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Cognitive Correlates of Timed Up and Go Subtasks in Older People With Preserved Cognition, Mild Cognitive Impairment, and Alzheimer’s Disease

Ansai, Juliana Hotta MSc; Andrade, Larissa Pires de PhD; Nakagawa, Theresa Helissa PhD; Vale, Francisco Assis Carvalho PhD; Caetano, Maria Joana Duarte MSc; Lord, Stephen Ronald PhD; Rebelatto, José Rubens PhD

American Journal of Physical Medicine & Rehabilitation: October 2017 - Volume 96 - Issue 10 - p 700–705
doi: 10.1097/PHM.0000000000000722
Original Research Articles

Objective: To determine whether impaired Timed Up and Go Test (TUG) subtask performances are associated with specific cognitive domains among older people with preserved cognition (PC), mild cognitive impairment (MCI), and mild Alzheimer’s disease (AD).

Design: TUG subtasks performances were assessed by the Qualisys motion system. Cognition was assessed by Addenbrooke’s Cognitive Examination and the Frontal Assessment Battery (FAB).

Results: The highest correlations with transition subtasks were with aspects of executive function, i.e. the fluency domain in the PC group (n = 40), FAB scores in the MCI group (n = 40), and the visuospatial domain in the AD group (n = 38). No significant associations were found between the walking subtasks and cognition in any group. Multivariate linear regression models identified the fluency domain as an independent predictor of turn-to-walk and turn-to-sit measures in the PC group, and the visuospatial domain as an independent predictor of turn-to-walk and turn-to-sit measures in the AD group, adjusted for age and sex.

Conclusions: Poorer executive functioning was associated with impaired transition mobility in all groups. The significant associations between visuospatial impairment and poor transition mobility in the AD participants may provide insight into why this group has an elevated fall risk.

From the Postgraduate Program in Physiotherapy (JHA, LPdA, JRR) and Department of Medicine (FACV), Federal University of São Carlos, São Carlos, SP, Brazil; Centro Universitário do Norte, Manaus, AM, Brazil (THN); and Neuroscience Research Australia, University of New South Wales, Sydney, Australia (MJDC, SRL).

All correspondence and requests for reprints should be addressed to: Juliana Hotta Ansai, MSc, Postgraduate Program in Physiotherapy, Federal University of São Carlos, Rodovia Washington Luiz, km 235, CEP 13565-905, São Carlos, SP, Brazil.

This study was supported by the Coordination for the Improvement of Higher Education Personnel (CAPES).

Financial disclosure statements have been obtained, and no conflicts of interest have been reported by the authors or by any individuals in control of the content of this article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.ajpmr.com).

Balance and mobility disorders have been documented in people with mild cognitive impairment (MCI) (a transitional state between the cognitive changes of normal aging and very early dementia) and mild Alzheimer’s disease (AD).1,2 The reasons for these disorders remain uncertain, and a better understanding about mobility impairments in these populations is needed to improve rehabilitation planning.

In this sense, recent quantitative mobility analysis studies have identified gait impairments associated with increased risk of falls in people with mild AD3,4 and more advanced cognitive impairment.5 Quantitative analysis of movement has also been used to measure Timed Up and Go Test (TUG) performance, a widely used test for assessing mobility, fall risk, and functional status in both cognitively intact and impaired older people.6–8 These analyses provide a range of quantitative parameters for each TUG subtask (sit-to-stand, walking forward, turn-to-walk, walking back and turn-to-sit).7

In a study contrasting TUG performance between older people with MCI and preserved cognition (PC), Mirelman et al.5 found that those with MCI took longer to complete the turn-to-walk subtask and also had less trunk angular velocity during the turn-to-walk. Greene and Kenny9 assessed TUG performance in relation to cognitive decline in community-dwelling older adults. They found that participants who had a three plus reduction in mini-mental state examination (MMSE) scores over 2 years demonstrated reduced TUG walking performance (Y axis angular velocity values at mid-swing). No significant associations between cognitive decline and TUG turn-to-walk parameters were found, but this may be caused by certain limitations: kinematic data were acquired using two body-worn inertial sensors, which is a less reliable technique than other methods using more sophisticated motion analysis systems10; the older participants were not categorized into mild AD or MCI; and the relationship between TUG subtasks and different cognitive domains were not evaluated.

TUG transition subtasks (sit-to-stand, turn-to-walk, turn-to-sit) may require additional cognitive resources related to planning and organization than walking subtasks.11–13 For example, we have reported14 the average velocity of the trunk during the TUG turn-to-walk subtask is higher in older people with PC and MCI compared to those with AD. This previous analysis, however, did not examine whether TUG subtask performances were related to deficits in specific cognitive domains. Although MCI and AD pathophysiology can differ, the associations between cognition, mainly executive function and visuospatial domains, and mobility performances might manifest similarly.5,13

Therefore, the aim in this study was to determine whether impaired performances in TUG subtasks are associated with specific aspects of cognitive capacity among older people with PC, MCI, and AD. The hypotheses were that (a) executive function and visuospatial measures would be the cognitive measures most strongly associated with TUG subtask performances; (b) associations between the cognitive measures and the transition subtasks would be stronger than those between the cognitive measures and the non-transition (simple walking) subtasks; and (c) compared to the PC group, associations between cognitive and TUG measures would be stronger within the MCI group and stronger again within the AD group.

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METHODS

Study Design and Participants

This cross-sectional study included community-dwelling adults aged 65 years and older recruited from health centers, the School Health Unit of Federal University of São Carlos (São Carlos—SP, Brazil), and Open Universities for older adults. Inclusion criteria were: ability to walk short distances (at least 10 m) without a walking aid and availability to participate in the assessments. Exclusion criteria were as follows: presence of motor impairment after a stroke, neurological disorders that interfered in cognition or mobility (e.g., Parkinson’s disease, multiple sclerosis, Huntington’s disease, epilepsy, and trauma brain injury), advanced or moderate AD, and severe uncorrected visual acuity and deafness. The Federal University of São Carlos Human Research Ethics Committee approved the study protocol, which accords with the Declaration of the World Medical Association, and all participants provided written informed consent before participation. This study conforms to all STROBE guidelines and reports the required information accordingly (see Supplementary Checklist, http://links.lww.com/PHM/A401).

A trained neurologist physician confirmed the diagnosis of PC, MCI, and AD (mild stage). Participants with PC were defined as those with normal global cognition adjusted for educational level (MMSE score of 20+ points for people who were illiterate, 25+ for those with 1–4 years of education, 26.5+ for 5–8 years, 28+ for 9–11 years, and 29+ for higher educational levels)15 and did not exhibit criteria for MCI or dementia. MCI criteria were as follows: cognitive complaint reported by the participant or an informant (a person who had contact with the participant at least half a day, four days per week), objective cognitive impairment not related to delirium with a score of 0.5 in the Clinical Dementia Rating scale (CDR), normal global cognitive function for educational level,15 preserved function, and absence of clinical dementia.16,17 AD was diagnosed according to the Diagnosis and Statistical Manual of Mental Disorders (DSM-V TR)16 and included only older people with a CDR score of 1.0.

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Measures

Participants were instructed to wear comfortable clothing and closed usual shoes, to eat at least 1 hour before the tests, to avoid vigorous exercise the day before assessment, and to bring visual or auditory aids, if applicable.

Depressive symptoms were assessed using the Geriatric Depression Scale (GDS)18 and leisure-time energy expenditure were assessed using the Minnesota Questionnaire.19 Educational level and history of falls20 in the past year were obtained by structured interview. If necessary, an informant assisted participants complete the questionnaires.

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TUG Subtasks

Qualisys motion-capture system with seven cameras (Qualisys AB, Gothenburg, Sweden) (1280 × 1024 resolution; 1.3 megapixels) was used for the TUG analysis. A single assessor (good intra-rater reliability in all segments) placed the reflective markers on iliac spines, iliac crests, bilateral greater trochanter, epicondyles of femur, lateral and medial malleolus, heel, 1st and 5th metatarsal heads, acromion, sternum, and 7th cervical vertebra. Markers of iliac spines, iliac crests, heel, and metatarsal were used to define the pelvis and foot. Clusters containing reflective marks affixed in a non-collinear form defined the trunk, thigh, and leg, which were placed on thoracic and lumbar areas and on distal thirds of thigh and leg.10

For the TUG, participants were asked to rise from a 45-cm-high chair with trunk support and armrests and adapted design for capturing markers, walk 3 m at their usual pace, turn 180°, return, and sit down. The instructions were as follows: “You will get up, walk the course, go back and sit down. Ready, go.” “Get up.” “Walk.” “Go back.” “Sit down.” The instructions were standardized for all participants to maximize the consistency of the mobility assessment in older people with varying comprehension difficulties. Participants also conducted one practice trial before undertaking the test trials.

The Qualisys Track Manager (Qualisys AB) and the Visual3D (C-Motion, Inc., Germantown, MD) software programs were used for data processing. The sampling frequency was 120 Hz.10 A MATLAB routine was applied to detect, separate, and analyze TUG subtasks (sit-to-stand, walking forward, turn-to-walk, walking back, and turn-to-sit). Because of capture field limitations, the TUG test was performed six times. Participants could rest between trials if required. The first three trials recorded sit-to-stand, walking forward, and turn-to-sit subtasks, and the average of these trials was used in the analysis. The three last trials recorded turn-to-walk and walking back subtasks, and the average of these three trials was also analyzed. Sit-to-stand, turn-to-walk, and turn-to-sit were categorized as transition subtasks and walking forward and walking back as non-transition subtasks.

The sit-to-stand subtask was detected by the minimum and maximum angular velocities of trunk in the X axis (i.e., flexion/extension).6 In the walking forward and walking back subtasks, the first step was detected by the linear velocity of the heel and 5th metatarsal marker in the Y axis (i.e., adduction/abduction).21 The turn-to-walk subtask was detected by the first and the second peak of foot progression angle in the lower limb that started the turn. The minimum angular velocity of the trunk in the Z axis (i.e., rotation) indicated the beginning of the turn-to-sit subtask and the maximum value in the X and Z axes (the last to occur) indicated its ending.6

For each TUG subtask, the measure that better distinguished older people with PC, MCI, and AD based on previous research5,14 was selected as the dependent variable for inclusion in the statistical analyses. Thus, the following measures were analyzed: peak velocity of trunk (X axis) (°/s) during the sit-to-stand subtask; minimum peak of knee (X axis) (°) during the stance phase of the walking forward subtask; average velocity of trunk (Z axis) (°/s) during the turn-to-walk subtask; maximum range of motion (ROM) of hip (Y axis) (°) from heel-strike until maximum peak of hip during the stance phase of the walking back subtask; and peak velocity of trunk (Z axis) (°/s) during the turn-to-sit subtask.

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Cognitive Function

The cognitive function assessments [Addenbrooke’s Cognitive Examination-Revised (ACE-R)22 and the Frontal Assessment Battery (FAB)] were conducted in a quiet room by a single assessor. ACE-R is useful for assessing early stages of dementia. The ACE-R has a score range from 0 to 100 points (higher scores indicating intact cognition) and contains six cognitive domains: attention and orientation, memory, verbal fluency (related to cognitive abilities of executive function), visuospatial, and language skills.22 Both total and domain subscores were used in the analyses. The FAB measures executive function associated with frontal cortex damage and comprises six tasks (similarities, lexical fluency, motor series, conflicting instructions, go/no-go, and comprehension behavior).23

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Statistical Analysis

Data were analyzed using SPSS software, version 20.0. The level of significance was set at 0.05. Data normality was tested using the Kolmogorov-Smirnov test. Differences between groups for descriptive measures were analyzed with ANOVAs (post hoc Tukey tests) and χ2 tests. To examine the associations between TUG subtask and cognitive performances in each group, Pearson product–moment correlations were applied. The magnitude of the correlations was based on Munro’s classification24 (low: 0.26–0.49; moderate: 0.50–0.69; high: 0.70–0.89; very high: 0.90–1.00). Backward stepwise linear regression was conducted for each TUG subtask if there was more than one significant association found in the Pearson product–moment analysis (i.e., coefficient correlation—r > 0.30, p < 0.05), adjusted for age and sex. When backward stepwise linear regression was performed, the probability of F to remove was ≥0.100—this method was chosen to lower risk of type II errors (i.e., missing a predictor that does in fact predict the outcome). The total performance of ACE was not included in the regression model, as this variable is composed by several cognitive domains.

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RESULTS

The sample comprised 40 people with PC, 40 with MCI, and 38 with mild AD. Descriptive data for each group are presented in Table 1. In general, the sample had a high prevalence of women (mainly in the MCI group), low educational levels, few depressive symptoms, little indication of low weekly caloric expenditure among groups, a high mean age in the AD group, and a high proportion of fallers in the MCI and AD groups. Table 2 displays the correlation coefficients between the TUG subtask and cognitive variables for each group.

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Associations Within the Preserved Cognition Group

In the PC group, turn-to-sit subtask performance was most strongly correlated (moderate magnitude) with the cognitive function measures. Specifically per subtask, the highest correlations were between sit-to-stand peak velocity and fluency (r = 0.362), turn-to-walk average velocity and fluency (r = 0.584), and turn-to-sit peak velocity and fluency (r = 0.626). No significant associations were found between non-transition subtasks and cognitive variables.

In multivariate regression models, the fluency domain variable was identified as the only variable that predicted the turn-to-walk subtask (B [95% confidence interval] = 3.56 [1.42–5.70], P = 0.002) and the turn-to-sit subtask (B [95% confidence interval] = 5.44 [2.69–8.19], P = 0.000) when adjusting for age and sex. These single-variable models explained 40.4% (adjusted R2 = 0.369) and 44.5% (adjusted R2 = 0.413) of variability in the two TUG subtask measures, respectively.

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Associations Within the Mild Cognitive Impairment Group

In the MCI group, the three transition variables (sit-to-stand peak velocity, turn-to-walk average velocity, and turn-to-sit peak velocity) were similarly correlated (low magnitude) with FAB scores (r = 0.335–0.373). There were no significant correlations between the transition variables and the other cognitive measures and no significant correlations between the non-transition subtasks and cognitive variables. Furthermore, the MCI group presented the weakest associations between cognition and TUG subtasks among the groups.

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Associations Within the Alzheimer’s Disease Group

In the AD group, turn-to-sit subtask performance was most strongly correlated (moderate magnitude) with the cognitive function measures. Specifically, visuospatial ability was the only cognitive variable associated with sit-to-stand peak velocity (r = 0.369) and the most strongly correlated variable with turn-to-walk average velocity (r = 0.498) and turn-to-sit peak velocity (r = 0.641). No significant associations were found between non-transition subtasks and cognitive variables.

In multivariate regression models, the visuospatial domain variable was identified as the only variable independently associated with the turn-to-walk subtask (B [95% confidence interval] = 2.44 [0.82–4.05], P = 0.004) and the turn-to-sit subtask (B [95% confidence interval] = 6.61 [3.89–9.33], P = 0.000) when adjusting for age and sex. These single variable models explained 31.8% (adjusted R2 = 0.278) and 41.0% (adjusted R2 = 0.393) of variability in the two TUG subtask measures, respectively.

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DISCUSSION

This study investigated the relationship between performance on TUG subtasks and cognitive domains in older people with PC, MCI, and mild AD. Fluency domain, visuospatial domain, and FAB measures were significantly correlated with the transition and not the non-transition TUG subtasks. Further, the fluency domain was identified as the strongest predictor for the turn-to-walk and turn-to-sit measures in the PC group, whereas the FAB and visuospatial measures were the strongest predictors for these TUG subtasks in the MCI and AD groups, respectively.

The first two hypotheses of this study, (a) executive function and visuospatial measures would be the cognitive measures most strongly associated with TUG subtask performances and (b) associations between cognitive measures and transition subtasks would be stronger than those between cognitive measures and non-transition subtasks, were supported. Performances in the transition subtasks were associated with ACE-fluency domain scores in the PC group and FAB scores in the MCI group. Both tests (ACE-fluency domain and FAB) evaluate cognitive flexibility of executive function.

Our findings are in accordance with previous studies showing a higher association between TUG performance and verbal fluency in healthy older people (MMSE score ≥ 25),11 and a relationship between slow perceptual speed (reflecting executive function abilities) and impaired visuospatial abilities, but not poor memory, with reduced turn-to-walk TUG subtask performance in a mixed sample of older people with MCI and PC.5 Further, despite different executive function measures, McGough et al.25 also found significant associations between executive function and TUG performance in older people with MCI. The FAB provides a composite measure and may be a particularly sensitive test for detecting subtle cognitive impairment in people with MCI.23 The above findings, therefore, indicate executive function is an important cognitive domain in planning and correct execution of mobility tasks.

In the AD group, in addition to executive function measures, visuospatial tasks were associated with the turn-to-walk and turn-to-sit subtasks, with the visuospatial domain measure identified as the independent predictor in the two multiple regression analyses. This is consistent with the suggestion by Sheridan and Hausdorff26 that gait disturbances in AD might be linked to disintegration of higher cortical perception, such as visuospatial integration, and in line with the findings of Menant et al.,13 who found that visuospatial tasks affect locomotor control more than non-spatial tasks in older people, possibly a result of competition for common networks between visuospatial and locomotor information. Thus, the assessment of the visuospatial domain can provide information about the quality of transition tasks in people with mild AD.

Deficits in visuospatial capacity have also been identified as a predictor of falls in older people with different types of dementia.3,27 In line with the suggestion by Amboni et al.28 that cognition plays a pivotal role in mobility and fall prevention, the findings of the present study suggest a need for integrated visuospatial and transition mobility tools to improve screening and the understanding of increased fall risk in AD, and a need for fall prevention interventions that include strategies to address visuospatial deficits and/or transition tasks for older people with AD.

No significant associations were found between the cognitive measures and non-transition subtask performance in any group. Herman et al.11 studied three different mobility tools (Berg Balance Test, Dynamic Gait Index, and TUG) and found that only TUG was significantly associated with executive function. The findings of this study confirm that transition tasks, such as turning and transferring components, may be more challenging and require additional cognitive resources related to planning and organization than straight-line walking. Turning involves more inter-limb coordination and modification of locomotor patterns, which requires frontal lobe cognitive, executive function, and perception. Turning also places greater demands on visual processing to enable clear directional movement.11,29

In contrast to our findings, Mirelman et al.5 found an independent association between perceptual speed and step regularity during non-transition (walking) TUG subtasks in a sample of older people with MCI and PC. However, Mirelman et al.5 assessed participants at home and because of this, a shorter distance was used to assess TUG and the conditions of assessment were not the same among participants. More information about TUG subtasks performances using a minimal and standardized path (3 m) in people with MCI and mild AD is needed to confirm the results.

Our third hypothesis was that associations between cognition and TUG measures would be stronger in people with MCI and stronger again in AD compared with the PC Group because of greater variance in both cognitive capacity and physical performance measures in the latter two groups. This would be consistent with Blackwood et al.12 who reported that executive function was significantly associated with TUG total times in older people with MCI, but not in cognitively intact older people. However, in contrast to the AD group, the MCI group presented the weakest associations between cognition and all TUG subtasks, which may be caused by the heterogeneity of this population and possibly relate to the pathophysiology of MCI and AD being different. Nonetheless, significant differences between cognitive and TUG transition subtask performances were evident and other research points to people with amnestic MCI being more likely to show subtle gait-related motor disturbances.30 Further studies with different MCI types using methods sensitive enough to detect a subtle decline in motor performance need to be undertaken to better understand relationships between impaired cognition and mobility.

The findings of the present study have important clinical applications in that they demonstrate the importance of considering new mobility assessments, specifically those involving transition tasks, to better understand gait impairments in older people with different cognitive profiles. Our findings indicate that the TUG test can provide more information than just the total time spent and suggest a quality assessment in each subtask is important even if sophisticated gait analysis equipment is unavailable. Further, the findings may help guide and improve specific prevention and treatment strategies for older people with PC, MCI, and mild AD, such as interventions aimed at enhancing or compensating for reduced visuospatial abilities in people with mild AD. Finally, the complementary use of cognitive assessments, especially executive function, can add to our understanding of transition mobility impairments and mechanisms of falls.

The strengths of this study include the novelty of the analysis that investigated kinematic measures of the TUG subtasks and their relationship with different cognitive domains. The authors also acknowledge the study has certain limitations. First, the MCI sample was not categorized into subtypes, which can influence mobility and cognitive performances, so the findings would not generalize to specific MCI types. Second, the small capture field to kinematic analysis of TUG prevented us from calculating other interesting measures, such as step variability, during non-transition subtasks. Future research including longitudinal designs is required to verify the current findings and identify other possible predictors of TUG subtasks performances in people with MCI and mild AD. Randomized trials are also needed to demonstrate that executive function/visuospatial training and/or transition tasks can be improved with intervention.

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CONCLUSION

In conclusion, no significant associations were found between TUG non-transition subtasks and cognition in older people with PC, MCI, and AD. In contrast, poorer executive function (indicated by one or multiple tests) was associated with impaired transition mobility in all three groups and visuospatial impairment was identified as the strongest cognitive predictor for poor TUG transition subtask performances in people with mild AD. This differential finding in the AD sample may elucidate into why people with AD have an elevated risk of falls.

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Keywords:

Aged; Kinematics; Functional Mobility; Alzheimer Disease

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