Heterogeneity of smartphone impact on everyday life and its relationship with personality and psychopathology: A latent profile analysis

Problematic smartphone use in a representative sample of US adults: Prevalence and predictors

The exponential growth in worldwide smartphone adoption has led to mounting concerns about problematic smartphone use (PSU), with some studies of variably defined PSU yielding prevalence rates as high as 20 to 30%. The present study aims to (a) estimate the prevalence of PSU while emphasizing functional impairment, and (b) examine the degree to which a set of demographic, contextual, behavioral and mental health variables can predict PSU. A representative U.S. sample of 1,989 participants completed an online survey using a cross-sectional design aimed at examining PSU. Data were analyzed using two different but complementary machine learning algorithms. The results indicate a PSU prevalence of 0.75% to 1.2 % when a functional impairment criterion is required. The most important predictors of PSU are related to smartphone use behavior, followed by contextual factors (being a parent of under-age children, increase in use during the COVID-19 pandemic), age and some psychopathological factors. Overall, the study suggests that the prevalence of functionally impairing PSU may be much lower than what has been reported based on less stringent definitions. The results highlight the necessity to account for impact on key spheres of functioning in diagnosing PSU to avoid pathologizing a ubiquitous behavior that may be intensive but not necessarily pathological. They also open up relevant perspectives for the prevention of PSU.

Social Aspects of Problematic Smartphone Use: Development and Validation of the PSSNUS and Mental Health Implications for Spanish Youth

Purpose

Addressing the complex pattern of digital behaviors and interactions among youth, this research introduces a novel comprehensive scale, the Problematic Smartphone and Social Network Use Scale (PSSNUS), meticulously developed and validated across five studies.

Methods

Using a mixed-methods approach across five stages, including focus groups (Study 1; n = 31), cognitive interviews (Study 2; n = 16), exploratory factor analysis (Study 3; n = 316), and expert panel (Study 4; n = 4), this procedure reduced 58 initial items to an 18-item scale. Subsequently, a confirmatory factor analysis and further analyses (Study 5; n = 355) examined the factorial structure's replicability, reliability, and validity of the scale.

Results

The PSSNUS manifested as a reliable (ω ranging 0.70-0.89) instrument, comprising a structure with five key factors (x2 = 173.994, df = 125, p = 0.002, CFI = 0.994, CFI = 0.993, SRMR = 0.039, and RMSEA = 0.027) that capture both individual and social aspects of this construct: Psychological dependence (PD), Online interaction preference (OIP), Digital recognition desire (DRD), Social networks control seeking (SNCS), and Productivity loss (PL). Minor gender differences in both the DRD factor and in the overall score were found, with females scoring higher. The PSSNUS showed convergent and divergent validity through significant but modest correlations with daily smartphone usage hours, procrastination, emotional intelligence and mental health symptomatology (anxiety, depression and stress). This measure further exhibited incremental validity, controlling for other possible predictors, regarding anxiety, depression, and stress (6.5, 11.5 and 7.5% of additional variance explained), highlighting the DRD dimension's unique predictive power.

Conclusion

Emerging as a solid instrument, the PSSNUS broadens the concept of problematic smartphone use among young Spaniards to include social functioning aspects, serving as an interesting tool for those aiming to explore further the adverse influence of digital media on youth.

Depression and stress are associated with latent profiles of problematic social media use among college students

Background

The previous literature has demonstrated that depression, anxiety, and stress are significant predictors of problematic social media use. However, the manifestation of problematic social media use varies, and the predictive relationship between depression, anxiety, and stress with different subgroups of problematic social media use remains unclear. The aim of this research was to evaluate latent subgroups of problematic social media use among college students and to investigate the impact of depression, anxiety, and stress on these latent subgroups.

Methods

A survey was carried out among college students in China using a cross-sectional approach. A total of 955 participants were included, with a mean age of 19.50 ± 1.22 years. Participants completed questionnaires containing the Bergen Social Media Addiction Scale (BSMAS) and the Depression, Anxiety, and Stress Scale-21 (DASS-21). The study employed latent profile analysis (LPA) to investigate latent subgroups of Chinese college students with problematic social media use, and a robust three-step approach was used to develop predictive regression mixed models of depression, anxiety, and stress on latent subgroups.

Results

Problematic social media use of Chinese college students can be categorized into four latent subgroups, namely, the high-risk group, the moderate-risk with pleasure group, the moderate-risk with compulsion group, and the low-risk group. The regression model showed that there was a significant difference between the high-risk group and the low-risk group on the stress scale. There was a significant difference between the moderate-risk with pleasure group and the moderate-risk with compulsion group on the depression scale.

Conclusion

Problematic social media use is heterogeneous, with depression and stress being potentially key factors influencing problematic social media use. Depression would make college students more likely to be moderate-risk with compulsion problematic social media users than moderate-risk with pleasure problematic social media users, and stress would make college students more likely to be high-risk problematic social media users than low-risk problematic social media users.

Breastfeeding Apps: A Descriptive Report

BACKGROUND

Women are increasingly using breastfeeding apps to facilitate and organize breastfeeding; however, no study has so far focused on maternal psychopathological risk and on the quality of dyadic exchanges in this field.

AIM AND METHODS

This preliminary, descriptive study aimed at evaluating levels of psychopathological symptoms (through the SCL-90/R) and for the quality of the interactions they have with their children during feeding (through the SVIA) in mothers who use breastfeeding apps with different grades of engagement.

RESULTS

Data analyses showed that mothers with a mild use of the apps present a higher quality of dyadic interactions during feeding and lower psychopathological risk.

CONCLUSIONS

The cross-sectional and descriptive nature of this study does not allow any causal conclusions. However, results suggest that the higher the engagement and use of breastfeeding apps, the lower the quality of feeding interactions and the higher the maternal psychopathological risk.

Determination of the cutoff point for Smartphone Application-Based Addiction Scale for adolescents: a latent profile analysis

BACKGROUNDS

The Smartphone Application-Based Addiction Scale (SABAS) is a validated 6-item measurement tool for assessing problematic smartphone use (PSU). However, the absence of established cutoff points for SABAS hinders its utilities. This study aimed to determine the optimal cutoff point for SABAS through latent profile analysis (LPA) and receiver operating characteristic curve (ROC) analyses among 63, 205. Chinese adolescents. Additionally, the study explored whether PSU screening with SABAS could effectively capture problematic social media use (PSMU) and internet gaming disorder (IGD).

METHOD

We recruited 63,205. adolescents using cluster sampling. Validated questionnaires were used to assess PSMU, IGD, and mental health (depression, anxiety, sleep disturbances, well-being, resilience, and externalizing and internalizing problems).

RESULTS

LPA identified a 3-class model for PSU, including low-risk users (38.6%, n = 24,388.), middle-risk users (42.5%, n = 26,885.), and high-risk users (18.9%, n = 11,932.). High-risk users were regarded as "PSU cases" in ROC analysis, which demonstrated an optimal cut-off point of 23 (sensitivity: 98.1%, specificity: 96.8%). According to the cutoff point, 21.1% (n = 13,317.) were identified as PSU. PSU adolescents displayed higher PSMU, IGD, and worse mental health. PSU screening effectively captured IGD (sensitivity: 86.8%, specificity: 84.5%) and PSMU (sensitivity: 84.5%, specificity: 80.2%).

CONCLUSION

A potential ideal threshold for utilizing SABAS to identify PSU could be 23 (out of 36). Employing SABAS as a screening tool for PSU holds the potential to reliably pinpoint both IGD and PSMU.