Evidence map›Paper›PMID 24741000›Full record

ArticleStatistical methods in medical research2016

Bayesian latent structure modeling of walking behavior in a physical activity intervention.

Andrew B Lawson, Caitlyn Ellerbe, Rachel Carroll, Kassandra Alia, Sandra Coulon, Dawn K Wilson, M Lee VanHorn, Sara M St George

Abstract read
In one paragraph

Article in Statistical methods in medical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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.

Andrew B LawsonDivision of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA lawsonab@musc.edu.
Caitlyn EllerbeDivision of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
Rachel CarrollDivision of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
Kassandra AliaDepartment of Psychology, University of South Carolina, Columbia, SC, USA.
Sandra CoulonDepartment of Psychology, University of South Carolina, Columbia, SC, USA.
Dawn K WilsonDepartment of Psychology, University of South Carolina, Columbia, SC, USA.
M Lee VanHornDepartment of Psychology, University of South Carolina, Columbia, SC, USA.
Sara M St GeorgeDepartment of Psychology, University of South Carolina, Columbia, SC, USA.

Funding

Improving safety and access for physical activity supplementR01DK067615 · NIDDK · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI WILSON, DAWN K · 2007 to 2010
$3.2M
NIDDK NIH HHS R01 DK067615
6 · The paper itself

Abstract

The analysis of walking behavior in a physical activity intervention is considered. A Bayesian latent structure modeling approach is proposed whereby the ability and willingness of participants is modeled via latent effects. The dropout process is jointly modeled via a linked survival model. Computational issues are addressed via posterior sampling and a simulated evaluation of the longitudinal model's ability to recover latent structure and predictor effects is considered. We evaluate the effect of a variety of socio-psychological and spatial neighborhood predictors on the propensity to walk and the estimation of latent ability and willingness in the full study.

Indexed as

Bayes TheoremAgedExerciseExercise TherapyFemaleHumansMaleMiddle AgedPatient DropoutsWalkingBayesianinterventionjoint modelLatent structurelongitudinal dataphysical activity

Identifiers

PMID24741000
PMCPMC5388556

What OpenQuestion holds

Textmetadata
LicenceTDM
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.