Problematic smartphone use: The role of reward processing, depressive symptoms and self-control

West, Robert; Ash, Carl; Dapore, Ashley; Kirby, Bridget; Malley, Kaitlyn; Zhu, Shasha

doi:10.1016/j.addbeh.2021.107015

Cited by 18 publications

(8 citation statements)

References 31 publications

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“…Among the female group, the cross-lagged effect coefficient from depression to SA was relatively larger than the reverse effect, suggesting that depressive symptoms may worsen SA to a greater extent. This echoes the previous findings, which suggested that SA or similar conditions can be caused by depressive symptoms ( 14 , 51 ). Studies have found that depressed individuals derive less pleasure from social interactions and have increased sensitivity to social rejection ( 52 , 53 ), but have a tendency to use social media more frequently ( 54 ).…”

Section: Discussionsupporting

confidence: 90%

A bidirectional association between smartphone addiction and depression among college students: A cross-lagged panel model

Zhang

Guo

Wang

et al. 2023

Front. Public Health

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BackgroundSmartphone addiction (SA) is associated with adverse consequences, especially for freshmen. Evidence indicates that SA is associated with depression, and it is necessary to conduct a longitudinal study to explore the association further.MethodsSA (measured by the Smartphone Addiction Scale-Short Version) and depression (measured by the Zung's Self-Rating Depression Scale) among 1,186 freshmen were surveyed at baseline and a respective 12-month follow-up for each participant. The application of a cross-lagged panel model approach (CLPM) revealed an association between SA and depression after adjusting for demographic variables.ResultsThe CLPM results showed a significant path from baseline SA to follow-up depression (β = 0.08, P < 0.001) and a significant path from baseline depression to follow-up SA (β = 0.08, P < 0.001). Compared with the overall cross-lagged model, the cross-lagged coefficient of the path from baseline SA to follow-up depression increased in the female group (β = 0.10, P = 0.015), and the cross-lagged coefficient of the path from baseline depression to follow-up SA also increased significantly (β = 0.15, P < 0.001). In contrast, the cross-lagged model in the male group showed no predictive effect between SA and depression (P > 0.05).ConclusionsThe current study showed a significant bidirectional association between smartphone addiction and depression among freshmen, but only in the female population.

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Section: Discussionsupporting

confidence: 90%

A bidirectional association between smartphone addiction and depression among college students: A cross-lagged panel model

Zhang

Guo

Wang

et al. 2023

Front. Public Health

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“…They always give in to temptation and then delay sleep during the COVID-19 outbreak [ 37 ]. Self-control plays an essential role in PSU [ 38 ] and bedtime procrastination [ 39 ], which facilitates interpreting the relationship between boredom, bedtime procrastination, and phubbing. This study aimed to analyze the mediating effects of self-control and bedtime procrastination on this relationship.…”

Section: Introductionmentioning

confidence: 99%

Boredom Proneness on Chinese College Students’ Phubbing during the COVID-19 Outbreak: The Mediating Effects of Self-Control and Bedtime Procrastination

Meng

Xuan

2023

Journal of Healthcare Engineering

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Objective. To analyze the relationship between boredom proneness and phubbing among Chinese college students and examine how self-control and bedtime procrastination mediate this relationship during the COVID-19 outbreak. Methods. A total of 707 Chinese college students were voluntarily surveyed. They completed the Generic Scale of Phubbing (GSP), Short Boredom Proneness Scale (SBPS), Bedtime Procrastination Scale (BPS), and Self-Control Scale (SCS). Results. (1) The results revealed that men scored higher on boredom than women. (2) The analysis revealed significant associations between each of the variables. Boredom proneness was positively correlated with bedtime procrastination (r = 0.318; P < 0.001 ) and phubbing (r = 0.418; P < 0.001 ) and negatively correlated with self-control (r = −0.518; P < 0.001 ). (3) Mediation analysis suggested that self-control and bedtime procrastination mediate the relationship between boredom proneness and phubbing (effect of self-control = 0.094, P < 0.001 , 95% CI [0.062∼0.128]; effect of bedtime procrastination = 0.025, P < 0.001 , 95% CI [0.011∼0.042]; and effect of self-control and bedtime procrastination = 0.032, P < 0.0 01, 95% CI [0.020∼0.046]). Conclusion. Self-control and bedtime procrastination mediate the association between boredom proneness and phubbing among Chinese college students during the COVID-19 pandemic.

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“…Problematic smartphone use has been associated with a variety of outcomes, aside from those explored here. Those include personality traits, such as impulsiveness and the ability to self-control [ 64 , 67 , 68 ]. An individual’s prevalent attachment style can also be related to her use of the smartphone; secure attachment prevents excessive use [ 69 , 70 ], whereas avoidant and insecure attachment increases excessive use [ 71 , 72 , 73 ], in what Billieux and colleagues [ 74 ] described as the reassurance-seeking pathway to problematic smartphone use.…”

Section: Discussionmentioning

confidence: 99%

Difficulties in Establishing “Truth” Conditions in the Assessment of Addictive Smartphone Use in Young Adults

García-Manglano

López-Madrigal

Sádaba

et al. 2021

IJERPH

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The smartphone revolution has placed powerful, multipurpose devices in the hands of youth across the globe, prompting worries about the potential negative consequences of these technologies on mental health. Many assessment tools have been created, seeking to classify individuals into problematic and non-problematic smartphone users. These are identified using a cutoff value: a threshold, within the scale range, at which higher scores are expected to be associated with negative outcomes. Lacking a clinical assessment of individuals, the establishment of this threshold is challenging. We illustrate this difficulty by calculating cutoff values for the Short Version of the Smartphone Addiction Scale (SAS-SV) in 13 Spanish-speaking samples in 11 countries, using common procedures (i.e., reliability, validity, ROC methodology). After showing that results can be very heterogeneous (i.e., they lead to diverse cutoff points and rates of addiction) depending on the decisions made by the researchers, we call for caution in the use of these classifications, particularly when researchers lack a clinical definition of true addiction—as is the case with most available scales in the field of behavioral addictions—which can cause an unnecessary public health alert.

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Problematic smartphone use: The role of reward processing, depressive symptoms and self-control

Cited by 18 publications

References 31 publications

A bidirectional association between smartphone addiction and depression among college students: A cross-lagged panel model

A bidirectional association between smartphone addiction and depression among college students: A cross-lagged panel model

Boredom Proneness on Chinese College Students’ Phubbing during the COVID-19 Outbreak: The Mediating Effects of Self-Control and Bedtime Procrastination

Difficulties in Establishing “Truth” Conditions in the Assessment of Addictive Smartphone Use in Young Adults

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