Central Hemodynamic and Thermoregulatory Responses to Food Intake as Potential Biomarkers for Eating Detection: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Diet-related diseases, such as type 2 diabetes, require strict dietary management to slow down disease progression and call for innovative management strategies. Conventional diet monitoring places a significant memory burden on patients, who may not accurately remember details of their meals and thus frequently falls short in preventing disease progression. Recent advances in sensor and computational technologies have sparked interest in developing eating detection platforms. OBJECTIVE: This review investigates central hemodynamic and thermoregulatory responses as potential biomarkers for eating detection. METHODS: We searched peer-reviewed literature indexed in PubMed, Web of Science, and Scopus on June 20, 2022, with no date limits. We also conducted manual searches in the same databases until April 21, 2024. We included English-language papers demonstrating the impact of eating on central hemodynamics and thermoregulation in healthy individuals. To evaluate the overall study quality and assess the risk of bias, we designed a customized tool inspired by the Cochrane assessment framework. This tool has 4 categories: high, medium, low, and very low. A total of 2 independent reviewers conducted title and abstract screening, full-text review, and study quality and risk of bias analysis. In instances of disagreement between the 2 reviewers, a third reviewer served as an adjudicator. RESULTS: Our search retrieved 11,450 studies, and 25 met our inclusion criteria. Among the 25 included studies, 32% (8/25) were classified as high quality, 52% (13/25) as medium quality, and 16% (4/25) as low quality. Furthermore, we found no evidence of publication bias in any of the included studies. A consistent postprandial increase in heart rate, cardiac output, and stroke volume was observed in at least 95% (heart rate: 19/19, cardiac output: 18/19, stroke volume: 11/11) of the studies that investigated these variables' responses to eating. Specifically, cardiac output increased by 9%-100%, stroke volume by 18%-41%, and heart rate by 6%-21% across these studies. These changes were statistically significant (P<.05). In contrast, the 8 studies that investigated postprandial thermoregulatory effects displayed grossly inconsistent results, showing wide variations in response with no clear patterns of change, indicating a high degree of variability among these studies. CONCLUSIONS: Our findings demonstrate that central hemodynamic responses, particularly heart rate, hold promise for wearable-based eating detection, as cardiac output and stroke volume cannot be measured by any currently available noninvasive medical or consumer-grade wearables. TRIAL REGISTRATION: PROSPERO CRD42022360600; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=360600.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".