摘要：达特茅斯的研究人员积极探索了无线移动技术应用于老年病患保健的可能性，包括测量其身体活动和社会交流状况，以及提高对他们健康状况的变化的监测。八位65岁以上的老年病患在腰间佩带了这种无线移动装置，装置上的感应器不间断地记录下了老人们用于行走、上下电梯及静止不动（坐或者站立）的时间，以及和其他人交谈的时间。研究人员发现，通过这种移动感应器收集到的数据与通过四种著名问卷调查——《SF-36健康调查表》、《耶鲁身体活动调查表》、《美国流行病学研究中心抑郁量表》和《友情量表》——获得的结果高度相关。而且，研究参与者认为这个装置容易掌握、佩带舒适，且比填写调查问卷更方便，后者主要依赖患者的回忆，也更容易产生偏差。文章作者总结道，这种定量上的稳健和定性上对这一技术的接受互相结合，从而使在老年人群中利用普遍存在的移动设备的自动行为推理 （automated inference of behavior）具有潜在的灵活性和合理性。他们指出，获取的数据可以与病人的电子健康记录相连，为临床医生提供丰富、客观的信息来源，从而能在家人及保健护士发现之前便提醒医生该病人行为上的变化。
生物探索推荐英文论文摘要：
Objective Measurement of Sociability and Activity: Mobile Sensing in the Community
Background Health behavior data are often collected in laboratory settings or through surveys or self-reports, but these measures have a number of limitations. Mobile sensing of health behavior in the patient's natural environment over extended periods of time holds promise for clinicians, patients, and researchers. This study tests an automated behavioral monitoring system for sensing, recognizing, and presenting a range of physical, social, and mental indicators of well-being in natural everyday settings in older adults.
What This Study Found The study offers a provocative glimpse into the possibilities of wireless mobile technology to measure elderly patients physical activity and social interactions and improve detection of changes in their health. Sensors on a waist-mounted wireless mobile device worn by 8 patients aged 65 years and older continuously measured patients time spent walking level, up or down an elevation, and stationary (sitting or standing), and time spent speaking with one or more other people. Data from the mobile sensors correlated highly with results obtained using four established questionnaires. Moreover, study participants found the device easy to use, comfortable to wear, and more convenient than written questionnaires, which rely on recall and are more prone to biases.
Implications
Automated inference of behavior using commonly available mobile devices is potentially feasible and valid in older populations.
Data obtained through mobile sensing could potentially link to patients electronic health records, providing clinicians a rich source of information that could alert them of changes in a patient behavior before it is identified by family or caregivers.
