Background: Evaluation of pain and stiffness in patients with arthritis is largely based on participants retrospectively reporting their self-perceived pain/stiffness. This is subjective and may not accurately reflect the true impact of therapeutic interventions. We now have access to sensor-based systems to continuously capture objective information regarding movement and activity. Objectives: We present an observational study aimed to collect sensor data from participants monitored while performing an unsupervised version of a standard motor task, known as the Five Times Sit to Stand (5×STS) test. The first objective was to explore whether the participants would perform the test regularly in their home environment, and do so in a correct and consistent manner. The second objective was to demonstrate that the measurements collected would enable us to derive an objective signal related to morning pain and stiffness. Methods: We recruited a total of 45 participants, of whom 30 participants fulfilled pre-defined criteria for osteoarthritis, rheumatoid arthritis, or psoriatic arthritis and 15 participants were healthy volunteers. All participants wore accelerometers on their wrists, day and night for about 4 weeks. The participants were asked to perform the 5×STS test in their own home environment at the same time in the morning 3 times per week. We investigated the relationship between pain/stiffness and measurements collected during the 5×STS test by comparing the 5×STS test duration with the patient-reported outcome (PRO) questionnaires, filled in via a smartphone. Results: During the study, we successfully captured accelerometer data from each participant for a period of 4 weeks. The participants performed 56% of the prescribed 5×STS tests. We observed that different tests made by the same participants were performed with subject-specific characteristics that remained consistent throughout the study. We showed that 5×STS test duration (the time taken to complete the 5×STS test) was significantly and robustly associated with the pain and stiffness intensity reported via the PROs, particularly the questions asked in the morning. Conclusions: This study demonstrates the feasibility and usefulness of regular, sensor-based, monitored, unsupervised physical tests to objectively assess the impact of disease on function in the home environment. This approach may permit remote disease monitoring in clinical trials and support the development of novel endpoints from passively collected actigraphy data.

Background: Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently. Objective: This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30). Methods: Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups. Results: No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t =-4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different. Conclusion: This study is the first to show that a

Objective. We evaluate the stride segmentation performance of the Adaptive Empirical Pattern Transformation (ADEPT) for subsecond-level accelerometry data collected in the free-living environment using a wrist-worn sensor. Approach. We substantially expand the scope of the existing ADEPT pattern-matching algorithm. Methods are applied to subsecond-level accelerometry data collected continuously for 4 weeks in 45 participants, including 30 arthritis and 15 control patients. We estimate the daily walking cadence for each participant and quantify its association with SF-36 quality of life measures. Main results. We provide free, open-source software to segment individual walking strides in subsecond-level accelerometry data. Walking cadence is significantly associated with the role physical score reported via SF-36 after adjusting for age, gender, weight and height. Significance. Methods provide automatic, precise walking stride segmentation, which allows estimation of walking cadence from free-living wrist-worn accelerometry data. Results provide new evidence of associations between free-living walking parameters and health outcomes.