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      The Impact of Information Technology on Patient Engagement and Health Behavior Change: A Systematic Review of the Literature

      research-article
      Author(s): , B Pharm,MPharmSc,PhD (c) 1 , , , B Pharm,MPharm,PhD 2 , , MSc,PhD (c) 1 , , RN,PhD 3 , , RN,PhD 1
      Editor(s):
      Other contributor(s): (Reviewer), (Reviewer), (Reviewer)
      Publication date (Electronic):
      Journal: JMIR Medical Informatics
      Publisher: Gunther Eysenbach
      Keywords: patient engagement, patient behavior, technology, Internet, web-based, cell phone, social media

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          Abstract

          Background

          Advancements in information technology (IT) and its increasingly ubiquitous nature expand the ability to engage patients in the health care process and motivate health behavior change.

          Objective

          Our aim was to systematically review the (1) impact of IT platforms used to promote patients’ engagement and to effect change in health behaviors and health outcomes, (2) behavior theories or models applied as bases for developing these interventions and their impact on health outcomes, (3) different ways of measuring health outcomes, (4) usability, feasibility, and acceptability of these technologies among patients, and (5) challenges and research directions for implementing IT platforms to meaningfully impact patient engagement and health outcomes.

          Methods

          PubMed, Web of Science, PsycINFO, and Google Scholar were searched for studies published from 2000 to December 2014. Two reviewers assessed the quality of the included papers, and potentially relevant studies were retrieved and assessed for eligibility based on predetermined inclusion criteria.

          Results

          A total of 170 articles met the inclusion criteria and were reviewed in detail. Overall, 88.8% (151/170) of studies showed positive impact on patient behavior and 82.9% (141/170) reported high levels of improvement in patient engagement. Only 47.1% (80/170) referenced specific behavior theories and only 33.5% (57/170) assessed the usability of IT platforms. The majority of studies used indirect ways to measure health outcomes (65.9%, 112/170).

          Conclusions

          In general, the review has shown that IT platforms can enhance patient engagement and improve health outcomes. Few studies addressed usability of these interventions, and the reason for not using specific behavior theories remains unclear. Further research is needed to clarify these important questions. In addition, an assessment of these types of interventions should be conducted based on a common framework using a large variety of measurements; these measurements should include those related to motivation for health behavior change, long-standing adherence, expenditure, satisfaction, and health outcomes.

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          Adherence to long-term therapies: evidence for action.

          Sabina De Geest, Eduardo Sabate (2004)
          Article 0 comments Cited 466 times – based on 0 reviews     Review now
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            The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes

            Dean J Wantland, Carmen J Portillo, William L Holzemer … (2004)
            Background A primary focus of self-care interventions for chronic illness is the encouragement of an individual's behavior change necessitating knowledge sharing, education, and understanding of the condition. The use of the Internet to deliver Web-based interventions to patients is increasing rapidly. In a 7-year period (1996 to 2003), there was a 12-fold increase in MEDLINE citations for “Web-based therapies.” The use and effectiveness of Web-based interventions to encourage an individual's change in behavior compared to non-Web-based interventions have not been substantially reviewed. Objective This meta-analysis was undertaken to provide further information on patient/client knowledge and behavioral change outcomes after Web-based interventions as compared to outcomes seen after implementation of non-Web-based interventions. Methods The MEDLINE, CINAHL, Cochrane Library, EMBASE, ERIC, and PSYCHInfo databases were searched for relevant citations between the years 1996 and 2003. Identified articles were retrieved, reviewed, and assessed according to established criteria for quality and inclusion/exclusion in the study. Twenty-two articles were deemed appropriate for the study and selected for analysis. Effect sizes were calculated to ascertain a standardized difference between the intervention (Web-based) and control (non-Web-based) groups by applying the appropriate meta-analytic technique. Homogeneity analysis, forest plot review, and sensitivity analyses were performed to ascertain the comparability of the studies. Results Aggregation of participant data revealed a total of 11,754 participants (5,841 women and 5,729 men). The average age of participants was 41.5 years. In those studies reporting attrition rates, the average drop out rate was 21% for both the intervention and control groups. For the five Web-based studies that reported usage statistics, time spent/session/person ranged from 4.5 to 45 minutes. Session logons/person/week ranged from 2.6 logons/person over 32 weeks to 1008 logons/person over 36 weeks. The intervention designs included one-time Web-participant health outcome studies compared to non-Web participant health outcomes, self-paced interventions, and longitudinal, repeated measure intervention studies. Longitudinal studies ranged from 3 weeks to 78 weeks in duration. The effect sizes for the studied outcomes ranged from -.01 to .75. Broad variability in the focus of the studied outcomes precluded the calculation of an overall effect size for the compared outcome variables in the Web-based compared to the non-Web-based interventions. Homogeneity statistic estimation also revealed widely differing study parameters (Qw16 = 49.993, P ≤ .001). There was no significant difference between study length and effect size. Sixteen of the 17 studied effect outcomes revealed improved knowledge and/or improved behavioral outcomes for participants using the Web-based interventions. Five studies provided group information to compare the validity of Web-based vs. non-Web-based instruments using one-time cross-sectional studies. These studies revealed effect sizes ranging from -.25 to +.29. Homogeneity statistic estimation again revealed widely differing study parameters (Qw4 = 18.238, P ≤ .001). Conclusions The effect size comparisons in the use of Web-based interventions compared to non-Web-based interventions showed an improvement in outcomes for individuals using Web-based interventions to achieve the specified knowledge and/or behavior change for the studied outcome variables. These outcomes included increased exercise time, increased knowledge of nutritional status, increased knowledge of asthma treatment, increased participation in healthcare, slower health decline, improved body shape perception, and 18-month weight loss maintenance.
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              Delivering interventions for depression by using the internet: randomised controlled trial.

              Helen Christensen, Kathleen Griffiths, Anthony F. Jorm (2004)
              To evaluate the efficacy of two internet interventions for community-dwelling individuals with symptoms of depression--a psychoeducation website offering information about depression and an interactive website offering cognitive behaviour therapy. Randomised controlled trial. Internet users in the community, in Canberra, Australia. 525 individuals with increased depressive symptoms recruited by survey and randomly allocated to a website offering information about depression (n = 166) or a cognitive behaviour therapy website (n = 182), or a control intervention using an attention placebo (n = 178). Change in depression, dysfunctional thoughts; knowledge of medical, psychological, and lifestyle treatments; and knowledge of cognitive behaviour therapy. Intention to treat analyses indicated that information about depression and interventions that used cognitive behaviour therapy and were delivered via the internet were more effective than a credible control intervention in reducing symptoms of depression in a community sample. For the intervention that delivered cognitive behaviour therapy the reduction in score on the depression scale of the Center for Epidemiologic Studies was 3.2 (95% confidence interval 0.9 to 5.4). For the "depression literacy" site (BluePages), the reduction was 3.0 (95% confidence interval 0.6 to 5.2). Cognitive behaviour therapy (MoodGYM) reduced dysfunctional thinking and increased knowledge of cognitive behaviour therapy. Depression literacy (BluePages) significantly improved participants' understanding of effective evidence based treatments for depression (P < 0.05). Both cognitive behaviour therapy and psychoeducation delivered via the internet are effective in reducing symptoms of depression.
              Article 0 comments Cited 249 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Suhila Sawesi:
                School of Informatics and Computing – IndianapolisDepartment of BioHealth InformaticsIUPUIWalker Plaza (WK) 120719 Indiana AvenueIndianapolis, IN, 46202United States1 317 274 80591 317 274 8059ssawesi@umail.iu.edu
                ORCID: http://orcid.org/0000-0002-3510-8543
                Journal
                Journal ID (nlm-ta): JMIR Med Inform
                Journal ID (iso-abbrev): JMIR Med Inform
                Journal ID (publisher-id): JMI
                Title: JMIR Medical Informatics
                Publisher: Gunther Eysenbach (JMIR Publications Inc., Toronto, Canada )
                ISSN (Electronic): 2291-9694
                Publication date Collection: Jan-Mar 2016
                Publication date (Electronic): 21 January 2016
                Volume: 4
                Issue: 1
                Electronic Location Identifier: e1
                Affiliations
                [1] 1School of Informatics and Computing – Indianapolis Department of BioHealth Informatics IUPUI Indianapolis, INUnited States
                [2] 2School of Pharmacy Chapman University Irvine, CAUnited States
                [3] 3Indiana University School of Nursing Science of Nursing Care Department Indiana University Indianapolis, INUnited States
                Author notes
                Corresponding Author: Suhila Sawesi ssawesi@ 123456umail.iu.edu
                Author information
                http://orcid.org/0000-0002-3510-8543
                http://orcid.org/0000-0002-4029-5427
                http://orcid.org/0000-0002-0229-1257
                http://orcid.org/0000-0003-3988-8855
                http://orcid.org/0000-0002-4996-8595
                Article
                Publisher ID: v4i1e1
                DOI: 10.2196/medinform.4514
                PMC ID: 4742621
                PubMed ID: 26795082
                SO-VID: 4533bec0-fd39-4997-b53e-2a5ab2b9eb7a
                Copyright © ©Suhila Sawesi, Mohamed Rashrash, Kanitha Phalakornkule, Janet S Carpenter, Josette F Jones. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 21.01.2016.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

                History
                Date received : 9 April 2015
                Date revision requested : 29 July 2015
                Date revision received : 7 September 2015
                Date accepted : 9 October 2015
                Categories
                Subject: Original Paper
                Subject: Original Paper

                Keywords: patient engagement,patient behavior,technology,internet,web-based,cell phone,social media
                Data availability:
                Keywords: patient engagement, patient behavior, technology, internet, web-based, cell phone, social media

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