Prolonged Smartphone Usage Duration With/without Physical Inactivity Is Not Associated With Cognitive Decline In University Students
Bibliographic record
Abstract
Recently, a meta-analysis highlighted that the duration of screen time has increased among young populations. Moreover, some review articles mentioned the concept of ‘digital dementia’ that prolonged screen time would increase the risk of dementia even in young individuals. However, little is known whether long screen time duration contributes to cognitive decline in young adults. Given that a longer screen time causes physical inactivity which is one of the factors for cognitive decline, it is possible that the duration of smartphone usage is associated with cognitive impairment. PURPOSE: We aimed to examine the relationships between screen time with/without removing the impact of physical activity and cognitive function in university students. METHODS: Sixty-three healthy adults took part in the present study (Male n = 37, Female n = 26; Age 22 ± 2 yrs). The average daily duration of screen time was determined from smartphone recording data for three weeks. The International physical activity questionnaire-short form assessed the level of physical activity. Some cognitive functions were measured using Montreal cognitive assessment, Grooved pegboard test (dominant and non-dominant hand), Digit symbol substitution test, Color-word Stroop test (congruent, neutral, and incongruent task), Memory recognition test, and n-back test (1 and 2). A one-tailed Spearman’s rank correlation validated the relationship between screen time duration and physical activity. Pearson’s and partial correlation analyses (i.e., without and with removing the effects of physical activity) were conducted to determine whether the duration of smartphone screen time is associated with cognitive functions. RESULTS: There was a weak correlation between a longer duration of screen time and physical inactivity (ρ = -0.24, P < 0.05). However, both Pearson’s and partial correlation analyses demonstrated no significant relationship between screen time duration and all cognitive functions. CONCLUSION: Prolonged smartphone usage duration may be one of the factors for physical inactivity among young populations. However, the results of the present study indicate that cognitive decline was not observed in university students who use smartphones for a long duration with/without physical inactivity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".