Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Apr 24, 2020
Date Accepted: Oct 20, 2020
Using Smartwatches to Assess the Temporal Relationship between Ecological Pain and Life-Space Mobility in Older Adults with Symptomatic Knee Osteoarthritis: A Remote Health Demonstration Study
ABSTRACT
Background:
Older adults who experience pain are more likely to reduce their community and life-space mobility (i.e. the usual range of places in an environment in which a person engages). However, there is significant day-to-day variability in pain experiences that offer unique insights into consequences on life-space mobility that are not well understood. This variability is complex and cannot be captured with traditional recall-based pain surveys. As a solution, ecological momentary assessments (EMA) record repeated pain experiences throughout the day in the natural environment.
Objective:
To examine the temporal association between EMA of pain and metrics from Global Positioning System (GPS) in older adults with symptomatic knee osteoarthritis by using a smartwatch platform called Real-time Online Assessment and Mobility Monitor (ROAMM).
Methods:
Participants (n=19, 73.1 ± 4.8 yrs, 68.4% female) wore a smartwatch for an average period of 13.16 (±2.94) days. Participants were prompted in their natural environment about their pain intensity (range 0-10) at random times in the morning, afternoon and evening. GPS coordinates were collected at 15-min intervals and aggregated each day into ten features: excursion, ellipsoid, clustering and trip frequency features. We fitted a three-level mixed effect model with random slopes and intercepts to evaluate the association between pain and GPS features. We binned pain intensities into two groups: low pain (< 2) and high pain (>= 2). Because this study was considered a demonstration project, statistical significance was confirmed at P <= 0.05 level, and P <= 0.10 were considered as a "trend" effect.
Results:
The daily average pain intensities reported by participants ranged between 0 and 8. Sixty percent of the samples had < 2 pain intensities and the remaining had ≥ 2 pain intensities. Pain intensity was negatively associated with most, but not all life-space mobility features. Pain was significantly associated with excursion span, total distance, and ellipse major axis features, and excursion size showed a statistical trend. High pain intensity was associated with a 2.7-miles lower excursion size, 2.8-miles lower excursion span, 7.3-miles lower total distance traveled per day, 14.9 miles^2 smaller ellipse area, 0.3 miles lower ellipse minor axis, and 3.5 miles lower ellipse major axis. While not statistically significant, the point estimates number of clusters, entropy, and homestay were negatively associated with pain intensity, but frequency of trips was positively associated with pain intensity.
Conclusions:
In this demonstration study, higher intensity of knee pain in older adults was associated with lower life-space mobility. Results demonstrate that a custom designed smartwatch platform is effective at simultaneously collecting rich information about ecological pain and life-space mobility. Such smart tools are expected to be important for remote health interventions that harness the variability in pain symptoms while understanding their impact on life-space mobility.
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