Feasibility, Acceptability, and Usability of Physiology and Emotion Monitoring in Adults and Children Using the Novel Time2Feel Smartphone Application
Bibliographic record
Abstract
The present study tests the feasibility, acceptability, and utility of the novel smartphone application—Time2Feel—to monitor family members’ emotional experiences, at the experiential and physiological level, and their context. To our knowledge, Time2Feel is the first of its kind, having the capability to monitor multiple members’ emotional experiences simultaneously and survey users’ emotional experiences when experiencing an increase in physiological arousal. In this study, a total of 44 parents and children used Time2Feel along with the Empatica E4 wrist-wearable device for 10 days. Engagement rates were within the acceptable range and consistent with previous work using experience sampling methods. Perceived ease of use and satisfaction fell mostly in the moderate range, with users reporting challenges with connectivity. We further discuss how addressing connectivity would increase acceptability. Finally, Time2Feel was successful at identifying physiological deviations in electrodermal activity for parents and children alike, and even though responses to those deviation-generated surveys were largely consistent with random survey responses, some differences were noted for mothers and fathers. We discuss the implications of using Time2Feel for understanding families’ emotional and stressful experiences day-to-day.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".