Posts Tagged accelerometry

[ARTICLE] A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training – Full Text

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions.

Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices.

A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover.

On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Neurologic rehabilitation has been testing a motor learning theory for the past quarter century that may be wearing thin in terms of leading to more robust evidence-based practices. The theory has become a mantra for the field that goes like this. Repetitive practice of increasingly challenging task-related activities assisted by a therapist in an adequate dose will lead to gains in motor skills, mostly restricted to what was trained, via mechanisms of activity-dependent induction of molecular, cellular, synaptic, and structural plasticity within spared neural ensembles and networks.

This theory has led to a range of evidence-based therapies, as well as to caricatures of the mantra (eg, a therapist says to patient, “Do those plasticity reps!”). A mantra can become too automatic, no longer apt to be reexamined as a testable theory. A recent Cochrane review of upper extremity stroke rehabilitation found “adequately powered, high-quality randomized clinical trials (RCTs) that confirmed the benefit of constraint-induced therapy paradigms, mental practice, mirror therapy, virtual reality paradigms, and a high dose of repetitive task practice.”1 The review also found positive RCT evidence for other practice protocols. However, they concluded, no one strategy was clearly better than another to improve functional use of the arm and hand. The ICARE trial2 for the upper extremity after stroke found that both a state-of-the-art Accelerated Skill Acquisition Program (motor learning plus motivational and psychological support strategy) compared to motor learning-based occupational therapy for 30 hours over 10 weeks led to a 70% increase in speed on the Wolf Motor Function Test, but so did usual care that averaged only 11 hours of formal but uncharacterized therapy. In this well-designed RCT, the investigators found no apparent effect of either the dose or content of therapy. Did dose and content really differ enough to reveal more than equivalence, or is the motor-learning mantra in need of repair?

Walking trials after stroke and spinal cord injury,38 such as robot-assisted stepping and body weight-supported treadmill training (BWSTT), were conceived as adhering to the task-oriented practice mantra. But they too have not improved outcomes more than conventional over-ground physical therapy. Indeed, the absolute gains in primary outcomes for moderate to severely impaired hemiplegic participants after BWSTT and other therapies have been in the range of only 0.12 to 0.22 m/s for fastest walking speed and 50 to 75 m for 6-minute walking distance after 12 to 36 training sessions over 4 to 12 weeks.3,9 These 15% to 25% increases are just as disappointing when comparing gains in those who start out at a speed of <0.4 m/s compared to >0.4 to 0.8 m/s.3

Has mantra-oriented training reached an unanticipated plateau due to inherent limitations? Clearly, if not enough residual sensorimotor neural substrate is available for training-induced adaptation or for behavioral compensation, more training may only fail. Perhaps, however, investigators need to reconsider the theoretical basis for the mantra, that is, whether they have been offering all of the necessary components of task-related practice, such as enough progressively difficult practice goals, the best context and environment for training, the behavioral training that motivates compliance and carryover of practice beyond the sessions of formal training, and blending in other physical activities such as strengthening and fitness exercise that also augment practice-related neural plasticity? These questions point to new directions for research….

Continue —> A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training – Mar 01, 2017

Figure 1. Components of a Rehabilitation-Internet-of-Things: wireless chargers for sensors (1), ankle accelerometers with gyroscopes (2) and Android phone (3) to monitor walking and cycling, and a force sensor (4) in line with a stretch band (5) to monitor resistance exercises.

 

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[ARTICLE] Hemispheric asymmetry in myelin after stroke is related to motor impairment and function – Full Text

Fig. 1

Abstract

The relationships between impairment, function, arm use and underlying brain structure following stroke remain unclear. Although diffusion weighted imaging is useful in broadly assessing white matter structure, it has limited utility in identifying specific underlying neurobiological components, such as myelin. The purpose of the present study was to explore relationships between myelination and impairment, function and activity in individuals with chronic stroke. Assessments of paretic upper-extremity impairment and function were administered, and 72-hour accelerometer based activity monitoring was conducted on 19 individuals with chronic stroke. Participants completed a magnetic resonance imaging protocol that included a high resolution T1 anatomical scan and a multi-component T2 relaxation imaging scan to quantify myelin water fraction (MWF). MWF was automatically parcellated from pre- and post-central subcortical regions of interest and quantified as an asymmetry ratio (contralesional/ipsilesional). Cluster analysis was used to group more and less impaired individuals based on Fugl-Meyer upper extremity scores. A significantly higher precentral MWF asymmetry ratio was found in the more impaired group compared to the less impaired group (p < 0.001). There were no relationships between MWF asymmetry ratio and upper-limb use. Stepwise multiple linear regression identified precentral MWF asymmetry as the only variable to significantly predict impairment and motor function in the upper extremity (UE). These results suggest that asymmetric myelination in a motor specific brain area is a significant predictor of upper-extremity impairment and function in individuals with chronic stroke. As such, myelination may be utilized as a more specific marker of the neurobiological changes that predict long term impairment and recovery from stroke.

1. Introduction

Improved medical management of stroke has resulted in decreasing mortality rates (Grefkes and Ward, 2014). As a result, the number of individuals living with long-term disability as a result of stroke is rising (Krueger et al., 2015). Due to the heterogeneity of clinical presentation following stroke, it is imperative to identify biomarkers that may predict long-term impairment and function in order to appropriately individualize clinical rehabilitation goals and objectives (Bernhardt et al., 2016). With advances in diagnostic and prognostic tools, it is necessary to isolate modalities that can predict long-term outcomes for individuals with stroke, and to understand the underlying neurobiology that contributes to the predictive value of those measures.

Neuroimaging can be utilized to aid in the identification of biomarkers that may predict recovery status in individuals with stroke. White matter imaging is often used as a predictor of stroke recovery (Feng et al., 2015 and Stinear et al., 2012). Diffusion tensor imaging (DTI) can be performed within 10 days post stroke to quantify initial post stroke structural degeneration (Werring, 2000). Such indices have been found to strongly predict upper-extremity motor function at both 3- and 6-months post stroke (Puig et al., 2010 and Stinear et al., 2012). The combination of acute corticomotor function, derived from DTI and motor evoked potentials, using transcranial magnetic stimulation, has also been demonstrated to strongly predict recovery from upper-extremity impairment after stroke (Byblow et al., 2015). Although these modalities are predictive of long-term upper-extremity impairment, the underlying neurobiological bases driving the relationship between white matter microstructure and motor capacity remains unclear. Although relationships between white matter integrity, quantified with DTI, and motor impairment have been established after stroke, it is important to note that DTI measures are not a specific marker for myelination (Arshad et al., 2016). While DTI can grossly identify water movement, it is unable to differentiate between individual white matter substrates, which may produce the observed signal. Multiple structural features can be individually or collectively responsible for the observed changes in DTI measures, including: 1) axonal membrane status, 2) myelin sheath thickness, 3) number of intracellular neurofilaments and microtubules, and 4) axonal packing density (Alexander et al., 2007 and Beaulieu, 2002). To understand the neurobiological components contributing to the change in motor outcome observed there is a need to adopt neuroimaging techniques that can quantify these structural features.

Myelin formation has been identified as a specific target for therapeutic intervention following stroke, as recovery of axonal fibres is not complete without adequate myelination (Mifsud et al., 2014). Oligodendrocytes are responsible for initiating a cascade of events that result in the formation of myelin. Acute cerebral ischemia, such as that caused by a stroke, causes a rapid breakdown of oligodendrocytes and demyelination (Tekkök and Goldberg, 2001), which greatly limits overall axonal integrity in the lesioned area (Saab and Nave, 2016). Although animal work has underlined the importance of active myelination on motor recovery after stroke (Chida et al., 2011 and McKenzie et al., 2014), it is unclear how these findings transfer to humans.

Until recently, technical limitations prevented the imaging of myelin in vivo. Myelin water fraction (MWF) can be derived in humans non-invasively in vivo from multi-component T2-relaxation imaging (Alonso-Ortiz et al., 2014 and Prasloski et al., 2012b). Formalin-fixed human brains yield T2 distributions similar to those found in vivo, and histopathological studies show strong correlations between MWF and staining for myelin (Laule et al., 2004 and Moore et al., 2000). With the development of non-invasive imaging techniques, myelin can be quantified in the human brain (Prasloski et al., 2012b), both cross-sectionally and longitudinally (Lakhani et al., 2016) Work form the Human Connectome Project and others have identified that the primary motor and sensory regions are among the most densely myelinated and most easily delineated in the human brain, allowing for more reliable automatic identification and parcellation of myelinated regions (Glasser et al., 2016, Glasser and Van Essen, 2011 and Nieuwenhuys and Broere, 2016). In addition, myelination of corticospinal projections from these regions may vary based on the length of the tract and the size the axon. As such, quantification of corticospinal tract (CST) myelin using in vivo neuroimaging has not been validated to date (Glasser and Van Essen, 2011). Previous work from our group did not reveal a relationship between ipsi- and contralesional CST MWF, measured from the posterior limb of the internal capsule, and motor function or impairment (Borich et al., 2013). In order to limit variability arising from CST tract heterogeneity between individuals with stroke, the current study focused on the most well defined, myelinated regions of interest, located in precentral and postcentral areas.

Recent work has demonstrated that oligodendrocyte precursor cell proliferation and myelin structure are associated with motor learning in rodent models (Gibson et al., 2014 and Xiao et al., 2016). In particular, this work emphasized the possibility that functional motor activity may influence myelination of redundant neural pathways and improve conduction velocity via more efficient neural synchrony (Fields, 2015). The current study will extend previous lines of inquiry by exploring the relationship between real-world activity in the upper-extremity to myelination in humans. The ability to use the stroke-affected upper-limb in ‘everyday tasks’ is cited as a primary goal for individuals living with stroke (Barker and Brauer, 2005 and Barker et al., 2007). Monitoring upper-extremity usage after stroke using accelerometers is a low-cost, non-invasive way to measure functional activity and to quantify overall real-world activity (Hayward et al., 2015). Use of the stroke affected upper-limb correlates with long-term motor impairment as greater activity generally results in reduced impairment (Gebruers et al., 2014, Lang et al., 2007 and Shim et al., 2014). Identifying relationships between accelerometer based measures of activity and myelination will inform future investigations about the potential specificity of myelin as predictive biomarker for understanding what people can do, via measurement of impairment and function, versus what people actually do in the real-world.

Given the important relationships between white matter, activity and post-stroke impairment as well as the recent advances in imagining techniques, it is imperative to consider the contribution of myelination to post-stroke impairment, function and activity in humans. In order to identify potential differences in myelination based on the level of impairment after stroke, the current study identified ‘more impaired (M)’ and ‘less impaired (L)’ groups of participants. Therefore, the primary objective of the current investigation is to understand whether MWF in sensorimotor regions of interest is a biomarker of long term impairment, function or arm use in a population of individuals living with chronic stroke. Furthermore, we sought to identify if there were differences in MWF in sensorimotor regions of interest between individuals classified as ‘more impaired’ versus those who were ‘less impaired’.

Continue —> Hemispheric asymmetry in myelin after stroke is related to motor impairment and function

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[Abstract] A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

Abstract

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope.

We review some possible explanations, then potential technology-enabled solutions. Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices. A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement.

Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover. On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Source: A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

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[Abstract] A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

Abstract

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions. Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices. A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover. On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Source: A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

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