longitudinal predictors of non-aggressive agitated behaviors in the elderly

LONGITUDINAL PREDICTORS OFNON-AGGRESSIVE AGITATEDBEHAVIORS IN THE ELDERLY

JISKA COHEN-MANSFIELD1* AND PERLA WERNER2

1Research Institute of the Hebrew Home of Greater Washington,George Washington University Medical Center, Rockville, USA

2University of Haifa, Haifa, Israel

ABSTRACT

Longitudinal predictors of physically and verbally non-aggressive inappropriate behaviors were examined in200 community-dwelling elderly persons attending senior day care centers. Models based on ratings obtained fromsta� members and family caregivers were compared. Multiple factors contributed simultaneously to the predictionof non-aggressive behaviors. Similar to previous cross-sectional results, physically non-aggressive behaviors werepredictedmainly by good health and cognitive impairment. In addition, depression emerged consistently as a predictorof physically non-aggressive behaviors in all models. Verbally non-aggressive behaviors were predicted by depresseda�ect and pain, con®rming previous suggestions that these behaviors are related to discomfort. The relation-ship of these behaviors with cognitive functioning was relatively weak. Understanding the etiologies of non-aggressive problem behaviors can aid in developing appropriate care for this population. Copyright # 1999 JohnWiley & Sons, Ltd.

KEY WORDSÐnon-aggressive behavior; longitudinal predictors; pacing; agitated behaviors; nursing homes

The past several years have witnessed a growinginterest in the topic of agitation in the elderlypopulation. Numerous papers and chapters havedescribed issues related to di�erent aspects of theproblem. Prevalence rates have been examined inthe nursing home (Burgio et al., 1988; Cariaga etal., 1991; Hallberg, Norberg and Eriksson, 1990;Jackson et al., 1989; Sloane and Mathew, 1991;Wayne et al., 1991), as well as in the community(Baumgarten, Becker and Gauthier, 1990; Burns,Jacoby and Levy, 1990; Dawson and Reid, 1987;Hamel et al., 1990; Spector, 1991; Swearer, 1994).Several studies examined the correlates and charac-teristics of the elderly person manifesting speci®cbehaviors (Burns et al., 1990; Cohen-Mans®eldet al., 1991; Cohen-Mans®eld, Marx and Werner,1992; Cohen-Mans®eld and Werner, 1995; Lloyd,Hafner and Holme, 1995; Patterson et al., 1990;

Spector and Jackson, 1994). Moreover, new assess-ment scales are being developed and validated(Baumgarten et al., 1990; Drachman et al., 1992;Molloy et al., 1991; Patel and Hope, 1992; Rosen etal., 1992; Sultzer et al., 1992; Tariot et al., 1995; Teriet al., 1992), and both pharmacological and non-pharmacological treatment alternatives are receiv-ing consideration as well (Hinchli�e et al., 1995;Swearer, 1994; Devanand and Levy, 1995; Swanick,1995;MellowandAronson, 1995; Tariot, Schneiderand Katz, 1995).

Findings from our previous studies show thatagitated behaviors are characterized by di�erentsyndromes among both nursing home residents(Cohen-Mans®eld, Marx and Rosenthal, 1989) andcommunity-dwelling participants of adult day carecenters (Cohen-Mans®eld et al., 1989). Speci®cally,four distinct categories of agitation have beenreported: (1) physically non-aggressive behavior;(2) verbally non-aggressive behavior; (3) physicallyaggressive behavior; and (4) verbally aggressivebehavior. The behaviors included in the physicallyand verbally non-aggressive categories are amongthe most commonly manifested agitated behaviorsin old age.

CCC 0885±6230/99/010831±14$17.50 Received 3 November 1998Copyright # 1999 John Wiley & Sons, Ltd. Accepted 4 February 1999

INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY

Int. J. Geriat. Psychiatry 14, 831±844 (1999)

*Correspondence to: Jiska Cohen-Mans®eld, Ph.D., ResearchInstitute, Hebrew Home of Greater Washington, 6111MontroseRoad, Rockville, MD 20852, USA. Tel: 301-770-8449. Fax: 301-770-8455.

Contract grant sponsor: National Institute on Aging.Contract grant number: #AG08675.

PHYSICALLY NON-AGGRESSIVEAGITATION

Physically non-aggressive agitated behaviorsinclude pacing, wandering, general restlessness,performing repetitious mannerisms, trying to getto a di�erent place, handling things inappro-priately, and inappropriate dressing or undressing.Reported rates of the prevalence of pacing orwandering among the elderly range from a mini-mum of 3% among outpatients of a dementia clinic(Reisberg et al., 1987) to 59% in a heterogeneoussample of patients with irreversible dementia whowere cared for at home, who lived alone, or wholived in a nursing home (Rabins, Mace and Lucas,1982). Among the concomitants of physically non-aggressive agitation, increased cognitive impair-ment probably has the most robust relationship towandering and pacing behaviors (Teri, Larson andRei¯er, 1988; Burns et al., 1990; Cohen-Mans®eldet al., 1991; Cooper, Mungas and Weiler, 1990;Dawson and Reid, 1987; Snyder et al., 1978). Inaddition, wanderers/pacers in the nursing homehave been reported to be more physically healthy,as demonstrated by the fact that they have fewermedical problems and better appetites than others(Cohen-Mans®eld et al., 1991). Finally, wanderers/pacers are also more likely to experience delusions(Lachs et al., 1992).

VERBALLY NON-AGGRESSIVE AGITATION

Verbally non-aggressive agitated behaviors includeconstant requests for attention, verbal bossiness,complaining, negativism, disruptive interruptionsand whining. In the nursing home, prevalence ofdisruptive or inappropriate behaviors has beenfound to range from 11% (Cariaga et al., 1991) to30% (Ray et al., 1992). Among community-dwelling individuals with dementia, reports of theprevalence of verbally non-aggressive agitationwere even higher. For example, Baumgarten et al.(1990) reported that 72% asked repetitious ques-tions and 20% cried or laughed inappropriately.Similarly, Devanand et al. (1992) reported that36% of outpatients of a dementia clinic manifestedcrying as one of their behavioral symptoms. Incontrast to physically non-aggressive behaviors,certain types of verbally non-aggressive agitationsuch as complaining are not related to severedementia, but rather to mild and intermediatestages of the disorder (Cohen-Mans®eld, Marx and

Rosenthal, 1990), a ®nding which intuitively mightbe expected, since verbal ability preservation isnecessary in order to engage in those behaviors.Other types of vocal agitation, such as screaming,are associated with high levels of cognitive impair-ment (Cohen-Mans®eld et al., 1990). The relation-ship between dementia and verbally non-aggressiveagitation is therefore less strong than for dementiaand physically non-aggressive agitation (Cohen-Mans®eld, Culpepper and Werner, 1995). Anotherpsychological factor which relates to verbally non-aggressive agitation is depressed a�ect (Cohen-Mans®eld and Marx, 1988). It is possible that thisis due to the higher cognitive function of indi-viduals who display verbally non-aggressive beha-vior (in comparison to other nursing homeresidents), enabling them to express their emotionalstate. Physical health also relates di�erently toverbally non-aggressive agitation than to physicallynon-aggressive agitation. Whereas older personsmanifesting physically non-aggressive behaviorstend to be in better health, verbal agitation hasbeen related to having more medical conditionsand higher levels of pain (Cohen-Mans®eld et al.,1990), as well as to the presence of fevers (Hurleyet al., 1992). Finally, it has been found that womenare more likely to display verbally non-aggressiveagitation than are men (Jackson et al., 1989).

HYPOTHESES AND RATIONALE

Cross-sectional studies have shown that di�erentcharacteristics pertain to elderly persons who man-ifest physically non-aggressive behaviors versusthose who manifest verbally non-aggressive be-haviors (Cohen-Mans®eld et al., 1992). Thosedi�erences are likely to be due to di�erences in theetiology of the behaviors. Further evidence isrequired, however, particularly work that can assesschanges in agitation and its correlates over time.Such longitudinal work would facilitate testingseveral hypotheses, including:

1. Cognitive impairment which approaches highlevels of severity should lead to an increase inphysically non-aggressive agitation, but not inverbally non-aggressive agitation, where im-pairment reaching moderate levels would pre-dict the latter form of agitation

2. Healthier individuals will be more likely toexhibit physically non-aggressive agitation,whereas poorer health should result in moreverbally non-aggressive agitation

Copyright # 1999 John Wiley & Sons, Ltd. Int. J. Geriat. Psychiatry 14, 831±844 (1999)

832 J. COHEN-MANSFIELD AND P. WERNER

3. Increases in depressed a�ect should result inmore in verbally non-aggressive agitation, butnot in more physically non-aggressive agitation

4. Women should display more verbally non-aggressive agitation, but not more physicallynon-aggressive agitation

METHODS

Participants

Two hundred and seventy-six community-resid-ing participants of ®ve senior day care centers inMaryland, who were at least 60 years of age andEnglish-speaking, were approached for participa-tion in the study. Two hundred (72.5%) consentedto participate. Sixty-six per cent of the participants(N � 132) were female. The age range of the parti-cipants was 60±97 years (mean age � 80.0). Overhalf of the participants were widowed (53.5%),35% were married, 6% were divorced and 5.5%were single. The participants had been attendingday care for an average of 1.4 years, with a range ofless than 2 months to 13.3 years. Participantsattended day care on the average 3.7 days from10:30 am to 3 pm during the 2 weeks preceding theinitial interview.

Number of years of education for each partici-pant ranged from 0 to 25 (mean � 12.3, SD � 4.7).The participants' previous occupations included:homemaker (25%), professional (20.9%), clerical(17.9%), blue collar (16.8%) and business (14.8%).The remaining 4.6% of participants had otheroccupations, such as: military careers, poets andartists and clergymen. Most of the participants(87.5%) were Caucasian, with 9.5% African-American,, 1% Hispanic, 1% Asian-American,and 1% other. Participants' demographic datawere similar to those of the participants in anational survey of day care users, although a greaterproportion of the participants in this study werewhite as compared to the national survey and agreater proportion were disabled in the perform-ance of ADL (Cohen-Mans®eld et al., 1995).

Informed consent was obtained for all partici-pants. For those residents who were unable to pro-vide informed consent (as judged by a member ofthe day care sta� who was well acquainted with theparticipant), a close relative was contacted andasked to provide consent. Additional informationconcerning the informed consent procedure isavailable elsewhere (Cohen-Mans®eld et al., 1988).

Day care centers

Five senior adult day care centers inMontgomery County, Maryland, were included inthis study. All were Medicaid-certi®ed and weresta�ed to provide the level of care of a skillednursing facility or an intermediate care facility.Private fees provided less than half of the centers'funds. Medicaid was the largest single source offunding. Funding was received from the federal,state and local governments, but the countygovernment provided no funds for operatingexpenses. All of the centers included in the studyprovided transportation for at least some of theparticipants. The areas the centers served over-lapped.

Procedure

Participants were assessed at 6-month intervalson a variety of self-report measures, sta� and rela-tive ratings, and chart assessments. Medical exam-inations by a geriatric physician were performedonce a year.

The activity directors were selected to completethe information about the participants because oftheir close contact with the participants. A researchassistant was present when the ratings were com-pleted. Family members of the day care partici-pants also provided information concerning theparticipant via a mailed questionnaire.

Instruments

Physically and verbally non-aggressive beha-viors. Physically and verbally non-aggressivebehaviors were rated using the Cohen-Mans®eldAgitation Inventory for Community (CMAI-C) byday care sta� members and family caregivers. TheCMAI-C is an expanded version of the Cohen-Mans®eld Agitation Inventory (CMAI). It is aninformant rating questionnaire which consists of36 behaviors, each rated on a seven-point scale offrequency (`1' indicates that the participant nevermanifests the behavior, `2' indicates that the partic-ipant manifests the behavior less than once a week,`3' indicates that the participant manifests thebehavior once or twice a week, `4' indicates that theparticipant manifests the behavior several times aweek, `5' indicates that the participant manifeststhe behavior once or twice a day, `6' indicatesthat the participant manifests the behavior severaltimes a day, and `7' indicates that the participant


LONGITUDINAL PREDICTORS OF NON-AGGRESSIVE BEHAVIORS 833

manifests the behavior on average of severaltimes an hour. For additional information on theCMAI-C, see Cohen-Mans®eld et al. (1995).Interrater agreement rates (with agreement de®nedas 0- or 1-point discrepancy) for th CMAI-C wereassessed for 20 participants. Two day care sta�members who were similarly involved with the20 participants rated independently the partici-pants' level of agitation. Agreement rates averaged92.1% for the 36 items.

Cognitive functioning. Cognitive functioning wasmeasured by the Brief Cognitive Rating Scale(BCRS; information on reliability, validity andinteraxes correlations of the BCRS is presented inReisberg et al. (1983) and in Cohen-Mans®eld et al.(1990)), which was completed by activity therapistsor daytime nurses in the appropriate setting (adultday care or nursing home, respectively). The BCRSis a seven-point scale ranging from 1 indicatingnormal functioning to 7 indicating total cognitiveimpairment. Cognitively intact individuals usuallyscore under 3; scores of 6 and 7 are usuallysymptomatic of severe dementia. The BCRS wasslightly modi®ed for our purposes in order toimprove clarity and use of the scale by day care andnursing sta�, and tapped four axes of cognitivefunctioning: concentration, recent memory, pastmemory and orientation; each of the axes was ratedon a seven-point scale (ranging from normal tocomplete deterioration).

In addition, the Mini-Mental State Examination(MMSE; Folstein, Folstein and McHugh, 1975)was administered to each participant by a trainedresearch assistant. The MMSE is a direct assess-ment (ie the subject is tested) of cognitive functionthat consists of two parts: the ®rst part tapsorientation, memory and attention, and the secondpart assesses the participant's ability to namethings, follow verbal and written commands,write and draw (ie copy a complex ®gure). Themaximum score is 30, with lower test scoresindicating greater degree of cognitive impairment.

Depressed a�ect. Family caregivers and day carecenter sta� members rated the participants' severityof depressed a�ect using the Raskin DepressionScale (Raskin, 1988). This scale includes threeitems: (1) verbal depression, such as talking aboutfeeling helpless, hopeless or worthless, and com-plaints of loss of interest; (2) behavioral manifes-tations of depression, eg the participant looks sad,cries easily, speaks in sad voice, etc; (3) depression

through secondary symptoms, such as insomnia,dry mouth, history of recent suicide attempts, etc.

Each item is rated on a ®ve-point scale: 1 � notat all depressed, 5 � very much depressed. A meanscore of the severity of the depressed a�ect(ie averaged across the three items) was derivedfor each participant as suggested by Raskin (1988),Liang and Zeger (1986) and Paykel et al. (1980).

Measurement of pain. Day care sta� membersand family caregivers were asked to rate theseverity of the participant's physical pain duringthe 2 weeks preceding the completion of thequestionnaire. The pain item was assessed on ascale rating from 1Ðno pain to 6Ðexcruciatingpain, based on the Short Form McGill PainQuestionnaire (Melzack, 1987). Interrater reli-ability was assessed using independent ratings bytwo day care sta� members who were similarlyinvolved and well acquainted with 16 participants.The interrater Pearson correlation coe�cient wasr � 0.85.

Quality of the relationship. Day care sta�members rated the quality of the relationship ofthe participants with the sta� and with otherparticipants. Both items were rated on a seven-point scale ranging from 1 � always negative to7 � always positive. Because of the high correla-tion between the two items (r � 0.73, p5 0.01), anaveraged score of the two items was computed.

Frequency of social contacts. Family membersprovided information regarding how frequently theparticipant socialized with other relatives andfriends. This six-point item ranged from 1 � neverto 6 � several times a day.

Medical information. In addition to the indicesof dementia, a number of other variables wereobtained from the physical examinations of theparticipants. The variable used in these analyses isthe number of medical diagnoses (includes diag-nosis of dementia).

Demographic information. Basic demographicinformation on age, gender, race, marital statusand length of time in day care at initial testing wasrecorded from day care records. All data (cognitive,functioning, medical, depressed a�ect and demo-graphic) were coded and entered into an IBM-compatible 486-PC and analyzed using SPSS/PC � version 4.1 software.



Recruitment and nursing home placement

Participants were recruited throughout thestudy. At each 6-month interval all participantsof the adult day care centers who were over 60 yearsold were approached for participation in the study.Those who agreed were followed with an assess-ment every 6 months. Participants continued part-icipation in the study even when they stoppedparticipating in adult day care. Interviews werecontinued when they moved to new facilities suchas a group home or a nursing home. Length ofparticipation ranged from completing only baselineto completing nine assessments over 4 years. Themean number of assessments was 5.2 (SD � 0.17),meaning that participants participated in the studyan average of 2 years.

Eighty-three participants left the study before itstermination: 57 died, 12 moved out of the area and14 asked to be withdrawn.

Participants who moved or withdrew from thestudy (N � 26) had fewer years of education(10.8 vs 13.1, t(104) � 2.3, p5 0.05) than thosewho remained in the study. No other signi®cantdi�erences were found between participants whostayed in the study and participants who did not.

Fifty-two participants entered a nursing home(assessments continued while they were in anursing home) during the study. Participantsplaced in a nursing home (N � 52) tended to bewhite compared to participants who did not enter anursing home (96.2% vs 84.5, w2 � 4.8, p5 0.05),were rated by sta� members as manifesting lessphysically non-aggressive behavior (PNAB) thanparticipants who did not enter a nursing home(45.1% vs 61.0, w2 � 3.9, p5 0.05) and as signi®-cantly more depressed at baseline (t(146) � 3.22),and had more psychiatric diagnoses other thandementia (0.92 vs 0.49, t(146) � 2.29, p5 0.05; doesnot include dementia diagnosis) at initial testingthan participants measured over 2 years whoremained in the community.

Statistical analysis

Because of the variability in length of participa-tion in the study, a statistical method whichcould account for di�ering assessment lengths bydi�erent participants was sought. As a result ofconsultations with several statisticians, we chosethe generalized estimating equations (GEE)repeated measures regression model of Liang andZeger (1986), as implemented by a SAS/PC macro

(Karim, 1989). GEE provides consistent estimatorsof the regression coe�cients and the correspondingvariances under weak assumptions. For example,the GEE model can handle both normal and non-normal outcome variables such as the Poissondistribution of our data. Also, it is insensitive tomissing observations provided that they do notdi�er systematically from available values. Unlikeother methods for analyzing repeated measuresdesigns, GEE does not require a balanced design,thereby being una�ected by data that are missingat random. The GEE approach describes themarginal expectation of the outcome variable as afunction of the covariates, while accounting for thecorrelation among the repeated observations for agiven participant (Liang and Zeger, 1986). Finally,both time-independent and time-dependent covari-ates can be used (Davis, 1993).

Analytic approach

In order to explore the longitudinal predictors ofnon-aggressive behaviors, several approaches weretaken:

1. The prediction of hypothesized models, basedon our correlational results in the nursing homepopulation, was tried. A study of over 400 nur-sing home residents preceded this study andincluded di�erent participants, di�erent re-search assistants and, to some extent, di�erentassessment instruments from the current study(Cohen-Mans®eld et al., 1992). Findings of thisstudy showed that PNABwere related to greatercognitive impairment and better health (asre¯ected in a lower number of active diagnosesand less pain).

Based on ®ndings from the same study, verb-ally non-aggressive behaviors (VNAB) wereassociated with female gender, less cognitiveimpairment than other nursing home residents,a poorer state of health (ie more medical diag-noses and more pain), depressed a�ect andnegative interpersonal experiences. Accord-ingly, a model in which VNAB was predictedby these independent variables was tried.

2. In addition to the hypothesized models, generalmodels were attempted in order to examinewhether variables which were not related tonon-aggressive behavior in the nursing homestudy a�ect non-aggressive behaviors in com-munity-dwelling elderly persons.



The general model for PNAB included depresseda�ect, quality of relationship, age and sex, in addi-tion to the variables in the hypothesized model.The general model for VNAB added age to thevariables in the hypothesized model.

The predictive utility of the hypothesized andgeneral models was assessed using the informationobtained at baseline, and the information collectedconcurrently with the assessment time. Addition-ally, change in agitation over time was also invest-igated as predicted by change in the independentvariables.

For each model of prediction, the followingfunctions were examined:

Predictors of levels of non-aggressive behaviors

1. Prediction from baseline status:(a) Agitation(at time t) � function of (hypo-

thesized model variables(at baseline))(a1) Agitation(at time t) � function of (general

model variables(at baseline))

2. Concurrent prediction (cross-sectional):(b) Agitation(at time t) � function of (hypo-

thesized model variables(at time t))(b1) Agitation(at time t) � function of (general

model variables(at time t))

Predictors of change in non-aggressive behaviours

3. Prediction of change from baseline:(c) Change in Agitation(at time t from base-

line) � function of (change in independentvariables of the hypothesized model(at time t

from baseline))

(c1) Change in Agitation(at time t from base-

line) � function of (change in generalmodel variables (at time t from baseline))

4. Prediction of change from previous assessment:(d) Change in Agitation(at time t from previous

time) � function of (change in independentvariables of the hypothesized model(at time t

from time tÿ1))(d1) Change in Agitation(at time t from time

tÿ1) � function of (change in generalmodel variables(at time t from time tÿ1))

The samemodels were examined based on ratingsby sta� members and by the relatives of theparticipants. Models based on our hypothesizedmodel variables were tested using one-tailed testslevels, while models using general variables weretested using two-tailed tests.

RESULTS

Physically non-aggressive behaviors

Sta� members' based data.Prevalence of physically non-aggressive behaviorsat baseline.

Twenty-seven per cent of the participants mani-fested physically non-aggressive behaviors at leastonce a week during the 2 weeks preceding theinterviews with sta� members at the day carecenters.

Predictors of levels of physically non-aggressivebehaviors based on sta� ratings (PNAB)

1. Prediction from baseline status:

(a) Agitation(at time t) � function of (hypo-thesized model variables(at baseline))

The beta coe�cients, corresponding Z statisticsand signi®cance levels for the model predictinglevels of physically non-aggressive behaviors basedon baseline status concerning the variables in thehypothesized model are presented in Table 1. In it,number of active diagnoses and level of physicalpain are used as indicators of health. The hypo-thesized constructs (ie impaired cognition andhealthy status) were statistically signi®cant pre-dictors of physically non-aggressive behaviors.Cognitive impairment was found to be the mostpowerful predictor by far.

(a1) Agitation(at time t) � function of (generalmodel variables(at baseline))

When age, sex, depressed a�ect and quality ofrelationship were included as predicting variables,cognitive impairment and healthy status remainedsigni®cant predictors, while depressed a�ect atbaseline also emerged as a signi®cant predictor ofphysically non-aggressive behavior.

2. Concurrent predictors:

(b) Agitation(at time t) � function of (hypo-thesized model variables(at time t))

Cognitive functioning still emerged as the mostpowerful predictor (Table 1) based on the hypothe-sized model.

(b1) Agitation(at time t) � function of (generalmodel variables(at time t))

Concurrent prediction of physically non-aggressive behaviors based on the general modelshowed cognitive impairment, physical health



(indicated by lack of physical pain), young age anddepressed a�ect emerging as signi®cant predictors.

3,4. Prediction of change in physically non-aggressive behaviors:

None of the variables indicating change frombaseline included in the hypothesized model weresigni®cant predictors of change in PNAB. In thegeneral model, the only signi®cant predictor ofchange was that of increase in levels of depresseda�ect from baseline (Z � 2.42; p5 0.05;N � 540).

The only signi®cant predictor of change inPNAB from previous assessment was decrease incognitive functioning, both for the hypothesizedand for the general models (increase in BCRS; Zstatistic � 2.58, p5 0.01, and Z � 2.52, p5 0.05,respectively; N � 540).

Relatives' based data.Prevalence of physically non-aggressive behaviors

Based on relatives' ratings, as many as 43% ofthe participants manifested at least one physicallynon-aggressive behavior at baseline at least once aweek.

Predictors of levels of physically non-aggressivebehaviors based on relatives' ratings (PNABR)

1. Prediction from baseline status:

(a) Agitation(at time t) � function of (hypoth-esized model variables(at baseline))

Signi®cant predictors of physically non-aggressive behaviors based on relatives' datacollected at baseline are presented in Table 2.Cognitive impairment was the only signi®cantpredictor based on the hypothesized model.Number of active diagnoses was in the

Table 1. GEE models for predicting levels of physically non-aggressive behaviors based on sta� generatedassessments

Predictors Behavior predicted by baseline assessment

Hypothesized model variablesc General modeld

Number of observations � 895 Number of observations � 872

Beta coe�cient Z statistic Beta coe�cient Z statistic

Intercept 0.91 7.05** 1.82 2.25*

Cognitive functioning a 0.26 9.25** 0.26 9.11**

Number of active diagnoses ÿ0.07 ÿ2.40** ÿ0.08 ÿ2.93*Physical pain 0.01 0.21 ÿ0.07 ÿ1.36Age ÿ0.01 ÿ1.33Depressed a�ect 0.27 3.92**

Quality of relationship ÿ0.05 ÿ0.83Sex1 0.11 0.90

Predictors Behavior predicted by concurrent assessmentb




Intercept 0.72 7.33* 1.92 2.39*

Cognitive functioninga 0.25 8.99** 0.25 8.47**

Physical pain ÿ0.01 ÿ0.37 ÿ0.08 ÿ2.10*Age ÿ0.02 ÿ2.08*Depressed a�ect 0.22 3.71**

Quality of relationship ÿ0.03 ÿ0.55Sex1 0.14 1.24

* p5 0.05; ** p5 0.01; one-tailed for hypothesized model, two-tailed for general. aBCRSÐlarger score indicates more impairment.bNumber of active diagnoses was not included because not enough concurrent data were available and it would therefore limit theN. cHypothesized model was that PNAB would relate to greater cognitive impairment and better health (as re¯ected in a lowernumber of active diagnoses and less pain). dThe general model for PNAB included depressed a�ect, quality of relationship, age andsex, in addition to the variables in the hypothesized model. 10 � female; 1 � male.



hypothesized direction but did not reach statisticalsigni®cance.

(a1) Agitation(at time t) � function of (generalmodel variables(at baseline))

Depressed a�ect emerged as a statistically signi®-cant predictor in the general model and cognitiveimpairment remained the strongest predictor.Number of active diagnoses and physical painwere in the hypothesized direction but did notreach statistical signi®cance.2. Concurrent predictors:

(b) Agitation(at time t) � function of (hypoth-esized model variables(at time t))

Similar to the ®ndings based on baselineinformation, cognitive impairment was a power-ful predictor (Table 2) when information from the

concurrent assessment time was used forprediction.

(b1) Agitation(at time t) � function of (generalmodel variables(at time t))

Cognitive impairment and increased depresseda�ect were statistically signi®cant in this modelsimilar to the model based on baseline information.

3,4. Prediction of change in physically non-aggressive behaviors:

Worsening cognitive impairment ( for the hypo-thesized model) and increased depressed a�ect forthe general model were the predictors of changefrom baseline (Z � 2.28, N � 475, and Z � 2.80,N � 388, respectively, p5 0.01). Only increaseddepressed a�ect predicted change in PNAB

Table 2. GEE models for predicting levels of physically non-aggressive behaviors based on relatives' generatedassessments





Intercept 3.200 12.72** 1.44 1.59

Cognitive functioninga ÿ0.040 ÿ4.42** ÿ0.04 ÿ4.15*Number of active diagnoses ÿ0.070 ÿ1.49e ÿ0.08 ÿ1.66fPhysical pain ÿ0.001 ÿ0.01 ÿ0.13 ÿ1.51gAge 0.01 1.31

Depressed a�ect 0.34 3.16**

Quality of relationship 0.07 1.13

Sex1 ÿ0.13 ÿ0.75Predictors Behavior predicted by concurrent assessmentb




Intercept 2.73 15.68 1.00 1.15

Physical pain 0.04 0.61 ÿ0.01 ÿ0.09Cognitive functioninga ÿ0.04 ÿ5.40** ÿ0.04 ÿ5.74**Age 0.02 1.68


Frequency of social contacts 0.05 1.18

Sex1 ÿ0.17 ÿ0.94* p5 0.05; ** p5 0.01; one-tailed for hypothesized model, two-tailed for general. aMMSEÐsmaller score indicates moreimpairment. bNumber of active diagnoses was not included because not enough concurrent data were available and it wouldtherefore limit the N. cHypothesized model was that PNAB would relate to greater cognitive impairment and better health(as re¯ected in a lower number of active diagnoses and less pain). dThe general model for PNAB included depressed a�ect, qualityof relationship, age and sex, in addition to the variables in the hypothesized model. ep � 0.068, one-tailed. fp � 0.097, two-tailed;0.0485, one-tailed. gp � 0.131, two-tailed; 0.0655, one-tailed. 10 � female; 1 � male.



from previous assessment (Z � 2.44; p5 0.05;N � 388).

Verbally non-aggressive behaviors

Although age was included as an additionalpredictor variable in the general model, it did notreach statistical signi®cance in any of the predictingmodels (data not shown). Therefore, results forVNAB will be presented only for the hypothesizedmodel.

Sta� members' based data.Prevalence of verbally non-aggressive behaviors

Based on sta� members' ratings, 19% of theparticipants manifested verbally non-aggressivebehaviors at baseline at least once a week.

Predictors of levels of verbally non-aggressivebehaviors based on sta� ratings (VNAB)1. Prediction from baseline status:

(a) Agitation(at time t) � function of (variables

(at baseline))

The beta coe�cients, corresponding Z statisticsand signi®cance levels for the model predicting

levels of verbally non-aggressive behaviors basedon baseline status are presented in Table 3. Depres-sed a�ect, cognitive impairment and poor qualityof relationship with sta� members and otherparticipants at day care emerged as the main pre-dictors of VNAB based on baseline information.The e�ects of pain and number of medical diag-noses were in the hypothesized direction, althoughonly pain was marginally signi®cant. Similarly,female gender was marginally signi®cant.

2. Concurrent predictors:

(b) Agitation(at time t) � function of (variables

(at time t))

In the concurrent analysis, depressed a�ect,cognitive impairment, pain and poor quality ofrelationship predicted VNAB (Table 3).

3,4. Prediction of change in verbally non-aggressive behaviors:

Models predicting changes in VNAB are shownin Table 4.

(c) Change in Agitation(at time t from base-

line) � function of (change in independentvariables)(at time t from baseline))

Table 3. GEE models for predicting levels of verbally non-aggressive behaviors basedon sta� generated assessments


Number of observations � 872

Beta coe�cient Z-statistic

Intercept 1.15 3.40


Number of active diagnoses 0.01 0.26

Physical pain 0.08 1.64c

Quality of relationship ÿ0.11 ÿ2.70**Cognitive functioninga 0.09 4.02**

Sex1 ÿ0.16 ÿ1.61d

Predictors Behavior predicted by concurrent assessmentb


Beta coe�cient Z-statistic

Intercept 0.88 2.31


Physical pain 0.10 1.66*

Quality of relationship ÿ0.08 ÿ1.71*Cognitive functioninga 0.10 3.75**

Sex1 ÿ0.10 ÿ0.91p5 0.05; **p5 0.01, one-tailed. aBCRSÐlarger score indicates more impairment. bNumber ofactive diagnoses was not included because not enough concurrent data were available and itwould therefore limit the N. cp � 0.0505. dp � 0.0537. 10 � female; 1 � male.



Increases in depressed a�ect and increasedcognitive impairment from baseline were the mainpredictors of changes in VNAB as rated by sta�members.

(d) Change in Agitation(at time t from previous

time) � function of (change in independentvariables(at time t from time tÿ1))

Increases in depressed a�ect and in pain as com-pared to the previous assessment were found to bethe main predictors of changes in VNAB.

Relatives' based data.Prevalence of verbally non-aggressive behaviors.

Based on relatives' rating, 37% of the partici-pants manifested verbally non-aggressive behaviorsat least once a week.

1,2. Predictors of levels of verbally non-aggressivebehaviors based on relatives' ratings(VNABR):

Depressed a�ect emerged as the only predictor ofVNABR based on information collected either atbaseline or at the concurrent assessment time(Z � 4.08 and 8.27, respectively, p5 0.01). Sinceprevious ®ndings show that depressed a�ect isclosely related to pain in this population (Werneret al., 1995; Parmelee, Katz and Lawton, 1991), wehypothesized that the large depressed a�ect e�ectmight be masking other e�ects. We thereforeexamined the same models without depresseda�ect. Physical pain emerged indeed as a predictor

of VNABR in the models without depressed a�ect(Z � 3.11 and 3.86, p5 0.01, respectively, formodels based on baseline and concurrent infor-mation).

3,4. Prediction of change in verbally non-aggressive behaviors:

Increased depressed a�ect and poor health(as re¯ected in greater number of medical diag-noses and more pain) were the main predictors ofchanges in VNABR since baseline (Table 5).

Based on information re¯ecting changes fromthe last assessment, VNABR were predicted byincreased depressed a�ect.

DISCUSSION

Non-aggressive agitated behaviors are very com-mon in elderly persons attending adult day carecenters. Over a quarter of the participants (27%)were rated by sta� members as manifesting PNABat least once a week and 19% as manifestingVNAB. As previous ®ndings have already shown(Cohen-Mans®eld et al., 1995), relatives reportedeven higher rates (43% for PNAB and 37% forVNAB).

As hypothesized, this longitudinal examinationof predictors of non-aggressive behaviors andtheir correlates showed that physically and verballynon-aggressive behaviors are predicted by di�erentfactors. For example, physically non-aggressive

Table 4. GEE models for predicting changes in levels of verbally non-aggressivebehaviors based on sta� generated assessments

Predictors Change in behavior predicted by change from baseline assessment


Beta coe�cient Z statistic

Intercept 0.05 0.62


Physical pain ÿ0.07 ÿ0.74Quality of relationship ÿ0.06 ÿ1.22Cognitive functioninga 0.11 2.22*

Predictors Change in behavior predicted by change from last assessment



Intercept 0.01 0.23



Quality of relationship ÿ0.06 ÿ1.40Cognitive functioninga 0.06 1.21

*p5 0.05; ** p5 0.01, one-tailed. aBCRSÐlarger score indicates more impairment.



behaviors were predicted mainly by cognitiveimpairment in all models based on sta� members'ratings of the BCRS as well as in the relatives'based model, wherein direct assessment via theMMSE was considered. Finally, increased cogni-tive impairment predicted change in PNAB in thesta�-based models of change from previous assess-ment and family-based models (utilizing theMMSE) of change in PNAB from baseline. Goodhealth also emerged as a signi®cant predictor in thebaseline and concurrent models based on sta�members' ratings, and similar trends were observedin relative-based models of predictions from base-line assessments. Taken together, these longitudi-nal results demonstrated that PNAB (many ofwhich involve movement, such as pacing andwandering) are more likely in people with arelatively good health status and are at the latestages of dementia, outcomes which are in generalaccord with cross-sectional ®ndings.

In contrast with cross-sectional analyses ofnursing home residents (Cohen-Mans®eld et al.,1992), this longitudinal analysis of participants ofadult day care centers showed that, in addition tocognitive impairment and a healthy state, depres-sed a�ect is a signi®cant predictor of PNAB. Inmodels based on sta� members' as well as onrelatives' ratings, increased depressed a�ect

emerged consistently as a signi®cant predictor ofPNAB. The meaning of these ®ndings is somewhatpuzzling, because:

1. Previous studies have not pointed to such arelationship.

2. Observational ®ndings showed pacing andwandering (which are some of the mostcommon PNAB) to occur under positiveenvironmental conditions (such as adequatetemperature, light and noise) and, unlike otheragitated behaviors, not to relate to environ-mental disturbances (Cohen-Mans®eld andWerner, 1995). We therefore concluded thatthese behaviors were frequently not a sign ofdiscomfort.

3. Depressed a�ect is related to pain in olderpopulations (eg Parmelee et al., 1991), whereasour ®ndings point to lack of pain in thosemanifesting PNAB. In order to explore thislast point, we reanalyzed the data in a modelwithout depressed a�ect as an independentvariable but with pain, assuming that ifdepressed a�ect masks pain, it would emergeas a signi®cant predictor when depressed a�ectwas not entered. In these four analyses ( forrelatives and sta� members, prediction frombaseline and concurrent prediction), pain did

Table 5. GEE models for predicting changes in levels of verbally non-aggressive behaviorsbased on family generated assessments

Predictors Change in behavior predicted by change from baseline assessment



Intercept ÿ0.03 ÿ0.43Depressed a�ect 0.22 2.40**

Number of active diagnoses 0.18 2.28*


Frequency of social contacts 0.01 0.41

Cognitive functioninga 0.02 1.04

Predictors Change in behavior predicted by change from last assessment



Intercept ÿ0.06 ÿ1.88*Depressed a�ect 0.21 2.90**

Physical pain ÿ0.001 ÿ0.01Frequency of social contacts ÿ0.0003 ÿ0.01Cognitive functioninga 0.01 0.39

*p5 0.05; ** p5 0.01, one-tailed. aMMSEÐlower score indicates more impairment. bNumber ofactive diagnoses was not included because not enough concurrent data were available and it wouldtherefore limit the N.



not emerge as a predictor of PNAB. Whileadditional research is obviously needed toclarify these consistent ®ndings, we mightspeculate some preliminary explanations:(a) It is possible that a subset of the pacers

su�er from depressed a�ect. Based onclinical observation, it is possible thatsome pacers who are deeply upset or de-pressed are searching for home or for aloved oneÐat times for their parents or forothers who are not alive.

(b) The consequences of pacing could be thecause of depressed mood, ie given that thehome and the adult day care center are notdesigned to accommodate pacing and otherphysical behaviors, such as inappropriatehandling of things, the behaviors may betreated by negative consequences, suchas being stopped from going out or beingreproached for moving things around.These consequences may result in depres-sed mood of the older person.

In contrast to physically non-aggressive beha-viors, the main predictor of verbally non-aggressivebehaviors was depressed a�ect. It was the strongestpredictor in all the models of VNAB based on sta�and relatives' ratings. Indeed, in models based onrelatives' ratings, increased depressed a�ect was theonly signi®cant predictor. Some of this depresseda�ect seems to be explained by physical pain.Because previous studies (Parmelee et al., 1991;Cohen-Mans®eld and Marx, 1993) showed thatdepressed a�ect and pain are highly related in theelderly population, we explored a predictive modelfor relatives' ratings excluding depressed a�ect andfound that pain emerged as a signi®cant predictorof VNAB. For sta�-based models, pain emerged asa signi®cant predictor even when depressed a�ectwas included in the model. Consequently, VNABis perceived to be, at least in part, a verbal or vocalmanifestation of (or perhaps an attempt to com-municate) physical pain and depressed a�ect, bothof which have direct implications for treatment.

Cognitive impairment was also found to be apredictor of VNAB in all sta�-based models(utilizing the BCRS), but not in relatives' basedmodels (using the MMSE). These ®ndings arecongruent with the suggestion that verbal agitationoccurs in middle stages, rather than late stages ofdementia, and that the relationship with cognitiveimpairment is not very strong (Cohen-Mans®eld,1995).

A poor quality of relationship with sta� andother participants at the senior day care center wasalso a predictor of VNAB based on sta� ratings,similar to previous ®ndings based on sta� ratings ina nursing home population (Cohen-Mans®eld,1992). The study did not contain a similar assess-ment for relatives.

By assessing relatives and sta� members inde-pendently, this research was able to perform somevalidation of ®ndings within the same study. Thismultirater, multimethod approach and the exam-ination of both predictions from baseline andconcurrent models strengthens con®dence in the®ndings when a convergence occurs. Indeed, therelationship between PNAB and cognitive impair-ment and good health, as well as the relationshipbetween VNAB and depressed a�ect and pain,emerged as robust through multiple sources andmultiple analyses. The meaning of other ®ndings,such as the relationship between verbal behaviorand cognitive impairment, is less clear. On the onehand, they are corroborated by other cross-sectional research, yet their signi®cance as pre-dictors over time requires further clari®cation.Furthermore, the impact of the di�erence inassessment methods on the di�erent ®ndingsrequires further investigation.

From a methodological point of view, we foundthe models describing change in agitated behavioras predicted by change in independent variablesless useful than the other models. This is probablydue to two reasons: (1) change variables have largervariances; and (2) the N was smaller in analysesdescribing change. Both of these point to the needfor longitudinal studies with large samples forclarifying predictors for changes in agitation overtime.

Non-aggressive behaviors are complex pheno-mena. A number of factors contribute simultan-eously to their manifestation. Attempts to decrease,prevent or accommodate the manifestation of non-aggressive behaviors might therefore require amultidimensional approach in order to be e�ective.In particular, the domains of physical health andpain, cognitive functioning and depressed a�ectshould be considered.

ACKNOWLEDGEMENTS

This research was supported by grant #AG08675from the National Institute on Aging.



The contributions of the sta� and participants ofadult day care centers in Montgomery County aregreatly appreciated.

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