Related Experiment Video
Updated: Oct 5, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Design of an Integrated Acceptance Framework for Older Users and eHealth: Influential Factor Analysis
Jiyeon Yu1, Angelica de Antonio1, Elena Villalba-Mora2,3
1Research Group on Human-Computer Interaction and Advanced Interactive Systems, Madrid HCI Lab, Universidad Politécnica de Madrid, Madrid, Spain.
Background:
eHealth and telehealth play a crucial role in assisting older adults who visit hospitals frequently or who live in nursing homes and can benefit from staying at home while being cared for. Adapting to new technologies can be difficult for older people. Thus, to better apply these technologies to older adults' lives, many studies have analyzed the acceptance factors for this particular population. However, there is not yet a consensual framework that can be used in further development and to search for solutions.
Objective:
This paper aims to present an integrated acceptance framework (IAF) for older users' acceptance of eHealth based on 43 studies selected through a systematic review.
Methods:
We conducted a 4-step study. First, through a systematic review in the field of eHealth from 2010 to 2020, the acceptance factors and basic data for analysis were extracted. Second, we conducted a thematic analysis to group the factors into themes to propose an integrated framework for acceptance. Third, we defined a metric to evaluate the impact of the factors addressed in the studies. Finally, the differences among the important IAF factors were analyzed according to the participants' health conditions, verification time, and year.
Results:
Through a systematic review, 731 studies were found in 5 major databases, resulting in 43 (5.9%) selected studies using the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) methodology. First, the research methods and acceptance factors for eHealth were compared and analyzed, extracting a total of 105 acceptance factors, which were grouped later, resulting in an IAF. A total of 5 dimensions (ie, personal, user-technology relational, technological, service-related, and environmental) emerged, with a total of 23 factors. In addition, we assessed the quality of evidence and then conducted a stratification analysis to reveal the more appropriate factors depending on the health condition and assessment time. Finally, we assessed the factors and dimensions that have recently become more important.
Conclusions:
The result of this investigation is a framework for conducting research on eHealth acceptance. To elaborately analyze the impact of the factors of the proposed framework, the criteria for evaluating the evidence from the studies that have the extracted factors are presented. Through this process, the impact of each factor in the IAF has been presented, in addition to the framework proposal. Moreover, a meta-analysis of the current status of research is presented, highlighting the areas where specific measures are needed to facilitate eHealth acceptance.
More Related Videos
11:21Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
07:40Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
Published on: May 31, 2021
Related Concept Videos
Drug Dosing: Geriatric Patients
Factors Affecting Illness
For instance, risk factors are connected to illness,...
Models of Health Promotion and Illness Prevention II
The agent-host-environment model states that disease results...
Study Designs in Epidemiology
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Pharmacodynamics in Geriatric Patients: Effects of Age
Models of Health Promotion and Illness Prevention I
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...