Health Professionals' eHealth Literacy and System Experience Before and 3 Months After the Implementation of an Electronic Health Record System: Longitudinal Study.

BACKGROUND: The implementation of an integrated electronic health record (EHR) system can potentially provide health care providers with support standardization of patient care, pathways, and workflows, as well as provide medical staff with decision support, easier access, and the same interface across features and subsystems. These potentials require an implementation process in which the expectations of the medical staff and the provider of the new system are aligned with respect to the medical staff's knowledge and skills, as well as the interface and performance of the system. Awareness of the medical staff's level of eHealth literacy may be a way of understanding and aligning these expectations and following the progression of the implementation process. OBJECTIVE: The objective of this study was to investigate how a newly developed and modified instrument measuring the medical staff's eHealth literacy (staff eHealth Literacy Questionnaire [eHLQ]) can be used to inform the system provider and the health care organization in the implementation process and evaluate whether the medical staff's perceptions of the ease of use change and how this may be related to their level of eHealth literacy. METHODS: A modified version of the eHLQ was distributed to the staff of a medical department in Denmark before and 3 months after the implementation of a new EHR system. The survey also included questions related to users' perceived ease of use and their self-reported information technology skills. RESULTS: The mean age of the 194 participants before implementation was 43.1 (SD 12.4) years, and for the 198 participants after implementation, it was 42.3 (SD 12.5) years. After the implementation, the only difference compared with the preimplementation data was a small decrease in staff eHLQ5 (motivated to engage with digital services; unpaired 2-tailed t test; P=.009; effect size 0.267), and the values of the scales relating to the medical staff's knowledge and skills (eHLQ1-3) were approximately ≥3 both before and after implementation. The range of scores was narrower after implementation, indicating that some of those with the lowest ability benefited from the training and new experiences with the EHR. There was an association between perceived ease of use and the 3 tested staff eHLQ scales, both before and after implementation. CONCLUSIONS: The staff eHLQ may be a good candidate for monitoring the medical staff's digital competence in and response to the implementation of new digital solutions. This may enable those responsible for the implementation to tailor efforts to the specific needs of segments of users and inform them if the process is not going according to plan with respect to the staff's information technology-related knowledge and skills, trust in data security, motivation, and experience of a coherent system that suits their needs and supports the workflows and data availability.

How people with multiple sclerosis experience the influence of nutrition and lifestyle factors on the disease.

Purpose: Increasing knowledge suggests that nutrition and lifestyle factors affect multiple sclerosis. This study explores how people with multiple sclerosis experience daily multiple sclerosis disease activity and the influence of nutrition and lifestyle factors (e.g., stress, sleep, and environmental temperature).Methods: Four phases mix qualitative and quantitative elements in an exploratory study. The initial two phases consisted of an exploratory study with 14 participants followed by 15 semi-structured interviews. Results from the two first phases were substantiated in a survey completed by 425 respondents (response rate: 42.5%). Finally, findings and inconsistencies were elaborated in three focus group interviews.Results: In the initial exploratory study, several of the participants linked nutrition and lifestyle factors to disease activity. Results from the semi-structured interviews showed that particularly stress, meat, fatty foods, and processed sugar were perceived to have a negative impact on disease activity, and some participants had experienced immediate effects of these factors on their disease activity. The survey supported these findings that were further elaborated in focus groups.Conclusion: People with multiple sclerosis perceive nutrition and lifestyle to affect daily disease activity. Individuals who have experienced links between their multiple sclerosis, and nutrition and lifestyle attribute some of these changes to e.g., stress, and the consumption of sugar, meat, and fatty food.Implications for rehabilitationA majority of the participants in this study perceived nutrition and lifestyle factors to have an effect on their multiple sclerosis, particularly stress, meat, fatty foods, and processed sugar.Some participants with multiple sclerosis experienced that nutrition, stress, environmental temperature, and physical activity had a direct effect on the severity of daily symptom manifestations.Nutrition and lifestyle factors that potentially influence multiple sclerosis disease activity should be considered when organizing rehabilitation and care to better meet the needs of the individual with multiple sclerosis.

Identification of Factors That Motivate People With Multiple Sclerosis to Participate in Digital Data Collection in Research: Sequential Mixed Methods Study.

BACKGROUND: Digital data collection has the potential to reduce participant burden in research projects that require extensive registrations from participants. To achieve this, a digital data collection tool needs to address potential barriers and motivations for participation. OBJECTIVE: This study aimed to identify factors that may affect motivation for participation and adoption of a digital data collection tool in a research project on nutrition and multiple sclerosis (MS). METHODS: The study was designed as a sequential mixed methods study with 3 phases. In phase 1, 15 semistructured interviews were conducted in a Danish population of individuals with MS. Interview guide frameworks were based on dimensions from the electronic health literacy framework and the Health Education Impact Questionnaire. Data from phase 1 were analyzed in a content analysis, and findings were used to inform the survey design in phase 2 that validates the results from the content analysis in a larger population. The survey consisted of 14 items, and it was sent to 1000 individuals with MS (response rate 42.5%). In phase 3, participants in 3 focus group interviews discussed how findings from phases 1 and 2 might affect motivation for participation and adoption of the digital tool. RESULTS: The following 3 categories related to barriers and incentives for participation were identified in the content analysis of the 15 individual interviews: (1) life with MS, (2) use of technology, and (3) participation and incentives. Phase 1 findings were tested in phase 2's survey in a larger population (n=1000). The majority of participants were comfortable using smartphone technologies and participated actively on social media platforms. MS symptoms did cause limitations in the use of Web pages and apps when the given pages had screen clutter, too many colors, or too small buttons. Life with MS meant that most participants had to ration their energy levels. Support from family and friends was important to participants, but support could also come in the form of physical aids (walking aids and similar) and digital aids (reminders, calendar functions, and medication management). Factors that could discourage participation were particularly related to the time it would take every day. The biggest motivations for participation were to contribute to research in MS, to learn more about one's own MS and what affects it, and to be able to exchange experiences with other people with MS. CONCLUSIONS: MS causes limitations that put demands on tools developed for digital data collection. A digital data collection tool can increase chances of high adoption rates, but it needs to be supplemented with a clear and simple project design and continuous communication with participants. Motivational factors should be considered in both study design and the development of a digital data collection tool for research.

Differences in the Level of Electronic Health Literacy Between Users and Nonusers of Digital Health Services: An Exploratory Survey of a Group of Medical Outpatients.

BACKGROUND: Digitalization of health services ensures greater availability of services and improved contact to health professionals. To ensure high user adoption rates, we need to understand the indicators of use and nonuse. Traditionally, these have included classic sociodemographic variables such as age, sex, and educational level. Electronic health literacy (eHL) describes knowledge, skills, and experiences in the interaction with digital health services and technology. With our recent introduction of 2 new multidimensional instruments to measure eHL, the eHL questionnaire (eHLQ) and the eHL assessment (eHLA) toolkit, eHL provides a multifaceted approach to understand use and nonuse of digital health solutions in detail. OBJECTIVE: The aim of this study was to investigate how users and nonusers of digital services differ with respect to eHL, in a group of patients with regular contact to a hospital outpatient clinic. Furthermore, to examine how usage and nonusage, and eHL levels are associated with factors such as age, sex, educational level, and self-rated health. METHODS: Outpatients were asked to fill out a survey comprising items about usage of digital services, including digital contact to general practitioner (GP) and communication via the national health portal sundhed.dk, the eHLQ, and the eHLA toolkit, as well as items on age, sex, education, and self-rated health. In total, 246 patients completed the survey. A Mann-Whitney test was used to test for differences between users and nonusers of digital services. Correlation tests described correlations between eHL scales (eHEALSs) and age, education, and self-rated health. A significance level of .0071 was used to reject the null hypothesis in relation to the eHEALSs and usage of digital services. RESULTS: In total, 95.1% (234/246) of the participants used their personal digital ID (NemID), 57.7% (142/246) were in contact with their GPs electronically, and 54.0% (133/246) had used the national health portal (sundhed.dk) within the last 3 months. There were no differences between users and nonusers of sundhed.dk with respect to age, sex, educational level, and self-rated health. Users of NemID scored higher than nonusers in 6 of the 7 dimensions of eHLQ, the only one which did not differ was dimension 2: Understanding of health concepts and language. Sundhed.dk users had a higher score in all of the 7 dimensions except for dimension 4: Feel safe and in control. The eHLA toolkit showed that users of sundhed.dk and NemID had higher levels of eHL with regard to tools 2, 5, 6, and 7. Furthermore, users of sundhed.dk had higher levels of eHL with regard to tools 3 and 4. CONCLUSIONS: Information about patients' eHL may provide clinicians an understanding of patients' reasons for not using digital health services, better than sociodemographic data or self-rated health.

A new understanding of health related empowerment in the context of an active and healthy ageing.

BACKGROUND: Recently, several initiatives have focused on how to create true person-centred health services. This calls for a new understanding of health-related empowerment in relation to people living with one or more chronic conditions. We report on a Delphi investigation among participants in the European Innovation Partnership on Active and Healthy Ageing that has led to a new understanding of health-related empowerment. METHODS: The Delphi process was conducted in three sequential rounds. In the first round, we presented a suggested first version for a definition of "health-related empowerment" divided into nine statements. One hundred and twenty-two experts were then asked if they agreed or not with each individual statement, and in the case they disagreed, to state the reasons for their disagreement. After revisions, the experts who had replied to the first version were asked again, if they agreed or not with each individual statement of the second version and to elaborate on disagreements. Finally, in the third round the experts were asked to provide comments to the final proposed definition in general and not by each statement. RESULTS: A total of 33 experts responded to the first version. The following revision included a merging of two statements, and the addition of health literacy as part of the understanding. The second version was sent out to the 33 experts and a total of 19 experts commented with moderate consensus. Changes included removal of "self-esteem" and change of "self-confidence" to confidence. Third version was sent out to all 122 experts with 16 respondents. Strong consensus was obtained for this third version, and is with one minor change presented as the final version. CONCLUSION: We propose a new understanding of the concept health-related empowerment, by focusing on the individual as a co-manager with freedom to choose and focus on their own well-being.

Development of the Multidimensional Readiness and Enablement Index for Health Technology (READHY) Tool to Measure Individuals' Health Technology Readiness: Initial Testing in a Cancer Rehabilitation Setting.

BACKGROUND: The increasing digitization of health care services with enhanced access to fast internet connections, along with wide use of smartphones, offers the opportunity to get health advice or treatment remotely. For service providers, it is important to consider how consumers can take full advantage of available services and how this can create an enabling environment. However, it is important to consider the digital context and the attributes of current and future users, such as their readiness (ie, knowledge, skills, and attitudes, including trust and motivation). OBJECTIVE: The objective of this study was to evaluate how the eHealth Literacy Questionnaire (eHLQ) combined with selected dimensions from the Health Education Impact Questionnaire (heiQ) and the Health Literacy Questionnaire (HLQ) can be used together as an instrument to characterize an individual's level of health technology readiness and explore how the generated data can be used to create health technology readiness profiles of potential users of health technologies and digital health services. METHODS: We administered the instrument and sociodemographic questions to a population of 305 patients with a recent cancer diagnosis referred to rehabilitation in a setting that plans to introduce various technologies to assist the individuals. We evaluated properties of the Readiness and Enablement Index for Health Technology (READHY) instrument using confirmatory factor analysis, convergent and discriminant validity analysis, and exploratory factor analysis. To identify different health technology readiness profiles in the population, we further analyzed the data using hierarchical and k-means cluster analysis. RESULTS: The confirmatory factor analysis found a suitable fit for the 13 factors with only 1 cross-loading of 1 item between 2 dimensions. The convergent and discriminant validity analysis revealed many factor correlations, suggesting that, in this population, a more parsimonious model might be achieved. Exploratory factor analysis pointed to 5 to 6 constructs based on aggregates of the existing dimensions. The results were not satisfactory, so we performed an 8-factor confirmatory factor analysis, resulting in a good fit with only 1 item cross-loading between 2 dimensions. Cluster analysis showed that data from the READHY instrument can be clustered to create meaningful health technology readiness profiles of users. CONCLUSIONS: The 13 dimensions from heiQ, HLQ, and eHLQ can be used in combination to describe a user's health technology readiness level and degree of enablement. Further studies in other populations are needed to understand whether the associations between dimensions are consistent and the number of dimensions can be reduced.

Assessing Competencies Needed to Engage With Digital Health Services: Development of the eHealth Literacy Assessment Toolkit.

BACKGROUND: To achieve full potential in user-oriented eHealth projects, we need to ensure a match between the eHealth technology and the user's eHealth literacy, described as knowledge and skills. However, there is a lack of multifaceted eHealth literacy assessment tools suitable for screening purposes. OBJECTIVE: The objective of our study was to develop and validate an eHealth literacy assessment toolkit (eHLA) that assesses individuals' health literacy and digital literacy using a mix of existing and newly developed scales. METHODS: From 2011 to 2015, scales were continuously tested and developed in an iterative process, which led to 7 tools being included in the validation study. The eHLA validation version consisted of 4 health-related tools (tool 1: "functional health literacy," tool 2: "health literacy self-assessment," tool 3: "familiarity with health and health care," and tool 4: "knowledge of health and disease") and 3 digitally-related tools (tool 5: "technology familiarity," tool 6: "technology confidence," and tool 7: "incentives for engaging with technology") that were tested in 475 respondents from a general population sample and an outpatient clinic. Statistical analyses examined floor and ceiling effects, interitem correlations, item-total correlations, and Cronbach coefficient alpha (CCA). Rasch models (RM) examined the fit of data. Tools were reduced in items to secure robust tools fit for screening purposes. Reductions were made based on psychometrics, face validity, and content validity. RESULTS: Tool 1 was not reduced in items; it consequently consists of 10 items. The overall fit to the RM was acceptable (Anderson conditional likelihood ratio, CLR=10.8; df=9; P=.29), and CCA was .67. Tool 2 was reduced from 20 to 9 items. The overall fit to a log-linear RM was acceptable (Anderson CLR=78.4, df=45, P=.002), and CCA was .85. Tool 3 was reduced from 23 to 5 items. The final version showed excellent fit to a log-linear RM (Anderson CLR=47.7, df=40, P=.19), and CCA was .90. Tool 4 was reduced from 12 to 6 items. The fit to a log-linear RM was acceptable (Anderson CLR=42.1, df=18, P=.001), and CCA was .59. Tool 5 was reduced from 20 to 6 items. The fit to the RM was acceptable (Anderson CLR=30.3, df=17, P=.02), and CCA was .94. Tool 6 was reduced from 5 to 4 items. The fit to a log-linear RM taking local dependency (LD) into account was acceptable (Anderson CLR=26.1, df=21, P=.20), and CCA was .91. Tool 7 was reduced from 6 to 4 items. The fit to a log-linear RM taking LD and differential item functioning into account was acceptable (Anderson CLR=23.0, df=29, P=.78), and CCA was .90. CONCLUSIONS: The eHLA consists of 7 short, robust scales that assess individual's knowledge and skills related to digital literacy and health literacy.

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

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.