# Adolescent sports participation and health in early adulthood: An
observational study

Ajinkya H. Kokandakar, Yuzhou Lin, Steven Jin,
Jordan Weiss, Amanda R. Rabinowitz, Reuben A. Buford May,
Dylan Small33footnotemark: 3, and Sameer K. Deshpande11footnotemark: 1
University of Wisconsin–MadisonHarvard UniversityUniversity of
PennsylvaniaStanford UniversityMoss Rehabilitation and Research
InstituteUniversity of Illinois Urbana-Champaign

###### Abstract

We study the impact of teenage sports participation on early-adulthood health
using longitudinal data from the National Study of Youth and Religion. We
focus on two primary outcomes measured at ages 23–28 — self-rated health and
total score on the PHQ9 Patient Depression Questionnaire — and control for
several potential confounders related to demographics and family socioeconomic
status. To probe the possibility that certain types of sports participation
may have larger effects on health than others, we conduct a matched
observational study at each level within a hierarchy of exposures. Our
hierarchy ranges from broadly defined exposures (e.g., participation in any
organized after-school activity) to narrow (e.g., participation in collision
sports). We deployed an ordered testing approach that exploits the
hierarchical relationships between our exposure definitions to perform our
analyses while maintaining a fixed family-wise error rate. Compared to
teenagers who did not participate in any after-school activities, those who
participated in sports had statistically significantly better self-rated and
mental health outcomes in early adulthood.

##  1 Introduction

More than 6 million high school students in the United States of America
participate in organized sports every year [50]. The prevalence of sports
participation — estimated to account for 50-60% of the high school population
over the past two decades [5] — reflects widespread interest in athletics and
underscores the integral role that sports play in the educational and social
development of American high school students. Prior work has established
positive associations between high school sports participation and physical
and mental health as well as life satisfaction [55, 11, 56, 26, 46].

While evidence of the positive effects of sports participation is voluminous,
risk of sports-related injury may deter some children and families from
involvement [15, 22]. Collision sports, in particular, have faced heightened
scrutiny due to perceived risks of chronic traumatic encephalopathy (CTE), a
neurodegenerative disease [36] that has received attention in both the
scientific literature [36, 9] and popular media [31, 38, 37, 19, 1, 51, 30,
47, 44, 6]. The potential long-term consequences of concussions must now be
considered by families when making decisions about adolescent participation in
sports. Indeed, there has been a decline in participation in organized sports
among American high school students for the first time in 30 years, with the
steepest decline in football, according to a 2019 report [49].

Decision-making about the relative benefits and risks associated with sports
participation is complicated by a lack of consensus within the scientific
community regarding the association between collision sports and
neurodegenerative diseases [54, 48, 29]. A systematic review of long-term
effects of sports-related concussion suggests that multiple concussions appear
to be a risk factor for cognitive impairment and emotional problems in some
people [33]. Nevertheless, the challenge lies in discerning how families
should interpret and incorporate this risk when contemplating sports
participation for their child. To date the majority of research on this topic
has focused on either contemporaneous outcomes or later-life functioning, with
relatively few studies examining the influence of sports participation in
adolescence on physical and mental health outcomes in early adulthood.
Regarding later-life health outcomes, an emerging body of research suggests
that, when examined collectively, individuals involved in contact sports fare
no worse than their peers with regard to cognitive and emotional functioning
later in life [9, 10, 52]. A recent study [3] examined a subsample
($n=10{,}951$) from the National Longitudinal Study of Adolescent to Adult
Health [Add Health; 21] to investigate the association between sports
participation and various mental health outcomes in early adulthood.
Participants were categorized as participating in no sports, non-contact
sports only, and contact sports. Multivariate and logistic regression models
were employed, and the analysis was repeated on a males-only sample
($n=5{,}008$). The authors found that intention to participate in contact
sports was not significantly linked to any of the outcomes in the full-sample
analysis. However, the intention to participate in football was associated
with reduced odds of depression diagnosis in adulthood compared to non-contact
sports participation in the males-only sample. Notably, football showed no
significant associations with impaired cognitive ability, increased depressive
symptoms, or heightened suicide ideation [3].

Amidst this backdrop, considerable uncertainty persists regarding the broader
health impacts of sports participation as compared to other extracurricular
activities. Motivated by this uncertainty, we use data from the National Study
of Youth and Religion [NSYR; 45] to estimate the effect of multiple types of
sports participation on physical and mental health in early-adulthood.
Specifically, we not only estimate the broad effect of playing any sports but
also estimate the more granular effects of playing non-contact (e.g., track,
swimming, and golf), non-collision contact (e.g., basketball, soccer, and
volleyball), and collision sports (e.g., football, wrestling, and hockey).
Although our approach involves testing several hypotheses, we are nevertheless
able to control the family-wise error rate (FWER) through a testing-in-order
approach [40] that exploits the logical relationships between different types
of participation. We find that the subjects in our sample who take part in any
after school activity are more likely to self-report as healthy, experience
fewer depressive symptoms, and have higher life satisfaction than comparable
subjects who do not participate in any activity. Subjects who participated in
contact sports were significantly more likely to self-report as healthy than
comparable subjects who did not participate in any extracurricular activities.
Additionally, subjects who participated in non-collision contact sports
experienced significantly fewer depressive symptoms than comparable subjects
who did not participate in any activities. Finally, we find that the subjects
who participated in collision sports were not statistically significantly
different from comparable control subjects in terms of self-reported health
and depression scores.

##  2 Data and Methods

Before analyzing the outcomes, we published our pre-analysis protocol [27],
which contains further details about our analysis sample, our matching and
testing approach, and the composition of our matched sets.

### Study Population

We use data from the NSYR, a nationally-representative survey following
American youth from adolescence into early-adulthood. The NSYR conducted four
survey waves between 2002-03 (Wave 1), when subjects were between 13 and 17
years old, and 2013 (Wave 4), when subjects were 23–28 years old. We analyze
outcomes collected in Wave 4 and control for covariates measured in Wave 1.
Out of the initial 3,730 Wave 1 subjects, $2{,}144\ (57.4\%)$ were included in
Wave 4. We excluded subjects with missing primary outcomes, leaving an
analytic sample of $n=2{,}088$ subjects.

### Primary and secondary outcomes

We consider two co-primary outcomes measured in Wave 4: self-reported health
and the total score on the Patient Health Questionnaire [PHQ-9; 28], a
standard instrument used to assess symptoms of depression. In the Wave 4
survey, participants were asked to rate their health as either excellent, very
good, good, fair, or poor. Following precedent from earlier works [23, 34], we
dichotomized these responses by combining excellent, very good, and good into
one category (coded as 0) and fair and poor responses into another (coded as
1).111This coding reverses that used in our pre-analysis protocol [27]. The
present reverse coding simplifies the interpretation of our causal estimand as
a difference in risk of self-reporting as unhealthy. PHQ-9 scores range from 0
to 27, with lower scores indicating better mental health. Our secondary
outcomes include body mass index; a binary indicator of being overweight;
self-rated physical well-being; binary indicators for problematic and binge-
drinking; and a composite life satisfaction scale score. We defer further
details about how we constructed these outcomes to Appendix A.

### Defining the exposure

The NSYR did not directly record sports participation; instead, respondents
were asked to list all after-school activities in which they participated (if
any). Although it is easy to categorize these responses as “sports” (exposed)
or “non-sports” (control), using such broadly defined conditions may obscure
important heterogeneity in the effects of participating in different kinds of
sports. At the same time, using a narrower exposure condition like “playing
football” discards subjects who played other sports, leading to much smaller
sample sizes and power. Ultimately, the NSYR data supports multiple
definitions of exposure and control conditions, and it is not a priori obvious
which to choose.

We defined a hierarchy of exposures that retain precision in their definition
while allowing sufficient sample size to power analyses that addressed our
research question. Instead of comparing “sports” to “non-sports”, we first
segmented our study population into increasingly finer subgroups based on
their self-reported extracurricular activities. First we divided our
population into an “any activity” group and a “no activity” group. Then, we
segmented those who reported participating in any activity into a “sports”
group and a “non-sports” group. We further segmented “sports” into “contact”
and “no contact” and finally segmented those who participated in contact
sports into two more groups: sports featuring a risk of collision and sports
with no risk of collision. Figure 1(a) illustrates these segmentation and our
definitions of contact and collision sports follow those in [35]. Briefly,
body-to-body and body-to-ground contact is legal and purposeful in collision
sports; can occur but is not legal in non-collision contact sports; and is
rare in non-contact sports; see Table B1 in Appendix B.

While comparing the any activity group to the no activity is broadly
informative about the risks and benefits of extracurricular activities,
comparing the “any collision” group to the no activity group provides more
insight into the specific benefits and risks associated with collision sports.
However, because the any collision group is strictly smaller than the any
activity group, the latter comparison is less powerful than the former. Rather
than arbitrarily selecting one of these groups to be the exposure group, we
instead conduct matched observational studies comparing each group to the “no
activity” group, which serves as our common control group. We conduct these
matched studies and test the effect of each exposure (compared to the no
activity cohort) while controlling FWER using exposure-specific Type-I error
rates.

(a)

(b)

Figure 1: (a) Hierarchy of exposure definitions; (b) Type-I error rate for
each hypothesis

### Matching and randomization inference

Letting $\tau^{(j)}$ be the constant additive exposure effect on health of the
exposure indexed by the node number $j$ in Figure 1(a), we wish to test the
null hypotheses $H_{0}^{(j)}:\tau^{(j)}=0$ for each exposure group $j.$
Because our exposures are non-random, simple comparisons of the health
outcomes between an exposed group and the common control are very likely to be
confounded by demographic characteristics like age, gender, and race and by
socioeconomic characteristics like family structure, income, and parental
education. Table 1 summarizes the baseline covariates (all of which were
measured in Wave 1) for which we control. To mitigate potential confounding,
we would ideally first partition our sample into _matched sets_ containing
exposed and control subjects with identical covariates and then compare the
outcomes within matched sets.

Unfortunately, it is impossible to achieve this ideal in practice, and we
instead create matched sets of subjects with similar baseline characteristics.
Specifically, we use optimal full matching [20] with a rank-based Mahalanobis
distance and propensity score caliper [41, §8.3, p 171]. The rank-based
Mahalanobis distance accounts for correlation between baseline covariates, is
scale-free and is insensitive to outliers. A propensity score caliper ensures
that matched subjects have similar estimated probability of being assigned to
exposure, by disallowing matching subjects whose propensity score difference
is larger than a preset threshold [2]. We estimated a covariate-balancing
propensity score [24] using the CBPS package [17] and built matched sets using
the optmatch [20] package in R [39].

To assess the quality of matching, we compute the standardized difference for
each covariate both before and after matching. The standardized difference for
a covariate is the difference in the mean value of the covariate in the
exposed and control units divided by the pooled standard error before
matching. For each covariate used in matching, we aimed for the standardized
difference in means between the matched exposure and control cohorts to be in
the range $(-0.2,0.2)$ and ideally in the range $(-0.1,0.1)$ [25, 43, 7]. For
each exposure, we first attempted to construct matched pairs, and then
iteratively constructed matched sets with 1 exposed and $k$ control subjects
or 1 control and $k$ exposed subjects, increasing $k$ until we achieved
adequate balance.

|  | any | any | no | any | no | any | no
---|---|---|---|---|---|---|---|---
Covariate | control | activity | sports | sports | contact | contact | collision | collision
n | 573 | 1515 | 921 | 594 | 757 | 164 | 303 | 454
age | 15.60 | 15.48 | 15.34 | 15.69 | 15.25 | 15.78 | 15.30 | 15.22
gender | 51.66 | 53.47 | 45.06 | 66.50 | 37.91 | 78.05 | 9.24 | 57.05
Race |  |  |  |  |  |  |  |
White (%) | 66.67 | 75.91 | 77.20 | 73.91 | 76.22 | 81.71 | 76.57 | 75.99
Black (%) | 12.74 | 11.35 | 10.42 | 12.79 | 10.44 | 10.37 | 11.88 | 9.47
Hispanic (%) | 12.91 | 7.19 | 6.84 | 7.74 | 7.27 | 4.88 | 5.94 | 8.15
Asian (%) | 1.75 | 1.45 | 1.19 | 1.85 | 1.45 | 0.00 | 1.65 | 1.32
Islander (%) | 0.52 | 0.20 | 0.11 | 0.34 | 0.13 | 0.00 | 0.00 | 0.22
Native American (%) | 1.75 | 1.12 | 1.09 | 1.18 | 1.19 | 0.61 | 0.99 | 1.32
Mixed (%) | 2.27 | 1.32 | 1.30 | 1.35 | 1.19 | 1.83 | 0.66 | 1.54
Other (%) | 0.52 | 0.73 | 0.98 | 0.34 | 1.06 | 0.61 | 1.65 | 0.66
Missing (%) | 0.87 | 0.73 | 0.87 | 0.51 | 1.06 | 0.00 | 0.66 | 1.32
Region |  |  |  |  |  |  |  |
Northeast (%) | 13.44 | 17.89 | 19.44 | 15.49 | 19.42 | 19.51 | 17.82 | 20.48
Midwest (%) | 22.51 | 25.68 | 27.90 | 22.22 | 26.95 | 32.32 | 29.04 | 25.55
South (%) | 39.79 | 37.16 | 33.55 | 42.76 | 33.69 | 32.93 | 33.66 | 33.70
West (%) | 24.26 | 19.27 | 19.11 | 19.53 | 19.95 | 15.24 | 19.47 | 20.26
Family Income |  |  |  |  |  |  |  |
1st quantile (%) | 26.00 | 13.20 | 12.49 | 14.31 | 12.55 | 12.20 | 14.52 | 11.23
2nd quantile (%) | 30.02 | 22.44 | 21.17 | 24.41 | 21.53 | 19.51 | 23.43 | 20.26
3rd quantile (%) | 15.01 | 17.95 | 17.05 | 19.36 | 17.44 | 15.24 | 14.85 | 19.16
4th quantile (%) | 16.93 | 23.70 | 24.00 | 23.23 | 23.91 | 24.39 | 24.42 | 23.57
5th quantile (%) | 12.04 | 22.71 | 25.30 | 18.69 | 24.57 | 28.66 | 22.77 | 25.77
Family Structure |  |  |  |  |  |  |  |
Two parent biological (%) | 51.31 | 60.33 | 62.54 | 56.90 | 62.62 | 62.20 | 59.74 | 64.54
Two parent nonbiological (%) | 13.44 | 12.54 | 12.92 | 11.95 | 12.95 | 12.80 | 12.54 | 13.22
Single parent/Other (%) | 35.25 | 27.13 | 24.54 | 31.14 | 24.44 | 25.00 | 27.72 | 22.25
Max. Parent education |  |  |  |  |  |  |  |
AA/vocational degree | 20.07 | 16.37 | 15.64 | 17.51 | 15.85 | 14.63 | 15.51 | 16.08
BA/BS degree | 19.20 | 27.59 | 27.14 | 28.28 | 27.08 | 27.44 | 28.71 | 25.99
High school degree (%) | 42.41 | 28.84 | 27.47 | 30.98 | 28.40 | 23.17 | 30.03 | 27.31
Higher degree (%) | 11.87 | 24.75 | 27.25 | 20.88 | 26.16 | 32.32 | 24.09 | 27.53
Less than high school (%) | 6.46 | 2.05 | 2.17 | 1.85 | 2.11 | 2.44 | 1.65 | 2.42
Missing (%) | 0.00 | 0.40 | 0.33 | 0.51 | 0.40 | 0.00 | 0.00 | 0.66
School |  |  |  |  |  |  |  |
Public school (%) | 84.47 | 85.94 | 85.78 | 86.20 | 84.68 | 90.85 | 88.12 | 82.38
Private school (%) | 8.03 | 11.29 | 12.38 | 9.60 | 13.21 | 8.54 | 10.56 | 14.98
Home schooled (%) | 4.36 | 1.85 | 1.19 | 2.86 | 1.32 | 0.61 | 0.99 | 1.54
Other (%) | 3.14 | 0.73 | 0.43 | 1.18 | 0.53 | 0.00 | 0.00 | 0.88
Missing (%) | 0.00 | 0.20 | 0.22 | 0.17 | 0.26 | 0.00 | 0.33 | 0.22

Table 1: Summary of pre-exposure covariates before matching for each exposure

### Attrition analysis for outcomes

We perform a separate attrition analysis for each exposure to check if the
availability of both primary outcomes differs between the exposure and control
cohorts. For each exposure, we model the joint outcome availability indicator
using logistic regression with the exposure indicator and pre-exposure
covariates as the explanatory variables.

### Tree-based hypothesis testing

Formally, we wish to test several null hypotheses $H_{0}^{(j)}:\tau^{(j)}=0,$
one for each of the seven exposure group (i.e., node $j$ in Figure 1(a)),
which introduces a multiple comparisons problem. To control FWER at level
$\alpha=0.05$, it is tempting to use a Bonferonni correction and test each
hypothesis at $\alpha/7.$ Unfortunately, because our matched sample sizes
diminish as the exposure becomes more specific, such correction can be
extremely conservative. Further, the Bonferonni correction ignores the logical
relationships between hypotheses. We instead deploy a tree-based testing-in-
order strategy that leverages these relationships and permits us to test
$H_{0}^{(j)}$ at level larger than $\alpha/7.$

To illustrate the basic idea, consider only the hypotheses
$H_{0}^{(1)},H_{0}^{(2)},$ and $H_{0}^{(3)},$ corresponding to comparing the
no activity group to each of the any activity, any sport, and no sport groups.
If participating in any activity truly has an effect on health (i.e.,
$\tau^{(1)}\neq 0$), then either sports activities or non-sports activities
must have some effect compared to no activity (i.e., at least one of
$\tau^{(2)}$ and $\tau^{(3)}$ is non-zero). That is, if $H_{0}^{(1)}$ is
false, at most one of $H_{0}^{(2)}$ and $H_{0}^{(3)}$ can be true. Proposition
1 of [40] shows that the sequential procedure that (i) tests $H_{0}^{(1)}$ at
level $\alpha$ and then (ii) tests both $H_{0}^{(2)}$ and $H_{0}^{(3)}$ at
level $\alpha$ _only if $H_{0}^{(1)}$ is rejected_ controls the FWER at level
$\alpha.$

We can extend this logic to the full hierarchy of hypotheses in Figure 1(a).
Our procedure begins by testing $H_{0}^{(1)}$ and testing each subsequent
hypothesis only if its parent is tested and rejected. If we fail to reject a
null hypothesis along a branch in the tree, we do not test any of its
descendents. The procedure stops when we have either reached a non-rejection
in every branch or all hypotheses have been tested. Figure 1(b) shows
significance levels at which we test each $H_{0}^{(j)}$ that guarantees the
overall FWER is controlled at $\alpha$; see Appendix E of [27] for a
derivation. This tree-based testing-in-order strategy is powered to detect
small effects of broad exposures and large effects of narrow exposures [27,
§5].

Since we have two co-primary outcomes, we set $\alpha=2.5\%$ for each of them,
yielding a $5\%$ overall FWER considering both primary outcomes. We use
randomization inference for the individual tests. For the continuous outcomes,
we use the null hypothesis of no constant additive effect using the m-test
implemented by the senfm function in the sensitivityfull R package [42]. For
binary outcomes, we test the composite null hypothesis of no difference in
proportions described in [16].

##  3 Results

### Attrition Analysis

There were $3{,}370$ respondents in Wave 1, of which only $2{,}144$
respondents were available in Wave 4. Among them, $2{,}088$ respondents had
measurements for both co-primary outcomes, i.e. there were $2{,}088$ subjects
with both outcomes available (coded as 1) and $1{,}282$ subjects for whom at
least one of the two primary outcomes was missing (coded as 0). Thus, the lack
of outcome availability was primarily to dropout between Wave 1 and Wave 4 of
the survey. Subjects in each exposure group were somewhat more likely to have
both primary outcomes available for analysis as compared to the control group
after adjusting for baseline covariates using logistic regression (See Table
2).

Table 2: Odds-ratio (95% confidence interval) and p-values for outcome availability after adjusting for potential confounders. Exposure | Odds-ratio (95% CI) | p-value
---|---|---
any activity | 1.29 (1.10, 1.52) | 0.002
any sports | 1.20 (1.00, 1.44) | 0.048
no sports | 1.46 (1.19, 1.80) | 0.000
any contact | 1.20 (0.99, 1.45) | 0.059
no contact | 1.14 (0.81, 1.62) | 0.441
any collision | 1.33 (1.03, 1.72) | 0.032
no collision | 1.13 (0.91, 1.41) | 0.273

### Matching results

Figure 2 shows the sizes of the exposure and control cohort both before and
after matching, for each exposure. The size of the matched exposure and
control cohorts reduces as we go down the exposure hierarchy. The matched
control cohorts shrink by a larger amount than the matched exposure cohorts.
The matching procedure succeeded for each exposure i.e., all the matched
covariates had a standardized mean difference of less than 0.2 units between
the matched exposure and control groups for every exposure. Moreover, there
were only two instances where the standardized mean difference for a covariate
exceeded 0.1 units. In the matching process for the any activity exposure, the
standardized difference for “Northeast region” indicator was 0.1032 which is
very close to the 0.1 threshold, and therefore can be ignored. In the second
instance, the “other race” indicator had a standardized mean difference of
0.17 between the matched exposure and control cohorts for the no contact
exposure. Since only four units in this category were available for matching —
a very small subset of the available units — we are not concerned with the
small bias that may be incurred due to this lack of balance.

Figure 2: Number of available exposed (E) and control (C) units before and
after matching for each exposure

### Primary outcomes

Our testing-in-order strategy reached every hypothesis for self-rated health
and depression score. Table 3 shows the estimates and marginal 95% confidence
intervals for the effect of each exposure on the binary self-rated health
indicator. All the exposure cohorts indicated better self-reported health
(i.e., lower risk) as compared to the control cohort consisting of those who
did not participate in any organized after-school activity. Moreover, the any
activity, any sports, and any contact sports exposures had a statistically
significant and beneficial effect on self-reported health. The risk difference
between the exposure and the control groups are between 5 and 8 percentage
points, with all exposure cohorts having lower risk as compared to the control
group.

Table 3: Effect estimates (marginal 95% confidence intervals) and p-values for the hypothesis test for each exposure. The p-values in bold denote those that are lower than the significance level in Figure 1(b). For both outcomes negative differences denote better health in the exposure cohort. | Self-reported health | Depression Score
---|---|---
Exposure | Risk Difference (95% CI) | p-value | Score Difference (95% CI) | p-value
any activity | -0.07 (-0.13, -0.01) |  0.01 | -0.78 (-1.37, -0.18) | 0.00
any sports | -0.07 (-0.13, -0.01) |  0.01 | -1.16 (-1.79, -0.51) | 0.00
no sports | -0.05 (-0.12, 0.01) | 0.08 | -0.17 (-0.94, 0.58) | 0.61
any contact | -0.08 (-0.14, -0.01) |  0.01 | -1.38 (-2.07, -0.72) | 0.00
no contact | -0.05 (-0.15, 0.07) | 0.35 | -1.06 (-2.15, 0.04) | 0.03
any collision | -0.06 (-0.15, 0.04) | 0.17 | -1.12 (-2.10, -0.14) | 0.01
no collision | -0.07 (-0.15, 0.00) | 0.03 | -1.25 (-2.04, -0.45) | 0.00

All exposure groups also displayed lower depression scores compared to the
matched controls. That said, the depression scores for the non-sports activity
and the any collision cohort are statistically indistinguishable from those
for the control cohort. The any activity, any sports, any contact sports, and
no collision sports cohorts all had significantly lower depression scores than
the matched controls.

### Secondary outcomes

In our matched sample, subjects who participated in the any activity have a
slightly higher BMI (effect estimate = $0.13$, CI = $(-0.48,0.74)$), are less
likely to be overweight (risk difference = $-0.02$, CI = $(-0.08,0.04)$), are
more likely to engage in problematic drinking (risk difference = $0.008$, CI =
$(-0.04,0.06)$), and have a higher rate of binge-drinking (risk difference =
$0.001$, CI = $(-0.044,0.045)$) as compared to the subjects in the no activity
control cohort. However, none of these differences were statistically
significant. For each of these outcomes, our testing strategy terminated at
the root node comparing any activity to no activity.

In contrast, we are able to test the null hypotheses for all the exposures for
the life satisfaction score. The life satisfaction score is higher for each
exposure cohort when compared to the control cohort. Moreover, the null
hypothesis of no effect on life satisfaction score can be rejected for each
exposure except for the “no sports” exposure. That is, compared to the no
activity cohort, the exposure cohorts other than the non-sports cohort had a
significantly higher life satisfaction score.

##  4 Discussion

Engaging in organized after-school activities during teenage years is
associated with better overall health in the medium term (i.e., during young
adulthood). The association between better health and activity seems to be
stronger for those who engaged in sports activities during their teenage
years. The finding that sports participation does have significant (albeit
small) beneficial effect on mental and physical health, in line with prior
findings [12, 3]. For non-sports activities the association was not
statistically significant. One of the chief motivations of looking at
different kinds of sports was concern that collision sports may potentially
have substantial negative effects on health. In fact, we find that collision
sports show a small —though not statistically significant— improvement in
health. This finding is broadly in line with prior research that did not find
large harmful effects of participating in collision sports like American
football on several mental and physical health outcome [18, 9, 10, 53, 3].

Our stepwise comparison approach suggests that, while engaging in after-school
activities does improve mental health as evidenced by the decrease in
depression scores, this is driven mostly by sports participation. In fact, the
control cohort and the non-sports activities cohort had very similar
depression scores on average. This seems to suggest that sports activities
confer unique mental health benefits that are not associated (at least not in
the same magnitude) with non-sports organized activities.

There are multiple pathways by which sports participation could manifest these
benefits, including by promoting physical activity, providing venues for
socialization, fostering teamwork, and encouraging positive identity
development and self-esteem. While it is certainly the case that many of the
positive aspects of sports participation are also features of other after
school activities, the present findings suggest that there is something about
the synergy of features present in sports that confers these health benefits.

Our study has strengths and limitations. The greatest strength is being able
to determine which subgroups among those who engage in after school activities
drive the statistically significant differences in health as compared to the
control cohort (i.e., those who engage in no activity). This is enabled by
exploiting the logical relationships between the null hypotheses, which allows
us to test each exposure effect at a higher Type I error threshold as compared
to the Bonferroni correction.

One might be concerned that our conclusions may be affected by differential
attrition from the NSYR. In particular, one might worry that subjects who
participated in certain sports like football may experience severe negative
outcomes that lead to drop-out from the NSYR. Under such attrition, our
estimated effects would biased upwards. In fact, we found that subjects in
each exposure group were _less likely_ to drop out of the NSYR between Waves 1
and 4 than the controls. If attrition in the control group was due to
experiencing extremely harmful health outcomes, then our estimated effects are
biased downwards, which only amplifies the benefits of sports participation
suggested by our analyses.

Throughout the study, we compare the different exposure cohorts to the common
control cohort consisting of students who do not take part in after school
activities. However, it is not possible to compare two different exposure
cohorts using our method, while controlling for FWER. For instance, we cannot
test if there is any effect of playing contact sports as compared to non-
contact sports. We are able to obtain a higher Type I error threshold for each
individual test as compared to a Bonferroni correction due to logical
hypothesis among the hypotheses. However, such logical relationships may not
exist for hypotheses related to comparisons between two exposure groups. We
leave the problem of comparing the health effects between two exposure cohorts
while maintaining FWER for future work.

We classified sports as contact, non-contact, and collision, due to an
interest in how sports-related concussion risk might moderate the health
benefits of sports-participation. However, the sports we classified in this
way may differ in other respects that are relevant to physical and emotional
health. For example, individual sports such as track, cross country, swimming,
and golf, predominated the non-contact sports category, whereas the majority
of athletes included in the contact and collision category played team sports.
Hence, we are unable in the present study to disentangle the possible benefits
and risks that are due to the contact or collision nature of a sport versus
those related to playing on a team.

We tested a sharp null hypothesis of no effect for each exposure, which may be
considered restrictive. In [4] the authors show that randomization inference
can indeed be used to test for bounded null hypotheses beyond just the sharp
null. Whether their analysis extends to our tree based testing procedure
remains to be seen, and we hope to explore this in future work.

Finally, because we did not have data on the incidence of sports-related
concussion in this cohort, we could not evaluate whether outcomes related to
sports participation are different for those who have sustained a brain
injury. While our findings speak to the impact of adolescent sports
participation on adult health in the aggregate, there may be subgroups of
individuals for whom the risk/benefit profile differs. Understanding the
contributions of concussion history and other potential risk factors is an
important area for future research.

In conclusion, we find that participating in after-school activities does have
health benefits. When compared to participating in no activity, sports
participation and more specifically contact sports confer health benefits.
Therefore, our study lends credence to the idea that sports participation is a
net benefit for adolescents. In future work, we would like to extend our tree
based testing procedure to enable cross-exposure group comparisons while
controlling the family wise error rate.

## References

  * Arnold [2019] Chad Arnold.  New York lawmakers are considering a ban on tackle football for kids under 12.  USA Today, October 2019.  https://www.usatoday.com/story/news/nation/2019/10/31/new-york-considers-ban-youth-tackle-football-sparks-cte-debate/4107795002/.
  * Austin [2011] Peter C. Austin.  Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies.  _Pharmaceutical Statistics_ , 10(2):150–161, 2011\.
  * Bohr et al. [2019] Adam D. Bohr, Jason D. Boardman, and Matthew B. McQueen.  Association of adolescent sport participation with cognition and depressive symptoms in early adulthood.  _Orthopaedic Journal of Sports Medicine_ , 7(9), 2019.
  * Caughey et al. [2023] Devin Caughey, Allan Dafoe, Xinran Li, and Luke Miratrix.  Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects.  _Journal of the Royal Statistical Society Series B: Statistical Methodology_ , 85(5):1471–1491, 08 2023.
  * Centers for Disease Control and Prevention (2021) [CDC] Centers for Disease Control and Prevention (CDC).  1991-2021 High school youth risk behavior survey data.  Technical report, Centers for Disease Control and Prevention, 2021.
  * Chuck [2018] Elizabeth Chuck.  Despite evidence, skeptics try to cast doubt on CTE-football link.  NBC News, April 2018.  https://www.nbcnews.com/news/sports/despite-evidence-skeptics-try-cast-doubt-cte-football-link-n897416.
  * Cochran and Rubin [1973] William G. Cochran and Donald B. Rubin.  Controlling bias in observational studies: A review.  _Sankhyā: The Indian Journal of Statistics, Series A (1961-2002)_ , 35(4):417–446, 1973.
  * Culver [2021] Julian Culver.  How consistency in closeness to god predicts psychological resources and life satisfaction: Findings from the national study of youth and religion.  _Journal of Social and Personal Relationships_ , 38(1):103–123, 2021.
  * Deshpande et al. [2017] Sameer K. Deshpande, Raiden B. Hasegawa, Amanda R. Rabinowitz, John Whyte, Carol L. Roan, Andrew Tabatabaei, Michael Baiocchi, Jason H. Karlawish, Christina L. Master, and Dylan S. Small.  Association of playing high school football with cognition and mental health later in life.  _JAMA Neurology_ , 74(8):909–918, 08 2017.  ISSN 2168-6149.
  * Deshpande et al. [2020] Sameer K. Deshpande, Raiden B. Hasegawa, Jordan Weiss, and Dylan S. Small.  The association between adolescent football participation and early adulthood depression.  _PLOS ONE_ , 15(3):1–14, 03 2020.
  * Donaldson and Ronan [2006] Sarah J. Donaldson and Kevin R. Ronan.  The effects of sports participation on young adolescents’ emotional well-being.  _Adolescence_ , 41(162):369–389, 2006.
  * Eime et al. [2013] Rochelle M. Eime, Janet A. Young, Jack T. Harvey, Melanie J. Charity, and Warren R. Payne.  A systematic review of the psychological and social benefits of participation in sport for children and adolescents: informing development of a conceptual model of health through sport.  _International Journal of Behavioral Nutrition and Physical Activity_ , 10(1):98, 2013.
  * Ewing [1984] John A. Ewing.  Detecting alcoholism: The cage questionnaire.  _JAMA_ , 252(14):1905–1907, 10 1984.
  * Fiellin et al. [2000] David A. Fiellin, M. Carrington Reid, and Patrick G. O’Connor.  Screening for Alcohol Problems in Primary Care: A Systematic Review.  _Archives of Internal Medicine_ , 160(13):1977–1989, 07 2000.
  * Fishman et al. [2017] Michael Fishman, Eleanor Taranto, Meryl Perlman, Kyran Quinlan, Holly J. Benjamin, and Lainie Friedman Ross.  Attitudes and counseling practices of pediatricians regarding youth sports participation and concussion risks.  _The Journal of Pediatrics_ , 184:19–25, 2017.  ISSN 0022-3476.  URL https://www.sciencedirect.com/science/article/pii/S0022347617301580.
  * Fogarty et al. [2017] Colin B. Fogarty, Pixu Shi, Mark E. Mikkelsen, and Dylan S. Small.  Randomization inference and sensitivity analysis for composite null hypotheses with binary outcomes in matched observational studies.  _Journal of the American Statistical Association_ , 112(517):321–331, 2017.
  * Fong et al. [2022] Christian Fong, Marc Ratkovic, and Kosuke Imai.  _CBPS: Covariate balancing propensity scores_ , 2022.
  * Gaulton et al. [2020] Timothy G. Gaulton, Sameer K. Deshpande, Dylan S. Small, and Mark D. Neuman.  Observational study of the associations of participation in high school football with self-rated health, obesity, and pain in adulthood.  _American Journal of Epidemiology_ , 189(6):592–601, June 2020.
  * Gregory [2020] Sean Gregory.  Is it unethical to watch football with your kids?  Time, January 2020.  https://time.com/5770136/football-dangers-children/.
  * Hansen and Klopfer [2006] Ben B. Hansen and Stephanie Olsen Klopfer.  Optimal full matching and related designs via network flows.  _Journal of Computational and Graphical Statistics_ , 15(3):609–627, 2006.
  * Harris [2013] Kathleen M. Harris.  The Add health study: Design and accomplishments.  Technical report, Carolina Population Center, University of North Carolina, 2013.
  * Hibshman et al. [2022] Natalie Hibshman, Aaron Yengo-Kahn, Alyssa Wiseman, Patrick D. Kelly, Jeffanie Wu, Steve Monk, Glenn Harris, Stephen Gannon, Chevis Shannon, and Christopher M. Bonfield.  Child participation in collision sports and football: what influences parental decisions?  _The Physician and Sportsmedicine_ , 50(2):171–180, 2022.
  * Idler and Benyamini [1997] Ellen L. Idler and Yael Benyamini.  Self-rated health and mortality: A review of twenty-seven community studies.  _Journal of Health and Social Behavior_ , 38(1):21–37, 1997.
  * Imai and Ratkovic [2014] Kosuke Imai and Marc Ratkovic.  Covariate balancing propensity score.  _Journal of the Royal Statistical Society: Series B_ , 76(1):243–263, 2014.
  * Jain et al. [2023] Siddharth Jain, Paul R. Rosenbaum, Joseph G. Reiter, Omar I. Ramadan, Alexander S. Hill, Sean Hashemi, Rebecca T. Brown, Rachel R. Kelz, Lee A. Fleisher, and Jeffrey H. Silber.  Mortality among older medical patients at flagship hospitals and their affiliates.  _Journal of General Internal Medicine_ , 2023.
  * Jewett et al. [2014] Rachel Jewett, Catherine M. Sabiston, Jennifer Brunet, Erin K. O’Loughlin, Tanya Scarapicchia, and Jennifer O’Loughlin.  School sport participation during adolescence and mental health in early adulthood.  _Journal of Adolescent Health_ , 55(5):640–644, 2014.
  * Kokandakar et al. [in press] Ajinkya H. Kokandakar, Yuzhou Lin, Steven Jin, Jordan Weiss, Amanda R. Rabinowitz, Reuben A. Buford May, Dylan Small, and Sameer K. Deshpande.  Pre-analysis protocol for an observational study on the effects of adolescent sports participation on health in early adulthood.  _Observational Studies_ , in press.
  * Kroenke et al. [2001] Kurt Kroenke, Robert L. Spitzer, and Janet B. W. Williams.  The PHQ-9.  _Journal of General Internal Medicine_ , 16(9):606–613, 2001.
  * Kuhn et al. [2017] Andrew W. Kuhn, Aaron M. Yengo-Kahn, Zachary Y. Kerr, and Scott L. Zuckerman.  Sports concussion research, chronic traumatic encephalopathy and the media: repairing the disconnect.  _British Journal of Sports Medicine_ , 51(24):1732–1733, 2017.  ISSN 0306-3674.
  * Landesman [2015] Peter Landesman.  Concussion.  Sony Picture Releasing, 2015.
  * Lannan [2019] Kate Lannan.  Concussion threat behind youth football ban bill.  GBH, October 2019.  https://www.wgbh.org/news/politics/2019-10-22/concussion-threat-behind-youth-football-ban-bill.
  * Manglos [2012] Nicolette D. Manglos.  Faith Pinnacle Moments: Stress, Miraculous Experiences, and Life Satisfaction in Young Adulthood.  _Sociology of Religion_ , 74(2):176–198, 12 2012\.
  * Manley et al. [2017] Geoff Manley, Andrew J. Gardner, Kathryn J. Schneider, Kevin M. Guskiewicz, Julian Bailes, Robert C. Cantu, Rudolph J. Castellani, Michael Turner, Barry D. Jordan, Christopher Randolph, Jiří Dvořák, K. Alix Hayden, Charles H. Tator, Paul McCrory, and Grant L. Iverson.  A systematic review of potential long-term effects of sport-related concussion.  _British Journal of Sports Medicine_ , 51(12):969–977, 2017.  ISSN 0306-3674.
  * Manor et al. [2000] Orly Manor, Sharon Matthews, and Chris Power.  Dichotomous or categorical response? Analyzing self-rated health and lifetime social class.  _International Journal of Epidemiology_ , 29(1):149–157, 2000.
  * Meehan, III et al. [2016] William P. Meehan, III, Alex M. Taylor, Paul Berkner, Noah J. Sandstrom, Mark W. Peluso, Matthew M. Kurtz, Alvaro Pascual-Leone, and Rebekah Mannix.  Division III collision sports are not associated with neurobehavioral quality of life.  _Journal of Neurotrauma_ , 33(2):254–259, 2016\.
  * Mez et al. [2017] Jesse Mez, Daniel H. Daneshvar, Patrick T. Kiernan, Bobak Abdolmohammadi, Victor E. Alvarez, Bertrand R. Huber, Michael L. Alosco, Todd M. Solomon, Christopher J. Nowinski, Lisa McHale, Kerry A. Cormier, Caroline A. Kubilus, Brett M. Martin, Lauren Murphy, Christine M. Baugh, Phillip H. Montenigro, Christine E. Chaisson, Yorghos Tripodis, Neil W. Kowall, Jennifer Weuve, Michael D. McClean, Robert C. Cantu, Lee E. Goldstein, Douglas I. Katz, Robert A. Stern, Thor D. Stein, and Ann C. McKee.  Clinicopathological evaluation of chronic traumatic encephalopathy in players of American football.  _JAMA_ , 318(4):360–370, 07 2017.  ISSN 0098-7484.
  * Nowinski [2019] Christopher J. Nowinski.  Youth tackle football will be considered unthinkable 50 years from now.  Vox, April 2019.  https://www.vox.com/2019/3/27/18174368/football-concussion-brain-injury-cte-youth-football.
  * Omalu [2015] Bennet Omalu.  Don’t let kids play football.  The New York Times, December 2015.  https://www.nytimes.com/2015/12/07/opinion/dont-let-kids-play-football.html.
  * R Core Team [2023] R Core Team.  _R: A Language and Environment for Statistical Computing_.  R Foundation for Statistical Computing, Vienna, Austria, 2023.  URL https://www.R-project.org/.
  * Rosenbaum [2008] Paul R. Rosenbaum.  Testing hypotheses in order.  _Biometrika_ , 95(1):248–252, 2008.
  * Rosenbaum [2010] Paul R. Rosenbaum.  _Design of observational studies_.  Springer, 2010.
  * Rosenbaum [2017] Paul R. Rosenbaum.  _sensitivityfull: Sensitivity Analysis for Full Matching in Observational Studies_ , 2017.  URL https://CRAN.R-project.org/package=sensitivityfull.  R package version 1.5.6.
  * Rosenbaum and Rubin [1985] Paul R. Rosenbaum and Donald B. Rubin.  Constructing a control group using multivariate matched sampling methods that incorporate the propensity score.  _The American Statistician_ , 39(1):33–38, 1985\.
  * Schaller [2017] Vincent E. Schaller.  Opinion: Stop blaming football for concussions.  The Philadelphia Inquirer, September 2017.  https://www.inquirer.com/philly/health/opinion-stop-blaming-football-for-concussions-20170913.html.
  * Smith and Denton [2003] Christian Smith and Melinda Lundquist Denton.  Methodological design and procedures for the National Study of Youth and Religion (NSYR).  Technical report, University of North Carolina at Chapel Hill, 2003.
  * Song and Shi [2023] Yagang Song and Chongyan Shi.  Association between sports participation and overall health in children and adolescents.  _Complementary Therapies in Clinical Practice_ , 51:101718, 2023.  URL https://www.sciencedirect.com/science/article/pii/S1744388122001864.
  * Staropoli [2015] Nicholas Staropoli.  Concussion Doc calls for ban on youth football.  American Council on Science and Health, December 2015.  https://www.acsh.org/news/2015/12/08/the-concussion-doc-calls-for-ban-on-youth-football.
  * Stewart et al. [2019] William Stewart, Kieren Allinson, Safa Al-Sarraj, Corbin Bachmeier, Karen Barlow, Antonio Belli, Mark P. Burns, Alan Carson, Fiona Crawford, Kristen Dams-O’Connor, Ramon Diaz-Arrastia, C. Edward Dixon, Brian L. Edlow, Scott Ferguson, Bruce Fischl, Rebecca D. Folkerth, Steve Gentleman, Christopher C. Giza, M. Sean Grady, Adel Helmy, Mark Herceg, Janice L. Holton, David Howell, Peter J. Hutchinson, Diego Iacono, Juan E. Iglesias, Milos D. Ikonomovic, Victoria E. Johnson, C. Dirk Keene, Julia K. Kofler, Vassilis E. Koliatsos, Edward B. Lee, Harvey Levin, Jonathan Lifshitz, Helen Ling, David J. Loane, Seth Love, Andrew I.R. Maas, Niklas Marklund, Christina L. Master, Damien M. McElvenny, David F. Meaney, David K. Menon, Thomas J. Montine, Benoit Mouzon, Elliott J. Mufson, Joseph O. Ojo, Mayumi Prins, Tamas Revesz, Craig W. Ritchie, Colin Smith, Richard Sylvester, Cheuk Y. Tang, John Q. Trojanowski, Kathryn Urankar, Robert Vink, Cheryl Wellington, Elisabeth A. Wilde, Lindsay Wilson, Keith Yeates, and Douglas H. Smith.  Primum non nocere: a call for balance when reporting on CTE.  _The Lancet Neurology_ , 18(3):231–233, 2019\.  ISSN 1474-4422.
  * The National Federation of State High School Associations [2019] The National Federation of State High School Associations.  2018-19 high school athletics participation survey.  Technical report, The National Federation of State High School Associations, 2019.
  * The National Federation of State High School Associations [2023] The National Federation of State High School Associations.  High school participation survey.  Technical report, The National Federation of State High School Associations, 2023.
  * The New York Times [2016] The New York Times.  The N.F.L.’s tragic C.T.E. roll call, February 2016.  https://www.nytimes.com/interactive/2016/02/03/sports/football/nfl-brain-disease-cte-concussions.html.
  * Weiss et al. [2021a] Jordan Weiss, Amanda R. Rabinowitz, Sameer K. Deshpande, Raiden B. Hasegawa, and Dylan S. Small.  Participation in collision sports and cognitive aging among Swedish twins.  _American Journal of Epidemiology_ , 190(12):2604–2611, 06 2021a.  ISSN 0002-9262.
  * Weiss et al. [2021b] Jordan Weiss, Amanda R. Rabinowitz, Sameer K. Deshpande, Raiden B. Hasegawa, and Dylan S. Small.  Participation in collision sports and cognitive aging among Swedish twins.  _American Journal of Epidemiology_ , 190(12):2604–2611, December 2021b.
  * Wolfson et al. [2020] Daniel I. Wolfson, Andrew W. Kuhn, Zachary Y. Kerr, Benjamin L. Brett, Aaron M. Yengo-Kahn, Gary S. Solomon, and Scott L. Zuckerman.  Chronic traumatic encephalopathy research viewed in the public domain: What makes headlines?  _Brain Injury_ , 34(4):528–534, 2020.
  * Zarrett et al. [2018] Nicole Zarrett, Philip Veliz, and Don Sabo.  Teen sport in America: why participation matters.  Technical report, Women’s Sports Foundation, 2018.
  * Zullig and White [2011] Keith J. Zullig and Rebecca J. White.  Physical activity, life satisfaction, and self-rated health of middle school students.  _Applied research in quality of life_ , 6:277–289, 2011\.

##  Appendix A Outcome construction

Self-rated health: Self-reported health is a binary indicator obtained by
dichotomizing the subjects’ response to the survey question “Overall, would
you say your health is: Excellent, Very good, Good, Fair, or Poor?” Our
dichotomization combines the top three responses (excellent, very good, and
good) into one category (coded as 1 and interpreted as “healthy”) and the
bottom two responses (fair and poor) into another category (coded as 0 and
interpreted as “unhealthy”). This outcome was constructed using the NSYR
variable health_w4.

PHQ-9 score: The PHQ-9 is a standard survey instrument used to assess symptoms
of depression [28] based on answers to questions related to nine different
problems like feeling tired, having little energy, or feeling down, depressed,
or hopeless. Respondents are asked how often they experience these symptoms
and are given the choices “nearly every day”, “more than half the days”,
“several days”, or “not at all”. These responses are recorded in the NSYR
variables symptom1_1_w4, symptom1_2_w4, …, symptom1_9_w4. These responses are
scored from 0 to 3 and a total score from 0 to 27 is computed for each
respondent, with lower scores indicating better mental health.

Body mass index: We use the body mass index (BMI) is a numeric variable
recorded in the NSYR variable bmi_w4, and is computed as the ratio of
respondent’s weight (in lbs) to the square of their height (in inches)
multiplied by $703$.

Overweight indicator: The NSYR variable bmicat_w4 records whether the
respondent is underweight, normal weight, overweight, or obese based on NIH
guidelines. We convert this to a binary indicator by combining the categories
overweight and obese (coded as 1), and the other two categories normal weight
and underweight (coded as 0).

Problematic Drinking: We use the CAGE score [13] to construct a binary
indicator of problematic drinking. The CAGE score is constructed from four
questions that were asked in the NSYR survey, “Within the past 12 months have
you ever”: (1) “felt that you ought to cut down on your drinking?”
(drinkcutdown_w4), (2) “been annoyed with people criticizing your drinking?”
(drinkcriticize_w4), (3) “felt bad about your drinking?” (drinkfeltbad_w4),
and (4) “had a drink in the morning to steady your nerves or get rid of a
hangover?” (drinkhangover_w4). Each question gets a score of $0$ for a “No”
response, and $1$ for a “Yes” response, adding up to a CAGE score of 0 through
4. Note that we automatically assign a CAGE score of $0$ for subjects who
indicated they never drink. A subject is defined as engaging in problematic
drinking if the CAGE score is greater than or equal to $2$ [14].

Binge-drinking: We define binge-drinking as five or more episodes drinking at
least drinks in one night for females or at least 5 drinks in one night for
males in a two-week period. We constructed a binary binge-drinking indicator
based on the NSYR variable drunk_w4. Additionally, subjects who indicated that
they never drink (drink_w4, not the same as the aforementioned binge-drinking
variable) were automatically classified as not binge-drinking.

Life Satisfaction Score: Similar to the scoring scheme in [8] we assign a
score in the range 0 (strongly disagree) to 3 (strongly agree) for each of the
following survey items: (1) “The conditions of your life are excellent”, (2)
“You are satisfied with your life”, (3) “So far you have gotten the important
things you want in life” and (4) “In most ways your life is close to ideal”
[32]. The life satisfaction score was then constructed as the sum of the
individual scores, and ranges from 0 to 12, with higher scores indicating
better life satisfaction.

##  Appendix B Additional Tables

Table B1: Sports included in our dataset, with counts in parentheses. Non-contact | Contact | Collision
---|---|---
Track (217) | Basketball (509) | Football (367)
Volleyball (135) | Soccer (263) | Wrestling (80)
Cross Country (70) | Baseball (185) | Martial Arts (37)
Tennis (64) | Softball (120) | Lacrosse (32)
Swimming (59) | Gymnastics (22) | Hockey (30)
Golf (47) | Field Hockey (16) | Boxing (6)
Racquetball (3) | Fencing (4) | Diving (6)
Crew (9) | Flag Football (4) | Rugby (1)
| Water Polo (3) |
| Roller Hockey (2) |