The purpose of the workshop was to identify and evaluate innovative technologies for the remote measurement, collection, and transmittal of medical, biological, environmental, and exposure-related data for the National Children’s Study (Study). The workshop was structured around three workgroups:
These workgroups were chaired, respectively, by Richard Shiffman, M.D., Yale University; David Songco, NICHD, NIH, DHHS; and James J. Quackenboss, M.S., EPA.
Medical and Biological Measurements Workgroup
The workgroup reviewed the five thematic areas of the Study to focus their discussion on technologies that could be applied to medical and biological measurements. The five thematic areas are asthma, obesity and physical development, neurodevelopment and behavior, injury, and pregnancy outcomes.
The workgroup determined that there were several data and specimen collection processes that would be overarching among the thematic areas of the Study. The workgroup labeled these processes “big picture items” and determined that each of these items would need to be considered when developing the application of technology for data collection within each of the five thematic areas. The “big picture items” identified by the workgroup were:
-
Remote physiological measurements
-
Specimen collection
-
Health records collection (from physicians, pharmacists, and image information)
-
Other clinical sources
-
Genetic information.
The workgroup evaluated the different technologies that could be developed to obtain medical and biological measurements during the Study.
Key Findings of the Medical and Biological Measurements Workgroup
The workgroup evaluated the different technologies that could be developed to obtain medical and biological measurements during the National Children’s Study. The workgroup categorized the technologies using the following framework:
-
The availability of or time to develop the technology
– Currently available
– Short development time
– Long development time
-
The value quadrant of the technology to the Study
– 1 = high value and low effort
– 2 = high value and high effort
– 3 = low value and low effort
– 4 = low value and high effort
-
The burden that using the technology would impose on the participant or the provider
– 0 = none
– 1 = very low burden
– 2 = low burden
– 3 = high burden
– 4 = very high burden
– N/A = not applicable.
The following table lists only those technologies that have high value and low burden. The other technologies were deemed less desirable due to high effort or burden.
|
Technology |
Time to Develop |
Value Quadrant |
Burden to Patient |
Burden to Provider |
|
Physical Parameters |
|
Diaper sensors |
Long |
2 |
1 |
N/A |
|
Clothing sensors |
Short |
2 |
1 |
N/A |
|
Electronic game (child focused) |
Long |
2 |
1 |
N/A |
|
Home spirometry game package |
Current |
1 |
1 or 2 |
N/A |
|
Accelerometer |
Short |
3 |
1 |
N/A |
|
Accelerometer in helmet |
Current |
1 |
1 |
N/A |
|
Accelerometer in shoe |
Short |
1 |
1 |
N/A |
|
Accelerometer in temporary tattoo |
Current |
1 |
2 or 3 |
N/A |
|
Diary |
Current |
1 |
2 or 3 |
N/A |
|
Specimen Collection |
|
Filter paper blood sample |
Current |
1 |
2 |
N/A |
|
Home chemistry kit, for example, cartridge analysis of urine |
Current |
1 |
1 |
N/A |
|
RF ID tracking |
Current |
1 |
0 |
N/A |
|
RF ID urine analysis |
Short/Long |
2 |
1 |
N/A |
|
Blood draws by experienced staff |
Current |
2 |
2 or 3 |
N/A |
|
Swab it and mail |
Current |
1 |
1 |
N/A |
|
Hair—at home |
Current |
1 |
1 |
N/A |
|
Semen |
Current |
2 |
2 |
N/A |
|
Milk |
Current |
2 |
2 |
N/A |
|
Primary Care Physician Medical Records
and Pharmacy Records |
|
Electronic medical records |
Current/Long |
2 |
0 |
2 or 3 |
|
Web |
Current |
2 |
0 |
2 or 3 |
|
Personal data assistants (PDAs) |
Current |
2 |
0 |
2 or 3 |
|
Photocopy and fax/scan |
Current |
2 |
0 |
2 or 3 |
|
Abstraction by Study personnel |
Current |
2 |
0 |
1 |
|
Interface to pharmacy database |
Current |
2 |
0 |
1 or 2 |
|
Scannable bar code on prescriptions |
Short |
2 |
1 |
1 or 2 |
|
Record to smart card for
patient history record |
Current/Long |
2 |
2 |
3 |
Images |
|
Pregnancy ultrasounds (2-D) |
Current |
1 |
1 |
N/A |
|
Pregnancy ultrasounds (3-D) |
Current |
2 |
1 |
N/A |
|
Digitized spirometry signals |
Current |
2 |
1 |
N/A |
|
Noninvasive images of arteries to identify blood plaques in youngsters |
Current |
3 |
1 |
N/A |
|
Digital photos of neighborhoods |
Current |
1 or 2 |
1 |
N/A |
|
Images of birth defects |
Current |
1 |
1 |
N/A |
|
Images for bone age |
Current |
1 |
3 |
N/A |
|
DEXA fat/lean mass |
Current |
1 |
3 |
N/A |
|
Bioimpedance assessment |
Current |
3 |
1 |
N/A |
|
|
Current |
|
|
N/A |
Genetic Information |
|
Buccal cells |
Current |
1 |
1 |
N/A |
|
Blood current |
Current |
1 |
2 |
1 |
|
Cord blood |
Current |
1 or 2 |
0 |
2 |
|
Nails |
Current |
1 |
1 |
N/A |
|
Mouthwash |
Current |
1 |
1 |
N/A |
|
Tissues as available |
Current |
1 |
0 |
2 |
|
Computer-Assisted Telephone Interviewing (CATI ) |
Current |
1 |
1 or 2 |
N/A |
|
|
|
|
|
|
|
|
|
Data Collection Workgroup
The goals of the workgroup were to:
-
Discuss and discover innovative approaches to the collection of data that could benefit the Study
-
Determine data collection architectural directions that will facilitate the adoption of new technology
-
Establish criteria for evaluation of innovative data collection technologies in the Study
-
Define approaches to leverage technology to reduce respondent burden and improve retention of participants
-
Define parameters to improve and assure the quality of data.
The presentations by the invited workgroup members covered the following topics:
-
Criteria for evaluating innovative data collection technologies
-
Considerations in data architecture and data management
-
Lessons learned from multisite studies
-
Hybrid communications architecture networks
-
Secure centralized portals
-
Quality assurance considerations
-
Automated remote data collection
-
Leveraging technology to improve participant retention.
Key Findings of the Data Collection Workgroup
The key areas identified included:
-
Data collection objectives
-
Study design
-
Standard operating procedure
-
Standards versus flexibility
-
Study model
-
Human subject protection
-
Intervention and study bias
-
Burden on participants and staff
-
Training; interpersonal communications
-
Security strategies
-
Audit trails
-
Quality assurance
-
Database maintenance
-
System support
-
System equipment
-
Evaluation criteria for technology selection
-
Validation and verification of instrumentation
-
Incorporating new technology
-
Multiple technology modes
-
Equipment procurement
-
Data characteristics
-
Data format
-
Data ownership
-
Data sharing
-
Data quality
-
Other types of data.
In addition to the key areas to be considered in the remote collection of data, other considerations that were discussed include:
Environmental Measurements Workgroup
The Environmental Measurements Workgroup focused on three general topics:
-
Environmental measurements and samples
-
Questionnaires and interviews
-
Location and activity patterns.
The workgroup compiled the following list of technologies that were ranked as being high in their potential value to the Study and could be developed with relatively low effort:
-
Sensors for triggers, data capture
-
Screening samples and field laboratories
-
PDAs for sampling information, questionnaires, and bar codes
-
GPS/accelerometers/GIS for location and activity information
-
RF IDfor consumer products and medicine use
-
Standards (open architecture)
-
Portable instruments.
Portable instruments in multiple homes, bar codes (burden/compliance), and PDA/diary (compliance) were considered to have lower value and low effort. Prepregnancy enrollment, sensor data capture, and developing new sensors (for PM, chemicals) are potentially high value but with a high level of effort required. The workgroup recognized that the characterization of the technologies is based on many assumptions. The following table illustrates those technologies that yield the highest value with the least effort.
Key Findings of the Environmental Measurements Workgroup
|
Technology |
Value |
Effort |
|
Sensors for triggers, data capture |
High |
Low |
|
Screening samples and field laboratories |
High |
Low |
|
PDA for sampling information, questionnaires, and bar codes |
High |
Low |
|
GPS, accelerometers, and GIS for location and activity information |
High |
Low |
|
RF ID for consumer product and medicine use |
High |
Low |
|
Standards (open architecture) |
High |
Low |
|
Portable Instruments |
High |
Low |
Participants
Gerry Akland, RTI
Charlotte Andersen, Cincinnati Children’s Hospital Medical Center
Steve Bedosky, Levine Fricke
Arthur Bennett, NICHD, NIH, DHHS
Lew Berman, NHANES, CDC, DHHS
Greg Binzer, Westat
Margo Brinkley, RTI
Rick Chestek, Booz Allen Hamilton Inc.
Bob Clickner, Westat
Carry Croghan, EPA
Tom Dumyahn, Harvard School of Public Health
Kai Elgethun, University of Washington
Dan Ewert, North Dakota State University
Lara Gundel, Lawrence Berkeley Labs
Debbie Hillard, NHANES/Westat
Joel Jorgenson, North Dakota State University
Sarah Keim, NICHD, NIH, DHHS
Alex Lu, University of Washington
Juliana Maantay, Lehman College
Penny Manasco, First Genetic Trust
Marsha Marsh, EPA
John Menkedick, Battelle
Judie Mopsik, Abt
Marcia Nishioka, Battelle
Anna Orlova, John Hopkins University
Yechiam (Ami) Ostchega, NHANES, CDC, DHHS
Haluk Ozkaynak, EPA
Jennifer Peck, Texas A&M University
John P. Pestian, Cincinnati Children’s Hospital Medical Center
James J. Quackenboss, M.S., EPA
Charles Rhodes, RTI
Michael Rozendaal, Iowa Foundation for Medical Care
Kathy Schneider, Iowa Foundation for Medical Care
Brian Smith, Pennsylvania State University
David Songco, NICHD, NIH, DHHS
Paul Swidersky, Quality Associates
John Terrell, Booz Allen Hamilton Inc.
Jeffrey White, Miami Children’s Hospital