Evidence map›Paper›PMID 42129912›Full record

ReviewPilot and feasibility studies2026

Examining predictors of scale-up penalty in preliminary studies of health behavior interventions and subsequent larger trials: a meta-epidemiological review of diverse fields.

Lauren von Klinggraeff, Christopher D Pfledderer, Sarah Burkart, Kaitlyn Ramey, Michal Smith, Alexander C McLain, Bridget Armstrong, R Glenn Weaver, Anthony Okely, David Lubans and 6 more

Abstract readReview
In one paragraph

Review in Pilot and feasibility studies, 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

16 authors.

Lauren von KlinggraeffDepartment of Community and Behavioral Health Sciences, School of Public Health, Augusta University, 2500 Walton Way, Augusta, GA, 30904, USA. lvonklinggraeff@augusta.edu.ORCID http://orcid.org/0000-0002-4417-0701
Christopher D PfleddererSchool of Public Health in Austin, The University of Texas Health Science Center Houston, 1836 San Jacinto Blvd, Austin, TX, 78701, USA.
Sarah BurkartDepartment of Exercise Science, University of South Carolina, 921 Assembly Street, Columbia, SC, 29208, USA.
Kaitlyn RameyDepartment of Exercise Science, University of South Carolina, 921 Assembly Street, Columbia, SC, 29208, USA.
Michal SmithDepartment of Exercise Science, University of South Carolina, 921 Assembly Street, Columbia, SC, 29208, USA.
Alexander C McLainArnold School of Public Health, Department of Epidemiology and Biostatistics, University of South Carolina, 921 Assembly Street, Columbia, SC, 29208, USA.
Bridget ArmstrongDepartment of Exercise Science, University of South Carolina, 921 Assembly Street, Columbia, SC, 29208, USA.
R Glenn WeaverDepartment of Exercise Science, University of South Carolina, 921 Assembly Street, Columbia, SC, 29208, USA.
Anthony OkelySchool of Health and Society, University of Wollongong, Northfields Ave, Wollongong, New South Wales, 2500, Australia.
David LubansCentre for Active Living and Learning, University of Newcastle, University Dr, Callaghan, New South Wales, 2308, Australia.
John P A IoannidisDepartments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, 300 Pasteur Drive Stanford, Stanford, CA, 94305, USA.
Russell JagoBristol Medical School, Population Health Sciences, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK.
Gabrielle Turner-McGrievyDepartment of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, 921 Assembly Street, Columbia, SC 29208, USA.
James ThrasherDepartment of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, 921 Assembly Street, Columbia, SC 29208, USA.
Xiaoming LiDepartment of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, 921 Assembly Street, Columbia, SC 29208, USA.
Michael W BeetsDepartment of Exercise Science, University of South Carolina, 921 Assembly Street, Columbia, SC, 29208, USA.

Funding

Targeting Behavioral Adjustment and Healthy Lifestyle in Preschool-Age Children Using an Integrated Family-Based InterventionP20GM130420 · NIGMS · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI BURKART, SARAH · 2020 to 2024
$11.2M
A Meta-Epidemiological Assessment of the Role of Pilot Studies in the Design of Well-Powered Trials - the Pilot ProjectR01HL149141 · NHLBI · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI BEETS, MICHAEL W · 2019 to 2022
$2.4M
The Longitudinal Influence of Home Disorganization on Children's Sleep VariabilityF32HL154530 · NHLBI · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI BURKART, SARAH · 2020 to 2021
$137k
Predictors of Effective Scaling: A Meta-Epidemiological Study of Bias in Early-Stage Studies to Prevent Chronic DiseaseF31HL158016 · NHLBI · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI VON KLINGGRAEFF, LAUREN ELIZABETH · 2021 to 2023
$99k
NHLBI NIH HHS F31HL158016NHLBI NIH HHS F32HL154530NHLBI NIH HHS R01HL149141NIGMS NIH HHS P20GM130420
6 · The paper itself

Abstract

backgroundScale-up penalty, a common phenomenon in which the promising effects found in early preliminary studies are substantially reduced when evaluated in a subsequent larger trial, can stall the advancement of health behavior interventions. In obesity-related behavioral interventions, changes to key features between a preliminary study and subsequent larger trials inflate scale-up penalty. The purpose of this study is to examine whether changes in intervention features occur in other behavioral disciplines that utilize a similar developmental continuum wherein smaller-scale preliminary studies inform larger-scale trials, and whether changes in key features inflate scale-up penalty.

methodsWe conducted a systematic review identifying preliminary studies followed by a larger trial conducted by the same author(s) (i.e., a study pair) in four areas-tobacco/smoking cessation, alcohol use, interpersonal violence, and sexually transmitted diseases. We coded intervention features in the preliminary study and larger trial to capture changes in key study features (e.g., who delivered the intervention). Multi-level meta-regressions estimated the association between the changes to key study features and change in standardized mean difference for health outcomes and calculated scale-up penalty.

resultsWe identified 222 effects across 69 study pairs of preliminary studies with subsequent larger trials. Fifty-eight study pairs (84%) contained at least one change to a key study feature. Forty-nine study pairs were included in the meta-regressions (k = 154 effects). Changing the delivery agent, outcome measure, intervention duration, or implementation support in subsequent larger trials followed the hypothesized pattern with larger scale-up penalty (i.e., less effective at-scale) relative to study pairs that did not change those features.

conclusionsMultiple disciplines outside the obesity field make changes to key study features between preliminary studies and subsequent larger trials. Across disciplines, changes to the intervention delivery agent, intervention duration, outcome measurement, and reducing implementation support in subsequent larger trials are associated with inflated scale-up penalty. Recognition of the importance of key study features in preliminary studies and iterative intervention development could improve intervention scale-up.

Identifiers

PMID42129912
PMCPMC13340402

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.