Determining the Influencing Factors on Acceptance of eHealth Pain Management Interventions Among Patients With Chronic Pain Using the Unified Theory of Acceptance and Use of Technology: Cross-sectional Study

Abstract

Background Chronic pain is a complex disease with high prevalence rates, and many individuals who are affected 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 future use is associated with the acceptance of these interventions. Objective This study aims to assess the acceptance of eHealth pain management interventions among patients with chronic pain and identify the influencing factors on acceptance. A further objective of the study is to evaluate the viability of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and compare it with its extended version in terms of explained variance of acceptance. Methods We performed a cross-sectional web-based study. In total, 307 participants with chronic pain, as defined according to the International Association for the Study of Pain criteria, were recruited through flyers, posters, and web-based 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 (mean 3.67, SD 0.89). There was significant difference in acceptance among age groups (W=9674.0; r=0.156; P=.04). 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 (including the original UTAUT factors as well as sociodemographic, medical, and eHealth-related factors) model explained 66.4% of the variance in acceptance, thus supporting the viability of the model. Compared with the original UTAUT model (performance expectancy, effort expectancy, and social influence), the extended model explained significantly more variance (F25,278=1.74; P=.02). Conclusions Given the association between acceptance and future use, the knowledge of the influencing factors on acceptance should be used in the development and promotion of eHealth pain management interventions. Overall, the acceptance of eHealth pain management interventions was moderate to high. In total, 8 predictors proved to be significant predictors of acceptance. The UTAUT model is a valuable instrument for determining acceptance as well as the factors that influence acceptance of eHealth pain management interventions among patients with chronic pain. The extended UTAUT model provided the greatest predictive value for acceptance.

Acceptance, drivers, and barriers to use eHealth interventions in patients with post-COVID-19 syndrome for management of post-COVID-19 symptoms: a cross-sectional study

“…In addition, patients who are still moderately to significantly impaired after COVID-19 infection expressed higher acceptance than patients in currently good condition. This is in line with the results of Stoppok et al 27 showing higher acceptance in more burdened individuals affected by chronic pain. In addition, patients with moderate to severe symptoms during the initial COVID-19 infection also showed higher acceptance.…”

“…In addition, the comparison between the original and the extended UTAUT model indicated that the extended version, which includes additional predictors, is able to explain a higher level of variance of acceptance. This result has been previously described in other studies, 19,27 whereas another study reported opposite results. 26…”

“…This relationship was also observed in studies analyzing individuals with obesity and overweight, diabetes, and chronic pain. 19,21,26,27 Furthermore, PE was found to be the predictor with the highest association with acceptance, 19,20,21 which highlights that the acceptance is dependent on the beliefs of post-COVID-19 patients that the interventions could help them. Besides, it could be shown that the original UTAUT model is a reasonable way to predict the acceptance toward eHealth interventions for symptom management in post-COVID-19 patients.…”

“…24,26,34 In addition, other studies show similar positive connections between digital confidence and acceptance. 19,27,35 Digital confidence is, therefore, a relevant intervention factor to increase acceptance and actual usage.…”

“…24 The model has been applied in patients with diabetes, patients with obesity, patients with chronic pain, and aftercare in inpatients and relapse prevention. 19,20,21,26,27 The UTAUT consist of four core predictors including performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating condition (FC). 20,25 Acceptance is operationalized as behavioral intention (BI) and is predicted by the first three core predictors.…”