging. Patient demographics, symptoms, Health-related Quality of Life, pharmacy, and routinely reported CMR features were provided to both machine learning (ML) and competing risk Fine-Gray-based models (FGM) for the prediction of time to HF hospitalization.ResultsThe mean age was 59 years with a mean LVEF of 36 ± 11%. The population was evenly distributed between ischemic (52%) and idiopathic non-ischemic cardiomyopathy (48%). Over a median follow-up of 2.79 years (IQR: 1.59-4.04) 333 patients (19%) experienced HF related hospitalization. Both ML and competing risk FGM based models achieved robust performance for the prediction of time to HF hospitalization. Respective 90-day, 1 and 2-year AUC values were 0.87, 0.83, and 0.80 for the ML model, and 0.89, 0.84, and 0.80 for the competing risk FGM-based model in a holdout validation cohort. Patients classified as high-risk by the ML model experienced a 34-fold higher occurrence of HF hospitalization at 90 days vs. the low-risk group.ConclusionIn this study we demonstrated capacity for routinely reported CMR phenotypic markers and patient health information to be combined for the delivery of patient-specific predictions of time to HF hospitalization. This work supports an evolving migration toward multi-domain data collection for the delivery of personalized risk prediction at time of diagnostic imaging.

Project description:BACKGROUND:The limitations of existing HIV risk prediction tools are a barrier to implementation of pre-exposure prophylaxis (PrEP). We developed and validated an HIV prediction model to identify potential PrEP candidates in a large health-care system. METHODS:Our study population was HIV-uninfected adult members of Kaiser Permanente Northern California, a large integrated health-care system, who were not yet using PrEP and had at least 2 years of previous health plan enrolment with at least one outpatient visit from Jan 1, 2007, to Dec 31, 2017. Using 81 electronic health record (EHR) variables, we applied least absolute shrinkage and selection operator (LASSO) regression to predict incident HIV diagnosis within 3 years on a subset of patients who entered the cohort in 2007-14 (development dataset), assessing ten-fold cross-validated area under the receiver operating characteristic curve (AUC) and 95% CIs. We compared the full model to simpler models including only men who have sex with men (MSM) status and sexually transmitted infection (STI) positivity, testing, and treatment. Models were validated prospectively with data from an independent set of patients who entered the cohort in 2015-17. We computed predicted probabilities of incident HIV diagnosis within 3 years (risk scores), categorised as low risk (<0·05%), moderate risk (0·05% to <0·20%), high risk (0·20% to <1·0%), and very high risk (?1·0%), for all patients in the validation dataset. FINDINGS:Of 3?750?664 patients in 2007-17 (3?143?963 in the development dataset and 606?701 in the validation dataset), there were 784 incident HIV cases within 3 years of baseline. The LASSO procedure retained 44 predictors in the full model, with an AUC of 0·86 (95% CI 0·85-0·87) for incident HIV cases in 2007-14. Model performance remained high in the validation dataset (AUC 0·84, 0·80-0·89). The full model outperformed simpler models including only MSM status and STI positivity. For the full model, flagging 13?463 (2·2%) patients with high or very high HIV risk scores in the validation dataset identified 32 (38·6%) of the 83 incident HIV cases, including 32 (46·4%) of 69 male cases and none of the 14 female cases. The full model had equivalent sensitivity by race whereas simpler models identified fewer black than white HIV cases. INTERPRETATION:Prediction models using EHR data can identify patients at high risk of HIV acquisition who could benefit from PrEP. Future studies should optimise EHR-based HIV risk prediction tools and evaluate their effect on prescription of PrEP. FUNDING:Kaiser Permanente Community Benefit Research Program and the US National Institutes of Health.

Dataset Information

Investigating Flow State and Cardiac Pre-ejection Period During Electronic Gaming Machine Use.

Publications

Investigating Flow State and Cardiac Pre-ejection Period During Electronic Gaming Machine Use.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets