Objective. It is not clear why some individuals develop pain with knee osteoarthritis (OA). We undertook this study to identify pain susceptibility phenotypes (PSPs) and their relationship to incident persistent knee pain (PKP) 2 years later.Methods. We identified individuals free of PKP from the Multicenter Osteoarthritis Study, a longitudinal cohort of older adults with or at risk of knee OA. Latent class analysis was used to determine PSPs that may contribute to development of PKP apart from structural pathology. These included widespread pain, poor sleep, and psychological factors as well as pressure pain threshold and temporal summation (TS) as determined by quantitative sensory testing (QST). We used logistic regression to evaluate the association of sociodemographic factors with PSPs and the relationship of PSPs to the development of PKP over 2 years.Results. A total of 852 participants were included (mean age 67 years, body mass index 29.5 kg/m 2 , 55% women). Four PSPs were identified, primarily characterized by varying proportions (low/absent, moderate, or high) of the presence of pressure pain sensitivity and of facilitated TS, reflecting different measures of sensitization. Subjects in the PSP with a high proportion of pressure pain sensitivity and a moderate proportion of facilitated TS were twice as likely to develop incident PKP over 2 years (odds ratio 1.98 [95% confidence interval 1.07-3.68]) compared with subjects in the PSP having a low proportion of sensitization by both measures.Conclusion. Four PSPs were identified, 3 of which were predominated by QST evidence of sensitization and 1 of which was associated with developing PKP 2 years later. Prevention or amelioration of sensitization may be a novel approach to preventing onset of PKP in OA.

A common problem of mid-air interaction is excessive arm fatigue, known as the "Gorilla arm" effect. To predict and prevent such problems at a low cost, we investigate user testing of mid-air interaction without real users, utilizing biomechanically simulated AI agents trained using deep Reinforcement Learning (RL). We implement this in a pointing task and four experimental conditions, demonstrating that the simulated fatigue data matches human fatigue data. We also compare two effort models: 1) instantaneous joint torques commonly used in computer animation and robotics, and 2) the recent Three Compartment Controller (3CC-r) model from biomechanical literature. 3CC-r yields movements that are both more efficient and relaxed, whereas with instantaneous joint torques, the RL agent can easily generate movements that are quickly tiring or only reach the targets slowly and inaccurately. Our work demonstrates that deep RL combined with the 3CC-r provides a viable tool for predicting both interaction movements and user experience in silico, without users.

Supplemental Digital Content is Available in the Text.Resistance training prevented muscle pain in both male and female mice and was mediated through androgen receptors. Resistance training alleviated muscle pain in male mice only.

This paper presents the background and history of the virtual human Santos TM developed by the Virtual Soldier Research (VSR) Program at The University of Iowa. The early virtual human environment was called Mira TM . This 15-degree-of-freedom (DOF) upper-body model with posture and motion prediction was funded by John Deere Inc. and US Army TACOM Automotive Research Center. In 2003 US Army TACOM began funding VSR to develop a new generation of virtual humans called Santos (109 DOFs), which was to be another generation of Mira. Later on, Caterpillar Inc., Honda R&D North Americas, Natick Soldier System Center, and USCAR (GM, Ford, and Chrysler) joined the VSR partnership. The objective is to develop a new generation of digital humans comprising realistic human models including anatomy, biomechanics, physiology, and intelligence in real time, and to test digital mockups of products and systems before they are built, thus reducing the significant costs and time associated with making prototypes. The philosophy is based on a novel optimization-based approach for empowering these digital humans to perform, un-aided, in a physics-based world. The research thrusts include the following areas: (1) predictive dynamics, (2) modeling of cloth, (3) hand model, (4) intuitive interface, (5) motion capture, (6) muscle and physiology modeling, (7) posture and motion prediction, (8) spine modeling, and (9) real-time simulation and virtual reality (VR). Currently, the capabilities of Santos include whole-body posture prediction, advanced inverse kinematics, reach envelope analysis, workspace zone differentiation, muscle force and stress analysis, muscle fatigue prediction, simulation of walking and running, dynamic motion prediction, physiologic assessment, a user-friendly interface, a hand model and grasping capability, clothing modeling, thermo discomfort assessment, muscle wrapping and sliding, whole-body vibration analysis, and collision avoidance.

Supplemental Digital Content is Available in the Text.Individuals with post–Coronavirus-19 condition report elevated pain, fatigue, psychological impact, and functional limitation similar, but less severe, than those with fibromyalgia and chronic fatigue syndrome.