Between Human and System Agency: Coping with Negative Incidents for Continued Effective Use of Wearables
Bibliographic record
Abstract
Wearable devices hold significant promise for promoting healthy behaviors, yet they often fall short of this potential when users disengage or use them ineffectively. This research examines how individuals respond to negative incidents—such as frustrating feedback, misaligned goals, or perceived surveillance—and how these responses influence continued effective use. Effective use means interacting with the wearable in ways that help achieve health-related goals, beyond merely logging steps or checking data. Based on in-depth accounts from long-term users, the study reveals that, although some cope by re-engaging with the technology, others manipulate data, disengage, or selectively avoid features, undermining the benefits wearables are meant to deliver. Crucially, sustained effective use depends not just on motivation or usability but on the alignment between human and system agency. When wearables assert their own logic too strongly or misalign with user goals, maladaptive responses are more likely. These findings offer actionable guidance for designers, healthcare providers, and policymakers: Rather than focusing solely on adoption or persuasive design, efforts should support user autonomy and recovery after setbacks. Wearables that accommodate breakdowns and empower users in the face of friction are more likely to sustain engagement and improve long-term health outcomes.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".