We receive a large number of inquiries about our approach and the research platform, and we attempt to address some of the most frequently asked questions here. Regarding the most frequently asked question, Beiwe is a transliteration of a Nordic goddess of sunlight and mental health. We pronounce it bee-we.
What is digital phenotyping?
We have defined digital phenotyping as the “moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices,” in particular smartphones. This is our definition of the concept and it highlights some of the important aspects of digital phenotyping, such as using existing personal devices rather than introducing additional instrumentation. To truly leverage moment-by-moment data collected in situ, in the wild, one must rely on the use of passive data, i.e., smartphone sensor and usage data.
Who developed Beiwe?
The Beiwe research platform has been developed by the Onnela Lab at the Harvard T.H. Chan School of Public Health by funding from the National Institutes of Health (NIH). Specifically, the large majority of the development work has been enabled by a 2013 NIH Director’s New Innovator Award to Dr. Jukka-Pekka Onnela. The lab has worked with two different software development groups to create the front-end smartphone applications for Android and iOS devices, as well as the development of the back-end data collection system. The lab has developed the data analysis methods and data analysis pipeline internally.
What is the difference between a smartphone app and a research platform?
A smartphone app simply a software application that runs on a smartphone. The Beiwe app is just one of the three components of the Beiwe platform. The other components are the Beiwe back-end and the Beiwe data analysis pipeline. The Beiwe back-end makes use of Amazon Web Services (AWS) cloud computing infrastructure and is used to manage studies (e.g., study creation, addition of users, regeneration of passwords) and collect data. For the latter, it uses AWS Elastic Beanstalk, which automatically handles the details of capacity provisioning and load balancing, making it essentially infinitely scalable. The data analysis pipeline performs data pre-processing, checks data quality, transforms data, carries out imputation, and computes summary statistics of interest.
Why does Beiwe collect raw data?
In short, research requires research-grade raw data. Software development kits for Android (ResearchStack, etc.) and Apple iOS (ResearchKit, HealthKit, CareKit, etc.) collect processed data summaries rather than raw sensor and phone usage data. This introduces an opaque layer between the data generating process and data analysis, making it difficult to compare data across devices or pool data across studies as the data summaries are likely different. The use of pre-defined data summaries results in a loss of information, narrowing down potential use cases of data to those conceived at the time of data collection (e.g., number of steps taken), and as such diminishes the value of data biobanking. Collection and storage of raw data makes it possible to compute any summaries of interest at a future data, thus enhancing study replicability and facilitating re-analyses of data. Downsides to collecting raw data are the large volume of data and the difficulty of keeping phone sensors awake, but both of these challenges are manageable. Raw data lets investigators ask and answer questions they care about and makes data collection and data analysis transparent.
What about reproducibility and replicability of Beiwe studies?
Only 6% of biomedical studies have been found to be completely reproducible (Prinz et al, 2011). From this point of view, we do not need more studies but rather we need more studies that are reproducible. To achieve reproducibility, it is key to focus on both data collection and data analysis. With the Beiwe platform, we attempt to address both. We started by building a platform that collects research-grade data. The old adage about data analysis captures the sentiment perfectly: garbage in, garbage out. Therefore, our first step was to improve the quality of measurements. Many researchers have advocated the role of better measurement in studies that involve any type of quantification of human behavior, a point that has been made repeatedly and vigorously by Andrew Gellman among others. Beiwe captures all study settings in human readable JSON formatted configuration files, and the platform enables an investigator to export and import these files with a single click. Therefore, an investigator wishing to replicate a previous Beiwe study only needs this one file to collect identical data in an identical manner. Data analysis can be replicated by studying the scripts that are used to analyze the output matrices of the Beiwe platform.
Want to find out more about Beiwe or our work in Digital Phenotyping? Detailed information is available on the Onnela Lab Website.
