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Published VersionCC BY V4.0, Open Access
Journal article
Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis
Journal of Medical Internet Research, Vol.22(11), pp.1-12
2020
PMCID: PMC7679205
PMID: 33105099
Abstract
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 environments. 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: This study aimed 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 methods, 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: We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall 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.
Details
- Title
- Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis
- Authors
- Roschelle L Fritz (Author) - Washington State UniversityMarian Wilson (Author) - Washington State UniversityGordana Dermody (Author) - Edith Cowan UniversityMaureen Schmitter-Edgecombe (Author) - Washington State UniversityDiane J Cook (Author) - Washington State University
- Publication details
- Journal of Medical Internet Research, Vol.22(11), pp.1-12
- Publisher
- J M I R Publications, Inc.
- Date published
- 2020
- DOI
- 10.2196/23943
- ISSN
- 1438-8871; 1439-4456
- PMID
- 33105099; PMC7679205
- Copyright note
- ©Roschelle L Fritz, Marian Wilson, Gordana Dermody, Maureen Schmitter-Edgecombe, Diane J Cook. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.11.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
- Organisation Unit
- School of Health - Nursing; University of the Sunshine Coast, Queensland; School of Nursing, Midwifery and Paramedicine - Legacy
- Language
- English
- Record Identifier
- 99511305902621
- Output Type
- Journal article
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