@Article{信息:doi 10.2196 / / jmir。2582，作者=“Piette, John D and Sussman, Jeremy B and Pfeiffer, Paul N and Silveira, Maria J and Singh, Satinder and Lavieri, Mariel S”，标题=“通过避免重复患者报告来最大化移动健康监测的价值:预测抑郁相关症状和自动健康评估服务中的坚持问题”，期刊=“J Med Internet Res”，年=“2013”，月=“july”，日=“05”，卷=“15”，号码=“7”，页=“e118”，关键词=“手机;远程医疗;抑郁症;背景:交互式语音应答(IVR)呼叫增强了卫生系统识别健康风险因素的能力，从而实现了有针对性的临床随访。然而，重复的评估可能会增加患者退出，并代表失去了收集更多临床有用数据的机会。目的:我们确定了之前的IVR评估在多大程度上预测了抑郁症诊断患者的后续反应，潜在地避免了重复收集相同信息的需要。我们还评估了频繁(即每周)的IVR评估尝试是否比每两周或每月收集的信息更能预测患者的后续报告。方法:使用来自208名抑郁症诊断患者的1050次IVR评估数据，我们检查了四种IVR报告结果的可预测性:中度/重度抑郁症状(PHQ-9得分≥10)，一般健康状况一般/较差，抗抑郁药物依从性差，以及由于精神健康状况不佳而卧床的天数。我们使用训练和测试样本的逻辑模型来预测患者的IVR反应，基于他们最近的五次每周、两周和每月的评估尝试。 The marginal benefit of more frequent assessments was evaluated based on Receiver Operator Characteristic (ROC) curves and statistical comparisons of the area under the curves (AUC). Results: Patients' reports about their depressive symptoms and perceived health status were highly predictable based on prior assessment responses. For models predicting moderate/severe depression, the AUC was 0.91 (95{\%} CI 0.89-0.93) when assuming weekly assessment attempts and only slightly less when assuming biweekly assessments (AUC: 0.89; CI 0.87-0.91) or monthly attempts (AUC: 0.89; CI 0.86-0.91). The AUC for models predicting reports of fair/poor health status was similar when weekly assessments were compared with those occurring biweekly (P value for the difference=.11) or monthly (P=.81). Reports of medication adherence problems and days in bed were somewhat less predictable but also showed small differences between assessments attempted weekly, biweekly, and monthly. Conclusions: The technical feasibility of gathering high frequency health data via IVR may in some instances exceed the clinical benefit of doing so. Predictive analytics could make data gathering more efficient with negligible loss in effectiveness. In particular, weekly or biweekly depressive symptom reports may provide little marginal information regarding how the person is doing relative to collecting that information monthly. The next generation of automated health assessment services should use data mining techniques to avoid redundant assessments and should gather data at the frequency that maximizes the value of the information collected. ", issn="14388871", doi="10.2196/jmir.2582", url="//www.mybigtv.com/2013/7/e118/", url="https://doi.org/10.2196/jmir.2582", url="http://www.ncbi.nlm.nih.gov/pubmed/23832021" }