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Pulantara IW, Wang Y, Burke LE, Sereika SM, Bizhanova Z, Kariuki JK, Beatrice B, Loar I, Cedillo M, Conroy MB, Parmanto B
Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture
Streamlining data collection and management of mHealth, Wearable, and IoT in Digital Behavioral Health Intervention with ADAM: Development of A Novel Informatics Architecture
I Wayan Pulantara;
Yuhan Wang;
Lora E. Burke;
Susan M. Sereika;
Zhadyra Bizhanova;
Jacob K. Kariuki;
Britney Beatrice;
India Loar;
Maribel Cedillo;
Molly B. Conroy;
Bambang Parmanto
ABSTRACT
The Awesome Data Acquisition Method (ADAM) is a versatile web-based system that is designed for integrating data from different sources and managing a large-scale multi-phase research study. As a data collecting system, ADAM allows the collection of real-time data from wearable devices through the device’s Application Programmable Interface (API) and mobile app adaptive real-time questionnaires. As a clinical trial management system, ADAM integrates clinical trial management processes and efficiently supports the recruitment, screening, randomization, data tracking, data reporting, and data analysis during the entire research study process. We used a weight-loss research study (SMARTER Trial) as a test case to evaluate the ADAM system. The study was a randomized controlled trial (RCT) that screened 1741 participants and enrolled 502 adults. The ADAM system was efficiently and successfully deployed to organize and manage the SMARTER Trial. Moreover, with its versatile integration capability, the ADAM system made the necessary switch to fully remote assessments/tracking performed seamlessly and promptly when the COVID-19 pandemic ceased in-person contacts. The remote-native features afforded by the ADAM system minimized the effects of the COVID-19 lockdown on the SMARTER study. Moreover, ADAM was designed to be generalizable and scalable to fit other studies with minimal editing, re-development and customization, making it suitable for various behavioral interventions and different populations.
NIH Grant #: R01HL131583 (PI: LE Burke); R01HL131583 Diversity Supplement (PI: JK Kariuki); F31HL156278 (PI: J Cheng) ClinicalTrials.gov #: NCT03367936
Citation
Please cite as:
Pulantara IW, Wang Y, Burke LE, Sereika SM, Bizhanova Z, Kariuki JK, Beatrice B, Loar I, Cedillo M, Conroy MB, Parmanto B
Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture