Implementing Ecological Momentary Assessment in Audiological Research: Opportunities and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ecological momentary assessment (EMA) is a way to evaluate experiences in everyday life. It is a powerful research tool but can be complex and challenging for beginners. Application of EMA in audiological research brings with it opportunities and challenges that differ from other research disciplines. This tutorial discusses important considerations when conducting EMA studies in hearing care. While more research is needed to develop specific guidelines for the various potential applications of EMA in hearing research, we hope this article can alert hearing researchers new to EMA to pitfalls when using EMA and help strengthen their study design. The current article elaborates study design details, such as choice of participants, representativeness of the study period for participants' lives, and balancing participant burden with data requirements. Mobile devices and sensors to collect objective data on the acoustic situation are reviewed alongside different possibilities for EMA setups ranging from online questionnaires paired with a timer to proprietary apps that also have access to parameters of a hearing device. In addition to considerations for survey design, a list of questionnaire items from previous studies is provided. For each item, an example and a list of references are given. EMA typically provides data sets that are rich but also challenging in that they are noisy, and there is often unequal amount of data between participants. After recommendations on how to check the data for compliance, reactivity, and careless responses, methods for statistical analysis on the individual level and on the group level are discussed including special methods for direct comparison of hearing device programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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