Centre for Exercise, Nutrition and Health Sciences, University of Bristol, 8 Priory Road, Bristol, BS8 1TZ, UK.
National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK.
Int J Behav Nutr Phys Act. 2018 Sep 21;15(1):91. doi: 10.1186/s12966-018-0724-y.
Increases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data.
The Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for 7 days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131,537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402,749 points), and 10 participants from a separate study (STAMP-2, 210,936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability.
Applying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals' travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5% agreement between the STAMP-2 study and predictions (mean sensitivity 85.5%, mean active travel sensitivity 78.9%).
We present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers.
通过积极出行增加身体活动，有可能通过更好的健康结果和减少机动交通，对人群产生巨大的有益影响。然而，在大型数据集准确识别出行方式是有问题的。在这里，我们提供了一个开源工具，可根据加速度计测量的身体活动数据，结合 GPS 和 GIS 数据，量化静止和四种出行模式（步行、骑自行车、乘火车、乘机动车）所花费的时间。
伦敦生活环境中检验邻里活动研究评估了建筑环境对健康行为的影响，包括身体活动。参与者在臀部佩戴加速度计和 GPS 接收器 7 天。我们将加速度计和 GPS 时间匹配，然后从 326 名成年参与者的通勤中提取数据，使用规定的通勤时间和模式，并手动检查以确认规定的出行模式。这产生了五种出行模式的示例：步行、骑自行车、机动车、火车和静止。我们使用这个示例数据对梯度提升树进行训练，这是一种监督机器学习算法，对每个数据点（131537 个点）进行训练，而不是对行程进行训练。在训练过程中使用五折交叉验证评估准确性。我们还手动识别了来自 ENABLE London 的 21 名参与者（402749 个点）和来自单独研究（STAMP-2，210936 个点）的 10 名参与者的出行行为，这些参与者未包含在训练数据中。我们将我们的预测与手动识别进行比较，以进一步测试准确性和测试通用性。
应用该算法，我们在交叉验证中正确识别出行模式的时间为 97.3%（平均灵敏度 96.3%，主动出行灵敏度 94.6%）。我们显示了 21 名个体出行模式的手动识别和预测之间的 96.0%一致性（平均灵敏度 92.3%，主动出行灵敏度 84.9%），以及 STAMP-2 研究与预测之间的 96.5%一致性（平均灵敏度 85.5%，主动出行灵敏度 78.9%）。
我们提出了一种可广泛应用的工具，可非常精确地识别静止时间和步行时间，精确地识别火车或车辆时间，适度精确地识别自行车时间。在既有加速度计又有 GPS 数据的研究中，该工具补充了对身体活动的分析，表明 PA 差异是否可以用出行模式的差异来解释。为了便于其他研究人员使用，我们提供了复制、拟合和预测其他数据集所需的所有必要代码。
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