Evidence map›Paper›PMID 42840830›Full record

ArticleJournal for the measurement of physical behaviour2026

Validating the Longitudinal Assessment of Sedentary Behavior in a Randomized Controlled Trial via a Deep Learning Algorithm.

Jasmine Morales, Rong W Zablocki, Sheri J Hartman, Arun Kumar, Animesh Kumar, Jordan A Carlson, Mikael Anne Greenwood-Hickman, Paul R Hibbing, John Staudenmayer, Chongzhi Di and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal for the measurement of physical behaviour, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Jasmine MoralesHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.
Rong W ZablockiHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.
Sheri J HartmanHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.
Arun KumarDepartment of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
Animesh KumarDepartment of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
Jordan A CarlsonCenter for Children's Healthy Lifestyles & Nutrition, Children's Mercy Hospital, Kansas City, MO, USA.
Mikael Anne Greenwood-HickmanKaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
Paul R HibbingDepartment of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, USA.
John StaudenmayerDepartment of Mathematics and Statistics, University of Massachusetts, Amherst, MA, USA.
Chongzhi DiDivision of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Jingjing ZouHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.
Steve NguyenHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.
Andrea Z LaCroixHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.
Lindsay DillonHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.
Howon RyuHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.
Lucy ShaoHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.
Weiwei ShiHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.
Fatima Tuz-ZahraSchool of Public Health, Boston University, Boston, MA, USA.
Loki NatarajanHebert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA.

Funding

Sedentary Behaviour Interrupted: Acute, medium and long-term effects on biomarkers of healthy aging, physical function and mortalityP01AG052352 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LACROIX, ANDREA Z. · 2017 to 2022
$9.0M
Statistical methods for analyzing objectively measured physical activity dataR01HL130483 · NHLBI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Chongzhi Di · 2016 to 2026
$4.4M
Novel computational techniques to detect the relationship between sitting patterns and metabolic syndrome in existing cohort studies.R01DK114945 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NATARAJAN, LOKI · 2018 to 2021
$2.6M
Leveraging deep learning to classify sitting posture and measure sedentary patterns from accelerometer data in diverse cohortsR01HL168535 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Loki Natarajan · 2024 to 2026
$2.1M
NHLBI NIH HHS R01 HL130483NHLBI NIH HHS R01 HL168535NIA NIH HHS P01 AG052352NIDDK NIH HHS R01 DK114945
6 · The paper itself

Abstract

Background: Sedentary behavior (SB) is a risk factor for cardiometabolic disease. Existing cut-points of hip-worn accelerometers to quantify SB underestimate sedentary bout durations by ignoring posture. To address this, we previously developed CHAP, Convolutional Neural Network Hip Accelerometer Posture, a deep learning algorithm that predicts sitting and breaks from sitting. In this study, we applied CHAP to a randomized controlled trial to externally validate its accuracy for classifying sitting versus non-sitting and estimating intervention-related changes in SB. Methods: CHAP was applied to 30Hz-tri-axial hip-worn ActiGraph-GT3X+ (AG) accelerometer data from free-living postmenopausal women who are overweight (≥55-yrs) in the Rise for Health RCT(n=406). CHAP-predicted SB accuracy (e.g., sensitivity) and daily pattern metrics (e.g., total sedentary time) were compared to a ground-truth device activPAL (AP) and standard cut-point method (AG100), at two timepoints for intervention arms-Healthy Living (control), Reduce Sitting, and Increase Transitions. Results: CHAP's SB accuracy metrics were above 84.8%. Mean (SE, p) intervention effect estimates (mean change, intervention minus controls) from generalized estimating equation models for (I) total sedentary time were AP 65.1 min/day (16.2, p<0.001), CHAP 38.2 min/day (15.4, p=0.013), and AG100 0.28 min/day (13.9, p=0.98) (Reduce Sitting); (II) breaks from SB were AP 22/day (4.3, p<0.001), CHAP 4/day (1.5, p=0.003), and AG100 <1/day (2.1, p=0.860) (Increase Transitions). Conclusions: This study validated CHAP on an independent cohort. CHAP exhibited high prediction accuracy, and although less sensitive than AP, CHAP detected changes in SB more effectively than AG100. Thus, CHAP could make a strong contribution to SB intervention research.

Indexed as

accelerometer datafree-livinghealthy agingintervention-related changemachine learning

Identifiers

PMID42840830
PMCPMC13641217

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.