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.

The study shows promising feasibility of the individually tailored nurse-navigation intervention and while no significant effects were observed after 6 months, we did find statistically significant effects on distress, anxiety and depression 12 months after diagnosis. Our results will assist in developing rehabilitation to the most vulnerable patients.

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Background: During recent years, there has been an increased focus on technology within cancer survivorship and physical activity rehabilitation. Failure to acknowledge the individuals wishes and/or limitations in the use of technology introduces risk of neglecting or excluding low-resource individuals, thus adding to an already existing inequity within cancer management. It is important to identify vulnerable sub-populations with particular needs when introducing health technology to offer appropriate rehabilitation and support individuals in taking advantage of technology in their physical activity rehabilitation. We report how a population of cancer survivors referred to municipality-based rehabilitation can be segmented, according to their receptiveness and readiness for health technology utilization, to understand their potential barriers towards using technology. Methods: We used a cross-sectional design with convenience sampling among Danish cancer survivors (n ¼ 305) referred to rehabilitation. Participants completed a questionnaire survey covering background information and a new tool to assess self-reported health technology readiness. Demographic, disease and behavioral factors were described in relation to the participants' receptiveness to use technology in physical activity rehabilitation. Participants were stratified according to their health technology readiness and the resulting groups described with regards to the above-mentioned factors. Results: Almost 30% of the participants were un-receptive to use technology in relation to physical activity rehabilitation and were characterized by being more vulnerable with regards to sociodemographic characteristics and scoring lower on dimensions related to eHealth literacy. Stratification of the participants according to their health technology readiness profile revealed four distinct profiles that were significantly different in sociodemographic, disease and behavioral factors. Conclusion:To reduce the risk of alienating low-resource individuals when introducing health technology, evaluation of the individuals' receptiveness to use technology in a rehabilitation context and their readiness for health technology may help tailor the extent to which technology should be offered to assist the cancer survivors.