Archives of Gerontology and Geriatrics 53 (2011) 146–151

The role of physical and psychological variables in predicting the outcome ofhospitalization in very old adults

Christina Bryant a,*, Henry Jackson a, David Ames b

a Psychological Sciences, University of Melbourne, VIC 3010, Australiab National Ageing Research Institute, PO Box 31, Parkville, VIC 3052, Australia

A R T I C L E I N F O

Article history:

Received 30 July 2010

Received in revised form 17 November 2010

Accepted 19 November 2010

Available online 28 December 2010

Keywords:

Functional outcomes

Predictors in older adults

Depression

Anxiety

Psychological factors

Oldest old

A B S T R A C T

The aim of this study was to examine predictors of functional outcome in hospitalized geriatric patients

with a focus on psychological variables, as these have been somewhat neglected in this population. A

prospective study was conducted in aged-care wards in Melbourne, Australia. Consecutively admitted

patients (n = 100, mean age 82 years) completed measures of health status, anxiety, depression, self-

efficacy, personality and coping. Two months later data were collected with respect to three outcomes,

namely overall functioning, ability to carry out activities of daily living as measured by the Barthel Index

(BI), and quality of life (QoL), as measured by the assessment of quality of life (AQoL) instrument.

Syndromal depression was highly prevalent (28%) and syndromal anxiety was less common (5%

prevalence), but neither was predictive of functional outcome. The strongest predictor of outcome was

physical health status on admission to hospital. The results suggest that in physically unwell, very old

populations, physical health factors may be stronger predictors of functioning than psychological

variables. The findings also highlight some difficulties in the use of psychological measures in old–old

populations, and the need for more research that recognizes the oldest old as a distinct group.

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1. Introduction

Older adults are heavy users of health services (HospitalEpisode Statistics); they have a higher rate of hospital admissionthan younger people and they stay there longer (AustralianInstitute of Health and Welfare, 1996). Given the high usage ofhospital services by older adults, and the rapid growth of the oldestold sector of the population, it is important to examine the factorsthat may contribute to improving the outcome of episodes of care,and to consider how best to assess the outcome of hospitalization.Campbell et al. (2004) reported a systematic literature review ofstudies that had measured outcome of hospital admission in oldermedical patients, and found that the most common indicators ofoutcome used were length of stay, mortality, discharge destinationand readmission; only one had measured functional status atdischarge. A few studies, for example those of Bradley et al. (2000)and Demers et al. (2004), have asked people who had recently beendischarged from hospital-based rehabilitation programmes whattheir goals had been. They found that the most frequentlymentioned goals were to improve mobility, followed by themaintenance of social relationships, the reduction of pain andincreased ability to perform abilities of everyday living.

* Corresponding author. Tel.: +61 3 8345 3906; fax: +61 3 8345 2076.

E-mail address: cbryant@unimelb.edu.au (C. Bryant).

0167-4943/$ – see front matter � 2010 Elsevier Ireland Ltd. All rights reserved.

doi:10.1016/j.archger.2010.11.023

With respect to the predictors of outcome, authors havenoted the importance of a range of clinical parameters, includingage, cognitive status and medical diagnosis (McCusker et al.,2002). A recent study set in a geriatric evaluation andmanagement unit in Taiwan (Chen et al., 2010) examined thetimed up and go test (TUG), ability to carry out activities of dailyliving, nutritional status and a test of dynamic balance aspredictors of functional improvement. With the exception ofdepression, psychological variables, however, have been largelyneglected in the literature on outcomes in older hospitalizedpatients.

This is surprising, given the growing literature that hasexamined the role of psychological factors in the treatment andoutcome of physical illnesses more broadly (Allgulander andLavori, 1993; Koenig, 1998; Kennedy, 2001). Psychological factorscan be of several kinds, ranging from enduring personality traits,such as neuroticism, to state phenomena that may be time-limited,such as symptoms of depression. A number of studies of olderadults have demonstrated that elevated levels of depression and, toa lesser extent, anxiety, are associated with increased rates ofmortality (Allgulander and Lavori, 1993), morbidity (Koenig, 1998)and disability (Lenze et al., 2001). Others have suggested thatpsychological resources, such as coping style and self-efficacybeliefs are related to the outcome of physical illness and themanagement of chronic diseases (Grembowski et al., 1993; Kraaijet al., 2002; Penrod et al., 2002).

C. Bryant et al. / Archives of Gerontology and Geriatrics 53 (2011) 146–151 147

Most of these studies, however, are carried out usingpopulations of community-dwelling older people, or relativelyyoung old in-patient populations, an issue that has beenhighlighted by a number of authors, including Baltes and Meyer(1999). The literature on older people as in-patients in generalhospitals has established the high prevalence of depression, and, toa lesser extent, of anxiety, in these settings (Bryant et al., 2009).Yet, few studies have examined whether psychological variables,including constructs such as self-efficacy, contribute to physicaland psychosocial outcomes in very old, frail populations. Indeed,some authors have suggested that that the role of psychologicalvariables is attenuated in those over the age of 75 (Wilms et al.,2000; Fleming and Brayne, 2005; Rapp et al., 2008).

The aim of the current study, therefore, was to address a gap inthe literature by examining both physical and psychologicalfactors as predictors of functional and psychosocial outcomes in asample of frail older adults, who were hospitalized for thetreatment of physical illness. The primary hypothesis was thathigher scores on measures of anxiety and depression would predictpoorer psychosocial outcomes of hospitalization in patients inaged care (geriatric evaluation and management) wards. The studyalso hypothesized that lower neuroticism, the use of task-oriented,rather than emotion-focused coping style, and higher self-efficacywould predict better outcomes.

The outcomes examined in the current study go some waytowards encompassing commonly used objective measures, suchas mortality and discharge destination, while also measuring theability to carry out everyday activities using the well-established BI(Mahoney and Barthel, 1965), and quality of life. Although the term‘quality of life’ was infrequently mentioned by the samples ineither Demers et al. (2004) study, or the study by Bradley et al.(2000), the AQoL (Hawthorne et al., 1999) measures five aspects ofhealth-related quality of life, that include pain, psychological well-being, the ability to maintain relationships with friends and family,and mobility, all ranked as important outcomes in the studies byBradley et al. (2000) as well as Demers et al. (2004).

2. Subjects and methods

2.1. Participants and setting

The study was carried out in aged care wards of a geriatric andrehabilitation hospital situated in Melbourne, Australia. To beeligible, participants had to be over the age of 65, have beenadmitted in the previous 5 days, be able to give informed consentand be able to speak sufficient English to be able to take part in averbally based assessment process. The initial screening forcompetence to give consent was by means of the abbreviatedmental test score (Hodkinson, 1972), using a cut-off of 6/10. Thestudy received ethics approval from the Human Ethics ResearchCommittee of the Royal Melbourne Hospital.

2.2. Measures

Demographic information was obtained from the participants’medical records. This included age, gender, marital status, date ofadmission to hospital, date of discharge and place to which theparticipant was discharged. The discharge destinations weredefined as return to home, placement in high level or low levelresidential care homes, admission to an acute hospital, admissionto a further rehabilitation facility, readmission to the samehospital, and deceased.

Physical illness information was collected as follows: followingthe suggestions of Burvill et al. (1990) for quantifying physicalillness in psychiatric research, the following details of theparticipants’ health were recorded: main diagnosis, severity of

the condition, whether the condition was chronic or acute (i.e., ofless than three months duration), number of other acute andchronic conditions with which the patient had been diagnosed andnumber of medications prescribed. Severity was assessed in thefollowing categories: mild (symptoms mild or absent; notreatment required or well controlled with treatment); moderate(moderately severe symptoms; the illness is not under full controlbut is not life threatening); and severe (severe symptoms thatrequire substantial medical treatment; illness expected to reducelife expectancy). Most of this information was derived from theparticipant’s hospital record and in case of any ambiguity, the warddoctor’s rating of severity was sought.

The BI was used as a measure of the ability to carry outactivities of daily living (ADLs) (Mahoney and Barthel, 1965). Itcomprises measures of 10 activities of daily living, namely, feeding,transferring, personal hygiene, toileting (bowels and bladder),walking, negotiating stairs, dressing and bathing. More points arescored for being able to carry out these activities independently.It is scored out of 100, with higher scores indicating betterfunctioning.

The geriatric mental state (GMS)/AGECAT was used to assesspsychiatric disorder. This is a well validated semi-structuredinstrument developed for the purpose of standardized psychiatricassessment of older people (Copeland et al., 1976). The informationobtained in the GMS interview was analysed with the AGECATcomputer programme (Copeland et al., 1986). This yieldsstandardized psychiatric diagnoses with specified confidencelevels.

The NEO—Personality Inventory (Costa and McCrae, 1992) wasused to measure personality. The full NEO PI-R inventory contains240 items, which would place a considerable burden on the studypopulation. In order to surmount this difficulty, and because of itsparticular relevance to health and physical functioning, theneuroticism scale only (48 items) was used in the currentinvestigation.

The assessment of quality of life (AQoL) instrument (Hawthorneet al., 1999) is a brief 15-item scale with good psychometricproperties covering five dimensions of health related quality of life,namely illness, independent living, social relationships, physicalsenses and psychological well being. Data are analysed usingsyntax created for SPSS, and the scores generated can range fromvalues below zero, to a maximum of 1, indicating high quality oflife.

The Coping Inventory for Stressful Situations (Endler andParker, 1999) is a 48-item self-report measure consisting of threemain scales which assess task, emotion and avoidance-basedcoping.

The generalized self-efficacy scale (Schwarzer, 1992) is a 10-itemscale which has been translated into a number of languages from anoriginal German version. Respondents are required to indicate theextent to which statements apply to them on a four-point scale from‘‘Not at all true’’ to ‘‘Exactly true’’. The statements include items suchas, ‘‘I can always manage to solve difficult problems if I try hardenough’’ and, ‘‘If I am in a bind, I can usually think of something todo’’. A total score is derived by summing the scores for each item,yielding a maximum score of 40. In the standardization sample, themean score was 29.28 (Schwarzer, 1992).

The outcome of hospitalization was measured by threeoutcome variables selected to encompass physical, functionaland subjective measures of change from Time 1 to Time 2. The firstoutcome measure indicated whether the participant had main-tained or improved their functioning (good outcome) or whethertheir functioning had declined (poor outcome). This binary good/poor outcome variable was created in a two-stage process. First,the setting from where the patient was admitted was cross-tabulated with their discharge destination. Then, a judgement was

Table 1Health characteristics of the sample.

Variables n Mean � S.D. Median

Age 100 82.1 � 8.02 83.00

Number of acute illnesses 100 1.9 � 1.23 2.00

Number of chronic illnesses 100 4.7 � 2.21 4.50

Number of medications on admission 97 10.3 � 4.34 10.00

Abbreviated mental test score 100 8.1 � 1.31 8.00

BI on admission 98 48.4 � 22.37 49.00

BI on discharge 78 64.7 � 32.24 79.00

Table 2Diagnosis of participants at time of admission (n = 100).

Medical condition Percentage

Neurological 15

Falls 26

Fracture 14

Infection 8

Cancer 5

Not coping 8

Respiratory 8

Cardiovascular 8

Endocrine 3

Renal 2

Arthritis 3

C. Bryant et al. / Archives of Gerontology and Geriatrics 53 (2011) 146–151148

made by the first author about whether the change from one settingto another between Time 1 and Time 2 represented improvement/stability, or worsening. For example, discharge to a nursing homeafter admission from an acute hospital was considered a worseningof functioning while return home after admission from an acutehospital was considered an improvement of functioning. Thismethod of assessing functioning has been used by others, includingMoss et al. (1995). By these criteria, 49% of participants had declined,and 51% had maintained or improved their functioning.

The second outcome was the ability to carry out activities ofdaily living or functional capacity as measured by the total score onthe BI. The third outcome was quality of life, as measured by scoreson the AQoL.

2.3. Procedure

Eligible patients were identified by nursing staff and thenapproached personally by the principal researcher (CB), whointroduced herself and the nature of the project to the patients. Theinitial data collection included all the measures noted above. Twomonths after the initial interview, participants were contactedagain for the second interview. This time interval was chosen toenable a range of outcomes to manifest themselves and to besomewhat longer than the average length of stay for these wards,which was known to be 42 days. The instruments used at Time 2were the GMS, and the AQoL. The participant’s BI score wasrecorded again at this time.

2.4. Data analysis

Data were entered into a database using the Statistical Packagefor the Social Sciences, SPSS 12.01. Using the recommendations ofCohen (1992), it was estimated that 100 participants would givethe study sufficient power (0.8) to detect a medium to large effectsize at a probability level of p = 0.05. Data were screened to ensurethat assumptions of normality had not been violated prior toanalysis, and a square root transformation applied to the scores onthe BI and the AQoL, as these were significantly skewed. Logisticregression was used to examine the relationship of the predictorvariables to the binary improvement/decline variable of outcome.Hierarchical regression analyses with Barthel scores and AQoLscores as the dependent variables were performed to examine thecontribution of the predictor variables.

3. Results

3.1. Demographic characteristics and physical health of the sample

Of 117 eligible participants, 100 were interviewed within oneweek of admission to hospital (mean 4.5 days, median 5 days). Thisis referred to as Time 1. Reasons for not taking part in the studyincluded being too unwell and not being interested. The mean timeto follow-up was 62.8 days and the median 63 days. In betweenTime 1 and Time 2, 9 participants had died, 5 were too unwell to beinterviewed, 4 could not be traced and 2 declined to be interviewedagain. There was no significant difference between completers andnon-completers in terms of gender, age or severity of illness at timeof admission. Although 83.7% of men were completers, ascompared to 77.2% of women, this was not statistically significant(x2 = 0.46, df = 1, ns). The mean age of those lost to follow-up was83.4 years, compared to 81.8 years for those who were available atTime 2 (t = 0.77, df = 98, p = 0.44, ns). The number of medicationsprescribed was taken as a crude measure of physical illness. Themean number of medications being taken by completers was 9.8,as compared to 11.8 in non-completers, but this failed to reachstatistical significance (t = 1.88, df = 95, p = 0.06).

The average age of the participants was 82.1 years (range 65–100) and females outnumbered males (53% female, 47% male). Interms of marital status, 42% were widowed, 31% were married,21% were single and 6% were divorced or separated. Seventy-twopercent had been admitted from an acute hospital, 18% fromhome, 7% from low level residential care (care homes) and theremainder (3%) from other sources, such as rehabilitation wards.Two months later, while 36% had been able to return to their ownhome, 26% remained either in the same hospital or had alreadybeen readmitted, indicating the high level of morbidity in thispopulation. Seven percent of the participants had been dischargedto further rehabilitation hospitals and 4% had been admitted to anacute hospital. Nearly one in five of the participants wasdischarged to residential care homes—13% to low level care and6% to high level care. Also notable is the 9% mortality in thesample.

The majority of the sample was admitted for treatment of achronic condition of at least moderate severity. Table 1 reportsfurther details of the health characteristics of the sample,highlighting the high morbidity and low level of functioning inthis group. Table 2 shows the main diagnosis for each patientrecorded on admission by the ward doctor. This demonstrates thediversity of conditions that lead to admission on these wards. Twocategories dominate, however, namely, neurological conditions,fractures and falls-related admissions, the latter constituting 41%of the admissions.

Quality of life was poor in the sample, and showed little changefrom Time 1 to Time 2. The mean score on the AQoL at Time 1 was0.20 � 0.20 (median = 0.14), while at Time 2, the score was0.22 � 0.24 (median = 0.13).

3.2. Psychological characteristics of the sample

Symptoms of anxiety and depression, as measured by theGeriatric Mental Status Schedule, were both very common in thissample, such that 60% of the sample reported at least twosymptoms of anxiety. So too were syndromal cases of depression,with a prevalence of 41%, and of anxiety, with a prevalence of 8%.These data have been reported in greater detail elsewhere (Bryant

Table 3Psychiatric symptoms as measured by the GMS AGECAT at Time 1 (n = 100) and

Time 2 (n = 78).

Sub-cases, % Syndrome

cases, %

Time 1 Time 2 Time 1 Time 2

Organic impairment 23 13 15 15

Schizophrenia/paranoid psychosis 10 5 0 1

Mania 2 0 0 0

Depression 25* 12* 0 0

Neurotic 0 0 39 29

Psychotic 0 0 9 8

Obsessional neurosis 4 0 0 0

Hypochondriacal neurosis 1 0 2 0

Phobic neurosis 13 6 13 5

Anxiety neurosis 60 54 16 6

* For subcase levels of depression the GMS AGECAT does not discriminate

between neurotic and psychotic subtypes.

C. Bryant et al. / Archives of Gerontology and Geriatrics 53 (2011) 146–151 149

et al., 2009). As reported in Table 3, there was a reduction inanxiety and depression between Time 1 and Time 2, but depressionat a syndromal level remained highly prevalent at 28%. Syndromalanxiety was less prevalent at 5%.

Table 4 reports the scores for the other psychological variableshypothesised to contribute to the outcome of hospitalization in thesample, namely neuroticism, self-efficacy and coping style. Fromthese, it can be seen that this sample scored somewhat lower thanthe norms provided by the authors of the measures on neuroticism,and emotion-focused coping, but the scores were comparable withnorms for the general population for self-efficacy and task-oriented coping.

3.3. Outcome of hospitalization

Neither the number of acute, nor the number of chronicillnesses significantly predicted the outcome of hospitalization(Wald statistic = 1.10, odds ratio = OR = 0.83, 0.58–1.18 df 1,p = 0.29 and Wald statistic = 2.16, OR = 0.89, 95% confidenceinterval = CI = 0.95–1.48, df = 1, p = 0.14, respectively) when theeffects of the other variables were taken into account. The numberof medications prescribed at the time of admission, however, wasstatistically significant as a predictor of outcome, and emerged asthe strongest predictor of the outcome of hospitalization (Waldstatistic = 4.00, OR = 1.40, 95%CI = 0.79–1.00, df = 1, p = 0.05).

It was hypothesized that the presence of depression and/oranxiety at Time 1 would contribute to a poorer outcome ofhospitalization. Logistic regression analyses entering depressionscores as the predictor variable, with the binary good/pooroutcome variable as the dependent variable showed that neitherdepression (Wald statistic = 0.07, OR = 0.90, 95%CI = 0.40–2.54,df = 1, p = 0.80), nor anxiety (Wald statistic = 0.50, OR = 0.73,95%CI = 0.13–2.67, df = 1, p = 0.48) at Time 1 was a statisticallysignificant predictor of hospital outcome. The omnibus test of themodel entering depression and anxiety was statistically notsignificant (x2 = 0.71, p = 0.70).

Neuroticism, coping style and self-efficacy as psychologicalvariables reflecting more enduring states, were hypothesized to

Table 4Psychological predictor variables at Time 1. Means and variation from norms as

measured by Z-test.

Variable n* Mean � S.D. Median Z-test p

Neuroticism 96 47.50 � 13.99 44.00 2.24 0.02

Self-efficacy 100 28.38 � 6.62 30.00 0.72 0.68

Task-oriented coping 98 49.69 � 9.27 49.00 �0.30 0.76

Emotion-focused coping 98 43.29 � 8.71 45.00 �6.51 0.001

* Data incomplete for some participants.

predict outcome in the sample. The bivariate association ofneuroticism with hospital outcome was not significant (rho = 0.03,p = 0.75, ns). The hypothesis that the use of task-oriented copingwould predict better outcome at Time 2 than emotion-focusedcoping was tested using the Hotelling–Williams test (Hotelling,1940). This test is specifically designed to test whether two samplecorrelations are significantly different when one variable iscommon to both correlations. No significant difference was foundin correlations of task oriented coping (rho = 0.05) and emotionfocused coping (rho = 0.01) with hospitalization outcome, Hotell-ing–Williams t (93) = 0.26, p = 0.80, ns. The final psychologicalvariable that was hypothesised to contribute to the outcomesobserved was self-efficacy, but this was not significantly correlatedwith hospitalization outcome (rho = 0.13), AQoL at Time 2(rho = 0.19), or the BI at Time 2 (rho = 0.08). In light of the non-significant correlations at the bivariate level, regression analyseswere not pursued.

3.4. Ability to carry out activities of daily living

Anxiety and depression at Time 1 failed to predict functionalability. The BI at Time 2 (with square root transformation) wasentered into a linear regression analysis as the dependent variable,and the AGECAT depression and anxiety variables were enteredsimultaneously as the independent variables. Depression andanxiety only explained 1.1% of the variance in the BI at Time 2(R2 = 0.01, p = 0.60). In contrast, the number of acute illnesses atTime 1 was strongly predictive of functional outcome at Time 2(b = 0.27, p = 0.01), but this did not hold for the number of chronicillnesses (b = �0.09, p = 0.42). Neither neuroticism, nor self-efficacy beliefs were correlated with BI scores at Time 2 (Spear-man’s rho = 0.01 and 0.17, respectively), and regression analyseswere therefore not pursued. Furthermore, no significant differencewas found in correlations of task-oriented coping (rho = 0.05) andemotion-focused coping (rho = 0.01) with BI score, Hotelling–Williams t (93) = 0.98, p = 0.33, ns.

3.5. QoL

Anxiety and depression at Time 1 failed to predict the thirdoutcome measure used in this study, namely quality of life andaccounted for only 2.3% of the variance on AQoL scores (R2 = 0.02;p = 0.42). Of the relationships between psychological variables andquality of life, only one was significant: lower levels of neuroticismpredicted better quality of life at Time 2, accounting for 15% of thevariance in quality of life (R2 = 0.15; p = 0.05).

4. Discussion

The current study examined the role of physical andpsychological measures in predicting three separate, but related,measures of the outcome of hospitalization in older adults. Thesemeasures of outcome, although not comprehensive, were chosento capture important aspects of functioning in older adults.Consistent with previous findings, depression and anxiety werehighly prevalent in this sample, but showed a decrease inprevalence from the time of admission to hospital to the seconddata collection point, two months later. Contrary to expectations,however, anxiety and depression did not predict poorer outcomesin this sample, and the psychological variables, carefully selectedfor their supposed relevance to health, contributed little to theoutcomes under consideration. It appeared that the effect ofphysical health, as measured by indicators such as the number ofmedications taken by participants, and the number of acuteillnesses, overwhelmed the effect of psychological factors in thispopulation.

C. Bryant et al. / Archives of Gerontology and Geriatrics 53 (2011) 146–151150

There are a number of possible explanations for these findings.Firstly, it is possible that, although there were very high rates ofanxiety and depression at the time of admission to hospital, thesewere not predictive of psychosocial outcome because theyrepresent an acute psychological response to being admitted tohospital rather than an enduring mental state. One wouldtherefore expect a decrease in symptoms as adaptation to newcircumstances takes place, as was observed in the current sample.This is consistent with a view that the symptoms observed at thattime represent distress, rather than depression or frank psychiatricdisturbance, as suggested by Lloyd and Cawley (1983), and withthe view that mental and physical health tend to change in tandemwith one another, rather than one being a clear predictor of theother (Katz, 1996; Koenig and George, 1998; Bruce, 2001).

An important feature of this study was that the participants inthis study were very old, with a mean age of 82, and had high levels offunctional impairment, as demonstrated by their low scores on theBI at the time of admission (mean 48.4). These scores are even lowerthan those reported in a recent study of frail patients in a geriatricevaluation and management unit in Taiwan, who had a mean BIscore 57.7 at the time of admission to hospital (Chen et al., 2010), andindicate the severe functional limitations of these patients. It seemsplausible to suggest that, faced with high levels of impairment andmultiple comorbidities, factors such as self-efficacy or task-focussedcoping, may not be enough to overcome this degree of physicaladversity. Thus, it may be that in this population, physical healthvariables are paramount. This is consistent with studies showingthat when older adults are asked about the factors they considermost important for quality of life, good health is frequently cited asthe most important (Bowling and Dieppe, 2005). Moreover, it alsoechoes the findings of studies reporting that depression does notpredict mortality in the oldest old (Rapp et al., 2008), and thatphysical health factors outweigh depression as a risk for functionalimpairment in the very old (Fleming and Brayne, 2005).

We would suggest that caution needs to be exercised in the use ofcurrent measures of psychological variables for research with oldand frail adults. Although normed on populations up to the age of 70,neither the NEO, nor the CISS and GSES have been widely used instudies using the very old as participants, and it may be that thesemeasures did not adequately capture the constructs of self-efficacyand coping as they are experienced by this sample. Measures ofcoping have been criticized on a number of grounds, including theweak relationship between what people say they do, and what theyactually do (Aldwin and Revenson, 1987). This may have beenanother factor in the weak relationships between coping andoutcomes observed in the study. Moreover, it is important to notethat these measures were not designed as predictors of healthoutcomes as such in older adults, though they have been very widelyused in studies of psychological factors in the context of physicalhealth problems (Grembowski et al., 1993; Kraaij et al., 2002).

Finally, these results need to be understood in the light of thestudy’s limitations. Although the study had sufficient power todetect medium-sized to large effects, the relatively small samplesize reduced our power to detect small effects, and it is possiblethat the contribution of the psychological variables could havebeen detected in a larger sample. Secondly, although the range ofoutcome measures was chosen to encompass a range of function-ing considered as important by older people themselves (Demerset al., 2004), and by experts (Campbell et al., 2004), they were notexhaustive, and it is possible that more significant relationshipswould have been found using different outcome variables.

5. Conclusion

These findings have implications both for health policy and forresearch. If physical health is crucial to maintaining functioning in

old age, policies are needed that encourage health maintainingbehaviors in younger adults, so as to promote the compression ofmorbidity (Kaplan, 1991). The authors do not wish to imply thatpsychological constructs, such as coping and self-efficacy, are notimportant in older adults, rather that further study may be requiredto develop measures that are appropriate for this population. Thiswill ensure that future research can elucidate the relationshipsbetween psychological variables and physical health outcomes.

Therefore, research needs to focus greater attention on theoldest old, and those with high levels of functional impairment, asthis group has been neglected. This is particularly important giventhe rapid growth in the numbers of the oldest old and is in accordwith the view expressed some years ago by Baltes and Smith(2003) that researchers need to address the needs of the oldest oldas a more clearly differentiated group.

Conflict of interest statement

None.

Acknowledgement

C. Bryant received scholarship support through NHMRC grantnumber 310655.

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