Smartphone Dependency and Its Impact on Emotional Fatigue: Mediated by Sleep Disturbance
Bibliographic record
Abstract
Objective: This study aimed to investigate the impact of smartphone dependency on emotional fatigue among Indian university students, with sleep disturbance examined as a potential mediating variable. Methods and Materials: The study employed a descriptive correlational design involving 433 participants selected based on the Morgan and Krejcie sampling table. Standardized tools were used to measure the variables: the Smartphone Addiction Scale–Short Version for smartphone dependency, the Pittsburgh Sleep Quality Index for sleep disturbance, and the Emotional Exhaustion subscale of the Maslach Burnout Inventory–General Survey for emotional fatigue. Data analysis included Pearson correlation using SPSS-27 to assess bivariate relationships, and Structural Equation Modeling (SEM) via AMOS-21 to test direct and indirect effects and evaluate model fit. All assumptions for correlation and SEM, including normality, linearity, and absence of multicollinearity, were confirmed prior to analysis. Findings: The results indicated that smartphone dependency was significantly correlated with both sleep disturbance (r = .51, p < .001) and emotional fatigue (r = .48, p < .001), and that sleep disturbance was significantly associated with emotional fatigue (r = .56, p < .001). SEM analysis showed that smartphone dependency had a significant direct effect on sleep disturbance (β = .51, p < .001) and emotional fatigue (β = .23, p = .005), while sleep disturbance also had a significant direct effect on emotional fatigue (β = .46, p < .001). The indirect effect of smartphone dependency on emotional fatigue via sleep disturbance was also significant (β = .23, p < .001), confirming partial mediation. The model fit indices indicated good model fit (χ²/df = 2.28, CFI = .97, RMSEA = .054). Conclusion: The findings highlight the detrimental effects of smartphone dependency on emotional fatigue, both directly and indirectly through its impact on sleep disturbance, suggesting the need for targeted interventions to manage digital behaviors and promote healthy sleep among young adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".