47
views
0
recommends
+1 Recommend
1 collections
    0
    shares

      Submit your digital health research with an established publisher
      - celebrating 25 years of open access

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

      Evidence-Based Evaluation of eHealth Interventions: Systematic Literature Review

      review-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          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

          Until now, the use of technology in health care was driven mostly by the assumptions about the benefits of electronic health (eHealth) rather than its evidence. It is noticeable that the magnitude of evidence of effectiveness and efficiency of eHealth is not proportionate to the number of interventions that are regularly conducted. Reliable evidence generated through comprehensive evaluation of eHealth interventions may accelerate the growth of eHealth for long-term successful implementation and help to experience eHealth benefits in an enhanced way.

          Objective

          This study aimed to understand how the evidence of effectiveness and efficiency of eHealth can be generated through evaluation. Hence, we aim to discern (1) how evaluation is conducted in distinct eHealth intervention phases, (2) the aspects of effectiveness and efficiency that are typically evaluated during eHealth interventions, and (3) how eHealth interventions are evaluated in practice.

          Methods

          A systematic literature review was conducted to explore the evaluation methods for eHealth interventions. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were followed. We searched Google Scholar and Scopus for the published papers that addressed the evaluation of eHealth or described an eHealth intervention study. A qualitative analysis of the selected papers was conducted in several steps.

          Results

          We intended to see how the process of evaluation unfolds in distinct phases of an eHealth intervention. We revealed that in practice and in several conceptual papers, evaluation is performed at the end of the intervention. There are some studies that discuss the importance of conducting evaluation throughout the intervention; however, in practice, we found no case study that followed this. For our second research question, we discovered aspects of efficiency and effectiveness that are proposed to be assessed during interventions. The aspects that were recurrent in the conceptual papers include clinical, human and social, organizational, technological, cost, ethical and legal, and transferability. However, the case studies reviewed only evaluate the clinical and human and social aspects. At the end of the paper, we discussed a novel approach to look into the evaluation. Our intention was to stir up a discussion around this approach with the hope that it might be able to gather evidence in a comprehensive and credible way.

          Conclusions

          The importance of evidence in eHealth has not been discussed as rigorously as have the diverse evaluation approaches and evaluation frameworks. Further research directed toward evidence-based evaluation can not only improve the quality of intervention studies but also facilitate successful long-term implementation of eHealth in general. We conclude that the development of more robust and comprehensive evaluation of eHealth studies or an improved validation of evaluation methods could ease the transferability of results among similar studies. Thus, the resources can be used for supplementary research in eHealth.

          Related collections

          Most cited references53

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

          Harnessing Context Sensing to Develop a Mobile Intervention for Depression

          Background Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder. Objective The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing. Methods We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients’ mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients’ self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks. Results Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (betaweek = –.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (betaweek = –.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (bweek = –.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (betaweek = –.71, P < .001, per-protocol Cohen d = 2.58). Conclusions Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed. Trial Registration Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n)
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A model for assessment of telemedicine applications: mast.

            Telemedicine applications could potentially solve many of the challenges faced by the healthcare sectors in Europe. However, a framework for assessment of these technologies is need by decision makers to assist them in choosing the most efficient and cost-effective technologies. Therefore in 2009 the European Commission initiated the development of a framework for assessing telemedicine applications, based on the users' need for information for decision making. This article presents the Model for ASsessment of Telemedicine applications (MAST) developed in this study. MAST was developed through workshops with users and stakeholders of telemedicine. Based on the workshops and using the EUnetHTA Core HTA Model as a starting point a three-element model was developed, including: (i) preceding considerations, (ii) multidisciplinary assessment, and (iii) transferability assessment. In the multidisciplinary assessment, the outcomes of telemedicine applications comprise seven domains, based on the domains in the EUnetHTA model. MAST provides a structure for future assessment of telemedicine applications. MAST will be tested during 2010-13 in twenty studies of telemedicine applications in nine European countries in the EC project Renewing Health.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A development and evaluation process for mHealth interventions: examples from New Zealand.

              The authors established a process for the development and testing of mobile phone-based health interventions that has been implemented in several mHealth interventions developed in New Zealand. This process involves a series of steps: conceptualization, formative research to inform the development, pretesting content, pilot study, pragmatic randomized controlled trial, and further qualitative research to inform improvement or implementation. Several themes underlie the entire process, including the integrity of the underlying behavior change theory, allowing for improvements on the basis of participant feedback, and a focus on implementation from the start. The strengths of this process are the involvement of the target audience in the development stages and the use of rigorous research methods to determine effectiveness. The limitations include the time required and potentially a less formalized and randomized approach than some other processes. This article aims to describe the steps and themes in the mHealth development process, using the examples of a mobile phone video messaging smoking cessation intervention and a mobile phone multimedia messaging depression prevention intervention, to stimulate discussion on these and other potential methods.
                Bookmark

                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                November 2018
                23 November 2018
                : 20
                : 11
                : e10971
                Affiliations
                [1 ] Centre for Healthcare Improvement Technology Management and Economics Chalmers University of Technology Gothenburg Sweden
                Author notes
                Corresponding Author: Amia Enam amiaenam@ 123456gmail.com
                Author information
                http://orcid.org/0000-0001-7249-6242
                http://orcid.org/0000-0002-6416-3744
                http://orcid.org/0000-0001-6464-7231
                Article
                v20i11e10971
                10.2196/10971
                6286426
                30470678
                ebcf3220-92ee-475e-bc96-96932dfe7b04
                ©Amia Enam, Johanna Torres-Bonilla, Henrik Eriksson. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 23.11.2018.

                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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/.as well as this copyright and license information must be included.

                History
                : 7 May 2018
                : 7 June 2018
                : 1 August 2018
                : 1 August 2018
                Categories
                Review
                Review

                Medicine
                evidence-based practice,program evaluation,systematic review,technology assessment
                Medicine
                evidence-based practice, program evaluation, systematic review, technology assessment

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content478

                Cited by67

                Most referenced authors845