Sensing Tool Measures Lung Function over the Phone
By HospiMedica International staff writers Posted on 12 May 2016 |
Image: A person using SpiroCall on a phone with and without a SpiroCall whistle (Photo courtesy of the University of Washington).
A new device allows lung function to be assessed from any phone, using the voice channel to transmit the sound of the spirometry effort.
Developed by researchers at the University of Washington (UW; Seattle, USA), the SpiroCall is a service that transmits an audio signal using the standard voice telephony channel. A server receives the data of degraded audio quality, calculates clinically relevant lung function measures using multiple regression algorithms, and reports the results using an audio message, text message, or both. The researchers also found several significant usability challenges more difficult to mitigate, such as how a user holds the phone, the distance from the user’s mouth to the phone, and how wide a user opens their mouth.
The researchers therefore designed a simple, low-cost 3D printed whistle accessory, which generates vortices as the user exhales through it, changing its resonating pitch in proportion to the flow rate. The whistle does not have any moving parts and is as simple as any spirometer mouthpiece. Advantages of the whistle include more consistent acoustic properties, audible sound at lower flow rates, removing the effect of distance from the user’s mouth, and precisely controlling mouth shape and phone orientation.
In a study to investigate viability of the call-in service approach with and without the whistle, the researchers evaluated SpiroCall in 50 patients, comparing it to approved spirometers and evaluating the effect of using the voice communication channel on performance. Each patient performed spirometry efforts with and without the whistle on two different phones, recording the audio through the cell phone network, and two smartphones recording the audio locally through an app. Participants also used two different sizes of vortex whistles to determine if different sizes work better for different individuals.
The results show that without a whistle, SpiroCall has a mean error of 7.2% for the four major clinically relevant lung function measures; for forced expiratory volume percentage (FEV1%), the mean error is 6.2%. With a whistle, SpiroCall has a mean error of 8.3% for the four measures, and 7.3% for FEV1%. Although the whistle shows higher average error in lung function estimates, it is more consistent for people with lower lung function and produces fewer overestimations. The study was presented at the Association for Computing Machinery conference, held during May 2016 in San Jose (CA, USA).
"People have to manage chronic lung diseases for their entire lives, so there's a real need to have a device that allows patients to accurately monitor their condition at home without having to constantly visit a medical clinic,” said lead author Mayank Goel, MSc, a computer science and engineering doctoral student. “Because this project has been around for four years, we've been able to talk to a lot of patients about how they're able to use the technology, and that feedback has really helped us make smart improvement.”
Related Links:
University of Washington
Developed by researchers at the University of Washington (UW; Seattle, USA), the SpiroCall is a service that transmits an audio signal using the standard voice telephony channel. A server receives the data of degraded audio quality, calculates clinically relevant lung function measures using multiple regression algorithms, and reports the results using an audio message, text message, or both. The researchers also found several significant usability challenges more difficult to mitigate, such as how a user holds the phone, the distance from the user’s mouth to the phone, and how wide a user opens their mouth.
The researchers therefore designed a simple, low-cost 3D printed whistle accessory, which generates vortices as the user exhales through it, changing its resonating pitch in proportion to the flow rate. The whistle does not have any moving parts and is as simple as any spirometer mouthpiece. Advantages of the whistle include more consistent acoustic properties, audible sound at lower flow rates, removing the effect of distance from the user’s mouth, and precisely controlling mouth shape and phone orientation.
In a study to investigate viability of the call-in service approach with and without the whistle, the researchers evaluated SpiroCall in 50 patients, comparing it to approved spirometers and evaluating the effect of using the voice communication channel on performance. Each patient performed spirometry efforts with and without the whistle on two different phones, recording the audio through the cell phone network, and two smartphones recording the audio locally through an app. Participants also used two different sizes of vortex whistles to determine if different sizes work better for different individuals.
The results show that without a whistle, SpiroCall has a mean error of 7.2% for the four major clinically relevant lung function measures; for forced expiratory volume percentage (FEV1%), the mean error is 6.2%. With a whistle, SpiroCall has a mean error of 8.3% for the four measures, and 7.3% for FEV1%. Although the whistle shows higher average error in lung function estimates, it is more consistent for people with lower lung function and produces fewer overestimations. The study was presented at the Association for Computing Machinery conference, held during May 2016 in San Jose (CA, USA).
"People have to manage chronic lung diseases for their entire lives, so there's a real need to have a device that allows patients to accurately monitor their condition at home without having to constantly visit a medical clinic,” said lead author Mayank Goel, MSc, a computer science and engineering doctoral student. “Because this project has been around for four years, we've been able to talk to a lot of patients about how they're able to use the technology, and that feedback has really helped us make smart improvement.”
Related Links:
University of Washington
Latest Health IT News
- Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients
- Strategic Collaboration to Develop and Integrate Generative AI into Healthcare
- AI-Enabled Operating Rooms Solution Helps Hospitals Maximize Utilization and Unlock Capacity
- AI Predicts Pancreatic Cancer Three Years before Diagnosis from Patients’ Medical Records
- First Fully Autonomous Generative AI Personalized Medical Authorizations System Reduces Care Delay
- Electronic Health Records May Be Key to Improving Patient Care, Study Finds
- AI Trained for Specific Vocal Biomarkers Could Accurately Predict Coronary Artery Disease
- First-Ever AI Test for Early Diagnosis of Alzheimer’s to Be Expanded to Diagnosis of Parkinson’s Disease
- New Self-Learning AI-Based Algorithm Reads Electrocardiograms to Spot Unseen Signs of Heart Failure
- Autonomous Robot Performs COVID-19 Nasal Swab Tests
- Statistical Tool Predicts COVID-19 Peaks Worldwide
- Wireless-Controlled Soft Neural Implant Stimulates Brain Cells