Time Spent on Mobile Apps Matters: A Latent Class Analysis of Patterns of Smartphone Use among Adolescents
Open accessAbstract
The aims of the present study are: (1) to determine classes of adolescents with homogeneous patterns of smartphone or social media use; and (2) to examine the level of distress across the empirically derived profiles. Three hundred and forty adolescents (Mage = 15.61, SD = 1.19; 38.2% females) participated in a cross-sectional survey. Participants provided objective trace data on time spent on smartphones and applications, as well as self-reported social media addiction, social media use intensity, online social comparison, emotion dysregulation, and psychological distress. Latent class analysis (LCA) with total smartphone use categorized participants into three classes. Participants in Class 3 (19%) showed a more impaired functioning profile, with a tendency towards social media addiction and greater levels of distress. LCAs with the amount of time devoted to specific applications are more heterogeneous, and results showed that heavy use of social media apps was not consistently connected to the most impaired psychosocial profiles. Although the amount of mobile screen time can be a characteristic of problematic users, the link between social media usage and an adolescent’s psychological characteristics is mixed. More research is needed to explore the interplay between mobile screen time and social media usage among adolescents.
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Ten studies differentiated the population between active and passive users, while only one conducted a content analysis of posts, hashtags, or videos.
“Given that, ten studies differentiated the population between active and passive users [42, 67, 70, 72, 74–79], while only one conducted a content analysis of the posts, hashtags, or videos [78].”
From Scrolling through adolescence: a systematic review of the impact of TikTok on adolescent mental health (2025) · cites this paper as [67]
The Methods (sections 2.2–2.3; page unavailable) describe measuring adolescents’ app use and using latent class analysis to group participants by usage patterns. The paper distinguishes usage levels, not active from passive users, and describes no content analysis of posts, hashtags, or videos. It therefore does not match the study attributed to this citation. (LLM, conf=0.84)
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Experimental — accuracy not yet measuredEleven studies used self-developed questionnaires for outcome evaluation, of which only six paired it with standardized measures.
“Eleven studies used self-developed questionnaires for outcome evaluation [42, 67–72, 74, 80–82], of which only six paired it with standardized measures [67–69, 71, 72, 80, 81].”
From Scrolling through adolescence: a systematic review of the impact of TikTok on adolescent mental health (2025) · cites this paper as [67]
Section 2.2 (page unavailable) describes questionnaire items on smartphone use alongside established scales, including the YP-CORE measure of psychological distress. The paper therefore supports using study-specific questions together with standardized measures, but it does not establish that the study-specific questions were used for outcome evaluation; §2.3 identifies CORE-YP as the distress outcome measure. The counts of eleven and six are the citing authors’ synthesis, not findings this individual study could establish. (LLM, conf=0.77)
Experimental — accuracy not yet measuredOther studies showed that the variable "time" is not sufficiently sensitive to predict PTU risk as it does not account for individual factors involved in dysfunctional TikTok usage patterns.
“Conversely, other studies showed that the variable “time” is not sufficiently sensitive to predict PTU risk as it does not account for individual factors (e.g., emotion regulation abilities) involved in dysfunctional TikTok usage patterns [67].”
From Scrolling through adolescence: a systematic review of the impact of TikTok on adolescent mental health (2025) · cites this paper as [67]
The TikTok Results (§3.2) identify groups with similar time spent on TikTok but different emotion-dysregulation and social-media-addiction scores, supporting the point that time alone misses relevant individual differences. The Methods (§2.2) measured general social media addiction, not problematic TikTok use specifically, and the study did not test prediction of PTU risk. (LLM, conf=0.85)
Experimental — accuracy not yet measuredFour of the included studies assessed wellbeing indicators, like creativity, innovation, life satisfaction, and health habits, while sixteen assessed mental disorder indicators such as depression, anxiety, stress or body image issues.
“Four of the studies [68, 75, 79, 80] assessed wellbeing indicators, like creativity, innovation, life satisfaction, and health habits, while sixteen assessed mental disorder indicators such as depression, anxiety, stress or body image issues [42, 67, 68, 70–73, 76–79, 81–85].”
From Scrolling through adolescence: a systematic review of the impact of TikTok on adolescent mental health (2025) · cites this paper as [67]
Among included studies, only four reported data about total daily screen time, and four detailed the amount of time spent on TikTok.
“Among the included studies, only four reported data about total daily screen time [67–70], and four detailed the amount the time spent on TikTok [42, 67, 71, 72].”
From Scrolling through adolescence: a systematic review of the impact of TikTok on adolescent mental health (2025) · cites this paper as [67]
Fortunato et al. revealed that, despite significant differences in time spent on the app (2 h vs. 9 h), groups did not differ in psychosocial characteristics.
“Fortunato et al. [67] divided their sample into groups based on time spent on TikTok and matched them with psychosocial characteristics such as social comparison, difficulties in emotional regulation, and psychological distress, revealing that, despite significant differences in time spent on the app (2 h vs. 9 h), groups did not differ in psychosocial characteristics.”
From Scrolling through adolescence: a systematic review of the impact of TikTok on adolescent mental health (2025) · cites this paper as [67]
Higher social anxiety and social comparison tendencies, as well as inadequate emotion regulation strategies, may play a crucial role in placing users at different susceptibility risk towards SM.
“Higher social anxiety and social comparison tendencies, as well as inadequate emotion regulation strategies, may play a crucial role in this regard [67].”
From Scrolling through adolescence: a systematic review of the impact of TikTok on adolescent mental health (2025) · cites this paper as [67]
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- Authors
- Fortunato, L., Lo Coco, G., Teti, A., Bonfanti, R. C., & Salerno, L.
- Affiliations
- University of Palermo (IT)
- Department of Psychology, Educational Science and Human Movement, University of Palermo, Viale delle Scienze, Edificio 15, 90128 Palermo, Italy
- Corresponding author
- Salerno, Laura
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2023-07-25
- Volume
- 20
- Issue
- 15
- Pages
- 6439
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC10418827
- OpenAlex
- W4385278269
- Cited by
- 20
- References
- 74
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 6 · 2023: 1
- FWCI
- 8.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Education › Child Development and Digital Technology
- Social Sciences › Psychology › Clinical Psychology › Sexuality, Behavior, and Technology
- Keywords
- adolescent smartphone use, social media use, social media addiction, psychological distress, emotional dysregulation, problematic media use, user profiles
- MeSH terms
- Smartphone, Latent Class Analysis, Adolescent, Adolescent Behavior, Cross-Sectional Studies, Female, Humans, Male, Time Factors, Behavior, Addictive, Social Media, Mobile Applications
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