Attrition Revisited: Adherence and Retention in a Web-Based Alcohol Trial

Background: Attrition is a noted feature of eHealth interventions and trials. In 2005, Eysenbach published a landmark paper calling for a "science of attrition," suggesting that the 2 forms of attrition-nonusage attrition (low adherence to the intervention) and dropout attrition (poor retention to follow-up)-may be related and that this potential relationship deserved further study. Objective: The aim of this paper was to use data from an online alcohol trial to explore Eysenbach's hypothesis, and to answer 3 research questions: (1) Are adherence and retention related? If so, how, and under which circumstances? (2) Do adherence and retention have similar predictors? Can these predictors adequately explain any relationship between adherence and retention or are there additional, unmeasured predictors impacting on the relationship? (3) If there are additional unmeasured predictors impacting on the relationship, are there data to support Eysenbach's hypothesis that these are related to overall levels of interest? Methods: Secondary analysis of data from an online trial of an online intervention to reduce alcohol consumption among heavy drinkers. The 2 outcomes were adherence to the intervention measured by number of log-ins, and retention to the trial measured by provision of follow-up data at 3 months (the primary outcome point). Dependent variables were demographic and alcohol-related data collected at baseline. Predictors of adherence and retention were modeled using logistic regression models. Results: Data were available on 7932 participants. Adherence and retention were related in a complex fashion. Participants in the intervention group were more likely than those in the control group to log in more than once (42% vs 28%, P<.001) and less likely than those in the control group to respond at 3 months (40% vs 49%, P<.001). Within each randomized group, participants who logged in more frequently were more likely to respond than those who logged in less frequently. Response rates in the intervention group for those who logged in once, twice, or >= 3 times were 34%, 46%, and 51%, respectively (P<.001); response rates in the control group for those who logged in once, twice, or >= 3 times were 44%, 60%, and 67%, respectively (P<.001). Relationships between baseline characteristics and adherence and retention were also complex. Where demographic characteristics predicted adherence, they tended also to predict retention. However, characteristics related to alcohol consumption and intention or confidence in reducing alcohol consumption tended to have opposite effects on adherence and retention, with factors that predicted improved adherence tending to predict reduced retention. The complexity of these relationships suggested the existence of an unmeasured confounder. Conclusions: In this dataset, adherence and retention were related in a complex fashion. We propose a possible explanatory model for these data.