Evidence map›Paper›PMID 37440308›Full record

Trial reportJournal of medical Internet research2023

Assessing Interventions on Crowdsourcing Platforms to Nudge Patients for Engagement Behaviors in Primary Care Settings: Randomized Controlled Trial.

Kay-Yut Chen, Yan Lang, Yuan Zhou, Ludmila Kosmari, Kathryn Daniel, Ayse Gurses, Yan Xiao

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 23% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. 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

7 authors at 3 institutions in 1 country.

Kay-Yut Chen *College of Business, University of Texas at Arlington, Arlington, TX, United States.ORCID 0000-0001-8558-6124
Yan Lang *Department of Business, State University of New York at Oneonta, Oneonta, NY, United States.ORCID 0000-0002-4764-081X
Yuan Zhou *Department of Industrial, Manufacturing, and Systems Engineering, University of Texas at Arlington, Arlington, TX, United States.ORCID 0000-0002-4326-9160
Ludmila Kosmari *College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX, United States.ORCID 0000-0002-1325-6019
Kathryn Daniel *College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX, United States.ORCID 0000-0001-6440-2015
Ayse Gurses *Armstrong Institute Center for Health Care Human Factors, Anesthesiology and Critical Care, Emergency Medicine, and Health Sciences Informatics, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0001-7422-6852
Yan Xiao *College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX, United States.ORCID 0000-0003-2805-0619
The University of Texas at Arlington · USJohns Hopkins University · USSUNY Oneonta · US

Funding

PROMIS Learning Lab: Partnership in Resilience for Medication SafetyR18HS027277 · AHRQ · UNIVERSITY OF TEXAS ARLINGTON · PI XIAO, YAN · 2019 to 2022
$2.9M
AHRQ HHS R18 HS027277
6 · The paper itself

Abstract

backgroundEngaging patients in health behaviors is critical for better outcomes, yet many patient partnership behaviors are not widely adopted. Behavioral economics-based interventions offer potential solutions, but it is challenging to assess the time and cost needed for different options. Crowdsourcing platforms can efficiently and rapidly assess the efficacy of such interventions, but it is unclear if web-based participants respond to simulated incentives in the same way as they would to actual incentives.

objectiveThe goals of this study were (1) to assess the feasibility of using crowdsourced surveys to evaluate behavioral economics interventions for patient partnerships by examining whether web-based participants responded to simulated incentives in the same way they would have responded to actual incentives, and (2) to assess the impact of 2 behavioral economics-based intervention designs, psychological rewards and loss of framing, on simulated medication reconciliation behaviors in a simulated primary care setting.

methodsWe conducted a randomized controlled trial using a between-subject design on a crowdsourcing platform (Amazon Mechanical Turk) to evaluate the effectiveness of behavioral interventions designed to improve medication adherence in primary care visits. The study included a control group that represented the participants' baseline behavior and 3 simulated interventions, namely monetary compensation, a status effect as a psychological reward, and a loss frame as a modification of the status effect. Participants' willingness to bring medicines to a primary care visit was measured on a 5-point Likert scale. A reverse-coding question was included to ensure response intentionality.

resultsA total of 569 study participants were recruited. There were 132 in the baseline group, 187 in the monetary compensation group, 149 in the psychological reward group, and 101 in the loss frame group. All 3 nudge interventions increased participants' willingness to bring medicines significantly when compared to the baseline scenario. The monetary compensation intervention caused an increase of 17.51% (P<.001), psychological rewards on status increased willingness by 11.85% (P<.001), and a loss frame on psychological rewards increased willingness by 24.35% (P<.001). Responses to the reverse-coding question were consistent with the willingness questions.

conclusionsIn primary care, bringing medications to office visits is a frequently advocated patient partnership behavior that is nonetheless not widely adopted. Crowdsourcing platforms such as Amazon Mechanical Turk support efforts to efficiently and rapidly reach large groups of individuals to assess the efficacy of behavioral interventions. We found that crowdsourced survey-based experiments with simulated incentives can produce valid simulated behavioral responses. The use of psychological status design, particularly with a loss framing approach, can effectively enhance patient engagement in primary care. These results support the use of crowdsourcing platforms to augment and complement traditional approaches to learning about behavioral economics for patient engagement.

Indexed as

CrowdsourcingMotivationPatient ParticipationBehavior TherapyHumansPrimary Health CareSurveys and QuestionnairesAmazon Mechanical Turkbehavioral interventionscrowdsourcingmedication safetyMturkpatient engagementprimary care

Identifiers

PMID37440308
PMCPMC10375278
OpenAlexW4378576145

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.