Abstract
Background
Individuals with mild cognitive impairment (MCI), often a precursor to dementia, experience limitations in completing daily activities. These limitations are particularly important to understand, as they predict risk for dementia. Relations between functional changes and both cognitive decline and upper extremity motor impairments have been reported, but the contribution of motor function to relations between cognitive function and functional independence remains poorly understood. We examined the relationship between cognition and upper extremity activities, and whether this relation was mediated by motor function.
Methods
A total of 430 community-dwelling primary care patients aged at least 65 years from the Boston Rehabilitative Impairment Study of the Elderly completed self-report measures of upper extremity function, tests of neuromuscular attributes to measure motor function (reaction time, pronosupination of the hands), and neuropsychological measures. Participants were classified based on cognitive performance into groups: MCI and without MCI, with MCI further classified by cognitive subtype. Regression and mediation analyses examined group differences and relations between cognitive function, upper extremity function, and neuromuscular attributes.
Results
MCI participants demonstrated poorer neuromuscular attributes and self-reported upper extremity function, and neuromuscular attributes significantly mediated positive relations between cognitive status and self-reported upper extremity function. Poorer self-reported upper extremity function was most prominent for groups with executive dysfunction.
Conclusions
Together with previous research, results suggest that the relationship between cognitive function, motor function, and functional activities is not confined to mobility tasks but universally related to body systems and functional activities. These findings inform new approaches for dementia risk screening and rehabilitative care.
Keywords: Cognitive aging, Functional performance, Motor control
Mild cognitive impairment (MCI) has been described as an intermediate stage of cognitive functioning between typical aging and dementia. MCI is traditionally characterized by greater cognitive decline than expected for an individual’s age and education without significant functional limitations, and a diagnosis of MCI increases risk for later diagnosis of dementia (1,2). Preserved activities of daily living has been an essential feature differentiating MCI from more significant cognitive impairment (eg, dementia), yet a growing literature suggests that individuals with MCI experience mild declines in their ability to complete daily activities such as meal preparation (3). These changes are particularly important given their strong predictive value for future cognitive and functional decline and conversion to dementia (4–6). However, the nature of these functional changes remains poorly understood, including the extent to which they are driven by cognitive (ie, central) factors associated with brain function such as memory and executive function and/or motor (ie, peripheral) factors such as muscle strength, speed, and coordination. Understanding this relationship would likely have important clinical implications to inform targets for intervention and prevention, characterize the progression of decline, and identify individuals at greatest risk for dementia.
Systematic examination of this question requires identification of the components that comprise overall function. The International Classification of Functioning, Disability and Health (ICF) (7) provides a useful taxonomy of the health-related components of functioning and disability, including body functions and structures (eg, mental or sensory functions), activities (eg, mobility, self-care), and participation (eg, interpersonal interactions, community life). This framework is helpful in examining functional limitations that arise from impairments in cognitive and physical function (ie, body functions and structure). Within the domain of body functions and structure, the ICF model specifically includes “neuromusculoskeletal and movement-related functions,” herein referred to as “neuromuscular attributes” and “motor function” for purposes of addressing the described peripheral, or physical, component of function using the terminology of this established model. It is acknowledged that aspects of movement-related functions also rely on central or cognitive factors, which will be further addressed in the Discussion section.
In evaluating the contribution of cognitive function to completion of daily activities, consideration should be given to specific cognitive domains. MCI subtypes have been identified based on the number of domains affected (ie, single- vs multi-domain MCI) as well as the type of impairment, specifically whether memory impairment is present (ie, amnestic MCI [aMCI]) or absent (ie, nonamnestic MCI [naMCI]) (8). Parsing MCI subtypes is particularly important given evidence of relations between specific cognitive domains (eg, memory, executive functions) and everyday activities in MCI (9–11).
Research has also highlighted the relevance of changes in motor function to the progression of cognitive decline. Specifically, cognitive functions have been linked to upper extremity motor functions, including fine motor dexterity, alternating forearm movements, and bimanual coordination (12–16), and poorer manual dexterity is associated with a higher risk of incident neurodegenerative disease (17). Further, changes in motor function, particularly in higher-level upper limb function, have been associated with reduced activities of daily living in aging (18). These studies highlight the importance of considering relations between higher-level motor ability of the hands and everyday activity completion in cognitively impaired individuals.
As described, cognitive and basic motor functions both relate to changes in daily activities that occur with age, and relations between cognitive and motor impairments are established. However, the combined contribution of cognitive and motor impairment to difficulty with upper limb functional activities (eg, reaching and grasping to complete tasks such as meal preparation) among older adults remains unknown, constituting an important open question. A relationship between cognitive function and lower extremity motor activities such as gait and mobility (19,20) has been established, with mobility limitations most strongly predicted by performance on measures of executive function (20). A similar relationship between upper extremity motor functions and cognitive function may exist that affects daily activities, with executive function performance relating to both motor functions of the hands and/or arms (ie, body functions) as well as upper extremity activities (eg, meal preparation, dressing). Such a relationship would be expected based on shared neurological underpinnings of these cognitive and motor functions, namely the reciprocal connectivity of subcortical structures, the cerebellum, and the frontal lobe and their contributions to planned, top-down movements (21).
This study evaluated the relationship between self-reported upper extremity ability (ie, at the level of activities) and cognitive function as measured by MCI status, and specifically whether upper extremity motor function as measured by neuromuscular attributes (ie, reaction time, pronosupination of the hands) mediates the relationship between cognitive function and upper extremity activities. We hypothesized a relationship between MCI status and self-reported upper extremity activities that is mediated (indirect effect) by performance-based neuromuscular attributes, such that participants with MCI would demonstrate weaker neuromuscular attributes, and in turn, poorer self-reported upper extremity activities. We expected these associations to be observed for all MCI subtypes. Although much of the literature has focused on the impact of lower extremity limitations on function, the research described suggests that evaluating upper extremity contributions may provide important, novel information on the mechanisms and potential treatment of cognitive and functional decline.
Methods
Participants
Baseline data were used from the Boston Rehabilitative Impairment Study of the Elderly (RISE), a cohort study of 430 community-dwelling primary care patients ages at least 65 with self-reported mobility challenges. Participants were recruited through primary care practices at Massachusetts General Hospital and Brigham and Women’s Hospital and underwent database screening and telephone interviews to determine eligibility prior to screening procedures. Eligible participants met the following criteria: living in the community, aged 65 and older, English proficiency, self-reported difficulty walking half a mile or climbing a flight of stairs. Exclusion criteria included severe cognitive impairment likely to reflect dementia as defined by Mini-Mental State Examination score less than 18 (22), terminal medical illness, severe visual impairment, uncontrolled hypertension, amputation of a lower extremity, use of oxygen supplementation, history of heart attack or major surgery in the last 6 months, planned major surgery, plans to move away from the Boston area within 2 years, and Short Physical Performance Battery score less than 4 (23). Details on Boston RISE methods can be found elsewhere (24). As described in this methods paper by Holt and colleagues (24), participants attended a baseline eligibility visit followed by a second visit (if eligible) within 2 weeks. If eligible, participants completed a physical examination, neuropsychological testing, and pain questionnaires conducted by a nurse practitioner at the initial visit and physical performance testing and questionnaires on functional ability, falls, rehabilitative care, and physical activity conducted by a research assistant at the second visit. This study was approved by the Spaulding Rehabilitation Hospital Institutional Review Board.
Upper Extremity Measures
Self-report of upper extremity activities
The upper extremity portion of the Late Life Function and Disability Instrument (LLFDI) assesses functioning in a number of daily activities requiring the hands/arms (25). The LLFDI is associated with performance-based measures of function (26) and is valid among older adults (25–28). This study used the Function component of LLFDI assessing self-reported difficulty in performing 32 physical activities, with 7 specific to upper extremity abilities (eg, using common utensils for meal preparation, holding a full glass of water). Scores on the LLFDI are transformed to scaled scores (0–100), with higher scores reflecting better functioning (25).
Neuromuscular attributes
Mean reaction time was measured using methods developed by Lord and colleagues (29) for older adults who were frequent fallers (specifically, the time required to click a computer mouse in response to a flashed light). Participants were instructed to press a right mouse button as fast as possible when a red light appeared on the mouse, with 5 practice trials followed by 10 test trials. Outlier responses were omitted: those more than 150–200 ms slower than practice trials and faster than 150 ms. Participants used the hand of their choice; mean reaction time served as the dependent variable.
Pronosupination of the hands was measured by instructing participants to switch between palm facing up and down positions as quickly as possible in 20 seconds. Right and left hands were tested independently, with two to three slow, controlled trials followed by two rapid practice trials prior to the test trial. The number of accurate movements was recorded, defined as complete cycles back to the starting position, and served as the dependent variable.
Cognitive Measures
Participants completed cognitive measures commonly used in the clinical setting and with demonstrated reliability and validity (30–32) Details on administration and scoring of these measures have been published previously (20). For this study, memory measures included Total Recall, Delayed Recall, and Recognition Discrimination indices for the Hopkins Verbal Learning Test–Revised (32,33). Completion time (seconds) for Trails A and B (30,31) and total correct for the Digit Symbol Substitution Test from the Wechsler Adult Intelligence Scale (34) served as nonmemory measures in the area of attention and executive function in classifying cognitive subtypes. Cognitive test performance determined MCI classification. For each cognitive measure, standardized (z) scores were compared across groups. Consistent with previous studies (1,20), impaired performance was defined as 1.5 SD below age-adjusted means, and individuals with impairment on two dependent variables were classified as having MCI (35).
Participants were classified following the model of previously established criteria that group individuals based on the cognitive domain of impairment (amnestic or nonamnestic) as well as the number of impaired domains (single or multiple) (35,36). Participants were thus classified as cognitively intact (no-MCI) or impaired (MCI) and those with MCI were further classified based on impairment domain. Participants demonstrating impairment on at least two memory measures and no other cognitive impairments were classified as having single-domain aMCI; participants with intact memory scores and impaired performance on at least two nonmemory (attention and/or executive function) measures were classified as having naMCI. Those demonstrating impairment on both memory and nonmemory measures were classified in a third group (multiple-domain aMCI [mdMCI]). Given the focus of the Boston RISE sample on functional activities and neuromuscular impairments, a circumscribed cognitive battery was administered to participants. Thus, more comprehensive cognitive characterization of the sample (eg, language, visuospatial ability) was not possible, and groups were solely classified based on memory impairment alone (aMCI), attention/executive function impairment alone (naMCI), or both memory and attention/executive function impairment (mdMCI).
Statistics
Descriptive characteristics for the study population are presented in Table 1. These characteristics were compared across MCI subtypes using t tests or nonparametric tests when needed and chi-square tests. Linear regression was performed using different models to (a) estimate the association between the key independent variables (MCI or MCI subtype) and the dependent variable (upper extremity function), and (b) to test the mediation effect described later. Covariates of sex, race, and education were applied the multivariate models based on clinical significance. Using the definition of mediation from previous studies (37,38), we tested whether neuromuscular attributes (ie, mean reaction time and pronosupination of the hands) mediated associations of MCI subtypes with upper extremity activities. Mediation was examined by estimating the following regression equations:
Table 1.
Study Population Characteristics by Mild Cognitive Impairment (MCI) Status
| No-MCI | aMCI | naMCI | mdMCI | p-value | |
|---|---|---|---|---|---|
| n (%) | n (%) | n (%) | n (%) | ||
| 249 (57.9) | 68 (15.8) | 15 (3.5) | 98 (22.8) | ||
| Age (years), mean (SD) | 76.48 (6.69) | 77.15 (6.84) | 74.2 (6.06) | 76.7 (7.98) | .51 |
| Male, n (%) | 77 (30.9) | 20 (29.4) | 3 (20) | 39 (39.8) | .26 |
| Living with spouse/partner (yes vs no) | 95 (38.2) | 17 (25) | 2 (13.3) | 34 (34.7) | .07 |
| White and non-Hispanic, n (%) | 226 (90.8) | 59 (86.8) | 6 (40) | 64 (65.3) | <.0001 |
| Education, n (%) | <.0001 | ||||
| Less than 12 grade | 17 (6.8) | 6 (8.8) | 6 (40) | 25 (25.5) | |
| Grade 12th or general education | 62 (24.9) | 26 (38.2) | 3 (20) | 39 (39.8) | |
| Undergraduate or vocational or technical school |
94 (37.8) | 23 (33.8) | 3 (20) | 20 (20.4) | |
| Graduate/professional school | 76 (30.5) | 13 (19.1) | 3 (20) | 14 (14.3) | |
| Comorbidity score, mean (SD) | 6.53 (3.62) | 5.76 (3.31) | 9.13 (5.04) | 6.89 (3.64) | .04 |
| Brief pain inventory total, mean (SD) | 2.39 (1.69) | 2.15 (1.85) | 3.6 (2.79) | 3.03 (2.08) | .01 |
| HVLT, mean (SD) | |||||
| Delayed recall | 7.35 (2.39) | 3.31 (1.99) | 7.13 (1.68) | 3 (2.17) | <.0001 |
| Total recall | 21.78 (4.47) | 14.51 (3.36) | 20.4 (1.96) | 13.67 (3.27) | <.0001 |
| Retention | 82.61 (20.78) | 55.06 (34.59) | 83.53 (17.52) | 49.71 (34.23) | <.0001 |
| Recognition | 10.45 (1.36) | 7.9 (1.84) | 10.67 (1.18) | 7.92 (2.62) | <.0001 |
| DSST, mean (SD) | 40.71 (9.17) | 38.69 (7.66) | 20.67 (8.36) | 25.76 (8.47) | <.0001 |
| Trail making test A | 42.35 (15.18) | 41.5 (10.67) | 94.39 (34.41) | 73.34 (32.09) | <.0001 |
| Trail making test B | 109.18 (52.74) | 115.51 (41.81) | 275.4 (38.13) | 246.63 (68.9) | <.0001 |
| Depression, n (%) | 8 (3.2) | 7 (10.3) | 4 (26.7) | 9 (9.2) | <.001 |
| SPPB score | 9.29 (2.06) | 8.54 (1.94) | 7.2 (2.4) | 7.55 (2.4) | <.0001 |
| Average reaction time, mean (SD) | 237.34 (39.01) | 245.21 (38.16) | 265.88 (39.7) | 277.39 (73.43) | <.0001 |
| Pronosupination of the hands, mean (SD) | 32.63 (8.08) | 31.96 (8.26) | 27.2 (4.84) | 29.69 (9.34) | <.001 |
| Late life upper extremity function | 75.69 (13.12) | 74.94 (12.17) | 70.85 (14.62) | 72.46 (14.6) | .09 |
| Sedative medications, N (%) | 66 (26.5) | 22 (32.4) | 6 (40.0) | 36 (36.7) | .21 |
Notes: aMCI = amnestic MCI; DSST = Digit Symbol Substitution Test; HVLT = Hopkins Verbal Learning Test; mdMCI = multi-domain MCI; naMCI = nonamnestic MCI; SPPB = Short Physical Performance Battery.
1. upper extremity function (Y) regressed on MCI subtypes (X)
2. each neuromuscular attribute (M) regressed on MCI subtypes
3. upper extremity function (Y) regressed on each neuromuscular attribute (M)
4. upper extremity function (Y) regressed on each neuromuscular attribute (M) and MCI subtypes (X)
Iacobucci (39) also indicated that the categorical variable X (MCI subtype) may be treated as a dummy variable with the mechanics of the mediation analyses remaining the same. Using the definition of mediation from previous studies (37), we then tested whether the relationship between MCI subtype and upper extremity function was explained by the neuromuscular attributes. A mediator effect was supported if the β-coefficient for mdMCI group was reduced by more than 10% when the mediator (ie, average reaction time and pronosupination of the hands) was included in the models (40) (Table 5). Statistical significance was set at an alpha level of .05. As a post hoc analysis, we investigated whether results differed after adjusting for psychotropic medication use and comorbidities. Psychotropic medications included any medications classified under the Iowa Drug Information Service as psychotherapeutic agents or anxiolytics. All analyses were conducted in SAS, version 9.4.
Results
Baseline Characteristics
Among 430 participants, 42.1% (n = 181) manifested MCI, with 15.8% (n = 68) having aMCI, 3.5% (n = 15) naMCI, and 22.8% (n = 98) mdMCI. MCI and no-MCI groups did not differ in age (p = .79), gender (p = .47), living with a spouse or partner (p = .07), body mass index (p = .49), or comorbid illness (p = .69). MCI and no-MCI groups differed significantly in race/ethnicity, with a greater proportion of white and non-Hispanic participants in the no-MCI group (p < .0001). No-MCI participants had greater educational attainment, with a greater proportion attending undergraduate or graduate school (ps ≤ .007). MCI and no-MCI groups differed in current health status, with fewer “poor and fair” ratings in the no-MCI group (p < .02). As expected, no-MCI participants performed better on measures of global cognitive function (Mini-Mental State Examination) and all cognitive measures (all ps < .0001). No-MCI participants also demonstrated stronger lower extremity performance-based scores (p < .0001) and lower rates of depression (p = .001). See Table 1 for demographic and clinical characteristics of individual MCI subtypes.
Relations Between Neuromuscular Attributes and MCI Status
MCI and no-MCI participants differed significantly in mean reaction time and pronosupination of the hands within unadjusted and adjusted models (all absolute value βs ≥ 2.29, all ps ≤ .02), with poorer performance observed in the MCI group across analyses (see Table 2). When considering MCI subtypes, the p-trend across groups was significant for both reaction time and pronosupination of the hands (both p’s < .005). Individual comparisons showed that participants with naMCI exhibited significantly poorer pronosupination of the hands compared to no-MCI (β = −5.54, p = .02), and mdMCI participants were significantly worse on both neuromuscular attributes compared to no-MCI (absolute value β’s ≥ 2.64, all ps ≤ .01). In contrast, aMCI and naMCI groups did not differ from participants without MCI in mean reaction time, and participants with aMCI did not differ significantly from those without MCI in pronosupination of the hands (see Table 3).
Table 2.
Mean Differences in Upper Extremity Function Between Mild Cognitive Impairment (MCI) and No-MCI Participants
| Unadjusted Model | Adjusted Model† | |||||||
|---|---|---|---|---|---|---|---|---|
| β | 95% CI | p-value | β | 95% CI | p-value | |||
| Late life upper extremity function | −2.43 | −5.00 | 0.14 | .06 | −2.28 | −4.99 | 0.44 | .10 |
| Average reaction time | 27.00 | 17.44 | 36.56 | <.0001 | 23.66 | 13.46 | 33.86 | <.0001 |
| Pronosupination of the hands | −2.29 | −3.90 | −0.68 | .01 | −2.01 | −3.72 | −0.30 | .02 |
†Adjusted for sex, race, and education.
Table 3.
Mean Differences in Upper Extremity Function Between Mild Cognitive Impairment (MCI) Subtypes From Multivariate Regression Models, Adjusted for Sex, Race, and Education
| Multivariate |
Late Life Upper Extremity Function (p-trend = .04) | Average Reaction Time (p-trend < .0001) | Pronosupination of the Hands (p-trend = .004) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| β | 95% CI | β | 95% CI | β | 95% CI | ||||
| No-MCI | Ref | Ref | Ref | ||||||
| aMCI | −0.57 | −4.14 | 3.01 | 7.03 | −6.26 | 20.33 | −0.41 | −2.66 | 1.84 |
| naMCI | −4.28 | −11.50 | 2.95 | 22.63 | −4.23 | 49.49 | −5.54 | −10.08 | −0.99 |
| mdMCI | −3.52 | −6.89 | −0.14 | 36.53 | 24.07 | 48.98 | −2.64 | −4.75 | −0.53 |
Notes: aMCI = amnestic MCI; mdMCI = multi-domain MCI (ie, impaired memory and executive function); naMCI = nonamnestic MCI.
Relations Between Self-reported Upper Extremity Function and MCI Status
There were no significant differences between MCI and no-MCI in self-reported upper extremity activities (see Table 2). However, adjusting for sex, race, and education, the p-trend across groups was significant (p = .04). Individual comparisons showed that participants with mdMCI reported significantly poorer upper extremity activities than those without MCI (β = −3.52, p = .04). In contrast, those with aMCI and naMCI did not differ from participants without MCI in self-reported upper extremity activities (see Table 3). However, inspection of confidence intervals suggests that the naMCI group may have been underpowered to detect a significant effect, and it will be important to replicate these findings in a larger sample in order to more closely examine individual MCI subtype comparisons.
Mediation by Neuromuscular Attributes
The relationship between cognitive status and self-reported upper extremity activities was mediated by neuromuscular attributes. Mean reaction time and pronosupination of the hands were statistically associated with self-reported upper extremity activities in both unadjusted and adjusted models (Table 4). The p-trend was significant for the association between MCI status and self-reported upper extremity activities across the groups, adjusting for demographic covariates (p = .04). This relationship was no longer significant when average reaction time (p = .11) or pronosupination of the hands (p = .08) was independently added to the model. For all MCI subtypes with no-MCI as the reference group, adding reaction time and pronosupination of the hands independently to the model reduced the size of the β estimate by at least 15% with the exception of reaction time in the naMCI group (10.09%), further supporting mediation by neuromuscular attributes (see Table 5). Further examination of MCI subtypes suggests that differences in self-reported upper extremity function are most strongly related to difficulties in executive function (naMCI and mdMCI absolute value of βs ≥ 3.52) rather than memory impairment (aMCI β = −0.57).
Table 4.
Association Between Average Reaction Time and Pronosupination of the Hands With Late Life Upper Extremity Function
| Unadjusted Model | Adjusted Model† | |||||||
|---|---|---|---|---|---|---|---|---|
| β-Coefficient | 95% CI | p-value | β-Coefficient | 95% CI | p-value | |||
| Average reaction time | −0.03 | −0.06 | −0.01 | .01 | −0.03 | −0.05 | −0.001 | .04 |
| Pronosupination of the hands | 0.25 | 0.10 | 0.40 | .001 | 0.21 | 0.06 | 0.36 | .01 |
†Adjusted for sex, race, and education.
Table 5.
Coefficient Estimates for the Association Between Mild Cognitive Impairment (MCI) Subtypes and Late Life Upper Extremity Function With and Without Adjustment for Average Reaction Time and Pronosupination of the Hands
| Model 1 | Model 2: Adjusting for Average Reaction Time | % Change | Model 3: Adjusting for Pronosupination of the Hands | % Change | |
|---|---|---|---|---|---|
| β-Coefficient | β-Coefficient | β-Coefficient | |||
| MCI subtypes | |||||
| No-MCI | Ref. | Ref. | Ref. | ||
| aMCI | −0.57 | −0.42 | 25.1 | −0.48 | 15.06 |
| naMCI | −4.28 | −3.84 | 10.09 | −3.32 | 24.43 |
| mdMCI | −3.52* | −2.77 | 21.31 | −2.95 | 16.04 |
| p-trend | .04 | .11 | .08 | ||
Notes: All models adjusted for sex, race, and education. aMCI = amnestic MCI; mdMCI: multi-domain MCI (ie, impaired memory and executive function); naMCI = nonamnestic MCI.
*p < .05.
Results of the post hoc analysis adjusting for medication use and comorbidities revealed that the association between MCI subtype and upper extremity function was attenuated and nonsignificant (0.06). However, this was likely due to lack of statistical power given the small number of participants in some of our MCI groups. Despite this, average reaction time and pronosupination of the hands continued to attenuate the relationship between MCI subtype and upper extremity function by at least 15%, suggesting a mediating effect.
Discussion
Results indicate that neuromuscular attributes (ie, body functions) and self-reported upper extremity ability (ie, activities) vary by MCI status, with poorer motor performance and self-reported activities in individuals with cognitive impairment. Further, neuromuscular attributes measured by reaction time and pronosupination of the hands mediated relations between MCI status and self-reported upper extremity activities. Poorer self-reported upper extremity activities were specifically found in the mdMCI group, whereas group differences were not found between MCI (all subtypes combined) and no-MCI. Given larger beta-coefficients and confidence intervals found for nonamnestic groups (ie, naMCI and mdMCI), it is likely that limited power because of the small sample size of the naMCI group accounts for the nonsignificant effect of self-reported upper extremity activities as well as the limited variance explained by the model adjusted for reaction time in this group.
Results extend previous research showing associations between cognitive functions, specifically executive functioning, and lower extremity mobility activities (19,20), suggesting that similar associations apply in the upper extremity. Mediation of this relationship by neuromuscular attributes highlights the possible shared impact of cognitive and motor deficiencies on everyday activities and is consistent with overlapping cognitive and motor neural circuitry (ie, reciprocal connectivity of subcortical structures, the cerebellum, and the frontal lobe and their contributions to planned, top-down movements) (21). The notion that executive functions strongly relate to the ability to carry out upper extremity everyday tasks is in line with previous research indicating relations between executive dysfunction and difficulty with everyday tasks related to health and safety such as medication management (41,42) as well as financial management (43).
These findings build on previous work and suggest that executive dysfunction may affect more discrete upper extremity components of these tasks such as reaching for and grasping objects central to task completion. It follows that frontally mediated cognitive processes (eg, processing speed, selective attention, self-monitoring) are required for the execution and/or coordination of seemingly basic motor components of daily tasks, which is further supported by research revealing relations between cognition and motor abilities (12–16). Further, that the mdMCI group specifically demonstrated poorer self-reported upper extremity activities than no-MCI participants is consistent with research showing that the combination of executive and memory impairment in MCI is associated with more severe functional activity limitation than single-domain MCI (44,45).
Although the current cross-sectional study precludes conclusions regarding causation that would explicitly inform clinical screening and intervention targets, observed relations suggest important directions for future prospective research studies. Specifically, results suggest the possibility that upper extremity tasks that include basic motor functions such as reaction time or basic motor coordination (eg, pronosupination of the hands) may be useful screening tools for risk for cognitive and functional decline. This has also been suggested by previous work relating manual dexterity to risk for future neurodegeneration (17). In addition, future work should examine whether interventions targeting neuromuscular attributes such as strength and motor dexterity may benefit cognitive and functional outcomes. When considered in the context of previous research demonstrating relations between cognition and mobility, results highlight that the relationship between cognition, motor functioning, and functional activities is not confined to lower limb or mobility tasks, but more universally applies to body systems and everyday activities. This has significant implications for care in the rehabilitation setting, as interventions targeting one body system may have more generalizable benefits than previously considered. Specifically, upper limb exercises may be useful to improve functional outcomes for patients with mobility problems, or lower limb activities addressing neuromuscular impairments may be beneficial with upper limb activities. These body limb exercises may also confer cognitive benefits, which constitutes an interesting area for future study. Though direct causal links cannot be concluded from this cross-sectional study, findings broaden the perspective of the interrelationships among cognitive impairments, neuromuscular impairments, and everyday activity limitations.
A number of potential limitations should be noted, including the cross-sectional analyses that preclude conclusions regarding causality or change over time. In addition, the study sample was recruited based in part on self-reported mobility problems, which limits the generalizability of these findings. Finally, the small sample size of MCI subtypes may have limited our power to detect small but meaningful effects, and the lack of a more comprehensive cognitive battery to assess relations with other domains (eg, language, visuospatial ability) is also acknowledged. Overall, however, this study examined a novel question about relations between upper extremity motor ability and cognitive function as they relate to completion of daily activities, which has important implications for informing geriatric care.
Funding
This work was supported by the National Institute on Aging (R01AG032052-03) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (1K24HD070966-01 to J.F.B.).
Conflict of Interest
None reported.
References
- 1. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56:303–308. doi: 10.1001/archneur.56.3.303 [DOI] [PubMed] [Google Scholar]
- 2. Ritchie K. Mild cognitive impairment: an epidemiological perspective. Dialogues Clin Neurosci. 2004;6:401–408. PMID: 22034212. PMCID: PMC3181815. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Lindbergh CA, Dishman RK, Miller LS. Functional disability in mild cognitive impairment: a systematic review and meta-analysis. Neuropsychol Rev. 2016;26:129–159. doi: 10.1007/s11065-016-9321-5 [DOI] [PubMed] [Google Scholar]
- 4. Gomar JJ, Bobes-Bascaran MT, Conejero-Goldberg C, Davies P, Goldberg TE; Alzheimer’s Disease Neuroimaging Initiative Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer’s disease neuroimaging initiative. Arch Gen Psychiatry. 2011;68:961–969. doi: 10.1001/archgenpsychiatry.2011.96 [DOI] [PubMed] [Google Scholar]
- 5. Peres K, Helmer C, Amieva H, et al. . Natural history of decline in instrumental activities of daily living performance over the 10 years preceding the clinical diagnosis of dementia: a prospective population-based study. J Am Geriatr Soc. 2008;56:37–44. doi: 10.1111/j.1532-5415.2007.01499.x [DOI] [PubMed] [Google Scholar]
- 6. Purser JL, Fillenbaum GG, Pieper CF, Wallace RB. Mild cognitive impairment and 10-year trajectories of disability in the Iowa established populations for epidemiologic studies of the elderly cohort. J Am Geriatr Soc. 2005;53:1966–1972. doi: 10.1111/j.1532-5415.2005.53566.x [DOI] [PubMed] [Google Scholar]
- 7. World Health Organization. Towards a Common Language for Functioning, Disability and Health: ICF, The International Classification of Functioning, Disability and Health. Geneva, Switzerland: World Health Organization; 2002. [Google Scholar]
- 8. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004;256:183–194. doi: 10.1111/j.1365-2796.2004.01388.x [DOI] [PubMed] [Google Scholar]
- 9. Artero S, Touchon J, Ritchie K. Disability and mild cognitive impairment: a longitudinal population-based study. Int J Geriatr Psychiatry. 2001;16:1092–1097. doi: 10.1002/gps.477 [DOI] [PubMed] [Google Scholar]
- 10. Tomaszewski Farias S, Cahn-Weiner DA, Harvey DJ, et al. . Longitudinal changes in memory and executive functioning are associated with longitudinal change in instrumental activities of daily living in older adults. Clin Neuropsychol. 2009;23:446–461. doi: 10.1080/13854040802360558 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Tucker-Drob EM. Neurocognitive functions and everyday functions change together in old age. Neuropsychology. 2011;25:368–377. doi: 10.1037/a0022348 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Ashendorf L, Vanderslice-Barr JL, McCaffrey RJ. Motor tests and cognition in healthy older adults. Appl Neuropsychol. 2009;16:171–176. doi: 10.1080/09084280903098562 [DOI] [PubMed] [Google Scholar]
- 13. Bangert AS, Reuter-Lorenz PA, Walsh CM, Schachter AB, Seidler RD. Bimanual coordination and aging: neurobehavioral implications. Neuropsychologia. 2010;48:1165–1170. doi: 10.1016/j.neuropsychologia.2009.11.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Bramell-Risberg E, Jarnlo GB, Elmstahl S. Slowing of alternating forearm movements is associated with cognitive impairment in community-dwelling older people. Dement Geriatr Cogn Disord. 2010;29:457–466. doi: 10.1159/000305093 [DOI] [PubMed] [Google Scholar]
- 15. Hayashi H, Nakashima D, Matsuoka H, et al. . Exploring the factor on sensory motor function of upper limb associated with executive function in communitydwelling older adults. Nagoya J Med Sci. 2016;78:285–291. PMID: 27578912. PMCID: PMC4995274. [PMC free article] [PubMed] [Google Scholar]
- 16. Rodriguez-Aranda C, Mittner M, Vasylenko O. Association between executive functions, working memory, and manual dexterity in young and healthy older adults: an exploratory study. Percept Mot Skills. 2016;122:165–192. doi: 10.1177/0031512516628370 [DOI] [PubMed] [Google Scholar]
- 17. Darwish S, Wolters FJ, Hofman A, Stricker B, Koudstaal PJ, Ikram MA. Simple test of manual dexterity can help to identify persons at high risk for neurodegenerative diseases in the community. J Gerontol A Biol Sci Med Sci. 2017;12:75–81. doi: 10.1093/gerona/glw122 [DOI] [PubMed] [Google Scholar]
- 18. Scherder E, Dekker W, Eggermont L. Higher-level hand motor function in aging and (preclinical) dementia: its relationship with (instrumental) activities of daily life—a mini-review. Gerontology. 2008;54:333–341. doi: 10.1159/000168203 [DOI] [PubMed] [Google Scholar]
- 19. Montero-Odasso M, Verghese J, Beauchet O, Hausdorff JM. Gait and cognition: a complementary approach to understanding brain function and the risk of falling. J Am Geriatr Soc. 2012;60:2127–2136. doi: 10.1111/j.1532-5415.2012.04209.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Pedersen MM, Holt NE, Grande L, et al. . Mild cognitive impairment status and mobility performance: an analysis from the Boston RISE study. J Gerontol A Biol Sci Med Sci. 2014;69:1511–1518. doi: 10.1093/gerona/glu063 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Middleton FA, Strick PL. Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Res Brain Res Rev. 2000;31:236–250. doi: 10.1016/S0165-0173(99)00040-5 [DOI] [PubMed] [Google Scholar]
- 22. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–198. doi:10.1002/(SICI)1099-1166(199805)13:5-285::AID-GPS753>3.0.CO;2-V [DOI] [PubMed] [Google Scholar]
- 23. Guralnik JM, Simonsick EM, Ferrucci L, et al. . A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49:M85–M94. doi: 10.1093/geronj/49.2.M85 [DOI] [PubMed] [Google Scholar]
- 24. Holt NE, Percac-Lima S, Kurlinski LA, et al. . The Boston rehabilitative impairment study of the elderly: a description of methods. Arch Phys Med Rehabil. 2013;94:347–355. doi: 10.1016/j.apmr.2012.08.217 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Haley SM, Jette AM, Coster WJ, et al. . Late life function and disability instrument: II. Development and evaluation of the function component. J Gerontol A Biol Sci Med Sci. 2002;57:M217–M222. doi: 10.1093/gerona/57.4.M217 [DOI] [PubMed] [Google Scholar]
- 26. Sayers SP, Jette AM, Haley SM, Heeren TC, Guralnik JM, Fielding RA. Validation of the late-life function and disability instrument. JAm Geriatr Soc. 2004;52:1554–1559. doi: 10.1111/j.1532-5415.2004.52422.x [DOI] [PubMed] [Google Scholar]
- 27. Bean JF, Olveczky DD, Kiely DK, LaRose SI, Jette AM. Performance-based versus patient-reported physical function: what are the underlying predictors?Phys Ther. 2011;91:1804–1811. doi: 10.2522/ptj.20100417 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Jette AM, Haley SM, Coster WJ, et al. . Late life function and disability instrument: I. Development and evaluation of the disability component. J Gerontol A Biol Sci Med Sci. 2002;57:M209–M216. doi: 10.1093/gerona/57.4.M209 [DOI] [PubMed] [Google Scholar]
- 29. Lord SR, Clark RD, Webster IW. Postural stability and associated physiological factors in a population of aged persons. J Gerontol. 1991;46:M69–M76. doi: 10.1093/geronj/46.3.M69 [DOI] [PubMed] [Google Scholar]
- 30. Bowie CR, Harvey PD. Administration and interpretation of the Trail Making Test. Nat Protoc. 2006;1:2277–2281. doi: 10.1038/nprot.2006.390. [DOI] [PubMed] [Google Scholar]
- 31. Lezak MD, Howieson DB, Loring DW, Hannay JH, Fischer JS.. Neuropsychological Assessment. 4th ed New York: Oxford University Press; 2004. [Google Scholar]
- 32. Shapiro AM, Benedict RH, Schretlen D, Brandt J. Construct and concurrent validity of the Hopkins Verbal Learning Test-revised. Clin Neuropsychol. 1999;13:348–358. doi: 10.1076/clin.13.3.348.1749 [DOI] [PubMed] [Google Scholar]
- 33. Benedict RHB, Schretlen D, Groninger L, Brandt J. Hopkins verbal learning test—revised: normative data and analysis of inter-form and test-retest reliability. Clin Neuropsychol. 1998;12:43–55. doi: 10.1076/clin.12.1.43.1726 [DOI] [Google Scholar]
- 34. Wechsler D. Wechsler Adult Intelligence Scale — Third Edition: Administration and Scoring Manual. San Antonio, TX: The Psychological Corporation; 1997. [Google Scholar]
- 35. Jak AJ, Bondi MW, Delano-Wood L, et al. . Quantification of five neuropsychological approaches to defining mild cognitive impairment. Am J Geriatr Psychiatry. 2009;17:368–375. doi: 10.1097/JGP.0b013e31819431d5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Petersen RC. Clinical practice. Mild cognitive impairment. N Engl J Med. 2011;364:2227–2234. doi: 10.1056/NEJMcp0910237 [DOI] [PubMed] [Google Scholar]
- 37. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51:1173–1182. doi: 10.1037/0022-3514.51.6.1173 [DOI] [PubMed] [Google Scholar]
- 38. MacKinnon DP, Krull JL, Lockwood CM. Equivalence of the mediation, confounding and suppression effect. Prev Sci. 2000;1:173–181. doi:10.1023/A:1026595011371 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Iacobucci D. Mediation analysis and categorical variables: the final frontier. J Consum Psychol. 2012;22:582–594. doi: 10.1016/j.jcps.2012.03.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Maldonado G, Greenland S. Simulation study of confounder-selection strategies. Am J Epidemiol. 1993;138:923–936. doi: 10.1093/oxfordjournals.aje.a116813 [DOI] [PubMed] [Google Scholar]
- 41. Bangen KJ, Jak AJ, Schiehser DM, et al. . Complex activities of daily living vary by mild cognitive impairment subtype. J Int Neuropsychol Soc. 2010;16:630–639. doi: 10.1017/S1355617710000330 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Mariani E, Monastero R, Ercolani S, et al. . Influence of comorbidity and cognitive status on instrumental activities of daily living in amnestic mild cognitive impairment: results from the ReGAl project. Int J Geriatr Psychiatry. 2008;23:523–530. doi: 10.1002/gps.1932 [DOI] [PubMed] [Google Scholar]
- 43. Okonkwo OC, Wadley VG, Griffith HR, Ball K, Marson DC. Cognitive correlates of financial abilities in mild cognitive impairment. J Am Geriatr Soc. 2006;54:1745–1750. doi: 10.1111/j.1532-5415.2006.00916.x [DOI] [PubMed] [Google Scholar]
- 44. Alexopoulos P, Grimmer T, Perneczky R, Domes G, Kurz A. Progression to dementia in clinical subtypes of mild cognitive impairment. Dement Geriatr Cogn Disord. 2006;22:27–34. doi:10.1159/000093101 [DOI] [PubMed] [Google Scholar]
- 45. Bombin I, Santiago-Ramajo S, Garolera M, et al. . Functional impairment as a defining feature of: amnestic MCI cognitive, emotional, and demographic correlates. Int Psychogeriatr. 2012;24:1494–1504. doi: 10.1017/S1041610212000622 [DOI] [PubMed] [Google Scholar]