Abstract
To monitor the experience of participants in a field evaluation of a home care Web support service we developed a survey to measure patient technology acceptance. Predictors of the acceptance model were selected from the technology acceptance literature. Cognitive interviewing was used to improve the validity of the survey items. We also describe the methods used to develop the survey.
Introduction & Background
In theory, health outcomes can be improved by providing patients with appropriate information through health information technologies. However, patients may reject the technology. While models to predict technology acceptance (TA) and use do exist, their constructs and items were developed among healthy employees or college students.1,2 We expanded the well-validated Technology Acceptance Model1 (TAM) to understand predictors of TA among homecare patients with heart disease (HD) enrolled in the HeartCare II study. The purpose of our study is to develop a survey instrument to measure TA among this mostly elderly patient population.
Methods
We first selected possible predictors of TA from the TAM1 and the unified TAM2 which specified performance expectancy, effort expectancy, facilitating conditions, attitude toward technology, and social influence that should predict TA. We also added potential predictors specifically related to mostly elderly patients with HD (e.g., perceived upper extremity function and visual acuity) and variables specific to electronic health information. Predictor selection was partially based on field observations with home visiting nurses3 and patients. A survey was generated based on the TA measurement scales developed in prior research. Cognitive interviewing was used to improve the validity of the survey. The interview is a verbal probing technique to investigate whether participants understand the questions as intended.4 Validity is severely compromised if participants do not comprehend survey items. Participants were recruited from a senior citizen center since many homecare patients with HD are elderly. Participants were interviewed to understand if they comprehended items as intended. Two rounds of interviews were conducted with 15 participants: 10 for the 1st and 5 for the 2nd round as recommended.4 Results from the 1st round of testing were applied to revise survey items. The revised items were tested during the 2nd round of testing.
Results
The resulting survey has 42 items to measure 15 TA constructs. The 1st round of the interviews revealed that participants did not understand some questions as intended because the sentences were too complex. Concepts in three items were not comprehended; these items were deleted. The revised survey was understood more easily and consistently in the 2nd round of testing.
Conclusions
The methods used in this paper facilitated designing measures to fit the population of interest and could be applied to develop measures of TA among other patient populations. The literature review and field observations helped develop the initial survey draft. Cognitive interviewing allowed us to further shape the instrument to match our target sample. The resulting instrument will be further tested among HeartCare II participants and used to assess patient technology acceptance.
Acknowledgement
This study was supported by a grant from the National Library of Medicine (Grant# LM 6249)
REFERENCES
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