Insights from the evaluation of a persuasive intervention for absent-minded smartphone use
Bibliographic record
Abstract
Problematic smartphone use (PSU) is a growing concern that hurts users' daily lives and activities. Previous studies have emphasised the importance of developing mindfulness and self-efficacy concerning smartphone use rather than solely focussing on reducing usage. However, there has been little research developing and evaluating digital interventions specifically targeting absent-minded smartphone use. There is also little knowledge on how best mindfulness practice using digital technologies can be integrated with persuasive designs which have been widely studied and implemented for behaviour change. In this paper, we developed a live wallpaper application for Android lock and home screens as a mindfulness-based intervention for absent-minded smartphone use. The application was evaluated over two weeks with 121 participants. This was followed by a semi-structured interview with 15 participants. The results of our analysis show that the intervention reduced absent-minded smartphone use overall. While participants found the various features (especially the customisation features) of the intervention to be persuasive, we found no correlation between perceived persuasiveness and behaviour change. This work provides valuable insights for advancing human-computer interaction (HCI) research on PSU. Additionally, the results raise important questions for future research, such as the relationship between perceived persuasiveness and behaviour change. Finally, we contribute to the discussion on the applications of mindfulness in persuasive technology, challenges and future research areas.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".