ABIPA:基于arima的基于加速度计的体育活动对青少年体重状况预测的集成

Yiyuan Wang, Guillaume Wattelez, S. Frayon, C. Caillaud, O. Galy, K. Yacef
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引用次数: 0

摘要

肥胖是一个全球性的健康问题,与包括身体活动在内的各种人口和生活方式因素有关。研究通常使用自我报告的PA数据,或者当使用基于加速度计的活动跟踪器时,使用高度汇总的数据(例如,每日平均值)。这表明详细的活动跟踪数据的丰富潜力在很大程度上没有得到充分利用,而更深入的分析可能有助于更好地理解这种关系。这在儿童和青少年中尤其如此,他们性格鲜明,更多地参与到PA的爆发中。本文介绍了ABIPA,一种基于机器学习的方法,将基于加速度计的PA数据的各个方面集成到青少年体重状态预测中。我们提出了一种利用自回归综合移动平均(ARIMA)来推导不同PA时间序列结构特征的方法。基于arima的PA特征与其他个体属性相结合,以预测体重状态,并进一步揭示这些特征的重要性。我们将ABIPA应用于一个关于青少年的数据集(N = 206),该数据集包含每个参与者连续7天的加速度计数据集(60 Hz,来自ActivInsights的GENEActiv跟踪器)以及他们的一系列社会人口统计学、人体测量学和生活方式信息。结果表明,我们的方法为将基于加速度计的PA模式整合到体重状态预测中提供了一种实用的方法,并为验证其在理解肥胖因素中的重要性铺平了道路。
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ABIPA: ARIMA-Based Integration of Accelerometer-Based Physical Activity for Adolescent Weight Status Prediction
Obesity is a global health concern associated with various demographic and lifestyle factors including physical activity (PA). Research studies generally used self-reported PA data or, when accelerometer-based activity trackers were used, highly aggregated data (e.g., daily average). This suggests that the rich potential of detailed activity tracker data is largely under-exploited and that deeper analyses may help better understand such relationships. This is particularly true in children and adolescents who are distinct and engage more in bursts of PA. This article presents ABIPA, a machine learning-based methodology that integrates various aspects of accelerometer-based PA data into weight status prediction for adolescents. We propose a method to derive features regarding the structure of different PA time series using Auto-Regressive Integrated Moving Average (ARIMA). The ARIMA-based PA features are combined with other individual attributes to predict weight status and the importance of these features is further unveiled. We apply ABIPA to a dataset about young adolescents (N = 206) containing, for each participant, a 7-day continuous accelerometer dataset (60 Hz, GENEActiv tracker from ActivInsights) and a range of their socio-demographic, anthropometric, and lifestyle information. The results indicate that our method provides a practical approach for integrating accelerometer-based PA patterns into weight status prediction and paves the way for validating their importance in understanding obesity factors.
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