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. 2023 Sep 16;23(1):675.
doi: 10.1186/s12888-023-05170-4.

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

Pu Peng  1   2 , Zhangming Chen  2 , Silan Ren  3 , Yi Liu  1 , Ruini He  4 , Yudiao Liang  4 , Youguo Tan  4 , Jinsong Tang  1 , Xiaogang Chen  2 , Yanhui Liao  5
Affiliations

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

Pu Peng et al. BMC Psychiatry. .

Abstract

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.

Keywords: Internet gaming disorder; Problematic smartphone use; Problematic social media use; Smartphone Application-Based Addiction Scale.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The latent profile of PSU based on SABAS scores The X-axis represented the items of SABAS. The Y-axis represented the scores on each item of SABAS
Fig. 2
Fig. 2
The overlap of PSMU, PSU, and IGD The Venn plot was used to visualize the overlap in the positive cases of PSMU, PSU, and IGD. Note: PSU: problematic smartphone use; IGD: internet gaming disorder; PSMU: problematic social media use
See this image and copyright information in PMC

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