Between Human and System Agency: Coping with Negative Incidents for Continued Effective Use of Wearables
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it