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Self-perceived health among sports participants and non-sports participants
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Sot. Sci. Med. Vol. 31, No. 9. pp. %3-969, 1990 Printedin Great Britain. All rights reserved
Copyright 0 1990Pcrgamon
SELF-PERCEIVED PARTICIPANTS
HEALTH
AMONG
AND NON-SPORTS
SPORTS
PARTICIPANTS
K. L. LAMB,’K. ROBERTS’and D. A. BRODIE~ Departments
of ‘Sociology and ‘Movement Science & Physical Education, Liverpool L69 3BX, England
University of Liverpool,
Abstract-This
paper examines and compares the self-perceived health (SPH) of a sample of sports participants (n = 1385) and a matched sample of non-participants (n = 292). Ratings of health were generally found to be favourable among both samples, but a non-parametric analysis of their distributions revealed that the SPH of sports participants was significantly (P < 0.0001) better than that of the non-participants. SPH improved with age among both samples, but above the age of 34, the non-participants’ perceived health ceased to be inferior to that of participants. Controlling for age and gender revealed no difference in SPH above 24 years among males and 34 years among females. SPH was related to a variety of additional health-related factors. Multiple regression analysis was used to identify the predictors of SPH for both samples, and highlighted marked differences between them in the type and number of contributory factors. It is suggested that participation in active sports may enhance health awareness, especially among the young, and that future studies of this kind among sports populations should take account of the levels of commitment (frequency, duration and intensity) to sport. Key words-self-perceived
health, health indicators, sports participation
INTRODUCTION Modem day endeavours by national governing sports bodies to improve the heatlh of their societies have focussed on highlighting the most common behaviour-related causes of chronic, life-threatening diseases, thereby enabling individuals the opportunity to adopt more favourable health practices. Of the many life-style factors associated with health, physical activity or exercise has emerged as one being of considerable importance. Apart from the numerous physiological benefits available through physical activity [I, 21, recent research has shown that level of physical activity is positively associated with aspects of psychological well-being [3,4]. In particular, significant relationships have been identified between self-perceived health and physical activity [5,6]. The argument exists, therefore, that the promotion of sports participation and active leisure pursuits may at least be rewarded by better health perceptions. Beneficial ‘consequences’ of favourably perceived health have been observed in the form of reduced risks of mortality [7-141, positive associations with levels of morale and returning to employment quickly following a first myocardial infarction [ 15, 161,a tendency to abandon the sick role soon after heart surgery (171 and postoperative recovery following hip-fracture [ 111. If sports participation can positively modify health perception, which in turn can likewise modify health behaviour, then efforts to promote participation would be wholly justified. However, the direction of causality is difficult to establish; it is feasible that participation in sports is a consequence of previously established health behaviour patterns and health perceptions. This paper will thus examine the extent to which sports activity affects (or is affected by) self-perceived health and its associations with other
health-related measures. In so doing, sports participation has been classified in terms of (i) a total abstention from sports activity (non-participant) and (ii) an involvement on a regular basis (participant). Owing to the confounding influence of gender and age on sports paticipation, controls for these were also introduced into the analysis. METHODS Our is from larger programme research which comparing for sport in U.K. cities Cardiff, Chester, Glasgow, Liverpool London), relating these provisions levels of among different of the populations, and local changes sport and types of leisure over two year period. addition, the research is the
K. L. LAMB et al.
964
The non-participant sample comprised respondents who at the time of interview were not regularly involved in active sports. Otherwise, this sample was matched with the participants for place of residence, sex, age and social class. We can be confident, therefore, that other differences between these samples could not be due to any of the above sociodemographic factors. Self-perceived
BMI-
SMOKING-
ALCOHOL-
SLEEP-
VISITS-
MEDICINES-
ILLNESSES-
INFIRMITIES-
INABILITIES-
DIET CHANGES-
HEALTH
CHANGES-
PERCEIVED
IMPORTANCE-
ACTIVITY-
health and health -related variables
Self-perceived health (SPH) was determined from the question, ‘How would you describe your health compared to other people of your own age?’ Relative (to age) health has been assessed elsewhere [7,9, 18-201 and reflects the manner in which people prefer to report their health [20]. The options in our study were ‘Excellent’, ‘Good’, ‘Fair’, ‘Poor’ or ‘Very Poor’. Responses were subsequently ranked from 1 (Excellent) to 5 (Very Poor). Fifteen other healthrelated variables were examined as potential determinants of SPH. Below is a list of these variables and a description of their format. All possible responses were assigned a rank on a ‘favourable-unfavourable’ basis.
DOCTOR
EXERCISE-
WEIGHT-
Body mass index. An index of adiposity [21] based on values of measured height and weight (~25 or >25). Subjects indicated whether they were currently a regular smoker (yes or no). Subjects indicated whether they consumed alcohol (no or rarely or regularly-at least once per week). Subjects reported their quality of sleep (very well or satisfactory or poorly). Subjects reported if they had visited their doctor or a specialist during the past six months (no or yes-once or yes-more than once). Subjects were asked if they were regularly receiving medicines or prescribed drugs (yes or no). The number of illnesses incurred during the past six months (none or one or z-one). The number of physically limiting illnesses, disabilities or infirmities currently experienced (none or one or > one). The number of everyday tasks found to be difficult to manage (none or one or > one). The number of positive dietary changes during the past year (none or 1-3 or 4-6 or >6). The number of lifestyle changes made over the past year in order to improve/ maintain current health (none or one or two or >two). Perceived weight for own height (too heavy or about right or too light).
Subjects reported whether they felt they got enough exercise (yes or no). Subjects reported how important exercise and keeping fit were to them (very or fairly or unimportant). Subjects described their overall physical activity, relative to age, in everyday life (more active or about average or less active).
It is recognised that all variables except body mass index relied upon subjects’ self-reports, which, either through error of judgement or dishonesty, can be inaccurate. We accept that some responses such as DOCTOR VISITS, MEDICINES, or SMOKING could be validated by independent means, but this was impractical within the context of the current study. Thus, most of the variables requiring subjective responses were assumed to have face validity. With regard to reliability, the variables were not examined on a test-retest basis owing to the nature of the main research survey preventing it. We can only assume, therefore, that the subjects’ responses were indeed reliable.
Statistical
analyses
With the exception of gender, all variables were treated as ordinal data since responses could be classified in a rank-order, or at least in a favourable-unfavourable manner (for example, a BMI below 25 was coded as favourable, and above 25 as unfavourable). For each sample, appropriate non-parametric tests were employed to assess differences between observed and expected frequencies of responses (I*), and associations between pairs of ranked variables (Spearman’s rho). Inter-sample differences were assessed with an alternative to the parametric t test, the Mann-Whitney U test [22], which compares the distributions of ranked scores. The level of confidence was set at P < 0.05. Though most correctly applicable to parametric data, stepwise multiple regressions were utilised to examine the determinants of SPH for both samples. Justification for employing this technique was provided by the finding that bivariate correlations between SPH and the health-related variables were found to be almost identical when calculated as Spearman’s rho and the parametric Pearson’s r. RESULTS
Owing to the study’s selection procedure, sports participants and nonparticipants were very similar in terms of their age and class distributions (see Table 1). In addition, it can be seen from Table 1 that no significant differences (P > 0.01) existed between the two samples in terms of their education, marital quality of status, BMI, alcohol consumption, sleep, visits to the doctor, consumption of medicines, experience of illnesses, physically limiting disabilities and infirmities, the number of inabilities and positive changes in diet, and the relative perception of body weight. Sports participants did, however, (a) report
965
Self-perceived health among sports and non-sports participants
Table
I. Participant
(P) and non-participant
Males Variable
$0,
AGE t25 25-34 >34 CLASS Middle Working EDUCATIONI6 MARITAL Single Married/co-habit Divorced/separated BMI <25 >25 SMOKING No YCS ALCOHOL No/rarely Regularly SLEEP Very well Satisfadory Poorly DOCTOR VISITS None One > One MEDICINES No Yes ILLNESSES None One > One INFIRMITIES One > One INABILITIES None One > One DIET CHANGES None l-3 4-6 >6 HEALTH CHANGES None One Two >Two PERCEIVED WEIGHT Too heavy About right Too light EXERCISE Yes No IMPORTANCE VCN Faiily Unimportant ACTIVITY More activity Average Less activity ‘P < 0.01. Percentages
rounded
(NP) characteristics Overall
Females
X1
X2
P (%)
X2
3s 32 34
36 24 40
4.0
27 33 40
31 31 38
I.1
31 33 31
34 28 39
2.6
42 58
39 61
0.5
43 57
42 58
0.1
42 58
40 60
0.3
56 44
52 48
0.8
52 48
51 49
0.0
54 46
51 49
0.4
47 SO 3
47 52 I
0.6
42 53 5
38 58 4
1.5
44 52 4
42 55 3
I.5
60 40
60 40
0.02
73 27
65 35
3.6
66 34
62 38
I.4
80 20
64 36
15.0.
79 21
68 32
8.2.
80 20
66 34
23.1.
32 68
37 63
I.4
48 52
49 51
0.0
40 60
44 56
0.3
54 37 9
58 30 I2
3.3
55 35 IO
59 32 9
0.9
54 36 IO
59 31 IO
2.6
5s 27 I8
59 22 19
I.5
53 26 21
43 29 29
6.2
54 27 19
so 26 24
3.0
83 I7
87 13
0.9
79 21
74 26
1.5
81 19
80 20
0.1
81 I7 2
82 I4 4
3.2
82 IS 3
76 I8 6
4.0
81 I6 3
79 16
6.1
82 II 7
4.2
83 13 4
a4 II 5
1.3
5
82 14 4
86 I2 2
I.4
84 I2 4
90 7 3
93 5 2
1.4
89 8 3
81 I3 6
7.6
a9 8 3
86 IO 4
2.2
5s 21 19 4
57 24 17 2
1.4
SO 24 22 4
48 23 23 6
I.0
53 23 21 4
52 23 21 4
0.1
38 34 21 7
58 32 a 3
24.4.
38 35 20 6
52 33 I4 0
17.0’
38 34 20 7
5s 33
38.4.
SO 13 37
47 I7 36
1.3
39 5 56
34 II 54
10.4’
44 9 47
40
61 39
39 61
21.5.
53 47
31 69
25.2’
57
34
48.89
59 35 6
21 54 25
82.5’
55 40 4
I9 SO 31
136.0’
57 38 5
20
213.6'
54 41 5
30 57 14
31.5.
53 43 4
I8 69 I4
77.6’
54 42 4
23
up to the nearest whole unit.
II 2 7.2
14 46
52 28
63 I4
105.1*
K. L. LAMBer al.
966
Table 2. Self-perceived health of
sports
(P) and non-sports (NP) participants
Males Health rating (rank)
Excellent (I) Good (2) Fair (3) Poor/Very Poor (4) n
P (%) 26.2 53.9 I 7.I 2.8 683
Females NP
Overall NP*
W)
P (%)
20.5 55.3 20.5 3.8 132
21.9 56. I 14.5 1.4 702
W)
P W)
NP* (%I
IS.0 54.4 25.0 5.6 160
27. I 55.0 15.8 2.1 1385
Il.5 54.8 22.9 4.8 292
*Denotes that these distributions are significantly different from the corresponding butions (P < 0.001) using the Mann-Whitney U statistic.
significantly more (P c 0.01) improvements in lifestyle (health changes), (b) perceive exercise and keeping fit to be more important, and (c) describe a greater age-related level of activity than non-participants. In addition, a lower proportion of participants were smokers and a higher proportion felt they exercised enough than non-participants. Overall, 82.1% of the sporting sample rated their health as good or excellent, while only 2.1% described it as poor (see Table 2). SPH assessment was not affected by gender, though a positive relationship existed with age-group (x2 = 39.1; P < 0.0001). Almost 10% more over-34s reported their health as excellent than under-25s (Table 3). Furthermore, when subjects over 64 years were analysed separately (n = 112), it was found that the proportion now rating their health as excellent was the non-sporting sample, over 38%. Among markedly lower proportions of respondents rated their health as being excellent, whilst more fell into the fair category than did sports participants. The effect of age on SPH was similar to that of participants (x2 = 27.5; P < 0.0001) with again noticably more of the over-34s rating their health as excellent than younger respondents. The Mann-Whitney U test (Table 2) revealed that sports participants overall rated their health significantly better than nonparticipants (z = -4.60; P < 0.001). However, whilst the same analysis among females produced an almost identical result (z = -4.76; P < O.OOl), this was not so among males (z = - 1.59; P < 0.06). Extending this analysis to account for age (Table 3) identifies that above age 34 years, sports participants no longer rated their health significantly better than nonparticipants. Table 4 shows the Spearman correlation coefficients between SPH and the health-related variables. Among participants, SPH was significantly related to 10 out of 15 of the health variables (P < 0.01). The highest correlation (rho = 0.23) was with the ACTIVITY variable, followed by INFIRMITIES (rho = 0.21). Among non-partici-
sports participant
distri-
pants, SPH was related to eight health indicators, but the highest correlation was now with ILLNESSES (rho = 0.26; P < 0.01). Smoking was significantly related to SPH among the sporting sample only (rho = 0.13; P < O.Ol), whilst DIET CHANGES was only related to SPH among the non-sporting sample (rho = 0.16; P c 0.01). As might be expected, the variable IMPORTANCE was positively associated with the SPH of sports participants (rho = 0.19; P < 0.01) but not with that of non-participants. The strength and/or existence of some of the above associations can be seen from Table 4 to differ between the sexes, particularly among the non-participants. For example, whilst there was a highly significant (P < 0.01)correlation of rho = 0.35 between SPH and INFIRMITIES of female non-participants, the two variables were unrelated for males. The same trend was also found for INABILITIES. Stepwise linear regression analysis revealed that ten of the health variables contributed significantly to the variation in SPH among sports participants. Together, these variables acounted for 23% of the variance in SPH (Table 5), with INFIRMITIES accounting for the highest proportion (7%). Among non-participants, whilst only five variables remained in the regression equation, they still accounted for 26% of the variance in SPH. Of this, age was the most dominant contributor. Controlling for gender highlighted noticeable differences in the factors affected SPH (see Tables 6 and 7). Whilst the strongest predictor of SPH among both male and female sports participants was INFIRMITIES, EXERCISE was far more influential among females, and ACTIVITY likewise among males. For male non-participants, only five variables were significant predictors of SPH, although they did account for 10% more of the variance in SPH than the ten variables among sports participants. Among female non-participants, INFIRMITIES, INABILITIES and age were the only three variables that remained in the regression equation, accounting for 23% of the variance in SPH.
Table 3. Self-perceived health by sample by age group Ane NP* (“/)
Health rating Excellent Good Fair Poor/Very Poor n
22.4 52.3 23.6 1.6 428
z-34
25-34
t25
7.1 54.1 34.7 4.1 98
25. I 59.6 13.5 1.8 451
NP* (“/)
(4)
NP (%)
13.6 56.8 23.5 6.2 81
32.8 53.2 II.3 2.8 506
29.2 54.0 12.4 4.4 II3
‘Denotes that these distributions are significantly different from the corresponding butions (P < 0.001) using the Mann-Whitney LI statistic.
sports participant
distri-
Self-perceived health among sports and non-sports participants Table 4. Spearman rank-order correlations
967
between SPH and other health-related variables
Sports participants
Non-sports participants
Health variable
Males
Females
Total
Maln
Females
Total
AGE CLASS EDUCATION MARITAL PERCEIVED WEIGHT DOCTOR VISITS MEDICINES ILLNESSES INFIRMITIES INABILITIES HEALTH CHANGES ACTIVITY IMPORTANCE EXERCISE DIET CHANGES SMOKING ALCOHOL SLEEP BMI
-0.09 0.08 -0.03 -0.03 0.07 0.17’ 0.19’ 0.13’ 0.24’ 0.23. -0.02 0.27’ 0.20. 0.15. -0.04 0.14. 0.01 0.20. -0.01
-0.16. 0.08 -0.08 -0.08 0.04 0.13. 0.11’ 0.16’ 0.18’ 0.15. 0.01 0.19’ 0.19. 0.22. 0.02 0.12’ -0.02 0.14. 0.03
-0.13’ 0.08’ -0.06 -0.06 0.05 0.15. 0.149 0.14. 0.21. 0.18. -0.01 0.23. 0.19. 0.18. -0.01 0.13. -0.01 0.18’ 0.01
-0.34. 0.02 0.09 -0.20. -0.02 0.18 0.05 0.28. 0.05 0.11 0.02 0.33. -0.09 0.19 0.18 0.06 -0.07 0.28’ -0.13
-0.219 0.10 -0.10 -0.06 -0.01 0.15 0.11 0.23’ 0.35’ 0.25. 0.08 0.14 -0.01 0.10 0.11 -0.04 0.14 0.19’ 0.03
-0.25* 0.06 -0.01 -0.11 -0.01 0.18’ 0.10 0.26. 0.23. 0.20. 0.06 0.24. -0.04 0.14. 0.16’ -0.06 0.04 0.23. -0.05
lf < 0.01.
Table 5. Stepwisc linear regression of self-perceived health Sports participants (n = 1385) Independent variable
Beta
I. INFIRMITIES 2. ACTIVITY 3. SLEEP 4. AGE 5. ILLNESSES 6. INABILITIES 7. IMPORTANCE 8. SMOKING 9. EXERCISE IO. MEDICINES Total I’
0.26 0.21 0.17 -0.16 0.14 0.12 0.11 0.10 0.09 0.08
Non-participants (n = 292) Multiple r 0.26 0.33 0.37 0.40 0.42 0.44 0.45 0.46 0.47 0.47 0.23
I. 2. 3. 4. 5.
Independent variable
Beta
Multiple I
AGE INFIRMITIES ACTIVITY SLEEP ILLNESSES
0.29 0.29 0.21 0.19 0.14
0.29 0.40 0.45 0.49 0.51
0.26
All variables arc significant (P < 0.001). Beta and multiple I arc regression coefficients on entering equation. Table 6. Stew&c linear rcnression of self-txrceived health-males Sports participants (n = 683) Independent variable
Beta
I. INFIRMITIES 2. ACTIVITY 3. SLEEP 4. INABILITIES 5. AGE 6. MEDICINES 7. SMOKING 8. ILLNESSES 9. EXERCISE 10. CLASS Total r2
0.30 0.22 0.17 0.15 -0.15 0.14 0.12 0.11 0.10 0.08
Non-participants (n = 132) Multiple r 0.30 0.37 0.41 0.43 0.46 0.47 0.49 0.50 0.51 0.51 0.26
I. 2. 3. 4. 5.
Independent variable
Beta
Multiple I
AGE ACTIVITY SLEEP ILLNESSES IMPORTANCE
-0.38 0.32 0.24 0.18 0.15
0.38 0.50 0.56 0.58 0.60
0.36
All variables are significant (P < 0.0001). Beta and multiple I are regression coefficients on entering equation.
DISCUSSION
Although many previous researchers have measured self-perceived health and many others have studied the health and fitness of sports players, as far as we are aware no-one has previously compared the self-perceived health of two populations different initially only in their leisure activities. That the populations were subsequently found to differ in terms of the health-related variables
SMOKING, HEALTH CHANGES, EXERCISE, IMPORTANCE, and ACTIVITY, reemphasises the uncertainty concerning the causes and consequences of sports participation. We acknowledge that from cross-sectional data such as presented here, no direction can be assigned; it is quite feasible that behaviour such as that above may stimulate participation in sport, or may be the result of sports participation. Indeed, whilst this study clearly shows that overall, non-sports participants do not rate their health as
K. L. LAMBer (11.
968 Table 7. Steuwix
linear rearcssion
of self-oemived
Sports participants (n = 702) Independent variable I. INFIRMITIES 2. EXERCISE 3. SLEEP 4. AGE 5. IMPORTANCE 6. ILLNESSES 7. ACTIVITY 8. SMOKING Total rz
Beta 0.21 0.19 0.17 -0.17 0.16 0.1s 0.11 0.08
All variables are sinnificant (P < O.OOOik Beta and multiple ; are regrksion coefficients
health-females Non-participants (n = 160)
Multiple I 0.21 0.29 0.33 0.37 0.41 0.43 0.44 0.45 0.20 on entering
favourably as sports participants, it cannot be proven at this stage that participation (or non-participation) is the underlying cause. Owing to the fact that many previous studies have either assessed SPH in a different manner and/or have not presented a simple breakdown of response frequencies, it is difficult to compare directly the SPH ratings of our sporting population with those of other populations. In addition, it is the health perceptions of elderly populations that have received particular attention in the literature. However, the proportion of sports participants reporting their health as good or excellent (82.1%) was high and not only superior to that of our non-sporting sample, but also to that of a representative sample of 9003 male and females of the United Kingdom [23]. Of these, a total of 71.5% rated their health as good or excellent, whilst 23.1% rated it as fair (compared with 15.8% of our sports participants). The figures for the U.K. population are almost identical to those from our sample of non-sports participants. Amongst the oldest sports participants, that is those above 64 years (not shown in the Results), almost 84% perceived their health as good or better, which compares favourably with studies reported by Linn and Linn [19] and Mossey and Shapiro [20], for whom only 68% and 62.2% respectively of their respondents (aged 65 and over) rated their health in the same way. Our results confirm previous findings that show the elderly to report their health as good as, if not better than younger people. Whilst this was true for both our samples, the differences were greater among the non-sports participants. Cockerham et al. [24] observed that persons aged 18-60 had similar health perceptions, but beyond 60 years SPH (relative to someone of the same age) improved considerably. With our sports participants, this improvement followed a continual trend throughout, reaching a peak of ‘excellent’ responses in the oldest age group. Whilst the same trend was apparent among the non-participants, an outstanding feature of their response distribution was the very small proportion of under-25s rating their health as excellent (7.1%), and the high proportion rating their health as only fair (34.7%). The next youngest age group (25-34) also perceived their health noticeably less favourably than the oldest group. Although the sports participants overall rated their health more favourably than non-participants, age was found to be a confounding factor. By age 35,
Independent vanable I. INFIRMITIES 2. AGE 3. INABILITIES
Beta 0.37 -0.24 0.21
Multi& I 0.37 0.44 0.48
0.23 equation.
sports participation seemed to have no effect on SPH, making it difficult at first glance to present a healthperception promoting case for sports participation during later life. Also the effect of gender on SPH cannot be overlooked, especially among a sporting population in which, because of a variety of social factors, males are typically over-represented. Female sports participants perceived, like the total sample, their health to be significantly (P < 0.0001) more favourable than female non-participants. The same was not true for males. The reasons for the above findings are undoubtedly rooted in the factors that determine or influence a person’s health perception. Situational or acute events, such as influenza or a broken limb (that may require medication and doctor visits, affect quality of sleep and such), can cause a real deviation from a ‘usual’ health status. Reports have supported, however, the notion that perceived health is fairly stable over time [25-271 and therefore resilient to such events. Further aspects of the study from which this report has been compiled will address this issue of stability and which factors continue to be related to SPH over time. Within this cross-sectional analysis, bivariate and multivariate analyses have re-affirmed the validity of this popular measure of SPH, though clear gender and age differences in the determinants of SPH emerged between the sporting and nonsporting samples. Tables 5-7 show sports participants to be influenced in their health perceptions by a wider range of factors than non-participants. This may reflect a greater awareness or appreciation of health-a sense of positive health-that is not fashioned just by illness or disability. Involvement in sport could be a catalyst in the development of this awareness, particularly among the young, although type (of sport) and persistence are also likely determinants. Indeed, by eliminating from our analysis those respondents who participated in only non-energetic sports (n = 175), such as bowls and snooker, the self-perceived health of male participants was subsequently found to be better (P c 0.03) than that of the male non-participants. CONCLUSIONS
Based on the premise that self-perceived health is independently associated with, amongst other things, well-being and mortality, a comparison of
Self-perceived health among sports and non-sports participants
the self-perceived health of sports participants and non-sports participants has revealed an apparent beneficial effect of participation in active sports. Overall, sports participants rated their health more highly than non-participants. However, controlling for gender showed there no longer to be a difference in perceived health among males. Controlling for age group showed significant differences only below 35 years (females) and 25 years (males). Though observed bivariate correlations with other health-related variables suggested that our SPH item was indeed measuring health, the low-order and inconsistency of these correlations confirm the difficulty of establishing just what determines how a person perceives his or her health. Furthermore, although multiple regression analyses yielded much unexplained variance in SPH, they did highlight marked sample differences in the pattern and number of correlates of SPH. Before satisfactory conclusions can be reached regarding the effects of sports activity or exercise on health perception, and the possible knock-on effects in terms of health behaviour, future examinations among sporting populations will need to distinguish between different levels of commitment, thus breaking down participation according to its frequency, duration and intensity. Acknowledgemenr-The
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15. Garrity T. F. Social involvement and activeness as predictors of morale six months after first myocardial infarction. Sot. Sci. Med. 7, 199-207, 1973. 16. Garrity T. F. Vocational adjustment after first myocardial infarction: comparative assessment of several variables suggested in the literature. Sot. Sci. Med. 7, 705-717, 1973. 17. Brown J. S. and Rawlinson M. Relinquishing the sick role following open-heart surgery. J. Hlrh sot. Behar. 16, 12-27, 1975.
18. Wan T. T. H. Predicting self-assessed health status: a multivariate approach. Hlrh Sent. Res. 11, 464477, 1976.
19. Linn B. S. and Linn M. W. Objective and self-assessed health in the old and very old. Sot. Sci. Med. 14A, 311-315, 1980. 20. Blaxter M. Self-definition of health status and consulting rates in primary care. Q. J. Sot. Afiirs 1, 131-I 71, 1985. 21. Keys A., Fidanza F., Karvonen M. J., Kimora N. and Taylor H. L. Indices of relative weight and obesity. J. chron. Dis. 25, 329-343, 1972. Statistics for the S. Nonparametric McGraw-Hill Kogakusha, Behavioral Sciences.
22. Siegel
Tokyo, 1956. 23. Health Promotion Research Trust. Health and Lifestyle Sumey. Health Promotion Research Trust, London, 1987. 24. Cockerham W. C., Sharp K. and Wilcox J. A. Aging and perceived health status. J. Geronf. 38, 349-355, 1983. 25. Maddox G. L. Self-assessment of health status: a longitudinal study of selected elderly subjects. J. chron. Dis. 17, 44940, 1964. 26. Lamb K. L., Roberts K. and Brodie D. A. Health assessment and sports participation among indoor sports participants. Paper presented at The Leisure Studies Association Annual Conference, University of Leeds, 20-22 March, 1989. 27. Goldstein M. S., Siegel J. M. and Boyer R. Predicting changes in perceived health status. Am. 1. publ. HIth 74, 611614. 1984.
Sot. Sci. Med. Vol. 31, No. 9. pp. %3-969, 1990 Printedin Great Britain. All rights reserved
Copyright 0 1990Pcrgamon
SELF-PERCEIVED PARTICIPANTS
HEALTH
AMONG
AND NON-SPORTS
SPORTS
PARTICIPANTS
K. L. LAMB,’K. ROBERTS’and D. A. BRODIE~ Departments
of ‘Sociology and ‘Movement Science & Physical Education, Liverpool L69 3BX, England
University of Liverpool,
Abstract-This
paper examines and compares the self-perceived health (SPH) of a sample of sports participants (n = 1385) and a matched sample of non-participants (n = 292). Ratings of health were generally found to be favourable among both samples, but a non-parametric analysis of their distributions revealed that the SPH of sports participants was significantly (P < 0.0001) better than that of the non-participants. SPH improved with age among both samples, but above the age of 34, the non-participants’ perceived health ceased to be inferior to that of participants. Controlling for age and gender revealed no difference in SPH above 24 years among males and 34 years among females. SPH was related to a variety of additional health-related factors. Multiple regression analysis was used to identify the predictors of SPH for both samples, and highlighted marked differences between them in the type and number of contributory factors. It is suggested that participation in active sports may enhance health awareness, especially among the young, and that future studies of this kind among sports populations should take account of the levels of commitment (frequency, duration and intensity) to sport. Key words-self-perceived
health, health indicators, sports participation
INTRODUCTION Modem day endeavours by national governing sports bodies to improve the heatlh of their societies have focussed on highlighting the most common behaviour-related causes of chronic, life-threatening diseases, thereby enabling individuals the opportunity to adopt more favourable health practices. Of the many life-style factors associated with health, physical activity or exercise has emerged as one being of considerable importance. Apart from the numerous physiological benefits available through physical activity [I, 21, recent research has shown that level of physical activity is positively associated with aspects of psychological well-being [3,4]. In particular, significant relationships have been identified between self-perceived health and physical activity [5,6]. The argument exists, therefore, that the promotion of sports participation and active leisure pursuits may at least be rewarded by better health perceptions. Beneficial ‘consequences’ of favourably perceived health have been observed in the form of reduced risks of mortality [7-141, positive associations with levels of morale and returning to employment quickly following a first myocardial infarction [ 15, 161,a tendency to abandon the sick role soon after heart surgery (171 and postoperative recovery following hip-fracture [ 111. If sports participation can positively modify health perception, which in turn can likewise modify health behaviour, then efforts to promote participation would be wholly justified. However, the direction of causality is difficult to establish; it is feasible that participation in sports is a consequence of previously established health behaviour patterns and health perceptions. This paper will thus examine the extent to which sports activity affects (or is affected by) self-perceived health and its associations with other
health-related measures. In so doing, sports participation has been classified in terms of (i) a total abstention from sports activity (non-participant) and (ii) an involvement on a regular basis (participant). Owing to the confounding influence of gender and age on sports paticipation, controls for these were also introduced into the analysis. METHODS Our is from larger programme research which comparing for sport in U.K. cities Cardiff, Chester, Glasgow, Liverpool London), relating these provisions levels of among different of the populations, and local changes sport and types of leisure over two year period. addition, the research is the
K. L. LAMB et al.
964
The non-participant sample comprised respondents who at the time of interview were not regularly involved in active sports. Otherwise, this sample was matched with the participants for place of residence, sex, age and social class. We can be confident, therefore, that other differences between these samples could not be due to any of the above sociodemographic factors. Self-perceived
BMI-
SMOKING-
ALCOHOL-
SLEEP-
VISITS-
MEDICINES-
ILLNESSES-
INFIRMITIES-
INABILITIES-
DIET CHANGES-
HEALTH
CHANGES-
PERCEIVED
IMPORTANCE-
ACTIVITY-
health and health -related variables
Self-perceived health (SPH) was determined from the question, ‘How would you describe your health compared to other people of your own age?’ Relative (to age) health has been assessed elsewhere [7,9, 18-201 and reflects the manner in which people prefer to report their health [20]. The options in our study were ‘Excellent’, ‘Good’, ‘Fair’, ‘Poor’ or ‘Very Poor’. Responses were subsequently ranked from 1 (Excellent) to 5 (Very Poor). Fifteen other healthrelated variables were examined as potential determinants of SPH. Below is a list of these variables and a description of their format. All possible responses were assigned a rank on a ‘favourable-unfavourable’ basis.
DOCTOR
EXERCISE-
WEIGHT-
Body mass index. An index of adiposity [21] based on values of measured height and weight (~25 or >25). Subjects indicated whether they were currently a regular smoker (yes or no). Subjects indicated whether they consumed alcohol (no or rarely or regularly-at least once per week). Subjects reported their quality of sleep (very well or satisfactory or poorly). Subjects reported if they had visited their doctor or a specialist during the past six months (no or yes-once or yes-more than once). Subjects were asked if they were regularly receiving medicines or prescribed drugs (yes or no). The number of illnesses incurred during the past six months (none or one or z-one). The number of physically limiting illnesses, disabilities or infirmities currently experienced (none or one or > one). The number of everyday tasks found to be difficult to manage (none or one or > one). The number of positive dietary changes during the past year (none or 1-3 or 4-6 or >6). The number of lifestyle changes made over the past year in order to improve/ maintain current health (none or one or two or >two). Perceived weight for own height (too heavy or about right or too light).
Subjects reported whether they felt they got enough exercise (yes or no). Subjects reported how important exercise and keeping fit were to them (very or fairly or unimportant). Subjects described their overall physical activity, relative to age, in everyday life (more active or about average or less active).
It is recognised that all variables except body mass index relied upon subjects’ self-reports, which, either through error of judgement or dishonesty, can be inaccurate. We accept that some responses such as DOCTOR VISITS, MEDICINES, or SMOKING could be validated by independent means, but this was impractical within the context of the current study. Thus, most of the variables requiring subjective responses were assumed to have face validity. With regard to reliability, the variables were not examined on a test-retest basis owing to the nature of the main research survey preventing it. We can only assume, therefore, that the subjects’ responses were indeed reliable.
Statistical
analyses
With the exception of gender, all variables were treated as ordinal data since responses could be classified in a rank-order, or at least in a favourable-unfavourable manner (for example, a BMI below 25 was coded as favourable, and above 25 as unfavourable). For each sample, appropriate non-parametric tests were employed to assess differences between observed and expected frequencies of responses (I*), and associations between pairs of ranked variables (Spearman’s rho). Inter-sample differences were assessed with an alternative to the parametric t test, the Mann-Whitney U test [22], which compares the distributions of ranked scores. The level of confidence was set at P < 0.05. Though most correctly applicable to parametric data, stepwise multiple regressions were utilised to examine the determinants of SPH for both samples. Justification for employing this technique was provided by the finding that bivariate correlations between SPH and the health-related variables were found to be almost identical when calculated as Spearman’s rho and the parametric Pearson’s r. RESULTS
Owing to the study’s selection procedure, sports participants and nonparticipants were very similar in terms of their age and class distributions (see Table 1). In addition, it can be seen from Table 1 that no significant differences (P > 0.01) existed between the two samples in terms of their education, marital quality of status, BMI, alcohol consumption, sleep, visits to the doctor, consumption of medicines, experience of illnesses, physically limiting disabilities and infirmities, the number of inabilities and positive changes in diet, and the relative perception of body weight. Sports participants did, however, (a) report
965
Self-perceived health among sports and non-sports participants
Table
I. Participant
(P) and non-participant
Males Variable
$0,
AGE t25 25-34 >34 CLASS Middle Working EDUCATION
rounded
(NP) characteristics Overall
Females
X1
X2
P (%)
X2
3s 32 34
36 24 40
4.0
27 33 40
31 31 38
I.1
31 33 31
34 28 39
2.6
42 58
39 61
0.5
43 57
42 58
0.1
42 58
40 60
0.3
56 44
52 48
0.8
52 48
51 49
0.0
54 46
51 49
0.4
47 SO 3
47 52 I
0.6
42 53 5
38 58 4
1.5
44 52 4
42 55 3
I.5
60 40
60 40
0.02
73 27
65 35
3.6
66 34
62 38
I.4
80 20
64 36
15.0.
79 21
68 32
8.2.
80 20
66 34
23.1.
32 68
37 63
I.4
48 52
49 51
0.0
40 60
44 56
0.3
54 37 9
58 30 I2
3.3
55 35 IO
59 32 9
0.9
54 36 IO
59 31 IO
2.6
5s 27 I8
59 22 19
I.5
53 26 21
43 29 29
6.2
54 27 19
so 26 24
3.0
83 I7
87 13
0.9
79 21
74 26
1.5
81 19
80 20
0.1
81 I7 2
82 I4 4
3.2
82 IS 3
76 I8 6
4.0
81 I6 3
79 16
6.1
82 II 7
4.2
83 13 4
a4 II 5
1.3
5
82 14 4
86 I2 2
I.4
84 I2 4
90 7 3
93 5 2
1.4
89 8 3
81 I3 6
7.6
a9 8 3
86 IO 4
2.2
5s 21 19 4
57 24 17 2
1.4
SO 24 22 4
48 23 23 6
I.0
53 23 21 4
52 23 21 4
0.1
38 34 21 7
58 32 a 3
24.4.
38 35 20 6
52 33 I4 0
17.0’
38 34 20 7
5s 33
38.4.
SO 13 37
47 I7 36
1.3
39 5 56
34 II 54
10.4’
44 9 47
40
61 39
39 61
21.5.
53 47
31 69
25.2’
57
34
48.89
59 35 6
21 54 25
82.5’
55 40 4
I9 SO 31
136.0’
57 38 5
20
213.6'
54 41 5
30 57 14
31.5.
53 43 4
I8 69 I4
77.6’
54 42 4
23
up to the nearest whole unit.
II 2 7.2
14 46
52 28
63 I4
105.1*
K. L. LAMBer al.
966
Table 2. Self-perceived health of
sports
(P) and non-sports (NP) participants
Males Health rating (rank)
Excellent (I) Good (2) Fair (3) Poor/Very Poor (4) n
P (%) 26.2 53.9 I 7.I 2.8 683
Females NP
Overall NP*
W)
P (%)
20.5 55.3 20.5 3.8 132
21.9 56. I 14.5 1.4 702
W)
P W)
NP* (%I
IS.0 54.4 25.0 5.6 160
27. I 55.0 15.8 2.1 1385
Il.5 54.8 22.9 4.8 292
*Denotes that these distributions are significantly different from the corresponding butions (P < 0.001) using the Mann-Whitney U statistic.
significantly more (P c 0.01) improvements in lifestyle (health changes), (b) perceive exercise and keeping fit to be more important, and (c) describe a greater age-related level of activity than non-participants. In addition, a lower proportion of participants were smokers and a higher proportion felt they exercised enough than non-participants. Overall, 82.1% of the sporting sample rated their health as good or excellent, while only 2.1% described it as poor (see Table 2). SPH assessment was not affected by gender, though a positive relationship existed with age-group (x2 = 39.1; P < 0.0001). Almost 10% more over-34s reported their health as excellent than under-25s (Table 3). Furthermore, when subjects over 64 years were analysed separately (n = 112), it was found that the proportion now rating their health as excellent was the non-sporting sample, over 38%. Among markedly lower proportions of respondents rated their health as being excellent, whilst more fell into the fair category than did sports participants. The effect of age on SPH was similar to that of participants (x2 = 27.5; P < 0.0001) with again noticably more of the over-34s rating their health as excellent than younger respondents. The Mann-Whitney U test (Table 2) revealed that sports participants overall rated their health significantly better than nonparticipants (z = -4.60; P < 0.001). However, whilst the same analysis among females produced an almost identical result (z = -4.76; P < O.OOl), this was not so among males (z = - 1.59; P < 0.06). Extending this analysis to account for age (Table 3) identifies that above age 34 years, sports participants no longer rated their health significantly better than nonparticipants. Table 4 shows the Spearman correlation coefficients between SPH and the health-related variables. Among participants, SPH was significantly related to 10 out of 15 of the health variables (P < 0.01). The highest correlation (rho = 0.23) was with the ACTIVITY variable, followed by INFIRMITIES (rho = 0.21). Among non-partici-
sports participant
distri-
pants, SPH was related to eight health indicators, but the highest correlation was now with ILLNESSES (rho = 0.26; P < 0.01). Smoking was significantly related to SPH among the sporting sample only (rho = 0.13; P < O.Ol), whilst DIET CHANGES was only related to SPH among the non-sporting sample (rho = 0.16; P c 0.01). As might be expected, the variable IMPORTANCE was positively associated with the SPH of sports participants (rho = 0.19; P < 0.01) but not with that of non-participants. The strength and/or existence of some of the above associations can be seen from Table 4 to differ between the sexes, particularly among the non-participants. For example, whilst there was a highly significant (P < 0.01)correlation of rho = 0.35 between SPH and INFIRMITIES of female non-participants, the two variables were unrelated for males. The same trend was also found for INABILITIES. Stepwise linear regression analysis revealed that ten of the health variables contributed significantly to the variation in SPH among sports participants. Together, these variables acounted for 23% of the variance in SPH (Table 5), with INFIRMITIES accounting for the highest proportion (7%). Among non-participants, whilst only five variables remained in the regression equation, they still accounted for 26% of the variance in SPH. Of this, age was the most dominant contributor. Controlling for gender highlighted noticeable differences in the factors affected SPH (see Tables 6 and 7). Whilst the strongest predictor of SPH among both male and female sports participants was INFIRMITIES, EXERCISE was far more influential among females, and ACTIVITY likewise among males. For male non-participants, only five variables were significant predictors of SPH, although they did account for 10% more of the variance in SPH than the ten variables among sports participants. Among female non-participants, INFIRMITIES, INABILITIES and age were the only three variables that remained in the regression equation, accounting for 23% of the variance in SPH.
Table 3. Self-perceived health by sample by age group Ane NP* (“/)
Health rating Excellent Good Fair Poor/Very Poor n
22.4 52.3 23.6 1.6 428
z-34
25-34
t25
7.1 54.1 34.7 4.1 98
25. I 59.6 13.5 1.8 451
NP* (“/)
(4)
NP (%)
13.6 56.8 23.5 6.2 81
32.8 53.2 II.3 2.8 506
29.2 54.0 12.4 4.4 II3
‘Denotes that these distributions are significantly different from the corresponding butions (P < 0.001) using the Mann-Whitney LI statistic.
sports participant
distri-
Self-perceived health among sports and non-sports participants Table 4. Spearman rank-order correlations
967
between SPH and other health-related variables
Sports participants
Non-sports participants
Health variable
Males
Females
Total
Maln
Females
Total
AGE CLASS EDUCATION MARITAL PERCEIVED WEIGHT DOCTOR VISITS MEDICINES ILLNESSES INFIRMITIES INABILITIES HEALTH CHANGES ACTIVITY IMPORTANCE EXERCISE DIET CHANGES SMOKING ALCOHOL SLEEP BMI
-0.09 0.08 -0.03 -0.03 0.07 0.17’ 0.19’ 0.13’ 0.24’ 0.23. -0.02 0.27’ 0.20. 0.15. -0.04 0.14. 0.01 0.20. -0.01
-0.16. 0.08 -0.08 -0.08 0.04 0.13. 0.11’ 0.16’ 0.18’ 0.15. 0.01 0.19’ 0.19. 0.22. 0.02 0.12’ -0.02 0.14. 0.03
-0.13’ 0.08’ -0.06 -0.06 0.05 0.15. 0.149 0.14. 0.21. 0.18. -0.01 0.23. 0.19. 0.18. -0.01 0.13. -0.01 0.18’ 0.01
-0.34. 0.02 0.09 -0.20. -0.02 0.18 0.05 0.28. 0.05 0.11 0.02 0.33. -0.09 0.19 0.18 0.06 -0.07 0.28’ -0.13
-0.219 0.10 -0.10 -0.06 -0.01 0.15 0.11 0.23’ 0.35’ 0.25. 0.08 0.14 -0.01 0.10 0.11 -0.04 0.14 0.19’ 0.03
-0.25* 0.06 -0.01 -0.11 -0.01 0.18’ 0.10 0.26. 0.23. 0.20. 0.06 0.24. -0.04 0.14. 0.16’ -0.06 0.04 0.23. -0.05
lf < 0.01.
Table 5. Stepwisc linear regression of self-perceived health Sports participants (n = 1385) Independent variable
Beta
I. INFIRMITIES 2. ACTIVITY 3. SLEEP 4. AGE 5. ILLNESSES 6. INABILITIES 7. IMPORTANCE 8. SMOKING 9. EXERCISE IO. MEDICINES Total I’
0.26 0.21 0.17 -0.16 0.14 0.12 0.11 0.10 0.09 0.08
Non-participants (n = 292) Multiple r 0.26 0.33 0.37 0.40 0.42 0.44 0.45 0.46 0.47 0.47 0.23
I. 2. 3. 4. 5.
Independent variable
Beta
Multiple I
AGE INFIRMITIES ACTIVITY SLEEP ILLNESSES
0.29 0.29 0.21 0.19 0.14
0.29 0.40 0.45 0.49 0.51
0.26
All variables arc significant (P < 0.001). Beta and multiple I arc regression coefficients on entering equation. Table 6. Stew&c linear rcnression of self-txrceived health-males Sports participants (n = 683) Independent variable
Beta
I. INFIRMITIES 2. ACTIVITY 3. SLEEP 4. INABILITIES 5. AGE 6. MEDICINES 7. SMOKING 8. ILLNESSES 9. EXERCISE 10. CLASS Total r2
0.30 0.22 0.17 0.15 -0.15 0.14 0.12 0.11 0.10 0.08
Non-participants (n = 132) Multiple r 0.30 0.37 0.41 0.43 0.46 0.47 0.49 0.50 0.51 0.51 0.26
I. 2. 3. 4. 5.
Independent variable
Beta
Multiple I
AGE ACTIVITY SLEEP ILLNESSES IMPORTANCE
-0.38 0.32 0.24 0.18 0.15
0.38 0.50 0.56 0.58 0.60
0.36
All variables are significant (P < 0.0001). Beta and multiple I are regression coefficients on entering equation.
DISCUSSION
Although many previous researchers have measured self-perceived health and many others have studied the health and fitness of sports players, as far as we are aware no-one has previously compared the self-perceived health of two populations different initially only in their leisure activities. That the populations were subsequently found to differ in terms of the health-related variables
SMOKING, HEALTH CHANGES, EXERCISE, IMPORTANCE, and ACTIVITY, reemphasises the uncertainty concerning the causes and consequences of sports participation. We acknowledge that from cross-sectional data such as presented here, no direction can be assigned; it is quite feasible that behaviour such as that above may stimulate participation in sport, or may be the result of sports participation. Indeed, whilst this study clearly shows that overall, non-sports participants do not rate their health as
K. L. LAMBer (11.
968 Table 7. Steuwix
linear rearcssion
of self-oemived
Sports participants (n = 702) Independent variable I. INFIRMITIES 2. EXERCISE 3. SLEEP 4. AGE 5. IMPORTANCE 6. ILLNESSES 7. ACTIVITY 8. SMOKING Total rz
Beta 0.21 0.19 0.17 -0.17 0.16 0.1s 0.11 0.08
All variables are sinnificant (P < O.OOOik Beta and multiple ; are regrksion coefficients
health-females Non-participants (n = 160)
Multiple I 0.21 0.29 0.33 0.37 0.41 0.43 0.44 0.45 0.20 on entering
favourably as sports participants, it cannot be proven at this stage that participation (or non-participation) is the underlying cause. Owing to the fact that many previous studies have either assessed SPH in a different manner and/or have not presented a simple breakdown of response frequencies, it is difficult to compare directly the SPH ratings of our sporting population with those of other populations. In addition, it is the health perceptions of elderly populations that have received particular attention in the literature. However, the proportion of sports participants reporting their health as good or excellent (82.1%) was high and not only superior to that of our non-sporting sample, but also to that of a representative sample of 9003 male and females of the United Kingdom [23]. Of these, a total of 71.5% rated their health as good or excellent, whilst 23.1% rated it as fair (compared with 15.8% of our sports participants). The figures for the U.K. population are almost identical to those from our sample of non-sports participants. Amongst the oldest sports participants, that is those above 64 years (not shown in the Results), almost 84% perceived their health as good or better, which compares favourably with studies reported by Linn and Linn [19] and Mossey and Shapiro [20], for whom only 68% and 62.2% respectively of their respondents (aged 65 and over) rated their health in the same way. Our results confirm previous findings that show the elderly to report their health as good as, if not better than younger people. Whilst this was true for both our samples, the differences were greater among the non-sports participants. Cockerham et al. [24] observed that persons aged 18-60 had similar health perceptions, but beyond 60 years SPH (relative to someone of the same age) improved considerably. With our sports participants, this improvement followed a continual trend throughout, reaching a peak of ‘excellent’ responses in the oldest age group. Whilst the same trend was apparent among the non-participants, an outstanding feature of their response distribution was the very small proportion of under-25s rating their health as excellent (7.1%), and the high proportion rating their health as only fair (34.7%). The next youngest age group (25-34) also perceived their health noticeably less favourably than the oldest group. Although the sports participants overall rated their health more favourably than non-participants, age was found to be a confounding factor. By age 35,
Independent vanable I. INFIRMITIES 2. AGE 3. INABILITIES
Beta 0.37 -0.24 0.21
Multi& I 0.37 0.44 0.48
0.23 equation.
sports participation seemed to have no effect on SPH, making it difficult at first glance to present a healthperception promoting case for sports participation during later life. Also the effect of gender on SPH cannot be overlooked, especially among a sporting population in which, because of a variety of social factors, males are typically over-represented. Female sports participants perceived, like the total sample, their health to be significantly (P < 0.0001) more favourable than female non-participants. The same was not true for males. The reasons for the above findings are undoubtedly rooted in the factors that determine or influence a person’s health perception. Situational or acute events, such as influenza or a broken limb (that may require medication and doctor visits, affect quality of sleep and such), can cause a real deviation from a ‘usual’ health status. Reports have supported, however, the notion that perceived health is fairly stable over time [25-271 and therefore resilient to such events. Further aspects of the study from which this report has been compiled will address this issue of stability and which factors continue to be related to SPH over time. Within this cross-sectional analysis, bivariate and multivariate analyses have re-affirmed the validity of this popular measure of SPH, though clear gender and age differences in the determinants of SPH emerged between the sporting and nonsporting samples. Tables 5-7 show sports participants to be influenced in their health perceptions by a wider range of factors than non-participants. This may reflect a greater awareness or appreciation of health-a sense of positive health-that is not fashioned just by illness or disability. Involvement in sport could be a catalyst in the development of this awareness, particularly among the young, although type (of sport) and persistence are also likely determinants. Indeed, by eliminating from our analysis those respondents who participated in only non-energetic sports (n = 175), such as bowls and snooker, the self-perceived health of male participants was subsequently found to be better (P c 0.03) than that of the male non-participants. CONCLUSIONS
Based on the premise that self-perceived health is independently associated with, amongst other things, well-being and mortality, a comparison of
Self-perceived health among sports and non-sports participants
the self-perceived health of sports participants and non-sports participants has revealed an apparent beneficial effect of participation in active sports. Overall, sports participants rated their health more highly than non-participants. However, controlling for gender showed there no longer to be a difference in perceived health among males. Controlling for age group showed significant differences only below 35 years (females) and 25 years (males). Though observed bivariate correlations with other health-related variables suggested that our SPH item was indeed measuring health, the low-order and inconsistency of these correlations confirm the difficulty of establishing just what determines how a person perceives his or her health. Furthermore, although multiple regression analyses yielded much unexplained variance in SPH, they did highlight marked sample differences in the pattern and number of correlates of SPH. Before satisfactory conclusions can be reached regarding the effects of sports activity or exercise on health perception, and the possible knock-on effects in terms of health behaviour, future examinations among sporting populations will need to distinguish between different levels of commitment, thus breaking down participation according to its frequency, duration and intensity. Acknowledgemenr-The
project on which this paper is based is supported by the Health Promotion Research Trust. REFERENCES I. American
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3.
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