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Testing the validity of the smartphone pervasiveness scale for adolescents with self-reported objective smartphone use data.

Shobhik ChakrabortyMarco GuiTiziano GerosaLaura Marciano
Published in: Digital health (2024)
An ongoing and heated scientific debate pertains to the conceptualization and quantification of adolescents' problematic smartphone use (PSU). To address the limitations of existing surveys, the smartphone pervasiveness scale for adolescents (SPS-A) has been designed to measure the subjective frequency of smartphone usage during significant moments within daily routines. Given the weak correlations in prior literature between self-reported PSU metrics and objective use data, this study investigates the relationships between diverse self-reported objective metrics of smartphone engagement-that is duration, frequency, and count of notifications-and the SPS-A scale, employing a cohort of Swiss adolescents ( N  = 1396; M age  = 15.8, SD age  = 0.81; 59% female). The findings reveal a substantial correlation between the total objectively measured duration of smartphone engagement and the SPS-A scale ( r  = .41 for iOS users and r  = .42 for Android users). Moreover, a similar trend emerges as users are categorized by their level of objective use, with each category displaying a linear augmentation in smartphone pervasiveness levels. Instead, modest correlations emerge when considering the quantity of device unlocks and notifications. Noteworthy, no gender disparities emerged. These results add to our knowledge about the usefulness of the concept and measurement of smartphone pervasiveness: not only the SPS-A is a valid alternative to scales on "smartphone addiction" to capture non-pathological PSU, but it is also a better predictor of smartphone objective duration of use than self-reported measures. The correlation found between self-reported pervasiveness and actual use is discussed in light of the debate about the relevance of screen time in the study of PSU.
Keyphrases
  • physical activity
  • young adults
  • systematic review
  • gene expression
  • mental health
  • electronic health record
  • cross sectional
  • deep learning
  • artificial intelligence
  • big data
  • depressive symptoms