Prediction of physical fitness and physical activity level in adulthood by physical performance and physical activity in adolescence - An 18-year follow-up study

Scand J Med Scr Sports 1998. 8: 299-308 Printed in Denmark . All rights reserved

Copyrrghr 0 Munksgoard 1998

Srundinovron Journal of MEDICINE & SCIENCE

IN SPORTS ISSN 090s-7188

Prediction of physical fitness and physical activity level in adulthood by physical performance adolescence -

and physical activity in - An 18-year follow-up study

Barnekow-Bergkvist M, Hedberg G, Janlert U, Jansson E. Prediction of physical fitness and physical activity level in adulthood by physical performance and physical activity in adolescence - An 18-year follow-up study. Scand J Med Sci Sports 1998: 8: 299-308. 0 Munksgaard, 1998

The aim of the study was to investigate relationships between physical fitness and self-reported physical activity in adulthood and to what extent the level of physical fitness and leisure-time physical activity in adulthood can be explained by anthropometric measures, physical performance, physical activity, attitudes to sports activities and socio-demographic characteristics at the age of 16. A group of 157 men and 121 women was tested at the ages of 16 and 34 by means of questionnaires and fitness tests. Physically active men and women had higher estimated VOZ max and performed better in curl ups and bench press than those who were inactive. Performance in physical tests, height, weight and physical activity at the age of 16 contributed best to explain adult physical performance and physical activity. The magnitude of explanation varied between 10% (9-min run test) and 56% (bench press test); it was in general lower in the men than in the women. The various fitness tests and physical activity were explained by different predictors and the predictors also differed between men and women. The findings about attitudes to sports and socio-demographic factors at a young age that influence adult physical activity habits and fitness are very complex and further research is required to identify specific inactivity risks.

Many studies have shown an inverse relationship between adult physical activity andlor fitness and various diseases, for example, coronary heart diseases, non-insulin-dependent diabetes mellitus, osteoporosis and musculoskeletal disorders (1-7). Therefore, it be- comes interesting to know which factors at a young age affect the level of physical activity and fitness in adulthood.

Earlier studies have investigated relationships between on one hand physical fitness, physical activity and social factors at a young age and on the other hand adult physical fitness andlor activity (8-1 3). However, many of the studies show different aspects of these relationships. Glenmark et al. (11) found that physical performance and physical activity in adolescence were associated with physical activity in

M. Barnekow-Bergkvist'l*, G. Hedberg', U. Janlert3, E. Jansson4 'Department of Musculoskeletal Research, National Institute for Working Life, UrneA, %epartment of Physical Medicine and Rehabilitation, UmeA, 3Department of Epidemiology and Public Health, Umed University, 4Department of Medical Laboratory Sciences and Technology, Division of Clinical Physiology, Huddinge University Hospital, Stockholm. Sweden

Key words. exercise; fitness, longitudinal, physical activity; sex

Margareta Barnekow-Bergkvst, Department of Musculoskeletal Research, National Institute for Working Life, PO. Box 7654, $90713 UmeA, Sweden

Accepted for publication 2 June 1998

men and women at 27 years of age. This was also found by Dennison et al. (9) regarding higher fitness test scores in childhood and physical activity in adult men. Engstrom (10) showed that early experience of physical activity was related to adult leisure-time physical activity and Kuh et al. (13) have reported that above average ability in sports at school was a positive predictor of high sporting activity in men and women at 36 years of age. Kuh et al. also found other factors of significance for adult physical activity such as youth health status and educational level. In a follow-up Dutch study of men and women, from 12 to 27 years, significant differences between physically active groups were found regarding aerobic power in adulthood but not regarding other physical performances, for ex-

299

Barnekow-Bergkvist et al.

ample strength tests (12). Beunen et al. (8) observed that, in additon to fitness test scores in boys at the age of 16, anthropometric dimensions, skeletal maturation and behavioural characteristics contributed to explain fitness levels at the age of 30.

Although in general significant, the strength of the relationships between childhood and adult physical activity and/or performance is often low to medium in the above-mentioned studies. This could to some extent be due to great changes in lifestyle during the transition from adolescence to adulthood resulting in some individuals becoming sedentaryhnfit while others remain or become activelfit in adulthood, and this increases the challenge to further investigate which factors at a young age may contribute to explain adult physical fitness and activity.

The aim of this investigation was to study the relationships between physical fitness and self-reported physical activity in adulthood and also to what extent anthropometric measures, physical performance, physical activity and socio-demographic characteristics at the age of 16 can explain the future level of physical fitness and physical activity in adulthood. An additional aim was to study in what respect these findings differ between men and women.

Methods Study population and test procedure In 1974, a randomly selected group of 220 male and 205 female Swedish students in their first year of upper secondary school was investigated regarding physical capacity, attitudes to and participation in sports activities (mean age= 16.1, standard deviation=0.33). Six geographic areas in Sweden were systematically selected with regard to different cli- mates, with and without possibilities for winter sports activities. In each area one secondary school region was randomly selected and thereafter the students were selected randomly (14). Sixty-five per cent, 157 men and 121 women, participated in the rein- vestigation 18 years later in 1992 (mean age=33.7, standard deviation=0.74). There were no significant differences between those who took part in the follow-up and those who did not regarding physical capacity, attitude to different sports activities, leisure- time participation in sports and membership of a sports club at the age of 16. This was also true of all other variables at the age of 16 except marks in physical education - more boys but not girls with the highest marks (4, 5) participated in the test at the follow-up. The subjects were invited to take part in the follow-up study which, in addition to answering a questionnaire, consisted of participating in measurements and tests at a field testing station. A detailed description of the study population, dropout and test procedure is given elsewhere (1 5) .

Questionnaires At the age of 16 the participants answered a standardised questionnaire regarding participation in leisure-time sports activity, number of sports activities and membership of sports clubs. The students rated their attitudes to 63 different sports activities on a scale with five options ranging from “I like it very much” (5) to “I dislike it” (1). The number of variables - only activities in which 260% had participated - was reduced by factor analysis to eight factors: 1) gymnastics and jumping athletics; 2) aerobic training; 3) volleyboll, table-tennis, badminton; 4) ice-hockey, bandy, floorball; 5) dance and movement; 6) soccer, handball; 7) strength athletics; 8) swimming. They also answered questions about how often they felt satisfied with their sports performances and worried about physical education lessons. They rated the answers on a five-point scale. Average marks in theoretical and practical subjects as well as marks in physical education were also obtained. A more detailed description is given elsewhere (1 6).

At the follow-up a new questionnaire with items on socio-demographic characteristics (marital status, number of children living at home, education, socio- economic status and their father’s and mother’s socio- economic status) and leisure-time physical activity was used. A test-retest examination of the questionnaire showed acceptable reliability: the pro- portion of the same answers in the retest was 270% and the reliability coefficient was 270 for the questions (16). Information about sports activities was collected regarding type of activity, intensity, fre- quency and duration separately for summer and winter, Walking/cycling to work was reported separately. The total number of months/years the activity had been practised was also reported. The amount of leisure-time physical activity during the last year was summed up as average times and hours per week. For detailed description see Barnekow-Bergkvist et al. (16).

The estimated energy expenditure of the activities was calculated as MET-hours per week. The MET factor (metabolic equivalent) was defined as a multiple of the resting metabolic rate (17); one MET equals approximately 1 kcal kg body weight-’ h-’ or 4.2 kJ - kg body weight-‘ h-I. The energy expenditure for each activity was classified as light (2 METs), medium (4 METs) and high (6 METs) (1 7- 19). The self-rated intensity for each activity was added: 20 for low, + I for medium and +2 for high intensity. Thus, adjusted METs (MET scores) for light activity could vary between 2 and 4, for medium between 4 and 6, and for hard activities between 6 and 8. This value was then multiplied by the number of hours per week of the activity to obtain MET- hours per week (MET - h - week-’). The MET-hours

300

Prediction of fitness and physical activity

data, and median and quartile deviation were used for tests with skewed and limited distribution. Group differences were statistically tested by Wilcoxon's sign-rank test or group t-test. Analysis of variance was applied to test differences between more than two groups and to analyse interactions between sex and duration, and between sex and intensity. A two-tailed significance level of P50.05 was chosen in the study.

To study whether adolescent characteristics contributed to explain physical activity andlor performance at the age of 34 and to study the influence of more than one variable on the outcome, multiple linear regression analyses were used. As dependent variables at the age of 34, aerobic fitness, curl ups, bench press, two-hand lift, standing balance and leisure-time physical activity were chosen to represent adult physical fitness and physical activity, respectively. As determinants, variables were selected from 27 adolescent characteristics grouped in five blocks: anthropometric measures and running performance; muscular endurance and strength; attitudes to physical activity; participation in leisure-time physical activity; marks in physical education and socio-demographic characteristics. The model for each outcome was based on significant (PIO. 1) variables in bivariate correlations. However, if a block lacked significant determinants for some aspect that might influence the outcome, the strongest related variable in the block was chosen. If more than one variable representing the same aspect was significant, only one was chosen for that model, to avoid co- linearity. Variables that did not contribute to improve the model were removed. As the amount of physical activity had a skewed distribution a logarithmic trans- formation was used to normalise the distribution. Since the focus of the analysis was to determine which variables at the age of 16 contributed best to explain adult physical activity and fitness, multiple correlations (adjusted r2) are reported.

The study was approved by the Research Ethics Committee at UmeA University and the data hand- ling procedures by the National Computer Data In- spection Board.

per week for the various leisure-time physical activities were added to form an Exercise index. Physically active was defined as participation in physical activity regularly at least once a week and exerciser as physically active at least at a level of the median value ( r M d ) MET-hours per week.

Physical measurements

Anthropometric measurements included height and weight. Body mass index (BMI) was calculated as body weight per squared body height (kg - m-2).

Aerobicjtness tests. In 1974 the boys and girls performed a run-walk test in which the distances covered in 9 min were measured (14). In 1992 a submaximal cycle ergometer exercise test with one workload was used to estimate maximal oxygen uptake (VO, ,,,) (20). The workload was chosen to give a heart rate between 120 and 160 beats/min. Average workload during the submaximal test was 141+-22 W for the men and 992 18 W for the women and average heart rate was 136211 and 141513 beats - min-' for men and women, respectively. Due to practical difficulties, this assessment of aerobic fitness at the follow-up re- placed the 9-min run test.

Muscular endurance and strength tests. Five tests were the same in both investigations: bench press, sit ups, two-hand lift, hand grip and Sargent jump. Two tests from 1974, hanging and lying leg-lift, were re- placed by two new tests, back extension and curl ups, at the follow-up. Dynamic endurance was thus represented by three tests: number of lifts in bench press - the weight of the barbell was 20 kg for men and 12 kg for women (1974 and 1992), number of sit ups (1974 and 1992) and number ofcurl ups (1992). Static endurance was represented by time to fatigue in lying leg-lift (1974) and lying back extension (1992). Maximal dynamic strength was represented by a standing vertical jump measured in centimeters - Sargent jump (1974 and 1992) and by hanging leg-lift - the angle of the lifted legs was measured in degrees (1974). Maximal static lifting strength was measured in kilo pound (kp) in the two-hand lift test (1 974 and 1992) and hand grip strength was measured in kilo pound per square cent- imeter by squeezing a rubber ball (1974 and 1992). For detailed description of the tests see Jansson et al. (14) and Barnekow-Bergkvist et al. (1 5) .

Standing balance (1992). The subject was to stand on one leg and turn the head from side to side (15).

Statistical methods

The results are presented separately for men and women. There was a small internal dropout (1-6 cases) in occasional variables due to low-back pain, ankle injury or pregnancy. Mean and standard deviation were used to describe normally distributed

Results Sociodemographic characteristics At the age of 16 years, 42% of the men and 35% of the women had chosen an advanced theoretical educational level program. At the age of 34 years, 38% of the men and 33% of the women were manual workers. Significantly more women (82%) than men (65%) had children living at home (data not shown).

Leisure-time physical activity Significantly more men than women participated in sports activities (70% vs 41%) and were members of

301


a sports club (72% vs 50%) at the age of 16 (data not shown); but at the age of 34 there was no significant difference between men and women regarding amount of leisure-time physical activity (Table 1). On average, men and women exercised for an hour per week. The median value (Md) of estimated weekly energy expenditure for leisure-time physical activity was 5.6 MET-hours per week for the men and 4.5 for the women (P=0.19). When walklcycle to work was included the energy expenditure increased to 7.2 and 6.0 MET-hours per week, for men and women, respectively (P=0.44, data not shown).

Approximately 50%0 of both men and women were physically active =once per week. In this group (data not shown) a significant difference between men and women appeared for total number of months the activity had been practised (Md=134 for men and 88 for women, P=0.002), and highest activity intensity, MET scores (Md=7 for men and 5 for women, P50.001). The median value of estimated weekly energy expenditure for physical activity, when both time and intensity were taken into account, was 14.8 MET

hours per week vs 9.0 for men and women, respec- t ivel y (P= 0.004).

Fitness characteristics in exercising and non-exercising adult men and women Results regarding height, weight, physical performance and physical activity at the age of 34 in exercising and non-exercising adult men and women are given in Table 2. Exercising adult men and women had, at the age of 34, higher estimated V02max and they performed better in the curl ups and bench press test than those who were inactive. However, exercising women, on average, also weighed more and had a higher BMI value than those who were inactive.

Figures 1 and 2 show relationships between physical performance and amount of physical activity or level of activity intensity at the age of 34. Aerobic fitness and curl ups at the age of 34 increased significantly with amount of physical activity among the men (Fig. lA), and number of bench press lifts increased with amount of physical activity among the

Table 1. Median (Md) value, quartile (a) and range (R) for leisure-time physical activity in men and women at the age of 34

Men Women P' n=157 n=121

Md Q R Md Q R

Total number of activities 1.0 2.0 0-6 1.0 2.0 0-4 0.47 Total number of months 48 138 0-300 24 84 0-288 0.13 Highest activity intensity (METscore) 7.0 7.0 l t 8 5.0 6.0 0-7 0.05

Exercise (times week-') 1.0 2.5 0-1 9 1.0 2.5 0-18 0.77 Exercise (h . week-') 1.0 3.0 0-25 1.0 2.0 0-19 0.40 Exercise index (MET. h * week-') 5.6 15.9 0-1 24 4.5 9.5 0-44 0.19

a) Wilcoxon Rank-Sum Test.

Table 2. Height, weight and physical performance (M, SO) in exercisers (Md of MET. h . week-') and not exercisers (<Md of MET. h week-') at the age of 34

Men Women

Exercisers Nan-exercisers Exercisers Non-exercisers n=81 n=73 n=66 n=54

M SO M SO P M so Height (cm) Weight (kg) BMI (kg . m2) VO, max ( I . min-I) VO, Back extension (s) Curl up (number) Bench press (number of lifts)

(ml . kg-I . min-'1

180.8 7.0 80.0 9.3 24.5 2.6 3.5 0.8

44.4 9.1 149.4 39.5 53.0 34.3 62.6 25.2

180.1 6.9 0.55 79.4 11.2 0.71 24.4 2.9 0.91 3.2 0.6 0.001

40.2 7.8 0.002 140.4 53.0 0.23 37.9 21.0 0.001 51.4 22.7 0.004

168.3 6.2 69.3 14.1 24.4 4.5

2.9 0.7 42.1 10.6

154.3 63.0 30.7 20.7 48.2 20.4

M SO

168.0 5.5 64.9 8.8 22.9 2.4 2.5 0.4

38.5 8.1 157.7 57.9 22.6 15.3 38.1 15.6

P

0.79 0.04 0.03

(0.001 0.04 0.76 0.02 0.003

0.94 0.2 0.32 Hand grip (kp . cm-*) 1.14 0.2 1.14 0.2 0.83 0.97 0.2 Two-hand lift (kp) 144.9 22.0 145.7 21.9 0.82 87.6 17.2 85.2 14.7 0.43 Sargent jump (cm) 45.6 7.4 44.8 6.6 0.50 30.4 6.9 30.5 6.3 0.94 Standing balance on one leg (s) 61.1 43.3 58.4 41.3 0.69 46.8 40.8 46.0 37.4 0.92

302


A VO,m?x rnl,kg rnin

v

48 !

44 + Men P=0.007

-0.. Women '*, : P=0.02

42

36 4' ! 34

* 32 J

Physical activity 0-0.9 1-1.9 2-2.9 23 hours.week.'

Curl up times 70 1 60

+ Men P=0.009

40

30 --om. Women Pr0.07 20 -

Physical actiyty 0-0.9 1-1.9 2-2.9 23 hours week.

Men 70 31 15 40 Numberof Women 59 27 18 17 subjects

B

40 50 i 46 44

42

40

38

36

Men P=O 005 Women P=O.OOl

Physical activity Light- High intensity Inactive

curl up times

T

A -9.. Women PC 0.001

5- Physical activity Inactive k$!tm High

Men 53 25 79 Numberof Women 30 58 25 subjects

Fig. I. Level of performance and 95% confidence intervals in VOr and curl up tests at the age of 34 in men and women hy A: hours of leisure-time physical activity at the age of 34 and B: level of hjghest activity intensity expressed as MET scores (not active 0, low-medium 2-6, high intensity 7-8). Due to small numbers of SUbJectS. the low and medium intensity groups were combined. Overall significances of differences between fitness levels are shown by P-values for men and women.

women (Fig. 2A). Unexpectedly, women who exercised for 2 3 h/week had lower estimated 002 max

(ml * kg-' - min-I) compared to the women who exercised for 1-2.9 h/week (Fig. IA). Interaction between sex and amount of physical activity was significant for VOz (P=0.02). Aerobic fitness, curl ups and bench press lifts at the age of 34 increased significantly with level of inrensiry among the women; among the men there was a similar trend. The associ- ation patterns between estimated VO, max (ml * kg-' + min-') and physical activity remained essentially the same when VO, was related to 314 or 2/3 power of the body weight (data not shown).

Predictors of adult physical fitness and activity

Linear regression models for explaining adult physical fitness and activity are shown in Table 3. All the regression models were significant ( P < O . O O l ) . Adult

level of aerobic fitness and performance in muscular endurance test (curl up, bench press) could be explained in 10-20%1 of the men and in 23-56% of the women from characteristics at 16 years of age. Strength performance (two-hand lift) could be explained in about 30% and balance and level of physical activity in about 15%) of both men and women. When change in height/weight was entered into the models, the explanation of aerobic fitness and performance in bench press among the men increased to 14% and 13'%1, respectively, but did not equal the level of explanation for the women (data not shown). An additional physical activity model built on activity intensity as dependent variable showed similar results as the model built on MET-h/week, both regarding pattern of explaining characteristics and magnitude of explanation (data not shown).

The various fitness tests and physical activity were explained by different predictors at the age of 16.

303


These predictors, however, differed between men and women (Table 3).

Anthropometric measurements: Among the men, height contributed significantly to explain two-hand lift and weight contributed inversely to explain estimated fro2 (ml * kg-’ * min-’) and curl up performance. Among the women, height contributed inversely to explain curl up and bench press performance. Fitness tests: The sit up, bench press and two- hand lift test contributed significantly to explain curl up, bench press and two-hand lift performance, respectively, among both men and women. The 9-min run contributed to explain relative V02 max among the women and physical activity among the men. At- titudes to different sports activities contributed significantly to explain one of six dependent variables among the men compared to four among the women. Physical activity: Membership of sports club was a significant predictor of V02 for the men, and leisure-time physical activity or membership of sports club was a significant predictor of level of physical

A Bench press’” :imes

T

activity for both men and women. Socio-demographic characteristics: Professional status of father or mother contributed significantly to the outcome of three of six dependent variables among the men compared to one among the women. Educational level program contributed to explain aerobic power and performance in bench press in the women but not in the men.

Discussion Physical fitness and leisure-time physical activity in general, aerobic fitness was higher in the physically active adults. Further, activity intensity was more important than the amount of physical activity for the relationship with aerobic fitness, especially among the women, which is supported by other studies of relationships between exercise intensity and level of estimated VOz max (21). Estimated V 0 2 max

(ml - kg-’ - min-I) increased with amount of exercise in the men, but decreased for women who exercised

B

Bench press” times ’’

+ Men P-0.002

P< 0.001

-0- Men 50

-0.- ‘Women 40 +..---‘q -0- Women P=0.03

P=O.O5

50

30 30 t- Physrcal activity 04.9 1-1.9 2-2.9 23 hours.week-’

Two-hand IiR kp 170

160

150 140

130 120 110 100

90 80 70

Men

1

+ Men P=O.90

P10.22

0-0.9 1-1.9 2-2.9 23 hours.week.’

70 31 15 40 Numberof Women 59 27 18 17 subjects

a) Diflerent weight for men (20 kg) and Women (12 kg)

’ % Physical activity lnacbve Light- Hlgh intensity

Medium

Twohand liR

160 1 kp

140 lSO $-€+

Physical activity lnacbve Light- High intensity

Medium

Men 53 25 79 Numberof Women 38 58 25 subjects

Fig. 2. Level of performance and 95% confidence intervals in bench press and two-hand lift in men and women at the age of 34 by A: hours of leisure-time physical activity at the age of 34 and B: level of highest activity intensity expressed as MET scores (not active 0, low-medium 2-6, high intensity 7-8). Due to small numbers of subjects, the not active and light intensity groups were combined. Overall significances of differences between fitness levels are shown by P-values for men and women.

304


Table 3. Adjusted multiple linear regression correlations (r2) for physical fitness scores and activity level in men and women at the age of 34 by characteristics observed at the age of 16. The variables and the beta values (p) are listed according to the order of the magnitude of their relationships with the dependent variable. Underlined variables correlate negatively with the test scores or physical activity level at the age of 34

Dependent variables at 34

Men Adjusted r2 Determinants at 16

Women Adjusted r2

I3 Determinants at 16

Fitness tests VO, max (ml . kg-' min-')

Curl up (number)

Bench press (number of lifts)

Two-hand lift (kp)

Standing balance on one leg (s)

Physical activity Participation in leisure- time physical activity (MET-hours . week-')

0.10 bench pressa) weighi) membership of sports cluba) positive attitude to swimming mother: working

0.1 6 weighi) two-hand lifta) sit upa) mother: working satisfied with performance

0.20 bench pressa) leisure-time physical activitya) worried about physical education lessona) weight mother: working

0.29 two-hand lifta) positive attitude to dance & moverneni) bench pressa) heighta) father: manual workera)

0.15 father: manual workeP) sargent jump marks in physical education number of sports activities 9-min run

0.16 leisure-time physical activitya) 9-min runa) mother: workinga) bench press sargent jump

0.24 -0.24

0.18 -0.14

0.05

-0.28 0.25 0.23 0.21 0.13

0.43 0.18 0.16

-0.06 0.01

0.43 -0.30 -0.21

0.18 0.17

-0.27 0.1 1 0.1 1 0.10 0.08

0.28 0.24 0.19

-0.15 -0.13

0.23 9-min runa) sargent jumpa) advanced educational levela) positive attitude to soccer & handball

0.26 9-min runa) positive attitude to soccer & handbalp)

sit upa) heighi) advanced educational level 0.56 bench pressa) sit upa) positive attitude to soccer & handbalP) heighi) advanced educational levela) 0.29 two-hand lifta) positive attitude to gymnastics & athleticsa) weight positive attitude to dance & movement

0.1 7

positive attitude to strengtha) positive attitude to ice hockey & bandp)

satisfied with performance father: manual worker

Slt ups)

0.16 membership of sports cluba) two-hand lifta) father: manual worker) advanced educational level positive attitude to soccer & handball

0.29 0.20 0.18

-0.13

0.28 -0.27

0.21 -0.17

0.16

0.56 0.26

-0.16 -0.16

0.16

0.42 0.33 0.1 1

-0.10

0.26 0.25

-0.24

0.12 -0.07

0.27 0.26 0.18 0.16

-0.15

a) The variable contributes significantly ( k 0 . 0 5 ) to explain the dependent variable.

for 1 3 Wweek compared to those who exercised for 2-3 h. The physically active women, that is exercisers, but not the men, weighed more and had higher BMI than those who were inactive. Therefore, estimated VOz was also related to 3/4 and 2/3 power of body weight in order to control for fatfree mass (22). When using VO, calculated in this way, the relationship between V02 max and amount of exercise remained similar to the earlier analysis with VO, that is the V 0 2 max was lower in the women who exercised for 2 3 h/week than those who exercised for 2-3 h. One explanation for this inverse relationship is prob-

ably that many of the women, who spent much time on physical activity, exercised with low intensity, for example walking. Moreover. earlier analysis of the same study population showed that many of the women exercised for weight control and for health benefits (16), which might indicate a favourable trend of physical activity among less fit women. Regarding health effects, the benefits of regular physical activty with low intensity is commonly acknowledged to be effective and might be as effective as with high intensity for some diseases such as non-insulin-dependent diabetes mellitus and colon cancer (cf. 23, 24).

305


Prediction of adult physical fitness and physical activity Earlier studies found inverse relationships between isometric trunk muscle strength (6) and isometric lifting strength (25) and low-back disorders. In the present study, characteristics at the age of 16 could explain performance 18 years later in curl ups in 16% and 26% of men and women respectively, and in two- hand lift in 29% of both men and women. In the present study population (26), high performance in adolescence in bench press among the men and two-hand lift among the women showed a positive preventive effect on adult neck-shoulder and low-back prob- lems, respectively. It was therefore interesting to see that characteristics at the age of 16 could explain performance 18 years later in two-hand lift in 29% among men and women and in bench press in 20% among the men and 56% among the women. Overall, the magnitude of the explanation of adult physical performance varied between 10% and 56%. It was in general lower among the men, which partly may be accounted for by changes in their height and weight which continued to increase beyond the age of 16. Influence of growth during adolescence on physical performance in adulthood has been reported earlier (27-29). When change in heightlweight was entered into the model for the men in the present study, the level of explanation for endurance tests increased, which supports the influence of growth on adult physical fitness. As expected, performance in physical tests, height and weight at the age of 16 were the most important predictors of adult physical fitness, indi- cating the genetic influence of performance (30). However, the fact that only a minor fraction of adult physical capacity was possible to explain indicates that in addition to the genetic influence there are other important factors, such as regular and continu- ous exercise, that are important for maintaining or achieving physical fitness.

Besides physical performance, height and weight also behavioural and socio-demographic characteristics at the age of 16 contributed to explain adult physical fitness in the present study. This agrees large- ly with the findings of Beunen et al. (31) who found that in addition to fitness scores, skeletal maturation and anthropometric dimensions, also behavioural characteristics contributed to explain adult fitness levels in men at 30 years of age. The relationships in the present study between behavioural and socio- demographic characteristics and adult physical fitness indicate a complex social influence. For example, positive attitude to and experience of strength performance may be more common among boys than girls from the working class. Another example is the inverse relationship between attitude to dance and movement at the age of 16 and performance in two- handlift in adult men and women, which may be ex-

pected due to the very different fitness expectations in these sports activities. Further, the inverse relationship between attitude to swimming at the age of 16 and adult V02max in relation to body weight may indicate that going to the swimhall is a more comfort- able way to participate in physical education for, for example, boys and girls with low fitness. Attitudes to sports activities and social adherence at a young age are therefore important factors to take into consider- ation when planning physical education lessons in order to create positive experiences of a great variety of physical activities and to minimise adverse social effects on developing physical activity habits.

The importance of early experience of physical activity as well as socio-economic factors for adult physical activity habits were reported earlier in sev- eral studies (9-1 1, 13, 31). Dennison et al. (9) also found that childhood fitness, especially the run and sit-up tests, could predict physical activity in adult men. In the present study, the 9-min run in the men and two-hand lift performance in the women at the age of 16, together with physical activity for both men and women, were the most important predictors of adult leisure-time physical activity in this study. We have earlier seen significant relationships between the strength, muscular endurance and aerobic fitness in the women but only between endurance and aerobic fitness in the men (15). This might contribute to explain the predictive strength of the two-hand lift test in the women for, for example, physical activity.

Although significant, adolescent characteristics only explained 16% of adult leisure-time physical activity in men and women. In a subgroup of the orig- inal study group which was followed up at the age of 27, Glenmark et al. (1 1 ) found considerably higher values of 47% and 82% in men and women, respectively. One explanation for the difference between Glenmarks’s study and our results might be that the subgroup in Glenmark’s study was selected on the basis of consent to muscle biopsy and thus more ad- dicted to physical exercise. Another explanation may be changed social circumstances after the age of 27, for example child care and different kinds of jobs.

Methodological considerations Physical activity is a very complex behaviour that can be characterised in many ways and accurate assessment in general populations is difficult (32, 33). A standardised questionnaire with assessed reliability was used in this study (1 6). Standardised tables (1 8) were also used for classification of activities in terms of METs. The validity of the questionnaire used in our study has not been investigated, but there are results from earlier attempts to validate physical activity questionnaires with both positive (13, 33) and negative experiences (12, 34, 35). Kemper (12) sug-


and further research is required to identify specific inactivity risk groups.

gests that low validity, such as low correlation between self-reported physical activity and measured indicators of physical activity, for example heart rate measurements, may probably be due to different kinds of activities, and that various methods for as- sessing physical activity should be considered for different aspects of physical activity. This is supported in the present study, where physical activity was significantly associated with aerobic fitness and performance in muscular endurance but not with performance in strength or balance tests (Fig. 1, 2, Table 2). Because the activities were weighted according to their energy expenditure they did not favour activities with demands on strength, coordination or flexibility. Similar results were found by Knapik et a]. (36) regarding aerobic fitness and muscular endurance.

Variables intending to measure the same dimensions over time have a tendency to correlate with each other (autocorrelation), which is a general problem in longitudinal studies. This points to the stability of certain characteristics, e.g. muscular endurance and strength. However, in most cases correlations are low and do not seriously affect the analyses.

Although the study population was representative of 16-year-old boys and girls in the first year of upper secondary school in Sweden in 1974 ( 1 51, 30% of the boys and girls leaving the 9th year of the compulsory school did not go on to upper secondary school in 1974 (37). Low level of education in adults was earlier found to be associated with physical inactivity (1 0, 13, 38), and this would imply an overestimation of the level of physical activity and eventually physical fitness in the present study in comparison with the entire population. The drop-out at the follow-up after 18 years was 35%, but there were no significant differences between those who participated in the follow- up tests and those who did not regarding BMI, physical capacity or participation in leisure-time sports activities at the age of 16, which supports that there was no systematic drop-out bias.

Conclusion The magnitude of explanation of adult physical performance and physical activity from predictors at age 16 varied between 10% and 56% and was in general lower in the men than in the woman. The various fitness tests and physical activity were explained by different performance and physical activity predictors, and the predictors also differed between men and women. The findings about attitudes to sports and socio-demographic factors at a young age that influence adult physical activity habits and fitness, may facilitate identification of inactivity risk groups. However, the relationships between attitudes and socio-demographic factors at a young age and adult physical activity habits and fitness are very complex

Acknowledgment This study was supported by grants from the Swedish Council for Social Research, Swedish National Center for Research in Sports and the Medical Faculty, Umei University.

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Prediction of physical fitness and physical activity level in adulthood by physical performance and physical activity in adolescence - An 18-year follow-up study