Evidence map›Paper›PMID 30291084›Full record

ArticleJMIR diabetes2017

Machine or Human? Evaluating the Quality of a Language Translation Mobile App for Diabetes Education Material.

Xuewei Chen, Sandra Acosta, Adam E Barry

Open access · goldAbstract read
In one paragraph

Article in JMIR diabetes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.9field-weighted citation impact, top 6% 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

20 citing papers in PubMed, 38 citations in OpenAlex.

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  6. MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025
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  20. Health Literacy and Use and Trust in Health Information.Journal of health communication · 2018
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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

3 authors at 1 institution in 1 country.

Xuewei ChenTransdisciplinary Center for Health Equity Research, Department of Health and Kinesiology, Texas A&M University, College Station, TX, United States.ORCID http://orcid.org/0000-0001-6391-1459
Sandra AcostaTexas A&M University, Department of Educational Psychology, Texas A&M University, College Station, TX, United States.ORCID http://orcid.org/0000-0002-6433-3256
Adam E BarryTexas A&M University, Department of Health and Kinesiology, Texas A&M University, College Station, TX, United States.ORCID http://orcid.org/0000-0001-6527-6866
Texas A&M University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes is a major health crisis for Hispanics and Asian Americans. Moreover, Spanish and Chinese speakers are more likely to have limited English proficiency in the United States. One potential tool for facilitating language communication between diabetes patients and health care providers is technology, specifically mobile phones.

objectivePrevious studies have assessed machine translation quality using only writing inputs. To bridge such a research gap, we conducted a pilot study to evaluate the quality of a mobile language translation app (iTranslate) with a voice recognition feature for translating diabetes patient education material.

methodsThe pamphlet, "You are the heart of your family…take care of it," is a health education sheet for diabetes patients that outlines three recommended questions for patients to ask their clinicians. Two professional translators translated the original English sentences into Spanish and Chinese. We recruited six certified medical translators (three Spanish and three Chinese) to conduct blinded evaluations of the following versions: (1) sentences interpreted by iTranslate, and (2) sentences interpreted by the professional human translators. Evaluators rated the sentences (ranging from 1-5) on four scales: Fluency, Adequacy, Meaning, and Severity. We performed descriptive analyses to examine the differences between these two versions.

resultsCronbach alpha values exhibited high degrees of agreement on the rating outcomes of both evaluator groups: .920 for the Spanish raters and .971 for the Chinese raters. The readability scores generated using MS Word's Flesch-Kincaid Grade Level for these sentences were 0.0, 1.0, and 7.1. We found iTranslate generally provided translation accuracy comparable to human translators on simple sentences. However, iTranslate made more errors when translating difficult sentences.

conclusionsAlthough the evidence from our study supports iTranslate's potential for supplementing professional human translators, further evidence is needed. For this reason, mobile language translation apps should be used with caution.

Indexed as

diabeteshealth communicationhealth educationhealth literacyhuman interpreterlanguage translationmachine translationmobile translation apptranslator

Identifiers

PMID30291084
PMCPMC6238892
OpenAlexW2725076736

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.