Bioimpedance for peripheral edema assessment in heart failure and clinical practice: a systematic review
Bibliographic record
Abstract
Abstract Objective. Peripheral edema is a common issue among elderly individuals with chronic conditions such as heart failure (HF). Continuous, non-invasive monitoring may enable earlier intervention, reduced hospital readmissions, and improved quality of life. This systematic review aims to evaluate the use of bioimpedance (BI) as a method for monitoring peripheral edema, with a particular focus on portable and wearable applications for remote health management. Approach. A systematic search was conducted across PubMed, IEEE Xplore, and Web of Science to identify studies utilizing BI for the detection or monitoring of lower limb edema with potential for portability or wearability. Main results. Fourteen studies met the inclusion criteria. Five studies focused on HF patients, while nine involved other populations, such as healthy individuals, patients with limb injuries, or those on hemodialysis. Ten studies featured or proposed portable BI devices, whereas four remained at the proof-of-concept stage without portable implementations. There was significant variability in device design, measurement protocols, and target populations. While existing results show promise, few studies evaluated systems in real-world or long-term monitoring scenarios. Significance. BI is a promising, non-invasive approach for the continuous monitoring of peripheral edema, particularly in remote and home-based settings. However, current research is limited by small sample sizes, lack of standardization, and minimal validation in diverse, real-world environments. Further development of wearable systems and robust clinical validation is essential to support broader clinical adoption.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".