Childhood and Adolescent Sports Participation as Predictors of Participation in Sports and Physical Fitness Activities During Young Adulthood

Abstract

This study examined whether organized sports participation during childhood and adolescence was related to participation in sports and physical fitness activities in young adulthood. The data were from the Michigan Study of Adolescent Life Transitions. The analyses include more than 600 respondents from three waves of data (age 12, age 17, and age 25). Childhood and adolescent sports participation was found to be a significant predictor of young adults’ participation in sports and physical fitness activities.

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Youth & Society
DOI: 10.1177/0044118X03261619
2004; 35; 495 Youth Society
Daniel F. Perkins, Janis E. Jacobs, Bonnie L. Barber and Jacquelynne S. Eccles
Fitness Activities During Young Adulthood
Childhood and Adolescent Sports Participation as Predictors of Participation in Sports and Physical
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10.1177/0044118X03261619ARTICLEYOUTH & SOCIETY / JUNE 2004Perkins et al. / SPORTS PARTICIPATION
CHILDHOOD AND ADOLESCENT
SPORTS PARTICIPATION AS
PREDICTORS OF PARTICIPATION IN
SPORTS AND PHYSICAL FITNESS
ACTIVITIES DURING YOUNG
ADULTHOOD
DANIEL F. PERKINS
JANIS E. JACOBS
The Pennsylvania State University
BONNIE L. BARBER
University of Arizona
JACQUELYNNE S. ECCLES
University of Michigan
This study examined whether organized sports participation during childhood and
adolescence was related to participation in sports and physical fitness activities in
young adulthood. The data were from the Michigan Study of Adolescent Life Transi-
tions. The analyses include more than 600 respondents from three waves of data (age
12, age 17, and age 25). Childhood and adolescent sports participation was found to
be a significant predictor of young adults’participation in sports and physical fitness
activities.
Keywords: sports participation; physical fitness; childhood; adolescence; young
adulthood
Physical activity and fitness have a well-established and empiri-
cally demonstrated positive influence on the health and well-being of
all individuals. Indeed, as a part of the Healthy People 2010 initiative,
the U.S. surgeon general outlined a 4-point prescription for health that
included as one of its pillars, “moderate physical activity, at least 5
495
YOUTH & SOCIETY, Vol. 35 No. 4, June 2004 495-520
DOI: 10.1177/0044118X03261619
© 2004 Sage Publications
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days a week, 30 minutes a day” (Satcher, 1999, p. 3). This pillar is im-
portant because the sedentary lifestyles and physical inactivity of
some Americans increase their risk for many chronic diseases, includ-
ing heart disease, stroke, colon cancer, diabetes, and osteoporosis (U.
S. Department of Health and Human Services, 2000). Regular physi-
cal activity in adults has been positively related to physical fitness and
inversely related to a variety of diseases (Blair, Jacobs, & Powell,
1985; Bouchard, Shepard, Stephens, Sutton, & McPherson, 1990;
Satcher, 1999; Vanreusel et al., 1993).
The sedentary lifestyle and physical inactivity can be seen in data
related to youth. According to the Centers for Disease Control and
Prevention (CDC, 2003), approximately 30% of students, age 12 to 17
years, are not getting an appropriate amount of physical exercise. For
instance, in 2001, 31% of students did not participate in at least 20 min
of vigorous physical activity on 3 or more of the past 7 days and did
not do at least 30 min of moderate physical activity on 5 or more of the
past 7 days. Moreover, approximately 10% of the students report not
participating in any vigorous or moderate physical activity during the
past 7 days (CDC, 2003).
Regular physical activity throughout the life cycle has been found
to be beneficial to physical fitness, yet there is a lack of empirical evi-
dence as to whether sports participation and physical activity in child-
hood and adolescence are related to later physical fitness in adulthood
496 YOUTH & SOCIETY / JUNE 2004
AUTHORS’ NOTE: This study was supported by The Penn State Agricultural Ex-
perimentation Project Number 3826. The data for this article were drawn from the
Michigan Study of Life Transitions (MSALT). MSALT has been funded by grantsfrom
National Institute of Child Health and Human Development (NICHD), National In-
stitutes of Mental Health, National Science Foundation, the Spencer Foundation, and
the William T. Grant Foundation to Jacquelynne Eccles and to Bonnie Barber.We
wish also to thank the following people for their contributions over the years to this
project: Carol Midgley, Allan Wigfield, David Reuman, Harriet Feldlaufer, Douglas
Mac Iver, Constance Flanagan, Christy Miller Buchanan, Andrew Fuligni, Deborah
Josefowicz, Pam Frome, Lisa Colarossi, Amy Arbreton, Laurie Meschke, Kim
Updegraff, Kristen Jacobson, Miriam Linver, Mina Vida, James Hunt, Margaret
Stone, and Sun-A Lee. Correspondence concerning this article should be directed to
Daniel F. Perkins, Ph.D., Associate Professor, Family and Youth Resiliency and Pol-
icy, Department of Agricultural and Extension Education, 323 Agricultural Adminis-
tration Building, The Pennsylvania State University, University Park, PA, 16802-
2601; e-mail: dfp102@psu.edu.
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(Malina, 1995, 1996; Vanreusel et al., 1992). Existing data suggest a
relationship between childhood and adolescent physical activity and
adult physical activity; however, the evidence for such links has been
limited (Malina, 1995, 1996) because most studies conducted in the
United States have been cross-sectional (Engstrom, 1991; Malina,
1994) or based on retrospective data. For example, retrospective stud-
ies provide evidence that participation in competitive sports in child-
hood or youth increases the probability of being physically active in
adulthood (Boucher & Shepard, 1974; Paffenbarger, Hyde, Wing, &
Steinmetz, 1984). A few longitudinal studies conducted in Europe
found evidence of the importance of childhood and adolescent physi-
cal activity (often organized sports) for continued involvement in
physical activity during adulthood (Engstrom, 1991; Frandin,
Mellstrom, Sundh, & Grimby, 1995; Kuh & Cooper, 1992, Vanreusel
et al., 1993).
This study employs longitudinal data to examine sports participa-
tion at three points in the lifespan (i.e., childhood, adolescence, and
young adulthood). The current study examines the relations between
organized sports participation during childhood and adolescence and
participation in sports and physical fitness activities during young
adulthood, focusing on the roles of specific demographic and environ-
mental factors.
CONTINUITY OF PHYSICAL ACTIVITY
Engstrom (1991) conducted a longitudinal study in Sweden, exam-
ining male individuals’and female individuals’participation in sports
activities from age 15 to age 30. After 19% attrition, the sample in-
cluded 1,675 participants for analysis. Based on a self-report measure,
those participants who had more prior experiences with sport and
physical activity at age 15 were found to possess a higher psychologi-
cal readiness for physical activity at age 30. Psychological readiness
for activity was defined as having a more positive view of the body and
its capabilities in sport, and a more positive attitude toward fitness ac-
tivities. Specifically, Engstrom (1991) found that individuals who at
age 15 participated in sport activities for at least 4 hr per week had
high grades in physical education and belonged to sport clubs were
Perkins et al. / SPORTS PARTICIPATION 497
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twice as likely to be psychologically ready for physical activity at age
30 than those who had not. He also found that four environmental fac-
tors were significantly linked to physical activity at age 30: a physi-
cally active spouse/mate, friends who were physically active, absence
of children, and high educational attainment. Finally, more male indi-
viduals (24%) were likely to devote themselves to keeping fit through
physical activity than female individuals (16%) (Engstrom, 1991).
Another Swedish longitudinal study was completed to examine the
extent to which physical activity in adulthood (age 27) can be pre-
dicted by physical characteristics, performance, and activity in ado-
lescence (age 16) (Glenmark, Hedberg, & Jansson, 1994). The sample
was composed of 62 male individuals and 43 female individuals.
Physical activity at age 16 was a self-report measure that included
number of physical activities, number of competitive activities, dura-
tion in months, and duration in minutes per week. Physical activity at
age 16 predicted 53% and 28% of the physical activity index at age 27
for women and for men, respectively. Similar to Engstrom’s (1991)
findings, female individuals were less likely than male individuals to
participate in physical activities at age 16 and age 27. In addition, the
authors reported that women at age 27 who had children under age 2
were less likely to be involved in physical fitness activities than
women without children under age 2. The same pattern of gender dif-
ferences has not been found in every study, however. For example, the
Amsterdam Growth Study (Mechelen & Kemper, 1995) examined ha-
bitual physical activity using information gathered from semi-
structured interviews with 182 adolescents over a period of almost 15
years. No differences were found between male adolescents and fe-
male adolescents in overall weekly time spent on habitual physical ac-
tivity, light, and medium-heavy physical activity; however, male ado-
lescents spent significantly more time than female adolescents on
heavy physical activities (Mechelen & Kemper, 1995).
In a longitudinal study of Belgian male individuals’sport participa-
tion, Vanreusel and colleagues (Vanreusel et al., 1993) followed 278
male individuals from age 13 to 30 years. The self-report measure in-
cluded the amount of time (i.e., hours per week considered over the
whole year) spent on sport participation during the year prior to the in-
quiry. The data were collected at ages 13, 14, 15, 16, and 17. The high-
est correlation (.39) was between sport participation at age 17 and at
498 YOUTH & SOCIETY / JUNE 2004
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age 30. Although the correlations between the other ages were low,
they were significant, except for the correlation between sports partic-
ipation at age 13 and at age 30. According to the same study, 38% of
sport nonparticipants at age 17 were active at age 30; however, 73% of
those who had been very active in youth (age 17) continued to be ac-
tive at age 30. These results provide evidence for a relationship be-
tween adolescent physical activity and adulthood physical activity for
Belgian male individuals (Vanreusel et al., 1993). Similar findings
were reported by Telama, Yang, Laakso, and Viikari (1997) in the na-
tional longitudinal study titled, Cardiovascular Risk in Young Finns.
In addition, they found that long-lasting participation in organized
sports (3 or more years) during adolescence makes a difference in
physical activity in young adulthood.
In a study of Danish youth followed from age 15 to 19 to age 23 to
27 years, a similar result was found for male youth but not for female
youth (Andersen & Haraldsdottir, 1993). Of the older adolescent male
individuals in the inactive group for physical activity, 53% remained
in that same group in their mid-20s, whereas only 8% of the older ado-
lescent female individuals remained in the inactive group during the
same period. The findings from these three studies suggest that there
is continuity and stability with regard to physical inactivity for male
individuals from adolescence to early adulthood.
Data from one U. S. longitudinal study were employed by Scott and
Willits (1989, 1998) to examine linkages between adolescent leisure
participation and involvement in similar activities in later life. Their
data were drawn from the Lives Through Time longitudinal study
(Willits & Crider, 1999) that began in 1947 with sophomores in rural
high schools across Pennsylvania. There were five leisure types of ac-
tivities that they examined: socializing activities (e.g., dancing, par-
ties, church socials, going to parks, and movies); creative activities
(e.g., drawing or painting, singing, writing poetry, and sewing); intel-
lectual activities (e.g., reading and studying); sports activities (e.g.,
tennis, baseball, ping pong, engaging in sports, bicycling, skating, and
climbing or hiking); and participation in formal organizations (e.g.,
fraternal or community organizations such as 4-H). Adolescent par-
ticipation was a positive and significant predictor of adult participa-
tion for all five types of activities, even after controlling for gender,
health rating, education, and income (Scott & Willits, 1989, 1998).
Perkins et al. / SPORTS PARTICIPATION 499
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The multiple regression analysis for adolescents’ sport participation
was highly significant. These findings support their conclusion from
an earlier study (Scott & Willits, 1989), in which leisure activity par-
ticipation in middle and later adulthood was linked to participation in
similar activities in adolescence. Moreover, Scott and Willits (1989,
1998) found significant relationships between education and sports
participation in adulthood and between gender and sports participa-
tion. Individuals with a higher level of education were more likely to
participate in sports than individuals with lower education levels.
Male individuals participated in sports significantly more often than
female individuals.
In summary, the research from the studies just reviewed indicates
that physical activity and sport participation in childhood and adoles-
cence are significant predictors of physical activity in adulthood.
However, the relationship is typically low and, in some cases, insignif-
icant (Telama et al., 1997). Moreover, no studies reviewed here exam-
ined whether mediation effects could potentially exist between child-
hood participation in sports, adolescent participation in sports, and
young adulthood participation in sports or physical fitness activities.
In addition to examining continuity of sports participation, several
studies investigated demographic and environmental variables to ex-
amine other influences on childhood, adolescent, and adult participa-
tion in sports or physical activities. As noted earlier, male individuals
appear to be more physically active in childhood and in adulthood
(Engstrom, 1991; Glenmark et al., 1994; Scott & Willits, 1998), espe-
cially in terms of intense activity (Mechelen & Kemper, 1995). How-
ever, not all studies found these gender differences. Socioeconomic
status (SES) of a child’s family was not found to be a significant pre-
dictor of a child’s participation in sports (Telama et al., 1997); how-
ever, a longitudinal study of 1,100 Icelandic adolescents conducted by
Vilhjalmsson and Thorlindsson (1998) found that higher social class
or SES was linked with more physical activity among adolescents.
Adolescents with families in the highest income bracket were most
likely to be in the highest category of moderate to vigorous physical
activity (Vilhjalmsson & Thorlindsson, 1998). In addition, Scott and
Willits (1989, 1998) found that SES predicted adults’ participation in
sports, with higher SES increasing the likelihood of sports participa-
tion (Gordon-Larsen, McMurray, & Popkin, 2000). Level of educa-
500 YOUTH & SOCIETY / JUNE 2004
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tion of the individual has also been found to be a significant predictor
of physical activity in adulthood (Dennison, Strauss, Mellits, &
Charnley, 1988; Scott & Willits, 1989, 1998; Stephens, Jacobs, &
White, 1985). The presence of children was found to significantly af-
fect an adult’s participation in physical activities and sports
(Glenmark et al., 1994).
Finally, the relationship between sports participation in childhood
and adolescence and participation in physical fitness activities has
been noted by the American Academy of Pediatrics’ Committee on
Sports Medicine and Fitness and Committee on School Health (2000)
and the CDC (1997). In his review of the literature related to physical
activity across the lifespan, Malina (2001) concluded that “the trends
emphasize the importance of a lifestyle of regular physical activity
during child and adolescence, which continues into and throughout
adulthood, for the health and well-being of the individual and the pop-
ulation” (p. 170). Yet as he noted more longitudinal research is needed
that examines physical fitness from childhood through adulthood.
The current study offers a unique opportunity to contribute to our
understanding of the continuity of sports participation in three ways.
First, the current study examined the contributions of childhood and
adolescent sports participation to sports participation in young adult-
hood and also considered the ways in which child and adolescent
sports participation interact with each other. Previous studies often
have examined only the adolescent period. Second, the current study
examined separately the relations between early sports participation
and two different outcome variables: participation in sports and physi-
cal fitness in young adulthood. We believe this is an important distinc-
tion because physical activity in adulthood is related to better health,
and such activities may not be found only in sports. Third, the current
investigation examined demographic (gender) and environmental fac-
tors in childhood (e.g., family SES and family structure) and young
adulthood (e.g., education level, SES, martial status, and children) be-
cause these factors have been predictive of varying levels of engage-
ment in physical activity and in sports during adulthood.
Employing a longitudinal study from the Midwest region of the
United States, the purpose of the current study was to examine
whether sports participation in childhood (age 12) and adolescence
(age 17) predicts sports participation and/or physical fitness activities
Perkins et al. / SPORTS PARTICIPATION 501
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in young adulthood (age 24). Specifically, the following questions
were addressed:
• Is sports participation during childhood and adolescence related to
sports participation during young adulthood, and what are the roles of
gender, SES, and family structure in sports participation during young
adulthood?
• Is sports participation during childhood and adolescence related to
physical fitness activities during young adulthood, and what are the
roles of gender, SES, and family structure in physical fitness activities
during young adulthood?
• Is continued sports participation during adolescence needed for active
participation in sports or physical fitness activities during young adult-
hood?
STUDY DESIGN AND SAMPLE
The data were drawn from the Michigan Study of Adolescent Life
Transitions (MSALT; Eccles & Barber, 1999). This longitudinal study
began (in 1983) with a cohort of sixth graders from 10 school districts
in southeastern Michigan. A majority of the sample is White and co-
mes from working- and middle-class families living in small indus-
trial cities around Detroit. Eccles and colleagues (Eccles et al., 1983)
followed approximately 1,800 of these youth through eight waves of
data, beginning in the sixth grade (1983-1984) and continuing into
1996-1997, when most were age 24 to 25 years. The analyses pre-
sented here include more than 600 respondents who completed the
survey items about sports and physical fitness from three waves of
data (Wave 1 at age 12, Wave 6 at age 17, and Wave 8 at age 25).
The children and adolescents were administered an extensive inter-
view during class time in school, with items tapping a wide range of
constructs. The surveys were mailed to young adults, completed, and
returned via postage-paid envelopes. On completion of the survey,
participants were sent $20. The constructs employed in the current
study are summarized below. Ethnicity was not employed in the cur-
rent study because there were empty cells in the analysis due to the
small sample size of ethnically diverse students. The demographic
variables of family origin are presented first, followed by the demo-
502 YOUTH & SOCIETY / JUNE 2004
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graphic variable of the young adult, and then the independent and the
dependent variables.
MEASURES
Family structure. This variable was assessed based on the mothers’
responses to questionnaires collected at Wave 1 (children in sixth
grade). The variable was dichotomized to characterize children who
lived in single-parent homes versus two-parent homes. The frequen-
cies for this variable are provided in Table 1.
Parental education. There were two items that dealt with parents’
education at Wave 1. The first was “What is the highest level of
schooling your father completed?” The second item was the same, ex-
cept that it addressed the mother’s education level. The range of
choices was 1 (completed grade school or less), 2 (some high school),
3(completed high school), 4 (some college), 5 (completed college),
and 6 (graduate or professional school after college). The highest
level of education obtained by either parent was the variable used in
the analysis. Parental education was used as an indicator for SES. The
frequencies for this variable are provided in Table 1.
Family income. One item dealt with the family’s current SES and
was completed by the parent during the first interview. The question
was “From all your sources of income, please indicate your total
household income.” There were nine response choices that ranged
from 1 (less than $5,000), 2 (between $5,000 and $9,999), 3 (between
$10,000 and $19,999), 4 (between $20,000 and $29,999), 5 (between
$30,000 and $39,999), 6 (between $40,000 and $49,999), 7 (between
$50,000 and $59,999), 8 (between $60,000 and $69,999), to 9 (more
than $70,000). These choices were condensed into four categories:
less than $20,000; between $20,000 and $40,000; between $40,000
and $60,000; and more than $60,000. The frequencies for this vari-
able are provided in Table 1.
Young adult marital status. This item had four response choices: “I
am married”; “I am living with someone in a steady, marriage-like re-
lationship”; “I am not living with him or her, but I have a steady, ro-
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504 YOUTH & SOCIETY / JUNE 2004
TABLE 1
Sample Characteristics (N = 1,127)
Variable Percentage (n) Percentage Missing (n)
Gender 2.0 (23)
Male 37.4 (422)
Female 62.6 (682)
Family structure 37.5 (423)
One-parent household 6.6 (75)
Two-parent household 55.8 (704)
Mother’s education 37.7 (425)
High school or less 32.8 (370)
Some college 12.8 (144)
College degree 13.3 (150)
Graduate studies 3.4 (38)
Father’s education 38.0 (428)
High school or less 27.2 (307)
Some college 13.4 (151)
College degree 16.8 (189)
Graduate studies 4.6 (52)
Family income 37.7 (425)
$20,000 or less 6.6 (74)
$20,001 to $40,000 14.5 (163)
$40,001 to $60,000 15.6 (176)
More than $60,000 12.3 (139)
Young adult martial status 0.4 (4)
Single 59.4 (669)
Married 40.3 (454)
Young adult education level 0.4 (5)
High school or less 17.6 (198)
Some college 42.1 (475)
College degree 28.6 (323)
Graduate studies 11.2 (126)
Young adult parental status 10.6 (119)
No children 71.9 (810)
Have at least one child 17.6 (198)
Young adult socioeconomic status 4.2 (47)
$20,000 or less 38.3 (432)
$20,001 to $40,000 37.3 (420)
$40,001 to $60,000 15.3 (172)
More than $60,000 5.0 (56)
Childhood sports participation 12.4 (140)
Low 15.7 (177)
Medium 34.0 (383)
High 37.9 (427)
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mantic relationship with one person”; or “none of the above.” The first
two choices were combined, and the last two choices were combined
to create a dichotomous variable. The frequencies for this variable are
provided in Table 1.
Young adult education level. There was one item that dealt with the
young adult’s education level at Wave 8. The question was “What is
the highest level of schooling you have completed?” The response cat-
egories were grouped into four choices: 1 (completed less than high
school, high school diploma, or GED), 2 (some college), 3 (completed
college), and 4 (graduate studies). The frequencies for this variable
are provided in Table 1.
Young adult’s parental status. There was one item that addressed
whether the young adult wanted or had any children of his or her own.
The question was “Do you want to have children in the future?” The
response choices were grouped into several choices: 1 (have chil-
dren), 2 (expecting), 3 (hope soon), 4 (sometime), 5 (unsure), 6 (prefer
not). For the current study, we focused on whether the young adults re-
ported having a child.
Young adult socioeconomic status. One item dealt with the young
adult’s current SES. The question was “From all your sources of in-
Perkins et al. / SPORTS PARTICIPATION 505
Adolescent sports participation 36.9 (416)
Low 22.7 (256)
Medium 24.5 (276)
High 15.9 (179)
Young adult sports participation 0.01 (7)
Low 52.6 (593)
Medium 24.4 (320)
High 18.4 (207)
Young adult fitness participation 0.9 (11)
Low 14.8 (167)
Medium 47.8 (539)
High 36.4 (410)
TABLE 1 (continued)
Variable Percentage (n) Percentage Missing (n)
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come, please indicate your total household income.” The respon-
dent was asked not to include his or her parents’ income in this figure.
There were nine response choices, ranging from 1 (less than $5,000),
2(between $5,000 and $9,999), 3 (between $10,000 and $19,999), 4
(between $20,000 and $29,999), 5 (between $30,000 and $39,999), 6
(between $40,000 and $49,999), 7 (between $50,000 and $59,999), 8
(between $60,000 and $69,999), to 9 (more than $70,000). These
choices were condensed into four categories: less than $20,000, be-
tween $20,000 and $40,000, between $40,000 and $60,000, and
more than $60,000. The frequencies for this variable are provided
in Table 1.
Sports participation in childhood. One item addressed children’s
participation in sports: “How much time do you spend on sports?” The
range of choices was 1 (less than 15 min a day), 2 (15 to 30 min a day),
3(30 min to an hour a day), and 4 (an hour or more a day). The item
was recoded as low (less than 15 min a day), medium (15 min to an
hour a day), or high (an hour or more a day).
Sports participation in adolescence. The scale for adolescents’
sports participation was derived from two items. These items were
“Think about the kinds of things you usually do after school and on
weekends. About how many hours do you usually spend each week
taking part in organized sport? Doing other athletic or sport activi-
ties?” All the items from this scale were scored on an 8-point scale: 1
(none), 2 (1 hr or less), 3 (2to3hr), 4 (4to6hr), 5 (7to10hr), 6 (11 to
15 hr), 7 (16 to 20 hr), and 8 (21 or more hr). The mean of the re-
sponses to the items was calculated. The Cronbach’s alpha for this
scale was .72. The scale was recoded as low sports participation
(none), medium (greater than 0 to 3 hr), and high (4 or more hr).
Sports participation in young adulthood. One item addressed
young adults’ sports participation. The item was “Think about the
kinds of things you usually do each week. About how many hours do
you usually spend each week . . . doing organized and/or competitive
athletic or sports activities?” This item was scored on an 8-point scale:
1(none), 2 (1 hr or less), 3 (2to3hr), 4 (4to6hr), 5 (7to10hr), 6 (11
to 15 hr), 7 (16 to 20 hr), and 8 (21 or more hr). The scale was recoded
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as low sports participation (none), medium (greater than 0 to 3 hr),
and high (4 or more hr).
Fitness participation in young adulthood. One item dealt with
young adults’ fitness participation: “Think about the kinds of things
you usually do each week. About how many hours do you usually
spend each week ...exercising or doing other fitness activities?” This
item was scored on an 8-point scale: 1 (none), 2 (1 hr or less), 3 (2to3
hr), 4 (4to6hr), 5 (7to10hr), 6 (11 to 15 hr), 7 (16 to 20 hr), and 8 (21
or more hr). The scale was recoded as low fitness participation (none),
medium (greater than 0 to 3 hr), and high (4 or more hr).
RESULTS
Several analyses were conducted to address the questions of the
current study. The analyses are presented in terms of the questions that
they addressed. First, a pattern matrix was created using the data
waves to examine sports participation over time (see Table 2). Due to
attrition and missing data, the sample for this analysis was composed
of 652 individuals. The results of the pattern matrix indicated that
most individuals had participated in some sports between the ages of
12 and 24 years. Approximately 7% of the sample reported not partic-
ipating in any sport at any time during the 14-year period, whereas
36% of the sample reported participating in sport during all three
Perkins et al. / SPORTS PARTICIPATION 507
TABLE 2
Participation in Sports Across Waves (Ages 12, 17, and 24)
Sports Participation Number (N = 652) Percentage
No participation 48 7.4
Participation in all waves 237 36.3
Wave 1 participation only 90 13.8
Wave 6 participation only 43 6.6
Wave 8 participation only 5 0.8
Waves 1 and 6 participation 162 24.8
Waves 1 and 8 participation 30 4.6
Waves 6 and 8 participation 37 5.7
NOTE: Percentage is the valid percentage that does not take missing numbers (1,819) into ac-
count.
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waves. Many individuals (24%) reported participating in sports dur-
ing Waves 1 and 2. However, less that 1% of the sample reported only
participating during Wave 3 (age 24).
LOGISTIC REGRESSION
Logistic regression was used to fit the models (see Figure 1). Logis-
tic regression was used because the dependent variable is dichoto-
mous. In addition, the logistic regression analysis was employed be-
cause it yields a probability of the event, the probability is transformed
into an odds (Vogt, 1993). A full model with main effects was fitted,
and backward elimination was used to obtain a reduced model. A term
was removed from the model if the corresponding p value was greater
than .20; this is a conservative criterion, and note that the level of sig-
nificance, alpha, remains at .05. In addition, the final reduced model
was selected based on the deviance and Pearson’s goodness-of-fit sta-
tistics, in which a large p value indicates a good model fit.
Question 1: Is sports participation during childhood and adolescence re-
lated to sports participation during young adulthood, and what are the
roles of gender, SES, and family structure in sports participation dur-
ing young adulthood?
The first logistic regression model examined sports participation in
young adulthood as the dependent variable with the following inde-
508 YOUTH & SOCIETY / JUNE 2004
Time in
Sports at
6
th
grade
Time in
Sports at
11
th
grade
Time in
sports at
age 28
Time in
fitness
activities at
age 28
Demographics at age 28:
Gender, Education level, SES,
Martial Status, Children
Demographics of Individual and
Family:
Gender, (ethnicity?), Family
Income, Parent’s Martial Status
FIGURE 1: The Overall Model to Be Tested
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pendent variables: gender, sports participation in childhood, family
structure, parental education, young adult education level, young
adult SES, young adult marital status, young adult parental status, and
sports participation in adolescence. Gender and sports participation in
adolescence were found to be significant predictors of sports partici-
pation in young adulthood. Although not statistically significant,
young adult education level and young adult parental status remained
in the model because removing these variables resulted in a large drop
in the deviance statistics (see Table 3). This model was based on 630
observations (individuals with complete records). The deviance and
Pearson goodness-of-fit statistics indicated a good model fit (1.03 and
.92, respectively). Two odds were estimated in this model: the odds of
a high level of participation versus a medium or a low level of
participation, and the odds of a high or medium level of participation
versus a low level of participation.
According to the odds ratio, men have twice the odds of participat-
ing in sports as young adults than women when high versus medium
or low levels of participation are compared and when low versus high
or medium levels of participation are compared. In addition, those
who participated in a medium amount of sports as adolescents are 3.67
times more likely to participate in sports as young adults than those
Perkins et al. / SPORTS PARTICIPATION 509
TABLE 3
Logistic Regression of Predictors of Sports Participation
in Youth Adulthood (N = 630)
Parameter β SE Wald df p
Gender 0.70 0.17 17.12 1 .0001
Young adult education .— .— 2.23 3 .5269
High school or less .— .— .—— .—
Some college –0.08 0.27 0.10 1 .7485
College degree –0.12 0.29 0.1844 1 .6677
Graduate studies –0.46 0.35 1.75 1 .1856
Young adult
Martial status –0.45 0.25 3.20 1 .0737
Adolescents’ sports
Participation .— .— 81.26 2 .0001
Low .— .— .—— .—
Medium 1.30 0.21 38.99 1 .0001
High 2.13 2.37 80.54 1 .0001
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who rated their adolescent sports participation as low, for both odds.
Adolescents who indicated a high amount of sports participation are
approximately 8 times more likely to participate in sports as young
adults than those adolescents who rated their adolescent sports partic-
ipation as low, for both odds.
Question 2: Is sports participation during childhood and adolescence re-
lated to physical fitness activities during young adulthood, and what
are the roles of gender, SES, and family structure in physical fitness ac-
tivities during young adulthood?
The second logistic regression examined young adult participation
in fitness activities as the dependent variable with the same independ-
ent variables noted in the first logistic regression: gender, sports par-
ticipation in childhood, family structure, parental education, young
adult education level, young adult SES, young adult marital status,
young adult parental status, and sports participation in adolescence.
Young adult education level and sports participation in adolescence
were found to be significant predictors of sports participation in
young adulthood (see Table 4). Although not statistically significant,
gender and family income remained in the model because removing
these variables resulted in a large drop in the deviance statistics. This
model was based on 411 observations (individuals with complete re-
cords). The deviance and Pearson goodness-of-fit statistics indicated
a moderate model fit (1.10 and .99, respectively). As before, two odds
were estimated in this model: the odds of a high level of participation
versus a medium or low level of participation, and the odds of a high or
medium level of participation versus a low level of participation.
As seen in the odds ratio, a person with a college degree or with
some graduate studies is more than 2 times (2.32 times and 2.28 times,
respectively) more likely to participate in fitness activities than a per-
son with no formal education beyond high school. In addition, an indi-
vidual with a medium level of adolescent sport participation is 2 times
(2.09) as likely to participate in fitness activities as an individual with
a low level of sports participation as an adolescent. Moreover, an indi-
vidual with a high level of adolescent sports participation is 3.49 times
more likely to participate in fitness activities as an adult than an indi-
vidual with a low level of sports participation as an adolescent.
510 YOUTH & SOCIETY / JUNE 2004
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TEST FOR MEDIATION
Question 3: Is continued sports participation during adolescence needed
for active participation in sports or physical fitness during young
adulthood?
Given that participation in sports during childhood has been found
in previous research to be related to sports participation in adoles-
cence, the third analysis involved a test of adolescent sports participa-
tion as a mediator between childhood sport participation and partici-
pation in sports during young adulthood. A variable functions as a
mediator when the following conditions are satisfied (Baron &
Kenney, 1986): (a) the initial variable (i.e., childhood sports participa-
tion) is correlated with the outcome (i.e., young adulthood sports par-
ticipation); (b) the initial variable is correlated with the potential me-
diator (i.e., adolescent sports participation); (c) the potential mediator
affects the outcome variable; and (d) the potential mediator com-
pletely mediates the relationship between the initial variable and the
outcome variable. If all four criteria are met, then there is enough evi-
Perkins et al. / SPORTS PARTICIPATION 511
TABLE 4
Logistic Regression of Predictors of Participation in Physical Fitness
Activities in Youth Adulthood (N = 411)
Parameter β SE Wald df p
Gender –0.33 0.20 2.67 1 .1026
Family income —— 6.85 3 .0768
$20,000 or less —— — — —
$20,001 to $40,000 0.45 0.33 1.90 1 .1683
$40,001 to $60,000 0.11 0.32 0.11 1 .7403
More than $60,000 0.67 0.34 3.98 1 .0462
Young adult education ——14.19 3 .0027
High school or less —— — — —
Some college 0.12 0.31 0.15 1 .7020
College degree 0.84 0.32 6.99 1 .0082
Graduate studies 0.83 0.39 4.47 1 .0344
Adolescents’ sports
Participation ——22.17 2 .0001
Low —— — — —
Medium 0.74 0.23 10.23 1 .001
High 1.25 0.27 21.24 1 .0001
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dence to establish the existence of complete mediation. If three steps
are met but the last one is not, then partial mediation is indicated.
Logistic regression was again employed to test the mediation.
Demographic variables (i.e., gender, family’s SES, martial status)
were held constant. The relationship between childhood participation
in sports and young adulthood participation in sports was completely
mediated by adolescent sports participation. A different result was
found when examining the outcome of physical fitness in young
adulthood. The relationship between childhood participation in sports
and young adulthood participation in physical fitness was only par-
tially mediated by adolescent sports participation because Step 4 of
testing for a complete mediator was not met.
Using the two logistic regressions from the last section as a back-
drop, two more logistic regressions were conducted to further investi-
gate the mediation effects. Thus, sports participation in adolescence
was removed from the equation to investigate whether sports partici-
pation in childhood would be a significant predictor of sports partici-
pation in young adulthood. The complete equation included sports
participation in young adulthood as the dependent variable with the
following independent variables: gender, sports participation in child-
hood, family structure, parental education, young adult education
level, young adult SES, young adult marital status, and young adult
parental status. Sports participation in childhood, gender, young adult
education level, and young adult marital status were found to be sig-
nificant predictors of sports participation in young adulthood. For the
final logistic regression, sports participation in adolescence was re-
moved from the equation to investigate whether sports participation in
childhood would be a significant predictor of physical fitness partici-
pation in young adulthood. The complete equation included physical
fitness participation in young adulthood as the dependent variable
with the following independent variables: gender, sports participation
in childhood, family structure, parental education, young adult educa-
tion level, young adult SES, young adult marital status, and young
adult parental status. Sports participation in childhood, young adult
education level, and young adult marital status were found to be sig-
nificant predictors of sports participation in young adulthood.
512 YOUTH & SOCIETY / JUNE 2004
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DISCUSSION
The current study’s primary goal was to examine whether orga-
nized sports participation during childhood and adolescence influ-
enced individuals’behavior as young adults in terms of their participa-
tion in sports and physical fitness activities. We began by focusing on
the relationships between sports participation during childhood and
adolescence with young adult’s sport participation and physical fit-
ness participation and the roles of gender, SES, and family structure.
Two logistic regressions were conducted to identify predictors of
young adults’sports participation and predictors of young adults’par-
ticipation in physical fitness activities. In both logistic regressions,
adolescent sports participation was found to be a significant pre-
dictor of young adults’ sports participation and participation in
physical fitness activities. The influence of adolescent sports par-
ticipation on adult sports participation and fitness is consistent with
findings from several European longitudinal studies (Glenmark et al.,
1994; Mechelen & Kemper, 1995; Telama et al., 1997; Vanreusel
et al., 1993). For example, Telama and colleagues (Telama et al.,
1997) found that a significant relationship existed between sports par-
ticipation at ages 9, 12, 15, and 18 and sports participation at age 30.
These findings led Engstrom (1991) to state, “The results strongly in-
dicate that early experience with physical activity during childhood
and adolescence, within school as well as during leisure time, is of im-
portance for the practice of keep-fit activities in adulthood” (p. 480).
In addition, the level of participation in sports during adoles-
cence had a significant influence on the young adults’sports partic-
ipation. For example, adolescents who reported being highly in-
volved in sports (i.e., 4 or more hr a week) were 8 times more likely
to participate in sports as young adults than adolescents who rated
their sports participation as low (i.e., spent no time in competitive ath-
letic or sports activities). Vanreusel and his colleagues (Vanreusel
et al., 1993) did not find a similar relationship between level of sports
participation during adolescence and sports participation at age 35.
However, several other researchers (Dennison et al., 1988; Glenmark
et al., 1994; Vanreusel et al., 1993) found a similar relationship
Perkins et al. / SPORTS PARTICIPATION 513
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between level of activity in childhood and adolescence and physi-
cal activity in adulthood. For example, Dennison and his colleagues
(Dennison et al., 1988) found that low childhood fitness scores were
predictive of physical inactivity in young men.
In our investigation, gender was found to be a significant predictor
of young adulthood sports participation but not of young adulthood
participation in physical fitness activities. Male individuals were
twice as likely to participate in sports as young adults than women, re-
gardless of level of participation (i.e., low, medium, or high). This
finding corroborates a previous finding from the study conducted by
Scott and Willits (1989, 1998). Although gender remained in the lo-
gistic regression equation for young adulthood participation in physi-
cal fitness, it was not a significant predictor. Although little research
has been conducted on this topic, Glenmark and his colleagues
(Glenmark et al., 1994) found that women devoted significantly less
time to physical activity than men at ages 16 and 27. The difference
may be due to changing norms in terms of female individuals’ partici-
pation in physical fitness activities, or it may be that other household
responsibilities preclude women’s spending time on sports and physi-
cal fitness. Nevertheless, female individuals need to be encouraged to
stay involved.
In addition, we found that young adult education level was a signif-
icant predictor of young adulthood participation in physical fitness
activities. Indeed, a young adult with a college degree is more than 2
times as likely to participate in fitness activities as a person with no
formal education beyond high school. Our finding that young adult
education level is a significant predictor of young adulthood partici-
pation in fitness activities, but not a significant predictor of young
adulthood sports participation, mirrors a study conducted by Kuh and
Cooper (1992) with individuals from the United Kingdom. Utilizing
the Medical Research Council National Survey of Health and Devel-
opment, a longitudinal study that employed a social class stratified
sample of single legitimate births that occurred during a week in
March, 1946 in England, Wales, and Scotland, Kuh and Cooper
(1992) examined factors in childhood and adolescence that predicted
high rates of participation in sports and recreational activities in adults
at age 36. They found that better educated individuals were more
likely to report some activities rather than none at all for leisure pur-
514 YOUTH & SOCIETY / JUNE 2004
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suits but not for sports. However, unlike our results, Kuh and Cooper
(1992) did not find that better educated individuals were any more
likely to participate at high levels of physical fitness activity than
those with less education. As noted earlier, we did not find young
adult education level to be predictive of young adulthood participa-
tion in sports. This contradicts Scott and Willits’(1989, 1998) finding
that education level was predictive of adults’ participation in sports.
Our research findings contribute to this area of research by provid-
ing unique evidence of the importance of early sports participation in
several ways. First, we find that individuals are not likely to begin par-
ticipating in sports if they have not participated in the past. Indeed,
less than 1% of this sample initiated participation in sports as young
adults, yet 36% of individuals participated in sports at all three waves.
Second, we find that gender plays a significant role in young adult-
hood participation in sports but not in young adult participation in fit-
ness. Third, our findings indicate that education level has a significant
role in physical fitness activities of young adults but not in young
adults’ sports participation.
We were particularly interested in the role of continued sports par-
ticipation as a mediator between early sports participation and later
participation in adulthood. We found that adolescent sports participa-
tion completely mediated the relationship between childhood sports
participation and participation in sports during young adulthood. In
addition, adolescent sports participation partially mediated the rela-
tionship between childhood sports participation and participation in
physical fitness activities during young adulthood. Using the models
from Questions 1 and 2, two logistic regressions were conducted with-
out the sports participation in adolescence variable to test whether
sports participation in childhood was a significant predictor of young
adults’ participation in sports and fitness activities. As in the Telama
et al. (1997) and the Engstrom (1991) studies, childhood participation
in sports (age 12) was a significant predictor of young adulthood
sports participation and also of young adulthood sports participation
for the current study. Thus, the lack of significance of childhood
sports participation in the logistic regressions from Questions 1 and 2
seems to be adolescents’ sports participation mediating the relation-
ship, rather than childhood sports participation nonpredictiveness. On
the contrary, childhood participation in sports was found to be a sig-
Perkins et al. / SPORTS PARTICIPATION 515
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nificant predictor of young adulthood sports participation and of
young adulthood physical fitness participation when adolescent
sports participation was removed from the equation. This suggests
that sports participation during early adolescence is likely to lead to
greater participation in adulthood, underscoring the importance of
getting youth involved in sport activities so that they develop lifelong
habits that include physical fitness.
Although the current study provided a unique opportunity to exam-
ine much needed research on the transitional years from adolescence
to adulthood for an American sample (Malina, 1996), it contained
several limitations. First, the measures are all based on self-reports of
children, adolescents, young adults, and parents, without validating
external measures. Clearly, having more objective measures of fitness
would have provided stronger data about the level of participation in
fitness activities. Second, this use of various definitions of sports par-
ticipation between waves increased the likelihood for measurement
error across time. The complexity of defining sports participation
(e.g., level and type) has been noted by several scholars (Engstrom,
1986, 1991; Malina, 1996). Third, the sample was drawn from one
Midwest town and comprised mostly European American individu-
als. In addition, the income data that were available seem to suggest
that the sample appears to be mostly middle class. Participation in
sports often involves a cost, thus the role of income may be an impor-
tant moderating variable. Given these concerns about the sample, gen-
eralizations must be made with caution. Fourth, although the attrition
was reasonable (1127, 62%) case-wise deletion for the two logistic re-
gressions decreased our numbers to be 56% and 36% of the available
data. Despite these limitations, the current study provided important
new evidence of the significant role of childhood and adolescent
sports participation in young adults’ participation in sports and their
participation in physical activity.
CONCLUSIONS
More than 40 million children, youth, and teenagers play sports in
school or within the community (Stryler, Tofler, & Lapchick, 1998).
516 YOUTH & SOCIETY / JUNE 2004
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Yet many children drop out of sports by their adolescence, and various
studies identified a noticeable decline in physical activity as children
pass through adolescence (Malina, 1996; Taylor, Blair, Cummings,
Wun, & Malina, 1999). The current study affirms the salience of
childhood and adolescent sports participation in shaping individuals’
participation in sports and fitness activities in later life. Given the re-
sults of the current study, future research is needed to examine the de-
terminants of participation in sports and fitness activities for adoles-
cents and adults and the relationship between them. Future applied
research is needed to identify and understand what motivates children
and adolescents to become engaged and stay engaged with organized
sports and what causes them to leave. For example, Taylor and col-
leagues (Taylor et al., 1999) found that being forced or encouraged to
exercise during childhood actually decreased the likelihood of contin-
ued activity in adulthood. Does this same relationship hold true for ad-
olescents? How can adults, communities, and policies foster skills and
habits in children and adolescents that encourage healthier lifestyles
without turning them off? In addition, future research is needed to
identify characteristics of quality sport experiences and what other
nonathletic skills and competencies are fostered through such oppor-
tunities.
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Pennsylvania State University, Department of Agricultural Economics and Rural Sociology.
Daniel F. Perkins is an associate professor of family and youth resiliency and policyin the
Department of Agricultural and Extension Education at The Pennsylvania State Univer-
sity. A human ecologist, he received a Ph.D. in family and child ecology in 1995 from
Michigan State University. His research examines factors and assets related to a young
person’s development, including community youth development, youth’s engagement of
sports, youth’s engagement in risky behavior, strength-based programming, family resil-
iency, and community resiliency. He coedited a book that proposes a community youth
development framework that intertwines tenets from the positive youth development
model and the community development perspective. His work also includes the evalua-
tion of community-based programs that are focused on promotion and prevention.
Janis E. Jacobs is vice provost for Undergraduate Education and International Pro-
grams at The Pennsylvania State University. She received her doctorate from the Univer-
sity of Michigan and is a professor of human development and family studies and profes-
sor of psychology. Her research and writing focus on the development of social cognitive
processes during childhood and adolescence. She has published numerous articles and
chapters related to the formation of judgment biases in real-world decisions and to gen-
der differences in achievement motivation, self-perceptions of achievement, and parents’
influence on achievement.
Bonnie L. Barber is a professor of family studies and human development at the Univer-
sity of Arizona. She received her Ph.D. in developmental psychology from the University
of Michigan in 1990 and has served on the faculties at The Pennsylvania State University
and the University of Arizona. Her research interests include adolescent and young adult
social relationships across life transitions, long-term benefits of activity participation,
and positive development in divorced families. She has also studied the effectiveness of
empirically based curricula for divorced mothers with adolescents in the United States
Perkins et al. / SPORTS PARTICIPATION 519
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and Australia and collaborated on a U.S. outcome evaluation of programs for youth and
families at risk.
Jacquelynne S. Eccles is the McKeachie Collegiate Professor of psychology at the Uni-
versity of Michigan. She received her Ph.D. from UCLA in 1974 and has served on the
faculty at Smith College and the University of Colorado. She is chair of the MacArthur
Foundation Network on Successful Pathways through Middle School and was a member
of the MacArthur Research Network on Successful Pathways through Adolescence. She
has conducted research on topics ranging from gender-role socialization, classroom in-
fluences on motivation to social development in the family, school, peer, and wider cul-
tural contexts. Much of this work focuses on the socialization of self-beliefs and the im-
pact of self-beliefs on many other aspects of social development.
520 YOUTH & SOCIETY / JUNE 2004
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