Using Mobiles to Monitor Respiratory Diseases
- It can eliminate the need of regular tests and hence reduce the cost of medical care and consequently provide healthcare for people with low income;
- It can reach patients in even the most remote locations;
- It can increase the reach and efficiency of healthcare;
- It empowers the patients because smartphones can help patients monitor their disease at home. Furthermore, it can be used as a tool for patients to manage appointments, renew prescriptions, or view medical records;
- Doctors are increasingly using smartphones, allowing them to access medical materials. They can also reach patients in rural areas through remote diagnostics and information alerts;
- Remotely monitoring hospital patients or the elderly can free up much needed capacity in hospitals and nursing homes.
2. Background and Related Work
- Forced vital capacity (FVC): The volume of air that can be expired after a maximum inspiration;
- Forced expiratory volume in one second (FEV1): Volume of air expelled in the first second of a forced expiration;
- Forced expiratory flow 25–75% (FEF 25–75): Average expiratory flow rate in the middle part of a forced expiration;
- FEV1/FVC: This is the ratio of the vital capacity that is expired in the first second of maximal expiration;
- Peak expiratory flow (PEF): Maximal expiratory flow rate achieved.
- Dyspnea that is progressive (worsens over time);
- Chronic cough that may be intermittent and may be unproductive;
- Chronic sputum production;
- History of exposure to risk factors such as tobacco smoke, smoke from home cooking, heating fuels, and occupational dusts and chemicals;
- Family history of COPD.
- Developing a “Pretest Activity” tool to be used as a first indicator about the presence of respiratory disease;
- Establishing and using appropriate techniques to extract the required physiological signals (exhalation and oxygen saturation);
- Developing an algorithm to analyze the collected physiological signals (analyze recorded patient exhalations);
- Developing a model that relates the frequency response of the exhalation recorded by the microphone to the actual flow rate of the exhalation;
- Implementing an Android application that makes use of the developed model to assist in diagnosing whether a patient suffers from COPD or asthma and analyzing the severity of the disease if present;
- Assessing the reliability of the mobile application by using it to examine 25 human subjects and then comparing the results with those obtained using a spirometer.
- FEV1/FVC > 0.7 and FEV1% predicted >80% with pretest possibility:
- Patient at risk and may have asthma. Patient should thus repeat the test after exercising or during a period of breathing difficulty in order to confirm the diagnosis.
- FEV1/VFC < 0.7 with pretest possibility:
- Patient has respiratory disease:
- If higher pretest possibility of asthma, then diagnose patient with asthma.
- If higher pretest possibility of COPD, then diagnose patient with COPD.
- SpO2 of 92% or less with FEV1/FVC < 0.7:
- Patient will be notified of impaired respiratory function and possible need of oxygen supplementation.
4. Experimental Work
4.1. Time-Domain Analysis
4.2. Frequency-Domain Analysis
5. The Mobile Application
- Spirometry parameters: FVC, FEV1, and FEV1/ FVC ratio;
- Diagnosis result: Whether or not the user has COPD or asthma;
- Disease severity: The level of the disease if the diagnosis is positive. COPD levels are mild, moderate, severe, and very severe, and asthma levels are mild intermittent, mild persistent, moderate persistent, and severe persistent;
- SpO2 warning: Active in case the user suffers from a poor blood oxygenation.
Analysis of Human Exhalations
6. Samples Collection and Discussion of Results
- Subjects are asked few questions about disease symptoms and family history of respiratory diseases in order to fill the Pretest Activity;
- Subjects are asked to measure their SpO2 using an external oximeter;
- Subjects are asked to perform spirometry on a handheld spirometer or clinical spirometer (for hospital subjects);
- Subjects are asked to perform spirometry using the mobile application by assuming a comfortable position while sitting down and using the proximity sensor to place the mouth at a distance of 5cm from the mobile microphone followed by a deep inhalation and blowing as hard as possible on the mobile. This activity is repeated three times.
Conflicts of Interest
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|Moderate||>=50 and <80|
|Severe||>=30 and <50|
|Severity||Age (Years)||Nighttime Awakenings||Interference with Normal Activities||FEV1% Predicted|
|Mild persistent||All||>2 days/week but not daily||Minor limitation||>80%|
|Moderate persistent||All||Daily||Some limitation||60–80%|
|>=5||>1x/week but not nightly|
|Severe persistent||All||Throughout the day||Extreme limitation||<60%|
|FEV1 % Mobile App.||Pretest Possibility of COPD||Pretest Possibility of Asthma||Clinical Diagnosis||Mobile App. Diagnosis|
|Subject 16||58.5||95.3||True||False||Mild COPD||Mild COPD|
|Subject 17||67.3||78.9||True||False||Moderate COPD||Moderate COPD|
|Subject 18||60.9||98.5||True||False||Mild COPD||Mild COPD|
|Subject 19||66.6||74.4||True||False||Moderate persistent Asthma||Moderate COPD|
|Subject 20||65.6||68||False||True||Moderate persistent Asthma||Moderate persistent Asthma|
|Subject 21||67.1||60.8||False||True||Moderate persistent Asthma||Moderate persistent Asthma|
|Subject 22||71.2||79.1||False||True||Moderate persistent Asthma||Moderate persistent Asthma|
|Subject 23||68.7||76.6||False||True||Moderate persistent Asthma||Moderate persistent Asthma|
|Subject 24||63.5||82.4||False||True||Intermittent Asthma||Intermittent Asthma|
|Subject 25||69.3||87.1||False||True||Intermittent Asthma||Intermittent Asthma|
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Zubaydi, F.; Sagahyroon, A.; Aloul, F.; Mir, H.; Mahboub, B. Using Mobiles to Monitor Respiratory Diseases. Informatics 2020, 7, 56. https://doi.org/10.3390/informatics7040056
Zubaydi F, Sagahyroon A, Aloul F, Mir H, Mahboub B. Using Mobiles to Monitor Respiratory Diseases. Informatics. 2020; 7(4):56. https://doi.org/10.3390/informatics7040056Chicago/Turabian Style
Zubaydi, Fatma, Assim Sagahyroon, Fadi Aloul, Hasan Mir, and Bassam Mahboub. 2020. "Using Mobiles to Monitor Respiratory Diseases" Informatics 7, no. 4: 56. https://doi.org/10.3390/informatics7040056