Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Thursday, January 30, 2025

Construct validity and responsiveness of clinical upper limb measures and sensor-based arm use within the first year after stroke: a longitudinal cohort study

 'Measurements' DO NOTHING UNLESS THEY ARE DIRECTLY MAPPED TO RECOVERY PROTOCOLS! This did nothing of the sort, so useless! I'd have you all fired!

Construct validity and responsiveness of clinical upper limb measures and sensor-based arm use within the first year after stroke: a longitudinal cohort study

Abstract

Background

Construct validity and responsiveness of upper limb outcome measures are essential to interpret motor recovery poststroke. Evaluating the associations between clinical upper limb measures and sensor-based arm use (AU) fosters a coherent understanding of motor recovery. Defining sensor-based AU metrics for intentional upper limb movements could be crucial in mitigating bias from walking-related activities. Here, we investigate the measurement properties of a comprehensive set of clinical measures and sensor-based AU metrics when gait and non-functional upper limb movements are excluded.

Methods

In this prospective, longitudinal cohort study, individuals with motor impairment were measured at days 3 ± 2 (D3), 10 ± 2 (D10), 28 ± 4 (D28), 90 ± 7 (D90), and 365 ± 14 (D365) after their first stroke. Using clinical measures, upper limb motor function (Fugl-Meyer Assessment), capacity (Action Research Arm Test, Box & Block Test), and perceived performance (14-item Motor Activity Log) were assessed. Additionally, individuals wore five movement sensors (trunk, wrists, and ankles) for three days. Thirteen AU metrics were computed based on functional movements during non-walking periods. Construct validity across clinical measures and AU metrics was determined by Spearman’s rank correlations for each time point. Criterion responsiveness was examined by correlating patient-reported Global Rating of Perceived Change (GRPC) scores and observed change in upper limb measures and AU metrics. Optimal cut-off values for minimal important change (MIC) were estimated by ROC curve analysis.

Results

Ninety-three individuals participated. At D3 and D10, correlations between clinical measures and AU metrics showed variability (range rs: 0.44–0.90). All following time points showed moderate-to-high positive correlations between clinical measures and affected AU metrics (range rs: 0.57–0.88). Unilateral nonaffected AU duration was negatively correlated with clinical measures (range rs: -0.48 to -0.77). Responsiveness across outcomes was highest between D10-D28 within moderate to strong relations between GRPC and clinical measures (rs: range 0.60–0.73), whereas relations were weaker for AU metrics (range rs: 0.28–0.43) Eight MIC values were estimated for clinical measures and nine for AU metrics, showing moderate to good accuracy (66–87%).

Conclusions

We present reference data on the construct validity and responsiveness of clinical upper limb measures and specified sensor-based AU metrics within the first year after stroke. The MIC values can be used as a benchmark for clinical stroke rehabilitation.

Trial registration

This trial was registered on clinicaltrials.gov; registration number NCT03522519.

Background

Stroke survivors frequently suffer from hemiparesis, a condition that disrupts their functioning and autonomy [1, 2], leading to substantial challenges in personal care, meaningful activities, and relationships [2, 3]. Holistically assessing an individual’s health status, guided by the International Classification of Functioning, Disability and Health [4] (ICF), is crucial when evaluating poststroke recovery. Consensus-based recommendations guide the selection of outcomes and integration of measurements throughout the recovery continuum [5,6,7]. These upper limb assessments evaluate constructs within the ICF domains of body funtions and structures, activities, and participation [4]. When evaluating activities, two dimensions can be distinguished: capacity and performance. Capacity refers to the maximal voluntary action within a standardised task (i.e., what a person can do), while performance describes real-life movement behaviour (i.e., what a person does) [4, 6]. Due to similarities in standardised assessment conditions, we use capacity as an umbrella term for upper limb function and activity capacity measures. Performance can be evaluated from a subjective and an objective perspective. Subjective performance is assessed through patient-reported outcome measures (PROMs), which capture the individual’s perspective on functioning in their daily life that clinicians can use for goal-setting and decision-making [8]. However, various factors influence the data quality of PROMs through differences in response behaviour, depending on the patient’s background and cognitive, psychological, and social factors [9,10,11].

Wearable movement sensors offer a discreet and objective way to quantify a patient’s physical activities and arm use (AU) performance [12, 13], providing valuable information for clinicians [14]. Numerous sensor-based AU metrics have been established in stroke, measuring real-life performance in terms of intensity, duration, and AU symmetry [15,16,17,18,19]. Therefore, clinical assessments should be complemented with real-world monitoring data to determine how individuals use their upper limb capacity in daily life.

Measurement properties of clinical and sensor-based outcomes play a crucial role in interpreting the results, as they allow qualitative meaning to be assigned to quantitative results [20]. The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) provide a taxonomy of measurement properties: reliability, validity, and responsiveness. Therein, validity refers to “the degree to which a measurement instrument purported the constructs to be measured [21]. Criterion validity is evaluated when comparing a measurement instrument to the construct’s gold standard, the best available measure of that domain [21]. Construct validity applies when a gold standard is unavailable, so construct validity involves investigating the dimensionality of a measure and relationships between other measurement constructs or populations of interest [21].

Research has shown strong relationships between upper limb capacity measured by the Fugl-Meyer Assessment upper extremity subscale (FMA-UE), the Action Research Arm Test (ARAT), and the Box and Block Test (BBT) [22,23,24]. However, motor capacity does not necessarily translate into performance. For example, perceived performance measured by the patient-reported Motor Activity Log (MAL) has shown low to moderate correlations with the ARAT (r = 0.23 to 0.51) in the subacute phase [25] and to the ARAT and BBT (r = 0.31 to 0.65) in the chronic phase after stroke [26,27,28].

Wearable movement sensors offer promise for understanding complex human movement behaviour but also present challenges to developing sound quantification methods. Associations between sensor-based AU performance and clinical measures greatly vary across studies [29, 30] due to factors such as different computation methods, small sample sizes, and differences in study designs [29].

Additionally, AU metrics are typically computed over the entire recording period without distinguishing between different physical activity types. This is especially concerning for walking activities, as upper limb movements during gait are primarily ballistic in nature, although there seem to be some effects of tonic cortical drive [31]. Gait, for instance, has been reported to inflate the AU duration (AU-d) of the affected upper limb by 31% in the subacute and 41% in the chronic phase after stroke [32]. Therefore, removing gait sequences is critical to maintaining the targeted outcome and avoiding including non-voluntary arm movements in the outcome calculation [32]. Specifying AU performance could enhance the understanding of relationships to clinical upper limb measures, which might facilitate the clinical integration of wearable sensors.

Quantifying change and interpreting its meaningfulness is essential in clinical stroke rehabilitation. Responsiveness is a vital measurement property defined as “the ability of a measurement instrument to reflect change over time” [21, 33]. Reference values for responsiveness in clinical upper limb measures are often derived from distribution-based methods (i.e. the significance of change magnitude) [25, 34,35,36,37]. These methods are criticised for neglecting clinical relevance [33, 38] considering that significant change does not necessarily have a meaningful impact on a person’s life.

In analogy to validity concepts, the COSMIN framework distinguishes between criterion responsiveness, which assesses the longitudinal association between observed and patient-perceived changes and construct responsiveness, which evaluates the relationship between changes in similar measurement constructs [21, 33]. Criterion responsiveness, considered a gold standard approach, enables estimating minimal important change (MIC) values, allowing clinicians to interpret whether a patient experienced meaningful change [38]. We refer to the MIC as a threshold for meaningful within-person changes over time [38]. Different terms, such as MID or MCID (minimal clinically important difference), are often used interchangeably. However, these terms address ‘differences’ and concern cross-sectional group differences rather than within-person change.

MIC values are widely used as benchmarks to evaluate the effectiveness of interventions in stroke research [39]. However, available criterion-based MIC values are often derived from high-dosed intervention trials in the chronic phase [40,41,42], which limits their applicability across different recovery phases and clinical settings. Additionally, it is unclear whether changes in AU performance reflect relevant changes from a patient’s perspective. For example, Lang et al. (2008) found no relationship between changes in affected AU duration and patient-perceived change in performance [40]; therefore, MIC values could not be estimated. Therefore, Based on this, criterion MIC values corresponding to motor recovery phases are still needed for clinical stroke rehabilitation.

This study aims to provide reference data on construct validity and responsiveness for clinical upper limb measures and sensor-based AU metrics within the first year post-stroke. We aimed to refine the AU metrics measurement construct by specifying functional AU performance and excluding walking activities. Given the extensive evidence on non-specified AU metrics, we aimed to provide reference data for two approaches: conventional AU metrics (AUconv) and AU specified for non-walking bouts. We hypothesised that construct validity was higher within similar measurement constructs (i.e., perceived performance versus AU performance) but lower between different constructs (i.e., perceived performance versus capacity). Regarding criterion responsiveness, we hypothesised that observed changes would be at least moderately correlated with patient-perceived change.

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