Murray Elizabeth, White Ian R, Varagunam Mira, Godfrey Christine, Khadjesari Zarnie, McCambridge Jim
e-Health Unit, Research Department of Primary Care and Population Health, University College London, London, United Kingdom.
J Med Internet Res. 2013 Aug 30;15(8):e162. doi: 10.2196/jmir.2336.
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.
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?
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.
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.
In this dataset, adherence and retention were related in a complex fashion. We propose a possible explanatory model for these data.
International Standard Randomized Controlled Trial Number (ISRCTN): 31070347; http://www.controlled-trials.com/ISRCTN31070347 (Archived by WebCite at http://www.webcitation.org/6IEmNnlCn).
损耗是电子健康干预措施和试验中一个显著的特征。2005年，艾森巴赫发表了一篇具有里程碑意义的论文，呼吁建立一门“损耗科学”，指出两种损耗形式——未使用损耗（对干预措施的低依从性）和退出损耗（随访时的低留存率）——可能存在关联，这种潜在关系值得进一步研究。
本文旨在利用一项在线酒精试验的数据来探究艾森巴赫的假设，并回答3个研究问题：（1）依从性和留存率相关吗？如果相关，是如何相关的，在何种情况下相关？（2）依从性和留存率有相似的预测因素吗？这些预测因素能否充分解释依从性和留存率之间的任何关系，或者是否存在其他未测量的预测因素影响这种关系？（3）如果存在其他未测量的预测因素影响这种关系，是否有数据支持艾森巴赫的假设，即这些因素与总体兴趣水平相关？
对一项在线干预措施减少重度饮酒者酒精摄入量的在线试验数据进行二次分析。两个结果分别是通过登录次数衡量的对干预措施的依从性，以及通过在3个月时（主要结局时间点）提供随访数据衡量的试验留存率。因变量是在基线时收集的人口统计学和与酒精相关的数据。使用逻辑回归模型对依从性和留存率的预测因素进行建模。
有7932名参与者的数据可用。依从性和留存率以复杂的方式相关。干预组的参与者比对照组的参与者更有可能登录不止一次（42%对28%，P<0.001），且在3个月时比对照组的参与者更不可能做出回应（40%对49%，P<0.001）。在每个随机分组中，登录更频繁的参与者比登录不那么频繁的参与者更有可能做出回应。干预组中登录一次、两次或≥3次的参与者的回应率分别为34%、46%和51%（P<0.001）；对照组中登录一次、两次或≥3次的参与者的回应率分别为
