Participant retention in an automated online monthly depression rescreening program: Patterns and predictors

Gill, Supria K.; Contreras, Omar Estrada; Muñoz, Ricardo F.; Leykin, Yan

doi:10.1016/j.invent.2014.02.003

Cited by 23 publications

(18 citation statements)

References 54 publications

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“…filling in the first questionnaires, does not change their commitment quickly, these participants are likely to discontinue early. Gill et al [6] also observed the importance of commitment in their online monthly depression rescreening program of similar length as our study and with short questionnaires every month. Our analyses showed that the need of receiving a reminder for the first questionnaire predicts attrition at later questionnaires (Gill et al did not send reminder emails, so we unfortunately cannot assess this observation in their study).…”

Section: Discussionsupporting

confidence: 82%

Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel

Rübsamen

Akmatov

Castell

et al. 2017

BMC Med Res Methodol

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BackgroundKnowing about predictors of attrition in a panel is important to initiate early measures against loss of participants. We investigated attrition in both early and late phase of an online panel with special focus on preferences regarding mode of participation.MethodsWe used data from the HaBIDS panel that was designed to investigate knowledge, attitudes, and practice regarding infections in the German general population. HaBIDS was divided into two phases: an initial phase when some participants could choose their preferred mode of participation (paper-and-pencil or online) and an extended phase when participants were asked to become members of an online panel that was not limited regarding its duration (i.e. participants initially preferring paper questionnaires switched to online participation). Using competing risks regression, we investigated two types of attrition (formal withdrawal and discontinuation without withdrawal) among online participants, separately for both phases. As potential predictors of attrition, we considered sociodemographic characteristics, physical and mental health as well as auxiliary information describing the survey process, and, in the extended phase, initial mode preference.ResultsIn the initial phase, higher age and less frequent Internet usage predicted withdrawal, while younger age, higher stress levels, delay in returning the consent form, and need for receiving reminder emails predicted discontinuation. In the extended phase, only need for receiving reminder emails predicted discontinuation. Numbers of withdrawal in the extended phase were too small for analysis. Initial mode preference did not predict attrition in the extended phase. Besides age, there was no evidence of differential attrition by sociodemographic factors in any phase.ConclusionsPredictors of attrition were similar in both phases of the panel, but they differed by type of attrition (withdrawal vs. discontinuation). Sociodemographic characteristics only played a minor role for both types of attrition. Need for receiving a reminder was the strongest predictor of discontinuation in any phase, but no predictor of withdrawal. We found predictors of attrition, which can be identified already in the early phase of a panel so that countermeasures (e.g. special incentives) can be taken.Electronic supplementary materialThe online version of this article (10.1186/s12874-017-0408-3) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 82%

Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel

Rübsamen

Akmatov

Castell

et al. 2017

BMC Med Res Methodol

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“…Greater adherence among older and employed participants has been observed previously. 11,29 The effect of quality of life suggests that poor health may be a barrier to research participation, whereas the effect of help-seeking intentions may reflect a propensity towards agreeableness being associated with both helpseeking intentions and survey completion. The version effect (depression v. social anxiety) may be an artefact of the recruitment method; although recruitment for both versions of the survey occurred simultaneously, there were times when recruitment for one version may have been dominant (due to Facebook algorithms and fluctuations in public interest).…”

Section: Discussionmentioning

confidence: 99%

Online screening and feedback to increase help-seeking for mental health problems: population-based randomised controlled trial

et al. 2016

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BackgroundCommunity-based screening for mental health problems may increase service use through feedback to individuals about their severity of symptoms and provision of contacts for appropriate services.AimsThe effect of symptom feedback on service use was assessed. Secondary outcomes included symptom change and study attrition.MethodUsing online recruitment, 2773 participants completed a comprehensive survey including screening for depression (n=1366) or social anxiety (n=1407). Across these two versions, approximately half (n=1342) of the participants were then randomly allocated to receive tailored feedback. Participants were reassessed after 3 months (Australian New Zealand Clinical Trials Registry ANZCTR12614000324617).ResultsA negative effect of providing social anxiety feedback to individuals was observed, with significant reductions in professional service use. Greater attrition and lower intentions to seek help were also observed after feedback.ConclusionsOnline mental health screening with feedback is not effective for promoting professional service use. Alternative models of online screening require further investigation.Declaration of interestNone.Copyright and usage© The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.

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“…Advanced digital interventions have also incorporated attributes of Web 2.0 such as social networking, text messaging, and the use of video. However, Internet interventions typically have high attrition and have so far failed to produce population effects [ 11 – 13 ].…”

Section: Introductionmentioning

confidence: 99%

Targeting Medication Non-Adherence Behavior in Selected Autoimmune Diseases: A Systematic Approach to Digital Health Program Development

2015

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Background29 autoimmune diseases, including Rheumatoid Arthritis, gout, Crohn’s Disease, and Systematic Lupus Erythematosus affect 7.6-9.4% of the population. While effective therapy is available, many patients do not follow treatment or use medications as directed. Digital health and Web 2.0 interventions have demonstrated much promise in increasing medication and treatment adherence, but to date many Internet tools have proven disappointing. In fact, most digital interventions continue to suffer from high attrition in patient populations, are burdensome for healthcare professionals, and have relatively short life spans.ObjectiveDigital health tools have traditionally centered on the transformation of existing interventions (such as diaries, trackers, stage-based or cognitive behavioral therapy programs, coupons, or symptom checklists) to electronic format. Advanced digital interventions have also incorporated attributes of Web 2.0 such as social networking, text messaging, and the use of video. Despite these efforts, there has not been little measurable impact in non-adherence for illnesses that require medical interventions, and research must look to other strategies or development methodologies. As a first step in investigating the feasibility of developing such a tool, the objective of the current study is to systematically rate factors of non-adherence that have been reported in past research studies.MethodsGrounded Theory, recognized as a rigorous method that facilitates the emergence of new themes through systematic analysis, data collection and coding, was used to analyze quantitative, qualitative and mixed method studies addressing the following autoimmune diseases: Rheumatoid Arthritis, gout, Crohn’s Disease, Systematic Lupus Erythematosus, and inflammatory bowel disease. Studies were only included if they contained primary data addressing the relationship with non-adherence.ResultsOut of the 27 studies, four non-modifiable and 11 modifiable risk factors were discovered. Over one third of articles identified the following risk factors as common contributors to medication non-adherence (percent of studies reporting): patients not understanding treatment (44%), side effects (41%), age (37%), dose regimen (33%), and perceived medication ineffectiveness (33%). An unanticipated finding that emerged was the need for risk stratification tools (81%) with patient-centric approaches (67%).ConclusionsThis study systematically identifies and categorizes medication non-adherence risk factors in select autoimmune diseases. Findings indicate that patients understanding of their disease and the role of medication are paramount. An unexpected finding was that the majority of research articles called for the creation of tailored, patient-centric interventions that dispel personal misconceptions about disease, pharmacotherapy, and how the body responds to treatment. To our knowledge, these interventions do not yet exist in digital format. Rather than adopting a systems level approach, digital health programs should focus...

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Participant retention in an automated online monthly depression rescreening program: Patterns and predictors

Cited by 23 publications

References 54 publications

Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel

Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel

Online screening and feedback to increase help-seeking for mental health problems: population-based randomised controlled trial

Targeting Medication Non-Adherence Behavior in Selected Autoimmune Diseases: A Systematic Approach to Digital Health Program Development

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