| Literature DB >> 30712150 |
Abstract
PURPOSE OF REVIEW: This paper offers a comprehensive review of interactive mobile allergy and asthma smartphone applications available within the USA in 2018, with an emphasis on interactive asthma apps. RECENTEntities:
Keywords: Allergy; App; Asthma; Digital health; Mobile application; Outcome measures; Peak expiratory flow; Smartphone; Symptom score test; mHealth
Mesh:
Year: 2019 PMID: 30712150 PMCID: PMC6394463 DOI: 10.1007/s11882-019-0840-z
Source DB: PubMed Journal: Curr Allergy Asthma Rep ISSN: 1529-7322 Impact factor: 4.806
Fig. 1Digital health stakeholders
Features preferred by 239 asthma and allergy patients
| Feature | % Prefer |
|---|---|
| Asthma education material | 94 |
| Symptom forecast | 92 |
| Asthma action plan | 92 |
| Telemedicine | 90 |
| Connect with local specialists | 90 |
| What to do in emergency | 90 |
| Monitor symptoms | 87 |
| Identify airborne triggers | 87 |
| Notifications from clinic | 86 |
Medication adherence was the least preferred feature by patients (83%)
Patient questionnaire: Would you use a mobile app if it:
1. Identifies nearby factors in the air that trigger your symptoms?
2. Provides you with your personal 4-day symptom forecast?
3. Advises you when to call your doctor?
4. Can monitor your symptoms over time?
5. Can show your caregivers how you are feeling?
6. Sends you a warning when your symptoms are out of control?
7. Allows you to receive secure notifications from your doctor’s office?
8. Has an Asthma Action Plan to help you if your asthma worsens?
9. Has educational materials about allergy and asthma?
10. Tells you what to do during a medical emergency?
11. Records your side effects from taking medications or other treatments?
12. Detects if your symptoms are better after you take a new medication?
13. Enables you to see your doctor in live Telemedicine video calls?
14. Connects you with nearby certified specialists in allergy and asthma?
15. Records when you use your inhaled medications?
Features preferred by 114 asthma specialists
| Feature | % Prefer |
|---|---|
| Symptom score tests | 89 |
| Air pollution | 89 |
| Pollen and mold information | 82 |
| Adherence with medications | 81 |
| Weather factors | 79 |
| Sleep patterns | 73 |
Diet, emotional stress, heart rate, fractional exhaled nitric oxide levels (FeNO), and spirometry were the least preferred features by physicians (54–63%)
Allergy and asthma specialist questionnaire: Which environmental factors and body measurements would you like to see in a mobile app?
1. Air pollution (ozone, particulate matter)
2. Allergens (pollens, mold spores)
3. Weather factors (temperature, humidity, wind)
4. Peak flow test results
5. Airway inflammation (FeNO) test results
6. Coughing episodes
7. Heart rate and activity levels
8. Emotional stress levels
9. When patients use their inhalers
10. What my patients eat
11. Sleep patterns
12. Symptom score test results (rhinitis, asthma)
13. Other factors or triggers: results of spirometry (FEV-1)
Fig. 2ROC curve: mobile asthma severity test vs. asthma control test. Receiver operating characteristic (ROC) curve confirming that the MAST® test and ACT® test are clinically equivalent (Kagen 2014)
Respiratory inhaler sensor studies
| App | Author | Study | Outcomes |
|---|---|---|---|
| Adherium (Hailie) | Foster (2014) | Study: | ACT: |
| 40 primary care physicians trained in asthma care and remote monitoring of asthma compare sensor users vs. non-users | No difference in ACT scores between remote sensor users receiving feedback and sensor non-user control group | ||
| Prednisone: | |||
| Patients: | No difference between sensor users and non-users re prednisone use (7/64 sensor users vs. 19/67 sensor non-users) | ||
| 113 asthma patients in a randomized controlled 6-month comparison study | |||
| Outcome measures: | Adherence: | ||
| Adherence declined in both groups, but was greater in sensor users than non-users (73% ± 26% vs. 28% ± 28%; | |||
| ACT scores, adherence, and prednisone use | Conclusion: | ||
| Remote sensor notifications increase patient compliance with inhaled asthma medication | |||
| Adherium (Hailie) | Morton (2016) | Study: | ACQ + FEV-1: |
| Randomized, controlled 12-month study of sensor users and non-users | No differences observed in ACQ or FEV-1 between sensor users and sensor non-users re symptom scores or FEV-1 | ||
| Patients: | |||
| 89 poorly controlled pediatric asthma patients (ACQ | Adherence: | ||
| Adherence declined in both groups, but sensor users were more compliant than non-users | |||
| Outcome measures: | Prednisone: | ||
| ACQ, FEV-1, hospitalization, unplanned office visits, and prednisone use | Sensor users were 53% less likely to need prednisone than sensor non-users | ||
| Hospitalization: | |||
| Rates of hospitalization per 100 child days were significantly less for sensor users than non-users | |||
| Conclusions: | |||
| Using remote sensors, patient reminders, and feedback decreased prednisone use and hospitalization rates in children with poorly controlled asthma | |||
| Propeller Health | Merchant (2017) | Study: | Hospitalization: |
| 5-year retrospective analysis of resource utilization rates per 100 patient-years by 507 asthma patients before and after using a remote sensor and app-based feedback | 79% decline in rate of use/100 patient-years | ||
| Patients: | |||
| 507 asthma patient records reviewed pre + post remote sensor use | |||
| Outcome measures: | |||
| Rates of hospitalization, emergency room visits, and outpatient office visits for asthma | Emergency room: | ||
| 57% decline rate of use/100 patient-years | |||
| Outpatient asthma visits: | |||
| 41% increase office visits for asthma | |||
| Conclusions: | |||
| Mobile app-based sensors and feedback was associated with increased office visits for asthma and reduced asthma-related resource utilization |
Mobile Application Rating Scale (MARS) scores
| Interactive asthma apps | AsthmaMD | Asthma Storylines | Hailie | KagenAir | Propeller Health |
|---|---|---|---|---|---|
| Engagement | 3.00 | 4.40 | 4.40 | 4.60 | 4.40 |
| Functionality | 4.50 | 4.75 | 5.00 | 5.00 | 4.50 |
| Esthetics | 4.50 | 4.50 | 5.00 | 5.00 | 4.50 |
| Information | 4.17 | 4.33 | 3.33 | 4.71 | 5.00 |
| App quality MARS mean | 4.04 | 4.50 | 4.43 | 4.83 | 4.60 |
| Subjective quality MARS mean | 2.00 | 3.75 | 4.75 | 4.75 | 4.60 |
Standalone mHealth allergy and asthma apps (2018)
| App | Owner | Website | Notes |
|---|---|---|---|
| AccuPollen | Len Bielory M.D. |
| NAB pollen information |
| Allergy 123 | Preventive Health Diagnostics, Inc. |
| Remote allergy practice sales and marketing |
| Allergy Alert | IQVIA |
| Commercial sales |
| AllergyCast | Johnson+Johnson |
| Commercial sales—Zyrtec |
| Allergy Pollen Count | Ghassan Safadi, M |
| Clinic business app |
| AP pal | Allergy Partners |
| Clinic business app |
| Asthma & Me | Anthem, Inc. |
| Must be a patient |
| Change 6401 | Cincinnati Children’s Hospital Med. Center |
| |
| Kiss My Asthma | Univ. of Sydney |
| For patients in Australia |
| MyAsthma | GlaxoSmithKline, PLC |
| Commercial sales, ACT® |
| My Asthma Pal | Children’s Med. Center Dallas |
| Clinic business app, ACT® |
| WebMD | KKR & Co Inc. |
| Commercial sales Education |
Features in mobile asthma apps—2018
| 27 Features | Asthma Storylines | AsthmaMD | KagenAir | Propeller | Hailie |
|---|---|---|---|---|---|
| Classification | Standalone | Standalone | Interactive | Interactive | Interactive |
| Cost | |||||
| Free to user | Yes | Yes | Yes | Yes | Yes |
| Clinic pays for access | – | – | Yes | Yes | Yes |
| Adherence | |||||
| User input data | Yes | Yes | Yes | Yes | Yes |
| Automatic input of data | – | – | – | Yes | Yes |
| Validated symptom score tests | |||||
| Public domain | – | – | Yes | – | – |
| Non-public domain | Yes | – | – | Yes | – |
| Alerts | |||||
| Inhaler use—user inputs | Yes | – | Yes | Yes | – |
| Inhaler use—auto | – | – | – | Yes | Yes |
| Asthma flares | – | – | Yes | Yes | Pending |
| Forecast symptoms | – | – | Yes | Yes | Pending |
| Trigger identification | |||||
| Artificial intelligence | – | – | Yes | Yes | Pending |
| Measurements | |||||
| FEV-1 user input data | – | Yes | Yes | Yes | – |
| FeNO user input data | – | – | Yes | – | – |
| Peak flow user inputs | Yes | Yes | – | Yes | Yes |
| Hypersensitivity test | – | – | Yes | – | – |
| Management | |||||
| Asthma action plan | Yes | Yes | – | Yes | Pending |
| Emergency instructions | – | Yes | – | Yes | Pending |
| Prednisone use user input | Yes | – | Yes | Yes | Pending |
| Population mgmt | – | – | Yes | Yes | Pending |
| ER + urgent visits | – | – | Yes | Yes | Pending |
| Communications | |||||
| Secure notifications | Yes | – | Yes | Yes | Yes |
| Telemedicine | – | – | Yes | – | – |
| Educational content | Yes | Yes | Yes | Yes | Yes |
| Monitor aeroallergens | |||||
| Local pollens + molds | – | – | Yes | – | Pending |
| User location | |||||
| GPS | – | – | Yes | Yes | Yes |
| Connect to caregivers | |||||
| Bd-certified allergists | Yes | – | Yes | – | – |
| Compare symptoms w/ | |||||
| Weather + pollution | – | – | Yes | Yes | Pending |
| Feature score | 10 | 7 | 20 | 21 | 9 |
Fig. 3Hailie sensor
Fig. 4Propeller sensor
Mobile asthma app performance scores
| App performance | Asthma Storylines | AsthmaMD | KagenAir | Propeller Health | Hailie |
|---|---|---|---|---|---|
| Classification | Standalone | Standalone | Interactive | Interactive | Interactive |
| 1. Easy to use | Yes | Yes | Yes | Yes | Yes |
| 2. Reliable | Yes | No | Yes | Yes | Yes |
| 3. Easy clinic integration | No | No | Easy | Easy | Easy |
| 4. Staff acceptance | – | – | Yes | Yes | N/A |
| 5. Privacy + security | Yes | Yes | Yes | Yes | Yes |
| 6. Design: bad-simple-good-excellent | Good | Simple | Excellent | Excellent | Excellent |
| 7. User feedback | Yes | No | Feedback | Feedback | Feedback |
| 8. Caregiver evaluations | No | No | No | Yes | Yes |
| 9. Support + updates | Yes | No | Yes | Yes | Yes |
| 10. Language | English | English | English | Eng + 11 languages | Eng, Fr Span Dutch |
| Total score [0–10] | 5 | 2 | 9 | 10 | 9 |