Evidence map›Paper›PMID 34982038›Full record

ArticleJMIR human factors2022

Using Health Concept Surveying to Elicit Usable Evidence: Case Studies of a Novel Evaluation Methodology.

Alex Mariakakis, Ravi Karkar, Shwetak N Patel, Julie A Kientz, James Fogarty, Sean A Munson

Open access · goldAbstract read
In one paragraph

Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 44% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

6 authors at 2 institutions in 2 countries.

Alex MariakakisDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-9986-3345
Ravi KarkarSchool of Computer Science & Engineering, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0003-1467-4439
Shwetak N PatelSchool of Computer Science & Engineering, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0002-6300-4389
Julie A KientzDepartment of Human Centered Design & Engineering, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0001-7437-7861
James FogartySchool of Computer Science & Engineering, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0001-9194-934X
Sean A MunsonDepartment of Human Centered Design & Engineering, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0002-0472-6138
University of Washington · USUniversity of Toronto · CA

Funding

Open Tools for Self-Tracking, Self Experimentation, and Patient-Provider Collaboration in Symptom Self-Management and Clinical CareR01LM012810 · NLM · UNIVERSITY OF WASHINGTON · PI FOGARTY, JAMES A · 2018 to 2021
$1.3M
Evaluating a novel, portable, self-administered device ("Flicker-App") that measures critical flicker frequency as a test for minimal hepatic encephalopathy in cirrhosisR21DK117431 · NIDDK · UNIVERSITY OF WASHINGTON · PI FOGARTY, JAMES A, IOANNOU, GEORGE · 2019 to 2020
$411k
NIDDK NIH HHS R21 DK117431NLM NIH HHS R01 LM012810
6 · The paper itself

Abstract

backgroundDevelopers, designers, and researchers use rapid prototyping methods to project the adoption and acceptability of their health intervention technology (HIT) before the technology becomes mature enough to be deployed. Although these methods are useful for gathering feedback that advances the development of HITs, they rarely provide usable evidence that can contribute to our broader understanding of HITs.

objectiveIn this research, we aim to develop and demonstrate a variation of vignette testing that supports developers and designers in evaluating early-stage HIT designs while generating usable evidence for the broader research community.

methodsWe proposed a method called health concept surveying for untangling the causal relationships that people develop around conceptual HITs. In health concept surveying, investigators gather reactions to design concepts through a scenario-based survey instrument. As the investigator manipulates characteristics related to their HIT, the survey instrument also measures proximal cognitive factors according to a health behavior change model to project how HIT design decisions may affect the adoption and acceptability of an HIT. Responses to the survey instrument were analyzed using path analysis to untangle the causal effects of these factors on the outcome variables.

resultsWe demonstrated health concept surveying in 3 case studies of sensor-based health-screening apps. Our first study (N=54) showed that a wait time incentive could influence more people to go see a dermatologist after a positive test for skin cancer. Our second study (N=54), evaluating a similar application design, showed that although visual explanations of algorithmic decisions could increase participant trust in negative test results, the trust would not have been enough to affect people's decision-making. Our third study (N=263) showed that people might prioritize test specificity or sensitivity depending on the nature of the medical condition.

conclusionsBeyond the findings from our 3 case studies, our research uses the framing of the Health Belief Model to elicit and understand the intrinsic and extrinsic factors that may affect the adoption and acceptability of an HIT without having to build a working prototype. We have made our survey instrument publicly available so that others can leverage it for their own investigations.

Indexed as

digital healthhealth belief modelhealth intervention technologyhealth screeninghealth technologymobile healthmobile phonepath analysissurvey instrumentuser design

Identifiers

PMID34982038
PMCPMC8764610
OpenAlexW4205159797

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.