Julie Muccini, MS, OTR/L

All Publications

• Treatments for Functional Neurological Disorder: A Practical Guide for Program Development. The Journal of neuropsychiatry and clinical neurosciences Martyna, M., Muccini, J., Sandoval, G., Fusunyan, M., Bullock, K., Bajestan, S., Barry, J. J., Lockman, J. 2025: appineuropsych20230213

  Abstract

  Functional neurological disorder (FND) is an often-disabling condition with a complex path to diagnosis, further challenged by limited availability of evidence-based treatment resources. Providers hoping to offer treatment resources face the challenge of identifying effective and sustainable implementation of interventions. The existing literature provides limited guidance on the logistics of creating specialized programs for FND outside of tertiary care centers or high-resource medical settings. Members of a multidisciplinary treatment team may find it challenging to identify program development resources that provide a unified perspective on each member's role and how they function together. The authors' FND program at the Stanford University School of Medicine has recently fielded a high number of requests by clinicians, health care staff, and administrators across the United States for collaboration to start new programs. Frequently asked questions include the criteria for patient selection, what personnel to include, how to ensure prompt staff responses to FND symptoms, when to hospitalize patients, how to obtain funding for services, and more. The intended audience for this review includes seasoned and new clinicians, allied health professionals, and nonclinicians, including administrators. The authors discuss diagnosis and evidence-based treatment strategies and provide guidance on practical issues, including work, disability, and driving. The authors' program experience is highlighted as an example, and alternative working models are discussed. The aim of this article is to improve providers' knowledge and confidence and remove frequently encountered barriers to program development. The authors seek to provide a resource that may help connect those in need of care to FND services.

  View details for DOI 10.1176/appi.neuropsych.20230213

  View details for PubMedID 41247147

• Virtual reality glove for home-based hand and arm stroke rehabilitation (vREHAB). PM & R : the journal of injury, function, and rehabilitation Yan, L., Muccini, J., Lugo, L., Mlynash, M., Michiels, L., Verheyden, G., Saenen, L., Dirlikov, B., Ali, A., Lanphere, J., Huie, H., Lemmens, R., Lansberg, M. G. 2025

  Abstract

  Upper extremity impairment is common after stroke. Virtual-reality rehabilitation systems may help restore hand and arm function.To assess the feasibility of the Neofect Smart Glove and its effect on functional recovery.Multicenter, prospective, randomized, open-label, blinded-endpoint phase 2 trial consisting of a 12-week active treatment period followed by a 12-week follow-up period.Patients with subacute and chronic stroke with upper extremity impairment.Patients assigned to the intervention group were instructed to use the Smart Glove for a minimum of one session per day for at least 5 days per week during the 12-week active treatment period, in addition to their usual care. Patients in the control group received their usual care only.Feasibility was assessed by the total dose of rehabilitation. The change from baseline to week 12 on the Jebsen-Taylor Hand Function Test (JTHFT) was the primary efficacy outcome and the change on the Upper Extremity Fugl-Meyer Assessment (UE-FMA) was secondary.Differences between treatment arms were compared using analysis of covariance in the overall population and, separately, in a post-hoc and exploratory analysis consisting of a subset of patients with mild to moderate upper extremity impairment (baseline JTHFT ≤500).During the 12-week active treatment period, there were no differences between the intervention (n = 18) and control (n = 24) groups in the change in the JTHFT (median -64 vs. -69 seconds, p = .88), the change in the UE-FMA (median 8 vs. 8 points, p = .61), or the total dose of rehabilitation (median 1434 vs. 584 minutes, p = .18). Among the subgroup of patients with mild to moderate symptoms (baseline JTHFT ≤500, n = 31), Smart Glove assignment was associated with a greater improvement on the JTHFT (median -72 vs. -40 seconds, p = .01) and a greater dose of rehabilitation (median 1739 vs. 510 minutes, p = .04) during the active treatment period, but there was no difference in the change in the UE-FMA (median 10 vs. 8 points, p = .15).The addition of the Smart Glove to traditional rehabilitation therapy did not improve hand and arm function in the overall study population but may increase the dose of rehabilitation and improve hand and arm function for patients with mild to moderate upper extremity impairment.

  View details for DOI 10.1002/pmrj.70014

  View details for PubMedID 40948414

• Video-Based Biomechanical Analysis Captures Disease-Specific Movement Signatures of Different Neuromuscular Diseases. NEJM AI Ruth, P. S., Uhlrich, S. D., de Monts, C., Falisse, A., Muccini, J., Covitz, S., Vogt-Domke, S., Day, J., Duong, T., Delp, S. L. 2025; 2 (9)

  Abstract

  Assessing human movement is essential for diagnosing and monitoring movement-related conditions like neuromuscular disorders. Timed function tests (TFTs) are among the most widespread types of assessments due to their speed and simplicity, but they cannot capture disease-specific movement patterns. Conversely, biomechanical analysis can produce sensitive disease-specific biomarkers, but it is traditionally confined to laboratory settings. Recent advances in smartphone video-based biomechanical analysis enable the quantification of three-dimensional movement with the ease and speed required for clinical settings. However, the potential of this technology to offer more sensitive assessments of human function than TFTs remains untested.To compare video-based analysis with TFTs, we collected an observational dataset from 129 individuals: 28 with facioscapulohumeral muscular dystrophy, 58 with myotonic dystrophy, and 43 controls with no diagnosed neuromuscular condition. We used OpenCap, a free open-source software tool, to capture smartphone video-based biomechanics of nine different movements in a median time of 16 minutes per participant. From these recordings, we extracted 34 interpretable movement features. Using these features, we evaluated the ability of video-based biomechanics to reproduce four TFTs (10-meter walk, 10-meter run, timed up-and-go, and 5-times sit-to-stand) while capturing additional disease-specific signatures of movement.Video-based biomechanical analysis reproduced all four TFTs (r>0.98) with similar test-retest reliability. In addition, video metrics outperformed TFTs at disease classification (P=0.021). Unlike TFTs, video-based biomechanical analysis identified disease-specific signatures of movement, such as differences in gait kinematics, that are not evident in TFTs.Video-based biomechanical analysis can complement existing functional movement assessments by capturing more sensitive, disease-specific outcomes from human movement. This technology enables digital health solutions for assessing and monitoring motor function, complementing traditional clinical outcome measures to enhance care, management, and clinical trial design for movement-related conditions. (Funded by the Wu Tsai Human Performance Alliance and others.).

  View details for DOI 10.1056/aioa2401137

  View details for PubMedID 40926960

  View details for PubMedCentralID PMC12416922

• Improved Strength Prediction Combining MRI Biomarkers of Muscle Quantity and Quality. NMR in biomedicine Mazzoli, V., Vainberg, Y., Hall, M. E., Barbieri, M., Asay, J., Muccini, J., Rosenberg, J., Kogan, F., Delp, S., Gold, G. E. 2025; 38 (9): e70112

  Abstract

  Muscle strength declines with aging at a faster rate compared with muscle mass, suggesting that not only muscle quantity but also muscle quality and architecture are age-dependent. This study tested the hypothesis that quantitative MRI (qMRI)-derived biomarkers of muscle quality (fractional anisotropy [FA], radial diffusivity [RD], axial diffusivity [AD], fat fraction [FF], and T2 relaxation time) and architecture (fascicle length) could improve the prediction of skeletal muscle strength over muscle mass alone. We recruited 24 adults (12 female, age range 30-79 years). Muscle mass was estimated as the volume and cross-sectional area (CSA) of the quadriceps. FA, RD, and AD parameters, together with fascicle length for the rectus femoris (RF) and vastus lateralis (VL), were derived from diffusion tensor imaging (DTI), and muscle-T2 was calculated from a multi-echo spin echo sequence. FF was determined using the Dixon approach. CSA values were combined with FF to calculate the lean CSA. Isometric, eccentric, and concentric knee extension torques were measured for the left and right leg using an isokinetic dynamometer. The univariable assessment of torque was performed using a linear regression. The statistical significance of adding qMRI parameters to the torque prediction models was tested using a mixed-effect regression. The best univariable predictor of isometric, eccentric, and concentric torque was lean CSA. Adding FA, RF fascicle length, and VL fascicle length to the model improved the prediction of concentric torque compared with CSA alone. The addition of FA, T2, RD, RF fascicle length, and VL fascicle length improved the prediction of eccentric torque over CSA alone. The addition of FF was not significant within the model. Our results confirmed the hypothesis that the inclusion of qMRI parameters of muscle composition and architecture leads to higher R2 coefficients for the prediction of muscle strength compared with models solely based on muscle quantity. These observations support the utility of qMRI for future research on sarcopenia prediction and management.

  View details for DOI 10.1002/nbm.70112

  View details for PubMedID 40769514

• Video-based biomechanical analysis captures disease-specific movement signatures of different neuromuscular diseases. bioRxiv : the preprint server for biology Ruth, P. S., Uhlrich, S. D., de Monts, C., Falisse, A., Muccini, J., Covitz, S., Vogt-Domke, S., Day, J., Duong, T., Delp, S. L. 2025

  Abstract

  Assessing human movement is essential for diagnosing and monitoring movement-related conditions like neuromuscular disorders. Timed function tests (TFTs) are among the most widespread assessments due to their speed and simplicity, but they cannot capture disease-specific movement patterns. Conversely, biomechanical analysis can produce sensitive disease-specific biomarkers but is traditionally confined to laboratory settings. Recent advances in smartphone video-based biomechanical analysis enable quantification of 3D movement with the ease and speed required for clinical settings. However, the potential of this technology to offer more sensitive assessments of human function than TFTs remains untested.To compare video-based analysis against TFTs, we collected an observational dataset from 129 individuals: 28 with facioscapulohumeral muscular dystrophy, 58 with myotonic dystrophy, and 43 controls with no diagnosed neuromuscular condition. We used OpenCap, a free open-source software tool, to capture smartphone video-based biomechanics of nine different movements in a median time of 16 minutes per participant. From these recordings we extracted 34 interpretable movement features. Using these features, we evaluated the ability of video-based biomechanics to reproduce four TFTs (10-meter walk, 10-meter run, timed up-and-go, and 5-time sit-to-stand) while capturing additional disease-specific signatures of movement.Video-based biomechanical analysis reproduced all four TFTs (r > 0.98) with similar test-retest reliability. In addition, video metrics outperformed TFTs at disease classification (p = 0.021). Unlike TFTs, video-based biomechanical analysis identified disease-specific signatures of movement such as differences in gait kinematics that are not evident in TFTs.Video-based biomechanical analysis can complement existing functional movement assessments by capturing more sensitive, disease-specific outcomes from human movement. This technology enables digital health solutions for assessing and monitoring motor function, complementing traditional clinical outcome measures to enhance care, management, and clinical trial design for movement-related conditions.

  View details for DOI 10.1101/2024.09.26.613967

  View details for PubMedID 40766489

  View details for PubMedCentralID PMC12324206

• Personalizing the shoulder rhythm in a computational upper body model improves kinematic tracking in high range-of-motion arm movements. Journal of biomechanics Maier, J. N., Bianco, N. A., Ong, C. F., Muccini, J., Kuhl, E., Delp, S. L. 2024; 176: 112365

  Abstract

  Musculoskeletal models of the shoulder are needed to understand the mechanics of overhead motions. Existing models implementing the shoulder rhythm are generic and might not accurately represent an individual's scapular kinematics. We introduce a method to personalize the shoulder rhythm of a computational model of the upper body that defines the orientations of the clavicle and scapula based on glenohumeral joint angles. During five static calibration poses, we palpate and measure the orientation of the scapula. We explore the importance of representing shoulder elevation by introducing clavicle elevation as a degree of freedom that is independent of the glenohumeral angles. For ten subjects, we record the five calibration poses, ten additional static poses, and dynamic arm raises covering the participants' full range of motion in each body plane using optical motion capture. We examine the data using a dynamically-constrained inverse kinematics analysis. Shoulder rhythm personalization, independent clavicle elevation, and both in combination reduce the average upper body marker tracking error compared to the generic model in the static poses (26 mm to 17-20 mm) and in the dynamic trials (22 mm to 14-17 mm). Only personalization reduces the average scapula marker error (51 mm to 36-38 mm) and scapula axis-angle error (15° to 10°) compared with the palpated ground truth measurements in the static poses, and in the dynamic trials at instances that best match the static poses (53 mm to 37-40 mm, 15° to 9°). Our results show that personalizing upper body models improves kinematic tracking. We provide our experimental data, model, and methods to allow researchers to reproduce and build upon our results.

  View details for DOI 10.1016/j.jbiomech.2024.112365

  View details for PubMedID 39426356

• EngageHealth: a mobile device application designed to deliver stroke rehabilitation exercises using asynchronous video recordings. Frontiers in stroke Song, A. J., Lugo, L., Muccini, J., Mlynash, M., Lansberg, M. G. 2024; 3: 1418298

  Abstract

  Stroke survivors who receive more rehabilitation therapy achieve better functional outcomes. The amount of rehabilitation that patients receive is, however, limited due to constraints of the healthcare system.To assess whether EngageHealth, a mobile device application designed to deliver stroke rehabilitation exercises using asynchronous video recordings, increases the amount of outpatient rehabilitation in stroke patients and improves their upper extremity function and quality of life.Prospective single-arm study consisting of a 2-week pre-intervention phase without EngageHealth followed by a 4-week intervention period with EngageHealth.Ambulatory care.Twenty-four stroke patients with upper extremity impairment were recruited at the Stanford Stroke Center outpatient clinic.Participants were instructed to use the EngageHealth application daily.Adherence, user experience, and change in the upper extremity Fugl-Meyer (UE-FM), Quality of Life in Neurological Disorders (Neuro-QoL), and Stroke Impact Scale (SIS).Of 23 participants, five (22%) used the application for 17 days, six (26%) used the application for 9-16 days, and 12 (52%) used it < 9 days. Sixty-three percent of participants would recommend the application to other stroke survivors, with fifty percent indicating they would continue using the application, if available. During the pre-intervention phase, there were no changes in hand function. During the intervention period, participants improved by 4 points on the UE-FM (P < 0.01), and 15 points in the hand-function domain of SIS (P = 0.03). Videos of participants' exercises were successfully recorded, allowing the clinician to review videos of the participants' completed tasks asynchronously. In-depth interviews revealed that participants viewed the EngageHealth application favorably, and that their perceived usefulness of the exercises affected their motivation.Use of the EngageHealth application in the home environment may improve upper extremity function in subacute/chronic stroke patients. Additional support strategies should be implemented in future studies to improve adherence. These findings from a prospective single-arm study, support the design of a randomized controlled trial to determine the efficacy of long-term use of the EngageHealth application.

  View details for DOI 10.3389/fstro.2024.1418298

  View details for PubMedID 41542231

  View details for PubMedCentralID PMC12802795

• Towards Video-Based Movement Biomarkers for Neuromuscular Diseases Uhlrich, S. D., Ruth, P. S., de Monts, C., Falisse, A., Muccini, J., Ataide, P., Day, J., Duong, T., Delp, S. L. edited by Pons, J. L., Tornero, J., Akay, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2024: 501-504

  View details for DOI 10.1007/978-3-031-77584-0_98

  View details for Web of Science ID 001462655900098

• OpenCap: Human movement dynamics from smartphone videos. PLoS computational biology Uhlrich, S. D., Falisse, A., Kidziński, Ł., Muccini, J., Ko, M., Chaudhari, A. S., Hicks, J. L., Delp, S. L. 2023; 19 (10): e1011462

  Abstract

  Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.

  View details for DOI 10.1371/journal.pcbi.1011462

  View details for PubMedID 37856442

  View details for PubMedCentralID PMC10586693

• Home-based Virtual Reality Therapy for Hand Recovery After Stroke. PM & R : the journal of injury, function, and rehabilitation Lansberg, M. G., Legault, C. n., MacLellan, A. n., Parikh, A. n., Muccini, J. n., Mlynash, M. n., Kemp, S. n., Buckwalter, M. S., Flavin, K. n. 2021

  Abstract

  Many stroke survivors suffer from arm and hand weakness, but there are only limited efficacious options for arm therapy available.To assess the feasibility of unsupervised home-based use of a virtual reality device (Smart Glove) for hand rehabilitation post stroke.Prospective single-arm study consisting of a 2-week run-in phase with no device use followed by an 8-week intervention period.Participants were recruited at the Stanford Neuroscience Outpatient Clinic.Twenty chronic stroke patients with upper extremity impairment.Participants were instructed to use the Smart Glove 50 minutes per day, 5 days per week for 8 weeks.We measured (1) compliance, (2) patients' impression of the intervention, and (3) efficacy measures including the upper extremity Fugl-Meyer (UE-FM), the Jebsen-Taylor hand function test (JTHFT) and the Stroke Impact Scale (SIS).Of 20 subjects, 7 (35%) met target compliance of 40 days use, and 6 (30%) used the device for 20-39 days. Eighty-five percent of subjects were satisfied with the therapy, with 80% reporting improvement in hand function. During the run-in phase there were no improvements in hand function. During the intervention, patients improved by a mean of 26.6 ± 48.8 seconds on the JTHFT (P = 0.03), by 16.1 ± 15.3 points on the hand-domain of the SIS (P < 0.01) and there was a trend towards improvement on the UE-FM (2.2 ± 5.5 points, P = 0.10).Unsupervised use of the Smart Glove in the home environment may improve hand/arm function in subacute/chronic stroke patients. A randomized controlled trial is needed to confirm these results. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1002/pmrj.12598

  View details for PubMedID 33773059

• Embodied Virtual Reality Mirror Visual Feedback for an Adult with Cerebral Palsy American Journal of Psychiatry and Neuroscience Bullock, K. D., Stevenson Won, A., Bailenson, J., Muccin, J., Paul, M., Bronte-Stewart, H. 2021; 9 (2): 59-67

  View details for DOI 10.11648/j.ajpn.20210902.16

• How to Design Woke Stroke Tech: The STORIES Project Eakin, M., Gian, A., Kim, F., Muccini, J., Lansberg, M., Flavin, K. LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for Web of Science ID 000590040201402