Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: JMIR mHealth and uHealth

Date Submitted: Jun 17, 2023
Date Accepted: Jun 5, 2024

The final, peer-reviewed published version of this preprint can be found here:

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

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

JMIR Mhealth Uhealth 2024;12:e50043

DOI: 10.2196/50043

PMID: 39113371

PMCID: 11322796

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

JMIR Mhealth Uhealth 2024;12:e50043

DOI: 10.2196/50043

PMID: 39113371

PMCID: 11322796

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.