[en] [en] AIM: To investigate whether a smartphone-based gait analysis tool can reliably output gait quality parameters that can be cross-analyzed to establish individual & disease-based changes in gait quality patterns.
METHODS: A cross-sectional study made up of a 48-patients undergoing disability certification at the "Dr. José Castro Villagrana" or the "Dr. David Fragoso Lizalde" Health Centers in Mexico City, Mexico. Their sensorimotor performance was evaluated through an in-house smartphone/IMU based digital tool. Gait was analyzed by means of frequency analysis of the acceleration of the body mass measured at the sternum. A composite gait quality score was determined through principal component analysis based primarily on the explainability and uniformity of gait. Quality independence against demographic variables (age & weight) was tested through ANCOVA. The association between gait quality and gait parameters was analyzed by using multiple linear regression.
RESULTS: A multiple regression model developed with a limited set of gait quality parameters successfully predicted gait smoothness with a 97.05 % accuracy with a mean square error of 0.085 between predicted and actual quality scores. The model demonstrates different predictive capacities across disease groups, with Osteoarthrosis + Osteoporosis having the highest R2 at 0.98 (p < 0.001) and Coxarthrosis having the lowest explained R2 at 0.79 (p < 0.001).
CONCLUSIONS: The assessment of gait quality, in family medicine, with low-cost digital tools is an area of opportunity yet to be explored. This tool can potentially disrupt the current disability workflow between primary and specialty care to have an objective method of assessing gait within a clinical consult. Individual patient-level benchmarking can give us insights into the patient's disease status, develop practical intervention strategies, and control the cost and quality of medical care by predicting an individualized course of disability or rehabilitation. Further studies are needed to validate digital gait assessments as clinical decision support tools for day-to-day clinical operations. MESH: Gait Analysis, Smartphone, Primary Health Care, Osteoarthrosis.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
CASTRO MEJIA, Alan ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Digital Medicine
Gulde, Philipp; Specialist Clinic for Neurology Medical Park Loipl, Bischofswiesen, Germany, Technical University of Munich, TUM School of Medicine & Health, Department Health & Sport Sciences, Chair of Human Movement Science, Munich, Germany
González Salinas, Consuelo; José Castro Villagrana Health Center, Mexico City, Mexico, Universidad Nacional Autónoma de México, Postgraduate Studies Division, Mexico City, Mexico
External co-authors :
yes
Language :
English
Title :
A clinical application of gait quality patterns in osteoarthritis.
Peña Ayala, A.H., Fernández-López, J.C., Prevalencia y factores de riesgo de la osteoartritis. Reumatol. Cl. ínica 3 (2007), 6–12.
C. Lavalle Montalvo, Osteoartritis, Med. Salud (2010). 〈http://www.medicinaysalud.unam.mx/temas/2010/06_jun_2k10.pdf〉.
Estadísticas a Proposito del Día Internacional de las Personas con Discapacidad (Datos Nacionales), INEGI, Mexico City, 2021. 〈https://www.inegi.org.mx/contenidos/saladeprensa/aproposito/2021/EAP_persdiscap21.pdf〉.
Tateuchi, H., Koyama, Y., Akiyama, H., Goto, K., So, K., Kuroda, Y., Ichihashi, N., Daily cumulative hip moment is associated with radiographic progression of secondary hip osteoarthritis. Osteoarthr. Cartil. 25 (2017), 1291–1298, 10.1016/j.joca.2017.02.796.
Bączkowicz, D., Skiba, G., Czerner, M., Majorczyk, E., Gait and functional status analysis before and after total knee arthroplasty. Knee 25 (2018), 888–896, 10.1016/j.knee.2018.06.004.
Duffell, L.D., Jordan, S.J., Cobb, J.P., McGregor, A.H., Gait adaptations with aging in healthy participants and people with knee-joint osteoarthritis. Gait Posture 57 (2017), 246–251, 10.1016/j.gaitpost.2017.06.015.
Dostanpor, A., Dobson, C.A., Vanicek, N., Relationships between walking speed, T-score and age with gait parameters in older post-menopausal women with low bone mineral density. Gait Posture 64 (2018), 230–237, 10.1016/j.gaitpost.2018.05.005.
La Obesidad en México, (2016). 〈https://www.gob.mx/issste/articulos/la-obesidad-en-mexico〉.
Health in the Americas+, 2017 Edition. Summary: Regional Outlook and Country Profiles., OPS, Washington, 2017. 〈https://www.paho.org/salud-en-las-americas-2017/wp-content/uploads/2017/09/Print-Version-Spanish.pdf〉.
Caderby, T., Caron, N., Verkindt, C., Bonazzi, B., Dalleau, G., Peyrot, N., Obesity-related alterations in anticipatory postural mechanisms associated with gait initiation. Exp. Brain Res 238 (2020), 2557–2567, 10.1007/s00221-020-05914-8.
Maktouf, W., Durand, S., Boyas, S., Pouliquen, C., Beaune, B., Interactions among obesity and age-related effects on the gait pattern and muscle activity across the ankle joint. Exp. Gerontol., 140, 2020, 111054, 10.1016/j.exger.2020.111054.
Lockhart, T.E., Frames, C.W., Soangra, R., Lieberman, A., Effects of obesity and fall risk on gait and posture of community-dwelling older adults. Int J. Progn. Health Manag, 10, 2019.
Zhang, C., Greve, C., Verkerke, G.J., Roossien, C.C., Houdijk, H., Hijmans, J.M., Pilot validation study of inertial measurement units and markerless methods for 3D neck and trunk kinematics during a simulated surgery task. Sensors, 22, 2022, 8342, 10.3390/s22218342.
Wade, L., Needham, L., McGuigan, P., Bilzon, J., Applications and limitations of current markerless motion capture methods for clinical gait biomechanics. PeerJ, 10, 2022, e12995, 10.7717/peerj.12995.
Clavijo-Buendía, S., Molina-Rueda, F., Martín-Casas, P., Ortega-Bastidas, P., Monge-Pereira, E., Laguarta-Val, S., Morales-Cabezas, M., Cano-de-la-Cuerda, R., Construct validity and test-retest reliability of a free mobile application for spatio-temporal gait analysis in Parkinson's disease patients. Gait Posture 79 (2020), 86–91, 10.1016/j.gaitpost.2020.04.004.
Gulde, P., Hermsdörfer, J., Rieckmann, P., Speed but not smoothness of gait reacts to rehabilitation in multiple sclerosis. Mult. Scler. Int, 2021, 2021, 5589562, 10.1155/2021/5589562.
Suri, A., Rosso, A.L., VanSwearingen, J., Coffman, L.M., Redfern, M.S., Brach, J.S., Sejdić, E., Mobility of older adults: gait quality measures are associated with life-space assessment scores. J. Gerontol. Ser. A 76 (2021), e299–e306, 10.1093/gerona/glab151.
Shin, S.Y., Lee, R.K., Spicer, P., Sulzer, J., Does kinematic gait quality improve with functional gait recovery? A longitudinal pilot study on early post-stroke individuals. J. Biomech., 105, 2020, 109761, 10.1016/j.jbiomech.2020.109761.
Fransen, B.L., Pijnappels, M., Butter, I.K., Burger, B.J., Van Dieën, J.H., Hoozemans, M.J.M., Patients’ perceived walking abilities, daily-life gait behavior and gait quality before and 3 months after total knee arthroplasty. Arch. Orthop. Trauma Surg. 142 (2022), 1189–1196, 10.1007/s00402-021-03915-y.
Carpinella, I., Gervasoni, E., Anastasi, D., Di Giovanni, R., Tacchino, A., Brichetto, G., Confalonieri, P., Rovaris, M., Solaro, C., Ferrarin, M., Cattaneo, D., Instrumentally assessed gait quality is more relevant than gait endurance and velocity to explain patient-reported walking ability in early-stage multiple sclerosis. Eur. J. Neurol. 28 (2021), 2259–2268, 10.1111/ene.14866.
Schootemeijer, S., Weijer, R.H.A., Hoozemans, M.J.M., Van Schooten, K.S., Delbaere, K., Pijnappels, M., Association between daily-life gait quality characteristics and physiological fall risk in older people. Sensors, 20, 2020, 5580, 10.3390/s20195580.
Shema-Shiratzky, S., Hillel, I., Mirelman, A., Regev, K., Hsieh, K.L., Karni, A., Devos, H., Sosnoff, J.J., Hausdorff, J.M., A wearable sensor identifies alterations in community ambulation in multiple sclerosis: contributors to real-world gait quality and physical activity. J. Neurol. 267 (2020), 1912–1921, 10.1007/s00415-020-09759-7.
Faul, F., Erdfelder, E., Lang, A.-G., Buchner, A., G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39 (2007), 175–191, 10.3758/BF03193146.
P. Gulde, Neuro Assessment Project, (2019).
International Physical Activity Questionnaire - Short Form, (2019). 〈https://youthrex.com/wp-content/uploads/2019/10/IPAQ-TM.pdf〉.
Gulde, P., Hermsdörfer, J., Smoothness metrics in complex movement tasks. Front. Neurol., 9, 2018, 10.3389/fneur.2018.00615.
Gulde, P., Hermsdörfer, J., Rieckmann, P., Introduction of the Watzmann severity scale: a sensorimotor approach to estimate the course of inpatient rehabilitation in multiple sclerosis. Mult. Scler. Relat. Disord., 48, 2021, 102674, 10.1016/j.msard.2020.102674.
Gulde, P., Vojta, H., Schmidle, S., Rieckmann, P., Hermsdörfer, J., Going beyond PA: Assessing sensorimotor capacity with wearables in multiple sclerosis—a cross-sectional study. J. Neuroeng. Rehabil., 20, 2023, 123, 10.1186/s12984-023-01247-z.
Gulde, P., Hermsdörfer, J., Rieckmann, P., Sensorimotor function does not predict quality of life in persons with multiple sclerosis. Mult. Scler. Relat. Disord., 52, 2021, 102986, 10.1016/j.msard.2021.102986.
Gulde, P., Hermsdörfer, J., Rieckmann, P., Inpatient rehabilitation: prediction of changes in sensorimotor performance in multiple sclerosis: a pilot study. J. Clin. Med, 10, 2021, 10.3390/jcm10102177.
Balasubramanian, S., Melendez-Calderon, A., Burdet, E., A robust and sensitive metric for quantifying movement smoothness. IEEE Trans. Biomed. Eng. 59 (2012), 2126–2136, 10.1109/TBME.2011.2179545.
Pinto, C., Schuch, C.P., Balbinot, G., Salazar, A.P., Hennig, E.M., Kleiner, A.F.R., Pagnussat, A.S., Movement smoothness during a functional mobility task in subjects with Parkinson's disease and freezing of gait – an analysis using inertial measurement units. J. Neuroeng. Rehabil., 16, 2019, 110, 10.1186/s12984-019-0579-8.
Bujang, M.A., Baharum, N., A simplified guide to determination of sample size requirements for estimating the value of intraclass correlation coefficient: a review. Arch. Orofac. Sci. 12 (2017), 1–11.
Koo, T.K., Li, M.Y., A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med 15 (2016), 155–163, 10.1016/j.jcm.2016.02.012.
Murtagh, E.M., Mair, J.L., Aguiar, E., Tudor-Locke, C., Murphy, M.H., Outdoor walking speeds of apparently healthy adults: a systematic review and meta-analysis. Sports Med 51 (2021), 125–141, 10.1007/s40279-020-01351-3.
Nguyen, T., Gad, E., Wilson, J., Mitigating footfall- induced vibration in long-span floors. Aust. J. Struct. Eng. 15 (2014), 97–109, 10.7158/S12-061.2014.15.1.
Ismailidis, P., Hegglin, L., Egloff, C., Pagenstert, G., Kernen, R., Eckardt, A., Ilchmann, T., Nüesch, C., Mündermann, A., Side to side kinematic gait differences within patients and spatiotemporal and kinematic gait differences between patients with severe knee osteoarthritis and controls measured with inertial sensors. Gait Posture 84 (2021), 24–30, 10.1016/j.gaitpost.2020.11.015.
Shirai, S., Yabe, I., Matsushima, M., Ito, Y.M., Yoneyama, M., Sasaki, H., Quantitative evaluation of gait ataxia by accelerometers. J. Neurol. Sci. 358 (2015), 253–258, 10.1016/j.jns.2015.09.004.
Horst, F., Mildner, M., Schöllhorn, W.I., One-year persistence of individual gait patterns identified in a follow-up study – a call for individualised diagnose and therapy. Gait Posture 58 (2017), 476–480, 10.1016/j.gaitpost.2017.09.003.
Halilaj, E., Le, Y., Hicks, J.L., Hastie, T.J., Delp, S.L., Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative. Osteoarthr. Cartil. 26 (2018), 1643–1650, 10.1016/j.joca.2018.08.003.
