Johann Bernoulli Institute for Mathematics and Computer Science, Faculty of Science and Engineering (FSE), University of Groningen, Nijenborgh 9, 9747 AG, Groningen, The Netherlands.
Institute of Communication, Hanze University of Applied Sciences, Media and ICT, Zernikeplein 11, 9746 AS, Groningen, The Netherlands.
Sensors (Basel). 2018 Feb 19;18(2):623. doi: 10.3390/s18020623.
Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88-0.99, and mean F1-score = 0.90, range = 0.87-0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.
久坐的生活方式是许多健康问题的主要原因之一。为了鼓励员工减少久坐，汉泽大学启动了一项健康促进计划。该计划的干预措施之一是使用活动追踪器记录参与者的日常步数。每日步数是每两周一次的教练课程的输入。在本文中，我们通过在一天中的任何时候为参与者提供实现个人目标进度的个性化反馈，研究了通过自动化部分体育活动教练程序来实现这一目标的可能性。收集的步数数据用于训练八种不同的机器学习算法，以对实现个性化日常步数目标的概率进行每小时估计。在 80%的个体案例中，随机森林算法是表现最好的算法（平均准确率=0.93，范围=0.88-0.99，平均 F1 得分为 0.90，范围=0.87-0.94）。为了展示这些模型的实际用途，我们开发了一个概念验证 Web 应用程序，该应用程序提供有关参与者是否有望达到其日常阈值的个性化反馈。我们认为，机器学习的使用可能成为自动化个性化教练过程中的宝贵资产。个性化算法允许预测白天的身体活动，并提供及时干预的可能性。
