PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am J Geriatr Psychiatry. Author manuscript; available in PMC 2012 February 1.
Published in final edited form as:
Am J Geriatr Psychiatry. 2011 February; 19(2): 142–150.
doi:  10.1097/JGP.0b013e3181e89894
PMCID: PMC3000466
NIHMSID: NIHMS221576

Depressive Symptoms Impair Everyday Problem-Solving Ability through Cognitive Abilities in Late Life

Abstract

Background

The association between depression and functional disability in late life remains unclear. This study aimed to explore the relationship between depressive symptoms and daily functioning through the mediation of cognitive abilities, measured by memory, reasoning, and speed of processing.

Methods

We recruited 2,832 older adults (mean age = 73.6 years, SD = 5.9) participating in the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) Study. Structural equation modeling (SEM) was applied to illustrate the relationship between depressive symptoms and everyday problem-solving ability through the mediation of cognitive abilities.

Results

Depressive symptoms were associated with impaired everyday problem-solving ability directly and indirectly mediated via learning and memory, and reasoning. Although depressive symptoms were associated with speed of processing, speed of processing was not significantly related to everyday problem-solving ability.

Conclusions

This study conceptualizes the possible relationships between depressive symptoms and daily functioning with mediation of cognitive abilities and provides a feasible model for the prevention of functional impairment related to geriatric depressive symptoms.

Keywords: depression, cognition, everyday function, elderly

Introduction

Dementia, delirium, and depression in late life call for our attention because they are related to functional disability, poorer quality of life, and the demands they put on family members or caregivers.1 Depression is predicted to be second to cardiovascular disease as a worldwide cause of disability in 2020.25 Functional disability related to depression in the older adults population thus becomes an important issue in attempts to prevent or intervene on disability in late life.

Studies on the association between depression and functional disability can be traced back to the 1960’s.6 Later studies suggested that functional disability may lead to depression, 711 whereas some other longitudinal or prospective cohort studies propose the possibility that depression may lead to functional disability.1222 Some researchers have proposed that the relationship between depression and disability in older adults may be reciprocal or potentially spiraling.23 In addition, cognitive impairment is considered to interact with depression in the relationship with functional disability.21,24

Symptoms or syndromes of depression have been noted to precede cognitive decline and dementia in previous studies.25,26 These results suggest that depression may play a role in the development of functional disability related to cognitive processes. However, the intermediate cognitive process between depression and functional disability had never been empirically tested, although previous researchers found that fluid and crystallized intellectual abilities were the direct predictors of functioning in everyday life, while working memory and speed of processing merely had an indirect effect on functioning.2931 The hypothesis of a “depression-executive dysfunction syndrome” was proposed to describe the conditions of elderly patients with decreased energy and cognitive impairment, principally the ability to planning and attention.32 Furthermore, slowed processing speed was found to be the core cognitive deficit of late-life depression and was closely followed by impaired executive function (Sheline et al., 2006).31 Later Gallo et al.17 concluded that memory and reasoning abilities were both important mediators in the relationship of depression and functional performance in a pilot study of the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) Study.33,34 The findings have not yet been replicated in another sample.

The relationship of depression to functional disability through the possible associations of depression and memory, reasoning, and speed of processing is important when interventions aim at decreasing the disability of older adults. Efficient management of depression as well as the improvement of cognitive abilities that may underlie the process of functional decline will then be justified for their roles in prevention of disability of older adults. To identify the possible pathways between depression and functional disability, this study examines the participants in the ACTIVE study with comprehensive measures of cognitive abilities and everyday problem-solving ability. The conceptual framework that guides the analysis of the potential relationship of depressive symptoms, cognition, and everyday problem-solving is shown in Figure 1. In the model, we propose that depressive symptoms affect memory, reasoning, speed of processing abilities, and everyday problem-solving ability, in accordance with previous studies.17, 28,29

Figure 1
Hypothesized models guiding the analysis of the relationships of depressive symptoms, memory, reasoning, speed of processing, and functioning.

Method

Subjects

Recruitment of participants for the ACTIVE trial was conducted from March 1998 to October 1999. Details of the subject sample and the recruitment procedures have been published elsewhere.3335 Self-reported data include age, years of education, gender, ethnicity, and the sum of reported health conditions, ranging from 0 to 8. Depressive symptoms, cognitive abilities, and everyday problem-solving ability of the participants were measured as described below. For this study, we used only the baseline data of the ACTIVE study participants.

Measures

Depressive symptoms were assessed using the Center for Epidemiological Studies-Depression (CES-D) scale.36 The 12-item version of this measure asks participants to report the frequency (never or less than once a day, 1–2 days, 3–4 days, 5–7 days) during the past week that they experienced certain symptoms or feelings. A total score derived from the four CES-D subscale scores was Blom transformed before analysis.

Learning and memory assessment focused on episodic verbal memory tasks. The Hopkins Verbal Learning Test (HVLT)37 evaluates new verbal learning and memory. Total words correctly recalled over three trials were employed in this study. In the Rey Auditory-Verbal Learning Test (AVLT),38 the number of words recalled across five trials provided an index of new learning. Total number of words correctly recalled over five trials was used as score in the analysis. The subscale of Rivermead Behavioral Memory Test39 that assesses memory for stories was employed in this study. The number of idea units presented in the story that were correctly recalled was used as the score on this task.

Reasoning assessment focused on tasks requiring identification of patterns in letter or word series problems. Word Series (WS)40 tested whether participants could determine the pattern of words in a list and then select the word that will come next in the series. The total number of word series correctly completed in six minutes was scored and used in the analysis. Letter Series (LS) was an adaptation of the number series and letter series tests of Thurstone.41 Participants were asked to discover one or more rules or patterns. The total number of series correctly completed in six minutes was recorded and used in the analysis. In Letter Sets (LT),42 participants viewed a set of letters and had to identify the set that did not use the same pattern rules as other sets in that group. The total number of letter sets correctly identified in seven minutes was recorded and used in analysis.

Speed of processing assessment focused on identifying the minimum stimulus duration at which participants could identify and localize information under varying levels of cognitive demand. Useful Field of View (UFOV) Tasks 2–44345 were included in the analysis. For each task, the dependent variable was the shortest presentation time needed by participants to complete the task 75% of the time. Task 1 was not included because of its simplicity and lack of ability to distinguish participants with mild impairment from ones without impairment.

Outcome measures were aspects of functional activities. Everyday problem-solving represented the ability to correctly identify and reason information in everyday stimuli (e.g., forms, charts, medical labels). The Everyday Problems Test (EPT)46 and the Observed Tasks of Daily Living (OTDL),47,48 both performance-based and measured via paper-and-pencil testing and behavioral stimulations of everyday tasks, were applied in this study.

Analysis

To evaluate the relationships between the above variables, data were analyzed in two phases: (1) Descriptive analyses included examination of distributions of the sociodemographics, health conditions, depressive symptoms, cognitive abilities, and everyday problem-solving ability. (2) An a priori structural equation model (SEM) (see Figure 1) was estimated to evaluate the conceptual models dealing with depressive symptoms, cognitive abilities, and everyday problem-solving ability. The a priori model was then modified and tested to explore the possible relationships between depressive symptoms, cognitive abilities, and everyday problem-solving ability. In those analyses, CES-D was treated as a single continuous variable measuring depressive symptoms. Four latent variables were created: 1) learning and memory ability represented by HVLT, AVLT, and the Rivermead Behavioral Memory Test; 2) reasoning ability by the Word Series, Letter Series, and Letter Sets tests; 3) speed of processing ability by the UFOV Tasks 2–4; and 4) everyday problem-solving ability by the EPT and OTDL tasks. Data on the depressive symptoms and the above indicator variables for the latent variables were Blom-transformed to better fit the hypothesis of normal distribution.49 Standardized measures of association were reported to account for the differing scales of the instruments employed. Models included covariates (gender, age, race, education, and health conditions) that are significantly associated with depression, cognitive abilities, and everyday problem-solving ability when using linear regression. Since participants were clustered in six sites, robust standard errors of measures of these associations were estimated. Differences in χ2 for nested models were examined to assess improvement in fit offered by freeing parameters of interest that relate depression to the variables under study. The value of Akaike Information Criterion (AIC) for each model was also compared.50 The AIC accounts for both model fit and parsimony.51 The model with smallest AIC value was selected. At the same time, three other indicators were used to examine the fitness of the model. Tucker-Lewis Index (TLI) and Comparative Fit Index (CFI) of the selected model should be 0.90 or more; the root mean square error of approximation (RMSEA) of the selected model should be less than 0.10. The direct and indirect effects (through cognitive abilities) of depression on everyday problem solving performance were calculated when standardized measures of association were reported. The structural equation models with latent variables were estimated with Mplus version 4.1.52 Two-tailed tests of significance with type I error rate set at .05 were used.

Results

Depressive symptoms, cognitive abilities, and everyday problem-solving ability for all participants are listed in Table 1. Seventy-six percent of participants were women and 72% were white, with a mean age of 73.6 (SD 5.9) years and a mean education of 13.5 (SD 2.7) years. Structural equation modeling was employed to operationalize the conceptual model of the mediation of cognitive process in the relationship of depressive symptoms and everyday problem-solving ability. Standardized parameter estimates and measures of model fit were estimated for all models that included paths from depressive symptoms to learning and memory, reasoning, processing speed abilities, or everyday problem-solving ability. In the selected model dealing with depressive symptoms (CES-D), cognitive abilities (measured by learning and memory, reasoning, and speed of processing), and everyday problem-solving ability (Figure 2), we found that all coefficients estimated for the paths from depressive symptoms to learning and memory, reasoning, speed of processing, and everyday problem-solving ability were significantly different from the null value of zero.

Figure 2
Structural equation model relating depression scores to performance measures of function through measures of memory, reasoning, and speed of processing.
Table 1
Characteristics of participants (N=2,812)

Depressive symptoms and cognitive abilities

Based on our estimate for the path from depressive symptoms to learning and memory, a one standard deviation increase in depressive symptoms can be expected to be associated with a 0.114 standard deviation decrease (95% confidence interval [CI], [−0.140, −0.088]) in learning and memory ability. Similarly, for the path from depressive symptoms to reasoning ability, a one standard deviation increase in depressive symptoms can be expected to be associated with a 0.041 standard deviation decrease (95% CI [−0.056, −0.026]) in reasoning ability. For the path from depressive symptoms to speed of processing, a one standard deviation increase in depressive symptoms can be expected to be associated with a 0.061 standard deviation decrease (95% CI [−0.030, −0.091]) in processing speed.

Depressive symptoms and everyday problem-solving ability

Direct effect

For the path directly from depressive symptoms to everyday problem-solving ability, a one standard deviation increase in depressive symptoms can be expected to be associated with a 0.043 standard deviation decrease (95% CI –[0.062, −0.023]) in everyday problem-solving ability. This direct effect accounts only for the association between depressive symptoms and everyday problem-solving ability without the mediation of learning and memory, reasoning, and speed of processing.

Through learning and memory

The effect of a one standard deviation increase in depressive symptoms through learning and memory to everyday problem-solving ability leads to an expected 0.064 standard deviation decrease in everyday problem-solving ability. It is calculated (see Figure 2) by adding −0.114×0.317 and −0.114×0.464×0.522 from two possible pathways in the selected model. It is notable that this learning and memory-mediated effect is greater than the direct effect of depressive symptoms to everyday problem solving ability. Through reasoning. A one standard deviation increase in depressive symptoms through reasoning ability leads to an expected 0.021 standard deviation decrease in everyday problem-solving ability. It is calculated (see Figure 2) by -0.041×0.522 from the only pathway in the model.

Through speed of processing

A one standard deviation increase in depressive symptoms through speed of processing leads to an expected 0.027 standard deviation decrease in everyday problem-solving ability. It is calculated (see Figure 2) by adding 0.061x(−0.389)×0.317, 0.061×(−0.389) ×0.464×0.522, 0.061× (−0.030), and 0.061× (−0.368) ×0.522 from four possible pathways in the model.

Total effect

Summing all possible pathways in the selected model, a one standard deviation increase in depressive symptoms is associated with a 0.155 standard deviation decrease in everyday problem-solving ability. The total effect of depressive symptoms on everyday problem-solving ability is composed of the direct effect (0.043) and the above three indirect effects through learning and memory, reasoning, and speed of processing (0.064, 0.021, and 0.027 respectively).

Discussion

Previous work has shown that depression affects the cognitive abilities of memory,5355 executive reasoning,32, 5658 and speed of processing,31,32, 5961 and that depression impairs daily function.62 Results of this analysis support the direct relationship of depression with both cognitive abilities and everyday problem-solving ability. The results move beyond what has been reported previously by showing that depression also affects everyday problem-solving ability through its effects on learning and memory, reasoning, and speed of processing. In addition, results of the model suggest that the effect of depression on one cognitive ability (processing speed) in turn has an additional effect on other cognitive abilities that interfere with everyday problem-solving, a finding supported by the work of Nebes et al.62 In our study, the effects of depressive symptoms (measured by the CES-D) on cognitive abilities and everyday problem-solving are small though statistically significant due to large sample size. On the other hand, the change of CES-D score in the clinical setting can be quite large if the patient has severe depressive symptoms or the treatment of depression is effective.63,64 In that case, the effect of depressive symptoms will not be trivial.

Common bases for depression and cognitive impairment in later life

Depression and cognitive impairment may independently or jointly contribute to the development of late-life daily functioning disability, yet the relationship between depression and cognitive impairment remains uncertain. Older adults appear to be at greater biological vulnerability to depression and cognitive impairment in comparison with younger adults. Considering the connection between depression and medical disorders, the National Institutes of Health consensus conference on depression in late life came to the conclusion that “The hallmark of depression in the elderly is its association with medical comorbidity.”65 Biological risk factors of late-life depression such as genetics, neurotransmitter dysfunction, endocrine changes, and cardiovascular diseases have been an increasing focus of research. Kumar et al.66 identified two neurobiological pathways to late-life major depressive disorder: one represented by vascular or non-vascular medical comorbidity that contributes to whole brain high-intensity lesions; the other one represented by smaller frontal lobe volume. These two neurobiological pathways lead to cognitive impairment as well. On the other hand, Yen et al. 67 noted that the APOE epsilon4 allele was associated not only with cognitive impairment but also with geriatric depression in a community study. This finding suggests that the APOE epsilon4 allele is closely related to the common neurobiology of geriatric depression and cognitive impairment in late life.

Reasoning ability in our study is a good measure of executive function, encompassing a set of cognitive skills responsible for planning, initiation, sequencing, and monitoring of complex goal-directed behaviors. Executive function impairment has been noted to be associated with lesions of the frontal cortex and its basal ganglia-thalamic connections in previous researches.68 Considerable overlap between those lesions and depression is noted. Patients of major depression mainly exhibit cognitive inhibition deficits, problem-solving impairment, and planning deficits. Frontal systems impairment in major depression patients may further cause functional disability.69,70

Strengths of the study

The sample of this study was composed of over 2,800 relatively healthy and independently living participants from six areas in the United States. Consistent with the intervention objective of preventing functional decline, a sampling goal for the ACTIVE study was to identify participants at risk but who had not yet experienced functional decline. Diversity in representation of older adults was another goal, with a particular emphasis on representation of African-Americans, who have been consistently under-represented in most previous cognitive training research with older adults.34 African-Americans were well-represented in our sample.

Structural equation modeling (SEM) was used in this study to explicitly model measurement error in the instruments employed in operationalizing the constructs within the conceptual model that guided analysis. Among the strengths of SEM is the ability to model constructs as latent variables — variables which are not measured directly, but are estimated in the model from measured variables, like learning and memory, reasoning, speed of processing, and everyday problem solving in this study. This allows explicit capture of unreliability of measurement in the model, allowing the structural relations between latent variables to be accurately estimated. Therefore, we could calculate separate estimates of the mediation of depressive symptoms with functional performance through learning and memory, reasoning, and speed of processing.

The measures of cognitive abilities and daily function in this analysis were performance-based tests. This may minimize the frequently documented effect that depressed persons tend to rate their abilities more poorly than non-depressed persons.71 As the functional outcomes were cognitively demanding, persons with functional impairment (i.e., with lower everyday problem-solving ability scores) may have represented the ‘preclinical’ phase of the disablement process, identifying a focus for prevention.72

Limitations of the study

The data reported here are cross-sectional. We are not sure the extent to which the association of depressive symptoms to cognitive abilities or functional performance occurred because people with poor cognition or functioning tend to be more depressed. Although depressive symptoms in late life may predict functional disability in older persons, persistent depression is thought to be associated with a greater linear increase in functional disability than remitted depression.73 On the other hand, certain neuropsychological abilities (e.g., visuospatial ability, attention, executive function) that may be relevant in the relationships of depression and functional status also were not evaluated in this study. If one or more of these measures were included, the observed relationships between depressive symptoms, cognitive abilities, and problem-solving ability might change.

In this study, overall level of depressive symptomatology was not high. The possibility of selection bias can’t be ruled out as more depressed older adults might tend to refuse to be recruited. Treatment for depression may be another explanation for the mild depressive symptomatology. However, diagnosis and treatment of depressive disorders was not included.

Our finding that the relationship of depressive symptoms to daily function disability occurs partly through the association of depressive symptoms and learning and memory, and reasoning suggests that interventions aimed at decreasing the disability due to geriatric depressive symptoms must take cognitive functioning into account. Treatment of depression that relies on the cognitive ability of older adults (e.g., to recognize negative thoughts or to be diligent in taking medications) may need to address learning and memory as well as reasoning concurrently. More studies on the relationships of depression and daily function incorporating other cognitive abilities or mediators are still needed. However, our study indicates a direction for future intervention research that targets the improvement of cognitive functioning among older persons with depressive symptoms to prevent associated disability and decline in quality of life.

Acknowledgments

This research was supported by a series of grants awarded from the National Institutes of Health to the six field sites and the coordinating center, including the Hebrew Rehabilitation Center for the Aged (R01NR04507), the Indiana University School of Medicine (R01 NR04508), The Johns Hopkins University (R01 AG14260), the New England Research Institutes (R01 AG 14282), The Pennsylvania State University (R01 AG 14263), the University Alabama at Birmingham (R91 AG14289), and Wayne State University (R01 AG014276). Dr. Rebok is an investigator with Compact Disc Incorporated for the development of an electronic version of the ACTIVE memory intervention. The opinions expressed here are those of the authors and do not necessarily reflect those of the funding agencies or academic, research, or governmental institutions involved.

Contributor Information

Yung-Chieh Yen, Department of Psychiatry, E-Da Hospital and College of Medicine, I-Shou University, Kaohsiung County, Taiwan.

George W. Rebok, Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland.

Joseph J. Gallo, Department of Family Medicine and Community Health, University of Pennsylvania, Philadelphia, Pennsylvania.

Richard N. Jones, Hebrew Senior Life, Institute for Aging Research, Boston, Massachusetts.

Sharon L. Tennstedt, New England Research Institutes, Watertown, Massachusetts.

References

1. Gallo JJ, Lebowitz BD. The epidemiology of common late-life mental disorders in the community: themes for the new century. Psych Services. 1999;50:1158–1168. [PubMed]
2. Murray CJL, Lopez AD. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Cambridge, MA: Harvard University Press; 1996.
3. Cowan C, Catlin A, Smith C, Sensenig A. National health expenditures, 2002. Health Care Financing Review. 2004;25:143–166. [PubMed]
4. General Accounting Office. Report to the Chairman, Special Committee on Aging, U.S. Senate. GAO/HEHS 96–16. 1996. Medicare: Home Health Utilization Expands While Program Controls Deteriorate.
5. Strahan GW. Advance Data from Vital and Health Statistics. 280. Hyattsville, MD: National Center for Health Statistics; 1997. An overview of nursing homes and their current residents: Data from the 1995 National Nursing Home Survey. [PubMed]
6. Humphrey M. Functional impairment in psychiatric outpatients. British Journal of Psychiatry. 1967;113:1141–1151. [PubMed]
7. Forsell Y, Jorm AF, von Strauss E, Winblad B. Prevalence and correlates of depression in a population of nonagenarians. British Journal of Psychiatry. 1995;167:61–64. [PubMed]
8. Forsell Y, Jorm AF, Winblad B. Association of age, sex, cognitive dysfunction, and disability with major depressive symptoms in an elderly sample. American Journal of Psychiatry. 1994;151:1600–1604. [PubMed]
9. Kennedy GL, Klerman HR, Thomas C. The emergence of depressive symptoms in late life: the importance of declining health and increasing disability. Journal of Community Health. 1990;15:93–104. [PubMed]
10. Lyness JM, Caine ED, Conwell Y, King DA, Cox C. Depressive symptoms, medical illness, and functional status in depressed psychiatric inpatients. American Journal of Psychiatry. 1993;150:910–915. [PubMed]
11. Steffens DC, O’Connor CM, Jiang WJ, Pieper CF, Kuchibhatla MN, Arias RM, Look A, Davenport C, Gonzalez MB, Krishnan KR. The effect of major depression on functional status in patients with coronary artery disease. Journal of the American Geriatrics Society. 1999;47:319–322. [PubMed]
12. Al Snih S, Markides KS, Ostir GV, Ray L, Goodwin JS. Predictors of recovery in activities of daily living among disabled older Mexican Americans. Aging & Clinical Experimental Research. 2003;15:315–320. [PubMed]
13. Broadhead WE, Blazer DG, George LK, Tse CK. Depression, disability days, and days lost from work in a prospective epidemiologic survey. Journal of the American Medical Association. 1990;264:2524–2528. [PubMed]
14. Bruce ML, Seeman TE, Merrill SS, Blazer DG. The impact of depressive symptomatology on physical disability: MacArthur Studies of Successful Aging. American Journal of Public Health. 1994;84:1796–1799. [PubMed]
15. De Ronchi D, Bellini F, Berardi D, Serretti A, Ferrari B, Dalmonte E. Cognitive status, depressive symptoms, and health status as predictors of functional disability among elderly persons with low-to-moderate education: The Faenza Community Aging Study. American Journal of Geriatric Psychiatry. 2005;13:672–685. [PubMed]
16. Gallo JJ, Rabins PV, Lyketsos CG, Tien AY, Anthony JC. Depression without sadness: functional outcomes of non-dysphoric depression in later life. Journal of the American Geriatrics Society. 1997;45:570–578. [PubMed]
17. Gallo JJ, Rebok GW, Tennstedt S, Wadley VG, Horgas A. the ACTIVE Study investigators. Linking depressive symptoms and functional disability in late life. Aging & Mental Health. 2003;7:469–480. [PubMed]
18. Horwath E, Johnson J, Klerman GL, Weissman MM. Depressive symptoms as relative and attributable risk factors for first-onset major depression. Archives of General Psychiatry. 1992;49:817–823. [PubMed]
19. Mehta KM, Yaffe K, Covinsky KE. Cognitive impairment, depressive symptoms, and functional decline in older people. Journal of the American Geriatrics Society. 2002;50:1045–1050. [PMC free article] [PubMed]
20. Mossey JM, Gallagher RM, Tirumalasetti F. The effects of pain and depression on physical functioning in elderly residents of a continuing care retirement community. Pain Medicine. 2000;1:340–350. [PubMed]
21. Penninx WJH, Guralnik JM, Ferrucci L, Simonsick EM, Deeg DJH, Wallace RB. Depressive symptoms and physical decline in community-dwelling older persons. Journal of the American Medical Association. 1998;279:1720–1726. [PubMed]
22. Pohjasvaara T, Vataja R, Leppavuori A, Kaste M, Erkinjuntti T. Depression is an independent predictor of poor long-term functional outcome post-stroke. European Journal of Neurology. 2001;8:315–319. [PubMed]
23. Bruce ML. Depression and disability in late life. American Journal of Geriatric Psychiatry. 2001;9:102–112. [PubMed]
24. Marengoni A, Aguero-Torres H, Cossi S, Ghisla MK, DeMartinis M, Leonardi R, Fratiglioni L. Poor mental and physical health differentially contributes to disability in hospitalized geriatric patients of different ages. International Journal of Geriatric Psychiatry. 2004;19:27–34. [PubMed]
25. Browner WS. Depressive symptoms and cognitive decline in non-demented elderly: a prospective study. Archives of General Psychiatry. 1999;56:425–430. [PubMed]
26. Devanand DP, Sano M, Tang MX, Taylor S, Gurland BJ, Wilder D, Stern Y, Mayeux R. Depressed mood and the incidence of Alzheimer’s disease in the elderly living in the community. Archives of General Psychiatry. 1996;53:175–182. [PubMed]
27. Cattell RB. Intelligence: its structure, growth, and action. New York: Elsevier Science; 1987.
28. Marsiske M, Willis SL. Dimensionality of everyday problem solving in older adults. Psychology and Aging. 1995;10:269–282. [PMC free article] [PubMed]
29. Willis SL, Jay GM, Diehl M, Marsiske M. Longitudinal change and prediction of everyday task competence in the elderly. Research on Aging. 1992;14:68–91. [PMC free article] [PubMed]
30. Butters MA, Whyte EM, Nebes RD, Begley AE, Dew MA, Mulsant BH, Zmuda MD, Bhalla R, Meltzer CC, Pollock BG, Reynolds CF, III, Becker JT. The nature and determinants of neuropsychological functioning in late-life depression. Archives of General Psychiatry. 2004;61:587–595. [PubMed]
31. Sheline YI, Barch DM, Garcia K, Gersing K, Pieper C, Welsh-Bohmer K, Steffens DC, Doraiswamy PM. Cognitive function in late life depression: relationships to depression severity, cerebrovascular risk factors and processing speed. Biological Psychiatry. 2006;60:58–65. [PubMed]
32. Alexopoulos GS. “The depression-executive dysfunction syndrome of late life”: a target for D3 agonists. American Journal of Geriatric Psychiatry. 2001;9:1–8. [PubMed]
33. Ball K, Berch DB, Helmers KF, Jobe JB, Leveck MD, Marsiske M, Morris JN, Rebok GW, Smith DM, Tennstedt SL, Unverzagt FW, Willis SL. Advanced Cognitive Training for Independent and Vital Elderly Study Group. Effects of cognitive training interventions with older adults: a randomized controlled trial. Journal of the American Medical Association. 2002;288:2271–2281. [PMC free article] [PubMed]
34. Jobe JB, Smith DM, Ball K, Tennstedt SL, Marsiske M, Willis SL, Rebok GW, Morris JN, Helmers KF, Leveck MD, Kleinman K. ACTIVE: A cognitive intervention trial to promote independence in older adults. Controlled Clinical Trials. 2001;22:453–479. [PMC free article] [PubMed]
35. Willis SL, Tennstedt SL, Marsiske M, Ball K, Elias J, Koepke KM, Morris JN, Rebok GW, Unverzagt FW, Stoddard AM, Wright E. for the Active Study Group. Long-term effects of cognitive training interventions with older adults: A randomized controlled trial. Journal of the American Medical Association. 2006;296:2805–2814. [PMC free article] [PubMed]
36. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measures. 1977;1:385–401.
37. Brandt J. The Hopkins Verbal Learning Test: Development of a new memory test with six equivalent forms. Clinical Neuropsychology. 1991;5:125–142.
38. Rey A. L’examen psychologique dans les cas d’encephalopathie tramatique. Archives de Psychologie. 1941;28:21.
39. Wilson BA, Cockburn J, Baddeley A. The Rivermead Behavioral memory Test. Reading, England: Thomas Valley Test Co; Gaylord, MI: National Rehabilitation Services; 1985.
40. Gonda J, Schaie KW. Schaie-Thurstone Mental Abilities Test: Word Series Test. Palo Alto, CA: Consulting Psychologists Press; 1985.
41. Thurstone LL, Thurstone TG. Examiner Manual for the SRA Primary Mental Abilities Test (Form 10–14) Chicago: Science Research Associates; 1949.
42. Ekstrom RB, French JW, Harman H, Derman D. Kit of Factor-Referenced Cognitive Tests. rev. Princeton, NJ: Educational Testing Service; 1976.
43. Ball K, Owsley C. The useful field of view test: a new technique for evaluating age-related declines in visual function. Journal of American Optometric Association. 1993;64:71–79. [PubMed]
44. Owsley C, Ball K, McGwin G, Jr, Sloane ME, Roenker DL, White MF, Overley ET. Visual processing impairment and risk of motor vehicle crash among older adults. Journal of the American Medical Association. 1998;279:1083–1088. [PubMed]
45. Owsley C, Ball K, Sloane ME, Roenker DL, Bruni JR. Visual/cognitive correlates of vehicle accidents in older drivers. Psychology & Aging. 1991;6:403–415. [PubMed]
46. Willis SL, Marsiske M. Manual for the Everyday Problem Test. University Park, PA: Pennsylvania State University; 1993.
47. Diehl M, Marsiske M, Horgas AL, Saczynski J. Psychometric Properties of the Revised Observed Tasks of Daily Living (OTDL-R). Poster session presented at the annual meeting of the Gerontological Society of America; Philadelphia. 1998.
48. Diehl M, Willis SL, Schaie KW. Everyday problem solving in older adults: Observational assessment and cognitive correlates. Psychology & Aging. 1995;10:478–491. [PubMed]
49. Blom G. Statistical estimates and transformed beta-variables. New York: Wiley; 1958.
50. Akaike H. Factor analysis and AIC. Psychometrika. 1987;52:317–332.
51. Schumacker RE, Lomax RG. A beginner’s guide to structural equation modeling. Mahwah, New Jersey: Lawrence Erlbaum Associates, Publishers; 1996.
52. Muthén LK, Muthén BO. Mplus version 4.1. Los Angeles, CA: 2006.
53. Brand AN, Jolles J, Gispen-de Wied C. Recall and recognition memory deficits in depression. Journal ofAffective Disorders. 1992;25:77–86. [PubMed]
54. Rapp MA, Dahlman K, Sano M, Grossman HT, Haroutuian V, Gorman JM. Neuropsychological differences between late-onset and recurrent geriatric major depression. American Journal of Psychiatry. 2005;162:691–698. [PubMed]
55. van Hooren SAH, Valentijn SAM, Bosma H, Ponds RWHM, van Boxtel MPJ, Jolles J. Relation between health status and cognitive functioning: A 6-year follow-up of the Maastricht Aging Study. Journal of Gerontology: Psychological Sciences. 2005;60B:57–60. [PubMed]
56. Hayslip B, Jr, Kennelly KJ, Maloy RM. Fatigue, depression, and cognitive performance among aged persons. Experimental Aging Research. 1990;16:111–115. [PubMed]
57. Lyness SA, Eaton EM, Schneider LS. Cognitive performance in older and middle-aged depressed outpatients and controls. Journal of Gerontology. 1994;49:129–136. [PubMed]
58. Rapp MA, Schnaider Beeri M, Schmeidler J, Sano M, Silverman JM, Haroutunian V. Relationship of neuropsychological performance to functional status in nursing home residents and community-dwelling older adults. American Journal of Geriatric Psychiatry. 2005;13:450–459. [PubMed]
59. Simpson S, Baldwin RC, Jackson A, Burns A. The differentiation of DSM-III-R psychotic depression in later life from nonpsychotic depression: comparisons of brain changes measured by multispectral analysis of magnetic resonance brain images, neuropsychological findings, and clinical features. Biological Psychiatry. 1999;45:193–204. [PubMed]
60. Nebes RD, Butters MA, Mulsant BH, Pollock BG, Zmuda MD, Houck PR, Reynolds CF., III Decreased working memory and processing speed mediate cognitive impairment in geriatric depression. Psychological Medicine. 2000;30:679–691. [PubMed]
61. Tsourtos G, Thompson JC, Stough C. Evidence of an early information processing speed deficit in unipolar major depression. Psychological Medicine. 2002;32:259–265. [PubMed]
62. Kiosses DN, Alexopoulos GS. IADL functions, cognitive deficits, and severity of depression. American Journal of Geriatric of Psychiatry. 2005;13:244–249. [PubMed]
63. Cole MG. Brief interventions to prevent depression in older subjects: A systematic review of feasibility and effectiveness. American Journal of Geriatric Psychiatry. 2008;16:435–443. [PubMed]
64. Smits F, Smits N, Schoevers R, Deeg D, Beekman A, Cuijpers P. A epidemiological approach to depression prevention in old age. American Journal of Geriatric Psychiatry. 2008;16:444–453. [PubMed]
65. National Institutes of Health: NIH consensus conference. Diagnosis and treatment of depression in late life. Journal of American Medical Association. 1992;268:1018–1024. [PubMed]
66. Kumar A, Mintz J, Bilker W, Gottlieb G. Autonomous neurobiological pathways to late-life major depressive disorder: clinical and pathophysiological implications. Neuropsychopharmacology. 2002;26:229–236. [PubMed]
67. Yen Y-C, Rebok GW, Gallo JJ, Yang M-J, Lung F-W, Shih C-H. APOE4 allele is associated with late-life depression: a population-based study. American Journal of Geriatric Psychiatry. 2007;15(10):858–868. [PubMed]
68. Royall DR, Lauterbach EC, Cummings JL, Reeve A, Rummans TA, Kaufer DI, LaFrance WC, Jr, Coffey CE. Executive control function: a review of its promise and challenges for clinical research. Journal of Neuropsychiatry & Clinical Neurosciences. 2002;14(4):377–405. [PubMed]
69. Royall DR. Frontal systems impairment in major depression. Seminars on Clinical Neuropsychiatry. 1999;4(1):13–23. [PubMed]
70. Fossati P, Ergis AM, Allilaire JF. Executive functioning in unipolar depression: a review. Encephale. 2002;28(2):97–107. [PubMed]
71. Miranda J, Persons JB. Dysfunctional attitudes are mood-state dependent. Journal of Abnormal Psychology. 1988;97:76–79. [PubMed]
72. Fried LP, Herdman SJ, Kuhn KE, Rubin G, Turano K. Preclinical disability: hypotheses about the bottom of the iceberg. Journal of Aging and Health. 1997;3:285–300.
73. Lenze EJ, Schulz R, Martire LM, Zdaniuk B, Glass T, Kop WJ, Jackson SA, Reynolds CF., III The course of functional decline in older people with persistently elevated depressive symptoms: Longitudinal findings from the Cardiovascular Health Study. Journal of the American Geriatrics Society. 2005;53:569–575. [PubMed]