Activity as a predictor of mental well-being among older adults

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1 Aging & Mental Health, September 2006; 10(5): ORIGINAL ARTICLE Activity as a predictor of mental well-being among older adults P. LAMPINEN 1, R.-L. HEIKKINEN 1, M. KAUPPINEN 1, & E. HEIKKINEN 1,2 1 The Finnish Centre for Interdisciplinary Gerontology and 2 Department of Health Sciences, University of Jyväskylä, Finland (Received 23 August 2005; accepted 16 January 2006) Abstract This eight-year follow-up study examines the roles of physical and leisure activity as predictors of mental well-being among older adults born in As part of the Evergreen project, 1224 (80%) persons aged years were interviewed at baseline (1988), and 663 (90%) persons in the follow-up (1996). Mental well-being factors including depressive symptoms, anxiety, loneliness, self-rated mental vigour and meaning in life were constructed using factor analysis. The predictors of mental well-being included physical and leisure activity, mobility status and number of chronic illnesses. We used a path analysis model to examine the predictors of mental well-being. At baseline, low number of chronic illnesses, better mobility status and leisure activity were associated with mental well-being. Baseline mental well-being, better mobility status and younger age predicted mental well-being in the follow-up. Explanatory power of the path analysis model for the mental well-being factor at baseline was 19% and 35% in the follow-up. These findings suggest that mental well-being in later life is associated with activity, better health and mobility status, which should become targets for preventive measures. Introduction Several recent studies confirm the hypothesis of the activity theory of aging (Lemon, Bengtson, & Peterson, 1972) that physical and leisure activity are positively correlated to subjective well-being (e.g., Everard, 1999; Herzog, Franks, Markus, & Holmberg, 1998; Kelly, 1993; Lawton, Winter, Kleban, & Ruckdeschel, 1999; Menec, 2003). Some reviews of the theory have concluded that there is substantial support for the assumption that the more active people are in their later years, the greater their subjective well-being (e.g., Kelly & Ross, 1989; Lawton, 1985). According to Litwin (2000), the activity theory of aging contends that involvement in activity on the part of elders reinforces their sense of subjective well-being. Subjective well-being is complex and multidimensional concept that is not easy to define. Indeed earlier studies on the relationships between activity and subjective well-being have applied various different definitions (e.g., Arent, Landers, & Etnier, 2000; Everard, 1999; Kahana et al., 2002; Smale & Dupuis, 1993; Stathi, Fox, & Mckenna, 2002). They have used either one single-item measures, multi-item scales or different dimensions of subjective well-being separately or together, such as life satisfaction, happiness, perceived health, depression, anxiety, meaning in life, and loneliness. It is accordingly virtually impossible to make meaningful comparisons between the results of separate studies. What is more, most of the research on the associations between activity and subjective well-being has been cross-sectional. This means that it is difficult to know whether activity predicts subjective well-being or whether subjective wellbeing contributes to higher levels of activity in older people. On the other hand, longitudinal research is regarded useful for disentangling temporal relations, but not necessarily for addressing causality. Epidemiological research among older adults suggests that physical activity may be associated with subjective well-being (Arent et al., 2000; Biddle & Faulkner, 2002; McTeer & Curtis, 1990; Morgan, Dallosso, Bassey, Ebrahim, Fentem, & Arie, 1991; Stathi et al., 2002; Taylor et al., 2004). Previous studies have also shown that a low level of physical activity is associated with increased depressive symptoms (American College of Sports Medicine [ACSM], 1998; Camacho, Roberts, Lazarus, Kaplan, & Cohen, 1991; Kritz-Silverstein, Barrett- Connor, & Corbeau, 2001; Lampinen, Heikkinen, & Ruoppila, 2000; Moore et al., 1999) and anxiety (Biddle, Fox, Boutcher, & Faulkner, 2001; Paluska & Schwenk, 2000; Watanabe, Okada, Takeshima, & Inomata, 2000). Furthermore, it has been indicated that a higher level of physical activity has a positive effect on meaning in life (Kahana et al., 2002; Correspondence: P. Lampinen, The Finnish Centre for Interdisciplinary, Gerontology University of Jyväskylä (Viveca), P.O. Box 35, FIN Jyväskylä, Finland. Tel: þ Fax: þ ISSN print/issn online/06/ ß 2006 Taylor & Francis DOI: /

2 Activity as a predictor of mental well-being 455 Takkinen, Suutama, & Ruoppila, 2001) and selfrated mental vigour (e.g., Engels, Drouin, Zhu, & Kazmierski, 1998; Lutgendorf et al., 2001). The authors have not found earlier research that has addressed the associations between physical activity and loneliness among older adults. Earlier studies have indicated a positive relationship between leisure activities and subjective wellbeing among elderly populations (Cummings, 2002; Everard, 1999; Lomranz, Bergman, Eyal, & Shmotkin, 1988; Smale & Dupuis, 1993). It has also been shown that leisure activities are associated with a low level of depressive symptoms (Dupuis & Smale, 1995; Herzog et al., 1998; Lomranz et al., 1988). A low level of social activity, on the other hand, predicts feelings of loneliness (Creecy, Berg, & Wright, 1985). There is only scarce information about possible gender differences in the associations between leisure activities and subjective well-being. Younger age, gender, higher socio-economic status, better health status and self-rated health, better functional status and cognitive performance and extensive social relationships are some of the factors that may positively contribute to or confound and modify the associations of activity and subjective well-being (Beekman et al., 1998; Cummings, 2002; Diener, 1984; Everard, 1999; Heidrich, 1993; Heikkinen, Berg, & Avlund, 1995; Palsson & Skoog, 1997; Tijhuis, De Jong-Gierveld, Feskens, & Kromhout, 1999). The intensity of these associations has varied in different studies. It is also possible that the effects of activity on subjective well-being are not direct, but mediated by other factors and processes. For the purposes of our studies we defined mental well-being on the basis of five different dimensions of well-being: depressive symptoms, anxiety, loneliness, self-rated mental vigour and meaning in life. Mental well-being factors were constructed on the basis of factor scores of these indicators. The main aim was to examine the roles of physical and leisure activity as predictors of mental well-being among people aged years at baseline during eight years. We also investigated the associations of age, health and mobility status with mental well-being. The direct and indirect effects of activity, health and functional capacity on the mental well-being factors were examined using path analysis model. The indicators of mental well-being, the independent variables of the model and the direction of the effects of different variables were selected on the basis of the earlier literature, our own previous studies and the preliminary analyses of the data of the present study. Hypothetical model of mental well-being The indicators of mental well-being, the independent variables of the model and the direction of the effects of different variables were selected on the basis of the earlier literature, our own previous research (Heikkinen, 1997; Lampinen & Heikkinen, 2003; Lampinen et al., 2000) and the preliminary analyses of the data of the present study. First, we hypothesized that activity and mobility would be associated with and predict mental well-being. Second, we presumed that health and age would be underlying factors which could modify and confound other factors and associations between them. Third, we supposed that mental well-being at baseline would predict mental well-being in the follow-up. Our path analysis model included three sequential constructs at different levels: (1) a number of chronic illnesses and age group; (2) physical and leisure activity and mobility status; and finally (3) a mental well-being factor at baseline and in follow-up as the dependent variables. Fourth, we expected that the pathway in our model would not only progress sequentially from one stage to another, but also affect the higher levels without passing through an intermediate level. Finally, we presumed different models for men and women on the basis of the differences between men and women in the levels of the applied variables. However, preliminary analyses showed that there were no significant differences between men and women in the correlation matrices or a path analysis models. For the final model data was pooled for men and women. Methods Sample The data for this study were collected as part of the Evergreen Project, which consists of a series of prospective studies on the health and functional capacity of the elderly residents of the city of Jyväskylä, central Finland (Heikkinen, 1998). At baseline, two random samples were drawn from the local population register, each containing 800 respondents born in and and living in the community. Approximately 84% of the eligible men (n ¼ 420) and 78% of the eligible women (n ¼ 804) were interviewed at baseline in About 91% of the men (n ¼ 212) and 87% of the women (n ¼ 451) took part in the follow-up interviews carried out among the survivors in Persons who had been institutionalized (n ¼ 56) were also interviewed at follow-up. Over the follow-up period 448 people died and 113 dropped out (moving to live elsewhere or refusing to participate in the follow-up study). The final sample (n ¼ 663) consisted of all the men and women who participated in both 1988 and For those who refused to participate in the study at baseline (n ¼ 275) and follow-up (n ¼ 86), basic data (age, gender, marital status, education, occupation, self-rated health and reasons for refusal) were inquired by telephone on both occasions. Refusal was more common among the older than the younger subjects. The most common reason for

3 456 P. Lampinen et al. refusal to participate was illness. People with poorer self-rated health and functional capacity refused to participate more often than those with better health and functional capacity. The people who were reluctant or unable to complete the questionnaire on depressive or anxiety symptoms at both points of measurement were older, had more chronic illnesses, were socially and physically more passive, had more difficulties in mobility and rated their health as poorer than did those who did take part in the interview. On the other hand, the results for those who were institutionalized during the follow-up did not differ from those for the population that remained in the community. Measures The data were collected in structured interviews. The participants were interviewed twice at their homes, for two hours per session at baseline. In the follow-up, interviews lasted on average about three hours. Trained students from the University of Jyväskylä were used as interviewers in both study years. Indicators of mental well-being Depressive symptoms. Depressive symptoms were screened using a modified version of Beck s depression scale (RBDI, Revised Beck s Depression Inquiry; Raitasalo, 1995), developed on the basis of Beck s 13-item abridged depression scale (BDI, Beck s Depression Inquiry; Beck, Rial, & Rickels, 1974). It is designed for use with people who live in the community rather than for diagnosing clinical depression. Separate analyses have shown a strong correlation between the BDI and the RBDI (r ¼ 0.88; Raitasalo, 1995), suggesting that both instruments measure largely the same thing: selfrated and experienced depressive symptoms. Every item is rated from 0 3 with ascending scores indicating greater severity of symptoms (Beck, Ward, Mendelson, Mock, & Erbaugh, 1961). The maximum score for the scale is 39 points. Those scoring four or less were deemed not to be experiencing depressive symptoms. The remainder (5 39 points), conversely, were judged to be experiencing depressive symptoms. Anxiety. Level of anxiety was assessed on a fivepoint scale: (1) I consider myself fairly relaxed and do not get anxious easily; (2) I do not feel anxious or nervous; (3) I get anxious and excited fairly easily; (4) I get especially easily distressed anxious or exited; and (5) I continually feel anxious and distressed as if I were at the end of my tether. Every item is rated from 0 3 with ascending scores indicating greater severity of anxiety. The question is a part of the revised Beck s depression inquiry (RBDI; Raitasalo, 1995) which is a widely used scale. Loneliness. Loneliness was inquired with a standard question: Do you think you are lonely? (1) I do not feel lonely; (2) I sometimes feel lonely; (3) I am fairly lonely; or (4) I am very lonely. This is a common way to inquire about older persons subjective loneliness (e.g., Creecy et al., 1985; Samuelsson, Andersson, & Hagberg, 1998). Self-rated mental vigour. Self-rated mental vigour (Heikkinen, 1996) was elicited by the question: How would you describe your self-rated mental vigour at the moment? (1) very good; (2) good; (3) moderate, satisfactory; (4) poor; or (5) very poor. This concept has rarely been applied in studies concerning the mental well-being of elderly people. A similar concept in some earlier studies of physical activity and mental health has been for example positive mood or psychological vigour (e.g., Engels et al., 1998; Lutgendorf et al., 2001). In this study, it was one indicator of positive mental well-being. Meaning in life. Meaning in life was measured using the question: Right now, how meaningful do you consider your life? (1) very meaningful; (2) meaningful; (3) neither meaningful nor meaningless; (4) meaningless; or (5) very meaningless. In the previous studies of the Evergreen project, this question has been successfully applied for describing meaning in life of older adults (Takkinen & Ruoppila, 2001; Takkinen et al., 2001). Mental well-being factors. Mental well-being factors for path analysis models were constructed from the factor scores of mental well-being indicators (depressive symptoms, anxiety, loneliness, self-rated mental vigour, meaning in life) using factor analysis. Independent variables Physical activity. Level of physical activity and its intensity was assessed on a seven-point scale (Hirvensalo, Lampinen, & Rantanen, 1998): (1) performance of necessary chores only; (2) walking 1 2 times/week; (3) walking several times/ week; (4) exercising 1 2 times/week to the point of perspiring and heavy breathing; (5) exercising several times/week to the point of perspiring and heavy breathing; (6) doing keep-fit exercises; and (7) participation in competitive sports. Leisure activity. Leisure activity was measured with an index based on the sum score of involvement in nine different interests (e.g., active and passive art interests, involvement in associations and religious activities, handicrafts, reading, studying), ranging from 0 ¼ never to 2 ¼ regularly. A high score indicated a higher frequency of leisure activity. Physical activity was not included in leisure activities. Many previous studies have used similar

4 Activity as a predictor of mental well-being 457 indices of leisure activities (e.g., Everard, 1999; Menec, 2003), but their contents have varied. Mobility status. Mobility status was assessed with two questions: Can you climb up one flight of stairs without resting? and Can you walk two kilometres non-stop? There were five preset response options: (1) yes, without difficulty; (2) yes, but with difficulty; (3) no; (4) don t know; and (5) isn t familiar with task. For the statistical analyses, mobility status was coded into three categories: (1) difficulties in both or unable to do either activity; (2) difficulties in one activity; or (3) without difficulty in both activities. The concept of mobility has fairly often been defined and measured among older adults as capability to climb stairs and walk a specified distance (Guralnik et al., 1993; Sakari- Rantala, Era, Rantanen, & Heikkinen, 1998). Number of chronic illnesses. Number of chronic illnesses was obtained by adding together the number of chronic conditions reported by the participants. The participants were asked: Are you currently suffering from any longstanding disease (duration over three months) or injury diagnosed by a physician? Age group. The younger age group included subjects born in (65 74-year-olds at baseline) and the older one subjects born in (75 84-year-olds at baseline). Statistical methods In the older age group (born in ) the observations were weighted by a coefficient of 0.736, and in the younger age group (born in ) by a coefficient of in order to produce correspondence with the real age distribution in 1988 of Jyväskylä residents born Because of weighting the proportion of men and women varied to some extent in the separate analyses. Statistical differences in the mental well-being and independent variables among men and women were tested by cross-tabulation and the 2 -test in the case of discrete variables, and Student s t-test in the case of continuous variables (Norusis, 1994). The difference between depressive symptoms at baseline and follow-up were tested by the t-test for paired samples. Transitions in the dichotomized mental well-being variables between the baseline and the follow-up were tested by the McNemar-test. For the statistical analyses, mental well-being factors were constructed on the basis of factor scores of the mental-well-being variables in 1988 and 1996 by factor analysis (Table I). The classes or scales of depressive symptoms, anxiety and loneliness were reversed so that the factors described positive mental well-being. The correlations between all the used indicators of mental well-being were significant. The mental well-being factors were extracted from factor scores of the applied mental well-being indicators using the method of factor analysis. The loadings and communalities were good and consequently, all the indicators were included in the factors. The factors and predictors of mental well-being were analyzed by structural equation methods using LISREL analysis, which allows us simultaneously to study the variables influencing the dependent factor as well as their interrelations. The path analysis model, one of the LISREL techniques, was used to test the plausibility of putative causal relationships between variables. Multivariate regression models were used for the analysis of factors explaining mental well-being variables at baseline. LISREL 8 software was used to accomplish the multivariate procedures ( Jöreskog & Sörebom, 1993a). The path analysis models were based on Pearson productmoment correlation coefficients between the continuous variables, polychoric correlation coefficients between the discrete variables and polyserial correlation coefficients between the continuous and discrete variables (Table IV) computed using PRELIS 2 ( Jöreskog & Sörebom, 1993b). A polychoric correlation matrix was used as the basis of model-building instead of a covariance matrix because both continuous and discrete-ordinal variables were included in the model. Results Changes in the mental well-being indicators Mental well-being factors in 1988 and Table I shows the factor loadings and communalities for the mental well-being indicators. At both baseline and follow-up, depressive symptoms seemed to have stronger loadings than the other indicators of mental well-being. However, all the selected variables were included in the mental well-being factors. The structure of the mental well-being factors remained similar in both study years. Table II shows the means, percentages and statistical significance of differences for the variables included in the mental well-being factors for men and women in 1988 and Depressive symptoms. Most of the men and women reported no depressive symptoms in Eight years later, both men (p50.001) and women (p50.001) reported more depressive symptoms. Women had more depressive symptoms than men in both study years. Anxiety. Only a small proportion of men and women suffered from anxiety in 1988 and Anxiety remained unchanged at the same level among both men and women during the

5 458 P. Lampinen et al. Table I. Factor loadings from principal axis factoring in 1988 and 1996 among men and women: Communalities, eigen-values and percentages of variance. Mental well-being factor 1988 Mental well-being factor 1996 Men Women Men Women Variables Factor loading Communality Factor loading Communality Factor loading Communality Factor loading Communality RBDI (depressive symptoms) Anxiety Loneliness Self-rated mental vigor Meaning in life Eigen-value % of variance

6 Activity as a predictor of mental well-being 459 Table II. Variables included in mental well-being factors at baseline (1988) and follow-up (1996) among men and women born in Means SD, percentage distributions and statistical significance of differences (t-test for continuous variables and 2 test for discrete variables) between men and women Variables Men (1) (n ¼ ) Women (2) Men (3) (n ¼ ) p (1,2) (n ¼ ) Women (4) (n ¼ ) p (3,4) p (1,3) p (2,4) Depressive symptoms % n % n % n % n Anxiety No Yes Loneliness No Yes Self-rated mental vigor Good Moderate Poor Meaning in life Meaningful Difficult to say Meaningless

7 460 P. Lampinen et al. eight-year follow-up. Women experienced more anxiety than men at baseline, but there was no gender difference in anxiety in follow-up. Loneliness. Approximately 20% of men and 30% of women experienced loneliness in Loneliness increased among both men (p ¼ 0.006) and women (p ¼ 0.002) during the eight years. Compared to men, women experienced more loneliness at both baseline and follow-up. Self-rated mental vigour. About half of both men and women regarded their experience of vigour as good at baseline. Both men (p50.001) and women (p50.001) had lower self-rated mental vigour in 1996 than in There were no significant gender differences in self-rated mental vigour. Meaning in life. About two-thirds of men and women regarded their life as meaningful at both baseline and follow-up. Men (p ¼ 0.020) regarded their life as more meaningless in follow-up than at baseline. There was no gender difference in experiences of meaning in life in 1988 or Path analysis model Path analysis model was constructed to examine the associations between the independent variables and mental well-being factors. The means, distributions and differences between men and women in the independent variables are presented in Table III. Table IV shows the correlation coefficients for the variables used in constructing the path analysis model. At baseline, a low number of chronic illnesses, better mobility status and higher leisure activity showed the strongest associations with better mental well-being (Figure 1). Physical activity contributed directly to a better mobility status and higher leisure activity. Physical activity (0.11, SE ¼ 0.02) and younger age ( 0.09, SE ¼ 0.02) associated indirectly with mental well-being at baseline. Younger age and a low number of chronic illnesses showed a direct association with a low level of physical activity and poorer mobility status. The baseline mental well-being was the most powerful predictor of better mental well-being in follow-up (Figure 1). Also, better mobility status and younger age predicted better mental well-being. In addition, higher physical activity (0.10, SE ¼ 0.02) and higher leisure activity (0.08, SE ¼ 0.02) and a low number of chronic illnesses ( 0.22, SE ¼ 0.03) had an indirect effect on mental well-being in follow-up. The model showed a good fit with the data. The explanatory power of the path analysis model for mental well-being at baseline was 19% and in follow-up it was 35%. Discussion This prospective eight-year follow-up study was mainly concerned with the roles and impacts of physical and leisure activity upon mental well-being among men and women aged years at baseline. Mental well-being was defined as a factor containing five different dimensions. The various indicators of health and functional capacity as associates and predictors of mental well-being were also investigated using a path analysis model. The sample included 663 men and women who were interviewed both in 1988 and The path analysis model at baseline showed that higher leisure activity was directly associated with better mental well-being. The results also showed that a low number of chronic illnesses and better mobility status were significantly associated with better mental well-being. The most important predictor of mental well-being in follow-up was the mental well-being at baseline. Also, better mobility Table III. Independent variables at baseline used in the path analysis models for mental well-being among men and women born in Means SD, percentage distributions and statistical significance of differences (t-test for continuous variables and 2 test for discrete variables) between men and women. Men Women Variables (n ¼ ) (n ¼ ) p Age Chronic illnesses Leisure activity Physical activity % n % n Necessary chores only Regular walking Strenuous exercise Mobility Without difficulties Difficulties in another action Difficulties in both actions

8 Activity as a predictor of mental well-being 461 Table IV. Correlation coefficients between selected variables among all subjects (n ¼ ). Variables Mental well-being factor Depressive symptoms ** Anxiety ** 0.62** Loneliness ** 0.41** 0.43** Self-rated mental vigor ** 0.28** 0.36** 0.25** Meaning in life ** 0.33** 0.21** 0.38** 0.24** Mental well-being factor ** 0.55** 0.39** 0.41** 0.23** 0.38** Age group ** 0.17** Chronic illnesses ** 0.36** 0.33** 0.19** 0.12** 0.08* 0.26** 0.12* Leisure activity ** 0.18** 0.25** 0.16** 0.21** 0.21** 0.15** ** Physical activity ** 0.23** 0.17** ** 0.24** 0.17** 0.18** 0.19** 0.22** Mobility ** 0.37** 0.27** 0.26** 0.21** 0.20** 0.33** 0.25** 0.44** 0.28** 0.46** 1.00 *p50.05; **p50.01.

9 462 P. Lampinen et al (0.04) 0.22 (0.03) 0.10 (0.02) CHRONIC ILLNESSES (0.04) 0.16 (0.04) PHYSICAL ACTIVITY 1988 R 2 = (0.04) 0.11 (0.02) 0.12 (0.04) 0.35 (0.03) 0.37 (0.03) AGE GROUP (0.03) MOBILITY STATUS 1988 R 2 = (0.04) 0.22 (0.04) MENTAL WELL-BEING 1988 R 2 = (0.03) 0.12 (0.03) MENTAL WELL-BEING 1996 R 2 = 0.35 LEISURE ACTIVITY 1988 R 2 = (0.04) 0.08 (0.02) 0.09 (0.02) 0.09 (0.03) χ 2 (7) = 5.13, p = 0.64, GFI = 1.00, NFI = 0.99, CFI = 1.00, RMR = Figure 1. The path analysis model of mental well-being for subjects born in over an eight-year follow-up period. The numbers show the maximum likelihood estimates of the path coefficients, standard errors are given in parentheses. R 2 ¼ squared multiple correlations. (Indirect effects in broken line.) status and younger age predicted better mental wellbeing. Physical activity was indirectly associated with and predicted better mental well-being through mobility status and leisure activity. The results of the present study revealed a positive correlation between leisure activity and mental wellbeing. Higher leisure activity also indirectly predicted better mental well-being. Physical activity and better mobility status were associated with higher leisure activity. These effects may modify the level of leisure activity, which independently seems to be associated with and to predict better mental wellbeing. Physically active persons with a good mobility status might have more leisure activities than those whose situation is poorer in this regard. On the other hand, the women in this study had more leisure activities than men, but they were physically more passive and had a poorer mobility status than men. One possible explanation lies in the differences between the traditional male and female roles: men perhaps attach greater importance to physical exercise as a leisure activity, while women engage more often in leisure activities in which social relationships are important as well as in handicrafts and housework (p50.001). It is also possible that among women, social ties are more closely associated with mental well-being than leisure activities. Lomranz et al. (1988) found that both indoor and outdoor activities were associated with depression and psychological well-being among men, while only outdoor activities were associated with psychological well-being among women. Some other earlier crosssectional studies have also shown that leisure activities are associated with subjective well-being, depressive symptoms and loneliness (Creecy et al., 1985; Cummings, 2002; Everard, 1999; Herzog et al., 1998; Smale & Dupuis, 1993; Tinsley, Colbs, Teaff, & Kaufman, 1987). Most of these studies have included physical activity among leisure activities, so they have not been able to explore their separate effects in the same way as in our study. According to our findings, physical activity was indirectly associated with and predicted mental wellbeing. It also had significant correlations with the mental well-being factors at baseline and follow-up. It contributed to a better mobility status and higher leisure activity at baseline. These paths may indicate that physical activity has a significant effect on mobility status and the level of leisure activity, which for their part are positively correlated with mental well-being in the elderly population. Our earlier study showed that mobility status is a stronger predictor of depressive symptoms than physical activity (Lampinen & Heikkinen, 2003). We assumed that increasing mobility problems with age might disguise the effect of physical activity upon developing depressive symptoms. However, previous studies have shown that physical activity is associated with higher subjective well-being (e.g., Arent et al., 2000; Biddle & Faulkner, 2002; Stathi et al., 2002), lower depressive symptoms (e.g., Fukukawa et al., 2004; Lampinen et al., 2000;

10 Activity as a predictor of mental well-being 463 Morgan & Bath, 1998) and lower anxiety (Watanabe et al., 2000), while a higher level of physical activity has a positive effect on meaning in life (Kahana et al., 2002; Takkinen et al., 2001) and self-rated mental vigour (e.g., Engels et al., 1998; Lutgendorf et al., 2001). Most of these studies have not controlled for other important factors, such as physical capacity, mobility and/or social activity, which contribute to the associations between physical activity and mental well-being. It seems that when mobility status is included in the analysis, the direct effect of physical activity disappears. A low number of chronic illnesses was directly associated with better mental well-being at baseline, and indirectly predicted better mental well-being in follow-up. A low number of chronic illnesses also contributed to physical activity and a better mobility status, and indirectly to higher leisure activity. In all probability, these associations show that various aspects of health modify other factors underlying mental well-being in old age. In particular, the increasing number of chronic illnesses with advancing age adversely affects general health and physical fitness and consequently poses a threat to mental well-being. Our findings are closely in line with the results of previous research on the associations of health and subjective well-being (e.g., Cummings, 2002; Diener, 1984; Kendig, Browning, & Young, 2000). Earlier studies have shown that illnesses and poor health are associated with depressive symptoms (Heidrich, 1993; Heikkinen et al., 1995; Lampinen et al., 2000; Mulsant, Ganguli, & Seaberg, 1997), anxiety (Beekman et al., 1998; Heidrich, 1993) and loneliness (Mullins & Elston, 1996; Tijhuis et al., 1999). Also, some studies have suggested that health positively contributes to meaning in life (Burbank, 1992; Ryff, 1989; Takkinen & Ruoppila, 2001). Mobility status played an important mediating role in the model. A better mobility status was directly associated with and predicted higher mental well-being. This is in agreement with earlier studies which have shown that mobility problems are associated with depressive symptoms (Cummings, 2002; Kennedy, Kelman, & Thomas, 1990; Lampinen & Heikkinen, 2003; Wallsten, Tweed, Blazer, & George, 1999), anxiety (de Beurs et al., 1999) and loneliness (Mullins & Elston, 1996). On the other hand, various factors, including chronic illnesses, physical activity and age contributed to and modified mobility status at baseline. Furthermore, previous research has indicated that mobility is modified by several interacting factors, including chronic diseases, physical inactivity and aging (Fried, 2000; Guralnik et al., 1993; Stuck et al., 1999). In our study, older age was associated with declining mobility status and lower physical activity at baseline. It also indirectly contributed to a lower leisure activity. Older age slightly predicted lower mental well-being in follow-up. The mental well-being factors remained more or less the same over the eight-year follow-up. It is possible that age is not directly associated with or does not predict mental well-being, but modifies in the models almost all other factors underlying mental well-being. Previous studies have also produced conflicting results concerning the associations between age and subjective well-being (Andersson, 1998; Beekman et al., 1998; Blanchard, 1996; Diener, 1984; Jorm, 2000). Subjective well-being is a complex and multidimensional concept that has been defined in various different ways in earlier research. In this study we set out to construct a multidimensional variable of mental well-being that would combine different dimensions of well-being identified in earlier research: depressive symptoms, anxiety, loneliness, self-rated mental vigour and meaning in life. The mental well-being factors seemed to fit the path analysis model fairly well. The factors also remained fairly constant over the eight-year follow-up period. The final model supports our hypothetical model of mental well-being. The explanatory power of the path analysis model was fairly low: at baseline 19% and in follow-up 35%. However, the model worked well and showed a statistically very good fit to the data. The main strengths of the current study are its fairly large random sample of older adults and its reasonably high rates of participation in both the baseline and follow-up interviews. The use of a longitudinal design allowed us to examine both the cross-sectional and the causal relationships between mental well-being and the baseline factors. The results can be considered fairly representative and generalizable to the western community-dwelling urban older population. These results also support the assumptions of the activity theory of aging. Among the limitations of our study, mention should be made, first, of its reliance on the accuracy of self-reports. Almost all of the mental well-being and independent variables were based on one-item scales; only depressive symptoms and leisure activity were assessed by multi-item scales. Although the one-item scales of mental well-being applied here have been used in earlier studies, the validity of these scales in the assessment of the phenomenon in question is not well known. On the other hand, the data was already gathered before our study. Second, we did not have any feasible theory of mental wellbeing that would have adequately addressed the different dimensions of mental well-being and thus provided a sound foundation for the logical construction of the path analysis model. For example, no attention was paid to baseline socio-economic status and social network or to life events during the follow-up period. Third, some evidence of sample selection was found in that the subjects who declined to take part, who did not answer and who had died were older and had poorer health and physical status

11 464 P. Lampinen et al. than those who participated in both study years. Fourth, the interval between the two assessments was eight years and little is known about individuals life changes over such a long period. Conclusion The findings of this study indicate that higher leisure activity, better mobility status, a low level of chronic conditions, and also physical activity indirectly, are associated with and predict mental well-being in later life. Activity may provide a global indicator of health and functioning through which the maintenance of health and functional performance is well perceived and reflected in everyday lives. Interventions aimed at preventing inactivity, mobility impairments and poor health would support the efforts of maintaining and improving mental well-being and successful aging in elderly people. Acknowledgements The authors acknowledge financial support from the Social Insurance Institution, the Ministry of Education, the Academy of Finland, the Ministry of Social Affairs and Health, the Association of Finnish Lions Clubs, the Scandinavian Red Feather project, the University of Jyväskylä, and the city of Jyväskylä. We express our warm thanks to all our colleagues who contributed to planning and implementing the study. We also wish to thank Professor Esko Leskinen (statistics) for his valuable advice and comments during the preparation of the manuscript. References American College of Sports Medicine (1998). Exercise and physical activity for older adults. Medicine and Science in Sports and Exercise, 30, Andersson, L. (1998). Loneliness research and interventions: A review of the literature. Aging & Mental Health, 2, Arent, S. M., Landers, D. M., & Etnier, J. L. (2000). The effect of exercise on mood in older adults: A meta-analytic review. 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