Approved research

Analysis of Gait Parameters assessed through Accelerometry in Rheumatic and Musculoskeletal Diseases (RMDs)

Lay summary

Painful rheumatic and musculoskeletal disease (RMD) conditions can impact the mobility and willingness to be active in patients. Furthermore, mobility is can be used as an indicator for fluctuations in disease severity. Passive recordings of patients' symptoms can be used by clinicians to obtain a more objective and comprehensive image of disease progression and intervention effectiveness then only asking the patients about their pain in the last two months. Episodes of flare-ups, if not documented, might be forgotten by the next doctor visit or downplayed. Furthermore, activity trackers, such as fit-bit, are ever more frequently used in everyday life and patients receive methods for self-tracking their conditions positively. Therefore, developing Apps for smart-watches, which can then be recommended by clinicians, promises to become a helpful and cheap diagnostic tool. We aim to identify walking behaviour and gait parameters that can be identified by such an App, using machine learning algorithms. Therefore we will take 2 years to compare parameters in patients with RMD conditions (such as osteoarthritis and rheumatoid arthritis) to healthy individuals, compare parameters between different RMDs and to observe the effect of covariates (such as obesity, medication history and self-reported physical activity) between individuals with the same RMD. The comparison between healthy and diseased individuals will help us to assess how reliably diseased individuals can be identified according to their gait pattern using wrist-worn accelerometers in real living conditions. Comparison between different RMDs can help further understand the different effect the diseases have on patient mobility. At last, conducting comparisons within the RMD groups allows for evaluating the effect of co-morbidities that affect an individual's gait, such as obesity. If possible, indication of disease severity could also be used to identify the effect of disease severity on gait and walking behaviour.