Longitudinal Relationships between Nomophobia, Addictive Use of Social Media, and Insomnia in Adolescents
Bibliographic record
Abstract
(1) Background: Temporal relationships between nomophobia (anxiety related to ‘no mobile phone phobia’), addictive use of social media, and insomnia are understudied. The present study aimed to use a longitudinal design to investigate temporal relationships between nomophobia, addictive use of social media, and insomnia among Iranian adolescents; (2) Methods: A total of 1098 adolescents (600 males; 54.6%; age range = 13 to 19) were recruited from 40 randomly selected classes in Qazvin, Iran. They completed baseline assessments. The same cohort was invited to complete three follow-up assessments one month apart. Among the 1098 adolescents, 812 (400 males; 49.3%; age range = 13 to 18) completed the baseline and three follow-up assessments. In each assessment, the participants completed three questionnaires, including the Nomophobia Questionnaire (NMP-Q), Bergen Social Media Addiction Scale (BSMAS), and Insomnia Severity Index (ISI); (3) Results: Multilevel linear mixed-effects regression analyses showed that participants demonstrated increased insomnia longitudinally over 3 months (B = 0.12 and 0.19; p = 0.003 and <0.001). Insomnia was associated with nomophobia (B = 0.20; p < 0.001) and addictive use of social media (B = 0.49; p < 0.001). Nomophobia and addictive use of social media interacted with time in associations with insomnia as demonstrated by significant interaction terms (B = 0.05; p < 0.001 for nomophobia; B = 0.13; p < 0.001 for addictive use of social media); (4) Conclusions: Both nomophobia and addictive use of social media are potential risk factors for adolescent insomnia. The temporal relationship between the three factors suggests that parents, policymakers, and healthcare providers may target reducing nomophobia and addictive use of social media to improve adolescents’ sleep.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".