Evidence map›Paper›PMID 42821586›Full record

ArticlePloS one2026

Impact of social media platforms excessive use on students performance in higher education institutions: A mathematical model approach.

Komi Afassinou, Narcisse Roland Loufouma Makala, Ousmane Koutou, Richard O Ocaya, Maba Boniface Matadi

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Komi AfassinouDepartment of Mathematics and Applied Mathematics, University of the Free State, QwaQwa Campus, Phuthaditjhaba, South Africa.ORCID https://orcid.org/0000-0002-5610-1076
Narcisse Roland Loufouma MakalaDepartment of Mathematics and Applied Mathematics, University of the Free State, QwaQwa Campus, Phuthaditjhaba, South Africa.ORCID https://orcid.org/0000-0002-2408-6844
Ousmane KoutouCentre Universitaire Polytechnique de Kaya, Université Joseph KI-Zerbo, Laboratoire de Mathématiques et Informatique, Ouagadougou, Burkina Faso.
Richard O OcayaDepartment of Physics, University of the Free State, QwaQwa Campus, Phuthaditjhaba, South Africa.ORCID https://orcid.org/0000-0002-5925-588X
Maba Boniface MatadiDepartment of Mathematical Sciences, University of Zululand, KwaDlangezwa, South Africa.ORCID https://orcid.org/0000-0002-4252-094X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article develops a compartmental mathematical model to investigate the impact of excessive social media use on the academic performance of higher education students. Students who are heavily subscribed are classified 'infected or addicted' individuals. Their influence on the other naive non-subscribers leading them to become one and by self-curiosity are defined as direct and indirect transmission routes, respectively. The model incorporates two primary control interventions: early awareness programs and continuous regulation of accessibility. Qualitative and numerical analyses of the model dynamics are thus carried out investigating the effectiveness and the benefits of these control measures. The results suggest that a combined strategy, implementing both control interventions at optimal efficacy levels, significantly reduces the 'addiction' rate, thereby fostering a healthier academic environment and promising a prospective academic performance from the students.

Indexed as

Academic PerformanceModels, TheoreticalSocial MediaStudentsAcademiaDigital MediaHumansMedia ExposureUniversities

Identifiers

PMID42821586
PMCPMC13630262

What OpenQuestion holds

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Registered trials

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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.