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Framework for a short muscle function battery using electronic handgrip dynamometry and accelerometry in older adults
Ryan McGrath1, Grant R Tomkinson2, Sarah Andrew3
1Healthy Aging North Dakota (HAND), North Dakota State University, Fargo, ND, USA; Department of Health, Nutrition, and Exercise Sciences, North Dakota State University, Fargo, ND, USA,; Department of Geriatrics, University of North Dakota, Grand Forks, ND, USA; Alliance for Research in Exercise, Nutrition, and Activity (ARENA), Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia; Fargo VA Healthcare System, Fargo, ND, USA.
Background:
Electronic handgrip dynamometry and accelerometry enables novel opportunities to collect additional attributes of muscle function beyond just maximal strength, but some muscle function attributes may already be related, which may warrant discerning these attributes into a short muscle function battery (SMFB).
Objectives:
We sought to determine the multivariate relationships between maximal strength, asymmetry, submaximal control, rate of force development, bimanual coordination, fatigability, and contractile steadiness in older adults.
Design:
A cross-sectional design was used for this investigation.
Setting:
Laboratory.
Participants:
The analytic sample included 121 generally healthy older adults aged 70.7 ± 4.7 years.
Measurements:
Electronic handgrip dynamometry and accelerometry measured strength, asymmetry, submaximal control, rate of force development, bimanual coordination, fatigability, and contractile steadiness. The handgrip variables were standardized before they were included in a factor analysis. Factors with eigenvalues >1.0 were kept. Items within a factor with a loading |>0.30| were similarly retained.
Results:
There were 3 factors retained with eigenvalues of 1.88, 1.56, and 1.10. The first factor (functional strength), which explained 39.9 % of the variance, included strength, submaximal control, and rate of force development. Factor 2 (lateral function), which explained 35.8 % of the variance, included asymmetry and bimanual coordination. The third factor (muscle endurance), which explained 24.3 % of the variance, included fatigability and contractile steadiness.
Conclusions:
Our findings suggest the surfacing of themes in the additional muscle function measures, thereby providing framework for a SMFB. More research is needed for electronic handgrip dynamometry and accelerometry derived muscle function on health before consideration of implementation in clinical practice.
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