Adherence to Internet-Based Mobile-Supported Stress Management: A Pooled Analysis of Individual Participant Data From Three Randomized Controlled Trials

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Abstract

Background

Nonadherence to treatment is a prevalent issue in Internet interventions. Guidance from health care professionals has been found to increase treatment adherence rates in Internet interventions for a range of physical and mental disorders. Evaluating different guidance formats of varying intensity is important, particularly with respect to improvement of effectiveness and cost-effectiveness. Identifying predictors of nonadherence allows for the opportunity to better adapt Internet interventions to the needs of participants especially at risk for discontinuing treatment.

Objective

The goal of this study was to investigate the influence of different guidance formats (content-focused guidance, adherence-focused guidance, and administrative guidance) on adherence and to identify predictors of nonadherence in an Internet-based mobile-supported stress management intervention (ie, GET.ON Stress) for employees.

Methods

The data from the groups who received the intervention were pooled from three randomized controlled trials (RCTs) that evaluated the efficacy of the same Internet-based mobile-supported stress management intervention (N=395). The RCTs only differed in terms of the guidance format (content-focused guidance vs waitlist control, adherence-focused guidance vs waitlist control, administrative guidance vs waitlist control). Adherence was defined by the number of completed treatment modules (0-7). An ANOVA was performed to compare the adherence rates from the different guidance formats. Multiple hierarchical linear regression analysis was conducted to evaluate predictors of nonadherence, which included gender, age, education, symptom-related factors, and hope for improvement.

Results

In all, 70.5% (93/132) of the content-focused guidance sample, 68.9% (91/132) of the adherence-focused guidance sample, and 42.0% (55/131) of the participants in the administrative guidance sample completed all treatment modules. Guidance had a significant effect on treatment adherence ( F _2,392=11.64, P<.001; ω ²=.05). Participants in the content-focused guidance (mean 5.70, SD 2.32) and adherence-focused guidance samples (mean 5.58, SD 2.33) completed significantly more modules than participants in the administrative guidance sample (mean 4.36, SD 2.78; t ₂₂₃=4.53, P<.001; r=.29). Content-focused guidance was not significantly associated with higher adherence compared to adherence-focused guidance ( t ₂₆₂=0.42, P=.67; r=.03). The effect size of r=.03 (95% CI –0.09 to 0.15) did not pass the equivalence margin of r=.20 and the upper bound of the 95% CI lay below the predefined margin, indicating equivalence between adherence-focused guidance and content-focused guidance. Beyond the influence of guidance, none of the predictors significantly predicted nonadherence.

Conclusions

Guidance has been shown to be an influential factor in promoting adherence to an Internet-based mobile-supported stress management intervention. Adherence-focused guidance, which included email reminders and feedback on demand, was equivalent to content-focused guidance with regular feedback while requiring only approximately a quarter of the coaching resources. This could be a promising discovery in terms of cost-effectiveness. However, even after considering guidance, sociodemographic, and symptom-related characteristics, most interindividual differences in nonadherence remain unexplained.

Clinical Trial

DRKS00004749; http://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL _ID=DRKS00004749 (Archived by WebCite at http://www.webcitation.org/6QiDk9Zn8); DRKS00005112; http://drks-neu.uniklinik-freiburg. de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00005112 (Archived by WebCite at http://www.webcitation.org/6QiDysvev); DRKS00005384; http://drks-neu.uniklinik-freiburg.de/ drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00005384 (Archived by WebCite at http://www.webcitation.org/6QiE0xcpE)

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

Saskia M Kelders, Robin N Kok, Hans Ossebaard … (2013)

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.

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Dropout from Internet-based treatment for psychological disorders.

Katherine M Melville, Leanne Casey, David J Kavanagh (2010)

The purpose of this review was to present an in-depth analysis of literature identifying the extent of dropout from Internet-based treatment programmes for psychological disorders, and literature exploring the variables associated with dropout from such programmes. A comprehensive literature search was conducted on PSYCHINFO and PUBMED with the keywords: dropouts, drop out, dropout, dropping out, attrition, premature termination, termination, non-compliance, treatment, intervention, and program, each in combination with the key words Internet and web. A total of 19 studies published between 1990 and April 2009 and focusing on dropout from Internet-based treatment programmes involving minimal therapist contact were identified and included in the review. Dropout ranged from 2 to 83% and a weighted average of 31% of the participants dropped out of treatment. A range of variables have been examined for their association with dropout from Internet-based treatment programmes for psychological disorders. Despite the numerous variables explored, evidence on any specific variables that may make an individual more likely to drop out of Internet-based treatment is currently limited. This review highlights the need for more rigorous and theoretically guided research exploring the variables associated with dropping out of Internet-based treatment for psychological disorders.

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A behavior change model for internet interventions.

Lee Ritterband, Frances Thorndike, Daniel Cox … (2009)

The Internet has become a major component to health care and has important implications for the future of the health care system. One of the most notable aspects of the Web is its ability to provide efficient, interactive, and tailored content to the user. Given the wide reach and extensive capabilities of the Internet, researchers in behavioral medicine have been using it to develop and deliver interactive and comprehensive treatment programs with the ultimate goal of impacting patient behavior and reducing unwanted symptoms. To date, however, many of these interventions have not been grounded in theory or developed from behavior change models, and no overarching model to explain behavior change in Internet interventions has yet been published. The purpose of this article is to propose a model to help guide future Internet intervention development and predict and explain behavior changes and symptom improvement produced by Internet interventions. The model purports that effective Internet interventions produce (and maintain) behavior change and symptom improvement via nine nonlinear steps: the user, influenced by environmental factors, affects website use and adherence, which is influenced by support and website characteristics. Website use leads to behavior change and symptom improvement through various mechanisms of change. The improvements are sustained via treatment maintenance. By grounding Internet intervention research within a scientific framework, developers can plan feasible, informed, and testable Internet interventions, and this form of treatment will become more firmly established.

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Author and article information

Contributors

Anna-Carlotta Zarski:

Department of Clinical Psychology and PsychotherapyFriedrich-Alexander University Erlangen-NurembergNägelsbachstr. 25aErlangen, 91052Germany49 9131 85 6757049 4131 677 171Anna-Carlotta.Zarski@fau.de

ORCID: http://orcid.org/0000-0002-0517-6668

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J. Med. Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: June 2016

Publication date (Electronic): 29 June 2016

Volume: 18

Issue: 6

Electronic Location Identifier: e146

Affiliations

[1] ¹Department of Clinical Psychology and Psychotherapy Friedrich-Alexander University Erlangen-Nuremberg ErlangenGermany

[2] ²Innovation Incubator Division of Online Health Training Leuphana University Lueneburg LuenburgGermany

[3] ³Institute for Psychology Department of Health Psychology and Applied Biological Psychology Leuphana University Luenburg LueneburgGermany

[4] ⁴Department of Clinical Psychology VU University Amsterdam AmsterdamNetherlands

[5] ⁵Telepsychiatric Centre University of Southern Denmark OdenseDenmark

Author notes

Corresponding Author: Anna-Carlotta Zarski Anna-Carlotta.Zarski@ 123456fau.de

Author information

Anna-Carlotta Zarski http://orcid.org/0000-0002-0517-6668

Dirk Lehr http://orcid.org/0000-0002-5560-3605

Matthias Berking http://orcid.org/0000-0001-5903-4748

Heleen Riper http://orcid.org/0000-0002-8144-8901

Pim Cuijpers http://orcid.org/0000-0001-5497-2743

David Daniel Ebert http://orcid.org/0000-0001-6820-0146

Article

Publisher ID: v18i6e146

DOI: 10.2196/jmir.4493

PMC ID: 4945816

PubMed ID: 27357528

SO-VID: 39adf809-d985-4e26-8faf-740a38a1ffb1

Copyright © ©Anna-Carlotta Zarski, Dirk Lehr, Matthias Berking, Heleen Riper, Pim Cuijpers, David Daniel Ebert. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.06.2016.

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 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

Date received : 1 April 2015

Date revision requested : 30 April 2015

Date revision received : 25 June 2015

Date accepted : 23 January 2016

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