Chronic low back pain (CLBP) is a common condition in elderly population. CLBP is associated with disability and poor quality of life worldwide. Studies have found that the lack of physical activity may negatively impact CLBP and show an inverse association with pain symptoms and limitations. For example, a study demonstrates that physically active patients with CLBP experience less pain [- 0.6, 95% confidence interval (CI): - 1.0 to - 0.1; 0-10 numerical pain scale] and disability (- 8.7, 95% CI: - 14.2 to - 3.1; 0-100 disability scale) compared to patients not adequately active. However, most reports are based on younger adults or using cross-sectional studies with no continuous or real-life monitoring.~Functionally, pain with movement is more important than resting pain. This is especially true of CLBP, which increases in severity with movement. Physical activity has health benefits, including for chronic pain conditions. However, many pain patients suffer from kinesiophobia and consider exercise to be unhelpful. There is uncertainty around exercise induced analgesia, as well as the type of activity in musculoskeletal pain. The present approach to pain recording, suffers from limitations including recall, and does not relate to associated physical activity. With this, the primary drivers of pain-sensory, psychological, and motor factors are studied in isolation. Additionally, objective markers of analgesia and neuroplasticity related to physical activity have not been well studied. There is a paradoxical relationship between physical activity to treat pain and participation in physical activity evoking pain. Most studies on movement evoked pain have been using paradigms of experimental movements which are not natural to patients, and do not consider the influence of contextual factors. Remote and real-time monitoring provides us possibility of measuring the subjects' real-time behaviors and experience. The wearable remote devices are developed to monitor activity and provide large data for various analyses.~The investigators will use an inertial measurement unit (IMU)-based device containing accelerometer, gyroscope and magnetometer worn on each ankle (like a watch) in the current study. The device is capable of measuring spatiotemporal gait parameters (such as asymmetry, variability, speed, stride time, stance to swing ratio, minimum foot clearance) along with type and amount of physical activity. The investigators will capture real-time pain intensity, type, and context of activity using an electronic diary in the form of a wristwatch. Data from both devices will be integrated for processing and analysis, including the use of machine learning and data analytics to develop predictive models. In the present proposal (the first phase), the investigators will use these devices on older adults with mobility (>65 years and <80 years) with (active group) or without CLBP (control group). In this phase, the investigators plan to complete the feasibility and acceptability of these devices and obtain pilot data to support the applications for the larger study funding. After the first phase, the investigators plan to conduct a larger cohort study in older adult CLBP patients using the study devices and applications at the participants' home/living settings.
Chronic low back pain (CLBP) is very common in older adults and is one of the most common reasons for disability and poor quality of life. Lack of physical activity is considered to negatively impact CLBP and show an inverse association with pain symptoms and limitations.~Remote and real-time monitoring can allow ecological momentary assessment which involves repeated sampling of participants' current behaviors and experiences in their natural environments. Recent advances allow monitoring of activity using inertial measurement units (consisting of accelerometers, gyroscopes and magnetometers) that can be worn by an individual at home or during work.~In our study, we plan to test the feasibility and acceptability of the wearable devices in elderly patients with CLBP and to explore the use of data analytics and machine learning on the recorded data, in order to demonstrate the feasibility of a larger cohort study.