ArticleJMIR formative research2022
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.
Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Virtual Preoperative Education in Bariatric Surgery: Patient Acceptance and Travel-Related Carbon Savings.Obesity surgery · 2026Article
- Acceptance, needs and demands of e-mental health interventions in adolescent elite athletes.Discover mental health · 2026Article
- Usability, acceptability, and tolerability of a virtual reality patient tour compared to an educational video in cardiac surgery patients - A feasibility study.PEC innovation · 2026Article
- Determining the adoption intention and influencing factors of telerehabilitation by people with rehabilitation needs: a cross-sectional survey.BMC public health · 2026Article
- Article
- Acceptance of Men Living With HIV Toward Treatment-Supportive Mobile Apps Using the Unified Theory of Acceptance and Use of Technology: Cross-Sectional Study.JMIR formative research · 2026Article
- Pricing, Popularity, and Customer Satisfaction in Online Pain-Relief Products: An AI-Powered Analysis.Journal of pain research · 2026Article
- Drivers and barriers of acceptance of eHealth interventions in postpartum mental health care: a cross-sectional study.BMC public health · 2025Article
- Acceptance, Drivers, and Barriers to Use of mHealth Apps to Improve Quality of Life in Female Patients Affected by Hypothyroidism: Cross-Sectional Study.JMIR formative research · 2025Article
- E-Mental Health Interventions in Inpatient Care: Scoping Review.Journal of medical Internet research · 2025Article
- Medical Students' Acceptance of Tailored e-Mental Health Apps to Foster Their Mental Health: Cross-Sectional Study.JMIR medical education · 2025Article
- Videoconferencing psychotherapy: determining acceptance, drivers and barriers of use.Frontiers in digital health · 2025Article
- Acceptance, Needs, and Demands for Nutritional mHealth Support in Patients with Cardiovascular Disease.Nutrients · 2024Article
- Association between eHealth literacy and health outcomes in German athletes using the GR-eHEALS questionnaire: a validation and outcome study.BMC sports science, medicine & rehabilitation · 2024Article
- Determining the acceptance of e-mental health interventions in elite athletes using the unified theory of acceptance and use of technology.Frontiers in sports and active living · 2024Article
- 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.Therapeutic advances in neurological disorders · 2023Article
- Telehealth during COVID-19: Perspectives on Usability by US Physicians.Journal of Brown hospital medicine · 2023Article
- Understanding the use intention and influencing factors of telerehabilitation in people with rehabilitation needs: a cross-sectional survey.Frontiers in public health · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChronic 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.
objectiveThis 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.
methodsWe 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.
resultsThe 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 (F
conclusionsGiven 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.