Physical Restraint Use and Cognitive Decline among - Nursing Home Residents Lynda C. Burton, ScD,* Pearl S. German, ScD,* B u r y W. Rovner, MD,t and Larry J. Braant$ Objective: This study investigated the association between physical restraint use and decline in cognition. Design: Cohort analytic study describing changes in resident characteristics. Setting: Eight nursing homes, both urban and suburban, operated by a proprietary corporation in a large metropolitan area. Participants: 437 nursing home admissions, with 201 remaining at 1 year. Main Outcome Measures: Cognitive status was measured by geropsychiatrists, using the Folstein Mini-Mental State Exam, during a psychiatric evaluation of the resident. Daily restraint use was documented from nursing orders. Observations were made at 2 weeks, 10 weeks, and 1 year. Results: Restraint use alone and in combination with neuroleptic use was associated with poor cognition. Other vari-

ables associated with poor cognitive scores were: ADL impairment, poor adaptive behavior, and longer time in the nursing home. The use of neuroleptics alone was not significant. Variables which were associated with good cognitive status were: being non-ambulatory but without dementia and having strong social support. Conclusions: These findings raise the possibility that restraint use may contribute to cognitive impairment, specifically among residents who have moderate to no cognitive impairment at admission; however, the findings do not exclude an alternative explanation that residents undergoing cognitive decline are more likely to be put in restraints. Further research is needed to understand whether factors which can be manipulated contribute to cognitive decline. J Am Geriatr SOC40811-816,1992.

he use of physical restraints on nursing home residents has become an important issue because of the extent of their use and the suspected negative impacts on quality of life of the residents. The controversy surrounding restraint use led to federal regulations' which attempt to define appropriate use. Yet many of the issues about the beneficial versus detrimental effects of restraints remain unresolved. There are two major concerns regarding restraint use: whether they are effective and whether they are appropriate. The purported benefits of physical restraints on reducing falls among this frail population have been ~hallenged,~-~ the value of physical restraints in controlling aggressive behavior has been q ~ e s t i o n e d , fatal ~ - ~ and serious mishaps from restraint use have been documented,2r7-9 and the detrimental effect on quality of life has been assumed." These studies have laid the ground work for further investigation of the consequences of restraint use and led to the current study, which investigated whether physical restraint use predicted cognitive decline in nursing home residents. From 60% to 66% of nursing home residents may have restraints applied at some time during their first year of stay.4*'I Residents most likely to be restrained are over age 80, di~oriented,~ cognitively impaired,"

functionally impaired, and in nursing homes that rely heavily on restraint use."

T

From the *Department of Health Policy and Management, Health Services Research and Development Center, School of Hygiene and Public Health, Johns Hopkins University, Baltimore, Maryland; tDepartment of Psychiatry and Human Behavior, School of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania; and $Gerontology Research Center, National Institute on Aging, Baltimore, Maryland. This study was supported by Grant #AGO6765 from the National lnstitute on Aging. Address correspondence to Lynda C. Burton, ScD, Johns Hopkins University, Health Services Research and Development Center, 624 North Broadway, Baltimore, MD 21205.

IAGS 40:811-816, 1992 0 1992 by the Americun Geriatrics Society

METHODS The study subjects were a nursing home admission cohort who were part of a larger study undertaken to assess the prevalence of mental morbidity and the impact of such illnesses on adaptation to a nursing home. The study and its major findings have been described by German et a15 and Rovner et a1'2*'3. All persons admitted to eight nursing homes, which were part of a regional proprietary chain, were eligible for the study, if they had not had a previous nursing home admission within 1 year. Of 562 eligible cases, 454 (81%) were enrolled. Eighty four (15%) refused participation, and 24 (4%) were excluded because all observations were not obtained within a specific time frame. An initial set of observations was done at 2 weeks (n = 454); 10 weeks following, the second set of observations was obtained to assess adaptation during the early months of stay (n = 313). At 1 year, a final observation was made (n = 201). By 1 year, 31% had died, 19% had been discharged and were alive, 3% had been discharged and were lost to follow-up, and 2% were still in the nursing home but had withdrawn from the study. Seventeen residents were eliminated from the analyses because of incomplete data, leaving 437, Admission characteristics are shown in Table 1. The cohort was predominantly white (88%), over twothirds women (77%), and modestly educated (44% received a high school education). While income data are not available on a resident level, the homes are located in middle income neighborhoods from which most of the residents came and their families continue to live. Sixty-eight percent were diagnosed by psychi-

0002-8614/92/$3.50

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TABLE 1. CHARACTERISTICS OF NURSING HOME RESIDENTS AT ADMISSION

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score was 16 on a scale in which 30 indicates no impairment. The diagnosis of dementia was made using criteria established by the American Psychiatric Age (mean years) 81 Ass~ciation’~ based on symptoms elicited by the exam. Race (% white) 88% Sex (% female) 77% The diagnosis and prevalence of mental disorders in High school education 44% this cohort has been described by Rovner et a1.” Dementia Dx. 68% Physical functioning was measured using a subscale Depression Dx. 13% of the Psychogeriatric Dependency Rating Scale.I6This Restraint use (any) 48% measure includes standard activities of daily living Neuroleptic use (any) 34% (dressing, eating, feeding, transferring, and getting to MMSE (mean)* 16 the toilet independently or with help or not at all) and ADL (mean)** 17 is enhanced for use with a nursing home population Non/ambulatory, no dementia 36% by adding items on urinary and fecal continence, ability Adaptation (mean)*** 2.3 to walk by oneself, hear, speak, and see, and to brush Social support (mean)# 1.5 teeth, comb hair, and wash the face. Mean ADL score * MMSE score ranges from 0 (most severely impaired) to 30 (no impairment). was 17 on a scale in which 35 indicates the most ** ADL score ranges from I (no impairment) to 35 (most severe impairseverely impaired resident. A variable for being nonment). ambulatory but without dementia was created to dis***Adaptation score ranges from 0 (well adapted) to 12.2 (severely tinguish physical dysfunction caused by physiological maladapted). factors from physical dysfunction which may be the #Social support score ranges from 0 (no family or friends visit) to 3 (weekly visits by family or friends). consequence of dementia. Thirty-six percent were in this category. Adaptation was defined as the absence of aggression atric exam with dementia and 13% with a major or passive hostility as well as demonstrated restlessness depression. and/or fearful behavior and was measured with a scale An evaluation of the resident’s behavior and adap- developed for the study.5 Mean adaptation score was tation to the nursing home was obtained from nursing 2.3 on a scale in which a score of 1 2 indicates severe staff by a research assistant. All data were obtained maladaptation. using a structured instrument. A physician researcher Social support was defined in terms of quantity of collected the chart data. contacts. Points were assigned if the resident mainPhysical restraints were defined as mechanical tained contact with friends outside of the nursing means to restrict or restrain action, including: trunk home, had any visits by family during the week, or restraints such as Posey vests or waist restraints, reclin- had any visit by friends during the week. Mean social ing chairs with tables across which prevent rising, support score was 1.5 on a scale in which 0 indicates commonly called geri-chairs, and extremity restraints no contacts with family or friends and 3 indicates such as mitts, wrist, or ankle restraints. Data on use of weekly visits. restraints were abstracted from daily nursing records Finally, the impact of the passage of time was incorin the residents’ charts, showing whether or not a porated into the model by adding a variable for the restraint was used during a day. Forty-eight percent of number of weeks in the nursing home at the time of residents had restraints applied at least 1 day during the observation: 2, 10, or 52 weeks. the first month; 52.2% of residents had not been restrained; 18.5% had been restrained ranging from 1% METHOD OF ANALYSIS to 79% of days; 29.3% had been restrained 80%-100% Resident characteristics were compared by whether of days. Because of this U-shaped distribution, which restraints were used, using chi-square and t tests for occurred at the 10-week and 1-year observation also, significance. Correlations of the MMSE score and the restraint use was recoded as a dichotomous variable admission predictor variables were obtained to guide indicating no use or any use. which variables to enter into the regression. Finally, a Medication records were reviewed using a list of all mixed-effects linear regression model used in the main neuroleptics available to determine the days of use for each month. For the analysis, neuroleptic use was recoded as an any use/no use variable: 33.8% had any TABLE 2. COMPARISON OF ANY USE OF use during the first month. A comparison of any use RESTRAINTS AND NEUROLEPTICS AT THE THREE of restraints and neuroleptics was made for the 201 OBSERVATIONS residents remaining for three observations (Table 2), One Ten Two showing an increase in combined use over the year. Year Weeks Weeks Variables were created for the regression to indicate use of neither, restraints only, neuroleptics only, or 42.3 42.8 29.3 Neither both. Restraint and neuroleptic variables were cumu28.9 26.3 31.3 Restraints only lative, indicating use from admission to the observa10.0 9.0 11.9 Neuroleptics only 16.9 20.9 30.4 Both tion. Cognitive status was measured by the study psychiTotal 100.0% 100.0% 100.0% atrists using The Folstein Mini-Mental State Exam (MMSE)I4during a psychiatric evaluation. The mean Residents remaining at one year, n = 201.

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PHYSICAL RESTRAINT USE A N D COGNITIVE DECLINE

study and described by Brant et all7 was used for the multivariate analysis. This model, developed for use with longitudinal data,’*-’l was chosen because of its ability to determine the relationship of restraint use and cognition while accounting for the correlation between repeated measurements and controlling statistically for the effects of the remaining covariates. (Please see the Appendix for the full regression model and assumptions). To examine for possible effects of multicollinearity among independent variables, the model was run leaving out ADL, whose correlation with restraint use was the highest correlation among the predictor variables. The size and significance of the estimated coefficients remained the same in this abridged model, reducing concern that correlation of the independent variables distorted the findings. Thus, the model with ADL included was selected because it explained more of the total variance in the data. Estimation of the parameters in the model was carried out using a restricted maximum likelihood (REML) estimation procedure described by Lindstrom and Bates.’l The software, developed by Lindstrom, was used on a VAX computer. The use of a REML procedure allows for the unbiased estimation of all the effects in the model, even though individuals may have different numbers of repeated observations. Tests of significance of the individual covariates in the model were obtained by calculating z ratios equal to the estimate of the parameter divided by the standard error of the estimates. These z values were then compared to the standard normal distribution critical values to determine their significance. One advantage of the model is that it accommodates unequal numbers of observations of individuals. Thus, three observations were included for 174 residents, two observations were included for 133 residents, and one observation was included for 130 residents, totaling 918 observations from 437 different residents. The preponderance of observations were from 2 weeks (431), with fewer at 10 weeks (291) and 1 year (196). Another benefit of this analytic technique is that it accounts for change over time with repeated measurement of individuals, avoiding the problem with correlated repeated observations that occurs with standard regression procedure.

813

TABLE 3. COMPARISON OF NURSING HOME RESIDENT CHARACTERISTICS (n = 437) BY USE OF RESTRAINTS Percent with Characteristics Total Race White Black Sex Female Male Dementia Yes* No Non/Ambulatory, No dementia Yes*

No

AnyUse n = 209

NoUse n = 228

47.8%

52.2%

47.5 50.0

52.5 50.0

46.4 52.5

53.6 47.5

58.6 25.0

41.4 75.0

34.2 55.3

65.8 44.7

Mean for Age MMSE score** ADL score** Maladaptation score** Social Suvvort score** * Chi-square test P value < 0.0001 ** t Test P value < 0.0001.

AnyUse 81.4 11.8 20.7 2.9 1.3

NoUse 80.6 19.9 12.7 1.6 1.7

dents who remained for 1 year, those with little to no impairment at admission (MMSE 24-30 points, n = 55) declined a mean of 4.93 points (SD 8.42); those with moderate impairment at admission (MMSE 18-23, n = 39) declined only .54 points (SD 4.83); and those with severe impairment (MMSE 0-17, n = 107) declined 1.6 points (SD 5.75). The outliers were reviewed by the principal geropsychiatrist to verify the dramatic change in score; in all cases, this change was attributed to onset or abatement of an acute condition. Relationships among variables hypothesized to predict change in cognition are shown in the correlation matrix (Table 4). While most attained statistical significance, the only correlation among these independent effects that reached .50 was between ADL and restraint use ( Y = .50). Trends in MMSE scores for residents with and without restraint use were followed by first grouping resiRESULTS dents by whether they had severe cognitive impairDuring the first month, 47.8% of residents had any ment or moderate-to-no impairment at admission and restraint use. Table 3 compares resident characteristics next looking at the mean scores at the 10-week and 1by whether any restraints were used. Race and sex year observation. For residents who entered the nurswere not associated with use. A greater proportion of ing home with moderate to no impairment (MMSE users had dementia (58.6%); a smaller proportion who score 18-30), the decline in MMSE scores was greater were non-ambulatory but without dementia (34.2%) for those with restraint use (Figure 1). Residents who had any use of restraints. Those with any use of entered with severe impairment showed virtually no restraints had significantly poorer mean scores in association with restraints and those with restraints MMSE, ADL, adaptation, and social support scores. actually declined less than those without restraints. Age was not associated with restraint use. The findings of the regression analysis, Table 5, show The general pattern of change in cognition over the that poor ADL score, combination of both restraint and year, without regard to restraint use, was modest de- neuroleptic use, restraint use only, poor adaptation cline, a mean of 2.3 points. Change vaned, based on score, and time in the nursing home had a negative residents’ admission MMSE scores. For the 201 resi- association with cognition. Being non-ambulatory but

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BURTON ET A L

JAGS-AUGUST 1992-VOL. 40, NO.8

TABLE 4. CORRELATIONS OF MMSE SCORE AND PREDICTOR VARIABLES Non-Ambulatory/No MMSE Restraint‘ Neuroleptic‘ Dementia ADL Restraint

Neuroleptic Non-ambulatory/no dementia ADL

Maladaptation Social sutmort

-.45** -.20** .65** -.50** -.37** .27**

.07 -.24** .50** .30** -.20**

Adaptation

-.23**

.oo

.25** -.09

-.09 -.27** .24**

.29** -.12

-.13*

* Pearson Correlation P value < 0.01. ** Pearson Correlation P value < 0.0001. Proportion of days restraints or neuroleptics were used over the first month of admission. 30

30

25 -

2 5 ~

20

-

20 -

1s

-

15 -

N.59 _ _ _ _ _ _ ~ N.35

-

i

c

-------___

N-18 10 -

10

~

N-89

5 -

S-

0 -

0

+Any Restraint --(t No Restraints a. Residents with Severe Cognitive Impairment at Admission MMSE score 0-17

+Any Restraint --*i No Restraints b. Residents with Moderate to No Impairment at Admission MMSE score 18-30

FIGURE 1. Mean MMSE scores at three observations for residents with any and no restraint use.

~

TABLE 5. ESTIMATES OF COGNITIVE FUNCTION (MMSE SCORE)‘ Parameter Standard z Value Error Estimate Parameter

Constant Non-ambulatory/no dementia ADL Both restraint and neuroleptic Restraint only Adaptation Time Social support Neuroleptic only

20.6813 7.3580

.6865 .5530

30.12** 13.31**

-0.3577 -4.2548

.0257 .7671

-13.90** -5.55**

-2.2968 -0.4416 -0.0181 0.5039 -1.3494

.5978 .0925 .0077 .1971 .9029

-3.84** -4.78** -2.36* 2.56* -1.49 ns

Fixed-effects obtained from a multivariate linear mixed-effects regression, n = 437. * P < 0.01. ** P < 0.0001.

without dementia and having social support had a positive association with cognition. Neuroleptic use only without restraint use was not significantly associated with cognition. DISCUSSION

Our data show an association between restraint use and cognitive decline, but our study design does not allow us to examine the direction of the effect. Typically, the association of poor cognition and restraint use has been interpreted as showing that persons with

declining cognition are put in restraints. However, we suggest an additional explanation, that restraint use may contribute to further decline in cognition. Conceptually, it is plausible that persons relatively intact cognitively feel the stress of restraint use, and this stress translates into confusion and disorientation, measured by the cognitive score. However, a randomized controlled trial will be necessary to support this theory. Assigning causality will be difficult because restraint use is closely associated with a combination of environmental factors, such as less personal interaction and less ability to control ones’ environment, which have been shown to lead to decline in cognition.”, 23 Mean cognitive decline was 2.3 points, with most decline occurring among residents with least cognitive impairment at admission. These findings are similar to those reported by Katzman et al,24which showed decline of 2.87 points in the Blessed Information-Memory-Concentration Test for a nursing home population during a period of 1 year, with least decline among those who started the year with the most errors. Restraint use alone and restraint use combined with neuroleptic use were associated with poor cognition as were poor ADL scores, poor adaptation, and passage of time. Neuroleptic use alone was not associated with low cognitive scores. This is consistent with findings of Barnes et al,25which showed that behaviorally disturbed dementia patients improved in confusion, impairment of recent memory, and disorientation while taking a neuroleptic, compared to a placebo.

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PHYSICAL RESTRAINT USE AND COGNITIVE DECLINE

The adverse effect on cognition of the passage of time is a hallmark of dementia of the Alzheimer’s t ~ p e . ’ ~ -The ~ * debilitating effect of reduced cognition on ability to perform ADLs is established, but less is understood about whether reduced ability to function physically contributes to reduction in cognition. Our findings show a highly significant association between restraint use and ADL function and raise the possibility that compromised physical function may affect cognition, an interpretation consistent with the theory of reduced environmental stimulation leading to reduced cognition. Nursing home residents with higher levels of social support had better cognitive scores. While the direction of the association is unknown, social support has been shown by many researchers to have a positive, though non-specific, effect on health.32-36In addition, it is possible that stimulation from social contacts increases cognitive activity, in effect provides practice, which has been shown to improve mental status.”

CONCLUSIONS AND IMPLICATIONS This study found a strong association between restraint use, the use of both restraints and neuroleptics, and poor cognitive scores. Neuroleptic use without restraint use was not associated with cognition. Restraints may contribute to decline in cognition of certain residents, those who enter the nursing home with moderate or no impairment, but appear to have little to no effect on decline in cognition of residents more cognitively impaired at admission. Restraint use and other extrinsic factors should be investigated further to understand their relationship with changes in cognition. REFERENCES 1. Omnibus Budget Reconciliation Act. Federal Register V. 54(21), Rules and Regulations, February 2, 1989.

2. Powell C, Mitchell-Pedersen L, Fingerote E, Edmund L. Freedom from restraint: Consequences of reducing physical restraints in the management of the elderly. Can Med Assoc J 1989;141:561-564. 3. Tinetti ME, Liu WL, Gmter SF. Mechanical restraint use and fall-related injuries among residents of skilled nursing facilities. Ann Intern Med 1992116:369-374. 4. Tinetti ME, Liu WL, Marottoli RA et al. Mechanical restraint use among residents of skilled nursing facilities. JAMA 1991;265(4):468-471. 5. German PS, Rovner BW,Burton LC et al. The role of mental morbidity in the nursing home experience. Gerontologist 1992;32(2):152-158. 6. Werner P, Cohen-Mansfield J, Braun J et al. Physical restraints and agitation in nursing home residents. J Am Geriatr SOC1989;371122-1126. 7. DiMaio VJ, Dana SE, Bux RC. Deaths caused by restraint vests. JAMA 1986;255(7):905. 8. Dube AH, Mitchell EK. Accidental strangulation from vest restraints. JAMA 1986;256(19):2725-2726. 9. Lofgren RP, M a c p h e m DS, Granieri K et al. Mechanical restraints on the medical wards: Are protective devices safe? Am J Public Health 1989;79:735-738, 10. U. S . Senate Special Committee on Aging and The Kendal Corporation. Untie the elderly: Quality care without restraints. Resource Packet prepared by the National Citizens’ Coalition for Nursing Home Reform, Silver Spring, MD. Manor H d t h Care Corporation, 1989. 11. Burton LC, German PS, Rovner BW et al. Mental illness and the use of restraints in nursing homes. Gerontologist 1992;32(2):164-170. 12. Rovner BW, German PS,Broadhead J et al. The prevalence and management of dementia and other psychiatric disorders in nursing homes. Int Psychogeriatrics 1990;147299-302. 13. Rovner BW, German PS, Brant LJ et al. Depression and mortality in nursing homes. JAMA 1991;265(8):993-996. 14. Folstein MF, Folstein SE, McHugh PR. Mini-mental state: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12389- 198.

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15. American PsychiatricAssociation Committeeon Nomenclature and Statistics. Diagnostic and Statistical Manual of Mental Disorders, DSM-IIIR. Washington, DC: American Psychiatric Association Press, 1987. 16. Wilkinson IM, Graham-White J. PsychogeriatricDependency Rating Scale (PGDRS): A method of assessment for use by nurses. Br J Psychiatry 1980;137:558-565. 17. Brant LJ, German PS, Rovner BS et al. A longitudinalapproach to modeling outcomes in a nursing home study. Gerontologist 1992;32(2):159-163. 18. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982;38(4):963-974. 19. Zeger SL. Regression analysis with longitudinal data. Proceedings of the 1987 Public Health Conference on Statistics, DHHS Publication PHS (881214), 1987. 20. Feldman HA. Families of lines: Random effects in linear regression analysis. Modeling methodology forum. J Appl Physiol 1988;64(4):1721-1732. 21. Lindstrom NJ, Bates DM. Newton-Raphson and EM algorithms for lmear mixed-effects models for repeated measures data. J Am Stat Assoc 1988;83:1014-1022, 22. Langer EJ. Old age: An artifact? In: McGaugh JL, Kiesler SB, eds. Aging: Biology and Behavior. New York Academic Press, 1981, pp 255-281. 23. Rodin J . Aging and health: Effects of the sense of control. Sdence 1986;223:1271-1276. 24. Katzman R, Brown T, Thal LJ et al. Comparison of rate of annual change of mental status score in four independent studies of patients with Alzheimer‘s Disease. Ann Neurol 1988;24(3):384-389. 25. Barnes R, Veith R, Okimoto J et al. Efficacy of antipsychotic medication in behaviorally disturbed dementia patients. Am J Psychiatry 1982;139:11701174. 26. Albert MS. Cognition and aging. In: Hazzard WR,Andres R, Bierman EL, Blass JP, eds. Principles of Geriatric Medicine and Gerontology. 2nd Ed. New York McGraw-Hill, 1990, pp 913-920. 27. Mayeux R. Alzheimer’s Disease. In: Hazzard WR, Andres R, Bierman EL, Blass JP, eds. Principles of Geriatric Medicine and Gerontology, 2nd Ed. New York McGraw-Hill, 1990, pp 934-948. 28. McKann G,Drachman D, Folstein M et al. Clinical diagnosisof Alzheimer’s disease: Report of the NINCDS-ADRDA work group under the auspices of the Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984;34:939-944. 29. Reisberg B, Borenstein J, Salob SP et al. Behavioral symptoms in Alzheimer’s disease: Phenomenology and treatment. J Clin Psycho1 1987;48:9-15. 30. Jenike MA. Geriatric Psychiatry and Psychopharmacology: A Clinical Approach. Chicago: Year Book Medical Publishers, 1989. 31. Katzman R. Alzheimer‘s Disease. N Engl J Med 1991;314(15):964-973. 32. Berkman LF, Syme SL. Social networks, host resistance and mortality: A nine-year follow-up study of Alameda County residents. Am J Epidemiol 1979;109:186-200. 33. Blazer DG. Social support and mortality in an elderly community population. Am J Epidemiol 1982;115:684-694. 34. Graham S,Reeder L. Social epidemiology of chronic disease. In: Freeman HE, Levine S, Reeder LG, eds. Handbook of Medical Sociology,3rd Ed., Englewood Cliffs, NJ: Prentice-Hall, 1979, pp 71-96. 35. Kasl SV, Berkman LF. Some psychosocial influences on the health status of the elderly: The perspective of social epidemiology. In: McGaugh JL, Kiesler SB, eds. Aging: Biology and Behavior. New York Academic Press, 1981, pp 345-386. 36. Antonucci TC, Jackson JS. Social support, interpersonal efficacy and health A life course perspective. in: Carstensen LA, Edelstein BA, eds. Handbook of Clinical Gerontology. Elmsford, N Y Pergamon Press, 1987, pp 291-311.

APPENDIX Model to Estimate MMSE Score If Yij represents the MMSE score for the ith individual at the jthtime of observation, Yjj = (Bo + bio) BMXM

+

+ BRXR + + BcXc + B p X p + + Bas+ BTXT + eij BNXN

BAXA

where Bo represents the fixed effect for the constant term and bio, the individual or random effect, and the covariates are: X R = restraint use only X N = neuroleptic use only Xc = combined restraint and neuroleptic use X P = non-ambulatory/no dementia X M = adaptation score XA = ADL score

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XS= social support XT = time ( 2 , l O or 52 weeks)

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have a multivariate normal distribution with mean vector of 0 and positive definite variance-covariance and eij represents the corresponding error term. structure a2D.This assumption provides the covariance The parameters for the fixed effects (Bo, BR, . . ., BT) structure which allows subjects to differ from the poprepresent the Yaverage"values for the intercept or ulation average. Thus, the random effect, bi, represents constant and the slopes for each covariate, while the the natural heterogeneity in the nursing home residents random effect parameter (bio)represents the deviations and reflects the fact that individuals start off differently for the ith individual from the average intercept or and change differently with time. initial MMSE score. The random effect is assumed to