Objective Measurement of Sociability and Activity: Mobile Sensing in the Community

Abstract

PURPOSE Automated systems able to infer detailed measures of a person's social interactions and physical activities in their natural environments could lead to better understanding of factors infl uencing well-being. We assessed the feasibility of a wireless mobile device in measuring sociability and physical activity in older adults, and compared results with those of traditional questionnaires.METHODS This pilot observational study was conducted among a convenience sample of 8 men and women aged 65 years or older in a continuing care retirement community. Participants wore a waist-mounted device containing sensors that continuously capture data pertaining to behavior and environment (accelerometer, microphone, barometer, and sensors for temperature, humidity, and light). The sensors measured time spent walking level, up or down an elevation, and stationary (sitting or standing), and time spent speaking with 1 or more other people. The participants also completed 4 questionnaires: the 36-Item Short Form Health Survey (SF-36), the Yale Physical Activity Survey (YPAS), the Center for Epidemiologic Studies-Depression (CES-D) scale, and the Friendship Scale.RESULTS Men spent 21.3% of their time walking and 64.4% stationary. Women spent 20.7% of their time walking and 62.0% stationary. Sensed physical activity was correlated with aggregate YPAS scores (r 2 = 0.79, P = .02). Sensed time speaking was positively correlated with the mental component score of the SF-36 (r 2 = 0.86, P = .03), and social interaction as assessed with the Friendship Scale (r 2 = 0.97, P = .002), and showed a trend toward association with CES-D score (r 2 = -0.75, P = .08). In adjusted models, sensed time speaking was associated with SF-36 mental component score (P = .08), social interaction measured with the Friendship Scale (P = .045), and CES-D score (P = .04).CONCLUSIONS Mobile sensing of sociability and activity is well correlated with traditional measures and less prone to biases associated with questionnaires that rely on recall. Using mobile devices to collect data from and monitor older adult patients has the potential to improve detection of changes in their health. INTRODUCTIONA n important goal of community health programs is to improve the overall quality of life by promoting cognitive, physical, and social/ emotional well-being.1,2 Everyday behaviors are often refl ective of physical and physiologic health states, and can be predictive of future health problems. The standard practice for collecting behavioral data in the health sciences relies on observational data collected in laboratory settings or through periodic surveys or self-reports. These proxy measures have several major limitations, however: (1) the time and resource requirements are too great to simultaneously gather data from a large number of individuals; (2) the measurements are prone to considerable bias, and the manual and sporadic recording of information often fails to capture the fi ner details of behavior that may be important; and (3) 345 ME A SUR...

In This Issue: Technology and Primary Care

“…2 The study, which validates the use of a wireless mobile sensing device to measure physical activity and social interactions, suggests a potentially important role for such devices in strengthening both access to and quality of care. Given the widespread acceptance of mobile devices within and outside medicine, the question is not whether mobile technology will have a role in primary care, but rather what that role will look like and how far it will extend.…”

Context-Awareness for Mobile Sensing: A Survey and Future Directions

“…Human Activity Recognition (HAR) intends to observe human related actions in order to obtain an understanding of what type of activities/routines that individuals perform within a time interval. By providing accurate information about HAR relevant data history could assist individuals on having better well-being, fitness level and situational-awareness [133]- [135]. For example, patients with diabetes, obesity, or heart disease are suggested to follow a predefined fitness program as a part of their treatment [136], [137].…”

The Impact of Information Quality on Quality of Life: An Information Quality Oriented Framework

“…While pattern recognition and signal analysis, clustering, classification and data-mining algorithms exist to manipulate such information [44] that is only one step in moving ubiquitous sensor systems toward clinical practice. These data validation and quality assurance techniques must be provided with better end-user interfaces to allow medical professionals to better qualify the healthcare data.…”