The Relationship Between Level of Disability

Rehabilitation Psychology 2002, Vol. 47, No. 2, 165–183

Copyright 2002 by the Educational Publishing Foundation 0090-5550/02/$5.00 DOI: 10.1037//0090-5550.47.2.165

The Relationship Between Level of Disability, Psychological Problems, Social Activity, and Social Networks Arne H. Eide SINTEF Unimed Espen Røysamb National Institute of Public Health

ABSTRACT. Objective: To test a theoretical model on the relationship between level of disability, psychological problems, social activity, and social networks. Study Design: A repeated cross-sectional study included in 2 representative studies in the general population in Norway. Structural equation modeling was applied to test different models. Results: Activity limitations contribute to the prediction of psychological problems and level of social activity over time, whereas the reverse effects were not demonstrated. However, cross-sectional associations between psychosocial variables and activity limitations were found. Conclusions: The study confirms that activity limitations predict level of psychosocial problems. Although the reverse longitudinal effect from psychosocial problems on activity limitations was not demonstrated, short-term effects cannot be ruled out with the current study design.

Despite the growing interest and body of knowledge on the role of psychosocial factors in relation to disability, much research still remains to fully elucidate this relationship (see, e.g., Bloom, 1990; Dunn, 2000). In this article, we present analyses from a longitudinal study on the relationship between psychosocial factors and level of difficulties with performing and participating in activities of daily living, or activity limitations. The data set comprises 1,391 respondents who were interviewed twice in representative surveys on living conditions conducted in Norway in 1987 and in 1995. Arne H. Eide, SINTEF Unimed, Oslo, Norway; Espen Røysamb, National Institute of Public Health, Oslo, Norway. Financial support for this research was provided by the Research Council of Norway. Mitch E. Loeb has given valuable support on language and statistics. Correspondence concerning this article should be addressed to Arne H. Eide, PhD, SINTEF Unimed, P.O. Box 124, Blindern, N-0314 Oslo, Norway. E-mail: arne.h.eide@ unimed.sintef.no 165

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Studies on the relationship between psychosocial factors and disability have generated considerable theoretical and empirical material (Antonak & Livneh, 1995). Psychological and social characteristics of the individual and his or her life situation influence the result of both social and psychological adaptation to disability. In most cases, the wide range of psychological and social factors that may have relevance for the disablement process are characteristics that were already present and independent of the disability. Bearing in mind the variation in psychological and social characteristics among people with disabilities, it is highly relevant to study how this variation may lead to differences in individuals’ assessment of their functional problems and the impact of these problems on their daily life. Previous research has found that the risk for clinically significant emotional distress is two to four times higher among persons with chronic diseases or disabilities as compared with nondisabled persons (Turner & Beiser, 1990). MacNeill and Lichtenberg (1998) reported that psychosocial problems may foreshadow loss of independence among older live-alone rehabilitation patients. Krause and Rohe (1998) studied the relationship between personality and multiple components of life adjustment after spinal cord injury and found that warmth, positive emotions, actions, and values were correlated with superior outcomes. In a study of the relationship among psychological factors, rehabilitation adherence, and short-term rehabilitation outcome after knee surgery following sports injury, Brewer, Van Raalte, Cornelius, and Petitpas (2000) demonstrated that athletic identity and lower psychological distress were associated with a more favorable outcome. Gatchel, Polatin, and Mayer (1995) found what they referred to as “a robust psychosocial disability factor” that was associated with the likelihood of developing low back pain disability problems. From a study of polio survivors, Tate et al. (1994) reported that positive self-acceptance, information seeking and sharing about the disability, and social activism constituted three factors that were important in the prediction of coping with the disability. Huebner, Thomas, and Berven (1996) examined attachment and interpersonal characteristics as potential risk or protective factors and demonstrated a combination of protective factors and latent resources among the college students in their sample that may have enhanced their resilience despite a disability. According to Chwalisz and Vaux (2000), research provides some evidence that social support is associated with coping, depression, adjustment, self-esteem, and so forth for individuals with disabling conditions. In most of the research on social support and disability, social support has been included as a predictor for specific outcomes, very often conceptualizing support in terms of the buffer model (see, e.g., Vaux, 1988). Psychological characteristics, health beliefs, as well as social influence are all identified as modifiers of the impact of disease and disability on an individual (Simmonds, Kumar, & Lechelt, 1996). The previous examples illustrate the diversity of relevant studies. Although not always explicit, the theoretical constructs underlying the various studies and analyses are also found in abundance.

Level of Disability and Psychosocial Functioning

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What is often missing, however, is a simple but general model that may guide the analyses of the relationship between psychological and social variables and disability and that recognizes the mutual influence in the relationships.

DISABILITY AND HEALTH Recent developments in the understanding and definition of disability [World Health Organization (WHO), 1997] have introduced a new conceptual scheme that underlines the importance of disability as a relative and relational phenomenon. In the revised International Classification of Impairments, Disabilities and Handicaps (ICIDH-2), the concepts of activities and participation have replaced the previous concepts of disability and handicap, reducing the influence of a medical paradigm for which ICIDH was heavily criticized (Pfeiffer, 1998). Such is the case that a person can have a health problem without being disabled, that is, if the interrelationship between the context and the individual does not affect the individual’s capability of participating and carrying out activities that could be expected of an individual in that particular context. Likewise, a person can experience difficulties in carrying out such activities without simultaneously having a medical condition. Having said this, disability and health are interrelated concepts— different elements in a broader understanding of the disablement process. Given a certain functional problem, an individual will face varying degrees of problems in performing and participating in a number of daily activities. Selfreports about such difficulties constitute the measurement of disability in many studies. This is also in line with the definition of disability applied in ICIDH-2 (WHO, 1997). According to Verbrugge and Jette (1994, p. 5), “the standard, and only economical, procedure (when measuring disability) is to interview individuals about difficulties (self-reports or proxy reports), with simple ordinal or interval scoring of degree-of-difficulty.” How individuals perceive these problems depends on social factors, psychological factors, as well as the functional problem itself. One could thus expect that individual variation in activity limitations is partly explained by variation in the level of psychological or social problems.

RESEARCH QUESTIONS The purpose of this study was to examine the interrelations between psychological problems, social activity, and social network on the one hand, and self-reported level of disability, or activity limitations, on the other. More specifically, we wanted to estimate both the unique and combined influence of the psychosocial variables on level of self-reported difficulties with performing and participating in daily life activities, cross-sectionally. Secondly, we wanted to test longitudinal relations between the psychosocial variables and activity

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limitations. We hypothesized that (a) activity limitations would contribute to variation in the psychosocial variables over time, and (b) the psychosocial variables would influence activity limitations over time.

METHOD Since 1980, Statistics Norway has carried out surveys on living conditions in the general population at regular intervals. These surveys have enabled the study of living conditions in the population, the distribution of living conditions among various population subgroups, the development of the living conditions and distributional aspects of this development. Psychological and social variables are also included in these surveys, and the respondents are explicitly asked to state whether they suffer from any disability or permanent health problem. Original Sample The surveys in 1987 and 1995 are based on stratified, random samples. In 1987, respondents were first drawn from individuals aged 16 to 79, followed by an additional sampling among individuals older than 80. In 1995, individuals 16 years or older were sampled. In both studies (1987 and 1995), the sample was drawn in two steps. In the first step, the population was stratified according to certain characteristics of their municipality (size of population and economic basis), yielding a total of 102 strata. Within each strata, a sampling area was drawn with a probability corresponding to the number of inhabitants in each area. Finally, individuals were drawn randomly within each of the 102 sampling areas. Persons living in institutions were exempted from the sample. A total of 5,865 respondents (gross sample, including the additional sample) were sampled in 1987, and 5,000 were sampled in 1995 (gross sample). Attrition rates for the two studies were 22% and 25%, respectively. Examination did, however, reveal only marginal differences between net and gross sample for gender composition, age, and place of residence. Study Sample The 1995 sample included 2,102 of the respondents who were sampled in 1987. However, because of attrition in both sampling years, the number actually interviewed in both 1987 and 1995 was 1,391 (the panel sample). Mean age in the panel sample in 1987 was 39.9, and proportion of male respondents was 47%. The sample is representative for the general population and comprises people with and without permanent health conditions. In the 1995 sample, a total of 35% (n ⫽ 222) reported a health problem of permanent character. The most common health problems were diseases of the musculoskeletal system (16%, n ⫽ 222), the circulatory system (6%, n ⫽ 76), the respiratory system (5%, n ⫽ 72), and the


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nervous system (5%, n ⫽ 66). Very few respondents reported psychic disorders (⬍1%, n ⫽ 3). Instruments for Data Collection The questionnaires used to survey level of living conditions in Norway fall within the Nordic tradition for such studies. A large number of questions are included on issues that constitute a broad definition of living conditions, level of education, employment, income, and health. The variables that are included in the analyses are all described in detail later in this article. Standardized interviews were carried out by professional interviewers at the respondents’ addresses. Measures Although largely comprising the same sections and questions, the questionnaires for the two surveys are not completely identical. Minor variations in wording may be found from one year to another. However, in the construction of variables for the analyses that are presented in this article, identical questions have been used. Age and gender are included as control variables in the model because it is known from other research that these may explain some of the variation in psychosocial variables. We did this to demonstrate that the findings in the theoretical model are not due to any confounding effects from these common predictors. The measures of psychological problems, social activity, and social network are relatively simple measures that could have been more refined. Self-Reported Activity Limitations Five different items measured various difficulties related to participation and daily life activities: “Do you, due to permanent health problems or disability, have difficulties with (a) moving around in or using your house (flat), (b) moving out of your house (flat) on your own, (c) participating in organizational life, (d) participating in other leisure activities?” The fifth question was “Do health problems or disability cause any limitations in your capacity to work?” All items had three response categories: No; Yes, some difficulties; and Yes, much difficulty (the first four items) and Not at all; To some extent; and To a large extent (the fifth item). Values are coded 1, 2, and 3, respectively. A scale intended to reflect self-reported activity limitations was constructed by adding the score on each item for each respondent. Much of the literature on disabilities deals with specific categories of functional problems. In this article, the data sample is composed of individuals with various medical conditions that may result in difficulties in participation and performing daily life activities. As this may represent a bias in the sample, the

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main categories of medical conditions were analyzed with respect to the activity limitation scale. It was shown that although there are differences in the activity limitations among various categories of health problems (varying between 9.1 and 10.8 on the Activity Limitations Index, dfs ⫽ 4 and 1168, p ⫽ .000), these differences are much smaller than the difference between those with a health problem and the general population (M ⫽ 6.58 in the general survey 1995). This difference would have been even greater if comparing between those with and those without a health problem. We have chosen to analyze the categories of different functional problems together as the focus of the analysis is the general relationship between psychosocial predictors and activity limitations. Psychological Problems The concept of “psychological problems” is broad and comprises a large number of possible components. There exists a wide variety of theoretically based instruments intended to tap various aspects of the human psyche. Anxiety and depression are among the most common psychological problems that have been discussed and studied in relation with disability (Simmonds et al., 1996). The measurement we applied is a combination of three questions related to anxiety and depression. The level of psychological problems was operationalized as a measure of the mean of the following three items: “During the past 6 months, have you often (3), sometimes (2) or never (1) suffered from (a) severe heartbeat without physical strain, (b) nervousness, anxiety, or restlessness, (c) depression or low moods to the extent that you could not take it any longer? Social Activity Social activity is defined as an individual’s tendency to take part in activities that are common in the context and that imply moving around among or together with other people. This is intended to reflect a tendency toward social isolation that has often been associated with disabilities in the literature (see, e.g., Simmonds et al., 1996). Participation in social activities has been identified by Tate et. al. (1994) as an important coping factor (with disability). Social withdrawal and interpersonal distancing are, according to Antonak and Livneh (1995), among the strategies individuals use when adapting to disability. A low level of social activity is here interpreted as reflecting an inclination to isolate oneself socially. For practical reasons—that is, considering the content of the two data sets—number of leisure time activities was applied to reflect “social activity.” Respondents were asked to estimate how many times during the previous 12 months they participated in the following activities: (a) walking by foot or skiing in the forest or in the mountains; (b) exercising in other ways; (c) spectating at a sporting event; (d) watching a movie; (e) dancing; (f) going to a restaurant or a cafe; (g) going to the theatre or the opera; (h) going to a classical music concert; (i) going to a jazz, folk, or pop concert; (j) going to an art exhibition; (k) going


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to a museum; (l) participating in a choir, an orchestra, or a band. Response categories were 0, 1–2 times, 3–9 times, 10 –20 times, 21–39 times, or 40⫹ times. The 12 questions were added together to form a scale on social activity. Social Network Social networks refer to structural qualities of social relations (House, Landis, & Umberson, 1988), and frequency of contact in a social network is a quantitative aspect of social support (Pearson, 1986). The social network measure that is applied here deals with frequency of contact and is understood as an indication of social support. The following four items were used: (a) How often do you spend time with good friends where you live? (Range ⫽ 0 –5: never, not every year, a few times per year but not every month, monthly but not every week, weekly but not daily, daily); (b) How many of your neighbors do you know so well that you visit each other every now and then? (Range ⫽ 0 – 4: no one, one, two, three to four, five or more); (c) How often do you see your brothers or sisters? (Range ⫽ 0 – 6: I have no brother or sister, more seldom than yearly, a few times per year but not every month, every month but not every week, every week but not every day, almost daily, I live with my brother or sister); (d) How often do you meet with your parents? (Range ⫽ 0 – 6: my parents are dead, more seldom than yearly, a few times per year but not every month, every month but not every week, every week but not every day, almost daily, I live with my parents). The five items were added together to form a social network frequency and intensity scale. Different from the previous reflexive scales, this scale is used as a formative (or composite) scale that is not intended to reflect an underlying construct that describes an individual predisposition or characteristic. The scale is constructed of items that measure different forms of social network, forms that will vary between life phases. The items are assumed to represent different potential sources of a total network. Single items in formative scales are not expected to correlate. The internal consistency criteria applied to assess the quality of reflexive (latent variable) scales do not apply to formative (composite) scales (Bollen, 1984; Mastekaasa, 1987). Analyses Analyses were performed by means of SPSS 6.1. for Windows and EQS (Bentler, 1995). Structural equation modeling (SEM), by means of EQS, was applied to test a longitudinal model in which the measures for social activity, social network, psychological problems, and self-reported activity limitations in 1987 and 1995 were combined. SEM is a comprehensive statistical approach to testing hypotheses about relations among variables. The application of SEM involves several advantages compared with more traditional multivariate methods. First, SEM enables the

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simultaneous test of a number of interrelations between variables. That is, a comprehensive model comprising several predictors, mediators, and dependent variables can be tested. Secondly, SEM analyses provide fit measures for the entire model in addition to regular regression coefficients and multiple Rs. Thus, a theoretically based model can be tested against the data. High fit measures imply that the proposed model accounts for the variances and covariances in the observed data matrix to a high degree. Generally, SEM can be seen as an integration of multiple regression, path analysis, and factor analysis. In addition to integrating these approaches, structural modeling requires the prespecification of one or several models to be tested against the observed data (Bollen, 1989; Hoyle, 1995; Jo¨ reskog & So¨ rbom, 1993; Loehlin, 1998). In the present SEM analyses, the scales described earlier were used as observed variables rather than the single items in the modeling analyses on which the covariance matrix was computed. This approach was chosen partly because the model comprises a combination of formative and reflexive indices, partly in order to avoid an unnecessarily complicated model, and partly to test the model against the multiple regression approach. It should therefore be noted that the resulting parameters might in fact represent slight underestimates of the true population values. Another reason for applying the indices as observed variables is that constructed scores may be considered to be of an interval level of measurement. The distribution of the interval scales will more closely reflect the (required) multivariate normal distribution than would the distribution of the single items. The various indices of model adequacy indicate the degree to which the pattern of fixed and free parameters specified in a model is consistent with the pattern of variances and covariances from a set of observed data. We used the following fit measures to evaluate the adequacy of the different models: (a) the root-mean-square error of approximation (RMSEA), for which a value below 0.08 typically indicates an acceptable model, and a value below 0.05 is considered an indication of good fit between model and data; (b) the consistent version of Akaike’s Information Criterion (CAIC) is typically applied to compare alternative models, with smaller values indicating better fit; (c) the comparative fit index (CFI) for which a value above 0.95 is considered indication of good fit. For more information on these and other fit indexes, see, for example, Bentler (1995), Hoyle (1995), Bollen (1989), and Jo¨ reskog and So¨ rbom (1993). In addition, we used the Pearson’s chi-square test as well as the Satorra-Bentler chi-square. Whereas the former implies an underlying assumption of multivariate normality, the latter is designed to handle situations involving deviations from normality (Bentler, 1995). A number of different models were tested to evaluate specific effects. That is, the strategy of analysis involved testing a sequence of increasingly less constrained models. The models were nested, thus allowing for chi-square difference tests that would enable the identification of the model with the best fit. All model testing was based on the covariance matrix of the variables involved, and maximum likelihood estimates were used.


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RESULTS Mean age for the respondents in 1987 was 39.9 years (SD ⫽ 15.38, skewness ⫽ 0.36). Gender difference in mean age (male ⫽ 39.40, female ⫽ 40.27) was not significant, t(1388) ⫽ -1.01, p ⫽ .315.

Scale Properties Properties for the scales included in the analysis are presented in Table 1. Because of skewness, logarithmic transformation was conducted before analyses for the scales measuring activity limitations and psychological problems.

Correlations Correlations between the variables in the two samples are shown in Table 2. The matrix reveals relatively high correlations between age and the measures for level of social activity and social network frequency (1987 and 1995). This is as expected and reflects a reduction in social activity with increasing age. Psychological problems appear to be increasing with age. Level of social activity correlates positively with social network frequency and negatively with the measure for psychological problems. Furthermore, there are negative associations between social network frequency and level of psychological problems. Although not very strong, the major gender difference appearing from the matrix is the correlation with level of psychological problems, in 1987 as well as in 1995. Women report higher levels of psychological problems than men, corresponding to repeatedly confirmed gender differences in this aspect (Blehar & Oren, 1997). Urbanity is the only variable in the matrix that is not correlated

Table 1. Scale Properties M Scale

Range 1987

SD

Skewness

1995 1987 1995 1987

Activity limitations 5–15 5.52 5.81 1.25 Psychological problems 3–9 3.56 3.52 1.01 Social activity 12–72 24.62 25.02 7.35 Social network 0–21 11.67 10.38 4.15

1995

Cronbach’s ␣ 1987 1995

1.67 3.31

2.72

.73

.78

1.02 2.21

2.30

.64

.67

7.32 0.37

0.25

.73

.73

3.68 0.11 ⫺0.14

Gender Urbanicity 1987b Age 1987 Social activity 1987 (12–72) Social activity 1995 (12–72) Social network 1987 (0–21) Social network 1995 (0–21) Psychological problems 1987 (3–9) Psychological problems 1995 (3–9) Activity limitations 1987 (5–15) Activity limitations 1995 (5–15)

a

.08**

.06*

.10**

.16**

⫺.05

⫺.08**

⫺.06*

⫺.06*

— .02 .03

1

.04

⫺.00

.03

.02

⫺.20**

⫺.22**

.16**

.18**

— ⫺.00

2

.29**

.27**

.12**

.09**

⫺.49**

⫺.55**

⫺.46**

⫺.49**

—

3

⫺.20**

⫺.22**

⫺.13**

⫺.13**

.25**

.33**

.67**

—

4

⫺.34**

⫺.27**

⫺.18**

⫺.17**

.26**

.30**

—

5

⫺.17*

⫺.15**

⫺.09**

⫺.12*

.66

—

6

⫺.17**

⫺.16**

⫺.13**

⫺.12**

—

7

.21**

.34**

.43**

—

8

.36**

.29**

—

9

.53**

—

10

—

11

Note. Pairwise deletion of missing values. N varied from 1,308 to 1,390. a 1 ⫽ male, 2 ⫽ female. b 1 ⫽ Rural area– dispersed population, 2 ⫽ 200 –1,999 inhabitants, 3 ⫽ 2,000 –19,999 inhabitants, 4 ⫽ 20,000 –99,999 inhabitants, 5 ⫽ 100,000-plus inhabitants. * p ⬍ .01. ** p ⬍ .05.

11.

10.

9.

8.

7.

6.

5.

1. 2. 3. 4.

Variable

Table 2. Bivariate Correlations (Pearson), Panel Sample, 1987 and 1995

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with activity limitations, and this variable is not included in the analyses that are presented later in this article. Relatively high correlations are found between the measures at the two points in time, indicating stability in the operationalized psychosocial phenomena. It appears from the matrix that activity limitations increase with increasing psychological problems and, although to a lesser extent, with increasing age. Women report somewhat higher activity limitations than men. The social variables correlate negatively with activity limitations.

SEM We used SEM (Bollen, 1989; Hoyle, 1995; Jøreskog & Sørbom, 1993) to investigate the cross-sectional and longitudinal relations between perceived activity limitations and the psychosocial variables. Multiple regression analyses with the independent variables alternating as dependent variables produced R2 values ranging from .04 to .30 in 1987 and from .02 to .28 in 1995. Multicollinearity was thus not observed (Berry & Feldman, 1985). Testing for interaction revealed a few small but significant effects. Social activity was found to be negatively associated with activity limitations among the oldest age groups only, the effect on activity limitations from level of psychological problems varied somewhat between age groups, and there was also some variation in the effect of psychological problems on activity limitations at different levels of social activity. As the detected interaction effects were found not to threaten the results from the overall analyses, we decided to present analysis of the entire sample rather than subgroups. The adopted strategy of analysis involved testing a highly restricted baseline model and subsequently testing different types of effects by relaxing constraints. A ⌬␹2 test was used to obtain a formal test between pairs of nested models, using a significance level of .01 as the cut-off point for accepting a model as better than the more restricted alternative. All tested models comprised 10 variables: sex and age as background variables, three psychosocial variables at two time points, and activity limitations at two time points. Fit measures of the different models are shown in Table 3. The first model (Number 0) involved only two kinds of effects; sex and age were allowed to influence the four T1 variables, and the only longitudinal effects allowed were within each of the main four variables (i.e., no cross-time crosstrait relations). As can be seen from the fit measures, this model clearly was not able to account for the covariance structure in the data. The next model (1) allowed for correlated errors among the three psychosocial variables at T1 and thus implied a test of whether age and sex were able to account for all the observed covariance among these three variables. Model 1 fit significantly better than Model 0, ⌬␹2(3, N ⫽ 1,213) ⫽ 25.34, p ⬍ .01. Model 2 relaxed the constraint of no cross-sectional effects from the psychosocial variables on Activity Limitations and yielded a better fit than Model 1, ⌬␹2(6, N ⫽ 1,213) ⫽

S–B␹2

␹2 df

.977 .994 .996 .995

.997

⫺34.32

.018–.045 ⫺100.05 ⫺64.30 ⫺82.63 ⫺89.79

.015–.049 .016–.047

.000–.040

.032 .032 .031

.024

.052–.076

.064

.977

.046–.068

.057

.868

⫺63.03

.072–.092

.082

241.89

.860

8–5

7–5

6–5

5–3

4–3

3–2

2–1

1–0

Change Models in fit compared

.943

.103–.121

.112

244.13

CAIC CFI

25.63

.101–.118

RMSEA CI

.109

RMSEA

⬍.01

ns

ns

⬍.01

ns

⬍.01

⬍.01

⬍.01

p

Note. Models 0 – 8 involve a sequence of nested models with an increased number of relaxed constraints. Model descriptions indicate type of relaxed constraint in comparison with the previous model. S–B ␹2 ⫽ Satorra-Bentler chi-square; RMSEA ⫽ root-mean-square error of approximation; CI ⫽ confidence interval; CAIC ⫽ consistent Akaike’s information criterion; CFI ⫽ comparative fit index.

0. Only sex and age effects at T1, cross-time same-trait effects 445.00 511.46 33 1. Correlated errors among psychosocial variables at T1 419.66 484.91 30 2. Cross-sectional effects on activity limitations from psychosocial variables 202.06 220.05 24 3. Cross-time effects of age on the four T2 variables 91.44 98.98 20 4. Cross-time effects of sex on the four T2 variables 85.97 95.30 16 5. Cross-time cross-trait effects of activity limitations to psychosocial variables 35.62 37.67 17 6. Cross-time cross-trait effects among the psychosocial variables 22.96 24.81 11 7. Correlated errors among psychosocial variables at T2 28.82 30.78 14 8. Cross-time cross-trait effects of psychosocial variables on activity limitations 23.55 23.62 14

Model

Table 3. Fit Measures of Tested Models

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217.6, p ⬍ .01. Next, in Model 3 effects were allowed from Age on the four main variables at T2, thus testing whether relative changes (from T1 to T2) in these variables were related to age. Model 3 fit better than Model 2, ⌬␹2(4, N ⫽ 1,213) ⫽ 110.62, p ⬍ .01. Correspondingly, Model 4 tested for similar effects of Sex on the four main variables at T2. However, Model 4 failed to yield a significantly better fit than Model 3, ⌬␹2(4, N ⫽ 1,213) ⫽ 5.44, ns, implying that no relative changes in the four main variables from T1 to T2 were due to sex differences. Model 5 allowed for cross-time cross-trait effects, that is effects from Activity Limitations at T1 on the three psychosocial variables at T2 (Hypotheses A) and yielded better fit than Model 3, ⌬␹2(3, N ⫽ 1,213) ⫽ 55.82, p ⬍ .01. Further, in Model 6, cross-time cross-trait effects were allowed among the three psychosocial variables. However, this model failed to yield significantly better fit than Model 5, ⌬␹2(6, N ⫽ 1,213) ⫽ 12.66, ns. Model 7 allowed for correlated errors among the psychosocial variables at T2, but also failed to fit better than Model 5, ⌬␹2(3, N ⫽ 1,213) ⫽ 6.8, ns. Next, in Model 8 cross-time cross-trait effects were allowed from the three psychosocial variables at T1 on Activity Limitations at T2 (Hypotheses B), and this model did fit significantly better than Model 5, ⌬␹2(3, N ⫽ 1,213) ⫽ 12.70, p ⬍ .01. However, Model 8 yielded a higher CAIC than did Model 5 (implying worse fit when parsimony is taken into account as in the CAIC index), and the confidence interval for the RMSEA of Model 8 included the point estimate of RMSEA in Model 5. Additionally, even though the ⌬␹2 test indicated better fit in Model 8, none of the three relaxed parameters reached significance. Thus, based on these findings, as well as the parsimony principle, it was decided to retain Model 5, which in general yielded high fit. Figure 1 shows the final model (5) with path coefficients. Only significant (p ⬍ .01) paths are depicted. Moreover, in order to avoid unnecessary visual complexity, the correlated errors among the psychosocial variables at T1 are not shown. The estimates for these correlations were .09 between Social Network and Social Activity, and -.09 for both Social Activity versus Psychological Problems, and Social Network versus Psychological Problems (all rs, p ⬍ .01). Multiple Rs for Activity Limitations were .41 (T1) and .60 (T2). There are strong direct effects of Age on Social Activity and Social Network, and moderate to low effects on Activity Limitations and Psychological Problems. Thus, Social Network Intensity and Social Activity decrease with increasing age, whereas Activity Limitations and Psychological Problems tend to increase. Regarding gender, females tend to have a slightly less intensive Social Network and a somewhat higher level of Psychological Problems than their male counterparts. The effects of Sex on Activity Limitations appear to be mediated through the level of Psychosocial Problems, whereas there are both direct and indirect effects of Age on Activity Limitations. Pronounced direct effects between the variables at the two points in time (i.e. cross-time same-variable relations) were demonstrated, indicating stability as an important characteristic of these phenomena. A major result is the direct crosstime effects of Activity Limitations (1987) on Social Activity (1995) and Psy-

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Figure 1. Structural model for the relationship between psychosocial variables and activity limitations. Psych. ⫽ psychological; T1 ⫽ Time 1; T2 ⫽ Time 2. chological Problems (1995). Thus, increasing Activity Limitations at T1 predicts lower level of Social Activity and higher level of Psychological Problems at T2. Any effects of the psychosocial variables at Time 1 on Activity Limitations at Time 2 appear to be mediated through the psychosocial variables at Time 2 (Social Network and Psychological Problems) or through Activity Limitations at Time 1 (Social Activity).

DISCUSSION The general purpose of these analyses has been to study how an individual’s self-reported level of difficulties in performing and participating in daily life activities (activity limitations) may vary with respect to the levels of certain psychosocial phenomena. At the cross-sectional level, the relationships were modeled as influences from the psychosocial variables on activity limitations, and effects were found for psychological problems and social activity, but not for social network. These effects remained when controlling for sex and age and when controlling for previous activity limitations. Whereas stability in activity limitations is represented by the T1–T2 relation, the residual variance at T2 represents change (in addition to measurement error). Thus, these findings are


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important in showing that changes in psychosocial variables are related to changes in activity limitations. Whether this relation is due to a one-way effect, a reciprocal process, or an influence from external variables remains an open question. With regard to causality, the longitudinal part of the model yields the strongest evidence. The findings clearly show that activity limitations at one point in time are predictive of scores on psychosocial variables several years later, thereby providing support for such a causal direction. The notion of opposite effects, that is, upon activity limitations, did not receive similar empirical support in the longitudinal perspective. However, there is support in the data for mediated effects of the psychosocial variables on activity limitations over time, indicating that testing a more complex model could have elucidated the relationship further. The lack of longitudinal effects upon activity limitations, however, does not imply a rejection of the hypothesis that psychosocial variables might have a short-term effect. That is, a person’s level of psychological problems and social activity might influence self-rated activity limitations in the immediate future despite lack of effects with a time perspective of several years. The notion of short-term effects fits with the findings in the cross-sectional part of the model; however, as already stated, the data do not provide for strong claims of causality, and this interpretation should thus be considered tentative and preliminary. It appears from the results that social network is not involved in the processes comprised by the model. In the current model, the two social measures (social network and social activity) differ in their basis for construction, but they also represent different social mechanisms. The social network measure is to a large extent defined by situational characteristics an individual finds him- or herself in (to have brothers or sisters is not a matter of personal choice), whereas the social activity measure may be regarded as reflecting the characteristics of an individual. The two social measures are both structural in character and provide no explicit information about the quality of the social relations or the social activity. We argue, however, that the level of social activity at least to some extent indicates a qualitative aspect of social life. According to Ganster and Victor (1988), functional measures are more likely to play the role of mediators as compared with structural measures. Although not included in the model, it is thus possible that social activity plays a mediating role between social network frequency and activity limitations. It is not unlikely that a variable tapping qualitative aspects of social networks (i.e., aspects of their contents) could have given somewhat different results. Rehabilitation and rehabilitation professionals are strongly influenced by medical science and practice. It is therefore understandable that physical problems are in the forefront and that psychosocial aspects may be minimized. This study has demonstrated that level of difficulties with performing and participating in daily life activities may lead to or contribute to increased psychosocial problems. It is known that psychosocial problems may influence the outcome and the pace of rehabilitation (see, e.g., Brewer et al., 2000). It is thus of importance that psychosocial prevention and support services find their place within the

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rehabilitation services. Although the findings here are less clear about the effect of psychosocial problems on level of difficulty, it is argued that putting more weight on preventing psychosocial problems from developing could reduce activity limitations. It is, however, necessary to study the latter relationship, particularly how the effects of psychosocial problems on activity limitations may be mediated. The current sample stems from a panel study with an 8-year interval included in the general studies of living conditions in Norway. This long interval between measurements may constitute a problem, as there is obviously little control of the wide spectrum of variables that may have influenced the results. On the other hand, the long interval considerably reduces control effect problems that are often associated with such studies (Finkel, 1995). Another problem with the current analyses is linked to the operationalization of both predictors and dependent variables. The questionnaire for the studies on living conditions is not optimal for testing such models as in the present study. One possible consequence of this relates to the internal reliabilities of the measure of psychological problems that are below the optimal level. However, our alphas of .64 and .67 are not far from the generally acceptable level of .70 (e.g., Cortina, 1993). Moreover, reliabilities slightly below the rule-of-thumb might lead to underestimation of the true structural patterns rather than overestimation of effects. Although the application of established and more sophisticated instruments for measurement of psychological and social problems could have enhanced the study, the present data nevertheless offer a rare opportunity to test the longitudinal effects that are used in the model. An improvement of the scales would most likely have contributed to strengthening rather than weakening the results that are presented. The type of health problem is not included in the analyses, and it may be argued that the various causes for activity limitations are so different that the relationship between activity limitations and psychosocial factors should be analyzed separately for each condition. We have shown that the variation in self-reported activity limitations between the different categories of health problems was relatively small and that there was a pronounced difference in activity limitations among the general population as compared with the individuals with one of the four dominating health problem categories. The strategy chosen as the purpose of this article has been to approach the relationships between activity limitations and psychological and social factors in general. This issue of analyzing across categories has been discussed by, for example, Ravesloot, Seekins, and Walsh (1997). In a study among adults who experienced mobility problems, it was found that primary impairments did not predict specific groupings of secondary conditions. They did, however, find that certain secondary conditions like depression would be evident across a variety of impairments. The authors suggest that the covariation of secondary conditions across specific impairments is related to a broader phenomena than influence from a specific impairment.


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CONCLUSIONS The purpose of this article was to present a statistical analysis of two hypotheses based on the relationship between psychosocial factors and difficulties with performing and participating in daily life activities (activity limitations). The longitudinal effect of activity limitations on psychosocial problems was supported. However, no direct longitudinal effect was found between psychosocial problems and activity limitations. The possible mediated effects of psychosocial problems on activity limitations may, however, indicate a need for the testing of more complex models to further elucidate this relationship. Furthermore, the results do not rule out a reciprocal short-term relationship. Future studies, including repeated cross-sectional studies with a combination of short and long time spans will be required to yield more conclusive evidence on this issue. The results presented here could have implications for the status and importance of psychosocial support services to people with participation and activity limitations. Strategies for preventing and treating such problems should be included when planning and coordinating individual rehabilitation as well as in the planning of such services. If psychosocial problems do in fact influence activity limitations, the integration of psychosocial support and treatment, when necessary, in the rehabilitation process may reduce individual problems as well as societal costs.

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