Evidence map›Paper›PMID 35072816›Full record

SynthesisJournal of medical systems2022

Development and Validation of Digital Health Technology Literacy Assessment Questionnaire.

Junghee Yoon, Mangyeong Lee, Jin Seok Ahn, Dongryul Oh, Soo-Yong Shin, Yoon Jung Chang, Juhee Cho

Registry-linked trialAbstract readSystematic Review
In one paragraph

Synthesis in Journal of medical systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07039682 (Developing a Digital-Based Education and Self-Screening Model to Improve Knowledge, Self-Awareness, and HIV Testing Coverage Among Adolescents), which is not on this map. Cited by 30 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 3 pooled it
–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.

NCT07039682 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

Developing a Digital-Based Education and Self-Screening Model to Improve Knowledge, Self-Awareness, and HIV Testing Coverage Among Adolescents

TypeinterventionalSponsorUniversitas Sebelas MaretRan2025 to 2025Enrolled400ConditionsHuman Immunodeficiency Virus (HIV) Infection, HIV Prevention, Adolescent Health, Digital Education InterventionsArmsDigital HIV Education and Self-Screening (EKSTRIM Model)
3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Efficacy of the PRO-CTCAE mobile application for improving patient participation in symptom management during cancer treatment: a randomized controlled trial.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2023
    Trial
  5. Review
  6. Article
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  16. eHealth Literacy Assessment Instruments: Scoping Review.Journal of medical Internet research · 2025
    Article
  17. Article
  18. Article
  19. Article
  20. 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.

Junghee YoonDepartment of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea.
Mangyeong LeeDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.
Jin Seok AhnDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Dongryul OhDepartment of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Soo-Yong ShinDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.
Yoon Jung ChangDepartment of Cancer Control and Population Health, National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Korea.
Juhee ChoDepartment of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea. jcho@skku.edu.

Funding

National Research Foundation NRF-2018R1D1A1B07047833
6 · The paper itself

Abstract

In clinical practice, assessing digital health literacy is important to identify patients who may encounter difficulties adapting to digital health using digital technology and service. We developed the Digital Health Technology Literacy Assessment Questionnaire (DHTL-AQ) to assess the ability to use digital health technology, services, and data. The DHTL-AQ was developed in three phases. In the first phase, the conceptual framework and domains and items were generated from a systematic literature review using relevant theory and surveys. In the second phase, a cross-sectional survey with 590 adults age ≥ 18 years was conducted at an academic hospital in Seoul, Korea in January and February 2020 to test face validity of the items. Then, psychometric validation was conducted to determine the final items and cut-off scores of the DHTL-AQ. The eHealth literacy scale, the Newest Vital Sign, and 10 mobile app task ability assessments were examined to test validity. The final DHTL-AQ includes 34 items in two domains (digital functional and digital critical literacy) and 4 categories (Information and Communications Technology terms, Information and Communications Technology icons, use of an app, evaluating reliability and relevance of health information). The DHTL-AQ had excellent internal consistency (overall Cronbach's α = 0.95; 0.87-0.94 for subtotals) and acceptable model fit (CFI = 0.821, TLI = 0.807, SRMR = 0.065, RMSEA = 0.090). The DHTL-AQ was highly correlated with task ability assessment (r = 0.7591), and moderately correlated with the eHealth literacy scale (r = 0.5265) and the Newest Vital Sign (r = 0.5929). The DHTL-AQ is a reliable and valid instrument to measure digital health technology literacy.

Indexed as

Biomedical TechnologyDigital TechnologyAdolescentAdultCross-Sectional StudiesHumansPsychometricsReproducibility of ResultsSurveys and QuestionnairesDigital health literacyDigital health technology literacyMeasurement scalePerformance-based task abilityTool development

Identifiers

PMID35072816
PMCPMC8784987

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.