Wearable accelerometers record physical activity with high resolution, potentially capturing the rich details of behaviour changes and habits. Detecting these changes as they emerge is valuable information for any strategy that promotes physical activity and teaches healthy behaviours or habits. Indeed, this offers the opportunity to provide timely feedback and to tailor programmes to each participant’s needs, thus helping to promote the adherence to and the effectiveness of the intervention. This article presents and illustrates U-BEHAVED, an unsupervised algorithm that periodically scans step data streamed from activity trackers to detect physical activity behaviour changes to assess whether they may become habitual patterns. Using rolling time windows, current behaviours are compared with recent previous ones, identifying any significant change. If sustained over time, these new behaviours are classified as potentially new habits. We validated this detection algorithm using a physical activity tracker step dataset (N = 12,798) from 79 users. The algorithm detected 80% of behaviour changes of at least 400 steps within the same hour in users with low variability in physical activity, and of 1600 steps in those with high variability. Based on a threshold cadence of approximately 100 steps per minute for standard walking pace, this number of steps would suggest approximately 4 and 16 min of physical activity at moderate-to-vigorous intensity, respectively. The detection rate for new habits was 80% with a minimum threshold of 500 or 1600 steps within the same hour in users with low or high variability, respectively.
Sudomotor dysfunction diagnostic test for early detection of diabetic neuropathy Background: Sudomotor dysfunction may appear in early stages of diabetic neuropathy. Aim: To evaluate the diagnostic capacity of the Neuropad test, based on the detection of sudomotor dysfunction, as an early indicator of diabetic neuropathy. Material and Methods: In Forty-two type 2 diabetic patients, the Neuropad test was compared with the 10 g monofilament test (proposed in the technical orientation of diabetic foot of the Ministry of Health of Chile), deep and thermal sensitivity. Results: The surface sensitivity assessed with a brush had a sensitivity and specificity of 18.8 and 100% respectively when compared with the 10 g monofilament. When compared with the Neuropad, the figures were 9 and 100%, respectively. Pain perception sensitivity and specificity were 13 and 100% respectively when compared with the 10 g monofilament. The figures were 6 and 100%, when compared with the Neuropad. Thermal discrimination had a sensitivity and specificity of 88 and 33% respectively when compared with the 10 g monofilament. The figures were 75 and 25% respectively when compared with the Neuropad. The deep sensitivity evaluated with a 128 Hz tuning fork had a sensitivity and specificity of 31 and 100% respectively when compared with the 10 g monofilament. The figures were 16 and 31% respectively when compared with the Neuropad. The Neuropad had a sensitivity and specificity of 94 and 29% respectively were compared with the 10 g monofilament. Conclusions: Neuropad had a good diagnostic yield for the early detection of sudomotor dysfunction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.