Aside from the obvious discomfort associated with experiencing bodily pain, the effects of pain on community participation have been documented for both committed activities like employment and discretionary activities like socializing. Most of this research was conducted using retrospective recall to measure pain and engagement which are subject to autobiographic recall error that can result in systematic measurement error.
Ecological momentary assessment (EMA) methods improve on retrospective recall by asking respondents to report on their experiences as they are happening (i.e., in situ). With these methods, validity of both the pain experience and engagement are more accurate and better reflect the daily experiences of the respondent. A variety of researchers have reported on methods for examining pain and participation using EMA technology. However, recent advances in mobile technology that include device capability to collect GPS coordinates adds another tool for examining the role of pain in the community living experiences of people.
With data that includes both self-report and GPS coordinates, we are able to use techniques developed within the time-geography framework (e.g., utilize space-time path and space-time prism visualization) to capture the impact of individual activity under space-time and pain constraints. The purpose of this paper is to explore the relationships between pain and participation through space-time prism visualization techniques created to explore pain level and community participation over time.
The ecological momentary assessment (EMA) participants were selected from the baseline Health and Community survey which queried survey participants about participating in additional research. Of the 525 survey participants, 366 indicated they would participate in future research. Participants were then recruited by phone to participate in EMA training and data collection. A total of 149 participants completed the EMA data collection, 128 of which provided valid GPS coordinates. Of the EMA participants, 99 were between the ages of 18 and 65 years, and 28 were between the ages of 65 and 75, no one who completed the EMA data collection was over the age of 75.
We collected the EMA data using four-inch touch screen devices (i.e., Samsung Galaxy Player 4.0, similar to a smart phone) programmed to prompt participants randomly within two-hour intervals (delivered between 9:00am and 9:00pm), six times daily, for 14 consecutive days. For more information on this method please see the Pain Interference Patterns Ecological Momentary Assessment research page.
For individuals who provided specific informed consent, the EMA devices collected GPS coordinates every 5 minutes between the hours of 9:00am and 9:00pm. If a GPS coordinate could not be obtained a Wi-Fi access point was recorded instead. The GPS positioning devices come with a specification on accuracy of 5-40 meters. We do not have an exact measure of the accuracy using WIFI. All location data was collected in the background and required no input from the respondent.
Participants were promoted randomly within two hour intervals to complete a survey that included questions such as:
- Where are you?
- What type of activity are you engaged in?
Participants reported on their current pain level using an 11-point scale programmed with a slide bar anchored on each end: “0” -no pain at all; to a “10”- pain as bad as you can imagine. The image below is a screen shot that illustrates how a participant may respond (Figure 1).
Figure 1: Pain question screen shot
“EMA data was collected using two programs created by the research team. One program asked participants to respond to a survey about their location and pain levels and the other ran in the background collecting GPS coordinates every 5 minutes between the hours of 9:00am and 9:00pm for the 14 day data collection period. If a GPS coordinate could not be obtained a Wi-Fi access point was recorded instead. The GPS positioning devices come with a specification on accuracy of 5-40 meters. We do not have an exact measure of the accuracy using WIFI.
The second program prompted the participants to complete a short survey randomly within two hour intervals, also between 9:00am and 9:00pm. Surveys completed by EMA participants included questions such as: Where are you? What type of activity are you engaged in? On a scale of 1-10, how much pain are you experiencing right now? Both programs wrote to CSV files which were stored separately on the devices and all data was date and time stamped at the point of collection. Finally, survey responses were linked to GPS data by matching date and time stamps.”
Respondents were grouped into three pain categories for these analyses:
- Consistent pain included participants who reported pain level of 7 or higher for 40 or more prompts
- Intermittent pain includes participants who responded with a pain level of 7 or higher for up to thirty-five prompts and no less than ten prompts
- Low pain includes participants who responded to nine or fewer prompts with a pain level of 7 or higher
- Individuals that never rated their pain as a 7 were not included in these analyses
Pain responses were linked to the GPS data by matching date and time stamps, and relationships were identified between path of travel and reported pain levels. The visualization techniques used here are based on Hägerstrand’s concept of space time paths to illustrate how a person navigates his or her way through the spatial temporal environment. This is shown in Figure 2. The vertical axis represents time, while the horizontal plane represents the geographical space of the individual's activity. If an individual stays stationary, the path will appear as a vertical line. If the participant moves, a line is drawn connecting the origin and destination of travel. The slope of the line represents the speed of movement. For days in which no community participation occurs, space time paths would be displayed as vertical lines rooted at their home locations.
Figure 2: Hägerstrand’s space-time path
We present results below for three individuals who are representative of the three pain groups. Space-time path’s show the travel path of individuals during the period along with their pain ratings at each measurement period. Overall, participants were at home during approximately 70% of the time periods assessed.
Figure 3 (shown below) shows the trips, time away from home and pain ratings for an individual who rated pain as 7 or greater, in 80 of the 84 possible time periods. The average of their pain ratings was 8.6. Over the course of 14 days, this participant took a total of 14 trips into the community, most were quite short in duration and close in distance to the participant’s home. When prompted while they were out in the community, the participant indicated high levels of pain.
Figure 3: Individual with consistent high pain
Figure 4 (shown below) shows a participant who was in an intermittent pain group. This individual reported pain of 7 or greater during 21 of the 75 time periods with average pain over all periods of 7.2. Over the course of 14 days this participant took a total of 7 trips into the community. None of the high pain episodes were captured during these trips, a phenomena that was observed consistently among people in the intermittent pain group.
Figure 4: Individual with intermittent pain
Figure 5 (shown below) shows a participant who is representative of low pain. This person reported pain at a 7 or above for only 1 prompt of the collection period. This participant took 15 trips into the community, many with long duration. This visualization demonstrates trips into the community that were both longer in duration and farther from home than individuals in the intermittent or high pain groups.
Figure 5: Individual with low pain
Space-time paths that include individual reports of pain are useful for examining the relationship between pain and travel in the community. In general, these results suggest that distance and duration of travel are related to the frequency of high pain events. People with consistently high pain report pain when they are out and tend to have fewer and shorter trips into the community. People with intermittent high pain are different. They rarely have high pain when they are out. This is consistent with regression results of lagged pain on being at home that indicated a 77% to 332% increase in the likelihood of being home subsequent to a one standard unit increase in pain intensity (Ravesloot, et al., 2015). This may reflect that individuals with intermittent high pain have learned to avoid having high pain away from home by curtailing activity when they detect an increase in pain.
Lastly, people with low levels of pain tend to take more trips and go further than people with high or intermittent pain. This group may not experience high pain consistently enough to warrant staying closer to home.
The space-time paths presented here help to visualize the relationship among travel and pain, but they do not establish a causal link between the two. Instead, they suggest hypotheses for future research that applies controls or institutes interventions to detect the effect of pain on travel.