Using the Technology Acceptance Model to Identify Factors That Predict Likelihood to Adopt Tele-Neurorehabilitation
journal contributionposted on 2021-01-27, 00:18 authored by Marlena KlaicMarlena Klaic, MP Galea
© Copyright © 2020 Klaic and Galea. Tele-neurorehabilitation has the potential to reduce accessibility barriers and enhance patient outcomes through a more seamless continuum of care. A growing number of studies have found that tele-neurorehabilitation produces equivalent results to usual care for a variety of outcomes including activities of daily living and health related quality of life. Despite the potential of tele-neurorehabilitation, this model of care has failed to achieve mainstream adoption. Little is known about feasibility and acceptability of tele-neurorehabilitation and most published studies do not use a validated model to guide and evaluate implementation. The technology acceptance model (TAM) was developed 20 years ago and is one of the most widely used theoretical frameworks for predicting an individual's likelihood to adopt and use new technology. The TAM3 further built on the original model by incorporating additional elements from human decision making such as computer anxiety. In this perspective, we utilize the TAM3 to systematically map the findings from existing published studies, in order to explore the determinants of adoption of tele-neurorehabilitation by both stroke survivors and prescribing clinicians. We present evidence suggesting that computer self-efficacy and computer anxiety are significant predictors of an individual's likelihood to use tele-neurorehabilitation. Understanding what factors support or hinder uptake of tele-neurorehabilitation can assist in translatability and sustainable adoption of this technology. If we are to shift tele-neurorehabilitation from the research domain to become a mainstream health sector activity, key stakeholders must address the barriers that have consistently hindered adoption.
JournalFrontiers in Neurology
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Science & TechnologyLife Sciences & BiomedicineClinical NeurologyNeurosciencesNeurosciences & Neurologystrokeneurorehabilitation after stroketele-neurorehabilitationtechnology—ICTtelehealth acceptanceVIRTUAL-REALITY SYSTEMTELEREHABILITATION PROGRAMPATIENT OUTCOMESACUTE STROKEREHABILITATIONHOMECAREEPIDEMIOLOGYFEASIBILITYSELECTIONtechnology—ICT