Rozet Alan, Kronish Ian M, Schwartz Joseph E, Davidson Karina W
Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, New York, NY, United States.
Feinstein Institute for Medical Research, Northwell Health, New York, NY, United States.
J Med Internet Res. 2019 Apr 26;21(4):e12910. doi: 10.2196/12910.
Investigations into person-specific predictors of stress have typically taken either a population-level nomothetic approach or an individualized ideographic approach. Nomothetic approaches can quickly identify predictors but can be hindered by the heterogeneity of these predictors across individuals and time. Ideographic approaches may result in more predictive models at the individual level but require a longer period of data collection to identify robust predictors.
Our objectives were to compare predictors of stress identified through nomothetic and ideographic models and to assess whether sequentially combining nomothetic and ideographic models could yield more accurate and actionable predictions of stress than relying on either model. At the same time, we sought to maintain the interpretability necessary to retrieve individual predictors of stress despite using nomothetic models.
Data collected in a 1-year observational study of 79 participants performing low levels of exercise were used. Physical activity was continuously and objectively monitored by actigraphy. Perceived stress was recorded by participants via daily ecological momentary assessments on a mobile app. Environmental variables including daylight time, temperature, and precipitation were retrieved from the public archives. Using these environmental, actigraphy, and mobile assessment data, we built machine learning models to predict individual stress ratings using linear, decision tree, and neural network techniques employing nomothetic and ideographic approaches. The accuracy of the approaches for predicting individual stress ratings was compared based on classification errors.
Across the group of patients, an individual's recent history of stress ratings was most heavily weighted in predicting a future stress rating in the nomothetic recurrent neural network model, whereas environmental factors such as temperature and daylight, as well as duration and frequency of bouts of exercise, were more heavily weighted in the ideographic models. The nomothetic recurrent neural network model was the highest performing nomothetic model and yielded 72% accuracy for an 80%/20% train/test split. Using the same 80/20 split, the ideographic models yielded 75% accuracy. However, restricting ideographic models to participants with more than 50 valid days in the training set, with the same 80/20 split, yielded 85% accuracy.
We conclude that for some applications, nomothetic models may be useful for yielding higher initial performance while still surfacing personalized predictors of stress, before switching to ideographic models upon sufficient data collection.
对压力的个体特异性预测因素的研究通常采用群体水平的通则法或个体化的个案法。通则法能够快速识别预测因素，但可能会受到这些预测因素在个体间和不同时间的异质性的阻碍。个案法可能会在个体层面产生更多的预测模型，但需要更长的数据收集时间来识别可靠的预测因素。
我们的目标是比较通过通则模型和个案模型识别出的压力预测因素，并评估依次结合通则模型和个案模型是否比仅依赖其中任何一种模型能产生更准确且可操作的压力预测。同时，尽管使用通则模型，我们仍力求保持获取压力个体预测因素所需的可解释性。
使用在一项为期1年的观察性研究中收集的数据，该研究涉及79名进行低强度运动的参与者。通过活动记录仪持续客观地监测身体活动情况。参与者通过移动应用程序进行的每日生态瞬时评估记录感知到的压力。从公共档案中获取包括日照时间、温度和降水量在内的环境变量。利用这些环境、活动记录仪和移动评估数据，我们构建了机器学习模型，采用通则法和个案法，运用线性、决策树和神经网络技术来预测个体压力评分。基于分类误差比较预测个体压力评分方法的准确性。
在所有患者组中，在通则递归神经网络模型中，个体近期的压力评分历史在预测未来压力评分时权重最大，而在个案模型中，温度和日照等环境因素以及运动时长和频率权重更大。通则递归神经网络模型是表现最佳的通则模型，在80%/20%的训练/测试分割中准确率为72%。使用相同的80/20分割，个案模型的准确率为75%。然而，将个案模型限制在训练集中有超过50个有效天数的参与者，在相同的80/20分割下，准确率达到85%。
我们得出结论，对于某些应用，通则模型可能有助于在足够的数据收集后切换到个案模型之前，在仍能揭示压力的个性化预测因素的同时，实现更高的初始性能。
