Direct and indirect effects of attention and visual function on gait impairment in Parkinson's disease: influence of task and turning (Commentary on Stuart <i>et al</i>. (2017))
Bibliographic record
Abstract
Falls are a growing concern among older adults and individuals living with a neurodegenerative disease that affects movement control. Growing evidence continues to encourage researchers and clinicians to consider vision when evaluating movement control and potential risk of falling (Lord & Dayhew, 2001; Lord et al., 2010); however, recommendations and guidelines for evaluating vision beyond standard classification of a visual impairment (e.g. near-sightedness) continue to be limited (AGS/BGS, 2010). Vision is not just the ability to see accurately, it also includes the ability to direct visual gaze in the surrounding environment, perceive the spatial relationships between external objects and the body and use this information to plan and execute movement or adapt ongoing motor patterns (Patla, 1997). Adaptation of motor patterns to environmental constraints, particularly during gait, is an essential part of mobility function in daily life, and reductions in the ability to adapt gait increase ones’ risk of falling. Therefore, visual function as a whole is an essential component to fall risk and mobility function. Further, visual function may indirectly play a role in other aspects of fall risk. Research in the last decade or so has supported the idea that divided attention during dual tasking (performance of simultaneous motor and cognitive tasks) results in negative effects on gait and/or decreased cognitive task performance, for example increased stride to stride variability and decreased gait velocity (Springer et al., 2006; Hollman et al., 2007; Yogev-Seligmann et al., 2010; Decker et al., 2016; Pelosin et al., 2016). Risk of falling increases as the ability to dual task decreases (Springer et al., 2006). In a recent study of a group of individuals with Parkinson's disease and a group of older adults prone to falling, reduced function of the cholinergic system, which plays a role in orienting attention, was associated with reduced gait speed while dual task walking (Pelosin et al., 2016). The ability to orient one's attention to appropriate cues in the environment has a significant impact on mobility and when considering orientation of attention, vision comes to the forefront. Research has shown that humans use vision to scan relevant objects or events within a space to plan possible motor responses for maintaining motion (Patla, 1997; Hollands et al., 2002). If our ability to orient attention is in fact related to an increased risk of falling, how might a concurrent reduction in visual function exacerbate this risk? How vision works with attention orientation may be a key causal factor in falls and strategies put in place to mitigate this increased risk may be critical for future fall prevention strategies. In addition to the known affects Parkinson's disease (PD) has on overall body movement control, PD also significantly affects movement of the eyes (Chan et al., 2005; Terao et al., 2013), resulting in reduced voluntary visual sampling of the movement environment. The experiment recently conducted by Stuart et al. (2017) ‘Direct and indirect effects of attention and visual function on gait impairment in Parkinson's disease: influence of task and turning ‘provides a basis for discussion regarding the interaction between the cognitive construct of attention and visual function in adaptive gait control. These researchers studied visual behaviour in a large group of individuals with Parkinson's disease (N = 60) and age-matched older adults (N = 40) during several gait tasks including adaptive tasks, such as turning. The authors reported a reduced number of saccadic eye movements while walking in individuals with PD compared to age-matched participants. Moreover, the authors found that the PD group increased the number of saccades during complex adaptive tasks such as turning but that saccades decreased again while turning under dual task, supporting the hypothesis that individuals with PD have limited visual sampling of the movement environment particularly when attentional resources to the motor task are limited. However, the most interesting part of the article was the second objective using structural equation modelling (SEM) to explore the relationship of visual function, attention and gait performance. SEM revealed that visual function did not significantly account for changes to gait parameters in PD but rather worked indirectly through attention, whilst poor attention directly related to both poor saccade number and lower gait velocity. These results suggest that while reduced visual function reduces adaptive gait, it does so indirectly through the interaction of visual attention. While there are a number of limitations to generalizing the results of this single study, these results raise interesting questions regarding how visual function and attention work together to control adaptive gait and how alterations to either of these systems play a role in risk of falling, especially in an at-risk population. Of course, one cannot speak about research on visual attention without acknowledging the methodological challenges faced by researchers. While discussing these challenges is beyond the scope of this commentary, a relevant point is that examining visual attention during gait does not have well-accepted and/or validated tests unlike other aspects of visual function such as visual acuity or contrast sensitivity. Differences in methods used across studies make comparisons between studies difficult and slow gains in knowledge. Therefore, methodological considerations need to be at the forefront of further research on visual attention and gait. In closing, the article by Stuart et al. (2017) is among the first to explore the interactions of visual function, attention and gait. It represents an important gain in knowledge when considering current clinical guidelines for fall prevention, which at present have limited recommendations to measures of visual acuity and/or visual impairments such as cataracts and glaucoma. Stuart et al. (2017) provide evidence to suggest that we must consider visual function as a whole, which includes attention, to understand how their interaction influences adaptive gait. With further research grounded in the knowledge base of neuroscience, psychology, motor control and motor learning, we will provide a better understanding of how and why vision is essential for evaluating and investigating movement control such as gait and a person's potential risk of a fall.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.046 | 0.044 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".