ArticleScientific reports2025
Facial emotion based smartphone addiction detection and prevention using deep learning and video based learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smartphone addiction among students has emerged as a critical issue, negatively impacting their academic performance, emotional well-being, and social behavior. This paper introduces the Theory of Mind integrated with Video Modelling (TMVM) framework, a novel deep learning-based approach aimed at recognizing and mitigating smartphone addiction. The TMVM framework leverages Theory of Mind AI to analyze students' facial emotions via smartphone cameras while watching videos. Based on detected emotions such as happiness, sadness, or anger, the system dynamically shuffles motivational videos using advanced algorithms like Fisher-Yates and Durstenfeld shuffling techniques to promote behavioral change. The framework also incorporates Behavior Parameters (BHP) evaluation, grounded in the Social Identity Model of Deindividuation Effects (SIDE) theory, to assess key behavioral metrics such as social identity, self-awareness, anonymity, responsibility, and accountability. Additionally, face emotion detection algorithms tuned with MnasNet-Teaching Learning Based Optimization (TLBO) and Convolution Neural Networks (CNN)-Cuckoo Search Optimization (CSO) are employed for accurate emotion recognition. Experimental results demonstrate significant improvements in students' behavior and reductions in smartphone usage post-intervention. The TMVM system achieves high accuracy in emotion detection and behavioral outcome prediction while fostering engagement in school and social activities. . TMVM method is tested in 750 students with low BHP and evaluated the behavioural parameters. After the intervention of TMVM the students showed more than 90% improvement in their BHP parameters. A paired sample t-test revealed notable reductions in mean scores from pre- to post-intervention across all measured dimensions. Social identity decreased from 4.07 to 2.21 (t(55) = 16.125, p < 0.001), anonymity from 4.11 to 2.01 (t(55) = 15.699, p < 0.001), self-awareness from 3.95 to 1.93 (t(55) = 15.103, p < 0.001), loss of individuality from 4.04 to 2.07 (t(55) = 13.364, p < 0.001), while sense of responsibility and accountability improved with mean differences of 1.18 and 2.0, respectively, both statistically significant at p < 0.001.The results showed 85% improvement in students' knowledge and attitudes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.