Low Back Pain-Induced Dynamic Trunk Muscle Control Impairments Are Associated with Altered Spatial EMG-Torque Relationships.

PURPOSE: We quantified the relationship between high-density surface electromyographic (HDsEMG) oscillations (in both time and frequency domains) and torque steadiness during submaximal concentric/eccentric trunk extension/flexion contractions, in individuals with and without chronic low back pain (CLBP). METHODS: Comparisons were made between regional differences in HDsEMG amplitude and HDsEMG-torque cross-correlation and coherence of the thoracolumbar erector spinae (ES), rectus abdominis (RA), and external oblique (EO) muscles between the two groups. HDsEMG signals were recorded from the thoracolumbar ES with two 64-electrode grids and from the RA and EO muscles with a single 64-electrode grid placed over each muscle. Torque signals were recorded with an isokinetic dynamometer. Coherence (δ band (0-5 Hz)) and cross-correlation analyses were used to examine the relationship between HDsEMG and torque signals. For this purpose, we used principal component analysis to reduce data dimensionality and improve HDsEMG-based torque estimation. RESULTS: We found that people with CLBP had poorer control during both concentric and eccentric trunk flexion and extension. Specifically, during trunk extension, they exhibited a higher HDsEMG-torque coherence in more cranial regions of the thoracolumbar ES and a higher HDsEMG cross-correlation compared with asymptomatic controls. During trunk flexion movements, they demonstrated higher HDsEMG amplitude of the abdominal muscles, with the center of activation being more cranial and a higher contribution of this musculature to the resultant torque (particularly the EO muscle). CONCLUSIONS: Our findings underscore the importance of evaluating torque steadiness in individuals with CLBP. Future research should consider the value of torque steadiness training and HDsEMG-based biofeedback for modifying trunk muscle recruitment strategies and improving torque steadiness performance in individuals with CLBP.

Factors Informing High-Risk Primary Care Patient Choice around Telehealth Use: a Framework.

BACKGROUND: While telehealth's presence in post-pandemic primary care appears assured, its exact role remains unknown. Value-based care's expansion has heightened interest in telehealth's potential to improve uptake of preventive and chronic disease care, especially among high-risk primary care populations. Despite this, the pandemic underscored patients' diverse preferences around using telehealth. Understanding the factors underlying this population's preferences can inform future telehealth strategies. OBJECTIVE: To describe the factors informing high-risk primary care patient choice of whether to pursue primary care via telehealth, in-office or to defer care altogether. DESIGN: Qualitative, cross-sectional study utilizing semi-structured telephone interviews of a convenience sample of 29 primary care patients between July 13 and September 30, 2020. PARTICIPANTS: Primary care patients at high risk of poor health outcomes and/or acute care utilization who were offered a follow-up primary care visit via audiovisual, audio-only or in-office modalities. APPROACH: Responses were analyzed via grounded theory, using a constant comparison method to refine emerging categories, distinguish codes, and synthesize evolving themes. KEY RESULTS: Of the 29 participants, 16 (55.2%) were female and 19 (65.5%) were Black; the mean age (SD) was 64.6 (11.1). Participants identified four themes influencing their choice of visit type: perceived utility (encapsulating clinical and non-clinical utility), underlying costs (in terms of time, money, effort, and safety), modifiers (e.g., participants' clinical situation, choice availability, decision phenotype), and drivers (inclusive of their background experiences and digital environment). The relationship of these themes is depicted in a novel framework of patient choice around telehealth use. CONCLUSIONS: While visit utility and cost considerations are foundational to participants' decisions around whether to pursue care via telehealth, underappreciated modifiers and drivers often magnify or mitigate these considerations. Policymakers, payers, and health systems can leverage these factors to anticipate and enhance equitable high-value telehealth use in primary care settings among high-risk individuals.

Training Health Policy Researchers on Policy Engagement and Research Translation for Greater Impact: Evaluation of the Amplify@LDI Program.

BACKGROUND: Few researchers receive formal training in research translation and dissemination or policy engagement. We created Amplify@LDI, a training program for health services and health policy researchers, to equip them with skills to increase the visibility of their research through translation and dissemination activities. AIMS: To describe the program's participants and curriculum, and evaluate the first 2 years of the program. SETTING: The Leonard Davis Institute (LDI) at the University of Pennsylvania (Penn). PARTICIPANTS: An annual cohort of 12 LDI Senior Fellows (Penn faculty) from multiple schools, disciplines, and ranks at Penn. PROGRAM DESCRIPTION: The Amplify@LDI curriculum includes 6 sessions on different aspects of research translation and dissemination, including media and social engagement, writing Op-Eds, and policy engagement. PROGRAM EVALUATION: Participants reported measurable increases in time spent on translation and dissemination activities, as well as new enthusiasm for and confidence in policy engagement. Participants' reach (as measured by Altmetric) increased during the program, compared to smaller increases or reductions in reach for two comparator groups. DISCUSSION: In our preliminary evaluation of Amplify@LDI, we find strong evidence of positive impact from participant evaluations, and suggestive evidence that participation in the program is associated with significant increases in the reach of their research.

Consumer Views on Privacy Protections and Sharing of Personal Digital Health Information.

Importance: Digital health information has many potential health applications, but privacy is a growing concern among consumers and policy makers. Consent alone is increasingly seen as inadequate to safeguard privacy. Objective: To determine whether different privacy protections are associated with consumers' willingness to share their digital health information for research, marketing, or clinical uses. Design, Setting, and Participants: This 2020 national survey with an embedded conjoint experiment recruited US adults from a nationally representative sample with oversampling of Black and Hispanic individuals. Willingness to share digital information across 192 different scenarios reflecting the product of 4 possible privacy protections, 3 uses of information, 2 users of information, and 2 sources of digital information was evaluated. Each participant was randomly assigned 9 scenarios. The survey was administrated between July 10 and July 31, 2020, in Spanish and English. Analysis for this study was conducted between May 2021 and July 2022. Main Outcomes and Measures: Participants rated each conjoint profile on a 5-point Likert scale measuring their willingness to share their personal digital information (with 5 indicating the most willingness to share). Results are reported as adjusted mean differences. Results: Of the 6284 potential participants, 3539 (56%) responded to the conjoint scenarios. A total of 1858 participants (53%) were female, 758 (21%) identified as Black, 833 (24%) identified as Hispanic, 1149 (33%) had an annual income less than $50 000, and 1274 (36%) were 60 years or older. Participants were more willing to share health information with the presence of each individual privacy protection, including consent (difference, 0.32; 95% CI, 0.29-0.35; P < .001), followed by data deletion (difference, 0.16; 95% CI, 0.13-0.18; P < .001), oversight (difference, 0.13; 95% CI, 0.10-0.15; P < .001), and transparency of data collected (difference, 0.08; 95% CI, 0.05-0.10; P < .001). The relative importance (importance weight on a 0%-100% scale) was greatest for the purpose of use (29.9%) but when considered collectively, the 4 privacy protections together were the most important (51.5%) factor in the conjoint experiment. When the 4 privacy protections were considered separately, consent was the most important (23.9%). Conclusions and Relevance: In this survey study of a nationally representative sample of US adults, consumers' willingness to share personal digital health information for health purposes was associated with the presence of specific privacy protections beyond consent alone. Additional protections, including data transparency, oversight, and data deletion may strengthen consumer confidence in sharing their personal digital health information.

People with chronic low back pain display spatial alterations in high-density surface EMG-torque oscillations.

We quantified the relationship between spatial oscillations in surface electromyographic (sEMG) activity and trunk-extension torque in individuals with and without chronic low back pain (CLBP), during two submaximal isometric lumbar extension tasks at 20% and 50% of their maximal voluntary torque. High-density sEMG (HDsEMG) signals were recorded from the lumbar erector spinae (ES) with a 64-electrode grid, and torque signals were recorded with an isokinetic dynamometer. Coherence and cross-correlation analyses were applied between the filtered interference HDsEMG and torque signals for each submaximal contraction. Principal component analysis was used to reduce dimensionality of HDsEMG data and improve the HDsEMG-based torque estimation. sEMG-torque coherence was quantified in the δ(0-5 Hz) frequency bandwidth. Regional differences in sEMG-torque coherence were also evaluated by creating topographical coherence maps. sEMG-torque coherence in the δ band and sEMG-torque cross-correlation increased with the increase in torque in the controls but not in the CLBP group (p = 0.018, p = 0.030 respectively). As torque increased, the CLBP group increased sEMG-torque coherence in more cranial ES regions, while the opposite was observed for the controls (p = 0.043). Individuals with CLBP show reductions in sEMG-torque relationships possibly due to the use of compensatory strategies and regional adjustments of ES-sEMG oscillatory activity.

A machine learning approach for the identification of kinematic biomarkers of chronic neck pain during single- and dual-task gait.

BACKGROUND: Changes in gait characteristics have been reported in people with chronic neck pain (CNP). RESEARCH QUESTION: Can we classify people with and without CNP by training machine learning models with Inertial Measurement Units (IMU)-based gait kinematic data? METHODS: Eighteen asymptomatic individuals and 21 participants with CNP were recruited for the study and performed two gait trajectories, (1) linear walking with their head straight (single-task) and (2) linear walking with continuous head-rotation (dual-task). Kinematic data were recorded from three IMU sensors attached to the forehead, upper thoracic spine (T1), and lower thoracic spine (T12). Temporal and spectral features were extracted to generate the dataset for both single- and dual-task gait. To evaluate the most significant features and simultaneously reduce the dataset size, the Neighbourhood Component Analysis (NCA) method was utilized. Three supervised models were applied, including K-Nearest Neighbour, Support Vector Machine, and Linear Discriminant Analysis to test the performance of the most important temporal and spectral features. RESULTS: The performance of all classifiers increased after the implementation of NCA. The best performance was achieved by NCA-Support Vector Machine with an accuracy of 86.85%, specificity of 83.30%, and sensitivity of 92.85% during the dual-task gait using only nine features. SIGNIFICANCE: The results present a data-driven approach and machine learning-based methods to identify test conditions and features from high-dimensional data obtained during gait for the classification of people with and without CNP.

How can healthcare organizations improve cost-of-care conversations? A qualitative exploration of clinicians' perspectives.

OBJECTIVES: Clinicians increasingly believe they should discuss costs with their patients. We aimed to learn what strategies clinicians, clinic leaders, and health systems can use to facilitate vital cost-of-care conversations. METHODS: We conducted focus groups and semi-structured interviews with outpatient clinicians at two US academic medical centers. Clinicians recalled previous cost conversations and described strategies that they, their clinic, or their health system could use to facilitate cost conversations. Independent coders recorded, transcribed, and coded focus groups and interviews. RESULTS: Twenty-six clinicians participated between December 2019 and July 2020: general internists (23%), neurologists (27%), oncologists (15%), and rheumatologists (35%). Clinicians proposed the following strategies: teach clinicians to initiate cost conversations; systematically collect financial distress information; partner with patients to identify costs; provide accurate insurance coverage and/or out-of-pocket cost information via the electronic health record; develop local lists of lowest-cost pharmacies, laboratories, and subspecialists; hire financial counselors; and reduce indirect costs (e.g., parking). CONCLUSIONS: Despite considerable barriers to discussing, identifying, and reducing patient costs, clinicians described a variety of strategies for improving cost communication in the clinic. PRACTICE IMPLICATIONS: Health systems and clinic leadership can and should implement these strategies to improve the financial health of the patients they serve.

Consumer Willingness to Share Personal Digital Information for Health-Related Uses.

Importance: Consumers routinely generate digital information that reflects on their health. Objective: To evaluate the factors associated with consumers' willingness to share their digital health information for research, health care, and commercial uses. Design, Setting, and Participants: This national survey with an embedded conjoint experiment recruited US adults from a nationally representative sample, with oversampling of Black and Hispanic panel members. Participants were randomized to 15 scenarios reflecting use cases for consumer digital information from a total of 324 scenarios. Attributes of the conjoint analysis included 3 uses, 3 users, 9 sources of digital information, and 4 relevant health conditions. The survey was conducted from July 10 to 31, 2020. Main Outcomes and Measures: Participants rated each conjoint profile on a 5-point Likert scale (1-5) measuring their willingness to share their personal digital information (with 5 indicating the most willingness to share). Results reflect mean differences in this scale from a multivariable regression model. Results: Among 6284 potential participants, 3543 (56%) responded. A total of 1862 participants (53%) were female, 759 (21%) identified as Black, 834 (24%) identified as Hispanic, and 1274 (36%) were 60 years or older. In comparison with information from electronic health care records, participants were less willing to share information about their finances (coefficient, -0.56; 95% CI, -0.62 to -0.50), places they visit from public cameras (coefficient, -0.28; 95% CI, -0.33 to -0.22), communication on social media (coefficient, -0.20; 95% CI -0.26 to -0.15), and their search history from internet search engines (coefficient, -0.11; 95% CI, -0.17 to -0.06). They were more willing to share information about their steps from applications on their phone (coefficient, 0.22; 95% CI, 0.17-0.28). Among the conjoint attributes, the source of information (importance weight: 59.1%) was more important than the user (17.3%), use (12.3%), and health condition (11.3%). Four clusters of consumers emerged from the sample with divergent privacy views. While the context of use was important, these 4 groups expressed differences in their overall willingness to share, with 337 participants classified as never share; 1116 classified as averse to sharing (mean rating, 1.64; 95% CI, 1.62-1.65); 1616 classified as uncertain about sharing (mean rating, 2.84; 95% CI, 2.81-2.86); and 474 classified as agreeable to sharing (mean rating, 4.18; 95% CI, 4.16-4.21). Respondents who identified as White and non-Hispanic, had higher income, and were politically conservative were more likely to be in a cluster that was less willing to share (ie, never or averse clusters). Conclusions and Relevance: These findings suggest that although consumers' willingness to share personal digital information for health purposes is associated with the context of use, many have strong underlying privacy views that affect their willingness to share. New protections may be needed to give consumers confidence to be comfortable sharing their personal information.

Substance Use and Overdose in Public Libraries: Results from a Five-State Survey in the US.

In the U.S., overdoses have become a health crisis in both public and private places. We describe the impact of the overdose crisis in public libraries across five U.S. states, and the front-line response of public library workers. We conducted a cross-sectional survey, inviting one worker to respond at each public library in five randomly selected states (CO, CT, FL, MI, and VA), querying participants regarding substance use and overdose in their communities and institutions, and their preparedness to respond. We describe substance use and overdose patterns, as well as correlates of naloxone uptake, in public libraries. Participating library staff (N = 356) reported witnessing alcohol use (45%) and injection drug use (14%) in their libraries in the previous month. Across states surveyed, 12% of respondents reported at least one on-site overdose in the prior year, ranging from a low of 10% in MI to a high of 17% in FL. There was wide variation across states in naloxone uptake at libraries, ranging from 0% of represented libraries in FL to 33% in CO. Prior on-site overdose was associated with higher odds of naloxone uptake by the library (OR 2.5, 95% CI 1.1-5.7). Although 24% of respondents had attended a training regarding substance use in the prior year, over 90% of respondents wanted to receive additional training on the topic. Public health professionals should partner with public libraries to expand and strengthen substance use outreach and overdose prevention efforts.

Machine learning approaches applied in spinal pain research.

The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to harness variability of patient presentation to enhance clinical prediction. The review includes a summary of current knowledge on the physiological adaptations present in people with spinal pain. We discuss how contemporary evidence highlights the importance of not relying on single features when characterizing patients given the variability of physiological adaptations present in people with spinal pain. The advantages and disadvantages of current analytical strategies in contemporary basic science and epidemiological research are reviewed and we consider how analytical machine learning approaches could provide the platform to harness the variability of patient presentations to enhance clinical prediction of pain persistence or recurrence. We propose that machine learning techniques can be leveraged to translate a potentially heterogeneous set of variables into clinically useful information with the potential to enhance patient management.

Consumer Views on Health Applications of Consumer Digital Data and Health Privacy Among US Adults: Qualitative Interview Study.

BACKGROUND: In 2020, the number of internet users surpassed 4.6 billion. Individuals who create and share digital data can leave a trail of information about their habits and preferences that collectively generate a digital footprint. Studies have shown that digital footprints can reveal important information regarding an individual's health status, ranging from diet and exercise to depression. Uses of digital applications have accelerated during the COVID-19 pandemic where public health organizations have utilized technology to reduce the burden of transmission, ultimately leading to policy discussions about digital health privacy. Though US consumers report feeling concerned about the way their personal data is used, they continue to use digital technologies. OBJECTIVE: This study aimed to understand the extent to which consumers recognize possible health applications of their digital data and identify their most salient concerns around digital health privacy. METHODS: We conducted semistructured interviews with a diverse national sample of US adults from November 2018 to January 2019. Participants were recruited from the Ipsos KnowledgePanel, a nationally representative panel. Participants were asked to reflect on their own use of digital technology, rate various sources of digital information, and consider several hypothetical scenarios with varying sources and health-related applications of personal digital information. RESULTS: The final cohort included a diverse national sample of 45 US consumers. Participants were generally unaware what consumer digital data might reveal about their health. They also revealed limited knowledge of current data collection and aggregation practices. When responding to specific scenarios with health-related applications of data, they had difficulty weighing the benefits and harms but expressed a desire for privacy protection. They saw benefits in using digital data to improve health, but wanted limits to health programs' use of consumer digital data. CONCLUSIONS: Current privacy restrictions on health-related data are premised on the notion that these data are derived only from medical encounters. Given that an increasing amount of health-related data is derived from digital footprints in consumer settings, our findings suggest the need for greater transparency of data collection and uses, and broader health privacy protections.

Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.

Neuromuscular impairments are frequently observed in patients with chronic neck pain (CNP). This study uniquely investigates whether changes in neck muscle synergies detected during gait are sensitive enough to differentiate between people with and without CNP. Surface electromyography (EMG) was recorded from the sternocleidomastoid, splenius capitis, and upper trapezius muscles bilaterally from 20 asymptomatic individuals and 20 people with CNP as they performed rectilinear and curvilinear gait. Intermuscular coherence was computed to generate the functional inter-muscle connectivity network, the topology of which is quantified based on a set of graph measures. Besides the functional network, spectrotemporal analysis of each EMG was used to form the feature set. With the use of Neighbourhood Component Analysis (NCA), we identified the most significant features and muscles for the classification/differentiation task conducted using K-Nearest Neighbourhood (K-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms. The NCA algorithm selected features from muscle network topology as one of the most relevant feature sets, which further emphasize the presence of major differences in muscle network topology between people with and without CNP. Curvilinear gait achieved the best classification performance through NCA-SVM based on only 16 features (accuracy: 85.00%, specificity: 81.81%, and sensitivity: 88.88%). Intermuscular muscle networks can be considered as a new sensitive tool for the classification of people with CNP. These findings further our understanding of how fundamental muscle networks are altered in people with CNP.

Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid.

BACKGROUND: The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers' perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter. OBJECTIVE: This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage in the United States, as an example. METHODS: We collected data from Twitter in two ways: via the public streaming application programming interface using Medicaid-related keywords (Corpus 1) and by using the website's search option for tweets mentioning agency-specific handles (Corpus 2). We manually labeled a sample of tweets in 5 predetermined categories or other and artificially increased the number of training posts from specific low-frequency categories. Using the manually labeled data, we trained and evaluated several supervised learning algorithms, including support vector machine, random forest (RF), naïve Bayes, shallow neural network (NN), k-nearest neighbor, bidirectional long short-term memory, and bidirectional encoder representations from transformers (BERT). We then applied the best-performing classifier to the collected tweets for postclassification analyses to assess the utility of our methods. RESULTS: We manually annotated 11,379 tweets (Corpus 1: 9179; Corpus 2: 2200) and used 7930 (69.7%) for training, 1449 (12.7%) for validation, and 2000 (17.6%) for testing. A classifier based on BERT obtained the highest accuracies (81.7%, Corpus 1; 80.7%, Corpus 2) and F1 scores on consumer feedback (0.58, Corpus 1; 0.90, Corpus 2), outperforming the second best classifiers in terms of accuracy (74.6%, RF on Corpus 1; 69.4%, RF on Corpus 2) and F1 score on consumer feedback (0.44, NN on Corpus 1; 0.82, RF on Corpus 2). Postclassification analyses revealed differing intercorpora distributions of tweet categories, with political (400778/628411, 63.78%) and consumer feedback (15073/27337, 55.14%) tweets being the most frequent for Corpus 1 and Corpus 2, respectively. CONCLUSIONS: The broad and variable content of Medicaid-related tweets necessitates automatic categorization to identify topic-relevant posts. Our proposed system presents a feasible solution for automatic categorization and can be deployed and generalized for health service programs other than Medicaid. Annotated data and methods are available for future studies.

Consumer Views on Using Digital Data for COVID-19 Control in the United States.

Importance: Curbing COVID-19 transmission is currently the greatest global public health challenge. Consumer digital tools used to collect data, such as the Apple-Google digital contact tracing program, offer opportunities to reduce COVID-19 transmission but introduce privacy concerns. Objective: To assess uses of consumer digital information for COVID-19 control that US adults find acceptable and the factors associated with higher or lower approval of use of this information. Design, Setting, and Participants: This cross-sectional survey study obtained data from a nationally representative sample of 6284 US adults recruited by email from the web-based Ipsos KnowledgePanel in July 2020. Respondents evaluated scenarios reflecting uses of digital data for COVID-19 control (case identification, digital contact tracing, policy setting, and enforcement of quarantines). Main Outcomes and Measures: Levels of support for use of personal digital data in 9 scenarios to mitigate the spread of COVID-19 infection, rated on a Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Multivariable linear regression models were fitted for each scenario and included factors hypothesized to be associated with views about digital data use for COVID-19 mitigation measures. Black and Hispanic survey respondents were oversampled; thus, poststratification weights were used so that results are representative of the general US population. Results: Of 6284 individuals invited to participate in the study, 3547 responded, for a completion rate of 56%. A total of 1762 participants (52%) were female, 715 (21%) identified as Black, 790 (23%) identified as Hispanic, and 1224 (36%) were 60 years or older; mean (SD) age was 51.7 (16.6) years. Approval of scenarios was low, ranging from 28% to 43% (52%-67% when neutral responses were included). Differences were found based on digital data source (smartphone vs social media: coefficient, 0.29 [95% CI, 0.23-0.35]; P < .001; smart thermometer vs social media: coefficient, 0.09 [95% CI, 0.03-0.16]; P = .004). County COVID-19 rates (coefficient, -0.02; 95% CI, -0.16 to 0.13 for quartile 4 compared with quartile 1) and prior family diagnosis of COVID-19 (coefficient, 0.00; 95% CI, -0.25 to 0.25) were not associated with support. Compared with self-described liberal individuals, conservative (coefficient, -0.81; 95% CI, -0.96 to -0.66; P < .001) and moderate (coefficient, -0.52; 95% CI, -0.67 to -0.38; P < .001) individuals were less likely to support the scenarios. Similarly, large political differences were observed in support of the Apple-Google digital contact tracing program, with less support from conservative (coefficient, -0.99; 95% CI, -1.11 to -0.87; P < .001) and moderate (coefficient, -0.59; 95% CI, -0.69 to -0.48; P < .001) individuals compared with liberal individuals. Respondents from racial/ethnic minority groups were more supportive of the scenarios than were White, non-Hispanic respondents. For example, compared with White respondents, Black respondents were more supportive of the Apple-Google contact tracing program (coefficient, 0.20; 95% CI, 0.07-0.32; P = .002). Conclusions and Relevance: In this survey study of US adults, many were averse to their information being used on digital platforms to mitigate transmission of COVID-19. These findings suggest that in current and future pandemics, public health departments should use multiple strategies to gain public trust and accelerate adoption of tools such as digital contact tracing applications.

Kinematic biomarkers of chronic neck pain measured during gait: A data-driven classification approach.

People with chronic neck pain (CNP) often present with altered gait kinematics. This paper investigates, combines, and compares the kinematic features from linear and nonlinear walking trajectories to design supervised machine learning models which differentiate asymptomatic individuals from those with CNP. For this, 126 features were extracted from seven body segments of 20 asymptomatic subjects and 20 individuals with non-specific CNP. Neighbourhood Component Analysis (NCA) was used to identify body segments and the corresponding significant features which have the maximum discriminative power for conducting classification. We assessed the efficacy of NCA combined with K- Nearest Neighbour (K-NN), Support Vector Machine and Linear Discriminant Analysis. By applying NCA, all classifiers increased their performance for both linear and nonlinear walking trajectories. Notably, features selected by NCA which magnify the classification power of the computational model were solely from the head, trunk and pelvis kinematics. Our results revealed that the nonlinear trajectory provides the best classification performance through the NCA-K-NN algorithms with an accuracy of 90%, specificity of 100% and sensitivity of 83.3%. The selected features by NCA are introduced as key biomarkers of gait kinematics for classifying non-specific CNP. This paper provides insight into changes in gait kinematics which are present in people with non-specific CNP which can be exploited for classification purposes. The result highlights the importance of curvilinear gait kinematic features which potentially could be utilized in future research to predict recurrent episodes of neck pain.

Variability in Primary Care Physician Attitudes Toward Medicaid Work Requirement Exemption Requests Made by Patients With Depression.

Importance: Medicaid work requirements seek to promote health and personal responsibility but can also jeopardize health care access. Physicians have a central function in assisting patients with exemption requests, but it is unclear how their role affects patient welfare, professionalism, and the ethical and legal justification of programs. Objective: To understand the degree of variability in physician response to assist patients with depression in receiving a Medicaid work requirement exemption. Design Setting and Participants: We conducted a mailed survey experiment among practicing primary care physicians in the first 4 approved states (Arkansas, Kentucky, Indiana, New Hampshire) in July and October of 2019. We report response, cooperation, refusal, and contact rates in line with American Association for Public Opinion Research (AAPOR) standards. Exposures: In each state, we used an experimental factorial design to randomize recipients to 1 of 4 patient clinical scenarios. Main Outcomes and Measures: The primary outcome was the indicator of willingness to assist a patient reporting depression with an exemption. Results: We received 715 responses (overall AAPOR response rate: 21%; cooperation rate: 84%; refusal rate: 4%; contact rate: 25%). Respondents' mean (SD) age was 54 (12) years; mean (SD) time since graduation, 26 (12) years; 435 (61%) identified as male; 177 as Democrat (25%); 156 as Republican (22%); 197 as Independent/other (28%); and 185 as declined/unknown (26%); the mean (SD) share of Medicaid patients was 29% (21%). We found that 97 of 387 physicians (25%) would offer assistance even when state policy would not support an exemption, and 170 of 315 (54%) would not offer assistance when regulations would require this. Moreover, 49 of 245 respondents (20%) who deemed an exemption appropriate indicated that they would not assist. State, administrative effort, political affiliation, and perceived appropriateness were statistically associated with the odds of assisting with an exemption. Conclusions and Relevance: In this survey study of primary care physicians, we found substantial variation regarding willingness to assist patients qualifying for a work requirement exemption where none should exist. Insofar as work requirements are implemented again, it is critical to proactively identify measures to ensure that patients qualifying for exemptions are not put at risk due to either the burdensomeness of exemption procedures, or physicians' political or personal views.

Engaging Consumers in Medicaid Program Design: Strategies from the States.

Policy Points As Medicaid programs grow in scale and complexity, greater consumer input may guide successful program design, but little is known about the extent to which state agencies are engaging consumers in the design and implementation of programs and policies. Through 50 semistructured interviews with Medicaid leaders in 14 states, we found significant variation in consumer engagement approaches, with many common facilitators, including leadership commitment, flexible strategies for recruiting and supporting consumer participation, and robust community partnerships. We provide early evidence on how state Medicaid agencies are integrating consumers' experiences and perspectives into their program design and governance. CONTEXT: Consumer engagement early in the process of health care policymaking may improve the effectiveness of program planning and implementation, promote patient-centric care, enhance beneficiary protections, and offer opportunities to improve service delivery. As Medicaid programs grow in scale and complexity, greater consumer input may guide successful program design, but little is known about the extent to which state agencies are currently engaging consumers in the design and implementation of programs and policies, and how this is being done. METHODS: We conducted semistructured interviews with 50 Medicaid program leaders across 14 states, employing a stratified purposive sampling method to select state Medicaid programs based on US census region, rurality, Medicaid enrollment size, total population, ACA expansion status, and Medicaid managed care penetration. Interview data were audio-recorded, professionally transcribed, and underwent iterative coding with content and thematic analyses. FINDINGS: First, we found variation in consumer engagement approaches, ranging from limited and largely symbolic interactions to longer-term deliberative bodies, with some states tailoring their federally mandated standing committees to engage consumers. Second, most states were motivated by pragmatic considerations, such as identifying and overcoming implementation challenges for agency programs. Third, states reported several common facilitators of successful consumer engagement efforts, including leadership commitment, flexible strategies for recruiting and supporting consumers' participation, and robust community partnerships. All states faced barriers to authentic and sustained engagement. CONCLUSIONS: Sharing best practices across states could help strengthen programs' engagement efforts, identify opportunities for program improvement reflecting community needs, and increase participation among a population that has traditionally lacked a political voice.