Rosalie van der Vaart

BACKGROUND: With the digitization of health care and the wide availability of Web-based applications, a broad set of skills is essential to properly use such facilities; these skills are called digital health literacy or eHealth literacy. Current instruments to measure digital health literacy focus only on information gathering (Health 1.0 skills) and do not pay attention to interactivity on the Web (Health 2.0). To measure the complete spectrum of Health 1.0 and Health 2.0 skills, including actual competencies, we developed a new instrument. The Digital Health Literacy Instrument (DHLI) measures operational skills, navigation skills, information searching, evaluating reliability, determining relevance, adding self-generated content, and protecting privacy. OBJECTIVE: Our objective was to study the distributional properties, reliability, content validity, and construct validity of the DHLI's self-report scale (21 items) and to explore the feasibility of an additional set of performance-based items (7 items). METHODS: We used a paper-and-pencil survey among a sample of the general Dutch population, stratified by age, sex, and educational level (T1; N=200). The survey consisted of the DHLI, sociodemographics, Internet use, health status, health literacy and the eHealth Literacy Scale (eHEALS). After 2 weeks, we asked participants to complete the DHLI again (T2; n=67). Cronbach alpha and intraclass correlation analysis between T1 and T2 were used to investigate reliability. Principal component analysis was performed to determine content validity. Correlation analyses were used to determine the construct validity. RESULTS: Respondents (107 female and 93 male) ranged in age from 18 to 84 years (mean 46.4, SD 19.0); 23.0% (46/200) had a lower educational level. Internal consistencies of the total scale (alpha=.87) and the subscales (alpha range .70-.89) were satisfactory, except for protecting privacy (alpha=.57). Distributional properties showed an approximately normal distribution. Test-retest analysis was satisfactory overall (total scale intraclass correlation coefficient=.77; subscale intraclass correlation coefficient range .49-.81). The performance-based items did not together form a single construct (alpha=.47) and should be interpreted individually. Results showed that more complex skills were reflected in a lower number of correct responses. Principal component analysis confirmed the theoretical structure of the self-report scale (76% explained variance). Correlations were as expected, showing significant relations with age (ρ=-.41, P<.001), education (ρ=.14, P=.047), Internet use (ρ=.39, P<.001), health-related Internet use (ρ=.27, P<.001), health status (ρ range .17-.27, P<.001), health literacy (ρ=.31, P<.001), and the eHEALS (ρ=.51, P<.001). CONCLUSIONS: This instrument can be accepted as a new self-report measure to assess digital health literacy, using multiple subscales. Its performance-based items provide an indication of actual skills but should be studied and adapted further. Future research should examine the acceptability of this instrument in other languages and among different populations.

BACKGROUND: The Internet increases the availability of health information, which consequently expands the amount of skills that health care consumers must have to obtain and evaluate health information. Norman and Skinner in 2006 developed an 8-item self-report eHealth literacy scale to measure these skills: the eHealth Literacy Scale (eHEALS). This instrument has been available only in English and there are no data on its validity. OBJECTIVES: The objective of our study was to assess the internal consistency and the construct and predictive validity of a Dutch translation of the eHEALS in two populations. METHODS: We examined the translated scale in a sample of patients with rheumatic diseases (n = 189; study 1) and in a stratified sample of the Dutch population (n = 88; study 2). We determined Cronbach alpha coefficients and analyzed the principal components. Convergent validity was determined by studying correlations with age, education, and current (health-related) Internet use. Furthermore, in study 2 we assessed the predictive validity of the instrument by comparing scores on the eHEALS with an actual performance test. RESULTS: The internal consistency of the scale was sufficient: alpha = .93 in study 1 and alpha = .92 in study 2. In both studies the 8 items loaded on 1 single component (respectively 67% and 63% of variance). Correlations between eHEALS and age and education were not found. Significant, though weak, correlations were found between the eHEALS and quantity of Internet use (r = .24, P = .001 and r = .24, P = .02, respectively). Contrary to expectations, correlations between the eHEALS and successfully completed tasks on a performance test were weak and nonsignificant: r = .18 (P = .09). The t tests showed no significant differences in scores on the eHEALS between participants who scored below and above median scores of the performance test. CONCLUSIONS: The eHEALS was assessed as unidimensional in a principal component analysis and the internal consistency of the scale was high, which makes the reliability adequate. However, findings suggest that the validity of the eHEALS instrument requires further study, since the relationship with Internet use was weak and expected relationships with age, education, and actual performance were not significant. Further research to develop a self-report instrument with high correlations with people's actual eHealth literacy skills is warranted.

Blended care, a combination of online and face-to-face therapy, is increasingly being applied in mental health care to obtain optimal benefit from the advantages these two treatment modalities have. Promising results have been reported, but a variety in descriptions and ways of operationalizing blended care exists. Currently, what type of "blend" works for whom, and why, is unclear. Furthermore, a rationale for setting up blended care is often lacking. In this viewpoint paper, we describe postulates for blended care and provide an instrument (Fit for Blended Care) that aims to assist therapists and patients whether and how to set up blended care treatment. A review of the literature, two focus groups (n=5 and n=5), interviews with therapists (n=14), and interviews with clients (n=2) were conducted to develop postulates of eHealth and blended care and an instrument to assist therapists and clients in setting up optimal blended care. Important postulates for blended care are the notion that both treatment modalities should complement each other and that set up of blended treatment should be based on shared decision making between patient and therapist. The "Fit for Blended Care" instrument is presented which addresses the following relevant themes: possible barriers to receiving blended treatment such as the risk of crisis, issues in communication (at a distance), as well as possible facilitators such as social support. More research into the reasons why and for whom blended care works is needed. To benefit from blended care, face-to-face and online care should be combined in such way that the potentials of both treatment modalities are used optimally, depending on patient abilities, needs, and preferences. To facilitate the process of setting up a personalized blended treatment, the Fit for Blended Care instrument can be used. By applying this approach in research and practice, more insight into the working mechanisms and optimal (personal) "blends" of online and face-to-face therapy becomes within reach.

BACKGROUND: Blending online modules into face-to-face therapy offers perspectives to enhance patient self-management and to increase the (cost-)effectiveness of therapy, while still providing the support patients need. The aim of this study was to outline optimal usage of blended care for depression, according to patients and therapists. METHODS: A Delphi method was used to find consensus on suitable blended protocols (content, sequence and ratio). Phase 1 was an explorative phase, conducted in two rounds of online questionnaires, in which patients' and therapists' preferences and opinions about online psychotherapy were surveyed. In phase 2, data from phase 1 was used in face-to-face interviews with therapists to investigate how blended therapy protocols could be set up and what essential preconditions would be. RESULTS: Twelve therapists and nine patients completed the surveys. Blended therapy was positively perceived among all respondents, especially to enhance the self-management of patients. According to most respondents, practical therapy components (assignments, diaries and psycho-education) may be provided via online modules, while process-related components (introduction, evaluation and discussing thoughts and feelings), should be supported face-to-face. The preferred blend of online and face-to-face sessions differs between therapists and patients; most therapists prefer 75% face-to-face sessions, most patients 50 to 60%. The interviews showed that tailoring treatment to individual patients is essential in secondary mental health care, due to the complexity of their problems. The amount and ratio of online modules needs to be adjusted according to the patient's problems, skills and characteristics. Therapists themselves should also develop skills to integrate online and face-to-face sessions. CONCLUSIONS: Blending online and face-to-face sessions in an integrated depression therapy is viewed as a positive innovation by patients and therapists. Following a standard blended protocol, however, would be difficult in secondary mental health care. A database of online modules could provide flexibility to tailor treatment to individual patients, which asks motivation and skills of both patients and therapists. Further research is necessary to determine the (cost-)effectiveness of blended care, but this study provides starting points and preconditions to blend online and face-to-face sessions and create a treatment combining the best of both worlds.