After lower-extremity orthopedic trauma and surgery, patients are often advised to restrict weight bearing on the affected limb. Conventional training methods are not effective at enabling patients to comply with recommendations for partial weight bearing. The current study assessed a novel method of using real-time haptic (vibratory/vibrotactile) biofeedback to improve compliance with instructions for partial weight bearing. Thirty healthy, asymptomatic participants were randomized into 1 of 3 groups: verbal instruction, bathroom scale training, and haptic biofeedback. Participants were instructed to restrict lower-extremity weight bearing in a walking boot with crutches to 25 lb, with an acceptable range of 15 to 35 lb. A custom weight bearing sensor and biofeedback system was attached to all participants, but only those in the haptic biofeedback group were given a vibrotactile signal if they exceeded the acceptable range. Weight bearing in all groups was measured with a separate validated commercial system. The verbal instruction group bore an average of 60.3±30.5 lb (mean±standard deviation). The bathroom scale group averaged 43.8±17.2 lb, whereas the haptic biofeedback group averaged 22.4±9.1 lb (P<.05). As a percentage of body weight, the verbal instruction group averaged 40.2±19.3%, the bathroom scale group averaged 32.5±16.9%, and the haptic biofeedback group averaged 14.5±6.3% (P<.05). In this initial evaluation of the use of haptic biofeedback to improve compliance with lower-extremity partial weight bearing, haptic biofeedback was superior to conventional physical therapy methods. Further studies in patients with clinical orthopedic trauma are warranted.

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Summary Blockchain has started to appear as a potentially reliable and underlying technology for various fields. There have been lots of surveys focusing on blockchain with respect to specific topics, such as security, architecture, applications, and so on. However, a systematic mapping study, including all related fields about blockchain, has been largely ignored. In this article, we revisit the problem of complex networks in the form of scientific collaboration networks. More specifically, we utilize the method of systematic mapping and implement them into blockchain technology. We collect 233 articles by searching Baidu scholar with the keyword “blockchain,” then construct two complex networks according to the relationship of keywords and authors, respectively. The keywords' complex network is a small‐world network while the authors' complex network is not. Furthermore, the tool of Netdraw provides a visualized graph for the complex network. Meanwhile, we find some subgroups in the network, which may highlight the future direction of blockchain.