Curbing Excessive Smartphone Use through Precommitment Apps: A Multiple Discrete-Continuous Extreme Value Approach
Bibliographic record
Abstract
The issue of smartphone addiction has been extensively studied, yet there is a lack of exploration into effective solutions to mitigate the impulsivity and temptation induced by these addictive technologies. Despite the proliferation of precommitment-based blocking apps introduced by major smartphone manufacturers, little is known about their effectiveness in curbing compulsive mobile indulgence—particularly among users with varying capacities for self-regulation. Drawing on a rational habit-formation framework, this study investigates how precommitment measures reduce susceptibility to mobile temptation over time, with particular attention to app characteristics, precommitment modes, and individual differences. To empirically validate our framework, we employ a structural model using a multiple discrete-continuous extreme value (MDCEV) approach, which endogenizes individuals’ choices of blocker modes (rigid vs. flexible) and simultaneously examines their app usage behaviors. Our unique dataset—capturing consumer app consumption with and without blocking features active—reveals that consistent use of precommitment apps not only fosters positive habit formation but also significantly reduces compulsive app usage across both hedonic and utilitarian categories. Furthermore, we find that flexible precommitment strategies outperform their rigid counterparts in reducing utilitarian app usage. Individual factors, such as gender and age, are found to play a moderating role in the efficacy of these precommitment strategies. Counterfactual simulations reveal that users who build stronger habit stock through repeated blocker use exhibit significantly lower app usage even after the tool is removed. Scaling the analysis to the population level further shows that expanding access to precommitment devices meaningfully reduces overall smartphone engagement and enhances digital well-being. Based on these empirical findings, we derived implications that can guide policymakers and managers in address excessive mobile dependence in public and workplace environments.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".