16
views
0
recommends
+1 Recommend
1 collections
    0
    shares

      Submit your digital health research with JMIR Publications, a leading publisher of open access digital health research

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Use Intention and User Expectations of Human-Supported and Self-Help eHealth Interventions: Internet-Based Randomized Controlled Trial

      research-article

      Read this article at

      ScienceOpenPublisherPMC
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Self-help eHealth interventions provide automated support to change health behaviors without any further human assistance. The main advantage of self-help eHealth interventions is that they have the potential to lower the workload of health care professionals. However, one disadvantage is that they generally have a lower uptake. Possibly, the absence of a relationship with a health care professional (referred to as the working alliance) could lead to negative expectations that hinder the uptake of self-help interventions. The Unified Theory of Acceptance and Use of Technology (UTAUT) identifies which expectations predict use intention. As there has been no previous research exploring how expectations affect the adoption of both self-help and human-supported eHealth interventions, this study is the first to investigate the impact of expectations on the uptake of both kinds of eHealth interventions.

          Objective

          This study investigated the intention to use a self-help eHealth intervention compared to a human-supported eHealth intervention and the expectations that moderate this relationship.

          Methods

          A total of 146 participants were randomly assigned to 1 of 2 conditions (human-supported or self-help eHealth interventions). Participants evaluated screenshots of a human-supported or self-help app–based stress intervention. We measured intention to use the intervention-expected working alliance and the UTAUT constructs: performance expectancy, effort expectancy, and social influence.

          Results

          Use intention did not differ significantly between the 2 conditions (t 142=–1.133; P=.26). Performance expectancy ( F 1,140=69.269; P<.001), effort expectancy ( F 1,140=3.961; P=.049), social influence ( F 1,140=90.025; P<.001), and expected working alliance ( F 1,140=26.435; P<.001) were positively related to use intention regardless of condition. The interaction analysis showed that performance expectancy ( F 1,140=4.363; P=.04) and effort expectancy ( F 1,140=4.102; P=.045) more strongly influenced use intention in the self-help condition compared to the human-supported condition.

          Conclusions

          As we found no difference in use intention, our results suggest that we could expect an equal uptake of self-help eHealth interventions and human-supported ones. However, attention should be paid to people who have doubts about the intervention’s helpfulness or ease of use. For those people, providing additional human support would be beneficial to ensure uptake. Screening user expectations could help health care professionals optimize self-help eHealth intervention uptake in practice.

          Trial Registration

          OSF Registries osf.io/n47cz; https://osf.io/n47cz

          Related collections

          Most cited references43

          • Record: found
          • Abstract: found
          • Article: not found

          G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

          G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and chi2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.
            • Record: found
            • Abstract: not found
            • Article: not found

            User Acceptance of Information Technology: Toward a Unified View

              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions

              Background Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence. Objective This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention. Methods We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence. Results We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p = .004), setup (p < .001), updates (p < .001), frequency of interaction with a counselor (p < .001), the system (p = .003) and peers (p = .017), duration (F = 6.068, p = .004), adherence (F = 4.833, p = .010) and the number of primary task support elements (F = 5.631, p = .005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence. Conclusions Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere.

                Author and article information

                Contributors
                On behalf of : the BENEFIT consortium
                Journal
                JMIR Form Res
                JMIR Form Res
                JFR
                JMIR Formative Research
                JMIR Publications (Toronto, Canada )
                2561-326X
                2024
                15 February 2024
                : 8
                : e38803
                Affiliations
                [1 ] Health, Medical, and Neuropsychology Unit Leiden University Leiden Netherlands
                [2 ] Department of Cardiology Leiden University Medical Center Leiden Netherlands
                [3 ] NDDO Institute for Prevention and Early Diagnostics (NIPED) Amsterdam Netherlands
                [4 ] Vital10 Amsterdam Netherlands
                [5 ] Department of Psychiatry Leiden University Medical Center Leiden Netherlands
                [6 ] Medical Delta Leiden University, Technical University of Delft, Erasmus University Rotterdam Leiden, Delft, Rotterdam Netherlands
                Author notes
                Corresponding Author: Talia R Cohen Rodrigues t.r.cohen.rodrigues@ 123456fsw.leidenuniv.nl
                Author information
                https://orcid.org/0000-0003-0000-5285
                https://orcid.org/0000-0002-2184-8727
                https://orcid.org/0000-0002-4441-2731
                https://orcid.org/0000-0002-9506-5274
                https://orcid.org/0000-0002-5102-0197
                https://orcid.org/0000-0002-9664-2985
                https://orcid.org/0000-0002-0090-5091
                Article
                v8i1e38803
                10.2196/38803
                10905349
                38358784
                37003d58-ef99-437b-9642-45b4694a59bc
                ©Talia R Cohen Rodrigues, Thomas Reijnders, Linda D Breeman, Veronica R Janssen, Roderik A Kraaijenhagen, Douwe E Atsma, Andrea WM Evers. Originally published in JMIR Formative Research (https://formative.jmir.org), 15.02.2024.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

                History
                : 16 April 2022
                : 12 September 2023
                : 28 October 2023
                : 20 November 2023
                Categories
                Original Paper
                Original Paper

                ehealth,human support,unified theory of acceptance and use of technology,use intention,utaut,working alliance

                Comments

                Comment on this article

                Related Documents Log