Evidence map›Paper›PMID 33218296›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2021

Using Clinical Trial Data to Estimate the Costs of Behavioral Interventions for Potential Adopters: A Guide for Trialists.

Louise B Russell, Laurie A Norton, David Pagnotti, Christianne Sevinc, Sophia Anderson, Darra Finnerty Bigelow, Lauren G Iannotte, Michael Josephs, Ryan McGilloway, Iwan Barankay and 8 more

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Trial
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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

18 authors.

Louise B RussellDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0003-3599-0991
Laurie A NortonDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
David PagnottiDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
Christianne SevincPenn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.
Sophia AndersonDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
Darra Finnerty BigelowDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
Lauren G IannotteDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
Michael JosephsDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
Ryan McGillowayDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
Iwan BarankayDepartment of Management and Department of Business Economics and Public Policy, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
Mary E PuttDepartment of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Peter P ReeseThe Leonard Davis Institute of Health Economics (LDI), University of Pennsylvania, Philadelphia, PA, USA.
David A AschDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
Lee R GoldbergDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
Shivan J MehtaDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
Monique S TannaDepartment of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Andrea B TroxelDivision of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY, USA.
Kevin G VolppDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.

Funding

Comparative effectiveness of process and outcomes incentives for lipid managementR01HL118195 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI BARANKAY, IWAN, REESE, PETER PHILIP · 2014 to 2019
$3.9M
RCT of automated hovering for congestive heart failure managementR01HL128465 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI ASCH, DAVID A, VOLPP, KEVIN G · 2015 to 2019
$3.9M
A Randomized Controlled Trial of Incentives for Habit FormationR01AG043844 · NIA · UNIVERSITY OF PENNSYLVANIA · PI BARANKAY, IWAN, REESE, PETER PHILIP · 2012 to 2015
$2.3M
NHLBI NIH HHS R01 HL118195NHLBI NIH HHS R01 HL128465NIA NIH HHS R01 AG043844
6 · The paper itself

Abstract

Behavioral interventions involving electronic devices, financial incentives, gamification, and specially trained staff to encourage healthy behaviors are becoming increasingly prevalent and important in health innovation and improvement efforts. Although considerations of cost are key to their wider adoption, cost information is lacking because the resources required cannot be costed using standard administrative billing data. Pragmatic clinical trials that test behavioral interventions are potentially the best and often only source of cost information but rarely incorporate costing studies. This article provides a guide for researchers to help them collect and analyze, during the trial and with little additional effort, the information needed to inform potential adopters of the costs of adopting a behavioral intervention. A key challenge in using trial data is the separation of implementation costs, the costs an adopter would incur, from research costs. Based on experience with 3 randomized clinical trials of behavioral interventions, this article explains how to frame the costing problem, including how to think about costs associated with the control group, and describes methods for collecting data on individual costs: specifications for costing a technology platform that supports the specialized functions required, how to set up a time log to collect data on the time staff spend on implementation, and issues in getting data on device, overhead, and financial incentive costs.

Indexed as

Health BehaviorBehavior TherapyClinical Trials as TopicCost-Benefit AnalysisHumansbehavioral interventionclinical trialcostingmedication adherenceoutcomes researchpragmatic trialpreventive servicestranslating research into practice

Identifiers

PMID33218296
PMCPMC7772275

What OpenQuestion holds

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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.