Objectively Measured Smartphone Use and Nonsuicidal Self-Injury Among College Students: Cross-Sectional Study
Bibliographic record
Abstract
Background: The impact of smartphone use on mental health is being rigorously debated. Some questionnaire-based research suggests that smartphone use correlates with nonsuicidal self-injury (NSSI). Self-reported data seem unlikely to capture actual smartphone use precisely, requiring objective measures to advance this field. Objective: The aim of the study is to examine whether objectively measured smartphone use was associated with NSSI among college students. Methods: This multicenter cross-sectional study was conducted from 2022 to 2024, enrolling college students from 559 classes across 6 universities in China. NSSI was measured by the Ottawa Self-Injury Inventory including 10 items of NSSI without suicidal intent within the past month. Participants answering "ever" were classified as having NSSI. Objectively measured smartphone screen time and number of smartphone unlocks were obtained from screenshots of smartphone use records. The association between objectively measured smartphone use and NSSI was analyzed using binary logistic regression models and restricted cubic spline regression. Results: Of 16,668 included participants, 627 (3.8%) reported NSSI. Mean (SD) smartphone screen time and number of smartphone unlocks were 48.8 (28.8) hours per week and 271.6 (291.0) times per week. The models adjusted for different factors showed a significant association between smartphone use and NSSI. Compared to participants with 0-21 hours per week of smartphone screen time, those with ≥63 hours per week of smartphone screen time had higher odds of NSSI (odds ratio [OR] 1.63, 95% CI 1.32-2.01). Likewise, compared to participants with 0-50 times per week of smartphone unlocks, those with ≥400 times per week of smartphone unlocks had higher odds of NSSI (OR 1.53, 95% CI 1.25-1.88). No significant NSSI risk increase was observed for participants with 21-42 and 42-63 hours per week of smartphone screen time nor for those with 50-150 and 150-400 times per week of smartphone unlocks. Moreover, restricted cubic spline analyses showed that the increasing risk of NSSI was associated with elevated smartphone screen time and number of smartphone unlocks. Conclusions: These findings emphasize that ≥63 hours per week of smartphone screen time and ≥400 times per week of smartphone unlocks are risk factors for NSSI among college students, and interventions targeting NSSI should consider the apparent association with smartphone use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".