Insecure Attachment and Technology Addiction Among Young Adults: The Mediating Role of Impulsivity, Alexithymia, and General Psychological Distress

Remondi, Chiara; Compare, Angelo; Tasca, Giorgio A.; Greco, Andrea; Pievani, Luca; Poletti, Barbara; Brugnera, Agostino

doi:10.1089/cyber.2019.0747

Cited by 33 publications

(35 citation statements)

References 40 publications

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“…In view of the harmful effects of SA on individual physical and mental health, several scholars have actively explored the influential factors of SA and found that alexithymia, anxious and avoidant attachment styles, and subjective well-being are important factors affecting SA ( Remondi et al, 2020 ; Satici and Deniz, 2020 ; Bermingham et al, 2021 ; Xiao et al, 2021 ). Alexithymia refers to the difficulties that an individual encounters when identifying and describing their own and others’ emotions, and distinguishing between feelings and bodily sensations and the externally oriented cognitive styles ( Taylor et al, 1999 ).…”

Section: Introductionmentioning

confidence: 99%

Correlations between smartphone addiction and alexithymia, attachment style, and subjective well-being: A meta-analysis

et al. 2022

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BackgroundSmartphone addiction (SA) has become a social problem that affects peoples’ quality of life and is frequently reported to be correlated with alexithymia, avoidant or anxious attachment styles, and subjective well-being. This study aimed to investigate the relationship between SA and alexithymia, attachment style, and subjective well-being.MethodsA meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The following electronic databases were searched: PubMed, Web of Science, Embase, PsycINFO, PsycArticles, China National Knowledge Infrastructure (CNKI), WANFANG DATA, and Chongqing VIP Information Co., Ltd. (VIP). Stata 16.0 was used to analyze the overall effect and test the moderating effect.ResultsOne hundred and ten studies were included, involving a total of 96,680 participants. SA had a significantly high positive correlation with alexithymia (r = 0.40), attachment anxiety (r = 0.37), and negative emotions (r = 0.31), and a low positive correlation with attachment avoidance (r = 0.17). In addition, there was a high negative correlation between SA and subjective well-being (r = –0.33) and a low negative correlation between SA, life satisfaction (r = –0.17), and positive emotions (r = –0.18). A moderation analysis revealed that age significantly moderated the relationship between SA and positive emotions. The tools for measuring SA significantly moderated the relationship between SA, alexithymia, attachment anxiety, and subjective well-being. Meanwhile, subjective well-being measurement tools significantly moderated the relationships between SA, subjective well-being, and negative emotions.ConclusionSA was closely related to alexithymia, attachment style, and subjective well-being. In the future, longitudinal research can be conducted to better investigate the dynamic changes in the relationship between them.Systematic review registration[www.crd.york.ac.uk/PROSPERO/], identifier [CRD42022334798].

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Section: Introductionmentioning

confidence: 99%

Correlations between smartphone addiction and alexithymia, attachment style, and subjective well-being: A meta-analysis

et al. 2022

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“…Based on Table 3, the effect size of studies number 3, 9, 10, and 11 is not significant. The study by Remondi et al (21) had a significant effect size and ranked first.…”

Section: Resultsmentioning

confidence: 96%

The Effect of Impulsivity on Addition and Addictive Tendencies: A Meta-analysis

Dezhkam

Bahri

Keshi

2022

J Health Rep Technol

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Background: Immature behaviors, including impulsivity and lack of control lead to personal and social pathologies, such as addiction. Methods: This meta-analysis study aimed to evaluate the effect of impulsivity on addition and addictive tendencies. This comprehensive interdisciplinary quantitative meta-analysis integrated the research findings regarding the effect of impulsivity on addiction and addictive tendencies based on secondary data. A checklist was developed for collecting data based on the article title, author specifications, publication year, setting, research type, hypothesis, data collection instrument, statistical population, sample size, significance level, and probability value to avoid bias in the study selection protocol. Results: The results of studies were divided into nine databases to calculate the effect size, and finally, 11 studies were analyzed. The descriptive data analysis and effect size calculation were performed in the Comprehensive Meta-Analysis Software (CMA) version 2.0. Based on the fixed-effects model, 0.153 of the studies contributed significantly to the actual fixed effect sizes. The difference in effect sizes was due to sampling error. The random-effects model (0.202) suggested the independence of the studies (P < 0.001) with moderate robustness. Conclusions: According to the results, the behavioral construct of impulsivity significantly and positively affected addictive behaviors and tendencies. Therefore, the mediating effect of impulsivity, promote resilience, and train protective strategies for individuals prone to addiction should be controlled to improve mental health quality and develop social, communication, and life skills.

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“…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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Insecure Attachment and Technology Addiction Among Young Adults: The Mediating Role of Impulsivity, Alexithymia, and General Psychological Distress

Cited by 33 publications

References 40 publications

Correlations between smartphone addiction and alexithymia, attachment style, and subjective well-being: A meta-analysis

Correlations between smartphone addiction and alexithymia, attachment style, and subjective well-being: A meta-analysis

The Effect of Impulsivity on Addition and Addictive Tendencies: A Meta-analysis

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

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