Abstract

The objective of this meta-analysis was to improve understanding of the heterogeneity in the relationship between cognition and functional status in individuals with mild cognitive impairment (MCI). Demographic, clinical, and methodological moderators were examined. Cognition explained an average of 23% of the variance in functional outcomes. Executive function measures explained the largest amount of variance (37%), whereas global cognitive status and processing speed measures explained the least (20%). Short- and long-delayed memory measures accounted for more variance (35% and 31%) than immediate memory measures (18%), and the relationship between cognition and functional outcomes was stronger when assessed with informant-report (28%) compared with self-report (21%). Demographics, sample characteristics, and type of everyday functioning measures (i.e., questionnaire, performance-based) explained relatively little variance compared with cognition. Executive functioning, particularly measured by Trails B, was a strong predictor of everyday functioning in individuals with MCI. A large proportion of variance remained unexplained by cognition.

Introduction

Evaluating the relationship between cognition and functional status in individuals with mild cognitive impairment (MCI) has become increasingly important because of the associated risk of MCI for the development of dementia. Recent literature has suggested that individuals with MCI experience early functional changes (e.g., Bondi & Smith, 2014; Schmitter-Edgecombe & Parsey, 2014a, 2014b). This is important given that restrictions in functional abilities may compromise an individual's independence, safety, or quality of life and contribute to caregiver burden and adverse healthcare outcomes (Aretouli & Brandt, 2010). Therefore, a better understanding of the relationship between cognitive deficits and everyday functioning in MCI could have important clinical and research applications.

Mild Cognitive Impairment

The concept of MCI has evolved over the past decade. The prevalence of MCI differs depending on clinical setting and inclusion criteria, but generally ranges between 11% and 20% (Petersen et al., 2010). MCI has been proposed as a transitional state between normal aging and dementia with considerable heterogeneity in etiology, clinical presentation, and prognosis and outcome. MCI is defined as a condition of objective cognitive impairment greater than expected for age, but insufficient to warrant a diagnosis of early dementia, with subjective memory complaints preferably corroborated by an informant (Petersen, 2005).

The initial diagnostic criteria for MCI specified generally intact everyday functioning (Petersen et al., 1999). Functional abilities refer to a range of self-initiated, multidimensional, everyday skills and activities necessary for independent daily living within the home and community (Gomar, Harvey, Bobes-Bascaran, Davies, & Goldberg, 2011). An international working group on MCI (Winblad et al., 2004, p. 243) later proposed the inclusion of “preserved basic [activities of daily living]/some minimal impairment in complex instrumental function” in the diagnostic process. A body of literature demonstrating the presence of difficulties in everyday abilities in MCI groups has accumulated (e.g., Pereira et al., 2010; Schmitter-Edgecombe & Parsey, 2014a, 2014b). Greater functional impairment in MCI has been associated with improved prediction of dementia and a more stable definition of MCI over time (e.g., Pérès et al., 2006), faster rates of subsequent cognitive and functional progression (e.g., Purser, Fillenbaum, Pieper, & Wallace, 2005), and increased rates of progression from MCI to dementia (e.g., Farias et al., 2013). These findings have encouraged some authors to argue that the explicit inclusion of functional deficits should be part of the diagnostic process (e.g., Nygard, 2003; Pérès et al., 2006; Ritchie, Artero, & Touchon, 2001), and this is further reflected in the more recent diagnostic criteria for MCI (i.e., Albert et al., 2011). However, the guidelines related to functional changes in individuals with MCI are unclear and reflect our currently limited, albeit growing, knowledge concerning the nature, extent, and types of functional change that develop with MCI. Currently, no standard criteria exist as to the practical or theoretical definition of minimal functional limitation in individuals with MCI (Gold, 2012).

Although earlier studies focused on the risk of progression to Alzheimer's dementia in those individuals with MCI with predominant memory impairment, more recent studies suggest that MCI is a clinically heterogeneous syndrome with different patterns of cognitive symptoms. To better characterize the underlying etiology and trajectory of MCI and facilitate earlier and more effective interventions, the concept of MCI was expanded to include subgroups based on patterns of cognitive impairment (i.e., Winblad et al., 2004). In addition to the classification of amnestic or nonamnestic, depending on the presence of memory impairment, categorization also exists based on whether cognitive impairment involves one or multiple cognitive domains. If viewed on a continuum of symptom severity, single-domain MCI may occur in the earliest stages with more cognitive domains becoming affected with disease progression. There is also some evidence (albeit inconsistent) to suggest that the etiology and clinical course of both cognition and everyday functioning differ within the MCI subtypes (e.g., Teng, Becker, Woo, Cummings, & Lu, 2010; Yeh et al., 2011). For example, Aretouli and Brandt (2010) found that individuals with multidomain MCI had more functional impairments than those with single-domain MCI, and the presence of cognitive impairment in multiple domains was associated with progression to dementia (Aretouli, Tsilidis, & Brandt, 2013).

Everyday Functioning

Functional abilities are multidimensional, and typically classified into basic activities of daily living (ADLs) and instrumental activities of daily living (IADLs) (Lawton & Brody, 1969). ADLs are lower-level, self-maintenance abilities such as bathing, dressing, eating, grooming, ambulating, and transferring. In contrast, IADLs are higher-level, complex everyday activities including telephone use, transportation, technology interaction, meal preparation, and medication and financial management. As IADLs are more complex and cognitively demanding than ADLs, they are more likely to be vulnerable to early changes in cognition and be affected earlier in the course of cognitive decline (Goldberg et al., 2010); however, the data do not always support this supposition (Royall et al., 2007).

Several general methods have been used in the literature for evaluating everyday functioning, including questionnaires and performance-based measures. Currently, questions remain as to which method best approximates behavior in real-world environments. For example, self- and informant-report questionnaires are subject to reporter biases but are also representations of performances across multiple unstructured environments and activities over an extended period of time (see Sikkes, de Lange-de Klerk, Pijnenburg, Scheltens, & Uitdehaag, 2009 for a review). Questionnaires are one of the cheapest and easiest methods of data collection. Although self-report questionnaires have shown poor relations with objective measures of cognition and poor reliability in individuals with deficits in self-awareness (e.g., Tabert et al., 2002), informant-report questionnaires have demonstrated moderate correlations with objective measures of cognition (e.g., Tsang, Diamond, Mowszowski, Lewis, & Naismith, 2012) and the ability to differentiate between diagnostic groups (e.g., Farias et al., 2008). Many questionnaires have been used to demonstrate the existence of functional impairment in the presence of dementia but are insensitive to functional changes occurring early in the spectrum of functional and cognitive decline. Only recently have self- and informant-based questionnaires been developed to capture the mild functional impairments seen in the earlier stages of decline (e.g., ECog, Farias et al., 2008; ADL-PI, Galasko et al., 2006; IADL-C, Schmitter-Edgecombe, Parsey, & Lamb, 2014). Unlike questionnaires, performance-based measures assess functional capacity directly by having an individual enact a task with formal evaluation of performance in an attempt to estimate actual abilities equivalent to those performed in the home environment (Marson & Hebert, 2006; see Moore, Palmer, Patterson, & Jeste, 2007 for a review of performance-based measures). However, they represent a single evaluation point and typically require completion of one task at a time in a controlled artificial laboratory setting. Three types of performance-based measures have been developed. Behavioral simulation measures require individuals to complete some sort of everyday task in a laboratory setting with the quality of performance examined relative to normative standards. Everyday problem-solving measures are paper-and-pencil type tasks that assess everyday cognition and problem solving through real-world problems. Direct observation measures involve observation of individuals completing everyday activities in a naturalistic environment.

Relationship Between Cognition and Functional Abilities

The ability to function independently in one's environment is determined by multiple influences, including social, physical, emotional, and cognitive factors. Among these, better general cognitive functioning has most consistently been associated with greater functional independence. In 2007, Royall and colleagues conducted a review of 68 articles examining the ability of cognitive tasks to predict functional abilities. Overall results were modest, and there was large variability between studies. The total variance in functional status measures accounted for by cognitive predictors ranged from 0% to 80% with an average of 21% (standard deviation [SD] = 20.20). Royall and colleagues also found that, aside from general cognition, executive functioning accounted for more of the variance in everyday functioning than memory, attention, visuospatial, and verbal domains. Furthermore, executive function measures involving reasoning and problem solving (e.g., word series) explained more of the variance than other executive function measures (e.g., Tower of London).

Although the study by Royall and colleagues (2007) included a range of distinct, and seemingly dissimilar, populations including neuropsychiatric, geriatric, and rehabilitation, other researchers have also reported large variability in the associations between cognition and everyday functioning (Tucker-Drob, 2011). Given the range of variability, and the fact that neuropsychologists often rely on cognitive data to predict everyday functioning, it is imperative to understand why different investigators have found such disparate relationships between cognition and functional abilities. The goal of this article is to improve understanding of the heterogeneity in the relationship between cognition and functional status in the MCI population by addressing the following questions:

  • What is the range of total variance in functional outcomes specifically attributed to cognitive measures in individuals with MCI?

  • Does age or education moderate the relationship between cognition and functional outcomes in individuals with MCI?

  • Does the total variance in functional outcomes specifically attributed to cognitive measures differ by MCI subtype or by referral source?

  • Does the total variance in functional outcomes specifically attributed to cognitive measures in individuals with MCI differ depending on type of functional status measure used?

  • What is the unique variance in functional outcomes explained by specific cognitive domains (e.g., memory, speeded processing, and executive functioning) and subdomain processes (e.g., switching, inhibition, and planning)?

Method

Search Strategy and Selection Criteria

The literature search was conducted in December 2013 by searching PsychInfo and PubMed electronic databases using the keywords “mild cognitive impairment” and combinations of “activities of daily living,” “ADLs,” “instrumental activities of daily living,” “IADLs,” “disability,” “functional impairment,” “functional outcomes,” “functional abilities,” “everyday functioning,” “daily functioning,” “independent living,” “functional status,” “everyday activity completion,” “self-report,” “informant-report,” “direct observation,” “performance based,” or “ecologic.” Given that MCI was first used as an independent diagnostic category (not linked to a defined stage on a rating scale) by Petersen and colleagues in 1995 (Golomb, Kluger, & Ferris, 2004), aside from date greater than 1995, no specific limits were imposed. All unique abstracts were screened, and full-text articles were obtained for all papers that appeared to meet study inclusion criteria (see subsequently). This process initially yielded 228 studies for inclusion. Figure 1 details the flow of studies included in the meta-analysis. Reference sections for all articles related to MCI or everyday functioning were also examined for papers not identified by the original search.

Fig. 1.

Flow of studies included in the meta-analysis.

Fig. 1.

Flow of studies included in the meta-analysis.

Inclusion Criteria

Studies were included if they investigated a sample of individuals with MCI not mixed with individuals with dementia or cognitively healthy older adults, and reported in the paper the means and SDs of at least one cognitive and one functional status measure for an MCI sample. In addition, only published English-language articles and dissertations were considered. Although some studies matched the inclusion criteria, they were not included because the respective samples overlapped with those already included in the analysis (k = 15). Studies also needed to use the more currently defined term, “MCI,” rather than earlier terms, such as cognitive impairment no dementia. Furthermore, MCI could not be due to other conditions such as Parkinson's disease. Both well-known (e.g., Trail Making Test) and lesser-known (e.g., Keep Track Task) cognitive and functional status measures were included (driving studies were excluded). Only baseline data were included if longitudinal studies were identified. Papers with data from multiple MCI subgroups were treated as separate study samples (k = 28, indicated with an asterisk in the reference section in Appendix) so that data from different MCI subtypes could be evaluated.

Coded Variables

The following information was extracted from the articles and coded: demographics (i.e., mean age and mean education as these were the only demographic variables consistently reported in most manuscripts), MCI subtype (i.e., amnestic, nonamnestic, single-domain, and multidomain), referral source (i.e., community and clinic), and name of test administered. In addition, domain and subdomains of the cognitive (e.g., memory and verbal memory) and functional status measures (e.g., performance-based and behavioral simulation) were extracted. To determine domain and subdomain classification for each measure, the test manuals and seminal books in the area were consulted, including the neuropsychological test compendium by Strauss, Sherman, and Spreen (2006) and the fifth edition of Lezak's book on neuropsychological assessment (Lezak, Howieson, Bigler, & Tranel, 2012). Each measure was coded in a single domain (and subdomain) that theoretically represented the primary ability tested. Classification of cognitive and functional domains used by study authors was changed when necessary. All of the papers were coded by the first author of the meta-analysis, an advanced doctoral candidate studying in the area of clinical neuropsychology. To assess coding quality, the second author classified all of the cognitive and functional status measures and then coded 10% of the studies. For the 10% of the studies that were then double coded, the raters showed considerable agreement, with interrater agreement as measured by Cohen's κ consistently falling in the excellent range (>0.81). Coding discrepancies for domain classification of the cognitive and functional status measures were discussed by the two coders with reference to the literature until a conclusion concerning how the test should be coded was reached. Tables 1 and 2 show how the cognitive and functional tests were grouped by domain and subdomain for the purposes of this meta-analysis. The inputted numbers used to create the effect sizes for the meta-analysis (i.e., means and SDs) were also checked for accuracy against the original article by a trained undergraduate research assistant.

Table 1.

Coding summary of cognitive measures for cognitive domains and subdomains

 k k  
Global  Working memory  
 Brief Mental Status   Digit span 12 
  ADAS-COG 18  WAIS-III Letter Number Sequencing 
  Short Test of Mental Status (STMS)  WAIS-R Digit Span 
  MMSE 120  Letter Number Sequencing 
  Modified MMSE  Reversed Visual Span 
  TICS  Digit Backward Span 
  TICS modified  Spatial Span Backward 
  MOCA  Spatial Span 
  ADAS nonmemory domain  WMS-R Digit Span Backward 
  Short Blessed Test  Self-Ordered Pointing Test (SOPT) 
 Extended cognitive screeners   Working Memory Paradigm 
  An overall composite  2 back task 
  CogState accuracy and speed   
  Global Cognition Factor Processing speed  
  Brief Neuropsychological Evaluation for Spanish-Speaking Subjects  WAIS-3 Digit Symbol 
  RBANS  Digit Symbol Coding 
  Consortium to Establish a Registry for AD Neuropsychological Battery (CERAD)  Digit Symbol Copy 
  Cambridge Cognitive Test (CAMCOG)  Trails A 26 
  Dementia Rating Scale, 1st & 2nd Editions 15  D-KEFS (Number Sequencing) 
   Digit Symbol Substitution 
Memory   Useful Field of View, task 1 
 Immediate   WAIS-R Digit Symbol 
  Benton Visual Retention Test (BVRT)b  Useful Field of View, processing speed 
  World Health Organization University of California Los Angeles Auditory Verbal Learning Test (immediate)a,d  Symbol Digit Modalities 
  Buschke Selective Reminding Test totala,d  SDMT oral 
  RAVLT total learninga,d  SDMT written 
  Brief Visual Memory Test (BVMT) learningb  D-KEFS Visual Scanning 
  Memory Assessment Scale (MAS) list learninga,d  Processing speed component 
  CVLT-II total learninga,d 12  Digit Symbol 
  10/36 Learningb   
  Chinese Auditory Verbal Learning Test—immediate recalla,d Attention  
  HVLT total recalla,d  Useful Field of View Divided Attention 
  Episodic Memory Compositea,d  Useful Field of View Selective Attention 
  Immediate Memory Compositea,d  Spatial Span Forward 
  Visual Reproduction Ib 10  Digit span forward measures 
  RBANS Immediate Memoryc,f  Forward visual span score 
  Logical Memory Ia,e 15  Conners' Continuous Performance Test- 2nd 
  A memory compositea,d,f  Visual search test 
 Short delay   Letter Cancellation (accuracy) 
  CERAD word list 5 min recalla,d  Digit cancellation (accuracy) 
  10 min recall of modified ROCFTb  Useful Field of View, task 2/3 composite 
  Chinese Version Verbal Learning Test (CVVLT)a,d  DRS-2 Attention 
  RAVLT immediate delaya,d  Brief Test of Attention 
  10/36 Delayb  Test of Everyday Attention 
  CVLT ba,d   
  CVLT short delaya,d Executive function  
  NYU paragraph immediate recalla,d  Shifting  
  Auditory Verbal Learning Test-Immediate recalla,d   Trails B 32 
  HVLT recalla,d   D-KEFS Number-Letter Switching 
  1- and 10-min delayed recall of ADAScoga,d   TMT B-A 
  ADAS delayed recalla,d   WCST categories 
  ADAS verbal memorya,d   WCST perseverative errors 
 Long delay    More-Odd Shifting Task 
  CERAD word list 30 min recalla,d   Brixton Test 
  Free and Cued Selective Reminding Test-delaya,d   D-KEFS Sorting Test 
  World Health Organization University of California Los Angeles Auditory Verbal Learning Test (delay)a,d  Inhibition  
  Object-Location Association Testb   D-KEFS Tower Test 
  Chinese Version Verbal Learning Test (CVVLT)a,d   Stroop measures 12 
  Buschke Selective Reminding delaya,d   Stop Signal Task 
  Item Recognition from Source Memory Paradigmc,f   Completions and Corrections 
  Content memory activity paradigmc,f   Hayling Test 
  RAVLT long delaya,d   D-KEFS Tower Test 
  Memory Assessment Scale (MAS) prose recalla,e   Random Number Generation 
  Brief Visual Memory Test (BVMT) long delayb  Initiation  
  Memory Assessment Scale (MAS) long delaya,d   Letter fluency measures 23 
  CVLT-II Long delaya,d 11   D-KEFS Design Fluency 
  Chinese Auditory Verbal Learning Test-delayeda,d   DRS-2 Initiation/Perseveration 
  NYU paragraph delayed recalla,e   Alternate Uses Test 
  Auditory Verbal Learning Test AVLT delayeda,d   Tinker Toy Test 
  HVLT delayed recalla,d  Reasoning  
  Delayed Memory Compositea,d   DRS-2 Conceptualization subscale 
  Logical Memory IIa,e 19   Matrix Reasoning 
  Visual Reproduction IIb 10   Similarities 
  A delayed memory compositea,d   Verbal Reasoning subtest of the Cognitive Competency Test 
 Not used in memory interval subdomain    Abstractions 
  Verbal Paired Associated Learning Testa,f   Word series 
  Prose recalla,e   Experimental Judgment Test 
  ADAS Memory Domaina,d   Iowa Gambling Test 
  ROCFT recallb   Stanford Binet Absurdities Test 
  Babcock story recalla,e   Verbal Concept Attainment Test 
  CVLT short delay cueda,d   Stanford Binet Absurdities Test 
  CVLT long delay cueda,d  Planning  
  Rivermead Behavioural Memory Testc,f   CLOX-1 
  Logical Memorya,e   Zoo Map (BADS) 
  A memory compositea,c,d,e,f   Clock drawing 
  Auditory Verbal Learning Test (AVLT) Recognitiona,d   Maze 
  Recognition Memory Compositea,d   ROCF organization 
    Tic tac toe 
Visuospatial    Porteus Maze Test 
 Constructional Praxis   Not used in subcomponent coding  
  Clox 2   BADS 
  Copy-a-Clock Test   Frontal Assessment Battery 
  Copy of ROCFT   Executive Interview (EXIT25) 
  Constructional Praxis   An executive function composite 
  Copy Drawing Test   Keep Track Task 
 Other visuospatial measures    
  Block Design Language  
  Spatial composite  Naming  
  Object Assembly   Boston Naming Test 22 
  Visual Object and Space Number Location   Semantic Memory Composite 
  Line Bisection  Fluency  
  Visual-spatial composite   Category fluency measures 34 
   Not used as a subdomain  
    Token Test 
    Syntax Comprehension 
 k k  
Global  Working memory  
 Brief Mental Status   Digit span 12 
  ADAS-COG 18  WAIS-III Letter Number Sequencing 
  Short Test of Mental Status (STMS)  WAIS-R Digit Span 
  MMSE 120  Letter Number Sequencing 
  Modified MMSE  Reversed Visual Span 
  TICS  Digit Backward Span 
  TICS modified  Spatial Span Backward 
  MOCA  Spatial Span 
  ADAS nonmemory domain  WMS-R Digit Span Backward 
  Short Blessed Test  Self-Ordered Pointing Test (SOPT) 
 Extended cognitive screeners   Working Memory Paradigm 
  An overall composite  2 back task 
  CogState accuracy and speed   
  Global Cognition Factor Processing speed  
  Brief Neuropsychological Evaluation for Spanish-Speaking Subjects  WAIS-3 Digit Symbol 
  RBANS  Digit Symbol Coding 
  Consortium to Establish a Registry for AD Neuropsychological Battery (CERAD)  Digit Symbol Copy 
  Cambridge Cognitive Test (CAMCOG)  Trails A 26 
  Dementia Rating Scale, 1st & 2nd Editions 15  D-KEFS (Number Sequencing) 
   Digit Symbol Substitution 
Memory   Useful Field of View, task 1 
 Immediate   WAIS-R Digit Symbol 
  Benton Visual Retention Test (BVRT)b  Useful Field of View, processing speed 
  World Health Organization University of California Los Angeles Auditory Verbal Learning Test (immediate)a,d  Symbol Digit Modalities 
  Buschke Selective Reminding Test totala,d  SDMT oral 
  RAVLT total learninga,d  SDMT written 
  Brief Visual Memory Test (BVMT) learningb  D-KEFS Visual Scanning 
  Memory Assessment Scale (MAS) list learninga,d  Processing speed component 
  CVLT-II total learninga,d 12  Digit Symbol 
  10/36 Learningb   
  Chinese Auditory Verbal Learning Test—immediate recalla,d Attention  
  HVLT total recalla,d  Useful Field of View Divided Attention 
  Episodic Memory Compositea,d  Useful Field of View Selective Attention 
  Immediate Memory Compositea,d  Spatial Span Forward 
  Visual Reproduction Ib 10  Digit span forward measures 
  RBANS Immediate Memoryc,f  Forward visual span score 
  Logical Memory Ia,e 15  Conners' Continuous Performance Test- 2nd 
  A memory compositea,d,f  Visual search test 
 Short delay   Letter Cancellation (accuracy) 
  CERAD word list 5 min recalla,d  Digit cancellation (accuracy) 
  10 min recall of modified ROCFTb  Useful Field of View, task 2/3 composite 
  Chinese Version Verbal Learning Test (CVVLT)a,d  DRS-2 Attention 
  RAVLT immediate delaya,d  Brief Test of Attention 
  10/36 Delayb  Test of Everyday Attention 
  CVLT ba,d   
  CVLT short delaya,d Executive function  
  NYU paragraph immediate recalla,d  Shifting  
  Auditory Verbal Learning Test-Immediate recalla,d   Trails B 32 
  HVLT recalla,d   D-KEFS Number-Letter Switching 
  1- and 10-min delayed recall of ADAScoga,d   TMT B-A 
  ADAS delayed recalla,d   WCST categories 
  ADAS verbal memorya,d   WCST perseverative errors 
 Long delay    More-Odd Shifting Task 
  CERAD word list 30 min recalla,d   Brixton Test 
  Free and Cued Selective Reminding Test-delaya,d   D-KEFS Sorting Test 
  World Health Organization University of California Los Angeles Auditory Verbal Learning Test (delay)a,d  Inhibition  
  Object-Location Association Testb   D-KEFS Tower Test 
  Chinese Version Verbal Learning Test (CVVLT)a,d   Stroop measures 12 
  Buschke Selective Reminding delaya,d   Stop Signal Task 
  Item Recognition from Source Memory Paradigmc,f   Completions and Corrections 
  Content memory activity paradigmc,f   Hayling Test 
  RAVLT long delaya,d   D-KEFS Tower Test 
  Memory Assessment Scale (MAS) prose recalla,e   Random Number Generation 
  Brief Visual Memory Test (BVMT) long delayb  Initiation  
  Memory Assessment Scale (MAS) long delaya,d   Letter fluency measures 23 
  CVLT-II Long delaya,d 11   D-KEFS Design Fluency 
  Chinese Auditory Verbal Learning Test-delayeda,d   DRS-2 Initiation/Perseveration 
  NYU paragraph delayed recalla,e   Alternate Uses Test 
  Auditory Verbal Learning Test AVLT delayeda,d   Tinker Toy Test 
  HVLT delayed recalla,d  Reasoning  
  Delayed Memory Compositea,d   DRS-2 Conceptualization subscale 
  Logical Memory IIa,e 19   Matrix Reasoning 
  Visual Reproduction IIb 10   Similarities 
  A delayed memory compositea,d   Verbal Reasoning subtest of the Cognitive Competency Test 
 Not used in memory interval subdomain    Abstractions 
  Verbal Paired Associated Learning Testa,f   Word series 
  Prose recalla,e   Experimental Judgment Test 
  ADAS Memory Domaina,d   Iowa Gambling Test 
  ROCFT recallb   Stanford Binet Absurdities Test 
  Babcock story recalla,e   Verbal Concept Attainment Test 
  CVLT short delay cueda,d   Stanford Binet Absurdities Test 
  CVLT long delay cueda,d  Planning  
  Rivermead Behavioural Memory Testc,f   CLOX-1 
  Logical Memorya,e   Zoo Map (BADS) 
  A memory compositea,c,d,e,f   Clock drawing 
  Auditory Verbal Learning Test (AVLT) Recognitiona,d   Maze 
  Recognition Memory Compositea,d   ROCF organization 
    Tic tac toe 
Visuospatial    Porteus Maze Test 
 Constructional Praxis   Not used in subcomponent coding  
  Clox 2   BADS 
  Copy-a-Clock Test   Frontal Assessment Battery 
  Copy of ROCFT   Executive Interview (EXIT25) 
  Constructional Praxis   An executive function composite 
  Copy Drawing Test   Keep Track Task 
 Other visuospatial measures    
  Block Design Language  
  Spatial composite  Naming  
  Object Assembly   Boston Naming Test 22 
  Visual Object and Space Number Location   Semantic Memory Composite 
  Line Bisection  Fluency  
  Visual-spatial composite   Category fluency measures 34 
   Not used as a subdomain  
    Token Test 
    Syntax Comprehension 

Note: k = number of studies. aVerbal, bVisual, cNot included in visual or verbal, dList, eStory, fNot included in list or story.

Table 2.

Coding summary of functional status measures for everyday functioning domains and subdomains

Performance-based measures k 
 Behavior Simulation 22 
  Financial Capacity Instrument and subscalesa 
  Independent Living Scale and subscalesa 
  Direct Assessment of Functional Status Scale (DAFS-R)a 
  Direct Assessment of Functional Status Scale (DAFS-BR)a 
  UCSD Performance-Based Skills Assessment (UPSA)a 
  Timed Instrumental Activities of Daily Living (TIADL)a 
  TIADLa 
  OTDL-Ra 
  Texas Functional Living Scale (TFLS)a 
  Naturalistic Action Test (NAT)a 
  MacArthur Competence Assessment Tool for Clinical Research and subscalea 
 Problem Solving 10 
  Test of Practical Judgment (TOP-J)a 
  Judgment Questionnaire from the Neurobehavioral Cognitive Status Exama 
  Judgment from NABa 
  Everyday Problems Testa 
  Capacity to Consent to Treatment Instrument subscalesa 
  Chinese Assessment of Capacity for Everyday Decision Making (ACED) Medication subscalesa 
  MacArthur Competence Assessment Tool for Clinical Research and subscalea 
 Direct Observation 
  Daily Activity Scenarioa 
  Day Out Taska 
  Virtual Action Planning Supermarketa 
 Self-report 26 
  Advanced ADLa 
  Interview for Deterioration of Daily Living in Dementiac 
  Five-item subscale of Instrumental Self-Maintenance of the Tokyo Metropolitan Institute of Gerontology-Index of Competence (TMIG-IC)a 
  Daily Functioning Composite from Minimum Data Set Home Interviewa,b,c 
  Alzheimer's Disease Cooperative Functional Assessment Scale (ADAS-FAS)a 
  Composite of IADL and ADLc 
  ADL, abbreviated version of the Cambridge ADL scale from the Cambridge Behavioral Inventorya 
  Bayer Activities of Daily Living Scale (B-ADL)a,c 
  Functional Assessment Questionnaire (PFAQ)a,c,d 
  Lawton IADL scalea 
  Katz ADL scaleb 
  Total World Health Organization Disability Assessment Schedule (WHODAS II)c 
  Blessed Dementia Rating Scale and subscalesa,b,c 
  Everyday Technology Use Questionnairea 
 Informant-report 90 
  ADCS-ADL Alzheimer's Disease Cooperative Study-Activities of Daily Living Study-ADCS-ADLc 
  IQCODEa 
  Disability Assessment in Dementia (DAD) and subscalesa,b,c 
  Functional Capacities of Activities of Daily Living (FC-ADL)b 
  WSU IADLsa 
  SV-ADLQc 
  Alzheimer's Disease Activities of Daily Living—International Scale (ADL-IS)a 
  ECOG subscalesa 
  Record of Independent Living Scale (ROIL)a 
  Korean Dementia Screening Questionnaire (KDSQ)d 
  Barthel Activities of Daily Living (B-ADL)d 
  Activities of Daily Living—Prevention Instrument (ADL-PI)a 
  Bristrol Activities of Daily Living (BADL)a 
  Barthel ADLb 
  Seoul IADLa 
  Korean-Instrumental Activities of Daily Livinga 
  IADL-Ea 
  Activities of Daily Livingb 
  Instrumental Activities of Daily Livinga 
  ADLb 
  Instrumental Activities of Daily Livinga 
  ADCS-MCI-ADL scalea 
  Bayer Activities of Daily Living Scale (B-ADL)a,c 
  Physical Self-Maintenance Scale (IADL-PSMS) subscalesa,b 
  Functional Assessment Questionnaire (PFAQ)a 15 
  Lawton IADL scalea 
  Katz ADL scaleb 
  Blessed Dementia Rating Scale and subscalesa,b,c 
 Both self- and informant-report 45 
  CDR-SOBc 38 
  Basic Activities of Daily Living (BADL)b 
  Instrumental Activities of Daily Living (BADL)a 
  Total World Health Organization Disability Assessment Schedule (WHODAS II)c 
  Everyday Technology Use Questionnairea 
 Not used in either self- or informant-report 17 
  Activities of Daily Living (ADL)b 
  Instrumental Activities of Daily Living (IADL)a 
  Chinese Activity of Daily Living Scalec 
  Activities of Daily Livingd 
  Instrumental Activities of Daily Livinga 
  Modified Activities of Daily Living Scaleb 
  Chinese version of ADLc 
  ADCS-MCI-ADL scalea 
  Physical Self-Maintenance Scale (IADL-PSMS) subscalesa,b 
  Lawton IADL scalea 
  Katz ADL scaleb 
Performance-based measures k 
 Behavior Simulation 22 
  Financial Capacity Instrument and subscalesa 
  Independent Living Scale and subscalesa 
  Direct Assessment of Functional Status Scale (DAFS-R)a 
  Direct Assessment of Functional Status Scale (DAFS-BR)a 
  UCSD Performance-Based Skills Assessment (UPSA)a 
  Timed Instrumental Activities of Daily Living (TIADL)a 
  TIADLa 
  OTDL-Ra 
  Texas Functional Living Scale (TFLS)a 
  Naturalistic Action Test (NAT)a 
  MacArthur Competence Assessment Tool for Clinical Research and subscalea 
 Problem Solving 10 
  Test of Practical Judgment (TOP-J)a 
  Judgment Questionnaire from the Neurobehavioral Cognitive Status Exama 
  Judgment from NABa 
  Everyday Problems Testa 
  Capacity to Consent to Treatment Instrument subscalesa 
  Chinese Assessment of Capacity for Everyday Decision Making (ACED) Medication subscalesa 
  MacArthur Competence Assessment Tool for Clinical Research and subscalea 
 Direct Observation 
  Daily Activity Scenarioa 
  Day Out Taska 
  Virtual Action Planning Supermarketa 
 Self-report 26 
  Advanced ADLa 
  Interview for Deterioration of Daily Living in Dementiac 
  Five-item subscale of Instrumental Self-Maintenance of the Tokyo Metropolitan Institute of Gerontology-Index of Competence (TMIG-IC)a 
  Daily Functioning Composite from Minimum Data Set Home Interviewa,b,c 
  Alzheimer's Disease Cooperative Functional Assessment Scale (ADAS-FAS)a 
  Composite of IADL and ADLc 
  ADL, abbreviated version of the Cambridge ADL scale from the Cambridge Behavioral Inventorya 
  Bayer Activities of Daily Living Scale (B-ADL)a,c 
  Functional Assessment Questionnaire (PFAQ)a,c,d 
  Lawton IADL scalea 
  Katz ADL scaleb 
  Total World Health Organization Disability Assessment Schedule (WHODAS II)c 
  Blessed Dementia Rating Scale and subscalesa,b,c 
  Everyday Technology Use Questionnairea 
 Informant-report 90 
  ADCS-ADL Alzheimer's Disease Cooperative Study-Activities of Daily Living Study-ADCS-ADLc 
  IQCODEa 
  Disability Assessment in Dementia (DAD) and subscalesa,b,c 
  Functional Capacities of Activities of Daily Living (FC-ADL)b 
  WSU IADLsa 
  SV-ADLQc 
  Alzheimer's Disease Activities of Daily Living—International Scale (ADL-IS)a 
  ECOG subscalesa 
  Record of Independent Living Scale (ROIL)a 
  Korean Dementia Screening Questionnaire (KDSQ)d 
  Barthel Activities of Daily Living (B-ADL)d 
  Activities of Daily Living—Prevention Instrument (ADL-PI)a 
  Bristrol Activities of Daily Living (BADL)a 
  Barthel ADLb 
  Seoul IADLa 
  Korean-Instrumental Activities of Daily Livinga 
  IADL-Ea 
  Activities of Daily Livingb 
  Instrumental Activities of Daily Livinga 
  ADLb 
  Instrumental Activities of Daily Livinga 
  ADCS-MCI-ADL scalea 
  Bayer Activities of Daily Living Scale (B-ADL)a,c 
  Physical Self-Maintenance Scale (IADL-PSMS) subscalesa,b 
  Functional Assessment Questionnaire (PFAQ)a 15 
  Lawton IADL scalea 
  Katz ADL scaleb 
  Blessed Dementia Rating Scale and subscalesa,b,c 
 Both self- and informant-report 45 
  CDR-SOBc 38 
  Basic Activities of Daily Living (BADL)b 
  Instrumental Activities of Daily Living (BADL)a 
  Total World Health Organization Disability Assessment Schedule (WHODAS II)c 
  Everyday Technology Use Questionnairea 
 Not used in either self- or informant-report 17 
  Activities of Daily Living (ADL)b 
  Instrumental Activities of Daily Living (IADL)a 
  Chinese Activity of Daily Living Scalec 
  Activities of Daily Livingd 
  Instrumental Activities of Daily Livinga 
  Modified Activities of Daily Living Scaleb 
  Chinese version of ADLc 
  ADCS-MCI-ADL scalea 
  Physical Self-Maintenance Scale (IADL-PSMS) subscalesa,b 
  Lawton IADL scalea 
  Katz ADL scaleb 

Note: k = number of studies. aIADL, bADL, cBoth IADL and ADL, dNot used as either IADL or ADL. Measures that included predominantly IADL items were coded as IADL.

The average age of the sample was not reported by five study samples. Education was either not reported or reported in non-U.S. equivalencies for 50 study samples. Twenty-four study samples did not report referral source. For MCI subtype, 22 study samples did not report if the sample was amnestic or nonamnestic, and 79 study samples did not report if the sample was single- or multidomain. For functional status measures, 15 study samples did not indicate if questionnaire measures were completed by self or informant, and three study samples did not indicate if measures were mainly composed of ADL or IADL items. If the study sample did not report the necessary information, it was not used in analyses involving the respective variable.

Effect Size Calculation

Effect sizes were calculated directly from the reported study means and account for the amount of variance in the relationship between the cognitive and functional measures. For each study sample, the means for all of the study sample cognitive and functional status measures were transformed to a standard Z score using the following equation: Z = Xi − μ/σ, where Xi is the mean of the individual measure, μ is the grand mean of the total cognitive and functional measures contained within a study sample, and σ is the SD. Using μ in this manner required the following two assumptions: (1) each of the means for each measure used to generate μ has equal probability of being selected from the overall population distribution and (2) the scales used to generate the individual means within each study measure the same construct and the measures share some level of measurement equivalence. Although the use of this equation allows for extraction of multiple effect sizes, there is a loss of sensitivity to individual means. Specifically, the authors assume that each of the cognitive measures and functional measures are congeneric; in the case of this study, we assume the underlying measurement of a unidimensional latent trait, cognitive or functional status. Due to the congeneric nature of the measures, the individual observed scores for each measure in the group contributing to μ are allowed to have (a) different units of measurement, (b) dissimilar scale origins (y intercept), and (c) unequal error variances (Dimitrov, 2014). When needed, Z-scores were recoded so that higher scores indicated better performance on the measures.

Z-scores were then used to calculate effect sizes using the following equation, d = ZX1ZX2/Sp, where ZX1 is the standardized mean of the cognitive measure, ZX2 is the standardized mean of the functional status measure, and (Sp) is the pooled standardized SD. The d score put everything on the same scale and is similar to a correlation calculation as it is accounting for the overlapping variance between the two standardized Z scores on each of the cognitive and functional status measures. For the subset of studies that included only one cognitive and one functional status measure, correlations were first calculated by computing and using the covariance. Resultant correlations were then converted to the d score in order to standardize effects within and between studies.

For interpretation, results are reported as correlations and variances (i.e., R2), converted from the d score. Cohen (1988) suggested that effect size magnitudes for variances of 0.01, 0.09, and 0.25 correspond to small, medium, and large effects, respectively. As the interpretation of effect size is driven by contextual factors associated with the sensitivity of the measure, one should use caution in overinterpreting the meaning of the effect size (Fritz, Morris, & Richler, 2012).

The unit of analysis was a comparison of the neuropsychological findings and functional ability of individuals with MCI. Thus, the relationship between each cognitive and functional status measure yielded its own effect size within a study sample. Studies that reported more than one cognitive or functional status measure created more than one comparison. In order to eliminate the potential for the lack of independence of observations, effect sizes were averaged to create a single mean effect size per study at the appropriate level of analysis (e.g., overall, domain, and subdomain) for the specific question being asked. For example, if a study had measures of immediate and delayed episodic memory, these standardized means were averaged from the individual means when evaluating the relationship between the domain of memory and functional status. However, when examining the relationship between cognition and functional status as a function of memory retention interval, we treated the two memory measures as separate subdomains. To increase the stability of the effect size estimate, we only calculated an average effect size when a measure was used in at least nine studies at the level of analysis of interest (e.g., domain and subdomain; Oswald, Mitchell, Blanton, Jaccard, & Tetlock, 2013), and all effects sizes were independent within a group.

Studies were weighted according to their inverse variance estimates to control for differences in sample size. The significance and magnitude of heterogeneity were calculated using Cochran's Q statistics and the I2 index (Braver, Thoemmes, & Rosenthal, 2014), respectively, with a 95% confidence interval (CI), approximating a χ2 distribution with k − 1 degrees of freedom, where k is equal to the number of study samples. Random-effects models were chosen for all analyses because included study samples employed vastly different methods of assessing cognition and functional status, and included heterogeneous samples of individuals with MCI. All computations were conducted using Excel 2013, SPSS 21 macros (Lipsey & Wilson, 2000; Wilson, 2005), and JMP software (version 11.1, SAS Institute, Cary, NC). Restricted maximum likelihood method was used to estimate all model parameters.

Potential effect size moderators were examined based on the Q statistic, subgroup analysis, and meta-regression techniques with random-effects models. Moderator analyses were conducted when the test for heterogeneity (Q) for a specific cognitive domain was statistically significant. Composite measures were only included if all of the contributing tests were specific to the cognitive domain or subdomain (see Table 1).

Results

The initial search identified 1,225 unique abstracts (see Fig. 1). Two-hundred and twenty-eight full-text articles of the initial 1,225 articles (18.6%) were selected for preliminary review as potentially meeting inclusion criteria. From these, 90 papers (39.0%) plus 8 papers included from backward searches, with 132 study samples, met inclusion criteria comprising 4,015 individual unique associations between a cognitive measure and a functional outcome. In total, data from 18,069 individuals with MCI were included [mean age = 73.65 (SD = 4.44) and education in years (U.S. equivalency) = 14.31 (SD = 1.69)]. Overall analysis of the relationship between cognition and functional outcomes for the 132 study samples revealed that cognition explained 23% of the variance in functional outcomes (95% CI for variance 0.20–0.26, p < .001, d = 1.08; see Fig. 2). The fail-safe N indicated that 238,830 studies would be needed to nullify the observed effect (Orwin, 1983). In addition, a funnel plot showed no publication bias (Fig. 3). Total variance explained in functional status by cognition for the 132 study samples ranged from 0.01% to 88% (median = 20%). Analysis of homogeneity of effect sizes (Q) showed significant variance across study effect sizes, whereas the I statistic quantifies the degree of heterogeneity by estimating the percentage of variance that is attributable between studies, Q(131) = 2007.31, p < 0.001, I2 = 93.47.

  • Question 1: What is the range of total variance in functional outcomes specifically attributed to cognitive measures in individuals with MCI?

Fig. 2.

Histogram of variance in functional measures accounted for by all cognitive measures.

Fig. 2.

Histogram of variance in functional measures accounted for by all cognitive measures.

Fig. 3.

Funnel plot, using data from 132 study samples, each point is two studies, with log-odds ratios displayed on the horizontal axis.

Fig. 3.

Funnel plot, using data from 132 study samples, each point is two studies, with log-odds ratios displayed on the horizontal axis.

Methodological, clinical, and demographic variables that could explain this heterogeneity among effect sizes were examined using categorical and continuous models. Among the study characteristics, the effect of participant mean age and education, referral method, and MCI subtype were examined as a set of possible variables generating the heterogeneity among effect size. The magnitude of the relationship between cognition and functional outcomes as a function of domains (e.g., memory) and subdomains (e.g., visual memory) of cognitive and everyday functioning measures was also examined (see Tables 1 and 2). We examined whether the relationship between cognition and functional outcomes in MCI populations could be attributed to age and education (U. S. equivalency) using random-effects inverse variance weighted meta-regression. Predictors were entered separately (k = 127 for age; k = 86 for education) and together into a meta-regression, and the results were similar. Neither age (β = −0.06, Z = −0.50, p = .62) nor education (β = −0.11, Z = −1.00, p = .32) was found to moderate the relationship between cognition and functional outcomes in the MCI population (R2 = .01, k = 86). Subgroup analyses were used to examine whether the strength of the relationship between cognition and functional outcomes differed by MCI subtype or referral source. As seen in Table 3, and supported by the nonsignificant Qb statistic, the relationship between cognition and functional outcomes did not significantly differ between amnestic or nonamnestic MCI subgroups. Studies using single-domain and multidomain MCI groups also did not differ in the percent of variance in functional status accounted for by cognition. Furthermore, studies with mixed MCI samples did not differ from those that specified a specific MCI subtype. In addition, as evidenced by the nonsignificant Qb statistic, the results did not indicate significant differences in variance accounted for (range 21%–23%) as a function of clinic, community, or mixed referral source (Table 3). As seen in Table 2, measures of functional status were categorized as either questionnaires or performance-based measures in addition to IADL, ADL, or both IADL and ADL measures. Questionnaires were further grouped into self-, informant-, or both self- and informant-report. Because there were so few studies that used performance-based problem-solving or direct observation measures, only performance-based behavioral simulation measures could be separately evaluated.

  • Question 2: Does age and education moderate the relationship between cognition and functional outcomes?

  • Question 3: Does the total variance in functional outcomes specifically attributed to cognitive measures differ by MCI subtype or referral source?

Table 3.

Effect sizes for subgroup analyses of study characteristics

Subgroups k R R2 (%) LCI of R2 (%) UCI of R2 (%) Qw Qb Qbp 
MCI subtypes 
 Amnestic 11 .50 25 20 28 13.70 0.84 .13 
 Nonamnestic .46 21 13 34 14.85   
 Mixed 33 .44 19 11 23 20.06   
 Single-domain 70 .49 24 14 37 85.14 4.05 .66 
 Multidomain 11 .48 23 34 3.32   
 Mixed 29 .41 17 13 26 16.05   
Referral source 
 Clinic 52 .46 21 16 27 44.60 0.11 .95 
 Community 40 .47 22 16 28 53.61   
 Mixed 16 .48 23 13 33 12.55   
Subgroups k R R2 (%) LCI of R2 (%) UCI of R2 (%) Qw Qb Qbp 
MCI subtypes 
 Amnestic 11 .50 25 20 28 13.70 0.84 .13 
 Nonamnestic .46 21 13 34 14.85   
 Mixed 33 .44 19 11 23 20.06   
 Single-domain 70 .49 24 14 37 85.14 4.05 .66 
 Multidomain 11 .48 23 34 3.32   
 Mixed 29 .41 17 13 26 16.05   
Referral source 
 Clinic 52 .46 21 16 27 44.60 0.11 .95 
 Community 40 .47 22 16 28 53.61   
 Mixed 16 .48 23 13 33 12.55   

Note: LCI = lower confidence interval; UCI = upper confidence interval.

  • Question 4: Does the total variance in functional outcomes specifically attributed to cognitive measures in individuals with MCI differ depending on type of functional status measure used?

The strength of the relationship between cognition and functional outcomes when using performance-based measures (25%) was comparable with questionnaires (24%), Qb = 0.02, p = .90, η2 = 0.07 (see Table 4). Questionnaires were the most common type of functional status measure used across studies, with informant-report being utilized most frequently. For performance-based measures, behavioral simulation measures were used most often with cognition explaining 32% of the variance. There were statistically significant differences between informant-, self-, and measures that included both self- and informant-report, F(2, 128) = 8.73, p < .001, η2 = 0.12. Tukey HSD post hoc comparisons revealed significant differences between informant-report (28%) and self-report (21%) measures in the amount of variance accounted for by cognition, t(32) = 3.28, p = .001, d = 1.15. The strength of the relationship between cognition and functional outcomes for IADL measures (25%) was comparable with ADL measures (27%) and to measures that were composed of both IADLs and ADLs (22%), F = 0.87, p = .42, η2 = 0.07. Individual cognitive tests were organized into several broad domains of cognitive abilities based on the neuropsychological literature (Lezak, Howieson, Bigler, & Tranel, 2012; Strauss et al., 2006). Specific domains identified included memory, executive functions, global cognitive status, attention, visuospatial abilities, working memory, processing speed, and language (see Table 1 for associated neuropsychological tests). Table 5 presents a summary of the mean effect sizes for each of the eight cognitive domains. All cognitive domains accounted for 20% or more of the variance in functional outcomes. There were statistically significant differences between cognitive domains, F = 4.31, p < .001, η2 = 0.20. Tukey HSD post hoc comparisons revealed significant differences between the amount of variance accounted for by the executive functioning domain (37%) and all other cognitive domains, ts > 3.69, ps > .003. Although there were no significant differences between the attention (33%), working memory (31%), and visuospatial domains (26%), ts < 1.86, ps > .06, these domains differed from the memory (23%), language (22%), processing speed (20%), and global cognitive status domains (20%), ts > 3.13, ps < .001. There were no differences between the memory, language, processing speed, or global cognitive status domains, ts < 1.86, ps > .92.

  • Question 5: What is the unique variance in functional outcomes explained by specific cognitive domains (e.g., memory, speeded processing, and executive functioning) and subdomain processes (e.g., switching, inhibition, and planning)?

Table 4.

Functional status domains and subdomains

Functional status domains and subdomains k R R2 (%) LCI of R2 (%) UCI of R2 (%) ZHeterogeneity
 
Q* I2 (%) 
Performance-based 31 .50 25 14 35 6.96 550.83 95.10 
 Behavioral simulation 20 .56 32 16 46 5.53 247.99 93.55 
Questionnaire 115 .49 24 20 27 20.17 2283.77 95.05 
 Self-report 16 .46 21 14 28 9.39 114.47 86.90 
 Informant-report 72 .53 28 24 33 18.66 1534.75 95.44 
 Both self- and informant 41 .39 15 50 7.05 1675.73 97.61 
IADL 105 .50 25 21 29 20.12 1707.37 94.08 
ADL 28 .52 27 14 40 5.9 2088.96 98.71 
Both IADL and ADL 64 .47 22 16 27 12.06 2014.4 96.87 
Functional status domains and subdomains k R R2 (%) LCI of R2 (%) UCI of R2 (%) ZHeterogeneity
 
Q* I2 (%) 
Performance-based 31 .50 25 14 35 6.96 550.83 95.10 
 Behavioral simulation 20 .56 32 16 46 5.53 247.99 93.55 
Questionnaire 115 .49 24 20 27 20.17 2283.77 95.05 
 Self-report 16 .46 21 14 28 9.39 114.47 86.90 
 Informant-report 72 .53 28 24 33 18.66 1534.75 95.44 
 Both self- and informant 41 .39 15 50 7.05 1675.73 97.61 
IADL 105 .50 25 21 29 20.12 1707.37 94.08 
ADL 28 .52 27 14 40 5.9 2088.96 98.71 
Both IADL and ADL 64 .47 22 16 27 12.06 2014.4 96.87 

Notes: LCI = lower confidence interval; UCI = upper confidence interval; IADL = instrumental activities of daily living; ADL = activities of daily living.

*

All significant at <.001.

Table 5.

Cognitive domains and subdomains.

Cognitive domains and subdomains k R R2 (%) LCI of R2 (%) UCI of R2 (%) ZHeterogeneity
 
QI2 (%) 
Memory 132 .48 23 19 29 22.93 2007.31 93.47 
 Immediate 42 .43 18 14 23 12.66 261.29 85.07 
 Short delay 32 .59 35 26 44 9.64 1076.59 97.31 
 Long delay 41 .56 31 23 39 10.67 1303.32 97.16 
 Verbal memory 66 .49 24 19 29 14.08 1276.16 95.14 
 Visual memory 18 .57 33 19 46 6.35 229.55 93.90 
 List 54 .46 21 16 27 11.56 886.48 94.36 
 Story 31 .53 28 20 36 10.34 621.37 95.65 
Global 129 .45 20 17 24 18.48 2635.97 95.14 
 Global 30 .51 26 15 36 6.95 641.01 95.48 
 Brief mental status 124 .45 20 16 24 17.43 2791.2 95.59 
Attention 16 .57 33 13 50 4.47 927.47 98.38 
Visuospatial 19 .51 26 16 36 7.23 238.39 92.45 
 Copy 13 .56 31 19 42 7.23 115.9 89.65 
 Other 11 .45 20 10 30 6.08 66.37 84.93 
Working Memory 32 .55 31 22 39 9.56 543.21 94.29 
Processing Speed 40 .44 20 15 24 13.55 417.05 90.65 
Language 37 .47 22 16 29 10.89 403.39 91.15 
 Naming 23 .46 21 12 31 6.67 266.81 92.13 
 Fluency 32 .47 22 15 29 9.62 399.02 92.23 
Executive Functions 56 .61 37 29 46 13.32 1107.55 94.94 
 Initiation 32 .34 11 17 7.16 195.24 85.66 
 Planning 23 .50 25 17 33 8.95 253.22 92.10 
 Reasoning 16 .33 11 21 4.18 209.76 93.33 
 Switching 39 .80 63 57 68 16.05 1604.57 97.69 
 Inhibition 13 .57 32 22 41 9.14 53.29 79.36 
 TMTB 37 .82 67 62 72 18.09 1425.80 97.48 
 COWAT 21 .29 14 6.35 93.15 78.53 
 STROOP 13 .58 33 24 43 9.23 71.89 83.31 
 CLOX 15 .51 26 13 39 5.76 389.34 96.40 
Cognitive domains and subdomains k R R2 (%) LCI of R2 (%) UCI of R2 (%) ZHeterogeneity
 
QI2 (%) 
Memory 132 .48 23 19 29 22.93 2007.31 93.47 
 Immediate 42 .43 18 14 23 12.66 261.29 85.07 
 Short delay 32 .59 35 26 44 9.64 1076.59 97.31 
 Long delay 41 .56 31 23 39 10.67 1303.32 97.16 
 Verbal memory 66 .49 24 19 29 14.08 1276.16 95.14 
 Visual memory 18 .57 33 19 46 6.35 229.55 93.90 
 List 54 .46 21 16 27 11.56 886.48 94.36 
 Story 31 .53 28 20 36 10.34 621.37 95.65 
Global 129 .45 20 17 24 18.48 2635.97 95.14 
 Global 30 .51 26 15 36 6.95 641.01 95.48 
 Brief mental status 124 .45 20 16 24 17.43 2791.2 95.59 
Attention 16 .57 33 13 50 4.47 927.47 98.38 
Visuospatial 19 .51 26 16 36 7.23 238.39 92.45 
 Copy 13 .56 31 19 42 7.23 115.9 89.65 
 Other 11 .45 20 10 30 6.08 66.37 84.93 
Working Memory 32 .55 31 22 39 9.56 543.21 94.29 
Processing Speed 40 .44 20 15 24 13.55 417.05 90.65 
Language 37 .47 22 16 29 10.89 403.39 91.15 
 Naming 23 .46 21 12 31 6.67 266.81 92.13 
 Fluency 32 .47 22 15 29 9.62 399.02 92.23 
Executive Functions 56 .61 37 29 46 13.32 1107.55 94.94 
 Initiation 32 .34 11 17 7.16 195.24 85.66 
 Planning 23 .50 25 17 33 8.95 253.22 92.10 
 Reasoning 16 .33 11 21 4.18 209.76 93.33 
 Switching 39 .80 63 57 68 16.05 1604.57 97.69 
 Inhibition 13 .57 32 22 41 9.14 53.29 79.36 
 TMTB 37 .82 67 62 72 18.09 1425.80 97.48 
 COWAT 21 .29 14 6.35 93.15 78.53 
 STROOP 13 .58 33 24 43 9.23 71.89 83.31 
 CLOX 15 .51 26 13 39 5.76 389.34 96.40 

Notes: LCI = lower confidence interval; UCI = upper confidence interval.

*

All significant at <.001.

The Q statistic revealed significant heterogeneity of effect sizes for all cognitive domains. In terms of potential moderators of this variability, we examined five cognitive domains as the number of studies that included potential moderators was sufficient for analysis and an a priori decision categorization was made of these factors as potential moderators. We did not identify any working memory subdomains, and there were not enough studies available to examine potential moderators for attention.

Cognitive Subdomains

For memory, we were interested in the effect of retention interval (immediate, short, and long delay) and study materials (list, story; verbal, visual) on the magnitude of observed effects. For executive function, the effects of switching, inhibition, initiation, planning, and reasoning were examined. We also examined the language, global, and visuospatial subdomains of naming and fluency, constructional praxis and other visuospatial measures, and brief mental status and extended cognitive screeners, respectively.

A summary of the mean effect sizes for each subdomain is presented in Table 5. For memory retention interval, short- and long-delay memory measures accounted for more variance (35% and 31%, respectively) in functional outcomes than immediate memory measures (18%), F = 5.14, p < .01, η2 = 0.08. Variance was 33% for visual memory tests and 24% for verbal memory tests but did not significantly differ, Qb = 1.25, p = .26, with confidence intervals surrounding the variances fully overlapping. Although not statistically different, Qb = 1.54, p = .21, contextual-based (i.e., story) memory tests accounted for 28% of variance, whereas non-contextual (i.e., list) memory tests accounted for 21% of variance.

Fluency and naming measures accounted for 22% and 21% of the variance, respectively, and did not statistically differ, Qb = 0.04, p = .83. Twenty-five percent of the variance was accounted for by extended cognitive screeners compared with 20% by brief mental status measures, with no significant differences, Qb = 1.43, p = .23. Variance was 31% for constructional praxis measures and 20% for other visuospatial measures but did not significantly differ, Qb = 1.28, p = .26, and confidence intervals surrounding the variances largely overlapped.

The variance accounted for by executive function subdomains differed significantly, F = 31.53, p < .001, η2 = 0.06. Examination of the subcomponent moderator analysis of the executive function subdomains revealed that the subcomponent accounting for the largest variance was switching (63%). The variance accounted for by inhibition (32%), planning (25%), reasoning (11%), and initiation (11%) did not significantly differ, ts < 0.41, ps > .68. Given the large amount of variance predicted by executive functioning, and switching measures in particular as the Trail Making Test B accounted for the majority of that data, we were interested in examining the mean effect sizes across popular tests of executive functioning. When examining individual measures of executive functioning, Trail Making Test part B accounted for a significantly greater amount of variance (67%) in functional outcomes, F = 15.27, p < .001, η2 = 0.14, compared with stroop (33%), CLOX (26%), and letter fluency (9%) measures (see Table 4), which did not significantly differ, F = 0.94, p = .57. We were not able to investigate other popular measures of executive functioning due to a low number of studies.

Given the suggestion by Gold (2012) that functional outcome measures may have different relations with executive functions, we examined the variance accounted for in functional outcome measures as a function of executive functioning tests. Tests of executive functioning accounted for 39% and 25% of the variance in questionnaires and performance-based measures, respectively, but did not statistically differ, Qb = 2.95, p = .09 (see Table 6). Twenty-nine percent of the variance in behavioral simulation measures was accounted for by executive functioning tests. Compared with questionnaires comprising both self- and informant-report (36%), executive function measures accounted for 44% of the variance in informant-based questionnaire measures, although there was no statistical difference, Qb = 3.00, p = .08. Furthermore, executive functioning tests accounted for 39% of the variance in IADLs, but did not statistically differ from measures including both IADL and ADL measures (34%), Qb = 0.63, p = .43. We were not able to investigate the variance accounted for by executive functioning in problem-solving, direct observation, self-report, or ADL measures due to a low number of studies.

Table 6.

Functional status domains and subdomains with executive function correlates

Functional status domains and subdomains k R R2 (%) LCI of R2 (%) UCI of R2 (%) ZHeterogeneity
 
QI2 (%) 
Performance-based 19 .50 25 43 3.94 863.80 97.92 
 Behavioral simulation 13 .54 29 54 2.80 467.12 97.43 
Questionnaire 48 .62 39 30 47 10.38 3176.15 98.52 
 Informant-report 32 .66 44 36 50 13.38 994.01 96.88 
 Both self- and informant 35 .60 36 30 42 14.58 503.70 93.25 
IADL 51 .62 39 29 48 9.87 3477.56 98.56 
Both IADL and ADL 27 .58 34 22 45 7.39 1423.13 98.17 
Functional status domains and subdomains k R R2 (%) LCI of R2 (%) UCI of R2 (%) ZHeterogeneity
 
QI2 (%) 
Performance-based 19 .50 25 43 3.94 863.80 97.92 
 Behavioral simulation 13 .54 29 54 2.80 467.12 97.43 
Questionnaire 48 .62 39 30 47 10.38 3176.15 98.52 
 Informant-report 32 .66 44 36 50 13.38 994.01 96.88 
 Both self- and informant 35 .60 36 30 42 14.58 503.70 93.25 
IADL 51 .62 39 29 48 9.87 3477.56 98.56 
Both IADL and ADL 27 .58 34 22 45 7.39 1423.13 98.17 

Notes: LCI = lower confidence interval; UCI = upper confidence interval; IADL = instrumental activities of daily living; ADL = activities of daily living.

*

All significant at <.01.

Discussion

Over the past decade, a considerable literature has established the presence of functional impairments in individuals with MCI. Although it appears that cognition significantly contributes to functional abilities, the magnitude of variance accounted for has varied greatly across studies. In this article, we used meta-analytic techniques to improve clinical understanding of the relationship between cognition and functional status in the MCI population. The results of this meta-analysis of 132 studies with 18,069 individuals with MCI revealed a significant relationship between cognition and functional outcomes with the overall explained variance of 23%. Our findings are consistent with the literature (e.g., Tucker-Drob, 2011) and comparable with Royall and Colleagues (2007) who found that overall cognition accounted for 21% of the variance in functional outcomes in a diverse sample of neuropsychiatric, geriatric, and rehabilitation patients. Findings regarding the relationship between age and education and functional abilities in MCI populations have been mixed (e.g., Tam, Lam, Chiu, & Lui, 2007; Triebel et al., 2010). The data showed that the strength of the association between cognition and functional outcomes was not significantly associated with age or education. The findings from this meta-analysis suggests that in a cognitively compromised population of individuals with MCI, cognition accounts for more of the variance in everyday functioning than age or education. Consistent with some prior study findings (e.g., Schmitter-Edgecomb, Woo, & Greeley, 2009), the current data did not support the idea that the strength of the relationship between cognition and functional abilities differed by MCI subtype (i.e., amnestic vs. nonamnestic; single- vs. multidomain) or referral source (i.e., clinic vs. community). This contrasts with some literature which suggests that the multidomain subtype of MCI is at higher risk of developing dementia (e.g., Alexopoulos, Grimmer, Perneczky, Domes, & Kurz, 2006) and that clinic samples show higher rates of conversion than community-based samples (e.g., Farias, Mungas, Reed, Harvey, & DeCarli, 2009). A limitation of the current findings is the heterogeneity in the different neuropsychological tests and batteries used across studies to diagnose MCI, some of which may not have been sensitive enough to distinguish between MCI subtypes. In addition, there was heterogeneity in the way the MCI subgroups were defined, as variation existed in criteria used to operationalize MCI subtypes (e.g., Jack et al., 2011; Petersen, 2004), likely leading to overlap in MCI subtypes across studies. More studies are needed to investigate cognition and everyday functioning within MCI subtypes and across referral sources. There is continued debate regarding the use of self- versus informant-report to measure everyday functioning in MCI populations. Although some studies suggest that individuals with MCI may overestimate functional abilities (e.g., Okonkwo et al., 2008), generally attributed to lack of awareness, other studies have reported that individuals with MCI may underestimate their functional abilities (e.g., Farias, Mungas, & Jagust, 2005). Findings from this meta-analysis suggest that the relationship between cognition and functional outcome is likely to be stronger for informant-report. Across studies, the strength of the relationship between cognition and informant-report questionnaires was larger than for self-report, with 28% and 21% of the variance explained, respectively. This finding is consistent with prior literature that has found stronger relationships between informant-report and cognition (e.g., Farias et al., 2005), as well as informant-report and prediction of dementia (e.g., Gifford et al., 2014; Tabert et al., 2002), compared with self-report.

  • Question 1: What is the range of total variance in functional outcomes specifically attributed to cognitive measures in individuals with MCI?

  • Question 2: Does age or education moderate the relationship between cognition and functional outcomes?

  • Question 3: Does the total variance in functional outcomes specifically attributed to cognitive measures differ by MCI subtype or referral source?

  • Question 4: Does the total variance in functional outcomes specifically attributed to cognitive measures in individuals with MCI differ depending on type of functional status measure used?

Although no “gold standard” has been established, some have suggested that performance-based measures may be more useful measures for assessing everyday functioning (e.g., Marcotte, Scott, Kamat, & Heaton, 2010), whereas others have suggested that questionnaires and performance-based measures may be capturing different aspects of everyday performance (e.g., Schmitter-Edgecombe, Parsey, & Cook, 2011). Data from this meta-analysis revealed that the strength of the relationship between cognition and functional outcomes was comparable for performance-based measures (25%) and questionnaires (24%). These data should not be taken to suggest that these methods for assessing everyday functioning are equivalent as there is evidence to suggest that different methods for assessing functional status are not highly correlated (e.g., Loewenstein et al., 2001; Schmitter-Edgecombe et al., 2011; Vaughan & Giovanello, 2010), perhaps because of the inherent differences between questionnaires and performance-based measures (Gold, 2012; Teng et al., 2010). It will be important for future studies to use several methods in clinical evaluation and examine the relative cognitive correlates across multiple methods.

Consistent with the findings by Royall and colleagues (2007), our results did not show a stronger relationship between cognition and IADLs (25%) compared with ADLs (27%). It should be noted, however, that many primarily IADL questionnaires do ask questions about ADLs and vice versa. In addition, the wording of a question may influence whether it is evaluating basic or complex abilities. For example, although grooming is typically considered a basic ADL, a question asking about grooming could assess basic abilities (e.g., ability to dress oneself) or more complex grooming abilities (e.g., ability to choose appropriate clothes for the day). Findings from the meta-analysis are consistent with the literature, suggesting that cognitive domains have differential relationships with functional outcomes. The cognitive domains of executive functioning, attention, and working memory were found to each account for more than 30% of the variance in functional outcomes, whereas visuospatial abilities, memory, language, processing speed, and global cognitive status accounted for between 20% and 26% of the variance. There was, however, significant heterogeneity within each cognitive domain suggesting a more complex relationship between cognitive subdomains and functional outcomes as described subsequently.

  • Question 5: What is the unique variance in functional outcomes explained by specific cognitive domains (e.g., memory, speeded processing, and executive functioning)?

One striking finding was that, although memory impairment is typically the cardinal feature of most MCI samples, memory (23%) did not emerge as the most significant cognitive correlate of functional ability. Instead, tests of executive functioning (37%) accounted for the largest percentage of variance in functional outcomes. Within the domain of episodic memory, however, the amount of variance in functional outcome accounted for by short- and long-delay memory measures (35% and 31%, respectively) was significantly larger than that accounted for by immediate recall measures (18%) and similar to that accounted for by the executive functioning domain (37%). The findings from this meta-analysis suggest that delayed memory measures may more accurately predict everyday functional abilities compared with immediate memory measures. Although not statistically different, contextually based memory measures (e.g., stories; 28%) accounted for a numerically greater proportion of the variance than noncontextually based memory measures (e.g., lists of words; 21%), as did visual study materials when compared with verbal study materials (33% and 24%, respectively). Given the large confidence intervals surrounding the variances for the story recall and visual memory measures, future studies are needed to identify whether story recall and visually based memory measures may be better predictors of everyday functioning than list learning and verbal memory measures.

Consistent with prior studies (e.g., Brandt et al., 2009; Zheng et al., 2012), the results of this meta-analysis suggest that subcomponents of executive functioning may have differential relationships with functional abilities in the MCI population. Although medium effect sizes (11%) were found for measures classified as assessing initiation/generativity and reasoning, large effects were found for planning (25%) and inhibition (32%). Furthermore, the largest relationship (63%) was found between the switching subdomain and functional abilities. Although studies are mixed with respect to which executive subcomponents may be the best predictors of everyday functioning, the findings from this meta-analysis are consistent with other studies in suggesting that inhibition (e.g., Jefferson, Paul, Ozonoff, & Cohen, 2006), switching (e.g., Bell-McGinty, Podell, Franzen, Baird, & Williams, 2002), and planning are most predictive (Lewis & Miller, 2007). To improve understanding of the independent fractionated roles of executive functioning, future studies should continue to investigate executive function subcomponents within MCI samples.

Given that Trails B comprised the majority of measures included in the switching subdomain, as an alternate method to investigate the superiority of the switching subcomponent of executive functioning, we examined neuropsychological tests commonly used to assess executive functioning (i.e., Trails B, stroop, clock drawing, letter fluency). We found that Trails B (67%) predicted a large amount of variance in everyday functioning and accounted for more than twice the amount of variance of the other executive functioning measures (clock drawing = 26%; stroop test = 33%, respectively, and letter fluency = 9%). There was not enough data available to examine other tests of executive functioning. These results are consistent with Yeh and colleagues (2011) findings that Trails B was the best predictor of ability to perform IADLs in a sample of MCI. However, Trails B, like other executive functioning tests, is not a process pure measure of executive functioning. For example, Trails B relies heavily on other cognitive domains along with switching including psychomotor speed and visual scanning. Most of the studies included in this meta-analysis used the completion time for Trails B rather than a correction for Trails A to better isolate the switching component, suggesting that the switching subdomain score likely included contributions from several cognitive domains. Thus, although Trails B demonstrated the strongest relationship with measures of everyday functioning, it is currently unclear whether it is the executive switching component of the task or a combination of the task processing components that make it such a sensitive measure.

The study results also revealed that attention and working memory accounted for a large percentage of variance in functional outcomes (33% and 31%, respectively). Although this was not hypothesized, this finding may not be surprising as attention, working memory, and executive functioning are all related to prefrontal cortical functions and circuitry (Baddeley & Wilson, 1988). It has been suggested that working memory capacity and executive function share a common underlying executive attention component (McCabe, Roediger, McDaniel, Balota, & Hambrick, 2010) and that individuals with MCI recruit alternate working memory networks compared with cognitively healthy older adults (Niu et al., 2013). Impaired attentional processing and working memory capacity have also been found in MCI samples (e.g., Saunders & Summers, 2010). Furthermore, studies have suggested a role for attention (e.g., Okonkwo, Wadley, Griffith, Ball, & Marson, 2006) in functional abilities with MCI, but studies with working memory are lacking. Across studies utilized in this meta-analysis, attention was assessed with a variety of different methods such that we could not conduct any type of subdomain analysis. Future research should explore the contributions of working memory and the varying subdomains of attention to functional abilities in the MCI population.

Another surprising finding was that measures of global cognitive status accounted for only 20% of the variance in functional outcomes, among the smallest effect sizes found. In contrast, Royall and colleagues (2007) found that global cognitive status accounted for more variance than other cognitive domains. Given that most study criteria for MCI require that global cognitive status scores (e.g., MMSE) fall within the normal range (Petersen, 2005), the contrasting findings could reflect a more restricted range on the global cognitive status measures in this study. Although tests of global cognitive status were the most commonly used cognitive measure across studies, our data suggest that specific cognitive domains and subdomains may provide more sensitive information than global cognitive measures about the status of everyday functioning within the MCI population.

Consistent with our expectations based on the literature, relative to other cognitive domains and subdomains, language did not account for as large a percentage of the variance in functional abilities (22%). In addition, there were no differences between object naming (21%) and category fluency (22%) measures. Similarly, speeded processing measures accounted for a relatively lower percentage in the variance (20%). This result is surprising given others have found that impairments in processing speed were associated with greater functional deficits in amnestic MCI (e.g., Brown et al., 2013). Visuospatial abilities accounted for a larger percentage of the variance (26%). As the variances accounted for by the constructional praxis subdomain of visuospatial abilities (31%) and the domain of executive functioning (37%) were generally comparable, it may be that these visuospatial measures are being affected heavily by executive function-related organizational difficulties in the MCI population.

Implications and Suggestions for Future Research

Using meta-analytic techniques, across cognitive domains and subdomains, cognition accounted for a significant amount of variance in functional outcomes in individuals with MCI. Despite these findings, there was still a large proportion of variance in functional outcomes that was not explained by cognition. Clearly, the ability to function independently in one's environment is determined by multiple factors, including social, physical, psychiatric, behavioral, environmental, and demographic factors. Future work is needed to better understand these noncognitive correlates and the impact these noncognitive correlates have on the relationship between cognition and everyday functioning (e.g., direct and additive).

Cognitive assessment batteries may also need to be redesigned in order to increase their ability to capture larger amounts of variance in functional outcomes. For example, although not often included in cognitive batteries, noncontent memory processes (e.g., prospective memory and temporal order memory) have demonstrated stronger relationships with functional outcomes than the more traditionally assessed content memory processes (e.g., story recall) in the MCI population (e.g., Schmitter-Edgecombe, Woo, & Greeley, 2009). Current assessment batteries may, therefore, not be measuring the cognitive abilities that may be most important in supporting everyday functioning in the real world. Furthermore, it is currently unclear how well performance-based and questionnaire measures of functional status actually predict real-world functioning. Future research should continue to develop and evaluate the ecological validity of measures purported to assess functional outcome. Newer smart-home and wearable technologies that allow for continuous data collection are beginning to provide an avenue for collecting data about an individual's functional abilities within real-world environments (e.g., Rashidi, Cook, Holder, & Schmitter-Edgecombe, 2011; Salazar et al., 2014; Schmitter-Edgecombe & Robertson, in press).

Because the majority of studies focused on global functional capacities, we were not able to investigate whether there were differential relationships between cognition and specific functional domains. The cognitive correlates of specific functional capacities are widely believed to differ, both from each other and from general functional capacities (Royall et al., 2007). Data from several studies suggest that the ability to manage finances may be one of the earliest IADL changes in the MCI population (Gold, 2012). Further work is needed to explore specific domains of everyday abilities and relationships with cognition. We also assumed a linear relationship between cognitive measures and functional outcome, and it is possible that nonlinear transitions, such as threshold effects in level of memory impairment needed to produce everyday functional impairment, may better explain the data. Continued exploration of these relationships is important as a better understanding of everyday functioning in MCI and its cognitive and noncognitive correlates could have important clinical and research applications for the improvement and stability of the MCI diagnosis and development of interventions.

Limitations

In an effort to be comprehensive and reduce publication bias, we included all studies that reported at least one cognitive and one functional measure used with an MCI population regardless of the focus of the article. This resulted in a high degree of heterogeneity across studies as well as a large number of studies making it difficult to review and summarize the methodological quality of these studies. Although one of our study goals was to investigate possible sources of heterogeneity across studies, heterogeneity among the studies remained high even when more specific cognitive domain and subdomain analyses were conducted. For example, there were a large number of functional status measures that differed significantly in what everyday activities were evaluated to assess functional status, the sensitivity of items to early levels of decline, and the sensitivity of the response options or measurement scales. As the number of publications in this area of research increases, future meta-analytic reviews should be able to reduce the level of heterogeneity across studies.

The majority of the studies also came from U.S. samples that had some college education, which may limit generalizability. MCI was also characterized differently across samples, which may have obscured possible findings of differences across MCI subtypes. Among the study samples included in this meta-analysis, few had information on all or even most of the cognitive domains. Therefore, the aspects of cognition that could be compared with functional status were limited by the measures used in the studies. Furthermore, findings from the cognitive subdomain analyses may partially reflect how the cognitive tests were characterized for the purposes of this study. In addition, everyday functioning is complex, involving the interaction of many factors, and the functional status measures used in the studies were assumed to be valid and reliable.

Conclusion and Clinical Implications

Neuropsychologists are increasingly being tasked with answering questions regarding the effects of cognitive difficulties on everyday functioning (Marcotte et al., 2010). Concomitantly, there has been increased interest in understanding the cognitive correlates of everyday functioning in individuals with MCI over the past decade with inconsistent findings. Our meta-analytic results revealed a significant relationship between cognition and functional abilities, with cognition accounting for 23% of the variance and effect sizes for the cognitive domains and subdomains investigated in the medium to large range. Clinically, executive function measures, and Trail Making Test Part B in particular, were especially strong correlates of functional abilities in the MCI population while speeded processing and global cognitive measures explained the least amount of variance. The variance accounted for in functional performance was also larger for delayed memory measures compared with immediate recall measures, and the relationship between cognition and functional outcomes was stronger when assessed via informant-report compared with self-report. Furthermore, the magnitude of the relationship between cognition and functional outcomes in the MCI population was affected mostly by cognitive domains and relatively little by age and education. Early detection of subtle functional impairment in MCI and understanding of its cognitive and noncognitive correlates are essential in diagnosis because of its prediction for dementia progression and potential for treatment and intervention.

Funding

This work was partially supported by grants from the National Institute of Biomedical Imaging and Bioengineering (Grants #R01 EB009675, #R01 EB015853).

Conflict of Interest

None declared.

Appendix

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