Evaluating authentication options for mobile health applications in younger and older adults
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Bibliographic record
Abstract
OBJECTIVE: Apps promoting patient self-management may improve health outcomes. However, methods to secure stored information on mobile devices may adversely affect usability. We tested the reliability and usability of common user authentication techniques in younger and older adults. METHODOLOGY: Usability testing was conducted in two age groups, 18 to 30 years and 50 years and older. After completing a demographic questionnaire, each participant tested four authentication options in random order: four-digit personal identification number (PIN), graphical password (GRAPHICAL), Android pattern-lock (PATTERN), and a swipe-style Android fingerprint scanner (FINGERPRINT). Participants rated each option using the Systems Usability Scale (SUS). RESULTS: A total of 59 older and 43 younger participants completed the study. Overall, PATTERN was the fastest option (3.44s), and PIN had the fewest errors per attempt (0.02). Participants were able to login using PIN, PATTERN, and GRAPHICAL at least 98% of the time. FINGERPRINT was the slowest (26.97s), had an average of 1.46 errors per attempt, and had a successful login rate of 85%. Overall, PIN and PATTERN had higher SUS scores than FINGERPRINT and GRAPHICAL. Compared to younger participants, older participants were also less likely to find PATTERN to be tiring, annoying or time consuming and less likely to consider PIN to be time consuming. Younger participants were more likely to rate GRAPHICAL as annoying, time consuming and tiring than older participants. CONCLUSIONS: On mobile devices, PIN and pattern-lock outperformed graphical passwords and swipe-style fingerprints. All participants took longer to authenticate using the swipe-style fingerprint compared to other options. Older participants also took two to three seconds longer to authenticate using the PIN, pattern and graphical passwords though this did not appear to affect perceived usability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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