A nudge-based intervention to reduce problematic smartphone use: Randomised controlled trial
Bibliographic record
Abstract
Problematic smartphone use is rising across the world. We tested an intervention with ten strategies that nudge users to reduce their smartphone use, for example by disabling non-essential notifications and changing their display to greyscale. Participants first completed baseline measures of smartphone use, well-being, and cognition before choosing which intervention strategies to follow for two to six weeks. Study 1 (N = 51) used a pre–post design while Study 2 (N = 70) compared the intervention to a control group who monitored their screen time. Study 1 found reductions in problematic smartphone use, screen time, and depressive symptoms after two weeks. Study 2 found that the intervention reduced problematic smartphone use, lowered screen time, and improved sleep quality compared to the control group. Our brief intervention returned problematic smartphone use scores to normal levels for at least six weeks. These results demonstrate that various strategies can be combined while maintaining feasibility and efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".