Using Smartphone-Based Ecological Momentary Assessment in Audiology Research: The Participants' Perspective
Bibliographic record
Abstract
= 10) participated in a study using a smartphone-based EMA system to measure their auditory lifestyles. A 14-item survey was scheduled to deliver every 45 min by an EMA app. After a 1-week trial, participants were interviewed regarding their study experiences. The app log files were analyzed to understand how the participants interacted with the app. Results Across the two groups, 1,295 surveys were completed (compliance rate 74.4%). On average, HI participants completed 10.0 and NH participants completed 9.1 surveys per day. The mean survey completion time for HI and NH groups were 72 s and 51 s, respectively. For both groups, about 90% of the participants reported the app as easy to use; about 60% of the participants reported that repetitive surveys interrupted or somewhat interrupted their activities. Participants reported surveys disrupting situations, for example, working, driving, and social events, and that they were more likely to skip surveys in these situations. Additionally, 50% of NH and 30% of HI participants indicated that the survey was not delivered too frequently and none indicated that the survey was too long. Conclusion Overall, the app and EMA design seem to be appropriate. Insights from this study can help researchers design their studies to adequately assess listeners' experience in the field with optimal compliance and data quality.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".