Evidence map›Paper›PMID 35295620›Full record

ArticleFrontiers in digital health2022

Applying the Digital Health Social Justice Guide.

Caroline A Figueroa, Hikari Murayama, Priscila Carcamo Amorim, Alison White, Ashley Quiterio, Tiffany Luo, Adrian Aguilera, Angela D R Smith, Courtney R Lyles, Victoria Robinson and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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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

11 authors.

Caroline A FigueroaSchool of Social Welfare, University of California, Berkeley, Berkeley, CA, United States.
Hikari MurayamaD-Lab, University of California, Berkeley, Berkeley, CA, United States.
Priscila Carcamo AmorimD-Lab, University of California, Berkeley, Berkeley, CA, United States.
Alison WhiteD-Lab, University of California, Berkeley, Berkeley, CA, United States.
Ashley QuiterioD-Lab, University of California, Berkeley, Berkeley, CA, United States.
Tiffany LuoSchool of Social Welfare, University of California, Berkeley, Berkeley, CA, United States.
Adrian AguileraSchool of Social Welfare, University of California, Berkeley, Berkeley, CA, United States.
Angela D R SmithSchool of Information, University of Texas at Austin, Austin, TX, United States.
Courtney R LylesUCSF Center for Vulnerable Populations in the Division of General Internal Medicine San Francisco, Zuckerberg San Francisco General Hospital, San Francisco, CA, United States.
Victoria RobinsonEthnic Studies, University of California, Berkeley, Berkeley, CA, United States.
Claudia von VacanoD-Lab, University of California, Berkeley, Berkeley, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Digital health, the use of apps, text-messaging, and online interventions, can revolutionize healthcare and make care more equitable. Currently, digital health interventions are often not designed for those who could benefit most and may have unintended consequences. In this paper, we explain how privacy vulnerabilities and power imbalances, including racism and sexism, continue to influence health app design and research. We provide guidelines for researchers to design, report and evaluate digital health studies to maximize social justice in health. Methods: From September 2020 to April 2021, we held five discussion and brainstorming sessions with researchers, students, and community partners to develop the guide and the key questions. We additionally conducted an informal literature review, invited experts to review our guide, and identified examples from our own digital health study and other studies. Results: We identified five overarching topics with key questions and subquestions to guide researchers in designing or evaluating a digital health research study. The overarching topics are: 1. Equitable distribution; 2. Equitable design; 3. Privacy and data return; 4. Stereotype and bias; 5. Structural racism. Conclusion: We provide a guide with five key topics and questions for social justice digital health research. Encouraging researchers and practitioners to ask these questions will help to spark a transformation in digital health toward more equitable and ethical research. Future work needs to determine if the quality of studies can improve when researchers use this guide.

Indexed as

digital health (eHealth)equitymobile health (mHealth)privacy and securityracism and antiracismsocial justice

Identifiers

PMID35295620
PMCPMC8918521

What OpenQuestion holds

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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.