Identification of Latent Classes of Motor Performance in a Heterogenous Population of Adults

Allison S Hyngstrom; Chi C Cho; Reivian Berrios Barillas; Mukta Joshi; Taylor W Rowley; Kevin G Keenan; John Staudenmayer; Ann M Swartz; Scott J Strath

doi:10.1016/j.arrct.2020.100080

Identification of Latent Classes of Motor Performance in a Heterogenous Population of Adults

Arch Rehabil Res Clin Transl. 2020 Sep 19;2(4):100080. doi: 10.1016/j.arrct.2020.100080. eCollection 2020 Dec.

Authors

Allison S Hyngstrom¹, Chi C Cho², Reivian Berrios Barillas³, Mukta Joshi⁴, Taylor W Rowley⁵, Kevin G Keenan^{2

4}, John Staudenmayer⁶, Ann M Swartz^{2

4}, Scott J Strath^{2

4}

Affiliations

¹ Department of Physical Therapy, Marquette University, Milwaukee, WI.
² Center for Aging and Translational Research, University of Wisconsin-Milwaukee, Milwaukee, WI.
³ Department of Occupational Therapy, Concordia University, Mequon, WI.
⁴ Department of Kinesiology, University of Wisconsin Milwaukee, Milwaukee, WI.
⁵ Department of Kinesiology, Saginaw Valley State University, Saginaw, MI.
⁶ Department of Math and Statistics, University of Massachusetts, Amherst, MA.

Abstract

Objective: To determine classes of motor performance based on community deployable motor impairment and functional tests in a heterogeneous adult population.

Design: Sixteen tests of limb-specific and whole-body measures of motor impairment and function were obtained. Linear regression analysis was used to dichotomize performance on each test as falling within or outside the age- and sex-predicted values. Latent class analysis was used to determine 3 classes of motor performance. The chi-square test of association and the Fisher exact test were used for categorical variables, and analysis of variance and the Kruskal-Wallis test were used for continuous variables to evaluate the relationship between demographic characteristics and latent classes.

Setting: General community.

Participants: Individuals (N=118; 50 men) participated in the study. Quota sampling was used to recruit individuals who self-identified as healthy (n=44) or currently living with a preexisting chronic health condition, including arthritis (n=19), multiple sclerosis (n=18), Parkinson disease (n=17), stroke (n=18), or low functioning (n=2).

Intervention: Not applicable.

Main outcome measure: Latent classes of motor performance.

Results: Across the entire sample, 3 latent classes of motor performance were determined that clustered individuals with motor performance falling: (1) within predicted values on most of the tests (expected class), (2) outside predicted values on some of the tests (moderate class), and (3) outside predicted values on most of the tests (severe class).The ability to distinguish between the respective classes based on the percent chance of falling outside predicted values was achieved using the following community deployable motor performance tests: 10-meter walk test (22%, 80%, and 100%), 6-minute walk test (14.5%, 37.5%, and 100%), grooved pegboard test (23%, 38%, and 100%), and modified physical performance test (3%, 54%, and 96%).

Conclusions: In this heterogeneous group of adults, we found 3 distinct classes of motor performance, with the sample clustering into an expected test score group, a moderate test score deficiency group, and a severed test score deficiency group. Based on the motor performance tests, we established that community deployable, easily administered testing could accurately predict the established clusters of motor performance.

Keywords: 10MWT, 10-meter walk test; 6MWT, 6-minute walk test; BBS, Berg Balance Scale; BBT, Box and Block Test; DGI, Dynamic Gait Index; Functionally-Impaired, Rehabilitation; LCA, latent class analysis; MMT, manual muscle test; MPPT, modified physical performance test; MVC, maximal voluntary contraction; PEGB, grooved pegboard test.

Abstract

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