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      Investigating When, Which, and Why Users Stop Using a Digital Health Intervention to Promote an Active Lifestyle: Secondary Analysis With A Focus on Health Action Process Approach–Based Psychological Determinants

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          Abstract

          Background

          Digital health interventions have gained momentum to change health behaviors such as physical activity (PA) and sedentary behavior (SB). Although these interventions show promising results in terms of behavior change, they still suffer from high attrition rates, resulting in a lower potential and accessibility. To reduce attrition rates in the future, there is a need to investigate the reasons why individuals stop using the interventions. Certain demographic variables have already been related to attrition; however, the role of psychological determinants of behavior change as predictors of attrition has not yet been fully explored.

          Objective

          The aim of this study was to examine when, which, and why users stopped using a digital health intervention. In particular, we aimed to investigate whether psychological determinants of behavior change were predictors for attrition.

          Methods

          The sample consisted of 473 healthy adults who participated in the intervention MyPlan 2.0 to promote PA or reduce SB. The intervention was developed using the health action process approach (HAPA) model, which describes psychological determinants that guide individuals in changing their behavior. If participants stopped with the intervention, a questionnaire with 8 question concerning attrition was sent by email. To analyze when users stopped using the intervention, descriptive statistics were used per part of the intervention (including pre- and posttest measurements and the 5 website sessions). To analyze which users stopped using the intervention, demographic variables, behavioral status, and HAPA-based psychological determinants at pretest measurement were investigated as potential predictors of attrition using logistic regression models. To analyze why users stopped using the intervention, descriptive statistics of scores to the attrition-related questionnaire were used.

          Results

          The study demonstrated that 47.9% (227/473) of participants stopped using the intervention, and drop out occurred mainly in the beginning of the intervention. The results seem to indicate that gender and participant scores on the psychological determinants action planning, coping planning, and self-monitoring were predictors of first session, third session, or whole intervention completion. The most endorsed reasons to stop using the intervention were the time-consuming nature of questionnaires (55%), not having time (50%), dissatisfaction with the content of the intervention (41%), technical problems (39%), already meeting the guidelines for PA/SB (31%), and, to a lesser extent, the experience of medical/emotional problems (16%).

          Conclusions

          This study provides some directions for future studies. To decrease attrition, it will be important to personalize interventions on different levels, questionnaires (either for research purposes or tailoring) should be kept to a minimum especially in the beginning of interventions by, for example, using objective monitoring devices, and technical aspects of digital health interventions should be thoroughly tested in advance.

          Trial Registration

          ClinicalTrials.gov NCT03274271; https://clinicaltrials.gov/ct2/show/NCT03274271

          International Registered Report Identifier (IRRID)

          RR2-10.1186/s13063-019-3456-7

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          Most cited references64

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          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.
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            From game design elements to gamefulness

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              The Law of Attrition

              In an ongoing effort of this Journal to develop and further the theories, models, and best practices around eHealth research, this paper argues for the need for a “science of attrition”, that is, a need to develop models for discontinuation of eHealth applications and the related phenomenon of participants dropping out of eHealth trials. What I call “law of attrition” here is the observation that in any eHealth trial a substantial proportion of users drop out before completion or stop using the appplication. This feature of eHealth trials is a distinct characteristic compared to, for example, drug trials. The traditional clinical trial and evidence-based medicine paradigm stipulates that high dropout rates make trials less believable. Consequently eHealth researchers tend to gloss over high dropout rates, or not to publish their study results at all, as they see their studies as failures. However, for many eHealth trials, in particular those conducted on the Internet and in particular with self-help applications, high dropout rates may be a natural and typical feature. Usage metrics and determinants of attrition should be highlighted, measured, analyzed, and discussed. This also includes analyzing and reporting the characteristics of the subpopulation for which the application eventually “works”, ie, those who stay in the trial and use it. For the question of what works and what does not, such attrition measures are as important to report as pure efficacy measures from intention-to-treat (ITT) analyses. In cases of high dropout rates efficacy measures underestimate the impact of an application on a population which continues to use it. Methods of analyzing attrition curves can be drawn from survival analysis methods, eg, the Kaplan-Meier analysis and proportional hazards regression analysis (Cox model). Measures to be reported include the relative risk of dropping out or of stopping the use of an application, as well as a “usage half-life”, and models reporting demographic and other factors predicting usage discontinuation in a population. Differential dropout or usage rates between two interventions could be a standard metric for the “usability efficacy” of a system. A “run-in and withdrawal” trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                January 2022
                31 January 2022
                : 10
                : 1
                : e30583
                Affiliations
                [1 ] Department of Movement and Sports Sciences Faculty of Medicine and Health Ghent University Ghent Belgium
                [2 ] Department of Experimental-Clinical and Health Psychology Faculty of Psychology and Educational Sciences Ghent University Ghent Belgium
                Author notes
                Corresponding Author: Helene Schroé helene.schroe@ 123456ugent.be
                Author information
                https://orcid.org/0000-0001-8473-552X
                https://orcid.org/0000-0002-4744-8561
                https://orcid.org/0000-0001-9969-7597
                https://orcid.org/0000-0003-1783-9075
                Article
                v10i1e30583
                10.2196/30583
                8845016
                35099400
                f78c1259-3ef9-4b65-992c-d753d5e52c4e
                ©Helene Schroé, Geert Crombez, Ilse De Bourdeaudhuij, Delfien Van Dyck. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 31.01.2022.

                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 mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 20 May 2021
                : 30 July 2021
                : 1 September 2021
                : 20 December 2021
                Categories
                Original Paper
                Original Paper

                digital health,psychosocial determinants,health action process approach,physical activity,sedentary behavior,attrition,dropout,mobile health,healthy life style,health behaviors

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