Evidence map›Paper›PMID 32026745›Full record

Trial reportHealth informatics journal2020

Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes.

Stephanie P Goldstein, J Graham Thomas, Gary D Foster, Gabrielle Turner-McGrievy, Meghan L Butryn, James D Herbert, Gerald J Martin, Evan M Forman

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Health informatics journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 4 pooled it
2.2field-weighted citation impact, top 8% of its field
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

27 citing papers in PubMed, 4 syntheses or guidelines pooled it, 40 citations in OpenAlex.

  1. Pooled it
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  8. Review
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  12. Replicable Bandits for Digital Health Interventions.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025
    Article
  13. Review
  14. Longitudinal User Engagement with Microinteraction Ecological Momentary Assessment (μEMA).Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025
    Article
  15. Review
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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

8 authors at 5 institutions in 1 country.

Stephanie P GoldsteinORCID 0000-0002-9656-1675
J Graham ThomasThe Warren Alpert Medical School of Brown University, USA.
Gary D FosterWW (Weight Watchers), USA; University of Pennsylvania, USA.
Gabrielle Turner-McGrievyUniversity of South Carolina, USA.
Meghan L ButrynDrexel University, USA.
James D HerbertUniversity of New England, USA.
Gerald J Martin
Evan M FormanDrexel University, USA.
Drexel University · USBrown University · USCalifornia University of Pennsylvania · USUniversity of New England · USUniversity of South Carolina · US

Funding

Applying Novel Passive Sensing Technology to Target Adherence to Diet in Behavioral Obesity Treatment for Patients with Cardiovascular Disease RiskF32HL143954 · NHLBI · MIRIAM HOSPITAL · PI GOLDSTEIN, STEPHANIE PAIGE · 2018 to 2019
$119k
NHLBI NIH HHS F32 HL143954
6 · The paper itself

Abstract

Suboptimal weight losses are partially attributable to lapses from a prescribed diet. We developed an app (OnTrack) that uses ecological momentary assessment to measure dietary lapses and relevant lapse triggers and provides personalized intervention using machine learning. Initially, tension between user burden and complete data was resolved by presenting a subset of lapse trigger questions per ecological momentary assessment survey. However, this produced substantial missing data, which could reduce algorithm performance. We examined the effect of more questions per ecological momentary assessment survey on algorithm performance, app utilization, and behavioral outcomes. Participants with overweight/obesity (

Indexed as

Weight Reduction ProgramsAlgorithmsHumansObesityOverweightWeight Lossdietmachine learningmHealthmobile healthweight loss

Identifiers

PMID32026745
PMCPMC8925642
OpenAlexW3005359970

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.