生态瞬时评估(EMA)广泛用于健康研究,以捕获个人在日常生活中的经验。大多数EMA研究,然而,依靠非概率抽样方法,在EMA研究中留下了非随机参与感兴趣的个体特征的可能性。需要了解预测参与EMA研究的因素,以评估这种可能性,并且还可以告知最佳招聘策略。
本研究旨在研究愿意参与EMA研究与受访者特征相关的程度,并确定参与的最关键预测因素。
我们利用了有关潜在EMA参与者的一般年轻成年人群的全面数据的可用性,并使用和比较了逻辑回归,分类和回归树,和随机森林方法来评估受访者参与几十年到几分钟EMA研究的特征预测因子。
在未调整的逻辑回归模型中,性别,迁移背景,焦虑,注意缺陷多动障碍的症状,压力,和亲社会性是参与意愿的重要预测因子;在Logistic回归模型中,为所有预测因子相互调整,迁移背景,烟草使用,社会排斥是重要的预测因素。基于树的方法还确定了迁移状态,烟草使用,和亲社会是突出的预测因素。然而,总的来说,从受访者的特征来看,参与数十年至分钟EMA研究的意愿只能弱地预测。最佳模型的曲线下交叉验证面积仅在0.56至0.57的范围内。
结果表明,迁移背景是改善EMA参与和样本代表性的唯一最有希望的目标;然而,需要更多的研究来改善对健康EMA研究参与的预测。
Ecological momentary assessment (EMA) is widely used in health research to capture individuals\' experiences in the flow of daily life. The majority of EMA studies, however, rely on nonprobability
sampling approaches, leaving open the possibility of nonrandom participation concerning the individual characteristics of interest in EMA research. Knowledge of the factors that predict participation in EMA research is required to evaluate this possibility and can also inform optimal recruitment strategies.
This
study aimed to examine the extent to which being willing to participate in EMA research is related to respondent characteristics and to identify the most critical predictors of participation.
We leveraged the availability of comprehensive data on a general young adult population pool of potential EMA participants and used and compared logistic regression, classification and regression trees, and random forest approaches to evaluate respondents\' characteristic predictors of willingness to participate in the Decades-to-Minutes EMA
study.
In unadjusted logistic regression models, gender, migration background, anxiety, attention deficit hyperactivity disorder symptoms, stress, and prosociality were significant predictors of participation willingness; in logistic regression models, mutually adjusting for all predictors, migration background, tobacco use, and social exclusion were significant predictors. Tree-based approaches also identified migration status, tobacco use, and prosociality as prominent predictors. However, overall, willingness to participate in the Decades-to-Minutes EMA
study was only weakly predictable from respondent characteristics. Cross-validation areas under the curve for the best models were only in the range of 0.56 to 0.57.
Results suggest that migration background is the single most promising target for improving EMA participation and sample representativeness; however, more research is needed to improve prediction of participation in EMA studies in health.