The Multidimensional State Boredom Scale (MSBS) is a promising new self-report measure of state boredom. Two condensed versions of the scale have also been introduced. This study helped explore the psychometric qualities of these scales, using a large sample of Australian adults ( N = 1,716), as well as two smaller samples ( N = 199 and N = 422). Data analyses indicated strong convergent validity and very high internal consistency for the scales. Test-retest reliability over a 6- to 8-day period was moderately high. Confirmatory factor analyses of the MSBS authors' suggested factor structure indicated good fit for this model. However, some of the data analyses raise questions as to whether the scale includes meaningful subfactors. Overall, the MSBS (and Short Form) is recommended for researchers who wish to assess state boredom.
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http://dx.doi.org/10.1177/1073191116662910 | DOI Listing |
Front Psychol
December 2024
Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, United States.
Boredom and curiosity are common everyday states that drive individuals to seek information. Due to their functional relatedness, it is not trivial to distinguish whether an action, for instance in the context of a behavioral experiment, is driven by boredom or curiosity. Are the two constructs opposite poles of the same cognitive mechanism, or distinct states? How do they interact? Can they co-exist and complement each other? Here, we systematically review similarities and dissimilarities of boredom and curiosity with respect to their subjective experience, functional role, and neurocognitive implementation.
View Article and Find Full Text PDFJ Sex Marital Ther
December 2024
Medical School, University of Minnesota, Minneapolis, MN, USA.
This study presents the development and validation of the Sexual Boredom Inventory (SBI), a 6-item measure assessing sexual boredom as a temporary, context-dependent state. Initial items were drafted from data obtained through qualitative analysis, and the SBI was tested using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) with a sample of adults reporting on their sexual experiences in the past month. A single-factor model was found to be the best fit, with four items dropped during refinement.
View Article and Find Full Text PDFJ Neuroeng Rehabil
December 2024
Scientific Institute, IRCCS "E. Medea", Bosisio Parini, Italy.
Background: Robot-Assisted Gait Rehabilitation (RAGR) is an established clinical practice to encourage neuroplasticity in patients with neuromotor disorders. Nevertheless, tasks repetition imposed by robots may induce boredom, affecting clinical outcomes. Thus, quantitative assessment of engagement towards rehabilitation using physiological data and subjective evaluations is increasingly becoming vital.
View Article and Find Full Text PDFData Brief
December 2024
College of Educational Science and Technology, Zhejiang University of Technology, Hangzhou, China.
It is challenging for teachers to monitor each student's emotional state in real-time, making personalized learning difficult to achieve. Previous emotion recognition methods, such as support vector machines, are limited by technology and fail to meet practical application requirements. However, the development of deep learning technology offers new solutions for facial expression recognition, which makes emotional interaction and personalized support in education possible.
View Article and Find Full Text PDFJ Appl Gerontol
December 2024
The Gerontology Institute, Georgia State University, Atlanta, GA, USA.
Early in our longitudinal qualitative study on meaningful engagement and quality of life among assisted living (AL) residents with dementia, researchers observed differences between the activities scheduled on monthly engagement calendars and those taking place. Yet, we were unable to identify any research examining such deviations or their implications. Thus, drawing on data from three diverse AL communities studied over a one-year period, we aim to: 1) examine deviations in scheduled engagement programming; 2) identify influential factors; and 3) understand resident outcomes.
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