IEEE J Biomed Health Inform. 2019 May;23(3):999-1010. doi: 10.1109/JBHI.2018.2879805. Epub 2018 Nov 7.
In recent years, there has been growing interest in the use of fitness trackers and smartphone applications for promoting physical activity. Many of these applications use accelerometers to estimate the level of activity that users engage in and provide visual reports of a user's step counts. When provided, most recommendations are limited to popular general health advice. In our study, we develop an approach for providing data-driven and personalized recommendations for intraday activity planning. We generate an hour-by-hour activity plan that is based on the user's probability of adhering to the plan. The user's probability of adherence to the plan is personalized, based on his/her past activity patterns and current activity target. Using this approach, we can tailor notifications (e.g., reminders, encouragement) to each user. We can also dynamically update the user's activity plan at mid-day, if his/her actual activity deviates sufficiently from the original plan. In this paper, we describe an implementation of our approach and report our technical findings with respect to identifying typical activity patterns from historical data, predicting whether an activity target will be achieved, and adapting an activity plan based on a user's actual performance throughout the day.
近年来，人们越来越关注使用健身追踪器和智能手机应用程序来促进身体活动。许多这些应用程序使用加速度计来估计用户参与的活动水平，并提供用户步数的可视化报告。在提供建议时，大多数建议仅限于流行的一般健康建议。在我们的研究中，我们开发了一种方法，用于提供数据驱动和个性化的日间活动计划建议。我们根据用户遵守计划的概率生成一个小时到一个小时的活动计划。用户遵守计划的概率是个性化的，基于他/她过去的活动模式和当前的活动目标。使用这种方法，我们可以为每个用户定制通知（例如，提醒、鼓励）。如果用户的实际活动与原始计划有足够大的偏差，我们也可以在中午动态更新用户的活动计划。在本文中，我们描述了我们方法的实现，并报告了我们在从历史数据中识别典型活动模式、预测活动目标是否能够实现以及根据用户全天的实际表现调整活动计划方面的技术发现。
