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

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 (R2 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.

RGB-Depth Camera-Based Assessment of Motor Capacity: Normative Data for Six Standardized Motor Tasks

“…While we found low inter-individual variability and negligible confounding for transition times in our sample, differences in transition times were previously described between age-groups 25 and younger and 70 and older [ 16 , 20 ]. Furthermore, possible cultural bias has been observed for this task [ 25 ].…”

“…The Microsoft Kinect SDK (version 2.0.14) enables extraction of 25 three-dimensional time series of body landmarks from these recordings, which were used to extract spatiotemporal parameters with custom algorithms. Here, we extracted 43 parameters ( Table 2 ), most of which have been previously introduced [ 22 , 23 , 25 , 26 , 27 , 28 ]. Others were carefully vetted in terms of clinical interest and statistical properties when tested in an independent dataset [ 22 ].…”

“…For this study, a motion analysis system based on the use of a single RGB-Depth camera (Microsoft Kinect v2) was employed, which has been evaluated for accuracy and reliability [ 11 , 22 ] and has been used in various clinical populations [ 5 , 23 , 24 ]. Previous related works from our groups include outcome parameters from healthy controls [ 23 , 25 , 26 , 27 , 28 , 29 ], but these do not represent a robust normative database by themselves due to likewise limited sample sizes, restriction to single motor tasks, or general study design.…”

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

“…The latter 3 tasks were excluded from this study, as evaluation algorithms were still in an explorative stage at the time, yielding premature claims regarding data quality. A description of the remaining tasks except POCO-DUAL can be found in Otte et al [ 7 , 30 ]. POCO-DUAL equates to POCO with the addition of a cognitive task (Serial 3’s subtraction).…”

“…In this study, we used data acquired with the emerging Motognosis Labs system (Motognosis GmbH) that extracts kinematic parameters from depth camera recordings. In recent years, this system was extensively used in a research context at our site and our cooperating sites [ 24 - 29 ] with a standardized protocol for short motor tasks specifically designed to assess motor capacities of people with multiple sclerosis (MS) [ 7 , 30 ]. Regarding preventive QC, previously established SOPs for system operators and patient instructions were used for all data analyzed herein.…”