The Moderating Effects of Smart Phone Addiction on the Relationship between Life Satisfaction, Sleep Quality and Academic Achievement among College Students: A Systematic Review
Bibliographic record
Abstract
This systematic review investigated the intricate dynamics between smartphone addiction and the well-being of college students, specifically examining its moderating effects on the relationships between life satisfaction, sleep quality, and academic achievement. This review further strengthens the evidence presented in previous studies that emphasize the negative influence of excessive smartphone usage on sleep quality and academic performance. The study employed quantitative observational studies that were obtained from five databases. During the selection process, PRISMA guidelines were followed, and the study incorporated studies from various countries globally, all of which were in English. The studies enrolled 31678 people, with females ranging from 33.1% to 75.5%; studies quality ranged from low to moderate. The results show that life satisfaction is positively associated with perceived academic achievement and also sleep quality is positively associated with the initial level of pre-sleep cognitive arousal, which hence leads to good academic performance. In contrast, Smartphone addiction was positively associated with daytime sleepiness and school disengagement while also negatively associated with Grade Point Average (GPA). Therefore, this study shows excessive use of smartphones among the youth, which in correlation affects the relationship between their life satisfaction, sleep quality and academic achievement. An extensive amount of studies needs to be done on smartphone addiction so that better plans can be made for preventive measures.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".