Touchscreen Accuracy and Usability for Older Adults: A Comparison of Target Selection Methods
Bibliographic record
Abstract
Canada, along with the rest of the world, is experiencing a steep growth in their oldest demographics who, due to the effects of aging, require support as they adopt or continue to use smartphones in their advancing years. Target selection is a common task that is difficult for older adults to perform. An alternative selection method that is more accurate and usable would support existing smartphone users suffering age-related decline as well as encourage smartphone adoption by non-users who are 60 and over. Previous research suggests that lift selection will improve accuracy of target selection and be more usable than the method used currently, touch selection. This thesis investigated whether lift selection was more accurate and usable than touch selection for both older (60+) and younger (18-29) adults. Predictive relationships for accuracy and usability were also examined. Participants completed a target selection task on a smartphone using both lift and touch selection on targets of small (5 x 7 mm), average (10 x 13 mm) and large (15 x 20 mm) sizes before completing a survey of usability perceptions and self-reported smartphone habits. Results showed that touch selection was significantly more accurate than lift selection for both older and younger adults. Further analysis showed that regular smartphone usage was a weak, but significant positive predictor of touch selection accuracy and that age was a moderate, but significant negative predictor of touch selection accuracy. Some older adults showed improved accuracy during lift selection trials, and older adults were more likely to experience an accuracy improvement during lift selection compared to younger adults. Touch selection was rated significantly easier to use, more satisfying and more learnable by both older and younger adults. Smartphone owners were more likely to rate lift selection as less usable than non-smartphone owners, and there was no difference in usability ratings between touch and lift selection among non-smartphone owners.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".