Understanding User Perceptions of Personalized Feedback in Digital Health Tools
Why this work is in the frame
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Bibliographic record
Abstract
This study aimed to explore how users perceive and emotionally respond to personalized feedback in digital health tools. A qualitative research design was employed using semi-structured interviews with 20 adult participants from Mexico who had experience using digital health tools with personalized feedback features. Participants were selected through purposive sampling, and data collection continued until theoretical saturation was achieved. Interviews were audio-recorded, transcribed, and thematically analyzed using NVivo 14. The coding process followed three stages: open coding, axial coding, and selective coding, ensuring a comprehensive understanding of user experiences and interpretive patterns. Analysis revealed four core themes: perceived effectiveness of feedback, personal relevance and cultural fit, communication and design quality, and trust, privacy, and emotional resonance. Participants valued motivational and positively framed feedback that aligned with their health goals, but criticized messages that were overly generic, intrusive, or lacking emotional intelligence. Users expressed a preference for customizable settings, culturally and linguistically appropriate messages, and visual formats such as graphs or summaries. Trust in the system was strongly influenced by the tone, clarity, and perceived transparency of the feedback, while overly frequent or robotic messages sometimes triggered negative emotional reactions or disengagement. User perceptions of personalized feedback in digital health tools are multifaceted and shaped by emotional, cultural, cognitive, and technological factors. For feedback systems to be effective and engaging, they must be adaptive, empathetic, and contextually relevant. Incorporating user control, emotional intelligence, and cultural sensitivity into feedback design can enhance trust, increase adherence, and ultimately improve digital health outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it