Predictors of Basic and Instrumental Activities of Daily Living Performance in Persons Receiving Rehabilitation Services

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Predictors of Basic and Instrumental Activities of Daily Living Performance in Persons Receiving Rehabilitation Services Wendy Coster, PhD, OTR/L, Stephen M. Haley, PhD, PT, Alan Jette, PhD, PT, Wei Tao, BA, Hilary Siebens, MD ABSTRACT. Coster W, Haley SM, Jette A, Tao W, Siebens H. Predictors of basic and instrumental activities of daily living performance in persons receiving rehabilitation services. Arch Phys Med Rehabil 2007;88:928-35. Objective: To examine the relations among cognitive and emotional function and other patient impairment and demographic variables and the performance of daily activities. Design: Cohort. Setting: Acute inpatient rehabilitation, skilled nursing facilities, home care, and outpatient clinics. Participants: Adults (N⫽534) receiving services for neurologic (32.3%), lower-extremity orthopedic (42.7%), or complex medical (24.9%) conditions. Mean age was 63.8 years; 55% were women; 88.6% were white; and the time since condition onset ranged from 0.2 to 3.9 years. Interventions: Not applicable. Main Outcome Measures: Activity Measure for Post-Acute Care: applied cognitive, personal care and instrumental, and physical and movement scales; Mental Health Inventory–5 (MHI-5); and patient-identified problems (vision, grasp). Results: Path analyses resulted in good model fit both for the total sample and 3 patient subgroups (␹2 test, P⬎.05; comparative fit index ⬎.95). There was a significant (P⬍.05) direct relation between the applied cognitive, grasp, and personal care and instrumental variables in all patient groups. There were also significant indirect relations between the MHI-5, visual impairment, and grasp problems with the personal care and instrumental scale through an association with the applied cognitive scale. Strength and significance of associations between age, sex, and physical and movement and personal care and instrumental scales varied more across patient groups. The model R2 for the personal care and instrumental scale for the total sample was .60, with R2 values of .10, .72, and .62 for the lower-extremity orthopedic, neurologic, and complex medical groups, respectively. Conclusions: Results suggest that variations in cognitive function, along with visual impairment and lower perceived well-being are associated with a patient’s ability to complete

From the Department of Occupational Therapy and Rehabilitation Counseling, Sargent College of Health and Rehabilitation Sciences (Coster) and the Health and Disability Research Institute, School of Public Health (Haley, Jette, Tao), Boston University, Boston, MA; and the Department of Physical Medicine and Rehabilitation, University of Virginia, Charlottesville, VA (Siebens). Supported by the National Institute of Child Health and Human Development and the Agency for Healthcare Research and Quality (grant no. R01 HD043568) and an Independent Scientist Award (award no. K02 HD45354-01). A commercial party having a direct financial interest in the results of the research supporting this article has conferred or will confer a financial benefit upon 1 or more of the authors. Haley and Jette have a stock interest in CRE Care LLC, which distributes the Activity Measure for Post-Acute Care products. Correspondence to Wendy Coster, PhD, OTR/L, Dept of Occupational Therapy and Rehabilitation Counseling, Boston University Sargent College, 635 Commonwealth Ave, Boston, MA 02215, e-mail: [email protected]. Reprints are not available from the author. 0003-9993/07/8807-11443$32.00/0 doi:10.1016/j.apmr.2007.03.037

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daily activities. Rehabilitation professionals should consider cognitive and emotional factors as well as physical performance when planning treatment programs to restore daily activity function. Key Words: Activities of daily living; Cognition; Rehabilitation. © 2007 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation ERFORMANCE OF COMPLEX daily activities depends on a number of contributing abilities. Knowledge of these P relations is of great importance to rehabilitation practice in order to guide intervention to meaningful targets. Although the importance of movement skills such as grasp and manipulation has long been recognized, studies in a number of different populations have also identified a relation between cognitive function and the performance of activities of daily living (ADLs). In particular, researchers in gerontology have reported that performance of specific activities such as telephone use was associated with ratings of dementia1,2 and that even at levels below those qualifying as dementia, cognitive impairment was associated with difficulties in both basic (BADLs) and instrumental activities of daily living (IADLs).3 In a sample of high functioning elderly subjects, lower baseline levels of cognitive function were associated with subsequent decline on a variety of physical tasks.4 Rehabilitation researchers have examined this association in a variety of patient groups, most often in persons with stroke because this condition is associated with both cognitive and motor impairments. In general, results have indicated that cognitive limitations are an independent contributor to overall level of disability separate from motor limitations.5-8 Although the results of this research are suggestive, several factors limit the conclusions that can be drawn. First, although recent studies have attempted to identify more specific aspects of cognitive function that predict a person’s performance in daily activities,9 the majority of studies have used global measures of basic cognitive functions such as those covered in the Mini-Mental State Examination. These measures serve primarily to identify participants with significant limitations in basic cognitive functions such as alertness, memory, and orientation, but may not address more complex cognitive functions such as language comprehension, speed of information processing, planning, or problem-solving ability. Thus, the impact of less severe cognitive limitations on performance of daily activities remains uncertain. Rehabilitation studies in this area have also been limited by reliance on measures that focus primarily on BADLs, which may not provide a full perspective on the skills relevant for independent living, that is, IADLs. Studies in gerontology have suggested that decline in cognitive functioning is more strongly associated with decreased performance of instrumental tasks that require application of these skills, such as telephone use, managing finances, and meal preparation.10 In fact, factor

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analyses in this population consistently have identified 3 dimensions of daily activities, one of which has been labeled advanced or “cognitive ADL.”11,12 This advanced ADL factor is the one most strongly related to performance on a standardized assessment of cognitive function. In addition to studies examining the relation between cognitive function and daily activities in patients with stroke, other research has suggested an increased prevalence of cognitive difficulties in the general population of persons with chronic health conditions.13 Studies have reported an association between cognitive functioning and likelihood of return to independent living after hospitalization among older rehabilitation patients14 and with the amount of change likely during the course of rehabilitation.15 Thus, it is important to examine further the potential impact of cognitive limitations in the broader population of persons receiving rehabilitation services. Although functional outcomes are acknowledged to be determined by multiple factors, few studies of the relation between cognitive function and daily activities have used designs that take this complexity into account. Most previous research has relied on single-equation models in which hypothesized predictors are simultaneously entered into the analysis model to determine their net effect on performance of daily activities. This approach is not optimal to clarify the interrelationships among various impairment, behavioral, and demographic factors and the outcome of interest. Few studies have simultaneously considered the potential direct or indirect effects of other factors known to have an impact on daily activities in addition to cognitive function. Important factors associated with functional decline in the gerontology literature that are in need of further investigation are mental health (depression10,16 and anxiety17) and visual impairment.18,19 Clarification of the possible role of intermediary factors related to daily activity performance may suggest relevant approaches to improve functional performance in patients receiving rehabilitation services. We designed the present study to address some of the limitations of previous research in an attempt to clarify the relation between cognitive and emotional function and impairment variables and the performance of ADLs in persons receiving rehabilitation services. Specifically: (1) we examined this question in a large, functionally heterogeneous sample of adults with major disabling conditions who were receiving either inpatient or outpatient rehabilitation services; (2) we examined the hypothesized relationships within the total sample as well as in 3 subgroups of patients: those with neurologic, lowerextremity orthopedic, and complex medical conditions; (3) the data are derived from the Activity Measure for Post-Acute Care (AM-PAC), an outcome measure designed specifically to capture a wide range of function in the 2 primary areas of concern: cognitive activities (including communication, problem solving, decision making, processing complex information, and planning) and personal care and instrumental activities (including grooming and dressing as well as a variety of activities related to meal preparation, cleaning, and household management); and (4) we used path analysis to test a model of proposed direct and indirect relations among a set of relevant variables. Because we considered the simultaneous relations among variables, we believe that this approach will yield fruitful suggestions for future research. Because path analysis is a model-testing procedure, hypotheses were specified a priori based on an analysis of the existing literature: (1) there would be a direct relation between cognitive function and performance of personal care and instrumental daily activities; (2) there would be a direct relation between general physical function and performance of basic and instrumental daily activities; (3) there would be an inverse relation-

ship between impairments affecting grasp and vision, older age, and performance of daily activities; (4) there would be a direct relation between mental health or emotional well-being and performance of daily activities; and (5) there also would be an indirect association of mental health/emotional well-being and performance of daily activities through an association with cognitive function. This indirect path was proposed because of mixed findings in the literature about whether depression has an independent impact on function.8,13 METHODS Participants The sample consisted of 534 participants who were 18 years and older (mean age ⫾ standard deviation [SD], 63.8⫾15.7y; range, 18 –93y) from 6 New England rehabilitation networks. Patients were excluded if they were: (1) non-English speaking, due to costs associated with translation services or (2) in a coma, debilitated, or agitated to a degree that precluded participation in active rehabilitation. All patients were actively receiving skilled rehabilitation services at the time of assessment either in inpatient rehabilitation (39.7%) or skilled nursing facility (8.4%), or through home care (25%) or outpatient services (26.8%). The sample reflects the racial and ethnic distribution of the greater Boston metropolitan population. More subjects were women (55%) and white (88.6%) and 38.4% had only a high school education or less. About a third of the sample had been living alone prior to the onset of their current illness or disability; most were living in a house (62.7%) or apartment or condominium (28.8%). A wide range of time since onset of initial injury or illness (range, 0.2–3.9y) characterizes different levels of acuity and stages of recovery within both inpatient and community settings. Patients were categorized into 3 major impairment groups based on standard rehabilitation impairment codes: (1) 32.3% neurologic (eg, stroke, multiple sclerosis, Parkinson’s disease, brain injury, spinal cord injury, neuropathy); (2) 42.7% lower-extremity orthopedic (eg, fractures, joint replacements, orthopedic surgery); and (3) 24.9% medically complex (eg, debility resulting from illness, cardiopulmonary conditions, or postsurgical recovery). We classified patients on an adapted Modified Rankin Scale20 into 3 distinct levels of disability severity: slight (39.5%), moderate (39.3%), and severe (19.5%). The institutional review boards at all sites approved the study and all persons signed informed consent forms. Procedure As a first step, a subject’s ability to respond to self-report questions was assessed by the treating clinician or assigned data collector who determined if the participant could: (1) understand the interview questions; (2) sustain attention for an hour; and (3) reliably respond to questions. If the answer to any of these questions was no, the interview was completed by a clinician or family member. In addition, all participants were administered a short cognitive screen.21 Patients who failed the screen also did not complete a self-report interview. Based on these screening criteria, only 3% of interviews were completed by proxy report. We have found only minimal differences across proxy reports in previous data collection with the AM-PAC22 thus we have included proxy reports in the overall analyses. Because all data were collected by interview, there was little to no missing data. Data were collected through semi-structured interviews by 1 of 8 trained data collectors. Procedural reliability of the interArch Phys Med Rehabil Vol 88, July 2007

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viewers was checked through observation by the study coordinator approximately every tenth interview. Each interview (⬇45– 60min) was fully scripted, with standard instructions, practice questions, and an answer card to help subjects communicate which of 4 response options applied to the activity described by each item. The subject’s exact response was recorded and no interviewer evaluation or judgment was involved in the scoring. To avoid respondent fatigue and to avoid asking questions that were irrelevant in a subject’s current living situation, we did not ask every question to every participant. Instead, we used a design in which 49 items from the larger item pools that were applicable across both hospital and community settings were administered to all patients and served as scale anchors across the 3 AM-PAC scales. There were 14 physical and movement items, 11 personal care and instrumental items, and 24 applied cognitive items in this core set. In addition to these core items, patients were asked additional items in each domain that were specific to the particular setting where they were currently residing. With item response theory (IRT)– based measures such as the AM-PAC, reliable summary score estimates can be obtained for all persons who have item scores on any subset of the items from the item pool for a given domain. On average, subjects completed 58 (49%) of the physical and movement items, 49 (84%) of the personal care and instrumental items, and 34 (72%) of the applied cognitive items. Instruments AM-PAC. Functional data were gathered using the item pool for the AM-PAC. Previous work on this patient-reported outcome measure has been described in detail elsewhere and will be summarized here.23-26 The initial content domains and item definitions for the AM-PAC item pool were guided by the World Health Organization’s International Classification of Functioning, Disability and Health definition and categories of activity. Thus, each item was written to examine a specific functional activity as typically performed in the person’s daily life. Factor analyses and Rasch analyses of data from a sample of over 400 persons receiving rehabilitation services led to the definition of 3 separate activity scales: physical and movement, personal care and instrumental, and applied cognitive. Coverage range, unidimensionality, reliability, and validity of these scales were confirmed in subsequent analyses. Prior to the current study, we made revisions and additions to create final versions of the item pool. The rating scales were reduced from 5- (difficulty) or 6- (assistance) point scales to 4-point scales to increase reliability. Misfitting items were removed or rewritten and new items were added to the personal care and instrumental and applied cognitive pools to try to reduce the ceiling effect found in earlier studies. IRT analyses were then conducted on the revised item pool. Scores used in the present analyses were derived from these analyses; therefore the scores are interval-level data (see details published elsewhere).23-26 Scores on all 3 scales were converted to a standard T scale with a mean of 50 and SD of 10, with higher scores reflecting greater function (less difficulty, less use of assistance). Test-retest reliability estimates for the longer AMPAC versions from which these scales were derived ranged from .91 to .97.22 As noted above, subsequent revisions of the rating scales enhanced the overall reliability of derived scores. Recent published findings suggest that the personal care and instrumental (intraclass correlation coefficient [ICC], .90) and applied cognitive (ICC⫽.77) summary scores are consistent across patients and proxy respondents.27 Impairments. Impairment variables were derived from a patient-reported problem list, which asked “Do you have probArch Phys Med Rehabil Vol 88, July 2007

lems in the following areas that limit your daily activities?” We used the participant’s answers to 2 items: eyesight (vision variable), and grasping and use of fingers (grasp variable). These items had dichotomous (yes, no) response options. Mental health. We used the Mental Health Inventory–5 (MHI-5), which is a 5-item inventory derived from the 36-Item Short-Form Health Survey28-30 to examine emotional distress and well-being. Evidence has indicated that the MHI-5 is sensitive both to depression29 and anxiety30,31 in elderly patients with functional limitations. Demographics. We included age as a continuous variable. Sex was coded as a dichotomous variable with 1 representing female and 0 for male. Data Analysis We used a series of preliminary univariate analyses to determine whether any additional demographic variables should be considered for the model. Based on the finding of little to no association with the primary variable of the personal care and instrumental scale (point biserial or Pearson r⬍.15) we eliminated race, education, hearing problems, and living situation (alone or with others) from further consideration. Vision and grasp were entered as dichotomous variables with 1 representing presence of the corresponding problem and 0 representing absence of the problem. Physical and movement, personal care and instrumental, and applied cognitive scores are continuous, interval-level measures generated from IRT analyses on the respective item pools of the AM-PAC. A path analysis was repeated 4 times: first on the total sample and then separately on the neurologic, lower-extremity orthopedic, and medically complex subgroups. Standardized coefficients of paths were compared among the 3 patient groups as well as between each of the patient groups and the total population. For each analysis, sample size per parameter estimate, overall model fit indices, and standardized and unstandardized direct and indirect effects of interest were tested. We examined 3 model fit indices: the chi-square statistic, comparative fit index (CFI), and standardized root mean residual (SRMR). When the P value for the chi-square test is larger than the nominal ␣ level (set at ␣⫽.05), CFI is larger than 0.9, and SRMR is less than 0.1, the model is said to fit the data. According to Klem,32 the desirable ratio of number of subjects to number of model parameters in a path analysis is between 5:1 and 10:1. As with all modeling approaches the greater the ratio, the more replicable the model is likely to be. Thus, in the current study the total sample has the most optimal ratio, with lower ratios for the orthopedic, neurologic, and complex medical subgroup analyses. Subgroup analyses were conducted in order to examine the extent to which the results for the total sample did or did not represent the patterns to be found in different patient groups. RESULTS The first analysis examined the fit of the proposed model for the group as a whole (N⫽534). Results supported the model (␹2 test⫽.167, P⫽.68; CFI⫽1.0, SRMR⫽.003), however, not all of the hypothesized individual relations were significant. The proportion of overall variance accounted for in the personal care and instrumental scale score was .604. Results are presented in figure 1 and table 1 including the standardized and unstandardized path coefficients for each path. The standardized coefficients can be used to compare the relative strength of each path within the model. Unstandardized coefficients can be used to examine the relation between variables in terms of the unit change in each measure.

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Fig 1. Results of path analysis with total sample (Nⴝ534). R2 values for AM-PAC scale scores: physical and movement (PM), .403; personal care and instrumental (PCI), .604; and applied cognitive (AC), .205. Legend: Solid-lined arrow indicates significant paths marked with standardized coefficients; broken-line arrow indicates nonsignificant paths.

The direct relations between applied cognitive score, age, grasp impairment, and personal care and instrumental score were supported, but those with sex and vision impairment were not. Not surprisingly, problems with grasp had the strongest association with activity performance: presence of a problem was associated with a decrease of 7.6 points on the personal care and instrumental scale. For each point increase on the applied cognitive scale, there was a .38 increase in the personal care and instrumental score. In addition, the anticipated indirect relation between the MHI-5 and personal care and instrumental score through applied cognitive score was also significant: for each point indicating greater distress on the MHI-5, there was a .62 decrease in the personal care and instrumental score. However, several variables (vision impairment, grasp impairment, age) showed unanticipated indirect relations with the Table 1: Path Analysis Results for Total Sample (Nⴝ534) Endogenous Exogenous Unstandardized Variable Variable Coefficient

Sex ⫺1.562 Age ⫺0.008 Vision 1.352 Grasp 0.915 MHI-5 ⫺1.483 PCI 0.449 AC Age ⫺0.060 Vision ⫺5.126 Grasp ⫺4.080 MHI-5 ⫺1.639 PCI Sex ⫺0.373 Age 0.041 Vision 1.034 Grasp ⫺7.698 MHI-5 ⫺0.332 PM 0.278 AC 0.380 Indirect paths through AC to PCI Age ¡ AC ¡ PCI ⫺0.023 Vision ¡ AC ¡ PCI ⫺1.946 Grasp ¡ AC ¡ PCI ⫺1.549 MHI-5 ¡ AC ¡ PCI ⫺0.622 PM

SE

Standardized Coefficient

Critical Ratio

0.692 0.022 0.837 1.304 0.360 0.106 0.020 0.782 0.655 0.299 0.563 0.018 0.705 0.621 0.294 0.074 0.039

⫺0.077 ⫺0.012 0.055 0.044 ⫺0.157 0.438 ⫺0.116 ⫺0.257 ⫺0.243 ⫺0.213 ⫺0.019 0.066 0.043 ⫺0.382 ⫺0.036 0.285 0.316

⫺2.26* ⫺0.36 1.62 0.70 ⫺4.13* 4.22* ⫺2.93* ⫺6.56* ⫺6.13* ⫺5.49* ⫺0.66 2.32* 1.47 ⫺12.40* ⫺1.13 3.78* 9.65*

0.008 0.359 0.299 0.130

⫺0.037 ⫺0.081 ⫺0.077 ⫺0.067

⫺2.80* ⫺5.42* ⫺5.17* ⫺4.77*

Abbreviations: AC, applied cognitive; PCI, personal care and instrumental; PM, physical and movement item; SE, standard error. *Effects have critical ratios ⬎1.96 and therefore are significant at P⬍.05.

personal care and instrumental scale score by way of applied cognitive. Of interest, the indirect effect of age was smaller (standardized coefficient, ⫺.037) than that of vision impairment (⫺.81) or grasp impairment (⫺.077) (see table 1). The AM-PAC personal care and instrumental activities items were written to try to minimize the dependence of performance on general mobility. However, it was reasonable to hypothesize a relation between the physical and movement score and personal care and instrumental score. The results were somewhat surprising, indicating that the path from personal care and instrumental to physical and movement was stronger (ie, more predictive) than the reverse (.44 vs .28). A separate analysis on the lower-extremity orthopedic patient group (n⫽228) yielded an acceptable fit, but revealed some differences from the pattern for the total patient group (fig 2). The hypothesized direct path from applied cognitive to personal care and instrumental remained significant (standardized coefficient, .333) as did the direct path from grasp impairment (⫺.346). However, in this group, there was no direct path from age to personal care and instrumental score, although there was a direct path from sex (⫺.20). In contrast to the pattern for the total group, the MHI-5 was related to personal care and instrumental through both direct and indirect (through applied cognitive) paths. The proportion of variance in the personal care and instrumental score accounted for in this model was comparatively small (R2⫽.104). The analysis for the neurologic patient group (n⫽173) also yielded an acceptable fit and results replicated the main elements of the original model: direct paths from applied cognitive and grasp impairment to personal care and instrumental and an indirect path to personal care and instrumental (by way of applied cognitive) of the MHI-5. As with the orthopedic group, there were also some differences. Neither age nor sex had any significant association with personal care and instrumental and in this group only the direct path from physical and movement to personal care and instrumental was significant (fig 3). The proportion of variance accounted for in personal care and instrumental score in this model was much higher than in the orthopedic group (R2⫽.72). The analysis of the medically complex group (n⫽133) yielded an acceptable fit and results also replicated the main elements of the original model. Interestingly, the direct path from applied cognitive to personal care and instrumental was strongest in this group (.46) (fig 4). The same pattern of significant indirect paths from vision impairment, grasp problems, and the MHI-5 to personal care and instrumental by way Arch Phys Med Rehabil Vol 88, July 2007

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Fig 2. Results of path analysis with orthopedic patient group (nⴝ228). R2 values for AMPAC scale scores: physical and movement, .407; personal care and instrumental, .104; and applied cognitive, .168. Abbreviations and Legend: see fig 1.

of applied cognitive also held. Neither age nor sex had a significant direct association with personal care and instrumental, but there was an indirect path between age and personal care and instrumental by way of applied cognitive. The proportion of variance accounted for in the personal care and instrumental score was less than in the model for the neurologic group but still substantial (R2⫽.623). DISCUSSION The results revealed a consistent and statistically significant relation between cognitive function and the performance of personal care and instrumental activities in this sample of adults receiving rehabilitation services for major disabling conditions. Although the strength of the path varied somewhat, this relationship was observed in all 3 groups of patients included in this study and was not limited to those whose medical diagnosis indicated potential neurologic sequelae. Regardless of the reason for therapy services, those patients with higher levels of cognitive function were functioning at a higher level in their daily activities. This finding is even more striking when one considers that only patients who passed a basic cognitive function screen were included in this sample. Thus, the findings emphasize that variations in cognitive function within the nonimpaired range are associated with patients’ ability to complete daily activities satisfactorily.

This finding is consistent with the reports in the aging literature and with some studies in rehabilitation populations that have reported relationships between cognitive impairment and functional status decline.3,4,6,14,33 However, our work expands on previous studies by clarifying that cognitive function may have a significant relation with concurrent daily activity performance and this factor should be considered when evaluating whether a patient is prepared to resume daily activities at home. The results from the path analysis suggest that other patient variables also may influence activity performance by way of their impact on cognitive performance. This influence may be in addition to any direct effect these factors may have on ADL and IADL performance. In particular, emotional distress as measured by the MHI-5, which may include depression and/or anxiety, appears to show this pattern. This result is also consistent with the reports in the aging literature that have identified the impact of depression and anxiety both on current function and risk for decline in functional status.10,17,34,35 Two possible explanations of this association must be considered. First, emotional distress may be associated with experiences such as more effortful information processing and memory problems or more difficulty with problem-solving, which are assessed on the AM-PAC. These difficulties, in turn, may

Fig 3. Results of path analysis with neurologic patient group (nⴝ173). R2 values for AMPAC scale scores: physical and movement, .433; personal care and instrumental, .720; and applied cognitive, .194. Abbreviations and Legend: see fig 1.

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Fig 4. Results of path analysis with medically complex patient group (nⴝ133). R2 values for AM-PAC scale scores: physical and movement, .455; personal care and instrumental, .623; and applied cognitive, .255. Abbreviations and Legend: see fig 1.

affect the ease with which daily activities such as taking care of one’s home are completed. Alternatively, because all measures in this study are patientreport emotional distress or negative affect may “color” the person’s perception of performance and increase the likelihood of giving a negative evaluation. Studies in social and health psychology have begun to document the impact of positive and negative affectivity on self-reported health status.35-37 Positive affect is associated with a more positive perception of health and may protect against physical decline, whereas negative affect and depression are associated with increased physical disability or reported symptom severity. A longitudinal study of older adults found that those patients who remained depressed over 3 months reported more decline in functional ability.34 It has been suggested that positive and negative affect may operate as independent factors. However, we were unable to examine this possibility because the summary score on the MHI-5 reflects questions of both types. Considered together, these studies suggest that intervention to treat depression and anxiety may be an important component of effective intervention to enhance function in daily life. A second notable finding in this analysis was the association of visual impairments with daily activity performance. The visual impairment variable was measured with a simple question asking if the person had problems with vision that affected his/her daily activities. Results suggest that the impact on daily activity reflected in a patient’s positive response to this question was complex. Visual impairment had a significant direct association with cognitive function, but not with personal and instrumental activity performance. Rather, the relation with the latter variable was indirect, by way of its association with cognitive function. This pattern was found in the total group and was replicated in each of the separate group analyses. The results may reflect, in part, the presence of items on the AMPAC cognitive function scale that inquire about the ability to perform daily tasks that involve reading. For example, some items ask about reading a food label or getting information from bill statements. Although studies have consistently identified limitations in vision as a factor in IADLs in the aging population,3,38,39 this variable has received far too little attention in outcome studies for the rehabilitation population. Our results suggest that this factor should be examined routinely in research that is attempting to clarify the pathways to functional performance as well as in clinical assessments of persons receiving rehabilitation services. Although similar paths were found to be significant across all three patient subgroups, it should be noted that the

overall amount of variance in personal care and instrumental score accounted for by the model was much greater in the neurologic and medically complex groups than in the group with lower-extremity orthopedic conditions. This suggests that alternative models that incorporate other factors might better capture the sources of variation in daily activity performance in the latter group. We chose to use path analysis in order to represent more closely the complexity of relations that likely influence patient outcomes. The results confirm the value of such approaches by revealing complex inter-relations among variables that might not be detected with standard linear analytic methods. Our findings, along with those of other researchers,38 suggest that use of alternative approaches could help organize disparate results regarding factors affecting function in daily activities into a more coherent and ultimately testable understanding of patient outcomes. Study Limitations There are several limitations in this study’s design that must be kept in mind. First, the total sample size for the initial path analysis should be quite adequate for obtaining a stable model, whereas the analyses for the 3 separate groups were conducted with smaller samples that may be more influenced by idiosyncratic sample characteristics. Therefore, the subgroup analyses should be seen more as hypothesis-generating than confirmatory. Although results provide valuable information regarding the extent to which the model based on the total sample does and does not represent the patterns in patient subgroups, they should be considered preliminary and in need of further testing. A second limitation is the cross-sectional design of the study which, by definition, only describes associations among concurrent variables. Finally, this study only included a subset of potential factors that may affect the performance of daily activities. Inclusion of other factors, particularly environmental factors such as social support or accessibility of the home, might yield a somewhat different model. Although acceptable test-retest reliability and proxy-respondent agreement of the AM-PAC have been reported previously,22,27 these consistency indices were not reassessed with the current sample and version of the instrument. Therefore, it is possible that the magnitude of error variance in the scores used for these analyses was different. These results lead to several suggestions for further investigation. Replication of the analyses with a longitudinal sample will be most important to try to identify the chronologic sequence of effects reported here. Longitudinal analysis would be Arch Phys Med Rehabil Vol 88, July 2007

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a significant help to identify which of these associations may represent actual causal pathways and, therefore, which might be fruitful targets of intervention to improve patient outcomes. Further analysis of patient subgroups in a longitudinal design would also help clarify the extent to which some of these factors may be uniquely influential in the context of particular clinical disorders. Finally, using a combination of self-report and observational measures to examine both cognitive function and performance of daily activities would help examine the extent to which the association between these 2 variables in the present study may reflect effects on patient perceptions versus effects on actual performance. Disentangling these effects would help practitioners determine the most appropriate targets for intervention to improve or support successful patient function at home and in the community. CONCLUSIONS The results of this study suggest that variations in cognitive function, along with visual impairment and lower perceived well-being, are associated with a patient’s ability to complete daily activities. Rehabilitation professionals should consider cognitive and emotional factors as well as physical performance when planning treatment programs to restore daily activity function. References 1. Aguero-Torres H, Thomas VS, Winblad B, Fratiglioni L. The impact of somatic and cognitive disorders on the functional status of the elderly. J Clin Epidemiol 2002;55:1007-12. 2. Cromwell DA, Eager K, Poulos RG. The performance of instrumental activities of daily living scale in screening for cognitive impairments in elderly community residents. J Clin Epidemiol 2003;56:131-7. 3. Stuck AE, Walthert JM, Nikolaus T, Bula CJ, Hohmann C, Beck JC. Risk factors for functional status decline in community-living elderly people: a systematic literature review. Soc Sci Med 1999; 48:445-69. 4. Tabbarah M, Crimmins EM, Seeman TE. The relationship between cognitive and physical performance: MacArthur studies of successful aging. J Gerontol A Biol Sci Med Sci 2002;57:M228-35. 5. Mok VC, Wong A, Lam WW, et al. Cognitive impairment and functional outcome after stroke associated with small vessel disease. J Neurol Neurosurg Psychiatry 2004;75:560-6. 6. Claesson L, Linden T, Skoog I, Blomstrand C. Cognitive impairment after stroke: impact on activities of daily living and costs of care for elderly people. Cerebrovasc Dis 2005;19:102-9. 7. Pohjasvaara T, Erkinjuntti T, Vataja R, Kaste M. Correlates of dependent living 3 months after ischemic stroke. Cerebrovasc Dis 1998;8:259-66. 8. Zinn S, Dudley TK, Bosworth HB, Hoenig HM, Duncan PW, Horner RH. The effect of poststroke cognitive impairment on rehabilitation process and functional outcome. Arch Phys Med Rehabil 2004;85:1084-90. 9. Cahn-Weiner DA, Malloy PF, Boyle PA, Marran M, Salloway S. Prediction of functional status from neuropsychological tests in community-dwelling elderly individuals. Clin Neuropsychol 2000;14:187-95. 10. Patrick JH, Johnson JC, Goins RT, Brown DK. The effects of depressed affect on functional disability among rural older adults. Qual Life Res 2004;13:959-67. 11. Wolinsky FD, Johnson RJ. The use of health services by older adults. J Gerontol 1991;46:S345-57. 12. Fitzgerald JF, Smith DM, Martin DK, Freedman JA, Wolinsky FD. Replication of the multidimensionality of activities of daily living. J Gerontol 1993;48:S28-31. Arch Phys Med Rehabil Vol 88, July 2007

13. Lyketsos CG, Lopez O, Jones B, Fitzpatrick AL, Breitner J, DeKosky S. Prevalence of neuropsychiatric symptoms in dementia and mild cognitive impairment: results from the cardiovascular health study. JAMA 2002;288:1475-83. 14. MacNeill SE, Lichtenberg PA. Home alone: the role of cognition in return to independent living. Arch Phys Med Rehabil 1997;78: 755-8. 15. Sands LP, Yaffe K, Covinsky K, et al. Cognitive screening predicts magnitude of functional recovery from admission to 3 months after discharge in hospitalized elders. J Gerontol A Biol Sci Med Sci 2003;58:37-45. 16. Ganguli M, Du Y, Dodge HH, Ratcliff GG, Chang CH. Depressive symptoms and cognitive decline in late life: a prospective epidemiological study. Arch Gen Psychiatry 2006;63:153-60. 17. Brenes GA, Guralnik JM, Williamson JD, et al. The influence of anxiety on the progression of disability. J Am Geriatr Soc 2005;53: 34-9. 18. Sloan FA, Ostermann J, Brown DS, Lee PP. Effects of changes in self-reported vision on cognitive, affective, and functional status and living arrangements among the elderly. Am J Ophthalmol 2005;140:618-27. 19. Lin MY, Gutierrez PR, Stone KL, et al. Vision impairment and combined vision and hearing impairment predict cognitive and functional decline in older women. J Am Geriatr Soc 2004;52:1996-2002. 20. Rankin J. Cerebral vascular accidents in patients over the age of 60. II. Prognosis. Scott Med J 1957;2:200-15. 21. Callahan CM, Unverzagt FW, Hui SL, Perkins AJ, Hendrie HC. Six-item screener to identify cognitive impairment among potential subjects for clinical research. Med Care 2002;40:771-81. 22. Andres PL, Haley SM, Ni PS. Is patient-reported function reliable for monitoring postacute outcomes? Am J Phys Med Rehabil 2003;82:614-21. 23. Coster WJ, Haley SM, Andres PL, Ludlow LH, Bond TL, Ni P. Refining the conceptual basis for rehabilitation outcome measurement: personal care and instrumental activities domain. Med Care 2004;42(1 Suppl):I62-72. 24. Coster WJ, Haley SM, Ludlow LH, Andres PL, Ni PS. Development of an applied cognition scale to measure rehabilitation outcomes. Arch Phys Med Rehabil 2004;85:2030-5. 25. Haley SM, Coster WJ, Andres PL, et al. Activity outcome measurement for postacute care. Med Care 2004;42(1 Suppl):I49-61. 26. Haley SM, Andres PL, Coster WJ, Kosinski MA, Ni PS, Jette AM. Short-form Activity Measure for Post-Acute Care. Arch Phys Med Rehabil 2004;85:649-60. 27. Haley SM, Ni P, Coster WJ, Black-Schaffer R, Siebens H, Tao W. Agreement in functional assessment: graphical approaches to displaying respondent effects. Am J Phys Med Rehabil 2006;85:747-55. 28. Ware JE Jr, Sherbourne CD. The MOS 36-Item Short-Form Health survey (SF-36). Med Care 1992;30:473-83. 29. Friedman B, Heisel M, Delavan R. Validity of the SF-36 five-item mental health index for major depression in functionally impaired, community-dwelling elderly patients. J Am Geriatr Soc 2005;53: 1978-85. 30. Berwick DM, Murphy JM, Goldman PA, Ware JE Jr, Barsky AJ, Weinstein MC. Performance of a five-item mental health screening test. Med Care 1991;29:169-76. 31. Weinstein MC, Berwick DM, Goldman PA, Murphy JM, Barsky AJ. A comparison of three psychiatric screening tests using receiver operating characteristic (ROC) analysis. Med Care 1989; 27:593-607. 32. Klem L. Path analysis. In: Grimm LG, Yarnold PR, editors. Reading and understanding multivariate statistics. Washington (DC): American Psychological Association; 1995. p 65-97.

PREDICTORS OF PERFORMANCE, Coster

33. Atkinson HH, Cesar M, Kritchevsky SB, et al. Predictors of combined cognitive and physical decline. J Am Geriatr Soc 2005; 53:1197-202. 34. Roberson T, Lichtenberg PA. Depression, social support and functional abilities: longitudinal findings. Clin Gerontol 2003;26:55-67. 35. Sullivan MD, LaCroix AZ, Russo JE, Walker EA. Depression and self-reported physical health in patients with coronary disease: mediating and moderating factors. Psychosom Med 2001;63:248-56. 36. Ostir GV, Markides KS, Black SA, Goodwin JS. Emotional wellbeing predicts subsequent functional independence and survival. J Am Geriatr Soc 2000;48:473-8.

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37. Pettit JW, Kline JP. Are happy people healthier? The specific role of positive affect in predicting self-reported health symptoms. J Res Pers 2001;35:521-36. 38. Allore H, Tinetti ME, Araujo KL, Hardy S, Peduzzi P. A case study found that a regression tree outperformed multiple linear regression in predicting the relationship between impairments and social and productive activities scores. J Clin Epidemiol 2005;58: 154-61. 39. Fried LP, Bandeen-Roche K, Kasper JD, Guralnik JM. Association of comorbidity with disability in older women: the women’s health and aging study. J Clin Epidemiol 1999;52:27-37.

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