Exploring Sensing Technologies for Tracking Healthy Eating Behaviors: Systematic Review
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
Sensor-based technologies for healthy eating have gained attention in human-computer interaction research. With the prevalence of diet-related health issues such as obesity and diabetes, innovative interventions providing personalized dietary feedback are increasingly needed. Sensor-based technologies offer real-time monitoring of dietary habits. We reviewed 28 articles, published between 2014 and 2024, to uncover the state-of-the-art and research gaps. Sixteen articles (57.1%) reported on effectiveness: seven measured by machine learning model performance and nine through user study evaluation. We identified several eating behaviors that were supported: adequate food intake, meal size and quality, eating posture, swallowing sounds and difficulties, eating styles, and pace. These behaviors were associated with conditions such as diabetes, post-stroke issues, cardiovascular diseases, obesity, and binge eating. Limitations included little to no interventions for life-threatening conditions. Our findings present opportunities to develop tailored sensor-based interventions for various diet-related issues.
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 | 0.001 | 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 | 0.000 | 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