Feeling Moodie: Insights from a Usability Evaluation to Improve the Design of mHealth Apps
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
Despite the growing number of mHealth apps for tracking and helping users to form and sustain health habits, most apps are not evidence-based and are not evaluated by the users to uncover potential issues and determine effectiveness. To fill this gap, we used a mixed methods approach to evaluate a mood-self-tracking app called Feeling Moodie. Data was collected from 34 participants [age range: 18–55 years old, with 15/34 (44%) being between the ages 26 and 35 years old; sex: 17 males and 17 females] who used the app for 15 days and completed a questionnaire about their experience followed by an interview with 18 participants to uncover more qualitative insights. Results showed a positive range for attractiveness, perspicuity, efficiency, dependability, and stimulation, but not for novelty which suggests that Feeling Moodie can be improved by increasing the level of creativity to further captivate the user’s interest. Furthermore, interviews revealed that while some participants expressed doing mood check-ins felt like a “chore,” others reported that at first, they had to use it intentionally, but after a while, it became a “rhythm,” pulling them to the experience. Based on the insights, we offer practical guidelines for increasing the level of interactivity and gradually guiding the user by using a variety of features to help them to form good habits. The results obtained in this work can inform designers on how to design more personalized apps and increase the possibility that the app will be adopted. The article contributes to a better understanding of the emotional and technological implications for designing and improving the quality of mood-tracking apps.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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