There are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient’s physical behaviour while using these devices would enhance our understanding of the impact of different prosthetic products. The current approaches for monitoring human physical behaviour use a single thigh or wrist-worn accelerometer, but in a lower-limb amputee population, we have the unique opportunity to embed a device within the prosthesis, eliminating compliance issues. This study aimed to develop a model capable of accurately classifying postures (sitting, standing, stepping, and lying) by using data from a single shank-worn accelerometer. Free-living posture data were collected from 14 anatomically intact participants and one amputee over three days. A thigh worn activity monitor collected labelled posture data, while a shank worn accelerometer collected 3-axis acceleration data. Postures and the corresponding shank accelerations were extracted in window lengths of 5–180 s and used to train several machine learning classifiers which were assessed by using stratified cross-validation. A random forest classifier with a 15 s window length provided the highest classification accuracy of 93% weighted average F-score and between 88 and 98% classification accuracy across all four posture classes, which is the best performance achieved to date with a shank-worn device. The results of this study show that data from a single shank-worn accelerometer with a machine learning classification model can be used to accurately identify postures that make up an individual’s daily physical behaviour. This opens up the possibility of embedding an accelerometer-based activity monitor into the shank component of a prosthesis to capture physical behaviour information in both above and below-knee amputees. The models and software used in this study have been made open source in order to overcome the current restrictions of applying activity monitoring methods to lower-limb prosthesis users.

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The recommended treatment for idiopathic congenital clubfoot deformity involves a series of weekly castings, surgery, and a period of bracing using a foot abduction brace (FAB). Depending on the age of the child, the orthotic should be worn for periods that reduce in duration as the child develops. Compliance is vital to achieve optimal functional outcomes and reduce the likelihood of reoccurrence, deformity, or the need for future surgery. However, compliance is typically monitored by self-reporting, which is time-consuming to implement and lacks accuracy. This study presents a novel method for objectively monitoring FAB wear using a single 3-axis accelerometer. Eleven families mounted an accelerometer on their infant’s FAB for up to seven days. Parents were also given a physical diary that was used to record the daily application and removal of the orthotic in line with their treatment. Both methods produced very similar measurements of wear that visually aligned with the movement measured by the accelerometer. Bland Altman plots showed a −0.55-h bias in the diary measurements and the limits of agreement ranging from −2.96 h to 1.96 h. Furthermore, the Cohens Kappa coefficient for the entire dataset was 0.88, showing a very high level of agreement. The method provides an advantage over existing objective monitoring solutions as it can be easily applied to existing FABs, preventing the need for bespoke monitoring devices. The novel method can facilitate increased research into FAB compliance and help enable FAB monitoring in clinical practice.

Introduction: The COVID-19 lockdown introduced restrictions to free-living activities. Changes to these activities can be accurately quantified using combined measurement. Using activPAL3 and self-reports to collect activity data, the study aimed to quantify changes that occurred in physical activity and sedentary behavior between prelockdown and lockdown. The study also sought to determine changes in indoor and outdoor stepping. Methods: Using activPAL3, four participants recorded physical activity data prelockdown and during lockdown restrictions (February–June 2020). Single events (sitting, standing, stepping, lying) were recorded and analyzed by the CREA algorithm using an event-based approach. The analysis focused on step count, sedentary time, and lying (in bed) time; median and interquartile range were calculated. Daily steps classified as taking place indoors and outdoors were calculated separately. Results: 33 prelockdown and 92 in-lockdown days of valid data were captured. Median daily step count across all participants reduced by 14.8% (from 5,828 prelockdown to 4,963 in-lockdown), while sedentary and lying time increased by 4% and 8%, respectively (sedentary: 9.98–10.30 hr; lying: 9.33–10.05 hr). Individual variations were observed in hours spent sedentary (001: 8.44–8.66, 002: 7.41–8.66, 003: 11.97–10.59, 004: 6.29–7.94, and lying (001: 9.69–9.49, 002: 11.46–11.66, 003: 7.63–9.34, 004: 9.7–11.12) pre- and in-lockdown. Discrepancies in self-report versus algorithm classification of indoor/outdoor stepping were observed for three participants. Conclusion: The study quantitively showed lockdown restrictions negatively impacted physical activity and sedentary behavior; two variables closely linked to health outcomes. This has important implications for public health policies to help develop targeted interventions and mandates that encourage additional physical activity and lower sedentary behavior.

In free-living environments, continuous walking can be challenging to achieve without encountering interruptions, making it difficult to define a continuous walking event. While limited research has been conducted to define a continuous walking event that accounts for interruptions, no method has considered the intensity change caused by these interruptions, which is crucial for achieving the associated health outcomes. A sample of 24 staff members at the University of Salford were recruited. The participants wore an accelerometer-based device (activPAL™) for seven days continuously and completed an activity diary, to explore a novel methodological approach of combining short interruptions of time between walking events based on an average walking cadence. The definition of moderate-to-vigorous physical activity (MVPA) used was a minimum walking cadence of either 76, 100, or 109 steps/min. The average daily time spent in MVPA increased from 75.2 ± 32.6 min to 86.5 ± 37.4 min using the 76 steps/min, 48.3 ± 29.5 min to 53.0 ± 33.3 min using the 100 steps/min threshold, and 31.4 ± 20.5 min to 33.9 ± 22.6 min using the 109 steps/min threshold; the difference before grouping and after grouping was statistically significant (p < 0.001). This novel method will enable future analyses of the associations between continuous walking and health-related outcomes.