Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence

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Abstract

Background

In electronic health (eHealth) evaluations, there is increasing attention for studying the actual usage of a technology in relation to the outcomes found, often by studying the adherence to the technology. On the basis of the definition of adherence, we suggest that the following three elements are necessary to determine adherence to eHealth technology: (1) the ability to measure the usage behavior of individuals; (2) an operationalization of intended use; and (3) an empirical, theoretical, or rational justification of the intended use. However, to date, little is known on how to operationalize the intended usage of and the adherence to different types of eHealth technology.

Objective

The study aimed to improve eHealth evaluations by gaining insight into when, how, and by whom the concept of adherence has been used in previous eHealth evaluations and finding a concise way to operationalize adherence to and intended use of different eHealth technologies.

Methods

A systematic review of eHealth evaluations was conducted to gain insight into how the use of the technology was measured, how adherence to different types of technologies was operationalized, and if and how the intended use of the technology was justified. Differences in variables between the use of the technology and the operationalization of adherence were calculated using a chi-square test of independence.

Results

In total, 62 studies were included in this review. In 34 studies, adherence was operationalized as “the more use, the better,” whereas 28 studies described a threshold for intended use of the technology as well. Out of these 28, only 6 reported a justification for the intended use. The proportion of evaluations of mental health technologies reporting a justified operationalization of intended use is lagging behind compared with evaluations of lifestyle and chronic care technologies. The results indicated that a justification of intended use does not require extra measurements to determine adherence to the technology.

Conclusions

The results of this review showed that to date, justifications for intended use are often missing in evaluations of adherence. Evidently, it is not always possible to estimate the intended use of a technology. However, such measures do not meet the definition of adherence and should therefore be referred to as the actual usage of the technology. Therefore, it can be concluded that adherence to eHealth technology is an underdeveloped and often improperly used concept in the existing body of literature. When defining the intended use of a technology and selecting valid measures for adherence, the goal or the assumed working mechanisms should be leading. Adherence can then be standardized, which will improve the comparison of adherence rates to different technologies with the same goal and will provide insight into how adherence to different elements contributed to the outcomes.

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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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Efficacy of internet-delivered cognitive-behavioral therapy for insomnia - A systematic review and meta-analysis of randomized controlled trials.

Robert Zachariae, Marlene Lyby, Lee Ritterband … (2015)

Cognitive-behavioral therapy for insomnia (CBT-I) has been shown efficacious, but the challenge remains to make it available and accessible in order to meet population needs. Delivering CBT-I over the internet (eCBT-I) may be one method to overcome this challenge. The objective of this meta-analysis was to evaluate the efficacy of eCBT-I and the moderating influence of various study characteristics. Two researchers independently searched key electronic databases (1991 to June 2015), selected eligible publications, extracted data, and evaluated methodological quality. Eleven randomized controlled trials examining a total of 1460 participants were included. Results showed that eCBT-I improved insomnia severity, sleep efficiency, subjective sleep quality, wake after sleep onset, sleep onset latency, total sleep time, and number of nocturnal awakenings at post-treatment, with effect sizes (Hedges's g) ranging from 0.21 to 1.09. The effects were comparable to those found for face-to-face CBT-I, and were generally maintained at 4-48 wk follow-up. Moderator analyses showed that longer treatment duration and higher degree of personal clinical support were associated with larger effect sizes, and that larger study dropout in the intervention group was associated with smaller effect sizes. In conclusion, internet-delivered CBT-I appears efficacious and can be considered a viable option in the treatment of insomnia.

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A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments.

Annet M Kleiboer, L Quigley, Gerhard Andersson … (2013)

No recent meta-analysis has examined the effects of cognitive-behavioural therapy (CBT) for adult depression. We decided to conduct such an updated meta-analysis. Studies were identified through systematic searches in bibliographical databases (PubMed, PsycINFO, Embase, and the Cochrane library). We included studies examining the effects of CBT, compared with control groups, other psychotherapies, and pharmacotherapy. A total of 115 studies met inclusion criteria. The mean effect size (ES) of 94 comparisons from 75 studies of CBT and control groups was Hedges g = 0.71 (95% CI 0.62 to 0.79), which corresponds with a number needed to treat of 2.6. However, this may be an overestimation of the true ES as we found strong indications for publication bias (ES after adjustment for bias was g = 0.53), and because the ES of higher-quality studies was significantly lower (g = 0.53) than for lower-quality studies (g = 0.90). The difference between high- and low-quality studies remained significant after adjustment for other study characteristics in a multivariate meta-regression analysis. We did not find any indication that CBT was more or less effective than other psychotherapies or pharmacotherapy. Combined treatment was significantly more effective than pharmacotherapy alone (g = 0.49). There is no doubt that CBT is an effective treatment for adult depression, although the effects may have been overestimated until now. CBT is also the most studied psychotherapy for depression, and thus has the greatest weight of evidence. However, other treatments approach its overall efficacy.

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Author and article information

Contributors

Floor Sieverink:

Centre for eHealth and Wellbeing ResearchDepartment of Psychology, Health and TechnologyUniversity of TwenteP.O. Box 217Enschede, 7500AENetherlands31 534896631f.sieverink@utwente.nl

ORCID: http://orcid.org/0000-0002-7018-2634

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: December 2017

Publication date (Electronic): 06 December 2017

Volume: 19

Issue: 12

Electronic Location Identifier: e402

Affiliations

[1] ¹ Centre for eHealth and Wellbeing Research Department of Psychology, Health and Technology University of Twente Enschede Netherlands

[2] ² Optentia Research Focus Area North-West University Vanderbijlpark South Africa

Author notes

Corresponding Author: Floor Sieverink f.sieverink@ 123456utwente.nl

Author information

Floor Sieverink http://orcid.org/0000-0002-7018-2634

Saskia M Kelders http://orcid.org/0000-0001-8949-6871

Julia EWC van Gemert-Pijnen http://orcid.org/0000-0001-6511-7240

Article

Publisher ID: v19i12e402

DOI: 10.2196/jmir.8578

PMC ID: 5738543

PubMed ID: 29212630

SO-VID: 31999a20-feeb-4e14-bc55-12b2581559f6

License:

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

Date received : 27 July 2017

Date revision requested : 25 August 2017

Date revision received : 18 October 2017

Date accepted : 3 November 2017

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