Translation, cultural adaptation and validity assessment of the Dutch version of the eHealth Literacy Questionnaire: a mixed-method approach
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Translation, cultural adaptation and validity assessment of the Dutch version of the eHealth Literacy Questionnaire : a mixed-method approach. / Poot, Charlotte C.; Meijer, Eline; Fokkema, Marjolein; Chavannes, Niels H.; Osborne, Richard H.; Kayser, Lars.
In: BMC Public Health, Vol. 23, No. 1, 1006, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Translation, cultural adaptation and validity assessment of the Dutch version of the eHealth Literacy Questionnaire
T2 - a mixed-method approach
AU - Poot, Charlotte C.
AU - Meijer, Eline
AU - Fokkema, Marjolein
AU - Chavannes, Niels H.
AU - Osborne, Richard H.
AU - Kayser, Lars
N1 - Publisher Copyright: © 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - Background: The digitalization of healthcare requires users to have sufficient competence in using digital health technologies. In the Netherlands, as well as in other countries, there is a need for a comprehensive, person-centered assessment of eHealth literacy to understand and address eHealth literacy related needs, to improve equitable uptake and use of digital health technologies. Objective: We aimed to translate and culturally adapt the original eHealth Literacy Questionnaire (eHLQ) to Dutch and to collect initial validity evidence. Methods: The eHLQ was translated using a systematic approach with forward translation, an item intent matrix, back translation, and consensus meetings with the developer. A validity-driven and multi-study approach was used to collect validity evidence on 1) test content, 2) response processes and 3) internal structure. Cognitive interviews (n = 14) were held to assess test content and response processes (Study 1). A pre-final eHLQ version was completed by 1650 people participating in an eHealth study (Study 2). A seven-factor Confirmatory Factor Analysis (CFA) model was fitted to the data to assess the internal structure of the eHLQ. Invariance testing was performed across gender, age, education and current diagnosis. Results: Cognitive interviews showed some problems in wording, phrasing and resonance with individual’s world views. CFA demonstrated an equivalent internal structure to the hypothesized (original) eHLQ with acceptable fit indices. All items loaded substantially on their corresponding latent factors (range 0.51–0.81). The model was partially metric invariant across all subgroups. Comparison of scores between groups showed that people who were younger, higher educated and who had a current diagnosis generally scored higher across domains, however effect sizes were small. Data from both studies were triangulated, resulting in minor refinements to eight items and recommendations on use, score interpretation and reporting. Conclusion: The Dutch version of the eHLQ showed strong properties for assessing eHealth literacy in the Dutch context. While ongoing collection of validity evidence is recommended, the evidence presented indicate that the eHLQ can be used by researchers, eHealth developers and policy makers to identify eHealth literacy needs and inform the development of eHealth interventions to ensure that people with limited digital access and skills are not left behind.
AB - Background: The digitalization of healthcare requires users to have sufficient competence in using digital health technologies. In the Netherlands, as well as in other countries, there is a need for a comprehensive, person-centered assessment of eHealth literacy to understand and address eHealth literacy related needs, to improve equitable uptake and use of digital health technologies. Objective: We aimed to translate and culturally adapt the original eHealth Literacy Questionnaire (eHLQ) to Dutch and to collect initial validity evidence. Methods: The eHLQ was translated using a systematic approach with forward translation, an item intent matrix, back translation, and consensus meetings with the developer. A validity-driven and multi-study approach was used to collect validity evidence on 1) test content, 2) response processes and 3) internal structure. Cognitive interviews (n = 14) were held to assess test content and response processes (Study 1). A pre-final eHLQ version was completed by 1650 people participating in an eHealth study (Study 2). A seven-factor Confirmatory Factor Analysis (CFA) model was fitted to the data to assess the internal structure of the eHLQ. Invariance testing was performed across gender, age, education and current diagnosis. Results: Cognitive interviews showed some problems in wording, phrasing and resonance with individual’s world views. CFA demonstrated an equivalent internal structure to the hypothesized (original) eHLQ with acceptable fit indices. All items loaded substantially on their corresponding latent factors (range 0.51–0.81). The model was partially metric invariant across all subgroups. Comparison of scores between groups showed that people who were younger, higher educated and who had a current diagnosis generally scored higher across domains, however effect sizes were small. Data from both studies were triangulated, resulting in minor refinements to eight items and recommendations on use, score interpretation and reporting. Conclusion: The Dutch version of the eHLQ showed strong properties for assessing eHealth literacy in the Dutch context. While ongoing collection of validity evidence is recommended, the evidence presented indicate that the eHLQ can be used by researchers, eHealth developers and policy makers to identify eHealth literacy needs and inform the development of eHealth interventions to ensure that people with limited digital access and skills are not left behind.
KW - Digital health
KW - eHealth
KW - Health literacy
KW - Psychometrics
KW - Questionnaire design
KW - Translation
U2 - 10.1186/s12889-023-15869-4
DO - 10.1186/s12889-023-15869-4
M3 - Journal article
C2 - 37254148
AN - SCOPUS:85160671869
VL - 23
JO - BMC Public Health
JF - BMC Public Health
SN - 1471-2458
IS - 1
M1 - 1006
ER -
ID: 360385861