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首页> 外文期刊>BMC Medical Informatics and Decision Making >Applying machine learning to predict future adherence to physical activity programs
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Applying machine learning to predict future adherence to physical activity programs

机译：应用机器学习预测未来对体育锻炼计划的遵守

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Identifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data. We use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity. we had access to a physical activity trial data that were continuously collected every 60?sec every day for 9?months in 210 participants. By using the first 15?weeks of data as training and test on weeks 16–30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes. DiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted. ClinicalTrials.gov NCT01280812 Registered on January 21, 2011.
机译：确定不太可能参加体育锻炼的个人有可能改善体育锻炼的干预措施。本文的目的是在基于移动电话的体育锻炼教育计划（mPED）试验中使用客观测量的体育锻炼数据来开发和测试依从性预测模型。据我们所知，这是第一个使用机器学习方法通​​过加速度计记录的身体活动数据来预测运动复发的方法。我们使用逻辑回归和支持向量机方法设计了两个版本的中止预测得分（DiPS），该模型使用客观测量的过去数据（例如，步骤和目标达成情况）来提供一个数值，以指示即将发生的运动复发的可能性周。比较了这两种DiPS版本的各自预测准确性，然后进行了数值模拟，以探索使用DiPS选择性地向参与者分配经济激励措施以鼓励他们增加体育锻炼的潜力。我们获得了一项体育锻炼试验数据，该数据在210位参与者中连续9天每月每60秒每秒收集一次。通过使用前15周的数据作为训练并在16-30周进行测试，我们显示DiPS的两个版本的测试AUC均为0.9，在预测运动依从性的可能性方面具有很高的敏感性和特异性。假设不同干预方案的模拟结果表明，使用DiPS作为得分来分配体育活动干预计划中的资源的潜在好处是与其他分配方案相比，可以降低成本。 DiPS能够对未来几周做出准确而可靠的预测。最可预测的特征是步数和体育锻炼强度。此外，使用DiPS评分可能是一种有前途的方法，可以确定何时或是否提供即时消息以及逐步调整目标以提高合规性。必须进一步研究在体育锻炼促进计划的设计中使用DiPS。 ClinicalTrials.gov NCT01280812于2011年1月21日注册。

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