Assessing Older Adults’ Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators

Abstract

Interest in global positioning system (GPS)-based mobility assessment for health and aging research is growing, and with it the demand for validated GPS-based mobility indicators. Time out of home (TOH) and number of activity locations (#ALs) are two indicators that are often derived from GPS data, despite lacking consensus regarding thresholds to be used to extract those as well as limited knowledge about their validity. Using 7 days of GPS and diary data of 35 older adults, we make the following three main contributions. First, we perform a sensitivity analysis to investigate how using spatial and temporal thresholds to compute TOH and #ALs affects the agreement between self-reported and GPS-based indicators. Second, we show how daily self-reported and GPS-derived mobility indicators are compared. Third, we explore whether the type and duration of self-reported activity events are related to the degree of correspondence between reported and GPS event. Highest indicator agreement was found for temporal interpolation (Tmax) of up to 5 h for both indicators, a radius (Dmax) to delineate home between 100 and 200 m for TOH, and for #ALs a spatial extent (Dmax) between 125 and 200 m, and temporal extent (Tmin) between 5 and 6 min to define an activity location. High agreement between self-reported and GPS-based indicators is obtained for TOH and moderate agreement for #ALs. While reported event type and duration impact on whether a reported event has a matching GPS event, indoor and outdoor events are detected at equal proportions. This work will help future studies to choose optimal threshold settings and will provide knowledge about the validity of mobility indicators.

Feature-First Add-On for Trajectory Simplification in Lifelog Applications

“…The parameters of (T fixL , T fixF , D maxS , T movS , S minG , T movG , Θ minT ) are set to (4 s, 2 s, 1 m, 3 s, S slow = 15 km/h, 3 s, 30º). The corresponding thresholds of (n L , n F , m S , m G ) for counting become (5,3,3,4) considering the GPS logger's sampling frequency of f op = 1 Hz. Moreover, we set B = 3 to divide Swin K into two sub-windows of Swin Kf and Swin Kb .…”

“…We can say that, while day #6 takes more time than day #1 by 34.5 %, it spends extra time mostly on expressway and highway. These kinds of context of a trajectory data with FFTS can be used widely in trajectory data mining and lifelog applications [4,5,24].…”

“…People sleep at bedroom, watch TV at living room, and cook at kitchen but hardly eat at bathroom. User's location has a huge implication and is one of the most common information for logging a person's life [3][4][5]. By understanding one's spatial mobility we can infer the behavior and attitude of the person in various situations in everyday life.…”

“…The availability and affordability of GPS (Global Positioning System) technology provides a simple yet powerful harness for collecting mobility data [4][5][6]. With GPS, we can capture consistent and accurate spatial and temporal details of a movement.…”

Map-based assessment of older adults’ life space: validity and reliability

“…Additional qualitative information (e.g. on the purpose of an activity) cannot be captured directly from GPS, although, there are a number of health-related measures that can be derived from GPS tracking data, including use of transportation [20], time spent out of home [21], and exposure to certain environments (such as green spaces, fast-food restaurants or supermarkets [22]).…”