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Physical activity, mood and the functioning of daily living
Archives of Gerontology and Geriatrics 48 (2009) 1–9
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Physical activity, mood and the functioning of daily living A longitudinal study among former elite athletes and referents in middle and old age Heli M. Ba¨ckmand a, Jaakko Kaprio a,b, Urho M. Kujala c, Seppo Sarna a,* a b c
Department of Public Health, University of Helsinki, P.O. Box 41 (Mannerheimintie 172), FIN-00014 Helsinki, Finland Department of Mental Health and Alcohol Research, National Public Health Institute, Mannerheimintie 166, FIN-00300 Helsinki, Finland Department of Health Sciences, University of Jyva¨skyla¨, P.O. Box 35 (LL), FIN-40014 Jyva¨skyla¨, Finland
A R T I C L E I N F O
A B S T R A C T
Article history: Received 16 October 2006 Received in revised form 27 August 2007 Accepted 4 September 2007 Available online 17 October 2007
We studied whether factors related to type of sport participated in as young adults and level of and changes in physical activity later in life predict changes in mood as well as functioning during a 6-year follow-up. A cohort of male Finnish former athletes (N = 504), referents (N = 349) was followed up for changes in physical activity, in relation to subsequent self-reported mood and functioning of daily living in 1985, 1995, and 2001. The mean age of the cohort was 68.6 years in 2001. Multinomial logistic regression analysis was used to assess changes in mood and functioning between 1995 and 2001 in relation to baseline values and changes in exposure variables and covariates. A low level of physical activity in 1985 predicted a decrease in physical functioning between 1995 and 2001 in the lowest physical activity compared to the highest quintile as well as poor physical functioning at the end of follow-up in 2001. An increase in physical activity between 1985 and 1995 protected against onset of anxiety between 1995 and 2001. Physical activity for elderly seems to have an important role in reducing the progress of deficiencies in physical functioning and in preventing onset of anxiety. ß 2007 Elsevier Ireland Ltd. All rights reserved.
Keywords: Anxiety in the elderly Cohort study Depression of the elderly Functioning of daily living Physical activity
1. Introduction Current population trends project that adults over age of 65 represent one of the fastest growing population segments worldwide (Kalache and Kickbusch, 1997). Problems of mood, especially depression, and functioning of daily living are widely acknowledged in elderly people (Kivela¨, 1994). Physical inactivity may be the strongest behavioral predictor of subsequent disability and mood. Several cross-sectional studies show that depression (Camacho et al., 1991; Weyerer, 1992; Byrne and Byrne, 1993; Krits-Silverstein et al., 2001) and anxiety symptoms (Morgan, 1979; Byrne and Byrne, 1993) are more common among subjects with low levels of physical activity than among physically active subjects. Studies documenting changes in physical activity show that regular physical activity may be associated with reduced depressive symptoms (Farmer et al., 1988; Camacho et al., 1991; Ba¨ckmand et al., 2003) and clinical depression (Weyerer, 1992) among elderly people. Accordingly,
* Corresponding author. Tel.: +358 9 191 27524; fax: +358 9 191 27533. E-mail address: seppo.sarna@helsinki.fi (S. Sarna). 0167-4943/$ – see front matter ß 2007 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.archger.2007.09.002
previous prospective studies (Camacho et al., 1991; Lampinen et al., 2000) reported that those with low baseline physical activity or those who had reduced their baseline physical activity intensity were at greater risk for depression. Cross-sectional research shows that poor functioning is more common among people with low levels of physical activity than among physically active people (Lammi et al., 1989). Follow-up studies indicate that subnormal daily functioning predicts disability (Guralnik et al., 1994, 1995). Kaplan et al. (1993) demonstrated a positive independent relationship between exercise and a 6-year change in self-reported functional ability. Most of the surveys agree that low-level physical activity at baseline predicts greater disabilities at follow-up (Seeman et al., 1994; Beckett et al., 1996; Lampinen and Hirvensalo, 1999; Hirvensalo et al., 2000; Ba¨ckmand et al., 2006). Therefore, physical activity may increase the level of independence and the quality for life of older individuals, but less is known about the long-term predictive value of physical activity in early adulthood for functioning in older age. The purpose of the present study was to examine whether factors related to sport group as young adults and level of and changes of physical activity later in life predict changes in mood as
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well as functioning during a 6-year follow-up period among former elite male athletes and referents in middle and old age. 2. Subjects and methods
likewise for psychological functioning. Earlier studies (Kattainen et al., 2004a,b,c) have also used this dichotomous definition of functionality. A more detailed description of the scales and the total score distributions has been described elsewhere (Ba¨ckmand et al., 2006).
2.1. Participants 2.3. Measure of baseline physical activity factors A detailed description of study participants has been published previously (Ba¨ckmand et al., 2001). The prospective cohort study is based on 2448 male athletes who represented Finland at least once in international or inter-country events from 1920 to 1965 (Sarna et al., 1993). The referents were from a cohort of Finnish men who were classified as completely healthy at the compulsory medical examination for induction into military service at age 20 (Sarna et al., 1993). The size of referent group was 1712 men. A baseline questionnaire was mailed to participants in 1985. Follow-up questionnaire surveys were mailed to participants in 1995 and 2001. The response rate was 80–90% for athletes and 77% for referents in 1985, and 70–80% for athletes and 71% for referents in the 1995 study. In the 2001 study there were 1135 replies, with a response rate of 76% for athletes and 74% for referents. In the present study were included those 853 men (N = 504 athletes and N = 349 referents) who responded to all three questionnaires. The mean age of the cohort was 68.6 years (range 52.0–100.0) in 2001. The descriptive characteristics of the participants by age group are shown in Table 1. 2.2. Measures of main outcome factors in 1995 and 2001 2.2.1. Mood Mood was defined by two partial scales of the short stress symptom survey extracted from the Brief Symptom Inventory-53 (BSI-53) symptom survey (Derogatis and Melisaratos, 1983; Kronholm, 1993). The short stress symptom survey and Beck’s depression inventory correlate well with each other (Kronholm, 1993). Depression was measured with six items and is primarily a depressiveness scale. Anxiety defined as symptoms that manifest themselves as restlessness, nervousness and stress, was also measured with six items. The responses for the scale questions were as follows on scale of 0–4: never (0), seldom (1), sometimes (2), often (3), and very often (4). The range of the sum scores was 0– 24. The highest decile of the outcome variable (depression or anxiety) was considered a ‘‘positive outcome’’, with others classified as not affected for the purposes of analyzing onset of the disorder. The scales are not, however, diagnostic of clinical disease. A detailed description of the scales, correlation coefficients of scale items and total score distributions has been published previously (Ba¨ckmand et al., 2001, 2003). 2.2.2. Functioning of daily living Our questionnaire included five items of physical functioning, and four items of psychological functioning from the Mini Finland Health Survey (Aromaa et al., 1989), where the same items have been used in a national survey to estimate the occurrence of disability. The national survey questions were modified from classification of functional capacity initially introduced by Katz et al. (1963) and Lawton and Brody (1969). We did not specifically measure cognitive function. Participants were asked to assess their functional ability to perform each of the activities by choosing one of following alternatives: 1 = I perform this without difficulty, 2 = with some difficulty, 3 = with considerable difficulty, and 4 = not at all. Then, we defined those who reported poor function (score 3 or 4) for at least one of the tasks as having ‘‘poor physical functioning’’, while others were classified as having ‘‘normal functioning’’, and we did
The former elite athletes were categorized in five groups according to their sport: endurance athletes, power and combat athletes, power and individual athletes, team athletes, and shooters. These athletes were compared to referents in analysis. Physical activity was defined by Metabolic Equivalent Index (MET) values. As previously (Ba¨ckmand et al., 2003), an index for physical activity was calculated from the product of intensity times, duration times, frequency of activity using responses to three questions on the type of activity, the mean duration of each episode of physical activity, and the frequency per month of such episodes. The activity MET index was expressed as the score of MET hours per day. The subjects were classified into quintiles (Table 1). The fifth (V) quintile with the highest physical activity level was used as the reference quintile. 2.4. Covariates from the 1985 and 1995 surveys Alcohol consumption based on quantity-frequency measures of beverage-specific use (beer, wines, and spirits) was converted into grams of pure alcohol per month as previously reported (Romanov et al., 1987). Subjects were grouped as four groups: abstainers (0 g/ day), occasional (1–10), moderate (11–29) and heavy users (30), with abstainers as the reference group. Subjects’ smoking status was defined by reported current smoking habits (Kaprio and Koskenvuo, 1988) as non-smokers, ex-smokers or current smokers 1 cigarettes/day, with those who never smoked as reference group. Marital status was recorded as living alone or cohabited. Body mass index (BMI) was calculated as kg/m2. BMI was a continuous variable, ranging in 1985 from 18.3 to 40.7 and in 1995 from 16.2 to 46.3. Self-reported and physician-diagnosed current chronic diseases were grouped as five dichotomous variables (Fogelholm et al., 2000): coronary heart disease, pulmonary disease, diabetes, and arthritis. Data on cancer diagnoses before and throughout the present study were obtained by record linkage from the Finnish Cancer Registry. Personality characteristics. Extroversion and neuroticism were assessed using the abbreviated version of Eysenck’s Personality Inventory (Floderus, 1974). Life satisfaction was assessed according to Allardt’s four-item scale (Koivumaa-Honkanen et al., 2001), where increasing scores indicate decreasing life satisfaction. Hostility was assessed by a three-item scale (Koskenvuo et al., 1988). Personality characteristics were continuous variables. A detailed presentation on the scales, correlation coefficients of scale items and total score distributions have been described in detail earlier (Ba¨ckmand et al., 2001, 2003). Life events were stressful life events (Holmes and Rahe, 1967) recorded on baseline questionnaire. Social and interpersonal incidents involving different aspects of life were asked. The response alternatives (never, during the last 6 months, during the last 5 years, or earlier) were scored 0, 3, 2, and 1 to weight the most recent life events (during the last 6 months). Life events were used as a continuous variable. Occupational data were collected partly from the Central Population Registry of Finland and partly from questionnaires, and five groups were defined from the data: executives, clerical workers (reference group), skilled workers, unskilled workers, and
Table 1 Subjects of the study and participation in questionnaire surveys in 1985, 1995, and 2001 Characteristic
Age 59 years
Age 60–69 years
N = 440 1985
3.55 (3.70) 16.1
3.12 (3.39) 11.5
Psychological (range 4–16), mean (SD) Psychological, best functionality, score 1–2 (%) Psychological, poorest functionality, score 3–4 (%)
MET mean (SD) b c
1985
2.71 (3.30)
1995
2.91 (3.59) 9.1
17.4 19.8 16.3 26.7 19.8 4.25 (4.73)
N = 407
N = 244
2001
1985
2.51 (3.17)
p for group difference N = 357 1995
3.41 (3.02) 7.6
6.6
2.77 (3.01)
2.81 (2.98) 9.8
9.2
Functioning of daily living Physical (range 5–20), mean (SD) Physical, best functionality, score 1–2 (%) Physical, poorest functionality, score 3–4 (%)
a
2001
Age 70 years
9.2
Anxiety (range 0–24), mean (SD) Anxiety, positive outcome, score 7 (%) in 1995 Anxiety, positive outcome, score 7 (%) in 2001
Physical activity MET index quintilec, MET h/day (%) Quintile I (<0.39, <0.41 MET h/day) Quintile II (0.40–1.32, 0.42–1.60) Quintile III (1.33–2.78, 1.61–3.99) Quintile IV (2.79–6.42, 4.00–6.42) Quintile V (>6.43, >6.43)
1995
N = 480
2001
3.10 (3.53) 11.2
2.57 (3.00)
2.46 (2.52) 6.4
8.4
2.73 (3.09) 10.8
year year year year
1995, 1995, 2001, 2001,
0.153a; 0.047b; 0.022a; 0.084b
year year year year
1995, 1995, 2001, 2001,
0.281a; 0.147b; 0.212a; 0.534b
5.38 (1.15) 95.3 4.7
5.37 (1.20) 97.7 2.3
5.59 (1.35) 93.7 6.3
5.76 (1.63) 93.4 6.6
5.66 (1.41) 91.3 8.7
6.49 (2.68) 79.4 20.6
year year year year
1995, 1995, 2001, 2001,
0.010a; 0.284b; <0.001a; <0.001b
5.20 (1.67) 90.7 9.3
4.85 (1.32) 96.6 3.4
5.25 (1.66) 89.3 10.7
5.31 (1.70) 92.1 7.9
5.29 (1.47) 93.1 6.9
5.90 (2.21) 87.3 12.7
year year year year
1995, 1995, 2001, 2001,
0.156a; 0.183b; <0.001a; 0.010b
year year year year
1985, 1995, 1985, 1995,
0.614b; 0.053b; 0.459a; 0.263a
12.6 24.1 19.5 16.1 27.6 4.43 (4.86)
19.5 26.6 15.3 19.5 19.2 3.56 (4.92)
17.9 19.9 28.2 13.9 20.2 3.65 (4.14)
19.4 21.7 18.6 19.4 20.8 3.51 (4.14)
16.5 18.6 33.3 8.4 23.2
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Mood Depression (range 0–24), mean (SD) Depression, positive outcome, score 8 (%) in 1995 Depression, positive outcome, score 8 (%) in 2001
N = 87
3.77 (4.08)
p-Value by Kruskal–Wallis test. p-Value by Pearson chi-square asymp. (two-sided). MET was calculated by assigning a multiple of the resting metabolic rate to each activity and calculating the product of intensity duration frequency.
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farmers. Each person was classified according to the occupation he had practiced for the longest period. 2.5. Statistical analysis In the multinomial logistic model of the study, the outcome variables are the two mood variables, as well as the two functioning dimensions. Participants were classified into four groups separately according to each of the four outcomes: (1) improved status, those with depression/anxiety or poor functioning in 1995, but no depression/anxiety or normal functioning in 2001, (2) onset of depression/anxiety or decreased functioning, those with no depression/anxiety or normal functioning in 1995, but who were depressive or anxious or who had poor functioning in 2001, (3) depressive/anxious or having poor functioning in 1995
and 2001, and (4) normal status (i.e., non-affected) both in 1995 and 2001 (reference group). Multinomial logistic regression analysis was used to assess changes in mood and functioning between 1995 and 2001 in relation to baseline values and changes in exposure variables and covariates. Most exposure variables apart from sports groups were measured in both 1985 and 1995, and therefore we formed variables to indicate changes in the variables. For continuous variables, the difference in 1995 and 1985 variables was used in addition to the baseline variable. For binary variables, four classes were formed: no change for presence or absence of traits, appearance of trait (i.e., present in 1995 but not 1985), or disappearance of trait. We first studied all baseline factors (sports group, physical activity variables and covariates) one by one adjusted only for age.
Table 2 Nominal regression results for subjects on depression Status in 1995
Depressionc Sports group Endurance sports Depressive Normal mood
Change between 1995 and 2001a
5 81
3 4
12 106
11 9
8 72
5 4
Team sports Depressive Normal mood
11 176
2 10
Shooting Depressive Normal mood
1 32
1 1
Referents Depressive Normal mood
41 308
23 14
Physical activity MET 1985, quintile 1d Depressive Normal mood
21 142
14 7
MET 1985, quintile 2d Depressive Normal mood
23 181
12 7
MET 1985, quintile 3d Depressive Normal mood
9 133
4 6
MET 1985, quintile 4d Depressive Normal mood
17 154
12 12
(MET 1985, quintile 5d ref.) Depressive Normal mood
8 161
3 10
Power sports/combat Depressive Normal mood Power sports/individual Depressive Normal mood
Change in MET 1995–1985
Improved status
Onset of depression b
Abnormal mood Odds ratio (CI)
p-Valueb
0.642
0.81 (0.08–8.10)
0.858
2.95 (1.05–8.34)
0.041e
0.18 (0.01–2.77)
0.217
0.902
0.96 (0.26–3.63)
0.957
1.19 (0.23–6.32)
0.836
0.16 (0.03–0.91)
0.038e
1.36 (0.46–4.08)
0.579
2.45 (0.61–9.83)
0.205
0.72 (0.07–7.13)
0.783
nc
nc
nc
nc
Odds ratio (CI)
p-Value
0.662
0.72 (0.18–2.87)
2.54 (0.93–6.92)
0.069
0.92 (0.26–3.26)
Odds ratio (CI)
p-Value
0.67 (0.12–3.95)
1.00
1.00
b
1.00
5.31 (0.87–32.34)
0.070
0.80 (0.22–2.95)
0.741
2.38 (0.30–18.76)
0.410
3.68 (0.62–21.88)
0.151
0.59 (0.17–2.03)
0.340
3.37 (0.52–21.98)
0.203
1.65 (0.24–11.33)
0.610
0.56 (0.15–2.05)
0.383
2.48 (0.34–18.27)
0.372
8.43 (1.60–44.23)
0.012f
1.51 (0.52–4.36)
0.449
0.74 (0.09–6.09)
0.779
1.00
0.91 (0.80–1.03)
1.00
0.128
0.98 (0.90–1.07)
1.00
0.669
0.90 (0.77–1.04)
0.146
nc = not computable. a Changes in mood between 1995 and 2001: depressive—improved status (depressive in 1995 but no depression in 2001) and normal mood—incidence case (no depression in 1995 but depressive status in 2001). b Wald’s test. Only sports groups and physical activity results are presented in the table. c Independent variables (age, sport group, physical activity, lifestyle, BMI, diseases, personality characteristics, life events, socioeconomic status) are included in the model. d Quintiles are described in detail in Table 1. e p < 0.05. f p < 0.01.
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All factors were forced in the model. Confidence intervals (CI) of 95% were calculated. The x2-test was used to test the significances of the differences between categorical variables, and the differences in continuous variables were tested by a non-parametric or parametric ANOVA (Table 1). Statistical analyses were carried out using the SPSS for Windows software package version 12.0. 3. Results Table 1 presents the studied factors at the baseline and at the follow-ups by age group. The number of cases in 1995 and the changes between 1995 and 2001 in mood or functioning variables are shown in Tables 2–5. There was a high risk for onset of depression among those with a history of combat sports (Odds ratio (OR) = 3.0, 95% CI = 1.1–8.3) compared to referents (Table 2). Subjects from the three lowest
5
physical activity quintiles compared to the highest quintile at baseline tended to have a higher risk for depression at the end of follow-up in 2001. None of the sports groups or the levels of physical activity were significant predictors for onset of anxiety while an increase in physical activity (OR = 0.9, 95% CI = 0.8–1.0 compared to unchanged activity) between 1985 and 1995 protected against onset of anxiety between 1995 and 2001 (Table 3). A history of team sport participation statistically significantly protected against a decrease in physical functioning at the followup (OR = 0.3, 95% CI = 0.1–1.0 compared to referents) (Table 4). Compared to the high baseline physical activity quintile, subjects from all other quintiles tended to have more decreases in physical functioning at the follow-up. In particular, both low level of physical activity (lowest quintile vs. highest quintile) (OR = 5.2, 95% CI = 1.3–20.7) and relatively high level of physical activity
Table 3 Nominal regression results for subjects on anxiety Status in 1995
Anxietyc Sports group Endurance sports Anxious Normal mood
Change between 1995 and 2001a
5 81
3 4
8 110
5 9
6 74
4 3
Team sports Anxious Normal mood
15 172
8 9
Shooting Anxious Normal mood
1 32
0 4
Referents Anxious Normal mood
38 311
23 22
Physical activity MET 1985, quintile 1d Anxious Normal mood
18 145
8 7
MET 1985, quintile 2d Anxious Normal mood
20 184
12 14
MET 1985, quintile 3d Anxious Normal mood
12 130
7 10
MET 1985, quintile 4d Anxious Normal mood
15 156
11 11
(MET 1985, quintile 5d ref.) Anxious Normal mood
8 161
5 9
Power sports/combat Anxious Normal mood Power sports/individual Anxious Normal mood
Change in MET 1995–1985
Improved status
Onset of anxiety
Abnormal mood
Odds ratio (CI)
p-Valueb
Odds ratio (CI)
p-Valueb
Odds ratio (CI)
p-Valueb
2.32 (0.50–10.74)
0.280
1.20 (0.32–4.45)
0.786
1.13 (0.13–9.67)
0.914
0.50 (0.13–1.88)
0.304
1.76 (0.68–4.55)
0.241
1.38 (0.24–8.09)
0.723
0.96 (0.23–4.07)
0.958
0.57 (0.14–2.26)
0.423
0.57 (0.07–4.61)
0.600
0.61 (0.27–3.06)
0.873
0.68 (0.23–1.99)
0.486
2.49 (0.58–10.77)
0.222
nc
nc
2.18 (0.47–10.04)
0.319
2.10 (0.12–38.10)
0.616
1.00
1.00
1.00
1.60 (0.30–8.49)
0.583
1.78 (0.42–7.49)
0.431
1.48 (0.25–8.69)
0.665
2.30 (0.49–10.90)
0.294
2.38 (0.66–8.61)
0.187
1.88 (0.34–10.33)
0.470
2.42 (0.49–12.04)
0.281
1.81 (0.47–6.98)
0.389
1.27 (0.19–8.58)
0.810
3.24 (0.70–15.00)
0.132
2.76 (0.83–9.20)
0.099
0.91 (0.14–5.92)
0.918
1.00
1.03 (0.92–1.14)
1.00
0.651
0.89 (0.81–0.98)
1.00
0.015e
1.04 (0.91–1.19)
0.553
nc = not computable. a Changes in mood between 1995 and 2001: anxious—improved status (anxious in 1995 but no anxiety in 2001) and normal mood—incidence case (no anxiety in 1995 but anxious status in 2001). b Wald’s test. Only sports groups and physical activity results are presented in the table. c Independent variables (age, sport group, physical activity, lifestyle, BMI, diseases, personality characteristics, life events, socioeconomic status) are included in the model. d Quintiles are described in detail in Table 1. e p < 0.05.
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Table 4 Nominal regression results for subjects on the physical functioning of daily living Status in 1995
Change between 1995 and 2001a
Physical functioningc Sports group Endurance sports Poor functioning Normal functioning
3 80
2 9
Power sports/combat Poor functioning Normal functioning
14 102
7 12
3 73
2 5
Team sports Poor functioning Normal functioning
9 174
7 6
Shooting Poor functioning Normal functioning
3 30
2 1
Referents Poor functioning Normal functioning
27 308
12 29
Physical activity MET 1985, quintile 1d Poor functioning Normal functioning
24 134
11 13
MET 1985, quintile 2d Poor functioning Normal functioning
11 188
5 10
MET 1985, quintile 3d Poor functioning Normal functioning
6 132
4 12
MET 1985, quintile 4d Poor functioning Normal functioning
8 154
6 15
(MET 1985, quintile 5d ref.) Poor functioning Normal functioning
10 156
6 12
Power sports/individual Poor functioning Normal functioning
Change in MET 1995–1985
Improved status
Decrease in functioning b
Poor functioning Odds ratio (CI)
p-Valueb
0.568
nc
nc
1.16 (0.44–3.02)
0.766
1.81 (0.53–6.14)
0.341
0.617
0.35 (0.10–1.22)
0.099
0.41 (0.03–4.90)
0.481
1.26 (0.29–5.39)
0.757
0.32 (0.11–0.97)
0.045e
0.12 (0.01–1.25)
0.076
1.60 (0.19–13.63)
0.669
nc
nc
1.45 (0.13–16.26)
0.765
Odds ratio (CI)
p-Value
0.843
0.71 (0.21–2.34)
3.19 (0.82–12.34)
0.093
0.62 (0.09–4.07)
Odds ratio (CI)
p-Value
0.79 (0.08–8.22)
1.00
1.00
b
1.00
1.96 (0.37–10.50)
0.433
5.19 (1.30–20.69)
0.020e
18.79 (1.65–214.44)
0.018e
0.37 (0.06–2.38)
0.294
1.68 (0.44–6.41)
0.450
3.06 (0.28–33.72)
0.362
1.05 (0.19–5.79)
0.956
3.48 (0.94–12.92)
0.062
2.28 (0.16–32.48)
0.542
0.93 (0.19–4.58)
0.933
4.43 (1.33–14.80)
0.016e
2.31 (0.20–26.67)
0.504
1.00
0.96 (0.84–1.10)
1.00
0.554
0.86 (0.78–0.95)
1.00
0.004f
0.83 (0.71–0.98)
0.029e
nc = not computable. a Changes in functioning between 1995 and 2001: Poor functioning—improved status (poor functioning in 1995 but normal functioning in 2001) and normal functioning— incidence case (normal functioning in 1995 but poor functioning in 2001). b Wald’s test. Only sports groups and physical activity results are presented in the table. c Independent variables (age, sport group, physical activity, lifestyle, BMI, mood, diseases, personality characteristics, life events, socioeconomic status) are included in the model. d Quintiles are described in detail in Table 1. e p < 0.05. f p < 0.01.
(fourth quintile vs. highest quintile) (OR = 4.4, 95% CI = 1.3–14.8) in 1985 predicted a decrease in physical functioning between 1995 and 2001. Additionally, a physical activity increase between 1985 and 1995 protected against a decrease in the physical functioning (OR = 0.9, 95% CI = 0.8–1.0). None of the sports groups or the levels of physical activity were significant predictors for decreased psychological functioning (Table 5). 4. Discussion The most consistent finding of our longitudinal study, with high response rate, was that high leisure physical activity and increase in physical activity level protected against future decreases in the physical functioning. Concerning psychological functioning and
mood, the findings were not similarly straightforward, and former athlete status had effects on some findings, possibly due to differences in personality profiles. Our comprehensive data collection allowed us to adjust the results for most of the different covariates that are relevant for studying these outcomes. Questionnaires were used as the data collection method in our follow-up study. The follow-up used the same scales for mood and functioning in 1995 and 2001. We used a less commonly known scale on mood, the brief version of BSI-53. We examined the properties of the scale in our earlier study (Ba¨ckmand et al., 2003), and in our study it appears to function well as a symptom scale that reflects mood. Generally, other scales on depressiveness and anxiety use items largely similar to the BSI-53 items. We used the cutpoint of the highest decile of depression and anxiety to define a
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Table 5 Nominal regression results for subjects on the psychological functioning of daily living Status in 1995
Change between 1995 and 2001a
Psychological functioningc Sports group Endurance sports Poor functioning Normal functioning
0 82
0 10
Power sports/combat Poor functioning Normal functioning
13 103
5 7
4 74
1 2
Team sports Poor functioning Normal functioning
11 175
6 10
Shooting Poor functioning Normal functioning
2 31
2 3
Referents Poor functioning Normal functioning
45 297
15 16
Physical activity MET 1985, quintile 1d Poor functioning Normal functioning
21 138
6 10
MET 1985, quintile 2d Poor functioning Normal functioning
22 179
8 7
MET 1985, quintile 3d Poor functioning Normal functioning
7 133
4 8
MET 1985, quintile 4d Poor functioning Normal functioning
15 151
6 12
(MET 1985, quintile 5d ref.) Poor functioning Normal functioning
10 157
5 11
Power sports/individual Poor functioning Normal functioning
Change in MET 1995–1985
Improved status
Decrease in functioning b
Poor functioning Odds ratio (CI)
p-Valueb
0.459
nc
0.998
0.92 (0.25–3.42)
0.902
1.11 (0.35–3.57)
0.859
0.056
0.22 (0.03–1.58)
0.132
0.29 (0.05–1.59)
0.154
0.05 (0.00–0.93)
0.045e
1.05 (0.31–3.55)
0.938
0.28 (0.07–1.24)
0.093
9.12 (0.40–207.17)
0.165
0.30 (0.02–4.17)
0.373
nc
nc
Odds ratio (CI)
p-Value
0.998
1.71 (0.41–7.07)
1.36 (0.18–10.39)
0.767
0.02 (0.00–1.10)
Odds ratio (CI)
p-Value
nc
1.00
1.00
b
1.00
0.10 (0.01–1.34)
0.082
2.46 (0.51–11.99)
0.264
7.49 (1.17–47.95)
0.034e
0.20 (0.02–2.05)
0.174
0.23 (0.04–1.43)
0.115
3.46 (0.59–20.38)
0.170
0.39 (0.04–3.78)
0.417
0.77 (0.15–3.94)
0.748
0.60 (0.06–5.64)
0.653
1.73 (0.21–14.28)
0.613
3.56 (0.96–13.25)
0.058
7.78 (1.30–46.62)
0.025e
1.00
1.16 (0.97–1.38)
1.00
0.098
0.92 (0.81–1.04)
1.00
0.173
0.95 (0.85–1.06)
0.372
nc = not computable. a Changes in functioning between 1995 and 2001: poor functioning—improved status (poor functioning in 1995 but normal functioning in 2001) and normal functioning— incidence case (normal functioning in 1995 but poor functioning in 2001). b Wald’s test. Only sports groups and physical activity results are presented in the table. c Independent variables (age, sport group, physical activity, lifestyle, BMI, mood, diseases, personality characteristics, life events, socioeconomic status) are included in the model. d Quintiles are described in detail in Table 1. e p < 0.05.
‘‘disease’’ status, with the approximately nine-tenths remaining classified as not affected. While this is an arbitrary dichotomization of a semi-continuous scale, many estimates of the prevalence of depression and anxiety disorders in the male population (Lehtinen et al., 1990; Weismann and Klerman, 1992) suggest that the top decile of the population have clinically relevant symptoms of depression. It is therefore of interest to see what factors predict future membership in this category. The functioning scale that we use contains substantially similar questions as used in earlier scales on coping with daily functioning. The functional capacity scale used in this study was validated earlier (Kattainen et al., 2004a,b,c) though the scale is not a diagnostic tool. Nonetheless, earlier studies (Kattainen et al., 2004a,b,c) have shown that it functions well as an indicator about the relative level of functional ability. Furthermore, previous study
provide evidence that the data generated by these questions predict the future functioning ability of elderly people (Palmore et al., 1985) The classification of physical activity used in our study is based on the items in questionnaires concerning the frequency, intensity, and time used for exercise. According to several studies, all these factors are related to improved fitness and prevention of illnesses (American College of Sports Medicine Position Stand, 1990; Kujala et al., 1994). In the baseline situation of our study, we categorized physical activity of former elite athletes in 1985. We used the physical activity of baseline situation as a structural factor to enable analysis of sports activity change between 1985 and 1995. Thus, in the analysis and results, the actual predicting factor is change in physical activity with adjustment for baseline levels. Additionally, it has been shown that levels of physical activity can
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H.M. Ba¨ckmand et al. / Archives of Gerontology and Geriatrics 48 (2009) 1–9
be reliably assessed using self-report data on frequency and intensity of physical activity (Grimby, 1986). The adjustment of covariates had no remarkable influence on the results. Some limitations need to be taken into account when interpreting our results. Our data is based only on self-reported measures, and our data included former elite male athletes, who have already ended their active careers before answering the questionnaires, which thus may possibly restrict generalizability. However, our cohort was enriched with subjects having high physical activity level and lifestyle of physical activity varying more than average population. Boxers and wrestlers, compared to referents, had high risk of onset of depression at the follow-up. This is not surprising because these athletes had high depression scores at baseline (Ba¨ckmand et al., 2001). Chronic brain damage in boxers may be partly explained by the fact that boxers have a high risk of onset of depression. According to Kaste et al. (1982), computed tomography reveals pathological findings attributable to brain injury in boxers. One should also note that chronic brain damage may influence some personality traits. One of the reasons for combat/ power sport athletes being at high risk of depression at the followup may be that they were the athlete group with the highest mean neuroticism score at the baseline (Ba¨ckmand et al., 2001), although one previous study (Ma¨kela¨, 1974) reported that male wrestlers have similar neurotic index as the average population. The personality traits of neuroticism and life dissatisfaction increase the risk of depression (Ba¨ckmand et al., 2003), and neuroticism includes an elevated risk for mental morbidity (Kaprio et al., 1987). Expectedly, the history of team sport participation statistically significantly protected against a decrease in physical functioning. Our earlier research has shown that a history of participating in specific types of sports, especially among certain team sports, does show a significant protective effect against poor psychological functioning (Ba¨ckmand et al., 2006). One possible explanation is team athlete’s personality profile. Our early study suggested that those with history of team sports are less depressed and neurotic than referents (Ba¨ckmand et al., 2001). Also, they are more extroverted and satisfied with their lives than referents (Ba¨ckmand et al., 2001). It is possible that a personality profile with less depression may also have an influence on the level of physical activity in later years. It is known that physical activity supports physical daily functionality. Our present results may be partly explained in that elite male team athletes have a higher general psychological self-confidence than those from other sports groups. More importantly, our study showed that physical inactivity at baseline predicted a higher risk of depression, while an increase in physical activity between 1985 and 1995 protected against onset of anxiety in the follow-up between 1995 and 2001. In our early longitudinal study physical activity had no significant association with anxiety (Ba¨ckmand et al., 2003). The findings of our research are consistent with previous follow-up studies indicating that those with low or decreased physical activity at the baseline are at a greater risk for future depression or more depressed symptoms (Camacho et al., 1991; Lampinen et al., 2000). The results may partly be explained by the fact that some of the elderly people have had to decrease their physical activity, e.g. due to aging, restrictions caused by illness or losses. Generally, persons with a good mood may be more active overall. Our results add evidence to the idea that physical activity has a significant effect on depressive symptoms, especially in older people. Physical activity seems to play an important role in functional ability. Our study shows that compared to the highest physical activity quintile, subjects from all other quintiles at the baseline tended to have more decreases in physical functioning in the follow-up. Our results are consistent with earlier findings. The
follow-up studies have shown that physical inactivity at the baseline predicts greater disabilities of daily living at the follow-up (Seeman et al., 1994; Beckett et al., 1996; Hirvensalo et al., 2000; Ba¨ckmand et al., 2006). In addition to low physical activity, decreased functional capacity may be explained by the aging process and its related changes in the locomotor and cardiovascular system, occurrence of various diseases, as well as problems of mood. However, randomized controlled trials have shown that physical fitness and functional ability can also be increased with exercise in patients with chronic disease (Kujala, 2004). The results suggest that physical activity for elderly seems to have an important role in reducing progress of deficiencies in physical functioning, and also in preventing onset of anxiety. It is clear that elderly can experience remarkable improvements in quality of life and health as a consequence of physical active lifestyle. It is important that health care and service providers offer opportunities and encouragement for elderly people to lead a physically active lifestyle. Acknowledgements The study was supported financially by the Finnish Ministry of Education, the Yrjo¨ Jahnsson Foundation, the Juho Vainio Foundation, and by a grant from Suomen Urheiluopistosa¨a¨tio¨. References American College of Sports Medicine Position Stand, 1990. The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness in healthy adults. Med. Sci. Sports Exerc. 22, 265–274. Aromaa, A., Klaukka, T., Impivaara, O., Helio¨vaara, M., 1989. Aikuisten tyo¨- ja toimintakyky seka¨ avuntarve (in Finnish). In: Aromaa, A., Helio¨vaara, M., Impivaara, O., Knekt, P., Maatela, J., Joukamaa, M., Klaukka, T., Lehtinen, V., Melkas, T., Ma¨lkia¨, E., Nyman, K., Paunio, I., Reunanen, A., Sievers, K., Kalimo, E., Kallio, V. (Eds.), Health, Functional Limitations and Need for Care in Finland. Basic Results from the Mini-Finland Health Survey. Publications of the Social Insurance Institution of Finland, AL: 32, Helsinki and Turku, Finland, pp. 355–366, 751. Ba¨ckmand, H., Kaprio, J., Kujala, U., Sarna, S., 2001. Personality and mood of former elite male athletes—a descriptive study. Int. J. Sports Med. 22, 215–221. Ba¨ckmand, H., Kaprio, J., Kujala, U., Sarna, S., 2003. Influence of physical activity on depression and anxiety of former elite athletes. Int. J. Sports Med. 24, 609–619. Ba¨ckmand, H., Kaprio, J., Kujala, U., Sarna, S., Fogelholm, M., 2006. Physical and psychological functioning of daily living in relation to physical activity. A longitudinal study among former elite male athletes and controls. Aging Clin. Exp. Res. 18, 40–49. Beckett, L.A., Brock, D.B., Lemke, J.H., Mendes de Leon, C.F., Guralnik, J.M., Fillenbaum, G.G., Branch, L.G., Wetle, T.T., Evans, D.A., 1996. Analysis of change in selfreported physical function among older persons in four population studies. Am. J. Epidemiol. 143, 766–778. Byrne, A., Byrne, D.G., 1993. The effect of exercise on depression, anxiety and other mood states: a review. J. Psychosom. Res. 37, 565–574. Camacho, T.C., Roberts, R.E., Lazarus, N.B., Kaplan, G.A., Cohen, R.D., 1991. Physical activity and depression: evidence from the Alameda County Study. Am. J. Epidemiol. 134, 220–231. Derogatis, L.R., Melisaratos, N., 1983. The brief symptom inventory: an introductory report. Psychol. Med. 13, 595–605. Farmer, M., Locke, B., Moscicki, E., Dannenberg, A., Larson, D., Radloff, L., 1988. Physical activity and depressive symptoms: the NHANES I epidemiologic follow-up study. Am. J. Epidemiol. 128, 1340–1351. Floderus, B., 1974. Psychosocial factors in relation to coronary heart disease and associated risk factors. Nord. Hyg. Tidskr. Suppl. 6, 1–148. Fogelholm, M., Kujala, U., Kaprio, J., Sarna, S., 2000. Predictors of weight change in middle-aged and old men. Obes. Res. 8, 367–373. Grimby, G., 1986. Physical activity and muscle training in the elderly. Acta Med. Scand. Suppl. 711, 233–237. Guralnik, J.M., Simonsick, E.M., Ferrucci, L., Glynn, R.J., Berkman, L.F., Blazer, D.G., Scherr, P.A., Wallace, R.B., 1994. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J. Gerontol. 49, M85–M94. Guralnik, J.M., Ferrucci, L., Simonsick, E.M., Salive, M.E., Wallace, R.B., 1995. Lower extremity function in persons over the age of 70 years as predictor of subsequent disability. N. Engl. J. Med. 332, 556–561. Hirvensalo, M., Rantanen, T., Heikkinen, E., 2000. Mobility difficulties and physical activity as predictors of mortality and loss of independence in the communityliving older population. J. Am. Geriatr. Soc. 48, 493–498.
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Archives of Gerontology and Geriatrics journal homepage: www.elsevier.com/locate/archger
Physical activity, mood and the functioning of daily living A longitudinal study among former elite athletes and referents in middle and old age Heli M. Ba¨ckmand a, Jaakko Kaprio a,b, Urho M. Kujala c, Seppo Sarna a,* a b c
Department of Public Health, University of Helsinki, P.O. Box 41 (Mannerheimintie 172), FIN-00014 Helsinki, Finland Department of Mental Health and Alcohol Research, National Public Health Institute, Mannerheimintie 166, FIN-00300 Helsinki, Finland Department of Health Sciences, University of Jyva¨skyla¨, P.O. Box 35 (LL), FIN-40014 Jyva¨skyla¨, Finland
A R T I C L E I N F O
A B S T R A C T
Article history: Received 16 October 2006 Received in revised form 27 August 2007 Accepted 4 September 2007 Available online 17 October 2007
We studied whether factors related to type of sport participated in as young adults and level of and changes in physical activity later in life predict changes in mood as well as functioning during a 6-year follow-up. A cohort of male Finnish former athletes (N = 504), referents (N = 349) was followed up for changes in physical activity, in relation to subsequent self-reported mood and functioning of daily living in 1985, 1995, and 2001. The mean age of the cohort was 68.6 years in 2001. Multinomial logistic regression analysis was used to assess changes in mood and functioning between 1995 and 2001 in relation to baseline values and changes in exposure variables and covariates. A low level of physical activity in 1985 predicted a decrease in physical functioning between 1995 and 2001 in the lowest physical activity compared to the highest quintile as well as poor physical functioning at the end of follow-up in 2001. An increase in physical activity between 1985 and 1995 protected against onset of anxiety between 1995 and 2001. Physical activity for elderly seems to have an important role in reducing the progress of deficiencies in physical functioning and in preventing onset of anxiety. ß 2007 Elsevier Ireland Ltd. All rights reserved.
Keywords: Anxiety in the elderly Cohort study Depression of the elderly Functioning of daily living Physical activity
1. Introduction Current population trends project that adults over age of 65 represent one of the fastest growing population segments worldwide (Kalache and Kickbusch, 1997). Problems of mood, especially depression, and functioning of daily living are widely acknowledged in elderly people (Kivela¨, 1994). Physical inactivity may be the strongest behavioral predictor of subsequent disability and mood. Several cross-sectional studies show that depression (Camacho et al., 1991; Weyerer, 1992; Byrne and Byrne, 1993; Krits-Silverstein et al., 2001) and anxiety symptoms (Morgan, 1979; Byrne and Byrne, 1993) are more common among subjects with low levels of physical activity than among physically active subjects. Studies documenting changes in physical activity show that regular physical activity may be associated with reduced depressive symptoms (Farmer et al., 1988; Camacho et al., 1991; Ba¨ckmand et al., 2003) and clinical depression (Weyerer, 1992) among elderly people. Accordingly,
* Corresponding author. Tel.: +358 9 191 27524; fax: +358 9 191 27533. E-mail address: seppo.sarna@helsinki.fi (S. Sarna). 0167-4943/$ – see front matter ß 2007 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.archger.2007.09.002
previous prospective studies (Camacho et al., 1991; Lampinen et al., 2000) reported that those with low baseline physical activity or those who had reduced their baseline physical activity intensity were at greater risk for depression. Cross-sectional research shows that poor functioning is more common among people with low levels of physical activity than among physically active people (Lammi et al., 1989). Follow-up studies indicate that subnormal daily functioning predicts disability (Guralnik et al., 1994, 1995). Kaplan et al. (1993) demonstrated a positive independent relationship between exercise and a 6-year change in self-reported functional ability. Most of the surveys agree that low-level physical activity at baseline predicts greater disabilities at follow-up (Seeman et al., 1994; Beckett et al., 1996; Lampinen and Hirvensalo, 1999; Hirvensalo et al., 2000; Ba¨ckmand et al., 2006). Therefore, physical activity may increase the level of independence and the quality for life of older individuals, but less is known about the long-term predictive value of physical activity in early adulthood for functioning in older age. The purpose of the present study was to examine whether factors related to sport group as young adults and level of and changes of physical activity later in life predict changes in mood as
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H.M. Ba¨ckmand et al. / Archives of Gerontology and Geriatrics 48 (2009) 1–9
well as functioning during a 6-year follow-up period among former elite male athletes and referents in middle and old age. 2. Subjects and methods
likewise for psychological functioning. Earlier studies (Kattainen et al., 2004a,b,c) have also used this dichotomous definition of functionality. A more detailed description of the scales and the total score distributions has been described elsewhere (Ba¨ckmand et al., 2006).
2.1. Participants 2.3. Measure of baseline physical activity factors A detailed description of study participants has been published previously (Ba¨ckmand et al., 2001). The prospective cohort study is based on 2448 male athletes who represented Finland at least once in international or inter-country events from 1920 to 1965 (Sarna et al., 1993). The referents were from a cohort of Finnish men who were classified as completely healthy at the compulsory medical examination for induction into military service at age 20 (Sarna et al., 1993). The size of referent group was 1712 men. A baseline questionnaire was mailed to participants in 1985. Follow-up questionnaire surveys were mailed to participants in 1995 and 2001. The response rate was 80–90% for athletes and 77% for referents in 1985, and 70–80% for athletes and 71% for referents in the 1995 study. In the 2001 study there were 1135 replies, with a response rate of 76% for athletes and 74% for referents. In the present study were included those 853 men (N = 504 athletes and N = 349 referents) who responded to all three questionnaires. The mean age of the cohort was 68.6 years (range 52.0–100.0) in 2001. The descriptive characteristics of the participants by age group are shown in Table 1. 2.2. Measures of main outcome factors in 1995 and 2001 2.2.1. Mood Mood was defined by two partial scales of the short stress symptom survey extracted from the Brief Symptom Inventory-53 (BSI-53) symptom survey (Derogatis and Melisaratos, 1983; Kronholm, 1993). The short stress symptom survey and Beck’s depression inventory correlate well with each other (Kronholm, 1993). Depression was measured with six items and is primarily a depressiveness scale. Anxiety defined as symptoms that manifest themselves as restlessness, nervousness and stress, was also measured with six items. The responses for the scale questions were as follows on scale of 0–4: never (0), seldom (1), sometimes (2), often (3), and very often (4). The range of the sum scores was 0– 24. The highest decile of the outcome variable (depression or anxiety) was considered a ‘‘positive outcome’’, with others classified as not affected for the purposes of analyzing onset of the disorder. The scales are not, however, diagnostic of clinical disease. A detailed description of the scales, correlation coefficients of scale items and total score distributions has been published previously (Ba¨ckmand et al., 2001, 2003). 2.2.2. Functioning of daily living Our questionnaire included five items of physical functioning, and four items of psychological functioning from the Mini Finland Health Survey (Aromaa et al., 1989), where the same items have been used in a national survey to estimate the occurrence of disability. The national survey questions were modified from classification of functional capacity initially introduced by Katz et al. (1963) and Lawton and Brody (1969). We did not specifically measure cognitive function. Participants were asked to assess their functional ability to perform each of the activities by choosing one of following alternatives: 1 = I perform this without difficulty, 2 = with some difficulty, 3 = with considerable difficulty, and 4 = not at all. Then, we defined those who reported poor function (score 3 or 4) for at least one of the tasks as having ‘‘poor physical functioning’’, while others were classified as having ‘‘normal functioning’’, and we did
The former elite athletes were categorized in five groups according to their sport: endurance athletes, power and combat athletes, power and individual athletes, team athletes, and shooters. These athletes were compared to referents in analysis. Physical activity was defined by Metabolic Equivalent Index (MET) values. As previously (Ba¨ckmand et al., 2003), an index for physical activity was calculated from the product of intensity times, duration times, frequency of activity using responses to three questions on the type of activity, the mean duration of each episode of physical activity, and the frequency per month of such episodes. The activity MET index was expressed as the score of MET hours per day. The subjects were classified into quintiles (Table 1). The fifth (V) quintile with the highest physical activity level was used as the reference quintile. 2.4. Covariates from the 1985 and 1995 surveys Alcohol consumption based on quantity-frequency measures of beverage-specific use (beer, wines, and spirits) was converted into grams of pure alcohol per month as previously reported (Romanov et al., 1987). Subjects were grouped as four groups: abstainers (0 g/ day), occasional (1–10), moderate (11–29) and heavy users (30), with abstainers as the reference group. Subjects’ smoking status was defined by reported current smoking habits (Kaprio and Koskenvuo, 1988) as non-smokers, ex-smokers or current smokers 1 cigarettes/day, with those who never smoked as reference group. Marital status was recorded as living alone or cohabited. Body mass index (BMI) was calculated as kg/m2. BMI was a continuous variable, ranging in 1985 from 18.3 to 40.7 and in 1995 from 16.2 to 46.3. Self-reported and physician-diagnosed current chronic diseases were grouped as five dichotomous variables (Fogelholm et al., 2000): coronary heart disease, pulmonary disease, diabetes, and arthritis. Data on cancer diagnoses before and throughout the present study were obtained by record linkage from the Finnish Cancer Registry. Personality characteristics. Extroversion and neuroticism were assessed using the abbreviated version of Eysenck’s Personality Inventory (Floderus, 1974). Life satisfaction was assessed according to Allardt’s four-item scale (Koivumaa-Honkanen et al., 2001), where increasing scores indicate decreasing life satisfaction. Hostility was assessed by a three-item scale (Koskenvuo et al., 1988). Personality characteristics were continuous variables. A detailed presentation on the scales, correlation coefficients of scale items and total score distributions have been described in detail earlier (Ba¨ckmand et al., 2001, 2003). Life events were stressful life events (Holmes and Rahe, 1967) recorded on baseline questionnaire. Social and interpersonal incidents involving different aspects of life were asked. The response alternatives (never, during the last 6 months, during the last 5 years, or earlier) were scored 0, 3, 2, and 1 to weight the most recent life events (during the last 6 months). Life events were used as a continuous variable. Occupational data were collected partly from the Central Population Registry of Finland and partly from questionnaires, and five groups were defined from the data: executives, clerical workers (reference group), skilled workers, unskilled workers, and
Table 1 Subjects of the study and participation in questionnaire surveys in 1985, 1995, and 2001 Characteristic
Age 59 years
Age 60–69 years
N = 440 1985
3.55 (3.70) 16.1
3.12 (3.39) 11.5
Psychological (range 4–16), mean (SD) Psychological, best functionality, score 1–2 (%) Psychological, poorest functionality, score 3–4 (%)
MET mean (SD) b c
1985
2.71 (3.30)
1995
2.91 (3.59) 9.1
17.4 19.8 16.3 26.7 19.8 4.25 (4.73)
N = 407
N = 244
2001
1985
2.51 (3.17)
p for group difference N = 357 1995
3.41 (3.02) 7.6
6.6
2.77 (3.01)
2.81 (2.98) 9.8
9.2
Functioning of daily living Physical (range 5–20), mean (SD) Physical, best functionality, score 1–2 (%) Physical, poorest functionality, score 3–4 (%)
a
2001
Age 70 years
9.2
Anxiety (range 0–24), mean (SD) Anxiety, positive outcome, score 7 (%) in 1995 Anxiety, positive outcome, score 7 (%) in 2001
Physical activity MET index quintilec, MET h/day (%) Quintile I (<0.39, <0.41 MET h/day) Quintile II (0.40–1.32, 0.42–1.60) Quintile III (1.33–2.78, 1.61–3.99) Quintile IV (2.79–6.42, 4.00–6.42) Quintile V (>6.43, >6.43)
1995
N = 480
2001
3.10 (3.53) 11.2
2.57 (3.00)
2.46 (2.52) 6.4
8.4
2.73 (3.09) 10.8
year year year year
1995, 1995, 2001, 2001,
0.153a; 0.047b; 0.022a; 0.084b
year year year year
1995, 1995, 2001, 2001,
0.281a; 0.147b; 0.212a; 0.534b
5.38 (1.15) 95.3 4.7
5.37 (1.20) 97.7 2.3
5.59 (1.35) 93.7 6.3
5.76 (1.63) 93.4 6.6
5.66 (1.41) 91.3 8.7
6.49 (2.68) 79.4 20.6
year year year year
1995, 1995, 2001, 2001,
0.010a; 0.284b; <0.001a; <0.001b
5.20 (1.67) 90.7 9.3
4.85 (1.32) 96.6 3.4
5.25 (1.66) 89.3 10.7
5.31 (1.70) 92.1 7.9
5.29 (1.47) 93.1 6.9
5.90 (2.21) 87.3 12.7
year year year year
1995, 1995, 2001, 2001,
0.156a; 0.183b; <0.001a; 0.010b
year year year year
1985, 1995, 1985, 1995,
0.614b; 0.053b; 0.459a; 0.263a
12.6 24.1 19.5 16.1 27.6 4.43 (4.86)
19.5 26.6 15.3 19.5 19.2 3.56 (4.92)
17.9 19.9 28.2 13.9 20.2 3.65 (4.14)
19.4 21.7 18.6 19.4 20.8 3.51 (4.14)
16.5 18.6 33.3 8.4 23.2
H.M. Ba¨ckmand et al. / Archives of Gerontology and Geriatrics 48 (2009) 1–9
Mood Depression (range 0–24), mean (SD) Depression, positive outcome, score 8 (%) in 1995 Depression, positive outcome, score 8 (%) in 2001
N = 87
3.77 (4.08)
p-Value by Kruskal–Wallis test. p-Value by Pearson chi-square asymp. (two-sided). MET was calculated by assigning a multiple of the resting metabolic rate to each activity and calculating the product of intensity duration frequency.
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4
farmers. Each person was classified according to the occupation he had practiced for the longest period. 2.5. Statistical analysis In the multinomial logistic model of the study, the outcome variables are the two mood variables, as well as the two functioning dimensions. Participants were classified into four groups separately according to each of the four outcomes: (1) improved status, those with depression/anxiety or poor functioning in 1995, but no depression/anxiety or normal functioning in 2001, (2) onset of depression/anxiety or decreased functioning, those with no depression/anxiety or normal functioning in 1995, but who were depressive or anxious or who had poor functioning in 2001, (3) depressive/anxious or having poor functioning in 1995
and 2001, and (4) normal status (i.e., non-affected) both in 1995 and 2001 (reference group). Multinomial logistic regression analysis was used to assess changes in mood and functioning between 1995 and 2001 in relation to baseline values and changes in exposure variables and covariates. Most exposure variables apart from sports groups were measured in both 1985 and 1995, and therefore we formed variables to indicate changes in the variables. For continuous variables, the difference in 1995 and 1985 variables was used in addition to the baseline variable. For binary variables, four classes were formed: no change for presence or absence of traits, appearance of trait (i.e., present in 1995 but not 1985), or disappearance of trait. We first studied all baseline factors (sports group, physical activity variables and covariates) one by one adjusted only for age.
Table 2 Nominal regression results for subjects on depression Status in 1995
Depressionc Sports group Endurance sports Depressive Normal mood
Change between 1995 and 2001a
5 81
3 4
12 106
11 9
8 72
5 4
Team sports Depressive Normal mood
11 176
2 10
Shooting Depressive Normal mood
1 32
1 1
Referents Depressive Normal mood
41 308
23 14
Physical activity MET 1985, quintile 1d Depressive Normal mood
21 142
14 7
MET 1985, quintile 2d Depressive Normal mood
23 181
12 7
MET 1985, quintile 3d Depressive Normal mood
9 133
4 6
MET 1985, quintile 4d Depressive Normal mood
17 154
12 12
(MET 1985, quintile 5d ref.) Depressive Normal mood
8 161
3 10
Power sports/combat Depressive Normal mood Power sports/individual Depressive Normal mood
Change in MET 1995–1985
Improved status
Onset of depression b
Abnormal mood Odds ratio (CI)
p-Valueb
0.642
0.81 (0.08–8.10)
0.858
2.95 (1.05–8.34)
0.041e
0.18 (0.01–2.77)
0.217
0.902
0.96 (0.26–3.63)
0.957
1.19 (0.23–6.32)
0.836
0.16 (0.03–0.91)
0.038e
1.36 (0.46–4.08)
0.579
2.45 (0.61–9.83)
0.205
0.72 (0.07–7.13)
0.783
nc
nc
nc
nc
Odds ratio (CI)
p-Value
0.662
0.72 (0.18–2.87)
2.54 (0.93–6.92)
0.069
0.92 (0.26–3.26)
Odds ratio (CI)
p-Value
0.67 (0.12–3.95)
1.00
1.00
b
1.00
5.31 (0.87–32.34)
0.070
0.80 (0.22–2.95)
0.741
2.38 (0.30–18.76)
0.410
3.68 (0.62–21.88)
0.151
0.59 (0.17–2.03)
0.340
3.37 (0.52–21.98)
0.203
1.65 (0.24–11.33)
0.610
0.56 (0.15–2.05)
0.383
2.48 (0.34–18.27)
0.372
8.43 (1.60–44.23)
0.012f
1.51 (0.52–4.36)
0.449
0.74 (0.09–6.09)
0.779
1.00
0.91 (0.80–1.03)
1.00
0.128
0.98 (0.90–1.07)
1.00
0.669
0.90 (0.77–1.04)
0.146
nc = not computable. a Changes in mood between 1995 and 2001: depressive—improved status (depressive in 1995 but no depression in 2001) and normal mood—incidence case (no depression in 1995 but depressive status in 2001). b Wald’s test. Only sports groups and physical activity results are presented in the table. c Independent variables (age, sport group, physical activity, lifestyle, BMI, diseases, personality characteristics, life events, socioeconomic status) are included in the model. d Quintiles are described in detail in Table 1. e p < 0.05. f p < 0.01.
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All factors were forced in the model. Confidence intervals (CI) of 95% were calculated. The x2-test was used to test the significances of the differences between categorical variables, and the differences in continuous variables were tested by a non-parametric or parametric ANOVA (Table 1). Statistical analyses were carried out using the SPSS for Windows software package version 12.0. 3. Results Table 1 presents the studied factors at the baseline and at the follow-ups by age group. The number of cases in 1995 and the changes between 1995 and 2001 in mood or functioning variables are shown in Tables 2–5. There was a high risk for onset of depression among those with a history of combat sports (Odds ratio (OR) = 3.0, 95% CI = 1.1–8.3) compared to referents (Table 2). Subjects from the three lowest
5
physical activity quintiles compared to the highest quintile at baseline tended to have a higher risk for depression at the end of follow-up in 2001. None of the sports groups or the levels of physical activity were significant predictors for onset of anxiety while an increase in physical activity (OR = 0.9, 95% CI = 0.8–1.0 compared to unchanged activity) between 1985 and 1995 protected against onset of anxiety between 1995 and 2001 (Table 3). A history of team sport participation statistically significantly protected against a decrease in physical functioning at the followup (OR = 0.3, 95% CI = 0.1–1.0 compared to referents) (Table 4). Compared to the high baseline physical activity quintile, subjects from all other quintiles tended to have more decreases in physical functioning at the follow-up. In particular, both low level of physical activity (lowest quintile vs. highest quintile) (OR = 5.2, 95% CI = 1.3–20.7) and relatively high level of physical activity
Table 3 Nominal regression results for subjects on anxiety Status in 1995
Anxietyc Sports group Endurance sports Anxious Normal mood
Change between 1995 and 2001a
5 81
3 4
8 110
5 9
6 74
4 3
Team sports Anxious Normal mood
15 172
8 9
Shooting Anxious Normal mood
1 32
0 4
Referents Anxious Normal mood
38 311
23 22
Physical activity MET 1985, quintile 1d Anxious Normal mood
18 145
8 7
MET 1985, quintile 2d Anxious Normal mood
20 184
12 14
MET 1985, quintile 3d Anxious Normal mood
12 130
7 10
MET 1985, quintile 4d Anxious Normal mood
15 156
11 11
(MET 1985, quintile 5d ref.) Anxious Normal mood
8 161
5 9
Power sports/combat Anxious Normal mood Power sports/individual Anxious Normal mood
Change in MET 1995–1985
Improved status
Onset of anxiety
Abnormal mood
Odds ratio (CI)
p-Valueb
Odds ratio (CI)
p-Valueb
Odds ratio (CI)
p-Valueb
2.32 (0.50–10.74)
0.280
1.20 (0.32–4.45)
0.786
1.13 (0.13–9.67)
0.914
0.50 (0.13–1.88)
0.304
1.76 (0.68–4.55)
0.241
1.38 (0.24–8.09)
0.723
0.96 (0.23–4.07)
0.958
0.57 (0.14–2.26)
0.423
0.57 (0.07–4.61)
0.600
0.61 (0.27–3.06)
0.873
0.68 (0.23–1.99)
0.486
2.49 (0.58–10.77)
0.222
nc
nc
2.18 (0.47–10.04)
0.319
2.10 (0.12–38.10)
0.616
1.00
1.00
1.00
1.60 (0.30–8.49)
0.583
1.78 (0.42–7.49)
0.431
1.48 (0.25–8.69)
0.665
2.30 (0.49–10.90)
0.294
2.38 (0.66–8.61)
0.187
1.88 (0.34–10.33)
0.470
2.42 (0.49–12.04)
0.281
1.81 (0.47–6.98)
0.389
1.27 (0.19–8.58)
0.810
3.24 (0.70–15.00)
0.132
2.76 (0.83–9.20)
0.099
0.91 (0.14–5.92)
0.918
1.00
1.03 (0.92–1.14)
1.00
0.651
0.89 (0.81–0.98)
1.00
0.015e
1.04 (0.91–1.19)
0.553
nc = not computable. a Changes in mood between 1995 and 2001: anxious—improved status (anxious in 1995 but no anxiety in 2001) and normal mood—incidence case (no anxiety in 1995 but anxious status in 2001). b Wald’s test. Only sports groups and physical activity results are presented in the table. c Independent variables (age, sport group, physical activity, lifestyle, BMI, diseases, personality characteristics, life events, socioeconomic status) are included in the model. d Quintiles are described in detail in Table 1. e p < 0.05.
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Table 4 Nominal regression results for subjects on the physical functioning of daily living Status in 1995
Change between 1995 and 2001a
Physical functioningc Sports group Endurance sports Poor functioning Normal functioning
3 80
2 9
Power sports/combat Poor functioning Normal functioning
14 102
7 12
3 73
2 5
Team sports Poor functioning Normal functioning
9 174
7 6
Shooting Poor functioning Normal functioning
3 30
2 1
Referents Poor functioning Normal functioning
27 308
12 29
Physical activity MET 1985, quintile 1d Poor functioning Normal functioning
24 134
11 13
MET 1985, quintile 2d Poor functioning Normal functioning
11 188
5 10
MET 1985, quintile 3d Poor functioning Normal functioning
6 132
4 12
MET 1985, quintile 4d Poor functioning Normal functioning
8 154
6 15
(MET 1985, quintile 5d ref.) Poor functioning Normal functioning
10 156
6 12
Power sports/individual Poor functioning Normal functioning
Change in MET 1995–1985
Improved status
Decrease in functioning b
Poor functioning Odds ratio (CI)
p-Valueb
0.568
nc
nc
1.16 (0.44–3.02)
0.766
1.81 (0.53–6.14)
0.341
0.617
0.35 (0.10–1.22)
0.099
0.41 (0.03–4.90)
0.481
1.26 (0.29–5.39)
0.757
0.32 (0.11–0.97)
0.045e
0.12 (0.01–1.25)
0.076
1.60 (0.19–13.63)
0.669
nc
nc
1.45 (0.13–16.26)
0.765
Odds ratio (CI)
p-Value
0.843
0.71 (0.21–2.34)
3.19 (0.82–12.34)
0.093
0.62 (0.09–4.07)
Odds ratio (CI)
p-Value
0.79 (0.08–8.22)
1.00
1.00
b
1.00
1.96 (0.37–10.50)
0.433
5.19 (1.30–20.69)
0.020e
18.79 (1.65–214.44)
0.018e
0.37 (0.06–2.38)
0.294
1.68 (0.44–6.41)
0.450
3.06 (0.28–33.72)
0.362
1.05 (0.19–5.79)
0.956
3.48 (0.94–12.92)
0.062
2.28 (0.16–32.48)
0.542
0.93 (0.19–4.58)
0.933
4.43 (1.33–14.80)
0.016e
2.31 (0.20–26.67)
0.504
1.00
0.96 (0.84–1.10)
1.00
0.554
0.86 (0.78–0.95)
1.00
0.004f
0.83 (0.71–0.98)
0.029e
nc = not computable. a Changes in functioning between 1995 and 2001: Poor functioning—improved status (poor functioning in 1995 but normal functioning in 2001) and normal functioning— incidence case (normal functioning in 1995 but poor functioning in 2001). b Wald’s test. Only sports groups and physical activity results are presented in the table. c Independent variables (age, sport group, physical activity, lifestyle, BMI, mood, diseases, personality characteristics, life events, socioeconomic status) are included in the model. d Quintiles are described in detail in Table 1. e p < 0.05. f p < 0.01.
(fourth quintile vs. highest quintile) (OR = 4.4, 95% CI = 1.3–14.8) in 1985 predicted a decrease in physical functioning between 1995 and 2001. Additionally, a physical activity increase between 1985 and 1995 protected against a decrease in the physical functioning (OR = 0.9, 95% CI = 0.8–1.0). None of the sports groups or the levels of physical activity were significant predictors for decreased psychological functioning (Table 5). 4. Discussion The most consistent finding of our longitudinal study, with high response rate, was that high leisure physical activity and increase in physical activity level protected against future decreases in the physical functioning. Concerning psychological functioning and
mood, the findings were not similarly straightforward, and former athlete status had effects on some findings, possibly due to differences in personality profiles. Our comprehensive data collection allowed us to adjust the results for most of the different covariates that are relevant for studying these outcomes. Questionnaires were used as the data collection method in our follow-up study. The follow-up used the same scales for mood and functioning in 1995 and 2001. We used a less commonly known scale on mood, the brief version of BSI-53. We examined the properties of the scale in our earlier study (Ba¨ckmand et al., 2003), and in our study it appears to function well as a symptom scale that reflects mood. Generally, other scales on depressiveness and anxiety use items largely similar to the BSI-53 items. We used the cutpoint of the highest decile of depression and anxiety to define a
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Table 5 Nominal regression results for subjects on the psychological functioning of daily living Status in 1995
Change between 1995 and 2001a
Psychological functioningc Sports group Endurance sports Poor functioning Normal functioning
0 82
0 10
Power sports/combat Poor functioning Normal functioning
13 103
5 7
4 74
1 2
Team sports Poor functioning Normal functioning
11 175
6 10
Shooting Poor functioning Normal functioning
2 31
2 3
Referents Poor functioning Normal functioning
45 297
15 16
Physical activity MET 1985, quintile 1d Poor functioning Normal functioning
21 138
6 10
MET 1985, quintile 2d Poor functioning Normal functioning
22 179
8 7
MET 1985, quintile 3d Poor functioning Normal functioning
7 133
4 8
MET 1985, quintile 4d Poor functioning Normal functioning
15 151
6 12
(MET 1985, quintile 5d ref.) Poor functioning Normal functioning
10 157
5 11
Power sports/individual Poor functioning Normal functioning
Change in MET 1995–1985
Improved status
Decrease in functioning b
Poor functioning Odds ratio (CI)
p-Valueb
0.459
nc
0.998
0.92 (0.25–3.42)
0.902
1.11 (0.35–3.57)
0.859
0.056
0.22 (0.03–1.58)
0.132
0.29 (0.05–1.59)
0.154
0.05 (0.00–0.93)
0.045e
1.05 (0.31–3.55)
0.938
0.28 (0.07–1.24)
0.093
9.12 (0.40–207.17)
0.165
0.30 (0.02–4.17)
0.373
nc
nc
Odds ratio (CI)
p-Value
0.998
1.71 (0.41–7.07)
1.36 (0.18–10.39)
0.767
0.02 (0.00–1.10)
Odds ratio (CI)
p-Value
nc
1.00
1.00
b
1.00
0.10 (0.01–1.34)
0.082
2.46 (0.51–11.99)
0.264
7.49 (1.17–47.95)
0.034e
0.20 (0.02–2.05)
0.174
0.23 (0.04–1.43)
0.115
3.46 (0.59–20.38)
0.170
0.39 (0.04–3.78)
0.417
0.77 (0.15–3.94)
0.748
0.60 (0.06–5.64)
0.653
1.73 (0.21–14.28)
0.613
3.56 (0.96–13.25)
0.058
7.78 (1.30–46.62)
0.025e
1.00
1.16 (0.97–1.38)
1.00
0.098
0.92 (0.81–1.04)
1.00
0.173
0.95 (0.85–1.06)
0.372
nc = not computable. a Changes in functioning between 1995 and 2001: poor functioning—improved status (poor functioning in 1995 but normal functioning in 2001) and normal functioning— incidence case (normal functioning in 1995 but poor functioning in 2001). b Wald’s test. Only sports groups and physical activity results are presented in the table. c Independent variables (age, sport group, physical activity, lifestyle, BMI, mood, diseases, personality characteristics, life events, socioeconomic status) are included in the model. d Quintiles are described in detail in Table 1. e p < 0.05.
‘‘disease’’ status, with the approximately nine-tenths remaining classified as not affected. While this is an arbitrary dichotomization of a semi-continuous scale, many estimates of the prevalence of depression and anxiety disorders in the male population (Lehtinen et al., 1990; Weismann and Klerman, 1992) suggest that the top decile of the population have clinically relevant symptoms of depression. It is therefore of interest to see what factors predict future membership in this category. The functioning scale that we use contains substantially similar questions as used in earlier scales on coping with daily functioning. The functional capacity scale used in this study was validated earlier (Kattainen et al., 2004a,b,c) though the scale is not a diagnostic tool. Nonetheless, earlier studies (Kattainen et al., 2004a,b,c) have shown that it functions well as an indicator about the relative level of functional ability. Furthermore, previous study
provide evidence that the data generated by these questions predict the future functioning ability of elderly people (Palmore et al., 1985) The classification of physical activity used in our study is based on the items in questionnaires concerning the frequency, intensity, and time used for exercise. According to several studies, all these factors are related to improved fitness and prevention of illnesses (American College of Sports Medicine Position Stand, 1990; Kujala et al., 1994). In the baseline situation of our study, we categorized physical activity of former elite athletes in 1985. We used the physical activity of baseline situation as a structural factor to enable analysis of sports activity change between 1985 and 1995. Thus, in the analysis and results, the actual predicting factor is change in physical activity with adjustment for baseline levels. Additionally, it has been shown that levels of physical activity can
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be reliably assessed using self-report data on frequency and intensity of physical activity (Grimby, 1986). The adjustment of covariates had no remarkable influence on the results. Some limitations need to be taken into account when interpreting our results. Our data is based only on self-reported measures, and our data included former elite male athletes, who have already ended their active careers before answering the questionnaires, which thus may possibly restrict generalizability. However, our cohort was enriched with subjects having high physical activity level and lifestyle of physical activity varying more than average population. Boxers and wrestlers, compared to referents, had high risk of onset of depression at the follow-up. This is not surprising because these athletes had high depression scores at baseline (Ba¨ckmand et al., 2001). Chronic brain damage in boxers may be partly explained by the fact that boxers have a high risk of onset of depression. According to Kaste et al. (1982), computed tomography reveals pathological findings attributable to brain injury in boxers. One should also note that chronic brain damage may influence some personality traits. One of the reasons for combat/ power sport athletes being at high risk of depression at the followup may be that they were the athlete group with the highest mean neuroticism score at the baseline (Ba¨ckmand et al., 2001), although one previous study (Ma¨kela¨, 1974) reported that male wrestlers have similar neurotic index as the average population. The personality traits of neuroticism and life dissatisfaction increase the risk of depression (Ba¨ckmand et al., 2003), and neuroticism includes an elevated risk for mental morbidity (Kaprio et al., 1987). Expectedly, the history of team sport participation statistically significantly protected against a decrease in physical functioning. Our earlier research has shown that a history of participating in specific types of sports, especially among certain team sports, does show a significant protective effect against poor psychological functioning (Ba¨ckmand et al., 2006). One possible explanation is team athlete’s personality profile. Our early study suggested that those with history of team sports are less depressed and neurotic than referents (Ba¨ckmand et al., 2001). Also, they are more extroverted and satisfied with their lives than referents (Ba¨ckmand et al., 2001). It is possible that a personality profile with less depression may also have an influence on the level of physical activity in later years. It is known that physical activity supports physical daily functionality. Our present results may be partly explained in that elite male team athletes have a higher general psychological self-confidence than those from other sports groups. More importantly, our study showed that physical inactivity at baseline predicted a higher risk of depression, while an increase in physical activity between 1985 and 1995 protected against onset of anxiety in the follow-up between 1995 and 2001. In our early longitudinal study physical activity had no significant association with anxiety (Ba¨ckmand et al., 2003). The findings of our research are consistent with previous follow-up studies indicating that those with low or decreased physical activity at the baseline are at a greater risk for future depression or more depressed symptoms (Camacho et al., 1991; Lampinen et al., 2000). The results may partly be explained by the fact that some of the elderly people have had to decrease their physical activity, e.g. due to aging, restrictions caused by illness or losses. Generally, persons with a good mood may be more active overall. Our results add evidence to the idea that physical activity has a significant effect on depressive symptoms, especially in older people. Physical activity seems to play an important role in functional ability. Our study shows that compared to the highest physical activity quintile, subjects from all other quintiles at the baseline tended to have more decreases in physical functioning in the follow-up. Our results are consistent with earlier findings. The
follow-up studies have shown that physical inactivity at the baseline predicts greater disabilities of daily living at the follow-up (Seeman et al., 1994; Beckett et al., 1996; Hirvensalo et al., 2000; Ba¨ckmand et al., 2006). In addition to low physical activity, decreased functional capacity may be explained by the aging process and its related changes in the locomotor and cardiovascular system, occurrence of various diseases, as well as problems of mood. However, randomized controlled trials have shown that physical fitness and functional ability can also be increased with exercise in patients with chronic disease (Kujala, 2004). The results suggest that physical activity for elderly seems to have an important role in reducing progress of deficiencies in physical functioning, and also in preventing onset of anxiety. It is clear that elderly can experience remarkable improvements in quality of life and health as a consequence of physical active lifestyle. It is important that health care and service providers offer opportunities and encouragement for elderly people to lead a physically active lifestyle. Acknowledgements The study was supported financially by the Finnish Ministry of Education, the Yrjo¨ Jahnsson Foundation, the Juho Vainio Foundation, and by a grant from Suomen Urheiluopistosa¨a¨tio¨. References American College of Sports Medicine Position Stand, 1990. The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness in healthy adults. Med. Sci. Sports Exerc. 22, 265–274. Aromaa, A., Klaukka, T., Impivaara, O., Helio¨vaara, M., 1989. Aikuisten tyo¨- ja toimintakyky seka¨ avuntarve (in Finnish). In: Aromaa, A., Helio¨vaara, M., Impivaara, O., Knekt, P., Maatela, J., Joukamaa, M., Klaukka, T., Lehtinen, V., Melkas, T., Ma¨lkia¨, E., Nyman, K., Paunio, I., Reunanen, A., Sievers, K., Kalimo, E., Kallio, V. (Eds.), Health, Functional Limitations and Need for Care in Finland. Basic Results from the Mini-Finland Health Survey. Publications of the Social Insurance Institution of Finland, AL: 32, Helsinki and Turku, Finland, pp. 355–366, 751. Ba¨ckmand, H., Kaprio, J., Kujala, U., Sarna, S., 2001. Personality and mood of former elite male athletes—a descriptive study. Int. J. Sports Med. 22, 215–221. Ba¨ckmand, H., Kaprio, J., Kujala, U., Sarna, S., 2003. Influence of physical activity on depression and anxiety of former elite athletes. Int. J. Sports Med. 24, 609–619. Ba¨ckmand, H., Kaprio, J., Kujala, U., Sarna, S., Fogelholm, M., 2006. Physical and psychological functioning of daily living in relation to physical activity. A longitudinal study among former elite male athletes and controls. Aging Clin. Exp. Res. 18, 40–49. Beckett, L.A., Brock, D.B., Lemke, J.H., Mendes de Leon, C.F., Guralnik, J.M., Fillenbaum, G.G., Branch, L.G., Wetle, T.T., Evans, D.A., 1996. Analysis of change in selfreported physical function among older persons in four population studies. Am. J. Epidemiol. 143, 766–778. Byrne, A., Byrne, D.G., 1993. The effect of exercise on depression, anxiety and other mood states: a review. J. Psychosom. Res. 37, 565–574. Camacho, T.C., Roberts, R.E., Lazarus, N.B., Kaplan, G.A., Cohen, R.D., 1991. Physical activity and depression: evidence from the Alameda County Study. Am. J. Epidemiol. 134, 220–231. Derogatis, L.R., Melisaratos, N., 1983. The brief symptom inventory: an introductory report. Psychol. Med. 13, 595–605. Farmer, M., Locke, B., Moscicki, E., Dannenberg, A., Larson, D., Radloff, L., 1988. Physical activity and depressive symptoms: the NHANES I epidemiologic follow-up study. Am. J. Epidemiol. 128, 1340–1351. Floderus, B., 1974. Psychosocial factors in relation to coronary heart disease and associated risk factors. Nord. Hyg. Tidskr. Suppl. 6, 1–148. Fogelholm, M., Kujala, U., Kaprio, J., Sarna, S., 2000. Predictors of weight change in middle-aged and old men. Obes. Res. 8, 367–373. Grimby, G., 1986. Physical activity and muscle training in the elderly. Acta Med. Scand. Suppl. 711, 233–237. Guralnik, J.M., Simonsick, E.M., Ferrucci, L., Glynn, R.J., Berkman, L.F., Blazer, D.G., Scherr, P.A., Wallace, R.B., 1994. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J. Gerontol. 49, M85–M94. Guralnik, J.M., Ferrucci, L., Simonsick, E.M., Salive, M.E., Wallace, R.B., 1995. Lower extremity function in persons over the age of 70 years as predictor of subsequent disability. N. Engl. J. Med. 332, 556–561. Hirvensalo, M., Rantanen, T., Heikkinen, E., 2000. Mobility difficulties and physical activity as predictors of mortality and loss of independence in the communityliving older population. J. Am. Geriatr. Soc. 48, 493–498.
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