Evidence map›Paper›PMID 42490573›Full record

ArticleJMIR human factors2026

Japanese Health Information Technology Usability Evaluation Scale for Sexually Transmitted Infection-Related Chatbots: Development and Psychometric Validation Study.

Tomoko Hato, Hirono Ishikawa, Kense Todo, Keisuke Harada, Atsushi Yoshikawa, Yoshiharu Fukuda, Rebecca Schnall

Abstract readValidation Study
In one paragraph

Article in JMIR human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Tomoko HatoGraduate School of Public Health, Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-8605, Japan, 81 3-3964-1211.ORCID 0009-0002-1208-7281
Hirono IshikawaGraduate School of Public Health, Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-8605, Japan, 81 3-3964-1211.ORCID 0000-0003-1458-6957
Kense TodoCollege of Informatics, Kanto Gakuin University, Kanagawa, Japan.ORCID 0009-0008-8252-9536
Keisuke HaradaSchool of Computing, Institute of Science Tokyo, Tokyo, Japan.ORCID 0009-0007-1984-3680
Atsushi YoshikawaCollege of Informatics, Kanto Gakuin University, Kanagawa, Japan.ORCID 0000-0001-7020-5085
Yoshiharu FukudaGraduate School of Public Health, Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-8605, Japan, 81 3-3964-1211.ORCID 000-0003-2099-1924
Rebecca SchnallSchool of Nursing, Columbia University, New York, NY, United States.ORCID 0000-0003-2184-4045

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The rapid expansion of mobile technology has accelerated the integration of health applications and conversational AI into clinical and public health practices. To ensure these tools are effective and sustainable, usability evaluations and early user engagement during development are essential. The Health Information Technology Usability Evaluation Scale (Health-ITUES) is a validated and flexible usability assessment instrument that is available in multiple languages and applicable across diverse contexts. However, a Japanese version of this scale has not yet been developed. Objective: This study aimed to translate and validate a Japanese version of the Health-ITUES, customized for a sexually transmitted infection (STI)-related chatbot, and to support the usability assessment of emerging mobile health tools in Japan. Methods: We developed a Japanese version of the Health-ITUES using a chatbot under development as a consultation tool for young women regarding STIs. First, the original scale was customized to reflect the chatbot's specific purpose and intended usage context. Following established translation guidelines, we conducted forward translation from English to Japanese, back translation, expert review, and reconciliation. We then evaluated the reliability and validity of the Japanese version in a sample of 301 young women. Results: The Japanese version of the Health-ITUES demonstrated high internal consistency (Cronbach α=0.85-0.98). Confirmatory factor analysis supported acceptable construct validity (root mean square error of approximation is 0.10, comparative fit index>0.90). Additionally, the Health-ITUES scores showed strong correlations with satisfaction and usage intention for the tool (r=0.779 and 0.797, respectively). Conclusions: The Japanese version of the Health-ITUES provides initial evidence of reliability and validity in an STI-related scenario among young women and may facilitate more rigorous usability evaluations of mHealth and conversational AI tools in Japan.

Indexed as

PsychometricsSexually Transmitted DiseasesAdultEast Asian PeopleFemaleHumansJapanReproducibility of ResultsSurveys and QuestionnairesYoung Adultartificial intelligencechatbotconversational AImHealthmobile healthsexually transmitted infectionSTItranslationusabilityvalidation

Identifiers

PMID42490573
PMCPMC13394856

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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.