I am a postdoctoral scholar at Stanford University in the Neuromuscular Biomechanics Laboratory, where I also received my PhD in Bioengineering. I'm passionate about monitoring, improving, and motivating movement and increasing access to health care with digital technology. My research bridges the fields of biomechanics, psychology, and computer science to understand not just how we move, but how we think about movement and our motivation for being physically active. I value human-centered design and a holistic, lifestyle-focused approach to engineering and medicine. I enjoy cultivating creativity and humor in work and life, sharing ideas, and communicating science, particularly on my podcast, Biomechanics On Our Minds (my mom says it's her favorite biomechanics podcast).

Professional Education

  • Doctor of Philosophy, Stanford University, BIOE-PHD (2022)
  • Master of Science, Stanford University, BIOE-MS (2018)

Stanford Advisors

All Publications

  • Smartphone videos of the sit-to-stand test predict osteoarthritis and health outcomes in a nationwide study. NPJ digital medicine Boswell, M. A., Kidziński, Ł., Hicks, J. L., Uhlrich, S. D., Falisse, A., Delp, S. L. 2023; 6 (1): 32


    Physical function decline due to aging or disease can be assessed with quantitative motion analysis, but this currently requires expensive laboratory equipment. We introduce a self-guided quantitative motion analysis of the widely used five-repetition sit-to-stand test using a smartphone. Across 35 US states, 405 participants recorded a video performing the test in their homes. We found that the quantitative movement parameters extracted from the smartphone videos were related to a diagnosis of osteoarthritis, physical and mental health, body mass index, age, and ethnicity and race. Our findings demonstrate that at-home movement analysis goes beyond established clinical metrics to provide objective and inexpensive digital outcome metrics for nationwide studies.

    View details for DOI 10.1038/s41746-023-00775-1

    View details for PubMedID 36871119

    View details for PubMedCentralID 5009047

  • Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective. Annual review of public health Hicks, J. L., Boswell, M. A., Althoff, T., Crum, A. J., Ku, J. P., Landay, J. A., Moya, P. M., Murnane, E. L., Snyder, M. P., King, A. C., Delp, S. L. 2022


    Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as an exemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors. Expected final online publication date for the Annual Review of Public Health, Volume 44 is April 2023. Please see for revised estimates.

    View details for DOI 10.1146/annurev-publhealth-060220-041643

    View details for PubMedID 36542772

  • Personalization improves the biomechanical efficacy of foot progression angle modifications in individuals with medial knee osteoarthritis. Journal of biomechanics Uhlrich, S. D., Kolesar, J. A., Kidzinski, L., Boswell, M. A., Silder, A., Gold, G. E., Delp, S. L., Beaupre, G. S. 2022; 144: 111312


    Modifying the foot progression angle during walking can reduce the knee adduction moment, a surrogate measure of medial knee loading. However, not all individuals reduce their knee adduction moment with the same modification. This study evaluates whether a personalized approach to prescribing foot progression angle modifications increases the proportion of individuals with medial knee osteoarthritis who reduce their knee adduction moment, compared to a non-personalized approach. Individuals with medial knee osteoarthritis (N=107) walked with biofeedback instructing them to toe-in and toe-out by 5° and 10° relative to their self-selected angle. We selected individuals' personalized foot progression angle as the modification that maximally reduced their larger knee adduction moment peak. Additionally, we used lasso regression to identify which secondary kinematic changes made a 10° toe-in gait modification more effective at reducing the first knee adduction moment peak. Seventy percent of individuals reduced their larger knee adduction moment peak by at least 5% with a personalized foot progression angle modification, which was more than (p≤0.002) the 23-57% of individuals who reduced it with a uniformly assigned 5° or 10° toe-in or toe-out modification. When toeing-in, greater reductions in the first knee adduction moment peak were related to an increased frontal-plane tibia angle (knee more medial than ankle), a more valgus knee abduction angle, reduced contralateral pelvic drop, and a more medialized center of pressure in the foot reference frame. In summary, personalization increases the proportion of individuals with medial knee osteoarthritis who may benefit from a foot progression angle modification.

    View details for DOI 10.1016/j.jbiomech.2022.111312

    View details for PubMedID 36191434

  • Mindset is associated with future physical activity and management strategies in individuals with knee osteoarthritis. Annals of physical and rehabilitation medicine Boswell, M. A., Evans, K. M., Zion, S. R., Boles, D. Z., Hicks, J. L., Delp, S. L., Crum, A. J. 2022; 65 (6): 101634


    Despite the benefits of physical activity for individuals with knee osteoarthritis (KOA), physical activity levels are low in this population.We conducted a repeated cross-sectional study to compare mindset about physical activity among individuals with and without KOA and to investigate whether mindset relates to physical activity.Participants with (n = 150) and without (n = 152) KOA completed an online survey at enrollment (T1). Participants with KOA repeated the survey 3 weeks later (T2; n = 62). The mindset questionnaire, scored from 1 to 4, assessed the extent to which individuals associate the process of exercising with less appeal-focused qualities (e.g., boring, painful, isolating, and depriving) versus appeal-focused (e.g., fun, pleasurable, social, and indulgent). Using linear regression, we examined the relationship between mindset and having KOA, and, in the subgroup of KOA participants, the relationship between mindset at T1 and self-reported physical activity at T2. We also compared mindset between people who use medication for management and those who use exercise.Within the KOA group, a more appeal-focused mindset was associated with higher future physical activity (β=38.72, p = 0.006) when controlling for demographics, health, and KOA symptoms. Individuals who used exercise with or without pain medication or injections had a more appeal-focused mindset than those who used medication or injections without exercise (p<0.001). A less appeal-focused mindset regarding physical activity was not significantly associated with KOA (β = -0.14, p = 0.067). Further, the mindset score demonstrated strong internal consistency (α = 0.92; T1; n = 150 and α = 0.92; T2; n = 62) and test-retest reliability (intraclass correlation coefficient (ICC) > 0.84, p < 0.001) within the KOA sample.In individuals with KOA, mindset is associated with future physical activity levels and relates to the individual's management strategy. Mindset is a reliable and malleable construct and may be a valuable target for increasing physical activity and improving adherence to rehabilitation strategies involving exercise among individuals with KOA.

    View details for DOI 10.1016/

    View details for PubMedID 35091113

  • Biceps femoris long head sarcomere and fascicle length adaptations after three weeks of eccentric exercise training. Journal of sport and health science Pincheira, P. A., Boswell, M. A., Franchi, M. V., Delp, S. L., Lichtwark, G. A. 2021


    BACKGROUND: Eccentric exercise increases muscle fascicle lengths; however, the mechanisms behind this adaptation are still unknown. This study aimed to determine whether biceps femoris long head (BFlh)1 fascicle length increases in response to 3 weeks of eccentric exercise training are the result of an in-series addition of sarcomeres within the muscle fibers.METHODS: Ten recreationally active participants (age: 27 ± 3 years; mass: 70 ± 14 kg; height: 174 ± 9 cm; mean ± SD) completed 3 weeks of Nordic hamstring exercise (NHE)1 training on a custom exercise device that was instrumented with load cells. We collected in vivo sarcomere and muscle fascicle images of the BFlh in 2 regions (central and distal), using microendoscopy and 3D ultrasonography. We then estimated sarcomere length, sarcomere number, and fascicle length before and after the training intervention.RESULTS: Eccentric knee flexion strength increased after the training (15%; p < 0.001; etap2 = 0.75). Further, we found a significant increase in fascicle length (21%; p < 0.001; etap2 = 0.81) and sarcomere length (17%; p < 0.001; etap2 = 0.90) in the distal but not in the central portion of the muscle. The estimated number of sarcomeres in series did not change in either region.CONCLUSION: Fascicle length adaptations appear to be heterogeneous in the BFlh in response to 3 weeks of NHE training. An increase in sarcomere length, rather than the addition of sarcomeres in series, appears to underlie increases in fascicle length in the distal region of the BFlh. The mechanism driving regional increases in fascicle and sarcomere length remains unknown, but we speculate it may be driven by regional changes in the passive tension of muscle or connective tissue adaptations.

    View details for DOI 10.1016/j.jshs.2021.09.002

    View details for PubMedID 34509714

  • A neural network to predict the knee adduction moment in patients with osteoarthritis using anatomical landmarks obtainable from 2D video analysis. Osteoarthritis and cartilage Boswell, M. A., Uhlrich, S. D., Kidzinski, L., Thomas, K., Kolesar, J. A., Gold, G. E., Beaupre, G. S., Delp, S. L. 2021


    OBJECTIVE: The knee adduction moment (KAM) can inform treatment of medial knee osteoarthritis; however, measuring the KAM requires an expensive gait analysis laboratory. We evaluated the feasibility of predicting the peak KAM during natural and modified walking patterns using the positions of anatomical landmarks that could be identified from video analysis.METHOD: Using inverse dynamics, we calculated the KAM for 86 individuals (64 with knee osteoarthritis, 22 without) walking naturally and with foot progression angle modifications. We trained a neural network to predict the peak KAM using the 3-dimensional positions of 13 anatomical landmarks measured with motion capture (3D neural network). We also trained models to predict the peak KAM using 2-dimensional subsets of the dataset to simulate 2-dimensional video analysis (frontal and sagittal plane neural networks). Model performance was evaluated on a held-out, 8-person test set that included steps from all trials.RESULTS: The 3D neural network predicted the peak KAM for all test steps with r2=0.78. This model predicted individuals' average peak KAM during natural walking with r2=0.86 and classified which 15° foot progression angle modifications reduced the peak KAM with accuracy=0.85. The frontal plane neural network predicted peak KAM with similar accuracy (r2=0.85) to the 3D neural network, but the sagittal plane neural network did not (r2=0.14).CONCLUSION: Using the positions of anatomical landmarks from motion capture, a neural network accurately predicted the peak KAM during natural and modified walking. This study demonstrates the feasibility of measuring the peak KAM using positions obtainable from 2D video analysis.

    View details for DOI 10.1016/j.joca.2020.12.017

    View details for PubMedID 33422707