Monitoring Respiratory Health in Children With Acute Asthma Using Wearable Electrical Bioimpedance and Breath Sounds: Observational Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Asthma remains one of the most serious chronic diseases of childhood. Individuals with severe asthma experience sudden episodes of breathlessness due to acute airflow obstruction, leading to recurrent pediatric intensive care unit (PICU) admissions that often result in mechanical ventilation and even death. Existing clinical assessments lack temporal resolution to effectively track the rapidly changing physiology. OBJECTIVE: This study aimed to evaluate the feasibility of quantifying respiratory health during acute asthma in children using wearable multimodal sensing. METHODS: Wearable-based impedance pneumography (IP) and multichannel lung sounds (LSs) were measured on 17 children admitted to the PICU with an acute asthma attack and on 9 healthy controls. Short-term multimodal measurements were obtained throughout hospitalization, specifically at PICU admission (T1) and discharge (T2). Measurements were also obtained from controls without any signs of acute asthma or otherwise healthy. Statistical and clustering analyses were performed to identify trends in IP- and LS-derived respiratory markers from T1 to T2 across all patients with paired time points (n=13), as well as across the matched cohort (T1: n=10 and T2: n=7), who were compared against controls (n=9). Five features were computed from the IP signal: respiratory rate, inspiration time (Ti), expiration time (Te), expiration-to-inspiration time ratio (Te:Ti), and the normalized Ti by interbreath interval (Ti/IBI). Leveraging the breathing context provided by the IP signal, 4 spectral integrated intensity (SI) acoustic features were computed in 4 different subbands for the inspiration and expiration phases. RESULTS: Within the patient group (n=13), we found that respiratory rate decreased (W(12)=79; P=.02), whereas Te (W(12)=12; P=.02) and Ti (W(12)=13; P=.02) lengthened. Meanwhile, the SIs for the lowest subband (100-300 Hz) decreased for both inspiration and expiration phases (P<.01), while they increased for the highest subband (800-1000 Hz) for both inspiration and expiration phases (P<.01). Significant differences also existed between T1 and control and T2 and control of the matched cohort. We found that all features were significantly different between T1 and control (P<.05), and all SIs together with Te:Ti and Ti/IBI were significantly different between T2 and control (P<.05), all exhibiting trends toward normalcy. CONCLUSIONS: These results demonstrate the feasibility of quantifying and tracking respiratory health in children with acute asthma using wearable multimodal sensing, specifically with the fusion of IP- and LS-derived markers. Such technology may provide a new adjunctive clinical tool for real-time respiratory monitoring and enable timely titration of care, thereby improving patient outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".