Project overview
The current study aimed to identify the most sensitive combination of non-invasive biomarkers and hypothesised that novel technologies would improve the discriminant validity of motor function testing.
In 138 self-reported healthy males and females (65 young, mean age ± SD = 25.7±4.8 years; 73 older, 74.9±5.9 years), nine tests (totalling 25 parameters) were conducted: timed up and go; stair climbing test; balance; quadriceps strength test; hand grip strength; respiratory muscle strength (peak flow); thigh composition using ultrasound imaging; muscle tone and mechanical properties of rectus femoris and biceps brachii using the MyotonPRO device; kinematic parameters of upper limb function using the motor task manager. Four questionnaires were administered: health related quality of life (SF36), Physical Activity Scale for the Elderly, Rivermead Mobility Index and the Tinetti Performance Oriented Mobility Assessment scale. Four experiments tested hypotheses regarding the influence of recording conditions on MyotonPRO parameters.
Reliability of tests was confirmed using parametric statistics. As expected, all tests indicated reduced motor function with ageing (independent t-tests), with the majority showing gender differences. MyotonPRO parameters were significantly influenced by testing site, muscle length, contractile status and prior activity. More tests correlated with grip strength (HGS) than SF36, and strongest predictors of HGS from multiple regression analyses were strength and characteristics of other muscles. Seven of the 25 parameters had high discriminant ability for classifying healthy adults into their respective groups, analysed by linear discriminant function using a stepwise approach. Novel technologies, notably mechanical properties of muscle and ultrasound imaging of thigh composition, improved classification accuracy (from 75% to 89%) when combined with conventional tests, supporting the hypothesis.
A comprehensive battery of non-invasive biomarkers with high discriminant ability for indicating musculoskeletal health status was developed with reference data for comparison with clinical populations. Variables associated with strength, muscle size and mechanical properties were most important when classifying healthy people, highlighting the contribution of these novel technologies and the need to assess and monitor these aspects of physical function during routine clinical assessments. Additionally, five of the novel biomarkers do not require participant effort, making them suitable for testing those with pain or cognitive impairment.
In 138 self-reported healthy males and females (65 young, mean age ± SD = 25.7±4.8 years; 73 older, 74.9±5.9 years), nine tests (totalling 25 parameters) were conducted: timed up and go; stair climbing test; balance; quadriceps strength test; hand grip strength; respiratory muscle strength (peak flow); thigh composition using ultrasound imaging; muscle tone and mechanical properties of rectus femoris and biceps brachii using the MyotonPRO device; kinematic parameters of upper limb function using the motor task manager. Four questionnaires were administered: health related quality of life (SF36), Physical Activity Scale for the Elderly, Rivermead Mobility Index and the Tinetti Performance Oriented Mobility Assessment scale. Four experiments tested hypotheses regarding the influence of recording conditions on MyotonPRO parameters.
Reliability of tests was confirmed using parametric statistics. As expected, all tests indicated reduced motor function with ageing (independent t-tests), with the majority showing gender differences. MyotonPRO parameters were significantly influenced by testing site, muscle length, contractile status and prior activity. More tests correlated with grip strength (HGS) than SF36, and strongest predictors of HGS from multiple regression analyses were strength and characteristics of other muscles. Seven of the 25 parameters had high discriminant ability for classifying healthy adults into their respective groups, analysed by linear discriminant function using a stepwise approach. Novel technologies, notably mechanical properties of muscle and ultrasound imaging of thigh composition, improved classification accuracy (from 75% to 89%) when combined with conventional tests, supporting the hypothesis.
A comprehensive battery of non-invasive biomarkers with high discriminant ability for indicating musculoskeletal health status was developed with reference data for comparison with clinical populations. Variables associated with strength, muscle size and mechanical properties were most important when classifying healthy people, highlighting the contribution of these novel technologies and the need to assess and monitor these aspects of physical function during routine clinical assessments. Additionally, five of the novel biomarkers do not require participant effort, making them suitable for testing those with pain or cognitive impairment.
Staff
Lead researchers
Other researchers
Research outputs
Sandra Agyapong-Badu, Martin Warner, Dinesh Samuel, Vasiliki Koutra & Maria Stokes,
2022
Type: conference
Sandra Agyapong-Badu, Martin Warner, Dinesh Samuel & Maria Stokes,
2018, Journal of Musculoskeletal Research, 21(2)
Type: article