Literature DB >> 33782636

Cultural bias in motor function patterns: Potential relevance for predictive, preventive, and personalized medicine.

Karen Otte1,2, Tobias Ellermeyer1,3, Masahide Suzuki4, Hanna M Röhling1,2,5, Ryota Kuroiwa6, Graham Cooper1,5,7,8, Sebastian Mansow-Model2, Masahiro Mori4, Hanna Zimmermann1,5, Alexander U Brandt1,5,9, Friedemann Paul1,5,7,10, Shigeki Hirano4, Satoshi Kuwabara4, Tanja Schmitz-Hübsch1,5.   

Abstract

BACKGROUND: Quantification of motor performance has a promising role in personalized medicine by diagnosing and monitoring, e.g. neurodegenerative diseases or health problems related to aging. New motion assessment technologies can evolve into patient-centered eHealth applications on a global scale to support personalized healthcare as well as treatment of disease. However, uncertainty remains on the limits of generalizability of such data, which is relevant specifically for preventive or predictive applications, using normative datasets to screen for incipient disease manifestations or indicators of individual risks.
OBJECTIVE: This study explored differences between healthy German and Japanese adults in the performance of a short set of six motor tests.
METHODS: Six motor tasks related to gait and balance were recorded with a validated 3D camera system. Twenty-five healthy adults from Chiba, Japan, participated in this study and were matched for age, sex, and BMI to a sample of 25 healthy adults from Berlin, Germany. Recordings used the same technical setup and standard instructions and were supervised by the same experienced operator. Differences in motor performance were analyzed using multiple linear regressions models, adjusted for differences in body stature.
RESULTS: From 23 presented parameters, five showed group-related differences after adjustment for height and weight (R 2 between .19 and .46, p<.05). Japanese adults transitioned faster between sitting and standing and used a smaller range of hand motion. In stepping-in-place, cadence was similar in both groups, but Japanese adults showed higher knee movement amplitudes. Body height was identified as relevant confounder (standardized beta >.5) for performance of short comfortable and maximum speed walks. For results of posturography, regression models did not reveal effects of group or body stature.
CONCLUSIONS: Our results support the existence of a population-specific bias in motor function patterns in young healthy adults. This needs to be considered when motor function is assessed and used for clinical decisions, especially for personalized predictive and preventive medical purposes. The bias affected only the performance of specific items and parameters and is not fully explained by population-specific ethnic differences in body stature. It may be partially explained as cultural bias related to motor habits. Observed effects were small but are expected to be larger in a non-controlled cross-cultural application of motion assessment technologies with relevance for related algorithms that are being developed and used for data processing. In sum, the interpretation of individual data should be related to appropriate population-specific or even better personalized normative values to yield its full potential and avoid misinterpretation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-021-00236-3.
© The Author(s) 2021.

Entities:  

Keywords:  BMI; Balance; Cultural bias; Gait analysis; Motion capture; Motor biomarker; Neurodegenerative disorders; Personalized monitoring; Posturography; Predictive preventive personalized medicine (PPPM/3PM); Risk assessment; Sub-optimal health

Year:  2021        PMID: 33782636      PMCID: PMC7954970          DOI: 10.1007/s13167-021-00236-3

Source DB:  PubMed          Journal:  EPMA J        ISSN: 1878-5077            Impact factor:   6.543


  47 in total

1.  Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed.

Authors:  R I Spain; R J St George; A Salarian; M Mancini; J M Wagner; F B Horak; D Bourdette
Journal:  Gait Posture       Date:  2012-01-25       Impact factor: 2.840

2.  The diagnostic accuracy of static posturography in predicting accidental falls in people with multiple sclerosis.

Authors:  Luca Prosperini; Deborah Fortuna; Costanza Giannì; Laura Leonardi; Carlo Pozzilli
Journal:  Neurorehabil Neural Repair       Date:  2012-05-15       Impact factor: 3.919

3.  A comprehensive method for the translation and cross-cultural validation of health status questionnaires.

Authors:  Sonya L Eremenco; David Cella; Benjamin J Arnold
Journal:  Eval Health Prof       Date:  2005-06       Impact factor: 2.651

4.  Non-motor symptoms burden, mood, and gait problems are the most significant factors contributing to a poor quality of life in non-demented Parkinson's disease patients: Results from the COPPADIS Study Cohort.

Authors:  D Santos García; T de Deus Fonticoba; E Suárez Castro; C Borrué; M Mata; B Solano Vila; A Cots Foraster; M Álvarez Sauco; A B Rodríguez Pérez; L Vela; Y Macías; S Escalante; P Esteve; S Reverté Villarroya; E Cubo; E Casas; S Arnaiz; F Carrillo Padilla; M Pueyo Morlans; P Mir; P Martinez-Martin
Journal:  Parkinsonism Relat Disord       Date:  2019-07-29       Impact factor: 4.891

Review 5.  A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies.

Authors:  Alberto J Espay; Jeffrey M Hausdorff; Álvaro Sánchez-Ferro; Jochen Klucken; Aristide Merola; Paolo Bonato; Serene S Paul; Fay B Horak; Joaquin A Vizcarra; Tiago A Mestre; Ralf Reilmann; Alice Nieuwboer; E Ray Dorsey; Lynn Rochester; Bastiaan R Bloem; Walter Maetzler
Journal:  Mov Disord       Date:  2019-03-22       Impact factor: 10.338

6.  Individual changes in preclinical spinocerebellar ataxia identified via increased motor complexity.

Authors:  Winfried Ilg; Zofia Fleszar; Cornelia Schatton; Holger Hengel; Florian Harmuth; Peter Bauer; Dagmar Timmann; Martin Giese; Ludger Schöls; Matthis Synofzik
Journal:  Mov Disord       Date:  2016-10-26       Impact factor: 10.338

Review 7.  Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care.

Authors:  Matthew Barrett; Josiane Boyne; Julia Brandts; Hans-Peter Brunner-La Rocca; Lieven De Maesschalck; Kurt De Wit; Lana Dixon; Casper Eurlings; Donna Fitzsimons; Olga Golubnitschaja; Arjan Hageman; Frank Heemskerk; André Hintzen; Thomas M Helms; Loreena Hill; Thom Hoedemakers; Nikolaus Marx; Kenneth McDonald; Marc Mertens; Dirk Müller-Wieland; Alexander Palant; Jens Piesk; Andrew Pomazanskyi; Jan Ramaekers; Peter Ruff; Katharina Schütt; Yash Shekhawat; Chantal F Ski; David R Thompson; Andrew Tsirkin; Kay van der Mierden; Chris Watson; Bettina Zippel-Schultz
Journal:  EPMA J       Date:  2019-11-22       Impact factor: 6.543

Review 8.  The myth of generalisability in clinical research and machine learning in health care.

Authors:  Joseph Futoma; Morgan Simons; Trishan Panch; Finale Doshi-Velez; Leo Anthony Celi
Journal:  Lancet Digit Health       Date:  2020-08-24

9.  Instrumental Assessment of Stepping in Place Captures Clinically Relevant Motor Symptoms of Parkinson's Disease.

Authors:  Karen Otte; Tobias Ellermeyer; Tim-Sebastian Vater; Marlen Voigt; Daniel Kroneberg; Ludwig Rasche; Theresa Krüger; Hanna Maria Röhling; Bastian Kayser; Sebastian Mansow-Model; Fabian Klostermann; Alexander Ulrich Brandt; Friedemann Paul; Axel Lipp; Tanja Schmitz-Hübsch
Journal:  Sensors (Basel)       Date:  2020-09-23       Impact factor: 3.576

Review 10.  Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures.

Authors:  Ken J Kubota; Jason A Chen; Max A Little
Journal:  Mov Disord       Date:  2016-08-08       Impact factor: 10.338

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  1 in total

1.  Proposal for Post Hoc Quality Control in Instrumented Motion Analysis Using Markerless Motion Capture: Development and Usability Study.

Authors:  Hanna Marie Röhling; Patrik Althoff; Radina Arsenova; Daniel Drebinger; Norman Gigengack; Anna Chorschew; Daniel Kroneberg; Maria Rönnefarth; Tobias Ellermeyer; Sina Cathérine Rosenkranz; Christoph Heesen; Behnoush Behnia; Shigeki Hirano; Satoshi Kuwabara; Friedemann Paul; Alexander Ulrich Brandt; Tanja Schmitz-Hübsch
Journal:  JMIR Hum Factors       Date:  2022-04-01
  1 in total

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