Visuospatial cognition predicts performance on an obstructed vision obstacle walking task in older adults

Abstract

Walking performance and cognitive function demonstrate strong associations in older adults, with both declining with advancing age. Walking requires the use of cognitive resources, particularly in complex environments like stepping over obstacles. A commonly implemented approach for measuring the cognitive control of walking is a dual-task walking assessment, in which walking is combined with a second task. However, dual-task assessments have shortcomings, including issues with scaling the task difficulty and controlling for task prioritization. Here we present a new assessment designed to be less susceptible to these shortcomings while still challenging cognitive control of walking: the Obstructed Vision Obstacle (OBVIO) task. During the task, participants hold a lightweight tray at waist level obstructing their view of upcoming foam blocks, which are intermittently spaced along a 10 meter walkway. This forces the participants to use cognitive resources (e.g., attention and working memory) to remember the exact placement of upcoming obstacles to facilitate successful crossing. The results demonstrate that adding the obstructed vision board significantly slowed walking speed by an average of 0.26 m/s and increased the number of obstacle strikes by 8-fold in healthy older adults (n=74). Additionally, OBVIO walking performance (a score based on both speed and number of obstacle strikes) significantly correlated with computer-based assessments of visuospatial working memory, attention, and verbal working memory. These results provide initial support that the OBVIO task is a feasible walking test that demands cognitive resources. This study lays the groundwork for using the OBVIO task in future assessment and intervention studies.

Introduction

Community ambulation requires successful clearance of obstacles that pose a tripping hazard, such as uneven sidewalks, curbs, and objects or debris on the ground. Avoidance of these obstacles requires cognitive engagement to modify the typical steady state walking pattern. Cognitive and walking abilities often decline in older adults and these declines are likely interconnected. Higher cognitive scores predict less future decline in gait speed, and higher gait speeds predict less yearly decline in cognitive abilities.¹ Specifically, the cognitive domains of attention and memory have been shown by multiple studies to have significant associations with gait speed.^1–5 Incorporation of attention and memory demanding tasks with walking (e.g. walking while talking) often results in reduced gait speed, indicating the influence of cognitive resources while walking.^6–9

Cognitive control of walking is most often investigated using dual-task assessments, in which walking is performed simultaneously with a cognitive task. Completing a cognitive and motor task simultaneously creates a competition for attentional and memory resources.⁹ Task performance degrades when the supply of resources is less than task demands. Since both tasks in a walking dual task assessment require attentional and memory resources, the brain is forced to divide resources between the tasks, thus prioritizing performance of one task over the other.^2,10,11 Task prioritization can be affected by a variety of factors such as an individual’s postural reserve and hazard estimation of each task, which can shift prioritization towards either the motor or cognitive task.¹² Fully prioritizing the motor task, for example, would preserve walking speeds, but at the cost of cognitive task performance. Therefore, both outcome measures of a dual-task paradigm should be analyzed when evaluating dual-task performance.

There are also age related differences in the cognitive contributions to obstacle walking.^13–15 Compared to young adults, reaction time dual-tasks show that older adults require attentional resources for longer.¹⁶ Young adults only show reaction time deficits when approaching an obstacle, while older adults show deficits for both obstacle approach and crossing.¹⁶ Interestingly, vision strategies show similar age-related differences. When approaching an obstacle, young adults only fixate on an approaching obstacle approximately 2 steps away and use peripheral vision for crossing, while older adults fixate their gaze for both the approach and crossing rather than using peripheral vision.¹⁷ These results suggest that crossing, the phase where visual inputs of the obstacle are limited, shows the most age-related differences. Older adults may also be using attentional resources and vision to compensate for natural degradation of visuospatial working memory, since visuospatial cognition is typically used for obstacle crossing when limbs are not in the visual field, such as trail foot crossing.^18,19 This may be why certain older adults seem to be at an increased risk of falling when the lower visual field is either missing or distorted, such as when wearing bifocals.²⁰

In the present study, we report on a new walking assessment called the Obstructed View Obstacle (OBVIO) walking task (Figure 1). Participants walk across a course that contains several foam obstacles, and under a separate condition, they walk across the same course while holding a cardboard “tray” that obstructs their view several meters in front of them. The obstructed view condition places greater demands on visual attention and working memory to avoid striking obstacles that they can no longer see. We hypothesized that walking speed would slow, and more obstacles would be struck during OBVIO walking versus the normal obstacle walking course, confirming the heightened complexity of OBVIO walking. Further, we hypothesized that older adults who have poorer visuospatial function on a computer-based cognitive battery would perform worse on the OBVIO task. If confirmed, these findings would provide initial support for the OBVIO task as an outcome measure to assess visuospatial control of walking.

Figure 1:

OBVIO walking task: a lightweight tray is held at waist height that obstructs the vision of upcoming obstacles.

Methods

Seventy-seven older adults were recruited by mailing advertising flyers to patients of the North Florida/South Georgia Veterans Health System using a mailing list generated by the VA Informatics and Computing Infrastructure. Patients were included in the mailing list if their medical record indicated age 65 or greater, primary residence in a local zip code, and absence of major medical conditions (i.e., absence of ICD-9/ICD-10 codes for major diseases of the nervous system, circulatory system, musculoskeletal system, etc.). A standardized screening questionnaire was used to interview volunteers by telephone. Inclusion criteria included self-report of having some difficulty with walking, climbing stairs, or doing daily chores. The assessments described in this article were conducted as part of another study,²¹ which excluded volunteers who self-reported any of the following: diagnosed neurological disorder/injury; severe arthritis in lower extremities (such as awaiting joint replacement); major cardiac, vascular, pulmonary, or renal disease; cancer treatment in the past year (other than for early stage skin, breast, or prostate cancers); diagnosis of a psychological condition (e.g., schizophrenia, bipolar disorder); bone fracture or musculoskeletal surgical procedure within the prior six months; contra-indications to lumbar spinal electrical stimulation (e.g., low back pain, prior spinal surgical procedure); pacemaker or other electronic medical device; severe obesity; current use of prescription medications affecting the central nervous system; and current participation in physical therapy. Ten meter walking speed, Activities Specific Balance Confidence Scale,²² and the Trail Making Test of cognitive function²³ were performed to characterize the participants (Table 1). All study procedures were approved by an Institutional Review Board, and all participants provided written informed consent.

Table 1:

Participant characteristics

	Mean ± SD
Sample Size	74
Male/Female	56/18
Age (years)	76 ± 7.1
Preferred 10 m Walking Speed (m/s)	1.19 ± 0.32
Fastest 10 m Walking Speed (m/s)	1.57 ± 0.33
ABC scale (% confidence)	87.5 ± 15
Trailmaking Test (s; Part B - A)	62.9 ± 62
Body Mass Index (kg/m2)	26.8 ± 3.9

Walking Tasks

Participants were instructed to walk at their fastest safe speed over a 10 meter obstacle course that included nine foam obstacles (Obstacles task). The dimensions of each obstacle were 10.2 × 61 × 10.2 cm (length × width × height), which is a similar height to a small step-up or curb. On the 10 meter course, obstacles were placed at the 1 meter (m), 1.6 m, 3 m, 4.25 m, 5 m, 5.75 m, 7 m, 8.4 m, and 9 m mark. These obstacle placements allow for the course to be identical, regardless of which end a participant starts. The spacing between obstacles varied between 0.6 m and 1.4 m, which ensured that at least 1 obstacle would be hidden during the obstructed vision board throughout the trial (see below). However, the Obstacles task contained no obstructed vision board, meaning no restrictions to vision or arm swing. Participants walked an additional meter at the beginning and end of the course to mitigate the effect of acceleration and deceleration on walking speed measurements. A stopwatch was started when a participant’s trunk broke the plane of the start line and stopped when it broke the plane of the stop line 10 meters later. All participants wore a gait belt around their waist, and a research staff member was nearby to assist if a participant became unsteady.

The Obstructed Vision Obstacles (OBVIO) task was identical to the Obstacles task except that participants held a 56 × 71 cm (length × width) lightweight board at waist level against the body while walking (Figure 1). Holding this board would obstruct the average participant’s view of the floor by approximately 1.3 meters in front of their body. At any given moment on the course, the participant would need to remember the location of multiple (usually two) obstacles and hold this in memory longer than usual (i.e., multiple steps) prior to attempting an obstacle crossing step. A performance score was calculated for each trial by multiplying the percentage of obstacles successfully crossed by the walking speed. Therefore, a participant who struck 1 obstacle (i.e., 89% success rate) while walking at 1 m/s would have a score of 0.89. The best score from three trials was used for subsequent analysis. The Obstacle and OBVIO walking tasks were each done three times in random order.

Cognitive Assessments

Participants completed computerized tests using commercially available software (Creyos, Toronto, Canada)(https://creyos.com/features/tasks). These tests originate from validated pen and paper neuropsychological assessments and have been used in large-scale studies of cognition.^24,25 The tests used in the current study were tests of attention (Feature Match), verbal working memory (Digit Span), and visuospatial working memory (Spatial Span).

Feature Match Test

Attentional processing was assessed with a computerized variation of a classic feature-search task.²⁶ Two large squares were shown on the computer screen, each containing multiple shapes. Participants were directed to decide whether the position and characteristics of the shapes within each square were identical or mismatched. Mismatches could be due to a shape being in a different location within the square, a shape being rotated by 90 degrees, a shape being empty or filled, or a different shape being in the same location (i.e. a circle in one square, at the location of a cross in the other square). If the participant answered correctly, an additional shape was added to each square on the next trial. If the participant answered incorrectly, the number of shapes in the next trial was one fewer. Difficulty level was assessed by how many shapes were present in each box. Difficulty automatically increased after two correct answers at a given level. The program calculates the score as the sum of difficulty levels answered correctly minus the sum of difficulty levels answered incorrectly, with participants trying to get as many correct answers as possible in 90 seconds. Therefore, a participant who gets their first five trials correct, the sixth trial wrong, and then two more trials correct (i.e., ✓-✓-✓-✓-✓-X-✓-✓) in 90 seconds would see their score change in the following manner: 1–2-4–6-9–6-9–12.

Digit Span Test

A computerized version of the commonly used forward digit span task was used to assess verbal working memory. During this test, a sequence of digits appeared on the screen at a set interval. Once the sequence was completed, participants were instructed to remember and with a computer mouse click the same sequence that had just appeared. After three errors, the test was terminated and the average sequence length of each successful trial was calculated.

Spatial Span Test

A computerized variant of the Corsi block-tapping task was used to measure visuospatial working memory. A 4×4 grid of squares (16 squares) appeared on the computer screen. A series of squares would blink in a sequence, one square at a time. The starting level for all participants was a sequence of four squares. After the sequence was complete, participants clicked on the squares in the same order as what they just observed. If the participant completed the sequence correctly, the length of the next sequence would increase by one square. If the participant was incorrect, the length of the next sequence would decrease by one square. The test continued until the participant answered three trials incorrectly. The program calculates the score as the average level of the successful trials.

Statistical Analysis

To assess normality, we performed a Shapiro-Wilk Test and visual inspection of distribution plots, which demonstrated that the data was normally distributed. Pearson’s product moment correlation coefficient test was used for all 9 comparisons of walking to cognition (3 walking × 3 cognitive tests). Multiple comparisons were accounted for using a false discovery rate (FDR) adjustment. Nine-way adjustments were done for all 3 measures (performance score, walking speed, and obstacle strikes) of the 3 walking task metrics (obstacle, OBVIO, and the difference of those two tasks). FDR was chosen over the more conservative Bonferroni correction to reduce Type II error. All analysis was done in JMP Pro 15 (SAS, Cary, NC).

Results

Seventy-seven older adults enrolled in this study. All but one participant completed all three trials of both the Obstacles and OBVIO walking tasks. That participant along with two others were excluded from the analysis for not following the test instructions.

For the Obstacles task, the average walking speed over the 10 meter course was 1.21 m/s with participants hitting 0.33 obstacles per trial (i.e., 1 obstacle strike total for the 3 trials). Participants slowed their gait speed 21% (p<0.0001) to an average of 0.95 m/s during the OBVIO task (Figure 2), while striking 2.8 obstacles per trial, an 8-fold increase in errors (p<0.0001) compared to Obstacles. Similarly, 22% of Obstacles trials contained at least one obstacle strike while 75% of OBVIO trials contained at least one obstacle strike. After calculating each trial’s walking score (gait speed * % of obstacles successfully cleared), the best score was taken. The average of these (best) walking scores was 1.25 ± 0.33 for Obstacles and 0.89 ± 0.44 for OBVIO.

Figure 2:

Older adults walked slower in the OBVIO (obstacles + obstructed vision board) vs. Obstacles task.

Better performance on each of the three cognitive tests was associated with better performance on the Obstacles and OBVIO walking assessments. These associations were stronger for OBVIO than for Obstacles, based on FDR-corrected 2-tailed p-values (Table 2). Feature Match had a correlation of r=0.39 (p=0.005) and r=0.29 (p=0.05) for OBVIO and Obstacles, respectively. Digit Span had a correlation of r=0.28 (p=0.04) and r=0.15 (p=0.21) for OBVIO and Obstacles, respectively. Spatial Span had a correlation of r=0.28 (p=0.05) and r=0.17 (p=0.16) for OBVIO and Obstacles, respectively. Scatterplots for each cognitive assessment and the OBVIO task are shown in Figure 3.

Table 2:

Pearson correlations of walking tasks to cognitive assessments.

	Obstacle	OBVIO	Δ walking scores
Feature Match	0.29	0.39**	0.28*
Digit Span	0.15	0.28*	0.27*
Spatial Span	0.17	0.28*	0.25*

^*indicates p<0.05 after 9 way FDR correction.

^**indicates p<0.01.

Figure 3:

OBVIO walking scores significantly correlated with Feature Match, Digit Span, and Spatial Span. Higher cognitive scores predicted better walking outcomes.

To directly assess the effect of the obstructed vision component of the OBVIO task, we subtracted the Obstacles score from the OBVIO score for each participant. OBVIO – Obstacles walking scores were significantly associated with Feature Match (r = 0.28, p=0.03), Digit Span (r =0.27, p=0.03), and Spatial Span (r =0.25, p=0.04) (Figure 4) with lower cognitive scores predicting a more detrimental influence of the obstructed vision board on walking performance (Table 2).

Figure 4:

The difference between OBVIO and Obstacles scores (how much the obstructed vision board hindered performance) was associated with Feature Match, Digit Span, and Spatial Span performance, with higher cognitive scores predicting less walking degradation.

In addition to the associations reported between the cognitive assessments and the derived performance scores from each walking task, associations were also evaluated for the underlying measures of walking speed and obstacle strikes. Gait speed during the OBVIO task was only significantly associated with Feature Match (r =0.34, p=0.03), not Digit Span or Spatial Span. Gait speed during the Obstacles task did not significantly associate with any of the cognitive measures, as the Feature Match association did not survive FDR correction. OBVIO strikes significantly correlated with all 3 measures: Feature Match (r =0.33, p=0.01), Digit Span (r =0.36, p=0.01), and Spatial Span (r =0.32, p=0.02). Strikes during the Obstacles task did not significantly correlate with any of the cognitive measures, as Digit Span’s and Spatial Span’s association did not survive FDR correction. Directionality was the same for all relationships, with higher cognitive scores predicting faster gait speeds and fewer obstacle strikes across both tasks.

The three computerized cognitive tasks were also found to be associated with each other. Feature Match was positively associated with Digit Span (r =0.44, p<0.0001) and Spatial Span (r =0.49, p<0.0001). Spatial Span and Digit Span were also positively associated (r =0.35, p=0.002).

Discussion

The OBVIO walking task was shown to significantly slow walking speed and increase the number of obstacle strikes compared to the unobstructed Obstacles task. Participants walked 21% slower and had 2.5 more obstacle strikes per trial (on average), supporting that OBVIO is a difficult task for older adults. Furthermore, computer-based tests of attention, visuospatial and verbal working memory associated with performance on the OBVIO task. This finding supports that performance on the OBVIO task is related to the individual’s visuospatial cognitive function. The results of this study provide initial support for use of the OBVIO task as an outcome measure in future observational studies, or as a training task in future intervention studies that seek to investigate and improve walking performance in complex visuospatial conditions.

The OBVIO task is a form of dual-task walking, but has several notable differences compared to the majority of tasks that have been reported on previously. First, OBVIO is designed to primarily challenge visuospatial cognition whereas most dual-task studies primarily challenge other domains of cognition. For example, verbal fluency and serial subtraction tasks are common in dual-task walking studies but do not directly engage visuospatial cognition. Arguably, visuospatial cognition is more likely to have relevance to everyday community ambulation than most other cognitive domains.¹⁸

Second, most dual-task walking studies use a cognitive task that is completely unrelated to the walking task and therefore serves as a distractor or interference task. In contrast, the cognitive demand of attending to the walking environment during the OBVIO task is a crucial component of the walking task itself. This approach is suitable for studying varying levels of walking task complexity, as compared to traditional dual-task paradigms where the cognitive task diverts cognitive resources away from walking. An additional benefit is that both the motor and cognitive aspects of OBVIO contribute to the outcome measure of corrected walking speed (i.e., walking speed corrected for obstacle strikes). This single outcome measure can be advantageous versus trying to interpret outcome measures from two disparate tasks (i.e., walking task performance and cognitive task performance). Importantly, the intertwined motor and cognitive demands of OBVIO and the single outcome measure help to overcome the well-known problem of task prioritization in traditional dual-task designs.^10,11,27 With OBVIO, there is not an easy way to reduce/ignore the motor or cognitive demands. For example, a participant cannot simply perform the cognitive task at a slower rate to decrease the cognitive load. OBVIO helps to solve the task prioritization issue since the cognitive component (remembering obstacle location) is necessary for success on the walking task. Slower walking speed may even make the cognitive component more difficult, since the tray will block the view of the obstacles for a longer period of time which increasingly challenges visuospatial working memory.

Third, many dual-task walking paradigms are not suitable for making incremental adjustments in task difficulty. For example, incremental changes in a serial-7 subtraction task (e.g., subtracting by 3 versus 5 versus 7) do not necessarily make the task more difficult. Subtracting by 5 is generally easier than subtracting by 3 or 7, and some participants may differ in whether they find 3 or 7 to be more difficult. In contrast, the OBVIO task can be finely incremented by changing the length of the board. A longer board will increase the number of obstacle locations that must be held in working memory and/or will increase the duration of time that obstacle location(s) are held in working memory. Although adjusting the board length was not part of the present study, in future studies it may be helpful to make board length adjustments to evaluate how this difference in task complexity affects performance. Multiple complexity levels of OBVIO would also be a suitable task if studying the relationship between task complexity and brain activity in the context of the Compensation Related Utilization of Neural Circuits Hypothesis (CRUNCH), which would predict increasing use of neural resources, with increasing OBVIO board lengths, before hitting an asymptote.^28,29 Additionally, OBVIO could be used to test differences in cortical activation across age groups during varying levels of complex walking. OBVIO may also be suitable as a training task for intervention studies that wish to gradually increase task difficulty throughout the intervention. There are many possibilities for leveraging the fine-tuned adjustability of OBVIO task complexity. OBVIO can be modified to fit a wide variety of skill levels. Anyone who can step over obstacles can participate in the OBVIO task, and using a long enough tray would make OBVIO sufficiently difficult to challenge even high functioning participants.

While it is possible that the slower walking was due to a lack of arm swinging (since participants were holding a board during OBVIO walking), it is unlikely since a previous study instructed participants to walk with a similar obstructed vision board (but without obstacles) and they did not slow down their gait speed.³⁰ A more likely explanation for slowing of walking speed is the effect of blocking the lower visual field. For example, a prior study blocked the lower visual field with glasses (which does not inhibit natural arm swing), which caused both young and older adults to slow their gait speed while walking over multiple types of terrain.³¹

Performance on the OBVIO task was associated with multiple cognitive measures (Table 2, Figure 3) including Feature Match, Digit Span, and Spatial Span, which are tests of attention, working memory, and visuospatial working memory, respectively. Feature Match was the only test associated with gait speed of the two tasks, which is consistent with other measures of attention.^5,32 Feature Match, along with Digit Span and Spatial Span, were also significantly associated with the difference between Obstacle and OBVIO walking scores (Table 2, Figure 4), suggesting that a general association between walking function and cognitive function did not fully account for the relationship between OBVIO and the cognitive tasks. Rather, the specific challenge posed by the obstructed vision aspect of the task (after accounting for Obstacles walking performance) was also found to be associated with cognitive function.

Digit Span was also significantly associated with OBVIO performance, as well as the difference between OBVIO and Obstacle walking scores. This finding helps to validate that the working memory component of the OBVIO task (i.e., remembering the location of multiple obstacles being occluded from the participant’s vision) tests a similar construct as the more traditional computer-based test of working memory. Feature Match and Digit Span were significantly correlated (r=0.44, p<0.0001), consistent with previous work showing that verbal measures of working memory, such as Digit Span, correlate with goal-directed attentional selection.^33,34 It has previously been suggested that the ability to preserve working memory load by focusing one’s attention on only task relevant stimuli is a reason for this association.³⁵ In the context of walking, we can speculate that attentional capacity contributes to creating an accurate visuospatial map of the environment, which is necessary for successful use of working memory resources during task performance.³⁶

Some methodological decisions should be acknowledged. We chose to use the best performance out of three trials for each of the walking tasks. As naturally expected, some participants were cautious when initially attempting the OBVIO task and performed better after the first trial. We were mainly interested in each person’s best performance, not their cautious behavior when first exposed to the novel task. Three participants were excluded from analysis for not averaging at least one successful obstacle clearance during the OBVIO task. This means that the participants did not follow our instructions to attempt to step over each obstacle, but rather shuffled their feet, kicking every obstacle. While this strategy preserved their walk speed, it resulted in a low performance score (speed × obstacle clearance percentage). Additionally, recruitment was primarily through the North Florida/South Georgia Veterans Health System, so the majority of participants were male. It is important to note that males and females have been reported to use different strategies in spatial skill.³⁷ Therefore, reproducibility of this study’s exact results may vary based on characteristics of the sample.

The computerized cognitive measures were chosen based on being accepted and validated for measuring cognitive function, particularly the tests of visuospatial attention and working memory, which is the domain we considered to be important for OBVIO task performance. It is important to note that the OBVIO performance score is not intended to be a measure of cognition, but rather a measure of walking performance in a cognitively demanding environment. More research is needed before using the OBVIO task as an assessment tool to predict older adults at risk of falls.

The primary walking measure score combined walking speed and successful obstacle crossing. These two aspects are arguably the most meaningful measures of performance for this task, as they most directly align with the participant’s goals. They are also the most intuitive when presenting these results to a general audience, and the most feasible for future efforts translating this to the clinic.

Finally, the cognitive assessments were only weakly correlated with walking outcomes. Therefore, the role of cognition is just one potential contributor, as many other factors will contribute to performance.³⁸ For example, other contributors to walking function can include musculoskeletal, neuromuscular, coordination, balance, somatosensation, pain, orthopedic issues, and more. Therefore, it would be unusual for any single factor to have a dominant effect that produces high correlation coefficients. The presence of even a weak correlation implies a sufficiently important role that rises above the “noise” of this multifactorial task. Though the correlations in the present study were characterized as weak, these results align with prior investigations demonstrating weak to moderate correlations between cognitive function and walking performance.^1–5 Importantly, the increased difficulty due to the addition of an obstructed vision board is associated with measures of cognition. These results support that OBVIO walking is a cognitively demanding walking task that is promising for assessing age-related changes in the cognitive control of walking.

Conclusion

Performance on our novel OBVIO walking task was significantly associated with cognitive assessments of attention and working memory. These tests of cognition continued to be related to OBVIO performance after accounting for performance on the unobstructed obstacle walking task. There are multiple potential uses for the OBVIO task in future studies. These include studying brain activity under varying levels of task complexity, assessing the role of visuospatial cognition in the performance of complex walking tasks, and/or as a task to use within intervention paradigms to train visuospatial control of walking. Assessments and interventions that influence the interplay between visuospatial function and walking function could lead to improved safety of community ambulation, including for older adults with cognitive impairments who wish to “age in place” in their preferred environment.

Data Availability Statement

The raw data involved in this article will be made available to any reasonable request.