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

Drivers and barriers of patients' acceptance of video consultation in cancer care

Background

Due to digitization in the medical sector, many healthcare interactions are switched to online services. This study assessed the acceptance of video consultations (VCs) in cancer care, and determined drivers and barriers of acceptance.

Methods

A cross-sectional online-based survey study was conducted in Germany from February 2022 to February 2023. Recruitment took place at oncology outpatient clinics, general practitioners, oncology practices and via cancer-related social media channels. Inclusion criteria were a cancer diagnosis, cancer treatment and internet access. Sociodemographic, medical data, eHealth-related data were acquired via an online assessment. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used to determine the acceptance of VC and its predictors.

Results

Of N = 350 cancer patients, 56.0% (n = 196) reported high acceptance of VC, 28.0% (n = 98) stated moderate acceptance and 16.0% (n = 56) indicated low acceptance. Factors influencing acceptance were younger age (β = -.28, p < .001), female gender (β = .35, p = .005), stage of disease (β = .11, p = .032), high digital confidence (β = .14, p = .010), low internet anxiety (β = -.21, p = .001), high digital overload (β = -.12, p = .022), high eHealth literacy (β = .14, p = .028), personal trust (β = -.25, p < .001), internet use (β = .17, p = .002), and the UTAUT predictors: performance expectancy (β = .24, p < .001), effort expectancy (β = .26, p < .001), and social influence (β = .34, p < .001).

Conclusions

Patients' acceptance of VC in cancer care is high. Drivers and barriers to acceptance identified should be considered for personalized applications. Considering the growing demand for cancer care establishing digital healthcare solutions is justified.

Understanding the use intention and influencing factors of telerehabilitation in people with rehabilitation needs: a cross-sectional survey

Objective

This study aimed to investigate the use intention and influencing factors of telerehabilitation in people with rehabilitation needs.

Methods

This cross-sectional survey recruited a total of 183 participants with rehabilitation needs from May 2022 to December 2022. Sociodemographic and medical data were collected by a structured questionnaire. The factors influencing the use intention of telerehabilitation were measured by the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. Multiple hierarchical regression analyses were performed.

Results

A total of 150 valid questionnaires were included for analysis. The results indicated that the use intention of telerehabilitation was overall high in people with rehabilitation needs. Health condition (β = -0.21, p = 0.03), performance expectancy (β = 0.21, p = 0.01), facilitating conditions (β = 0.25, p = 0.03), perceived trust (β = 0.25, p < 0.01), and self-efficacy (β = 0.19, p = 0.04) were significant factors influencing the use intention of telerehabilitation.

Conclusion

Overall, the use intention of telerehabilitation is high in individuals with rehabilitation needs. Health conditions, performance expectancy, facilitating conditions, perceived trust, and self-efficacy are important factors influencing the use intention of telerehabilitation in individuals with rehabilitation needs.

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

Background

Post-COVID-19 syndrome is a new and debilitating disease without adequate treatment options. eHealth could be a reasonable approach for symptom management.

Objectives

This study aims to evaluate the acceptance for eHealth interventions for symptom management in individuals with post-COVID-19 syndrome, as well as drivers and barriers influencing acceptance.

Design

Cross-sectional study.

Methods

This study was conducted from January 19 until 24 May 2022. Recruitment took place with a web-based survey. Acceptance and predictors of eHealth interventions were measured by the extended UTAUT model. Included in the model were the core predictor performance expectancy, social influence, and effort expectancy. Previously diagnosed mental illness was estimated and mental health by using the well-established Generalized Anxiety Disorder Scale-7 and the Patient Health Questionnaire Depression Scale. The effect of sociodemographic and medical data was assessed. Multiple hierarchical regression analyses as well as group comparisons were performed.

Results

342 individuals with post-COVID-19 syndrome were examined. The acceptance of eHealth interventions for symptom management was moderate to high (M = 3.60, SD = 0.89). Acceptance was significantly higher in individuals with lower/other education, patients with moderate to severe symptoms during initial COVID-19 infection, still significantly impaired patients, and individuals with a mental illness. Identified predictors of acceptance were age (β = .24, p < .001), current condition including moderate (β = .49, p = .002) and still significantly impaired (β = .67, p < .001), digital confidence (β = .19, p < .001), effort expectancy (β = .26, p < .001), performance expectancy (β = .33, p < .001), and social influence (β = .26, p < .001).

Conclusion

Patients with post-COVID-19 syndrome reported a satisfying level of acceptance and drivers and barriers could be identified. These factors need to be considered for the implementation and future use of eHealth interventions.