Predictors of problematic smartphone use among university students

Abstract Predictors of problematic smartphone use have been found mainly in studies on elementary and high school students. Few studies have focused on predictors related to social network and messaging apps or smartphone model. Thus, the objective of our study was to identify predictors of problematic smartphone use related to demographic characteristics, loneliness, social app use, and smartphone model among university students. This cross-sectional study involved 257 Brazilian university students who answered a smartphone addiction scale, a questionnaire about smartphone usage patterns, and the Brazilian version of the UCLA-R loneliness scale. Women, iPhone owners, and users of Instagram and Snapchat had significantly higher smartphone addiction scores. We found correlations between scores for the Brazilian version of smartphone addiction scale and the importance attributed to WhatsApp, Facebook, Instagram, and Snapchat, and the Brazilian version of the UCLA-R loneliness scale. Our hierarchical regression model predicted 32.2% of the scores of the Brazilian version of the smartphone addiction scale, with the greatest increase in predictive capability by the step that added smartphone social app importance, followed by the step that added loneliness. Adding the smartphone model produced the smallest increase in predictive capability. The theoretical and practical implications of these results are discussed.

Can a lexical decision task predict efficiency in the judgment of ambiguous sentences?

Abstract The lexicon plays a fundamental role in reading, but little is known about how it influences reading efficiency. Thus, this study seeks to identify which lexical factors in a lexical decision task are relevant in a semantic decision test. A total of 33 university students were recruited to perform a lexical decision task and a semantic decision task. The results revealed differences between the three types of words in the lexical decision task for all measures, but only in the regressive saccades for the semantic decision task. Ambiguous sentences triggered fewer regressions than sentences related to objects. The only lexical measure found to predict efficiency was average time on regular words, which predicted 24% of the efficiency. We discuss the implications of the use of a lexical decision task and the use of the inverse efficiency score as a semantic measure, and we discuss how the lexicon can predict semantic comprehension.