A systematic review of digital and wearable sensor data in the ABCD study: Insights, methods, and emerging opportunities.
11 affiliations
- 1Psychology Department, University of Maine, Orono, ME, United States. Electronic address: [email protected].
- 2Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States.
- 3School of Medicine, University of Maryland at Baltimore, Baltimore, MD, United States.
- 4J. Craig Venter Institute, La Jolla, CA, United States.
- 5Center for Health Sciences, SRI International, Menlo Park, CA, United States.
- 6University of Texas Health Science Center at Houston (UTHealth) School of Public Health - Austin Campus, Austin, TX, United States.
- 7Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States.
- 8National Institute on Drug Abuse Intramural Research Program, Baltimore, MD, United States.
- 9Center for Behavioral Health Research, University of Tennessee Knoxville, Knoxville, TN, United States.
- 10Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States.
- 11Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, VA, United States.
Abstract
Research into key adolescent health domains, like problematic screen-based media use, sleep health, and physical activity, are frequently limited by error-prone self-report-based measures. To improve the measurement of such behaviors, the Adolescent Brain Cognitive Development Study (ABCD), a diverse longitudinal cohort study of nearly 12,000 U.S. youth (ages 9-10 at Baseline), includes remote sensing protocols during data collection. Consenting participants provided up to three weeks of Fitbit actigraphy data, beginning at the year-two follow up, and smartphone usage data, beginning at the year-four follow up, collected via the Effortless Assessment of Research Systems (EARS) smartphone application. This systematic review aimed to identify and synthesize all published empirical studies which have utilized the ABCD Study's passive sensor data, identifying 45 articles investigating measurement validity, and associations with adolescent health, development, and neurocognitive functioning. Fitbit devices broadly reproduced estimates of sleep and physical activity obtained from research-grade sensors. Smartphone sensor data were internally consistent, reproduced expected daily usage patterns, and were moderately correlated with self-report. Most substantive studies utilized Fitbit data and reported cross-sectional associations between sensor-derived single-item summary statistics and survey-based measures of mental and behavioral health, neurocognitive functioning, or environmental risk factors. A smaller number of studies utilized longitudinal designs, within-person analyses, machine learning approaches, and/or genetic or neuroimaging methods to reduce confounding and enhance biological and etiological insight into these observed relationships. While published findings were largely incremental in nature, ABCD's passive sensing resources nonetheless represent a powerful, widely available resource for incorporating sensor-based measures into adolescent health research.
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BibTeX
@article{alexander2026systematic,
title = {{A systematic review of digital and wearable sensor data in the ABCD study: Insights, methods, and emerging opportunities.}},
author = {Jordan D Alexander and Sawyer Mills and Kelsey Sumpter and Calen J Smith and Biplabendu Das and Ruby Fan and Ethan T Hunt and Sandra A Brown and Brenda L Curtis and Kevin M Cummins and Michael Mason and Michael C Neale and Susan F Tapert and Fiona C Baker and Bernard F Fuemmeler and Natasha E Wade},
journal = {Developmental cognitive neuroscience},
year = {2026},
month = sep,
doi = {10.1016/j.dcn.2026.101826},
url = {https://europepmc.org/article/MED/42843149},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A systematic review of digital and wearable sensor data in the ABCD study: Insights, methods, and emerging opportunities.
AU - Jordan D Alexander
AU - Sawyer Mills
AU - Kelsey Sumpter
AU - Calen J Smith
AU - Biplabendu Das
AU - Ruby Fan
AU - Ethan T Hunt
AU - Sandra A Brown
AU - Brenda L Curtis
AU - Kevin M Cummins
AU - Michael Mason
AU - Michael C Neale
AU - Susan F Tapert
AU - Fiona C Baker
AU - Bernard F Fuemmeler
AU - Natasha E Wade
PY - 2026
DA - 2026/09/21
JO - Developmental cognitive neuroscience
DO - 10.1016/j.dcn.2026.101826
UR - https://europepmc.org/article/MED/42843149
AB - Research into key adolescent health domains, like problematic screen-based media use, sleep health, and physical activity, are frequently limited by error-prone self-report-based measures. To improve the measurement of such behaviors, the Adolescent Brain Cognitive Development Study (ABCD), a diverse longitudinal cohort study of nearly 12,000 U.S. youth (ages 9-10 at Baseline), includes remote sensing protocols during data collection. Consenting participants provided up to three weeks of Fitbit actigraphy data, beginning at the year-two follow up, and smartphone usage data, beginning at the year-four follow up, collected via the Effortless Assessment of Research Systems (EARS) smartphone application. This systematic review aimed to identify and synthesize all published empirical studies which have utilized the ABCD Study's passive sensor data, identifying 45 articles investigating measurement validity, and associations with adolescent health, development, and neurocognitive functioning. Fitbit devices broadly reproduced estimates of sleep and physical activity obtained from research-grade sensors. Smartphone sensor data were internally consistent, reproduced expected daily usage patterns, and were moderately correlated with self-report. Most substantive studies utilized Fitbit data and reported cross-sectional associations between sensor-derived single-item summary statistics and survey-based measures of mental and behavioral health, neurocognitive functioning, or environmental risk factors. A smaller number of studies utilized longitudinal designs, within-person analyses, machine learning approaches, and/or genetic or neuroimaging methods to reduce confounding and enhance biological and etiological insight into these observed relationships. While published findings were largely incremental in nature, ABCD's passive sensing resources nonetheless represent a powerful, widely available resource for incorporating sensor-based measures into adolescent health research.
SN - 1878-9293
SN - 1878-9307
ER -