Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands.
Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands.
Adm Policy Ment Health. 2024 Jul;51(4):455-475. doi: 10.1007/s10488-023-01328-0. Epub 2024 Jan 10.
Social interactions are essential for well-being. Therefore, researchers increasingly attempt to capture an individual's social context to predict well-being, including mood. Different tools are used to measure various aspects of the social context. Digital phenotyping is a commonly used technology to assess a person's social behavior objectively. The experience sampling method (ESM) can capture the subjective perception of specific interactions. Lastly, egocentric networks are often used to measure specific relationship characteristics. These different methods capture different aspects of the social context over different time scales that are related to well-being, and combining them may be necessary to improve the prediction of well-being. Yet, they have rarely been combined in previous research. To address this gap, our study investigates the predictive accuracy of mood based on the social context. We collected intensive within-person data from multiple passive and self-report sources over a 28-day period in a student sample (Participants: N = 11, ESM measures: N = 1313). We trained individualized random forest machine learning models, using different predictors included in each model summarized over different time scales. Our findings revealed that even when combining social interactions data using different methods, predictive accuracy of mood remained low. The average coefficient of determination over all participants was 0.06 for positive and negative affect and ranged from - 0.08 to 0.3, indicating a large amount of variance across people. Furthermore, the optimal set of predictors varied across participants; however, predicting mood using all predictors generally yielded the best predictions. While combining different predictors improved predictive accuracy of mood for most participants, our study highlights the need for further work using larger and more diverse samples to enhance the clinical utility of these predictive modeling approaches.
社交互动对幸福感至关重要。因此，研究人员越来越试图捕捉个体的社交环境，以预测幸福感，包括情绪。不同的工具被用来衡量社交环境的不同方面。数字表型是一种常用的技术，用于客观地评估一个人的社交行为。体验抽样法（ESM）可以捕捉到特定互动的主观感知。最后，自我中心网络通常用于衡量特定关系特征。这些不同的方法在不同的时间尺度上捕捉到与幸福感相关的社交环境的不同方面，将它们结合起来可能有助于提高幸福感的预测。然而，在以前的研究中，它们很少被结合使用。为了解决这一差距，我们的研究调查了基于社交环境的情绪预测准确性。我们在学生样本中（参与者：N=11，ESM 测量：N=1313）收集了为期 28 天的多个被动和自我报告来源的密集个体内数据。我们使用每个模型中包含的不同预测因子训练了个性化的随机森林机器学习模型，并总结了不同时间尺度上的模型。我们的研究结果表明，即使使用不同的方法结合社交互动数据，情绪预测的准确性仍然很低。所有参与者的平均确定系数为 0.06（正性和负性情感），范围从-0.08 到 0.3，表明人与人之间的差异很大。此外，最佳预测因子集因参与者而异；然而，使用所有预测因子预测情绪通常可以产生最佳预测。虽然结合不同的预测因子可以提高大多数参与者情绪预测的准确性，但我们的研究强调需要使用更大、更多样化的样本进一步工作，以增强这些预测建模方法的临床实用性。
