It is vastly more important to create protocols that produce this Minimal Detectable Change. Where is that work being done? Or is everyone in stroke so fucking incompetent that they can't see how to solve stroke?
Minimal detectable change of gait and balance measures in older neurological patients: estimating the standard error of the measurement from before-after rehabilitation data thanks to the linear mixed-effects models
Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 44 (2024)
Abstract
Background
Tracking gait and balance impairment in time is paramount in the care of older neurological patients. The Minimal Detectable Change (MDC), built upon the Standard Error of the Measurement (SEM), is the smallest modification of a measure exceeding the measurement error. Here, a novel method based on linear mixed-effects models (LMMs) is applied to estimate the standard error of the measurement from data collected before and after rehabilitation and calculate the MDC of gait and balance measures.
Methods
One hundred nine older adults with a gait impairment due to neurological disease (66 stroke patients) completed two assessment sessions before and after inpatient rehabilitation. In each session, two trials of the 10-meter walking test and the Timed Up and Go (TUG) test, instrumented with inertial sensors, have been collected. The 95% MDC was calculated for the gait speed, TUG test duration (TTD) and other measures from the TUG test, including the angular velocity peak (ωpeak) in the TUG test’s turning phase. Random intercepts and slopes LMMs with sessions as fixed effects were used to estimate SEM. LMMs assumptions (residuals normality and homoscedasticity) were checked, and the predictor variable ln-transformed if needed.
Results
The MDC of gait speed was 0.13 m/s. The TTD MDC, ln-transformed and then expressed as a percentage of the baseline value to meet LMMs’ assumptions, was 15%, i.e. TTD should be < 85% of the baseline value to conclude the patient’s improvement. ωpeak MDC, also ln-transformed and expressed as the baseline percentage change, was 25%.
Conclusions
LMMs allowed calculating the MDC of gait and balance measures even if the test-retest steady-state assumption did not hold. The MDC of gait speed, TTD and ωpeak from the TUG test with an inertial sensor have been provided. These indices allow monitoring of the gait and balance impairment, which is central for patients with an increased falling risk, such as neurological old persons.
Trial registration
NA.
Background
In medicine, it is extremely important to determine whether a patient has worsened significantly, for example because of disease progression, or improved significantly, such as after rehabilitation [1, 2].
The Minimal Detectable Change (MDC) represents the smallest change in a measured variable not attributable to measurement error [2, 3]. The MDC is a confidence interval (CI), most commonly a 95% CI, built around a null difference between two measures. In front of a difference between measures exceeding the 95% MDC, one can be 95% confident that this difference is not due to a mere measurement error.
Central to the MDC calculation is the estimation of the Standard Error of the Measurement (SEM). As the standard deviation of the measurement error [4], SEM quantifies the precision of a single measure [5]: the lower the SEM, the smallest the measurement error and the better (i.e., the more precise) the measure. In turn, the lower the SEM, the lower the MDC since the MDC is the SEM multiplied by a constant.
Various neurological diseases, such as stroke, peripheral neuropathy of the lower limbs and Parkinson’s disease, impair gait and balance in older adults [6]. This gait and balance impairment reduces the person’s independence [7] and increases the risk of falling [8, 9]. Therefore, it is critical to identify people with impaired mobility, estimate the amount of this impairment, administer treatments that may improve it effectively, and monitor the impairment over time.
In this context, it is not surprising that numerous gait and balance measures have been developed so far. Among the gait measures, it is worth mentioning the gait speed, which has been shown to predict several adverse events (including falls, hospitalisation and mortality) [10]. Among the balance measures, i.e. mobility measures reflecting the ability to not fall, are those from the Timed Up and Go (TUG) test [11, 12]. In this test, examinees are asked to stand up from a chair, walk straight for a few meters, turn, walk back to the chair and sit down. A longer TUG test duration is associated with an increased risk of falls in older adults [6, 13].
Given the importance of adequately monitoring gait and balance in time, the MDC has been calculated for gait and balance measures, and even systematic reviews are available on this topic. However, these same reviews point out that the MDC is the least frequently assessed psychometric property of mobility measures [14], encouraging further research on its adoption in different clinical conditions, ranging from knee osteoarthritis [15] to stroke [16].
Conventionally, dedicated test-retest experiments are run to estimate SEM. Individuals are measured twice (i.e., tested and retested), with an interval between assessment sessions short enough so that no modification of the patient’s status occurs (e.g. there is no disease progression) but sufficiently delayed so that to prevent test recall. Central to these experiments is the steady-state assumption, i.e., the measured variable’s quantity does not change between the two assessment times.
Three methods are commonly used for estimating SEM from test-retest, steady-state experimental designs [5, 17], namely. SEM can be:
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1.
Derived from a reliability index like the intraclass correlation coefficient (ICC);
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2.
Estimated by the limits of agreement of a Bland-Altman analysis;
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3.
Calculated as the square root of the mean square error term from an Analysis of Variance (ANOVA) model.
Each approach has pros and cons, but it is interesting to note that the first one, likely the most commonly used, has been harshly criticised to the point that some psychometricians discourage its application [17].
For example, since different reliability indices are available (e.g. a family of ICCs [18, 19]), the choice of the reliability index can affect the SEM size [5] and comparing the SEM of different studies can be challenging. On the contrary, this limitation does not apply to the estimation of SEM from ANOVA residuals, making this method recommendable [5].
Recently a novel method for estimating the SEM, which applies the linear mixed-effects models (LMMs) to data collected before and after treatments, has been proposed [20,21,22,23,24]. Compliance with the steady-state assumption is hard to defend when patients receive treatment between the two assessment sessions. In this scenario, LMMs come to the rescue. With this approach, time and treatment effects are incorporated into the model and accounted for: statistical modelling creates a steady state in the analysis phase, rather than in the experimental phase, so that repeated measures with a different mean structure can be used to quantify the measurement error [20]. Similarly to the above-mentioned ANOVA analysis, the SEM is eventually estimated through variance decomposition and from the residual variance in the first place.
On these bases, the present study aims to calculate the SEM and the 95% MDC of common measures from the walking and TUG tests administered to older neurological patients. To this aim, SEM is estimated from LMMs run on data from older neurological patients collected before and after rehabilitation, which included balance training. Emphasis is placed on simple assessment procedures and low-tech measures that can be easily implemented in the normal clinic and research setting.
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