The widespread adoption of smart mobile platforms, coupled with the growing sensor ecosystem, including passive location tracking, and the ability to take advantage of external data sources, creates an opportunity to generate unprecedented depth of personal data
Mobile health technology can be used in chronic disease management and research to improve our understanding of common diseases such as asthma.
We conducted a prospective observational asthma study to evaluate the feasibility of this approach, clinical features of the cohort recruited through the mobile platform, effectiveness of data collection, user retention patterns, user data sharing preferences.
We describe the data and descriptive statistics of the asthma mobile health study, and participants participated in the iPhone app built using the Apple Research Kit framework.
6346 data from the United StatesS.
Participants agreed to share their data widely and to provide further research.
These resources have the potential to enable the research community to work together to improve our understanding of best practices in asthma and mobile health research.
AHA was released at the Apple App Store on March 9, 2015, and during the 21-month study, the app was downloaded 58,182 times, 48 of them 054 participants from the USS.
Potential participants can download the application and pass the qualification screening (
18 years and older with asthma, no pregnancy, English literacy)
And then continue to complete the rest of the emailConsent process.
The ethical oversight of the study was obtained from the Icahn Medical School in Mount SinaiGCO15-0063).
First, the user learned the details of the study from a series of mandatory consent screens.
Before E-signing an informed consent form, users must make sure they understand the key elements of the consent screen, including the risks and benefits of the study, data sharing options, privacy protection, by understanding the quiz.
Participants are obliged to actively choose to complete the consent process (
No default selection).
After they signed the consent form, they provided an email address to verify their identity and received a PDF copy of the consent form they signed.
Users need to verify their email address by clicking on the link in the email sent by the Sage Bionetwork server.
This helps ensure ways to get in touch with users and restore account information when they switch to a different iPhone.
Provides a simplified layout of initialization processes in the Study of Asthma mobile health applications.
In addition, participants can choose to share their data with Mount Sinai research teams and partners individually (
"Limited sharing ")
Or more broadly with qualified researchers around the world (
Please see the screenshot of this option).
Participants can also select "vacation study" in AHA, allowing them to withdraw from the study at any time during the study.
The data presented here are personally contributed by American residents who choose to share their data widely.
Describes the sample size of the user during the onboarding process.
A total of 10 ÷ 010 users agreed to participate in the study and provided an email address for the registration verification, of which 1611 participants later withdrew from the study.
Of the remaining 8399 participants, 6346 (76%)
Choose to share their data extensively with qualified researchers worldwide.
Among them, 5875 copies of task or survey data were submitted.
This queue is described in this article.
Due to licensing constraints, we were unable to directly use certain standard-validated asthma surveys in the study.
Instead, our asthma experts at Mount Sinai Health System develop AHA surveys by integrating the general content used by validated survey tools.
Specifically, a medical history survey was conducted based on the Charlson comorbidity index;
Conduct daily and weekly surveys to be able to capture symptoms and assess the severity and control of the disease; and EQ-5D-
Investigation and verification of general quality of life 5L (
Although this is the first time on a modern smartphone).
Most of the data from the AHA was collected through a series of surveys planned to appear on the participant's "activity" screen.
In the first three days after enrollment, baseline surveys emerged one after another.
These surveys are designed to collect data on: 1)
History of asthma, including frequency and time (day or night)
Symptoms and degree of limited activity due to symptoms; 2)
Asthma experience including triggers and personal management plans, 3)
Medical History (
Previous and present diseases and allergies); and 4)demographics (
Race, age, gender, income and education)[
: Synapses,: synapses].
In addition, baseline information such as healthcare utilization, asthma medications, self-
The reported controller compliance, rapid mitigation/rescue drug use, peak flow, and other clinically relevant data were collected.
Participants were also asked to determine what was wrong with their asthma and were prompted to set goals for asthma control.
Finally, participants were asked to complete EQ-5D-5u2009L (
5D-European version5u2009L)
Assess quality of life related to health: Synapse ].
In the above 3-
Users are required to complete additional daily and weekly surveys [
: Synapses,: synapses]
Quality of life in Europe, 6-
[Monthly milestone and app feedback survey]: Synapse ]
Occurs at less frequent intervals.
For detailed investigation questions, see.
The data obtained from these surveys is subject to the permission of the copyright owner.
AHA uses the device's GPS to passively collect the location and to connect it with air-
Quality Report of research data.
In the initial version of the app, the participant's most recent EPA air-
When participants view the dashboard tab, the quality readings and the city and state of the station are sent.
However, since the location information of the weather station does not provide satisfactory accuracy or resolution, start with version 1. 0.
6 released in May 5, 2015, whenever the user's location changes, the app starts to collect the original latitude and longitude per hour according to the device's GPS.
In this data release, 3-per day-zip-
Provide code location information for each user after May 5 [: Synapse ].
The application records all the data collected for this study through interaction with Bridge Server, a set of web services developed and operated by Sage Bionetworks.
A detailed description of the back-end design for health data encryption is provided in Chan.
Bridge is a technology platform designed to support biomedical research through smartphones and other sensor devices.
Bridge services support mobile registration and agree to participate in research, design and scheduling of survey and mobile sensors
Receive sensor and survey data from mobile sources.
These services are widely used to support a wide range of health studies, including all five initial research kit applications launched in March 2015 (
The reference will export the coded research data consisting of the measurement response and GPS coordinates to Synapse and distribute it to the researchers.
Synapses, Universal
Data and Analytics sharing services enable researchers to collaborate on analyzing data and sharing insights.
Synapse Bionetworks has developed and operated Synapse as a service to the biomedical research community.
Due to initial technical issues with data integration of health kits and research kits, some participants lacked population information.
Multiple versions of the AHA were released during the study to address these software-
Implement new functions (see in Chan ).
Asthma Health App version 1. 011 ()
Using Apple's ResearchKit framework ()
, This is open source and can be obtained on GitHub (). AppCore ()
Is a layer built on top of ResearchKit, shared in five original ResearchKit applications.
Bridge iOS SDK ()
Provides integration with the bridge server of Sage bionetworks, after
Final data services designed to collect research data donated by participants ().
Given the mobile health study of asthma (AMHA)
The study, conducted by the iphone, reflected its users and introduced socio-economic bias.
A 2016 Pew study found that iPhone users have higher education and income than other smartphone users, and their income and education level as a group is higher than that of the general population.
"Only 5% of AHA asthma patients are black, compared to 13% of the US population, which is a common underrepresentation in general clinical studies.
In the United States, 92% of Hispanics, 91% of whites, 94% of blacks reported using mobile phones, 64% of Hispanics, 66% of whites, and 64% of blacks used smartphones ".
Therefore, using learning apps on the Android platform may attract more different groups.
In addition, the combination of traditional research methods and digital research methods may maximize and effectively attract participants who are most representative of the general population.
A new aspect of this research data set is its association with asthma symptoms and geography.
For a subset of participants who agreed to share geo-location data with qualified researchers, we reported time-course geo-data for their 3-digit ZIP code.
Note that during the electronic informed consent process, participants agreed to share the data on the understanding that their personally identifiable information (PII)
/Protected health information (PHI)removed.
As a result, more granular geographic information such as full zip code or portrait/latitude is not posted.
The symptom data generated in this smartphone study is based on self
Investigation of the reportLee (2003)
Note that different methods of defining asthma severity provide different distribution for patients of different disease categories (mild to severe).
However, our research methodology is no different from the common practices in most asthma epidemiology studies where symptoms-
Use Survey-based in the absence of corresponding biomeasurements (i. e. lung function).
Finally, we observed a significant decrease in study retention over time.
As discussed in Chan, the attenuation observed in user retention is shared by multiple "digital" use cases (e. g.
, Mobile app including entertainment "games" app, tutorial video, open online course)
Therefore, the biological, psychological and social tendencies and behaviors of users may be hard-wired.
Given that the ultimate goal of digital health is often dependent on long-term engagement, the creators of these tools must devote attention and resources to integrating mental health
In addition to technical principles, social and behavioral principles in digital health Design.
One consideration is to provide financial incentives for learning engagement to increase retention rates
This is the standard practice of clinical research.
In view of the above limitations and lessons of AMHS, we believe that research hypotheses with the following features are applicable to current research kit approaches: minimum risk clinical studies, allowing the use of electronic consent forms, the requirements for rapid registration and frequent data collection of different geographical locations can be answered in a short period of time, passive data collection (
GPS, sports activities, etc. )
, There is no assumption that the results can be summarized as participants recruited through traditional methods, as well as a sample size and statistical analysis plan, the plan explains the loss/missing data known in the history of internet/mobile application research.