Validity and Reliability of the eHealth Analysis and Steering Instrument.

Abstract

Background: eHealth services can contribute to individuals' self-management, that is, performing lifestyle-related activities and decision making, to maintain a good health, or to mitigate the effect of an (chronic) illness on their health. But how effective are these services? Conducting a randomized controlled trial (RCT) is the golden standard to answer such a question, but takes extensive time and effort. The eHealth Analysis and Steering Instrument (eASI) offers a quick, but not dirty alternative. The eASI surveys how eHealth services score on 3 dimensions (ie, utility, usability, and content) and 12 underlying categories (ie, insight in health condition, self-management decision making, performance of self-management, involving the social environment, interaction, personalization, persuasion, description of health issue, factors of influence, goal of eHealth service, implementation, and evidence). However, there are no data on its validity and reliability.

Objective: The objective of our study was to assess the construct and predictive validity and interrater reliability of the eASI.

Methods: We found 16 eHealth services supporting self-management published in the literature, whose effectiveness was evaluated in an RCT and the service itself was available for rating. Participants (N=16) rated these services with the eASI. We analyzed the correlation of eASI items with the underlying three dimensions (construct validity), the correlation between the eASI score and the eHealth services' effect size observed in the RCT (predictive validity), and the interrater agreement.

Results: Three items did not fit with the other items and dimensions and were removed from the eASI; 4 items were replaced from the utility to the content dimension. The interrater reliabilities of the dimensions and the total score were moderate (total, κ=.53, and content, κ=.55) and substantial (utility, κ=.69, and usability, κ=.63). The adjusted eASI explained variance in the eHealth services' effect sizes (R(2) =.31, P<.001), as did the dimensions utility (R(2) =.49, P<.001) and usability (R(2) =.18, P=.021). Usability explained variance in the effect size on health outcomes (R(2) =.13, P=.028).

Conclusions: After removing 3 items and replacing 4 items to another dimension, the eASI (3 dimensions, 11 categories, and 32 items) has a good construct validity and predictive validity. The eASI scales are moderately to highly reliable. Accordingly, the eASI can predict how effective an eHealth service is in regard to supporting self-management. Due to a small pool of available eHealth services, it is advised to reevaluate the eASI in the future with more services.