Estimation of muscle forces during motion involves solving an indeterminate problem (more unknown muscle forces than joint moment constraints), frequently via optimization methods. When the dynamics of muscle activation and contraction are modeled for consistency with muscle physiology, the resulting optimization problem is dynamic and challenging to solve. This study sought to identify a robust and computationally efficient formulation for solving these dynamic optimization problems using direct collocation optimal control methods. Four problem formulations were investigated for walking based on both a two and three dimensional model. Formulations differed in the use of either an explicit or implicit representation of contraction dynamics with either muscle length or tendon force as a state variable. The implicit representations introduced additional controls defined as the time derivatives of the states, allowing the nonlinear equations describing contraction dynamics to be imposed as algebraic path constraints, simplifying their evaluation. Problem formulation affected computational speed and robustness to the initial guess. The formulation that used explicit contraction dynamics with muscle length as a state failed to converge in most cases. In contrast, the two formulations that used implicit contraction dynamics converged to an optimal solution in all cases for all initial guesses, with tendon force as a state generally being the fastest. Future work should focus on comparing the present approach to other approaches for computing muscle forces. The present approach lacks some of the major limitations of established methods such as static optimization and computed muscle control while remaining computationally efficient.Electronic Supplementary MaterialThe online version of this article (doi:10.1007/s10439-016-1591-9 contains supplementary material, which is available to authorized users.

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a forcemeasuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) CalibrateþMatch where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrateþ Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrateþ Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the CalibrateþMatch case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R 2 ! 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the PrecalibrateþPredict and CalibrateþPredict cases, synergy controls yielded better contact force predictions (0.61 < R 2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R 2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

Validation is critical if clinicians are to use musculoskeletal models to optimize treatment of individual patients with a variety of musculoskeletal disorders. This paper provides an update on the annual Grand Challenge Competition to Predict In Vivo Knee Loads, a unique opportunity for direct validation of knee contact forces and indirect validation of knee muscle forces predicted by musculoskeletal models. Three competitions (2010, 2011, and 2012) have been held at the annual American Society of Mechanical Engineers Summer Bioengineering Conference and two more competitions are planned for the 2013 and 2014 conferences. Each year of the competition, a comprehensive data set collected from a single subject implanted with a force-measuring knee replacement is released. Competitors predict medial and lateral knee contact forces for two gait trials without knowledge of the experimental knee contact force measurements. Predictions are evaluated by calculating root-mean-square (RMS) errors and R2 values relative to the experimentally measured medial and lateral contact forces. For the first three years of the competition, competitors used a variety of methods to predict knee contact and muscle forces, including static and dynamic optimization, EMG-driven models, and parametric numerical models. Overall, errors in predicted contact forces were comparable across years, with average RMS errors for the four competition winners ranging from 229 to 312 N for medial contact force and from 238 to 326 N for lateral contact force. Competitors generally predicted variations in medial contact force (highest R2 = 0.91) better than variations in lateral contact force (highest R2 = 0.70). Thus, significant room for improvement exists in the remaining two competitions. The entire musculoskeletal modeling community is encouraged to use the competition data and models for their own model validation efforts.

This qualitative focus group study was conducted to explore social facilitators and barriers to health behavior change in persons with serious mental illness engaged in a healthy lifestyle intervention. Six focus group interviews were conducted with a total of 30 clients stratified by "high" and "low" achievers in the program based on clinically significant weight loss or significant increase in fitness. Thematic analysis of focus group discussions revealed that emotional, practical, and mutual support from family members and significant others were social facilitators to health behavior change, while unhealthy social environments was a barrier. Participants in the "high" achiever group reported more mutual support for health behavior change than participants in the "low" achiever group. Results highlight the need for researchers and clinicians to consider the potential role of family and significant others as health supporters for persons with mental illness who could encourage healthy behavior in the social environment.

Effective and scalable interventions are needed to reach a greater proportion of individuals with serious mental illness (SMI) who experience alarmingly high rates of obesity. This pilot study evaluated the feasibility of translating an evidenced-based professional health coach model (In SHAPE) to peer health coaching for overweight and obese individuals with SMI. Key stakeholders collaborated to modify In SHAPE to include a transition from professional health coaching to individual and group-based peer health coaching enhanced by mobile health technology. Ten individuals with SMI were recruited from a public mental health agency to participate in a 6-month feasibility pilot study of the new model. There was no overall significant change in mean weight; however, over half (56 %) of participants lost weight by the end of the intervention with mean weight loss 2.7 ± 2.1 kg. Participants reported high satisfaction and perceived benefits from the program. Qualitative interviews with key stakeholders indicated that the intervention was implemented as planned. This formative research showed that peer health coaching for individuals with SMI is feasible. Further research is needed to evaluate its effectiveness.

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r ¼ 0.99 and root mean square error (RMSE) ¼ 52.6 N medial; average r ¼ 0.95 and RMSE ¼ 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE ¼ 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r ¼ 0.90 medial and À0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking.

Objective: Youth with mental illnesses often engage in unhealthy behaviors associated with early mortality from physical diseases in adulthood, but interventions to support positive health behaviors are rarely offered as part of routine mental health care for this group. Digital health technology that is desirable, accessible, and affordable has the potential to address health behaviors in public mental health settings where many adolescents with severe mental health problems receive care. The aims of this study were to examine how adolescents receiving public mental health services use digital technology and social media and to explore their preferences using technology to support health and wellness. Methods: Using a convergent parallel mixed methods design, we surveyed adolescents ages 13-18 from four community mental health centers in one state and conducted focus group interviews to explore their perspectives on using digital technology and social media to receive health coaching and connect with peers to support healthy behaviors. The survey and focus group data were merged to inform the future development of a digital health intervention for adolescents receiving public mental health services. Results: Of 121 survey respondents (mean age 15.2, SD = 1.5), 92% had a cell phone, 79% had a smartphone, 90% used text messaging, and 98% used social media. Focus group interviews revealed that adolescents were interested in receiving strengths-based mobile health coaching, and they preferred structured online peer-to-peer interactions in which a professional moderator promotes positive connections and adherence to privacy guidelines. Conclusions: Adolescents receiving public mental health services in this study had access to smartphones and were frequent social media users. These data suggest that digital health interventions to promote health and wellness among adolescents may be scalable in community mental health settings. Adolescent participants suggested that digital health interventions for this group should focus on strengths and online peer support for health promotion should include a professional moderator to foster and manage peer-to-peer interactions.

Background Individuals with serious mental illness (SMI) such as schizophrenia and bipolar disorder face a higher risk of early death due to cardiovascular disease and other preventable chronic illnesses. Young adulthood is a critical window of development for lifestyle interventions to improve the long-term health and quality of life in this population. Fit Forward is an NIH-funded randomized clinical trial examining the effectiveness of a group lifestyle intervention (PeerFIT) enhanced with mobile health technology compared to one-on-one mobile lifestyle coaching with Basic Education in fitness and nutrition supported by a wearable Activity Tracking device (BEAT) in achieving clinically significant weight loss and improved cardiorespiratory fitness in young adults with SMI. Methods Fit Forward targets 144 young adults (18 to 35 years) with SMI and a body mass index (BMI) of ≥ 25 receiving public mental health services. In a two-arm randomized clinical trial, participants will be randomly assigned with equal probability to PeerFIT or BEAT, stratified by birth sex and psychiatric diagnosis. Participants will be assessed at baseline, 6, and 12 months. The primary outcome is cardiovascular risk reduction indicated by either clinically significant weight loss (5% or greater) or increased fitness (>50 m on the 6-Minute Walk Test). Secondary outcomes include change in BMI, lipids, and hemoglobin A1c. Perceived self-efficacy for exercise and peer support will be evaluated as mechanisms underlying intervention effects. Conclusion If effective, PeerFIT will provide a potentially scalable approach to addressing health risks among young adults with SMI in mental health settings.