Automated Smart Home Assessment to Support Pain Management: Multiple Methods.

BACKGROUND Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients' natural environment. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference thereby creating new ways of assessing pain and supporting people living with pain. OBJECTIVE To determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. METHODS A multiple method secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength and a Random Forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. RESULTS Thirteen clinically relevant behaviors were illuminated revealing six pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique which yielded a classification accuracy of 0.70, a sensitivity of 0.72, a specificity of 0.69, an area under the ROC curve of 0.756, and an area under the PRC curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; p<.001). The regression formulation achieved moderate correlation with r=0.42. CONCLUSIONS Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians' real-world knowledge when developing pain-assessing machine learning models improves the model's performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance. CLINICALTRIAL Not a clinical trial.