Related Experiment Video
Updated: Apr 30, 2026

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
Published on: January 5, 2018
Restoring Engagement in Digital Self-Control Tools Using Nudge Reconfiguration Prompts: Quasi-Experimental Study
Awen Kidel Peña-Albert1, Sandy Ingram2, Yasser Khazaal3
1École Polytechnique Fédérale de Lausanne, Rte Cantonale, Lausanne, 1015, Switzerland, 33 0782662496.
Digital self-control tools (DSCTs) show promise for managing smartphone use. Observable user behaviors, not self-reports, predict engagement with these digital well-being interventions.
Area of Science:
- Digital Health
- Human-Computer Interaction
- Behavioral Science
Background:
- Digital self-control tools (DSCTs) aim to reduce excessive smartphone use but struggle with user engagement and retention.
- Understanding user engagement drivers, particularly observable behaviors versus self-reports, is crucial for DSCT effectiveness.
Purpose of the Study:
- To investigate if prompting users to reconfigure nudges increases interaction with DSCTs.
- To analyze engagement evolution and behavioral differences between users accepting or rejecting interventions.
- To compare the predictive power of observable in-app behaviors versus self-reported measures for intervention acceptance.
Main Methods:
- A quasi-experimental study with 252 participants who had disabled nudges.
- Random assignment to a nudge reconfiguration prompt (experimental) or control group.
- Analysis of DSCT logs and self-reported data using difference-in-differences, t-tests, and chi-square tests.
Main Results:
- 46% of experimental users accepted the prompt, significantly increasing their user-nudge interaction ratio.
- Users accepting the prompt exhibited pre-existing behavioral indicators of higher change readiness.
- Observable behaviors, like usage thresholds, predicted acceptance better than self-reported screen time goals or regret.
Conclusions:
- Observable in-app behaviors are more effective than self-reports in identifying users receptive to DSCT interventions.
- DSCT design should leverage adaptive strategies based on in-app behaviors to enhance user engagement and readiness to change.
- Behaviorally-informed adaptive DSCTs are likely to surpass static or self-report-reliant interventions.
Related Concept Videos
Self-Discrepancy Theory
Self-Regulation
Behavior Modification
A real-world application of operant conditioning principles is applied...
Impression Management Techniques III: Aligning Actions
Self-Evaluation Maintenance Model
Operant Conditioning Intervention
In operant conditioning, behaviors that are...

