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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.

ABSTRACT: 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.

SUBMITTER: Kayser L

PROVIDER: S-EPMC6404640 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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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.

Kayser Lars L Rossen Sine S Karnoe Astrid A Elsworth Gerald G Vibe-Petersen Jette J Christensen Jesper Frank JF Ried-Larsen Mathias M Osborne Richard H RH

Journal of medical Internet research 20190212 2

<h4>Background</h4>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 th ...[more]

PMID: 30747717

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Project description:ObjectiveTo map the extant literature evaluating the use of technology for goal setting in adult rehabilitation and the impact of technology for patient outcomes.DesignScoping review.MethodsMEDLINE (via Ovid), CINAHL (via EBSCO), AMED and Scopus were searched for articles describing observational or interventional studies. ProQuest Dissertations and Theses database were searched for grey literature. Two review authors independently screened all titles and abstracts for potentially relevant articles. We included articles describing studies that had evaluated the development or application of technology to facilitate goal setting in rehabilitation for adults. Articles were excluded if the technology described did not include features to facilitate goal setting or were not in English. Narrative reviews, opinion pieces and editorials were also excluded.ResultsAfter screening 1640 publications of potential interest, we identified 27 studies for inclusion. These 27 articles described studies involving a total of 16 different technologies including, seven mobile apps, three websites, two mobile apps/website hybrids, two apps and two websites connected to a pedometer. We found that most technologies described were designed to facilitate self-management with goal setting as a feature and that only five included a shared decision moment around goal setting. Only six of the 16 technologies had research providing evidence of effectiveness in terms of improved patient outcomes, with the best evidence of beneficial effects associated with technologies that linked goal setting to pedometer use.ConclusionsThe identified technologies for use in adult rehabilitation that included goal setting as a feature were largely accepted and valued by patients and health professionals. The limited data suggest that there is a need for further research; specific foci may include the impact of incorporation of a shared decision-making moment and evaluation of effectiveness on patient outcomes.

Project description:BackgroundFarming is a stressful occupation, and a growing body of research shows that farm stressors are associated with poor mental health. To date, there are few methodologically sound surveys that assess farm stressors, and none have been validated for the Canadian context. Our study aimed to: (a) investigate the types of stressors experienced by farmers, (b) develop a farm stress assessment tool and test its factor structure and internal consistency, and (c) assess its criterion-related validity to self-reported levels of anxiety, depression, burnout, and resilience among farmers.MethodsWe developed a 20-item survey based on a review of the literature, examining existing farm stress surveys, and consulting 10 farmers and agricultural industry experts. Then, a convenience sample of farmers living in Alberta, Canada (Sample 1, N = 354) completed a questionnaire containing the 20-item farm stress survey and four validated measures that assessed depression, anxiety, burnout, and resilience. Sample 1 was used to assess the factor structure using exploratory factor analysis (EFA), internal consistency, and criterion-validity of the survey. Next, a convenience sample of farmers living outside of Alberta (Sample 2, N = 138) was used to evaluate the factor structure of the survey using confirmatory factor analysis (CFA).ResultsThe results of the EFA revealed five underlying dimensions of farm stressors: Unexpected work disruptions, Agricultural hazards, Farm and financial planning, Isolation, and Regulations and public pressure. The subscales accounted for 61.6% of the variance, and the internal consistency (Cronbach's alpha) ranged from 0.66 to.75. Subscale correlations were below 0.44, indicating evidence of discriminant validity. Correlations between the five subscales and the four mental health outcome variables supported the criterion-related validity of the survey. The results of the CFA indicated that the data fit the model, and fit was further improved by correlating one pair of error terms.ConclusionsPreliminary analysis of our Farmer Stress Assessment Tool (FSAT) suggests it is a reliable and valid instrument for measuring a range of stressors farmers face. Implications for policy and community-based mental health interventions that help farmers manage the enduring stressors of agriculture is discussed.

Project description:BackgroundInformation technologies (IT) are increasingly implemented in type 2 diabetes (T2D) treatment as a resource for remotely supported health care. However, possible pitfalls of introducing IT in health care are generally overlooked. Specifically, the effectiveness of IT to improve health care may depend on the user's readiness for health technology.ObjectiveWe aim to investigate readiness for health technology in relation to mental well-being, sociodemographic, and disease-related characteristics among individuals with T2D.MethodsIndividuals with T2D (aged ≥18 years) who had been referred to self-management education, exercise, diet counseling, smoking cessation, or alcohol counseling completed a questionnaire survey covering (1) background information, (2) the 5-item World Health Organization Well-Being Index (WHO-5), (3) receptiveness to IT use in physical activity, and (4) the Readiness and Enablement Index for Health Technology (READHY), constituted by dimensions related to self-management, social support, and eHealth literacy. Individuals were divided into profiles using cluster analysis based on their READHY scores. Outcomes included differences across profiles in mental well-being, sociodemographic, and disease-related characteristics.ResultsParticipants in the study were 155 individuals with T2D with a mean age of 60.2 (SD 10.7) years, 55.5% (86/155) of which were men and 44.5% (69/155) of which were women. Participants were stratified into 5 health technology readiness profiles based on the cluster analysis: Profile 1, high health technology readiness; Profile 2, medium health technology readiness; Profile 3, medium health technology readiness and high level of emotional distress; Profile 4, medium health technology readiness and low-to-medium eHealth literacy; Profile 5, low health technology readiness. No differences in sociodemographic and disease-related characteristics were observed across profiles; however, we identified 3 vulnerable subgroups of individuals: Profile 3 (21/155, 13.5%), younger individuals (mean age of 53.4 years, SD 8.9 years) with low mental well-being (mean 42.7, SD 14.7) and emotional distress (mean 1.69, SD 0.38); Profile 4 (20/155, 12.9%), older individuals (mean age 66.3 years, SD 9.0 years) with less IT use (50.0% used IT for communication) and low-to-medium eHealth literacy; and Profile 5 (36/155, 23.2%) with low mental well-being (mean 43.4, SD 20.1) and low readiness for health technology.ConclusionsImplementation of IT in health care of individuals with T2D should be based on comprehensive consideration of mental well-being, emotional distress, and readiness for health technology rather than sociodemographic and disease-related characteristics to identify the individuals in need of social support, self-management education, and extensive IT support. A one-size-fits-all approach to IT implementation in health care will potentially increase the risk of treatment failure among the most vulnerable individuals.

Dataset Information

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.

Publications

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.

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