Understanding Fitness Tracker Users' Security and Privacy Knowledge, Attitudes and Behaviours
Bibliographic record
Abstract
Personal data collected by fitness trackers can leave users open to security and privacy threats, often without their knowledge. Using an online survey with 212 fitness tracker users, we asked questions to understand participants' knowledge, attitudes and behaviours related to security and privacy, associated with the use of their fitness trackers. We found that users do little to protect their data. While they seem confident about the type of data being collected, they are unsure about how it is being used. Understandably, users are more comfortable sharing their data with friends and work colleagues. We also found that users differentiate between the types of data they are willing to share, suggesting a need for improved sharing preferences. When considering scenarios describing data uses with security and privacy implications, participants recognized that many scenarios were plausible but frequently felt that the scenarios were unlikely to occur. Overall, our findings lead us to believe that fitness tracker users require a greater awareness of the collection, ownership, storage, and sharing practices related to the tracking of their data.
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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 | 0.005 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".