ArticleBMC medical informatics and decision making2015
A cross-sectional study assessing determinants of the attitude to the introduction of eHealth services among patients suffering from chronic conditions.
Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 40 citations in OpenAlex.
- The perception of facilitators and barriers to the use of e-health solutions in Poland: a qualitative study.BMC medical informatics and decision making · 2024Article
- eHealth tools use and mental health: a cross-sectional network analysis in a representative sample.Scientific reports · 2024Article
- Review
- Predictors of non-participation in a cardiac telerehabilitation programme: a prospective analysis.European heart journal. Digital health · 2022Article
- Readiness and Acceptance of eHealth Services for Diabetes Care in the General Population: Cross-sectional Study.Journal of medical Internet research · 2021Article
- Rapid Transition to Telehealth and the Digital Divide: Implications for Primary Care Access and Equity in a Post-COVID Era.The Milbank quarterly · 2021Article
- The association between Internet use and health-related outcomes in older adults and the elderly: a cross-sectional study.BMC medical informatics and decision making · 2021Article
- Telehealth and COVID-19: Using technology to accelerate the curve on access and quality healthcare for citizens in India.Technology in society · 2021Article
- E-health literacy and associated factors among chronic patients in a low-income country: a cross-sectional survey.BMC medical informatics and decision making · 2020Article
- Impact of Age on Patients' Communication and Technology Preferences in the Era of Meaningful Use: Mixed Methods Study.Journal of medical Internet research · 2020Article
- Assessment of Use and Preferences Regarding Internet-Based Health Care Delivery: Cross-Sectional Questionnaire Study.Journal of medical Internet research · 2019Article
- Sociodemographic Factors Influencing the Use of eHealth in People with Chronic Diseases.International journal of environmental research and public health · 2019Review
- Barriers and Facilitators to eHealth Use in Daily Practice: Perspectives of Patients and Professionals in Dermatology.Journal of medical Internet research · 2017Article
- From loquacious to reticent: understanding patient health information communication to guide consumer health IT design.Journal of the American Medical Informatics Association : JAMIA · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProvision of care to patients with chronic diseases remains a great challenge for modern health care systems. eHealth is indicated as one of the strategies which could improve care delivery to this group of patients. The main objective of this study was to assess determinants of the acceptance of the Internet use for provision of chosen health care services remaining in the scope of current nationwide eHealth initiative in Poland.
methodsThe survey was carried out among patients with diagnosed chronic conditions who were treated in three health care facilities in Krakow, Poland. Survey data was used to develop univariate and multivariate logistic regression models for six outcome variables originating from the items assessing the acceptance of specific types of eHealth applications. The variables used as predictors were related to the sociodemographic characteristics of respondents, burden related to chronic disease, and the use of the Internet and its perceived usefulness in making personal health-related decisions.
resultsAmong 395 respondents, there were 60.3% of Internet users. Univariate logistic regression models developed for six types of eHealth solutions demonstrated their higher acceptance among younger respondents, living in urban areas, who have attained a higher level of education, used the Internet on their own, and were more confident about its usefulness in making health-related decisions. Furthermore, the duration of chronic disease and hospitalization due to chronic disease predicted the acceptance of some of eHealth applications. However, when combined in multivariate models, only the belief in the usefulness of the Internet (five of six models), level of education (four of six models), and previous hospitalization due to chronic disease (three of six models) maintained the effect on the independent variables.
conclusionsThe perception of the usefulness of the Internet in making health-related decision is a key determinant of the acceptance of provision of health care services online among patients with chronic diseases. Among sociodemographic factors, only the level of education demonstrates a consistent impact on the level of acceptance. Interestingly, a greater burden of chronic disease related to previous hospitalizations leads to lower acceptance of eHealth solutions.
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.