Telespirometry 8th IPCRG International World Conference Amsterdam, NL James W. Stout MD MPH Professor of Pediatrics University of Washington Seattle, WA USA Spirometry 360 © University of Washington

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Background What if “ongoing management” meant Asthma and COPD are two of the most common chronic diseases in the world. If care is sought, primary care providers manage most patients Ample evidence that many patients don’t receive guidelines-based care Ample evidence that adherence to guidelines would markedly improve quality of life for patients with asthma and COPD, and save significant costs. Guidelines recommend spirometry for initial diagnosis and ongoing management. What if “ongoing management” meant “ongoing self-management”? Spirometry 360 © University of Washington

Sending Spirometry Tests Spirometry 360 Feedback Reporting System Sending Spirometry Tests Using EasyWare and Spirometry 360 Feedback Agent ks Spirometry 360 © University of Washington

A convergence of collaborations Inter-disciplinary: Computer science engineering: Shwetak Patel and his grad students Pediatrics/Medicine Public Health International: International Primary Care Respiratory Group (IPCRG) Spirometry 360 © University of Washington

The Current Scenario Diagnostic spirometry is generally only done in the clinical setting. Though home devices exist, they may lack complete platforms. Spirometers are relatively expensive, & have minimal coaching capability. No mobile apps currently measure spirometry. Clinical Spirometer Home Spirometer

Our Proposed Solution Screen sharing. 1 on 1 person. SpiroSmart: Mobile phone spirometry application Uses built-in microphone No additional hardware needed Built-in automatic feedback software (FRS-based) Capable of One-on-One remote coaching (in progress) Automatic over-reads (in progress) Screen sharing. 1 on 1 person. Customized feedback. Only show our solution JS Bottom line now reads: Automatic over-reads (proposed for Year 2)

Flow features Shwetak N. Patel - University of Washington

Auto-regressive estimate envelope detection Auto-regressive estimate Shwetak N. Patel - University of Washington

ground truth flow features spirometer feature 1 feature 2 To use machine learning we need a ground truth estimate of the flow rate over time. However, recall we couldn't collect data on spirosmart and the spirometer at the same time. so we have to randomly assign a ground truth curve from the clinical spirometer in the same session where the given audio file was recorded. Here are two example features and the ground truth. We align the signals according to their maximum value. now we can build our machine learning model. feature 2

Spirometry 360: Training through Feedback Reporting System Cycle Spirometry 360 © University of Washington

Deployments Seattle Children’s Hospital VA Hospital, Tacoma • Seattle Children’s Hospital VA Hospital, Tacoma Chest Research Foundation, India Community Respiratory Clinic, Bangladesh

Seattle Children’s Hospital: Children VA Hospital, Tacoma: +50 years Demographics Seattle Children’s Hospital: Children VA Hospital, Tacoma: +50 years Chest Research Foundation, India: +18 years Community Respiratory Clinic, Bangladesh: all age groups •

Participants ~ 2,200 participants with a lung function lower than 75% • ~ 2,200 participants with a lung function lower than 75% Around 20 new patients with paired data every day

Lung Function Distribution (FEV1/FVC)

Distance Calculation

Performance Percentage Error (%) PEF FEV1 FVC FEV1% 20 15 10 5 5 0 0

Machine Learning: Cough (example) FV Curve VT Curve Spirometry 360 © University of Washington

limitations no inhalation quiet surroundings coaching All in all we were happy with the results but we also want to try and put these results in perspective. In the current form of spirosmart, we really see this as a replacement for people already doing home spirometry, not as a replacement for all clinical spirometers. we draw this conclusion based on several current limitations. the first limitation is that we are doing audible sensing, so we need relatively quiet surroundings--this is easy to do if you are at home. Second we cannot measure quantities from the subject inhaling. These are almost never used in diagnosis but they are helpful when teaching someone how to use a spirometer. And coaching someone in general with this device is something we didn't address in the current study. Its unclear how easy it will be to coach someone when the technician can't say things like keep going and really push out! Although our community might be uniquely skilled to create interfaces and incentive graphics for coaching on spirosmart. Another limitation is that we currently require a smart phone with an internet connection because the computation just can't yet be done on a phone in real time. The audio must be sent to a server, processed, and then results sent back down. Finally, in the current study, no participants lung function changed drastically during the study so we don't' know if spirosmart can track trends over time.

Spirocall (maybe all you need is a phone like this). This could really lower the access barriers of spirometry, for instance in developing world, telemedicine, and in general to people that do not own a smart phone.

Sensing and Diagnosing Disease using Commodity Mobile Devices Overview Sensing and Diagnosing Disease using Commodity Mobile Devices Senosis develops a broad set of clinically validated mobile apps that empower patients and physicians to find and manage disease Pulmonary Non-invasive Blood Screening Cardiac Sleep Senosis is a University of Washington spinout, and includes five co-founders

Roadmap Spirometry 360: Remote training and feedback SpiroSmart: spirometry Bilicam: newborn jaundice SLEEPI: sleep quality SPApp: SP02 Product CoughSense: cough counting BPSense: blood pressure I took out iron on AnemiApp, unless it also measures iron. I AnemiApp: hemoglobin OsteoApp: osteoporosis screening 2016 2017 2018

Kyrgyzstan: Fresh Air H2020 Study and spirometry

Dr. Aizhamal Tabyshova, Coach Extraordinaire

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