Abstract
Background
People living with dementia (PLWD) with advanced illness are prone to respiratory distress yet often cannot self-report dyspnea, delaying recognition and treatment. Near-field radio-frequency (NFRF) sensors offer touchless, covert cardiopulmonary monitoring that may be better tolerated than tethered devices.
Objectives
To assess the feasibility and acceptability of NFRF bed sensor for home monitoring of PLWD and to estimate machine-learning (ML) performance for detecting respiratory distress.
Methods
In a 48-hour pilot study, PLWD were recruited from a geriatrics practice. A lab-designed NFRF bed sensor recorded cardiopulmonary waveforms. Recorded video enabled minute-level Respiratory Distress Observation Scale scoring as reference. Feasibility outcomes included adverse events, acceptability, and percentage of usable data. ML classifiers (eg, random forest, k-nearest neighbors) were evaluated using 5-fold cross-validation, and class imbalance was addressed through data augmentation.
Results
Ten patient–legally authorized representative dyads were enrolled. No adverse events were reported, and no participants intentionally removed the sensor. Usable data averaged 52% (range 34-68%). Caregivers reported minimal burden and no patient distress. With augmented data, the random forest performed best, achieving 74.6% sensitivity and 95.5% specificity in detecting RDOS scores.
Conclusions
NFRF bed sensors were feasible and acceptable to implement in the home setting with PLWD, with promising ML-based detection of respiratory distress. Larger, longer studies with a broader range of RDOS severity are needed to validate performance and refine deployment. As this technology develops and matures, it could provide a method for non-invasive continuous monitoring to detect respiratory distress in PLWD in palliative care settings.
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