Determining the Influencing Factors on Acceptance of eHealth Pain Management Interventions among Chronic Pain Patients Using the Unified Theory of Acceptance and Use of Technology: Cross-Sectional Study (Preprint)

Abstract

BACKGROUND Chronic pain is a complex disease with high prevalence rates, and many affected individuals do not receive adequate treatment. As a complement to conventional therapies, eHealth interventions could provide many benefits to a multimodal treatment approach for patients with chronic pain, whereby the future usage is associated with the acceptance of these interventions. OBJECTIVE This study aimed to assess the acceptance of eHealth pain management interventions among patients with chronic pain and to identify the influencing factors on acceptance. A further objective of the study was to evaluate the viability of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and to compare it to its extended version in terms of explained variance of acceptance. METHODS A cross-sectional online study was performed. 307 participants with chronic pain, as defined according to the International Association for the Study of Pain criteria, were recruited via flyers, posters, and online inquiries between December 2020 and July 2021. In addition to sociodemographic and medical data, the assessment included validated psychometric instruments and an extended version of the well-established UTAUT model. For statistical analyses, group comparisons and multiple hierarchical regression analyses were performed. RESULTS The acceptance of eHealth pain management interventions among patients with chronic pain was overall moderate to high (M = 3.67, SD = 0.89). There was significant difference in acceptance between age groups (W = 9674.0, P = .044, r = .156). Effort Expectancy (β = .37, P < .001), Performance Expectancy (β = .33, P < .001), and Social Influence (β = .34, P < .001) proved to be the most important predictors of acceptance. The extended UTAUT model explained 66.4% of variance in acceptance, thus supporting the viability of the model. Compared to the original UTAUT model (PE, EE, SI), the extended model explained significantly more variance (F (25,278) = 1.74, P = .018). CONCLUSIONS Given the association between acceptance and future usage, the knowledge of the influencing factors on acceptance should be used in the development and advertising of eHealth pain management interventions. Overall, the acceptance of eHealth pain management interventions was moderate to high. Eight predictors proved to be significant predictors of acceptance. The UTAUT model is a valuable instrument for determining acceptance as well as factors influencing the acceptance of eHealth pain management interventions among patients with chronic pain. The extended UTAUT model provided the greatest predictive value for acceptance.