Saeb Sohrab, Lattie Emily G, Kording Konrad P, Mohr David C
Center for Behavioral Intervention Technologies (CBITs), Department of Preventive Medicine, Northwestern University, Chicago, IL, United States.
Rehabilitation Institute of Chicago, Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States.
JMIR Mhealth Uhealth. 2017 Aug 10;5(8):e112. doi: 10.2196/mhealth.7297.
Is someone at home, at their friend's place, at a restaurant, or enjoying the outdoors? Knowing the semantic location of an individual matters for delivering medical interventions, recommendations, and other context-aware services. This knowledge is particularly useful in mental health care for monitoring relevant behavioral indicators to improve treatment delivery. Local search-and-discovery services such as Foursquare can be used to detect semantic locations based on the global positioning system (GPS) coordinates, but GPS alone is often inaccurate. Mobile phones can also sense other signals (such as movement, light, and sound), and the use of these signals promises to lead to a better estimation of an individual's semantic location.
We aimed to examine the ability of mobile phone sensors to estimate semantic locations, and to evaluate the relationship between semantic location visit patterns and depression and anxiety.
A total of 208 participants across the United States were asked to log the type of locations they visited daily, using their mobile phones for a period of 6 weeks, while their phone sensor data was recorded. Using the sensor data and Foursquare queries based on GPS coordinates, we trained models to predict these logged locations, and evaluated their prediction accuracy on participants that models had not seen during training. We also evaluated the relationship between the amount of time spent in each semantic location and depression and anxiety assessed at baseline, in the middle, and at the end of the study.
While Foursquare queries detected true semantic locations with an average area under the curve (AUC) of 0.62, using phone sensor data alone increased the AUC to 0.84. When we used Foursquare and sensor data together, the AUC further increased to 0.88. We found some significant relationships between the time spent in certain locations and depression and anxiety, although these relationships were not consistent.
The accuracy of location services such as Foursquare can significantly benefit from using phone sensor data. However, our results suggest that the nature of the places people visit explains only a small part of the variation in their anxiety and depression symptoms.
某人是在家中、朋友家、餐厅还是在户外享受时光？了解个人的语义位置对于提供医疗干预、建议及其他情境感知服务至关重要。这一信息在精神卫生保健中尤其有用，可用于监测相关行为指标以改善治疗效果。诸如四方网（Foursquare）之类的本地搜索与发现服务可用于根据全球定位系统（GPS）坐标检测语义位置，但仅靠GPS往往不准确。移动电话还能感知其他信号（如运动、光线和声音），利用这些信号有望更准确地估计个人的语义位置。
我们旨在研究移动电话传感器估计语义位置的能力，并评估语义位置访问模式与抑郁和焦虑之间的关系。
美国共有208名参与者被要求使用移动电话记录他们在为期6周的时间里每天访问的地点类型，同时记录其电话传感器数据。利用传感器数据和基于GPS坐标的四方网查询，我们训练模型来预测这些记录的地点，并在训练期间未见过的参与者身上评估其预测准确性。我们还评估了在每个语义位置花费的时间与在研究基线、中期和结束时评估的抑郁和焦虑之间的关系。
虽然四方网查询检测到真实语义位置的曲线下平均面积（AUC）为0.62，但仅使用电话传感器数据可将AUC提高到0.84。当我们将四方网和传感器数据一起使用时，AUC进一步提高到0.88。我们发现，在某些地点花费的时间与抑郁和焦虑之间存在一些显著关系，尽管这些关系并不一致。
诸如四方网之类的定位服务的准确性可通过使用电话传感器数据而显著提高。然而，我们的结果表明，人们访问的地点性质仅能解释其焦虑和抑郁症状变化的一小部分。
