Previous research has demonstrated the utility of using lag schedules of reinforcement to increase response variability of children with autism. However, little research has evaluated whether the lag schedule promotes variability from within an already-established repertoire or expands the current repertoire by promoting the use of new responses (i.e., those not previously demonstrated). Thus, the purpose of the current study was to evaluate the extent to which lag schedules of reinforcement produced already-established intraverbal responses or novel responses for 3 children with autism. Results showed that lag schedules alone were sufficient to increase the number of different responses emitted for 2 participants, whereas brief variability training was needed for 1 participant. Further, some participants emitted novel responses throughout the experiment, suggesting that lag schedules may be an effective method for expanding a response class.
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http://dx.doi.org/10.1002/jaba.271 | DOI Listing |
Augment Altern Commun
December 2024
Department of Special Education, College of Education, King Saud University, Riyadh, Saudi Arabia.
Request training can produce highly repetitive requests unless the setting is organized to encourage request diversity, particularly for individuals who depend on speech-generating devices (SGDs). Previous studies have shown that request training utilizing a lag schedule and progressive-time delay led to an increase in variability among children diagnosed with autism spectrum disorder (ASD). The impact of lag schedules on augmented requesting remains to be seen.
View Article and Find Full Text PDFNutrients
November 2024
Department of Preventive Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
Social jetlag, which refers to the misalignment between biological rhythms and social schedule, is linked to an increased risk of metabolic diseases. This cross-sectional study investigated the relationship between social jetlag and workers' dietary quality. : This secondary data analysis included a sample of workers from the Korea National Health and Nutrition Examination Survey ( = 11,430).
View Article and Find Full Text PDFPLoS One
December 2024
College of Computer Science and Software Engineering, Hohai University, Nanjing, China.
Accurate medium- to long-term runoff forecasting is of great significance for flood control, drought mitigation, comprehensive water resource management, and ecological restoration. However, runoff formation is a complex process influenced by various natural and anthropogenic factors, resulting in nonlinearity, nonstationarity, and long prediction periods, which complicate forecasting efforts. Traditional statistical models, which primarily focus on individual runoff sequences, struggle to integrate multi-source data, limiting their predictive accuracy.
View Article and Find Full Text PDFJ Sleep Res
December 2024
Department of Social Nutrition - Institute of Nutrition, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
We investigated the influence of chronotype on eating habits among adolescents attending schools with morning (07:00 hours-12:00 hours) or afternoon (13:00 hours-18:00 hours) shifts, hypothesizing that incompatibility of study schedules may be related to dietary choices. In this cross-sectional study, 734 adolescents (aged 14-19 years) completed an online questionnaire covering eating habits, chronotype, sleep duration, social jet lag, socioeconomic data, physical activity and anthropometric measures. Odds ratios with 95% confidence intervals were estimated using adjusted logistic regression, stratified by study shift.
View Article and Find Full Text PDFJ Sleep Res
December 2024
College of Nursing, University of Iowa, Iowa City, Iowa, USA.
The accurate estimation of circadian phase in the real-world has a variety of applications, including chronotherapeutic drug delivery, reduction of fatigue, and optimal jet lag or shift work scheduling. Recent work has developed and adapted algorithms to predict time-consuming and costly laboratory circadian phase measurements using mathematical models with actigraphy or other wearable data. Here, we validate and extend these results in a home-based cohort of later-life adults, ranging in age from 58 to 86 years.
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