A Multidimensional Tool Based on the eHealth Literacy Framework: Development and Initial Validity Testing of the eHealth Literacy Questionnaire (eHLQ)

Lars Kayser, Astrid Karnoe, Dorthe Furstrand, Roy Batterham, Karl Bang Christensen, Gerald Elsworth, Richard H Osborne, Lars Kayser, Astrid Karnoe, Dorthe Furstrand, Roy Batterham, Karl Bang Christensen, Gerald Elsworth, Richard H Osborne

Abstract

Background: For people to be able to access, understand, and benefit from the increasing digitalization of health services, it is critical that services are provided in a way that meets the user's needs, resources, and competence.

Objective: The objective of the study was to develop a questionnaire that captures the 7-dimensional eHealth Literacy Framework (eHLF).

Methods: Draft items were created in parallel in English and Danish. The items were generated from 450 statements collected during the conceptual development of eHLF. In all, 57 items (7 to 9 items per scale) were generated and adjusted after cognitive testing. Items were tested in 475 people recruited from settings in which the scale was intended to be used (community and health care settings) and including people with a range of chronic conditions. Measurement properties were assessed using approaches from item response theory (IRT) and classical test theory (CTT) such as confirmatory factor analysis (CFA) and reliability using composite scale reliability (CSR); potential bias due to age and sex was evaluated using differential item functioning (DIF).

Results: CFA confirmed the presence of the 7 a priori dimensions of eHLF. Following item analysis, a 35-item 7-scale questionnaire was constructed, covering (1) using technology to process health information (5 items, CSR=.84), (2) understanding of health concepts and language (5 items, CSR=.75), (3) ability to actively engage with digital services (5 items, CSR=.86), (4) feel safe and in control (5 items, CSR=.87), (5) motivated to engage with digital services (5 items, CSR=.84), (6) access to digital services that work (6 items, CSR=.77), and (7) digital services that suit individual needs (4 items, CSR=.85). A 7-factor CFA model, using small-variance priors for cross-loadings and residual correlations, had a satisfactory fit (posterior productive P value: .27, 95% CI for the difference between the observed and replicated chi-square values: -63.7 to 133.8). The CFA showed that all items loaded strongly on their respective factors. The IRT analysis showed that no items were found to have disordered thresholds. For most scales, discriminant validity was acceptable; however, 2 pairs of dimensions were highly correlated; dimensions 1 and 5 (r=.95), and dimensions 6 and 7 (r=.96). All dimensions were retained because of strong content differentiation and potential causal relationships between these dimensions. There is no evidence of DIF.

Conclusions: The eHealth Literacy Questionnaire (eHLQ) is a multidimensional tool based on a well-defined a priori eHLF framework with robust properties. It has satisfactory evidence of construct validity and reliable measurement across a broad range of concepts (using both CTT and IRT traditions) in various groups. It is designed to be used to understand and evaluate people's interaction with digital health services.

Keywords: computer literacy; eHealth; health literacy; questionnaire design.

Conflict of interest statement

Conflicts of Interest: None declared.

©Lars Kayser, Astrid Karnoe, Dorthe Furstrand, Roy Batterham, Karl Bang Christensen, Gerald Elsworth, Richard H Osborne. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 12.02.2018.

Figures

Figure 1
Figure 1
The eHealth literacy framework (eHLF).

References

    1. World Health Organization. 2016. [2016-08-17]. WHO Framework on integrated people-centred health services
    1. European Commission. 2016. Dec, [2018-01-08]. Blueprint for a digital transformation of health and care in an ageing society .
    1. European Commission. Brussels: Flash Eurobarometer 404 - TNS Political & Social; 2014. [2018-01-08]. European citizens' digital health literacy .
    1. Nutbeam D. Health promotion glossary. Health Promot Int. 1998 Jan 01;13(4):349–364. doi: 10.1093/heapro/13.4.349.
    1. Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011 Jul 19;155(2):97–107. doi: 10.7326/0003-4819-155-2-201107190-00005.
    1. Norman CD, Skinner HA. eHealth literacy: essential skills for consumer health in a networked world. J Med Internet Res. 2006 Jun 16;8(2):e9. doi: 10.2196/jmir.8.2.e9.
    1. Chan CV, Kaufman DR. A framework for characterizing eHealth literacy demands and barriers. J Med Internet Res. 2011 Nov 17;13(4):e94. doi: 10.2196/jmir.1750.
    1. Chan CV, Mirkovic J, Furniss S, Kaufman DR. eHealth literacy demands and cognitive processes underlying barriers in consumer health information seeking. Knowl Manag E-Learn Int J KMEL. 2015 Dec 26;7(4):550–575.
    1. Gilstad H. Toward a comprehensive model of eHealth literacy. Proceedings of the 2nd European Workshop on Practical Aspects of Health Information; May 19-20, 2014; Trondheim, Norway. 2014. May 19,
    1. Karnoe A, Kayser L. How is eHealth literacy measured and what do the measurements tell us? A systematic review. Knowl Manag E-Learn Int J KMEL. 2015 Dec 26;7(4):576–600.
    1. van der Vaart R, van Deursen AJ, Drossaert CH, Taal E, van Dijk JA, van de Laar MA. Does the eHealth Literacy Scale (eHEALS) measure what it intends to measure? Validation of a Dutch version of the eHEALS in two adult populations. J Med Internet Res. 2011 Nov 9;13(4):e86. doi: 10.2196/jmir.1840.
    1. Norgaard O, Furstrand D, Klokker L, Karnoe A, Batterham RW, Kayser L, Osborne RH. The e-health literacy framework: a conceptual framework for characterizing e-health users and their interaction with e-health systems. Knowl Manag E-Learn Int J KMEL. 2015 Dec 26;7(4):522–540.
    1. Buchbinder R, Batterham R, Elsworth G, Dionne CE, Irvin E, Osborne RH. A validity-driven approach to the understanding of the personal and societal burden of low back pain: development of a conceptual and measurement model. Arthritis Res Ther. 2011;13(5):R152. doi: 10.1186/ar3468.
    1. Osborne RH, Batterham RW, Elsworth GR, Hawkins M, Buchbinder R. The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ) BMC Public Health. 2013 Jul 16;13:658. doi: 10.1186/1471-2458-13-658.
    1. Osborne RH, Elsworth GR, Whitfield K. The Health Education Impact Questionnaire (heiQ): an outcomes and evaluation measure for patient education and self-management interventions for people with chronic conditions. Patient Educ Couns. 2007 May;66(2):192–201. doi: 10.1016/j.pec.2006.12.002.
    1. van Randeraad-van der Zee CH, Beurskens AJ, Swinkels RA, Pool JJ, Batterham RW, Osborne RH, de Vet HC. The burden of neck pain: its meaning for persons with neck pain and healthcare providers, explored by concept mapping. Qual Life Res. 2016 May;25(5):1219–25. doi: 10.1007/s11136-015-1149-6.
    1. Eremenco SL, Cella D, Arnold BJ. A comprehensive method for the translation and cross-cultural validation of health status questionnaires. Eval Health Prof. 2005 Jun;28(2):212–32. doi: 10.1177/0163278705275342.
    1. Bloom BS, Engelhart M, Furst E, Hill W, Krathwohl D. Taxonomy of Educational Objectives: The Classification of Educational Goals. Handbook I: Cognitive Domain. New York, NY: Longmans Green; 1956.
    1. García AA. Cognitive interviews to test and refine questionnaires. Public Health Nurs. 2011 Sep 1;28(5):444–50. doi: 10.1111/j.1525-1446.2010.00938.x.
    1. Bollen KA. Structural Equations with Latent Variables. New York: Wiley; 1989.
    1. van der Linden WJ, Hambleton RK. Handbook of Modern Item Response Theory. New York, NY: Springer-Verlag; 1997.
    1. Muthén B, Asparouhov T. Bayesian structural equation modeling: a more flexible representation of substantive theory. Psychol Methods. 2012 Sep;17(3):313–35. doi: 10.1037/a0026802.
    1. Asparouhov T, Muthén B, Morin AJ. Bayesian structural equation modeling with cross-loadings and residual covariances. J Manag. 2015 Jun 30;41(6):1561–1577. doi: 10.1177/0149206315591075.
    1. Elsworth GR, Beauchamp A, Osborne RH. Measuring health literacy in community agencies: a Bayesian study of the factor structure and measurement invariance of the health literacy questionnaire (HLQ) BMC Health Serv Res. 2016 Sep 22;16(1):508. doi: 10.1186/s12913-016-1754-2.
    1. Muraki E. A generalized partial credit model: application of an EM algorithm. Appl Psychol Meas. 1992 Jul 27;16(2):159–176. doi: 10.1177/014662169201600206.
    1. Olsbjerg M, Christensen KB. Department of Biostatistics, University of Copenhagen. 2014. [2018-01-08]. LIRT: SAS macros for longitudinal ITR models .
    1. Holland PW, Wainer H, editors. Differential Item Functioning. Hillsdale, NJ: Lawrence Erlbaum Associates; 1993.
    1. Zumbo BD. A Handbook on the Theory and Methods of Differential Item Functioning (DIF): Logistic Regression Modeling as a Unitary Framework for Binary and Likert-Type (Ordinal) Item Scores. Ottawa, ON: Directorate of Human Resources Research and Evaluation, Department of National Defense; 1999.
    1. Crane PK, Gibbons LE, Jolley L, van Belle G. Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar. Med Care. 2006 Nov;44(11 Suppl 3):S115–23. doi: 10.1097/01.mlr.0000245183.28384.ed.
    1. Swaminathan H, Rogers HJ. Detecting differential item functioning using logistic regression procedures. J Educ Meas. 1990;27(4):361–370.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300.
    1. Rosas SR, Ridings JW. The use of concept mapping in measurement development and evaluation: application and future directions. Eval Program Plann. 2017 Feb;60:265–276. doi: 10.1016/j.evalprogplan.2016.08.016.
    1. Kayser L, Kushniruk A, Osborne RH, Norgaard O, Turner P. Enhancing the effectiveness of consumer-focused health information technology systems through eHealth literacy: a framework for understanding users' needs. JMIR Hum Factors. 2015 May 20;2(1):e9. doi: 10.2196/humanfactors.3696.
    1. Beauchamp A, Batterham RW, Dodson S, Astbury B, Elsworth GR, McPhee C, Jacobson J, Buchbinder R, Osborne RH. Systematic development and implementation of interventions to OPtimise Health Literacy and Access (Ophelia) BMC Public Health. 2017 Dec 03;17(1):230. doi: 10.1186/s12889-017-4147-5.
    1. Batterham RW, Buchbinder R, Beauchamp A, Dodson S, Elsworth GR, Osborne RH. The OPtimising HEalth LIterAcy (Ophelia) process: study protocol for using health literacy profiling and community engagement to create and implement health reform. BMC Public Health. 2014 Jul 07;14:694. doi: 10.1186/1471-2458-14-694.
    1. van der Vaart R, Drossaert C. Development of the digital health literacy instrument: measuring a broad spectrum of health 1.0 and health 2.0 Skills. J Med Internet Res. 2017 Jan 24;19(1):e27. doi: 10.2196/jmir.6709.
    1. Norman CD, Skinner HA. eHEALS: the eHealth literacy scale. J Med Internet Res. 2006 Nov;8(4):e27. doi: 10.2196/jmir.8.4.e27.
    1. Kane MT. An argument-based approach to validity. Psychol Bull. 1992;112(3):527–535. doi: 10.1037/0033-2909.112.3.527.
    1. Dodson S, Good S, Osborne RH. . 2015. [2018-01-10]. Health literacy toolkit for low and middle-income countries: a series of information sheets to empower communities and strengthen health systems .
    1. Parker R. Measuring health literacy: What? So what? Now what? In: Institute of Medicine (US) Roundtable on Health Literacy , editor. Measures of Health Literacy: Workshop Summary. Washington, DC: The National Academies Press; 2009. pp. 91–98.
    1. World Health Organization. 2016. [2017-10-02]. Shanghai Declaration on promoting health in the 2030 Agenda for Sustainable Development

Source: PubMed

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