N. Ward1, A. Menta1, S. Peach1, S.A. White2, S. Jaffe2, C. Kowaleski3, K. Grandjean da Costa1, J. Verghese4,5, K.F. Reid2
1. Tufts University, Department of Psychology, Medford, MA, USA; 2. Nutrition, Exercise Physiology and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research on Aging at Tufts University, Boston, MA, USA; 3. City of Somerville Council on Aging, Health and Human Services Department, Somerville, MA, USA; 4. Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA; 5. Institute of Aging Research, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
Corresponding Author: Nathan Ward, PhD. Department of Psychology, Tufts University, Boston, MA, 02155. Telephone: +1-617-627-2645; Fax: +1-617-627-3181;
J Frailty Aging 2021;in press
Published online June 21, 2021, http://dx.doi.org/10.14283/jfa.2021.27
The purpose of this study was to characterize Cognitive Motor Dual Task (CMDT) costs for a community-based sample of older adults with Motoric Cognitive Risk Syndrome (MCR), as well as investigate associations between CMDT costs and cognitive performance. Twenty-five community-dwelling older adults (ages 60-89 years) with MCR performed single and dual task complex walking scenarios, as well as a computerized cognitive testing battery. Participants with lower CMDT costs had higher scores on composite measures of Working Memory, Processing Speed, and Shifting, as well as an overall cognitive composite measure. In addition, participants with faster single task gait velocity had higher scores on composite measures of Working Memory, Processing Speed, and overall cognition. Taken together, these results suggest that CMDT paradigms can help to elucidate the interplay between cognitive and motor abilities for older adults with MCR.
Key words: Motoric cognitive risk syndrome, cognitive motor dual task costs, cognition, mobility.
Cognition and mobility are intimately linked, such that motor tasks like walking require increased amounts of cognitive processing, and this linkage is especially important for older adults (1). Studies using Cognitive Motor Dual Task (CMDT) paradigms (i.e., gait paired with a cognitive task) find greater impairments for older adults compared to younger adults. These costs suggest overlapping mechanisms or at least competition for resources between cognition and gait (2). Understanding this interplay between cognition and gait is especially important for certain aging populations, such as older adults with dementia and even pre-dementia classifications who might be at an increased risk of fall (3).
Motoric Cognitive Risk Syndrome (MCR) is a classification in which older adults, despite having preserved activities of daily living, exhibit both subjective memory complaints and slow gait in addition to an elevated risk factor for dementia (4–6). While an MCR diagnosis shares features with a diagnosis of Mild Cognitive Impairment (MCI), MCR predicts risk of dementia even after accounting for overlap with MCI subtypes (7).
MCR has also been associated with reduced gray matter in prefrontal areas, as well as in supplementary motor areas [8,9]. In addition, MCR has been associated with worse cognitive performance in domains that rely on prefrontal and motor planning areas, such as attention and executive function , which are also crucial for dual tasking. In short, there may be an impaired executive system in MCR, and CMDT may further reveal these impairments; however, to date no studies have investigated or characterized this.
The purpose of this study was to investigate, for the first time, the association between cognitive performance and CMDT costs for a community-based sample of older adults with MCR. By leveraging a CMDT paradigm, we sought to further understand the mechanisms of cognitive and motor control important for navigating complex environments in which attention may be divided. Furthermore, CMDT costs have been associated with the risk of developing dementia (11, 12) and can provide us with additional insights into MCR and potential compensatory mechanisms involved in cognitive-motor interference.
This study used baseline data from a clinical trial examining the real-world effects of a community-based physical activity intervention in a group of older adults with MCR (NCT03750682). Data were collected at a community-based urban senior center in Greater Boston, MA, USA.
Participants were considered to have MCR if they met all of the following criteria: 1. Self-reported memory complaint as assessed using the Geriatric Depression Scale (13); 2. Objectively defined slow gait, defined as gait speed below previously described age-appropriate mean values (age 60-74 yrs: < 0.70 meters per second and age 75+: < 0.60 meters per second; (4); 3. Absence of mobility-disability (inability to ambulate even with assistance or walking aids); 4. Absence of dementia diagnosis. The recruitment of participants resulted from targeted community outreach conducted by the study investigators in close collaboration with the senior center’s Health and Wellness Coordinator. Participants who were interested in the study were pre-screened via telephone or in person and were considered eligible for a screening visit if they were 60-89 years, community-dwelling, sedentary, and reported a subjective memory complaint. Eligible participants were invited to participate in additional MCR screening procedures that consisted of a medical history questionnaire and an objective 4-meter assessment of gait speed as part of the short physical performance battery (SPPB) test (14). Participants were excluded if they had an acute or terminal illness, myocardial infarction or upper and lower extremity fracture in the previous 6 months, symptomatic coronary artery disease, uncontrolled hypertension (>180/100 mmHg), or significant cognitive impairment (Modified Mini-Mental State Examination Score (3MSE) <80; ). In addition, participants’ primary care physicians confirmed the absence of a diagnosis of dementia.
A signed informed consent was obtained from all study participants. This study was approved by the Tufts University Health Sciences Institutional Review Board.
Measurements and Procedures
Cognitive testing took place in a quiet room with a research assistant, and participants were offered breaks throughout the testing. The cognitive testing battery was conducted on a tablet device using the mobile application BrainBaseline. BrainBaseline is a scientifically-validated research tool (16), and in the current study, a testing battery of eight standard cognitive tasks was used. Cognitive tasks included the Digit Symbol Substitution Task (DSST), the Digit Span Task, the N-Back Task, the Speed Task, the Erikson Flanker Task, the Stroop Task, the Task Switching Task, and the Trail Making Task, which were combined into several different composite scores (17). Specifically, the Digit Span Task and N-Back Task comprised a Working Memory composite measure; the Flanker Task and Stroop Task comprised an Inhibitory Control composite measure; the Task Switching Task and Trail Making Task comprised a Shifting composite measure; and the Speed Task and DSST comprised a Processing Speed composite measure. In addition, an overall cognition composite was created by combining all of the individual measures from the computerized testing battery as well as the 3MSE measure (17). In order to create the cognitive composite scores, z-scores were calculated for the cognitive variables. Next the Stroop, Flanker, Speed, Task Switch, and Trails were multiplied by -1 so that for all measures, larger values indicate better performance whereas smaller values indicate poorer performance.
To assess dual task function in older adults, complex walking tasks were used (18). The Gait Speed Test was first used to measure speed of walking 7 meters at normal pace, without any other simultaneous task (i.e., Single Task). Next participants were given a letter (e.g., S, T, or M depending on the day of the month they were born) and asked to name as many animals as they could think of whose name started with that letter while walking the same 7-meter distance at their normal walking pace (i.e., Dual Task).
The primary outcome of interest for the current study was CMDT interference and its relationship to our computerized cognitive test battery. We created the CMDT cost metric using the following equation: (single gait speed – dual gait speed)/single gait speed (2). Next, we assessed the relationships between CMDT cost and cognitive composite scores using Spearman’s rank correlation coefficients. All statistical analyses were conducted in Jamovi version 1.1.9 (www.jamovi.org).
Descriptive statistics are detailed in Table 1. Our total sample size included 25 participants with MCR. Participants were predominately white (72%), and female (80%). Fifty-six percent reported having some amount of college education, and the average 3MSE score was 92 out of 100, indicating global cognitive deficits. Six participants reported having at least one fall in the past 12 months. The overall SPPB gait speed sub-scores were indicative of severe mobility and gait speed impairments. In addition, our participants with MCR consistently demonstrated reduced cognitive performance on the computerized cognitive tasks compared to a healthy older adult population (16).
Abbreviations: BMI, Body Mass Index; kg, kilograms; m/s, meters per second; 3MSE, Modified Mini Mental Status Exam; DSST, Digit Symbol Substitution Task, one-minute version; n, number of; %, percent correct; ms, milliseconds
Associations Between CMDT Costs and Cognitive Composite Scores
Figure 1 represents linear relationships between CMDT costs and cognitive composite scores that were significant. Specifically, we found that participants with lower CMDT costs performed higher on Working Memory (r = -0.36, p = 0.04), Processing Speed (r = -0.39, p = 0.03), Shifting (r = -0.39, p = 0.03), as well as overall cognition (r = -0.38, p = 0.03).
Associations Between Single Task Gait Speed and Cognitive Composite Scores
Given the exceptionally low average 4-meter gait speed of our sample (i.e., 0.52 m/s), we also wanted to test for associations between single task gait speed and cognitive composite scores. We found that participants with faster gait speed performed higher on Working Memory (r = 0.48, p = 0.01), Processing Speed (r = 0.57, p = 0.001), and overall cognition (r = 0.46, p = 0.01) (Figure 2).
This study is the first to investigate associations between CMDT costs and cognitive performance for a community-based sample of older adults with MCR. We found that participants with lower CMDT costs performed higher on three individual cognitive composite measures (i.e., Working Memory, Processing Speed, and Shifting), as well as higher on an overall cognitive composite metric. Previous research has found that compared to participants without MCR, participants with MCR had lower performance on a multitude of cognitive measures, including similar measures to those used in the current study (e.g., Digit Span, DSST, Trails) (10, 19–21). Building on this prior research, we found that lower cognitive performance for a sample of older adults with MCR was associated with higher cognitive motor dual task costs. Our study is the first to report on cognitive motor dual task costs for older adults with MCR (22). Furthermore, our results with an older adult MCR sample build on prior, non-MCR research that found that dual task gait speed was related to cognitive decline, which further emphasizes the potential for using cognitive motor dual task paradigms in clinically-meaningful settings (23).
To reiterate, we found that lower cognitive performance was associated with higher cognitive motor dual task costs for a sample of older adults with MCR. This could be due to reduced gray matter in prefrontal areas (8) that are important for dividing (24) or shifting (25) attentional resources between cognitive and motor demands, which might indicate an impaired executive system in MCR (26), although future studies with cognitive neuroscientific measures, such as fMRI or fNIRS, should verify this.
In addition to CMDT costs, we investigated associations between single task walking and cognitive performance as greater understanding of link between walking performance, cognitive frailty and the development of cognitive disorders remains a research area of significant clinical and gerontological importance (27–30). We found that participants with faster gait speed performed higher on two individual cognitive composite measures (i.e., Working Memory and Processing Speed), as well as higher on an overall cognitive composite metric. This aligns with previous research that has found associations between an MCR subtype based on single task gait velocity and global cognition (31), as well as studies that have found relationships between single task gait speed and cognitive decline (32). Furthermore, this reiterates the importance of even single task walking as a possible window into the mind.
As with any study, there are both strengths and limitations of the current investigations. By conducting all study procedures at a senior center, we were able to reach a community-based sample of older adults with MCR that would not likely be well-represented in clinical trials. That said, future research with larger sample sizes and healthy control groups without MCR are required to further understand how much our results generalize beyond our specific community-based sample. Future work should also consider using wearable sensors during single and dual task walking conditions, which would allow for a richer set of gait kinematics to explore how CMDT costs might differ across different MCR subtypes (31).
In conclusion, our results suggest that the interplay between cognitive and motor abilities is an important avenue to explore for older adults with MCR. CMDT paradigms like the one used in the current study could be useful in clinical settings for early diagnosis of cognitive decline (12, 33), as well as an intervention modality (34). Indeed, others have found promising results when using CMDT training for older adults with cognitive impairments, such as MCI or dementias (35), and future work should investigate whether this extends to MCR.
Funding: This research was supported by the Boston Claude D. Pepper Older Americans Independence Center (1P30AG031679), the National Center for Advancing Translational Sciences, National Institutes of Health (NIH) (UL1TR001064) and is based on the work supported by the U.S. Department of Agriculture, under agreement No. 58-8050-9-004. Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.
Acknowledgements: We thank the following individuals for supporting this project: Joan Severson, Digital Artefacts LLC; Joseph A. Curtatone, Mayor of Somerville, MA.
Conflicts of Interest: The authors state no conflicts of interest.
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