Is Going Unnoticed More Socially Acceptable?: An Exploration of the Relationship Between Social Acceptability and Noticeability of Fitness Trackers
Bibliographic record
Abstract
While fitness trackers are becoming increasingly popular, the majority of such devices are relatively smaller and almost always worn around a user’s wrist (e.g., smart watches). To expand the potential of novel design options for such devices, a study explored the link between social acceptability and device noticeability, in conjunction with two other factors; namely, the device size and the on-body location (i.e., on which body parts the user wears the tracker). The central question we investigated was: to develop a socially acceptable fitness tracker, should the device be less noticeable? For this exploration, an online questionnaire was distributed (N = 32), and results indicated that noticeability was correlated with social acceptability only in two situations: i) when the fitness tracker is large, or ii) when a female user wears it around their chest. That is, noticeability partially accounted for social acceptability only in these conditions. Jointly, the results point toward the great possibility for novel design ideas of fitness trackers in other conditions (e.g., when the device is smaller or worn around the arm) without compromising social acceptability.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".