Cheat, curse, or comply? Wearable users’ proactive, avoidant-reactive, and ameliorative-reactive coping with negative incidents
Bibliographic record
Abstract
Despite their promise in promoting healthy behaviour, wearable self-tracking devices often fall short of long-term effectiveness due to negative user experiences. This study adopts a coping-theoretic perspective to explore how users respond to negative incidents. Drawing on narrative data from 62 long-term users of wearable self-tracking devices in Switzerland, the analysis identifies different categories of coping: Proactive coping involves anticipatory strategies aimed at preventing incidents, such as cognitive reinterpretation, seeking social support, cheating and manipulating information, and selective use. Reactive coping emerged in two subcategories: Avoidant-reactive coping includes responses after an incident has occurred aimed at disengagement, denial, and distancing, including rationalizing and downplaying the incident, doubting and dismissing the wearable, or discontinuing use. Ameliorative-reactive coping also includes post-incident responses but aimed at adaptation and constructive engagement, leading to improvement of personal outcomes, such as changing one’s behaviour or adapting use practices. The study contributes to the information systems coping literature by extending ways of coping and introducing ameliorative-reactive coping as a novel category. It also contributes to wearable-specific research by offering a coping-informed explanation for high attrition and inconsistent usage patterns. Finally, the study provides practical insights for designers and providers of wearables.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".