Context:

Normative scores for patient-rated outcome (PRO) instruments are important for providing patient-centered, whole-person care and making informed clinical decisions. Although normative values for the Pediatric Quality of Life Generic Core Scale (PedsQL) have been established in the general, healthy adolescent population, whether adolescent athletes demonstrate similar values is unclear.

Objective:

To compare PedsQL scores between adolescent athletes and general, healthy adolescent individuals.

Design:

Cross-sectional study.

Setting:

Secondary schools.

Patients or Other Participants:

A convenience sample of 2659 interscholastic athletes (males = 2059, females = 600, age = 15.7 ± 1.1 years) represented the athlete group (ATH), and a previously published normative dataset represented the general, healthy adolescent group (GEN).

Intervention(s):

All participants completed the PedsQL during 1 testing session.

Main Outcome Measure(s):

The PedsQL consists of 2 summary scores (total, psychosocial) and 4 subscale scores (physical, emotional, social, school), with higher scores indicating better health-related quality of life (HRQOL). Groups were stratified by age (14, 15, or 16 years old). Independent-samples t tests were conducted to compare between-groups and sex differences.

Results:

The ATH group scored higher than the GEN group across all ages for total and psychosocial summary scores and for emotional and social functioning subscale scores (P ≤ .005). For physical functioning, scores of the 15-year-old ATH were higher than for their GEN counterparts (P = .001). Both 14- and 15-year-old ATH scored higher than their GEN counterparts for the school functioning subscale (P ≤ .013), but differences between 16-year olds were not significant (P = .228). Male adolescent athletes reported higher scores than female adolescent athletes across all scores (P ≤ .001) except for social functioning (P = .229).

Conclusions:

Adolescent athletes reported better HRQOL than GEN, particularly in emotional functioning. These findings further support the notion that ATH constitutes a unique population that requires its own set of normative values for self-reported, patient-rated outcome instruments.

Key Points
  • Adolescent athletes reported better health-related quality of life than their general, healthy adolescent peers, particularly in emotional functioning.

  • As part of the overall evaluation, health care providers should consider whether a patient participates regularly in physical activity because that may affect baseline emotional well-being.

  • Adolescent athletes appear to constitute a unique patient population for whom normative values on patient-related outcome instruments should be established.

Health-related quality of life (HRQOL) is a multidimensional concept that represents an individual's overall satisfaction with his or her life and general sense of well-being.1  Considered a clinically important patient-rated outcome (PRO) of patient care, HRQOL characterizes the perceived effect of a disease, condition, illness, injury, or treatment intervention on various health domains (eg, physical, emotional, social).26  Because health domains are typically influenced by an individual's experiences, expectations, and beliefs, HRQOL is often regarded as an essential component of patient-centered, whole-person health care.79  Given the importance of HRQOL in patient care, efforts have been directed over the last decade to developing generic PRO instruments to evaluate HRQOL during care.10 

In conjunction with the development of generic PRO instruments, researchers11  have attempted to establish normative values for distinct patient populations. Establishing normative values for distinct patient populations is important to the overall clinical utility of PRO instruments in providing a frame of reference by which clinicians can make informed clinical decisions for patient care.11  Similar to normative values of clinician-rated outcome measures (eg, normal limits for range-of-motion measurements), normative values of PRO instruments can be used to identify potential HRQOL deficits in patients after an injury (eg, values beyond normal limits) and to track the recovery of HRQOL during the rehabilitation process (eg, values returning to normal limits over time). It should be noted that normative values for PRO instruments, like clinician-rated measures, can vary across patient populations, highlighting the need for these values in distinct groups of patients, such as athletes.

Despite the noted benefits of regular activity,1223  including decreased risk of chronic conditions (eg, diabetes, hypertension, obesity)14  and improved mental well-being,15,16  academic performance,13  and self-esteem,17  athletes are often considered to be members of the general, healthy population. Although this classification is logical, evidence2426  suggests that athletes may constitute their own unique patient population. Snyder et al26  found that adolescent athletes generally reported higher scores than their nonathlete peers on 2 commonly used generic PRO instruments: the Medical Outcomes Short Form (SF-36) and the Pediatric Outcomes Data Collection Instrument (PODCI). Their findings suggest that adolescent athletes experience better overall HRQOL than their nonathlete counterparts, as well as enhanced mental, emotional, and social health.26  In addition, previous authors24,25  have noted similar trends in emotional and mental health when comparing adult athletes and their nonathlete peers using the SF-36. In fact, McAllister et al25  noted that, even when injured, elite collegiate athletes reported higher emotional- and mental-functioning scores than general, healthy individuals. These group differences suggest that athletes may comprise a distinct patient population with its own normative values and further highlight the importance of establishing normative values for distinct patient populations.

A commonly used generic PRO instrument in the adolescent population is the Pediatric Quality of Life Inventory (PedsQL), which assesses the physical, emotional, social, and school-functioning health domains. The PedsQL may be an appropriate PRO instrument for the practice of athletic training as compared with other instruments because it was designed specifically for the adolescent age group (as opposed to the broad age range of the SF-36) and requires minimal time to complete (as opposed to the 83 items of the PODCI). Although normative values of the PedsQL have been reported for healthy individuals27  and patients with chronic conditions (eg, cancer, asthma, diabetes, cerebral palsy),10,2831  little is known about normative values in adolescent athletes.

The purpose of our study was to compare HRQOL scores between adolescent athletes and general, healthy adolescent individuals by using a common pediatric-specific, generic PRO instrument, the PedsQL Generic Core Scales (GCS). We hypothesized that adolescent athletes would demonstrate higher scores across all scales on the PedsQL GCS, indicating better overall HRQOL than would general, healthy adolescents, further supporting the notion that adolescent athletes constitute their own distinct patient population with a different set of normative values. Additionally, we suggested that sex differences would occur in HRQOL between male and female adolescent athletes. Lastly, we proposed that no differences would be seen across age groups within the adolescent athlete group.

METHODS

Participants

Two distinct populations were selected to solicit participants for this study. One group, adolescent athletes (ATH), was represented by a convenience sample of healthy students who were participating in interscholastic sports at 16 high schools within the greater metropolitan area and were between the ages of 14 and 18 years. Participants were considered healthy if they were medically cleared for sport activity through a preparticipation examination, were currently involved in an interscholastic sport without restrictions, and did not self-report a current injury or illness.

The other group, general, healthy adolescents (GEN), was represented by values extracted from a published sample set of healthy adolescent individuals.32  This methodological approach has been used in previous investigations24,25  comparing HRQOL between adult athletes and their general, healthy counterparts. Although several studies have provided sample sets for a general, healthy adolescent population,10,3335  the values from these studies were often reported in a manner that made between-groups comparisons difficult for the present study (eg, delineated values for healthy individuals were reported as a single value for participants aged 2–18 years or delineated values for specific age groups were combined across study groups, such as a chronic condition group and a healthy group). Varni et al32  were the only investigators to provide values for healthy individuals and stratified by age. Furthermore, participants from the Varni et al32  study were similar to those in previously published sample sets for a general, healthy adolescent population with regard to age, sex, and ethnicity.10,3335 

Procedures

Before the study began, a parent or legal guardian of each participant in the ATH group signed an informed consent approved by the local institutional review board, which also approved the study. All ATH participants completed the PedsQL GCS during a preseason screening session in their high school's athletic training facilities. All GEN participants completed the PedsQL GCS during a routine wellness checkup at their physician's office.32 

The Pediatric Quality of Life Inventory

The PedsQL GCS (version 4.0) is a self-report, generic PRO instrument that evaluates HRQOL in patients aged 2 to 18 years. The adolescent version of the PedsQL GCS is specifically designed for individuals aged 13 to 18 years and has been found to be a valid (concurrent validity: heterotrait-monomethod correlations = 0.45–0.48) and reliable PRO instrument (Cronbach α = 0.79–0.91).3436  The 23-item PedsQL GCS consists of 2 summary scores and 4 subscale scores. The total score (TS, 23 items) is a summary score of all subscale scores, and the psychosocial functioning score (PSF, 15 items) is a summary score of the emotional-functioning (EF), social-functioning (SOF), and school-functioning (SCF) subscale scores. The PedsQL GCS subscales include physical functioning (PF, 8 items), EF (5 items), SOF (5 items), and SCF (5 items). Each item is rated on a 5-point Likert scale and is reverse scored and linearly transformed to a 0 to 100 scale (0 = 100, 1 = 75, 2 = 50, 3 = 25, 4 = 0), with higher scores indicating better HRQOL.3436 

Data Analysis

For between-groups comparisons (ATH versus GEN), independent-samples t tests were conducted to evaluate differences between age-stratified groups (ie, 14, 15, and 16 years old; Table 1) for the PedsQL GCS summary and subscale scores. Between-groups comparisons for 17- and 18-year olds were not conducted because the Varni et al32  dataset did not report scores for these ages. However, scores for 17- and 18-year-old ATH were calculated and reported to provide a reference of normative scores for these age groups. Three pairwise comparisons (14-year-old ATH versus 14-year-old GEN, 15-year-old ATH versus 15-year-old GEN, 16-year-old ATH versus 16-year-old GEN) were conducted for each family of summary and subscale scores. A Bonferroni correction was used to account for multiple comparisons and protect against type I errors. The 2-tailed significance level for between-groups comparisons was set at P < .017.

Table 1.

Participants Stratified by Age, No.

Participants Stratified by Age, No.
Participants Stratified by Age, No.

For within-group comparisons for the ATH group, independent-samples t tests were conducted to evaluate differences between sexes for the PedsQL GCS summary and subscale scores. Six pairwise comparisons (male versus female) were calculated across all summary and subscale scores. A Bonferroni correction was used to account for multiple comparisons. The 2-tailed significance level for within-group sex comparisons was set at P < .008. In addition, a 1-way analysis of variance assessed age differences (14-, 15-, and 16-year olds) for the PedsQL GCS summary and subscale scores. The 2-tailed significance level for within-group age comparisons was set at P < .05. We used SPSS software (version 18.0; SPSS Inc, Chicago, IL) for data analysis.

To describe group differences, we calculated effect sizes (Cohen d) by dividing the difference between the mean values of the ATH and GEN groups by the pooled standard deviation of the 2 groups.37  The same procedures were used to calculate effect sizes between males and females within the ATH group. The magnitude of the group effect was interpreted as a trace (<0.20), small (0.20–0.49), medium (0.50–0.79), or large (>0.80) effect.37 

RESULTS

A total of 2659 high school athletes (males = 2059, females = 600, mean age = 15.7 ± 1.1 years) participated in the study and represented 19 interscholastic sports (Table 2). The GEN group was represented by values extracted from a published dataset for 14-, 15-, and 16-year-old healthy adolescent individuals (n = 729).32 

Table 2.

Primary Sports for Adolescent Athlete Group

Primary Sports for Adolescent Athlete Group
Primary Sports for Adolescent Athlete Group

The ATH had higher scores than the GEN across all ages for TS (P < .001), PSF (P < .001), EF (P < .001), and SOF (P<.005; Table 3, Figures 13). For PF, 15-year-old ATH scored higher than their GEN counterparts (P = .001), but the differences in PF between the 14- and 16-year-old groups were not significant (Table 3). For SCF, 14- and 15-year-old ATH demonstrated higher scores than their GEN counterparts (P ≤ .01), but the difference in SCF scores between the 16-year-old groups was not significant (P = .228). Effect sizes for all scores ranged from trace (0.18) to medium (0.57), with the largest effect sizes reported for EF (d = 0.51–0.57; Table 3).

Table 3.

Pediatric Quality of Life Generic Core Scale: Summary and Subscale Scores of Adolescent Athletes and General, Healthy Adolescents

Pediatric Quality of Life Generic Core Scale: Summary and Subscale Scores of Adolescent Athletes and General, Healthy Adolescents
Pediatric Quality of Life Generic Core Scale: Summary and Subscale Scores of Adolescent Athletes and General, Healthy Adolescents
Figure 1.

Pediatric Quality of Life Generic Core Scale summary and subscale scores for 14-year-old adolescent athletes (ATH) and general, healthy adolescents (GEN). ATH scored higher than GEN for all scores except physical functioning (P < .017). Error bars represent 95% confidence intervals.

Figure 1.

Pediatric Quality of Life Generic Core Scale summary and subscale scores for 14-year-old adolescent athletes (ATH) and general, healthy adolescents (GEN). ATH scored higher than GEN for all scores except physical functioning (P < .017). Error bars represent 95% confidence intervals.

Figure 3.

Pediatric Quality of Life Generic Core Scale summary and subscale scores for 16-year-old adolescent athletes (ATH) and general, healthy adolescents (GEN). ATH scored higher than GEN for all scores except physical and social functioning (P < .017). Error bars represent 95% confidence intervals.

Figure 3.

Pediatric Quality of Life Generic Core Scale summary and subscale scores for 16-year-old adolescent athletes (ATH) and general, healthy adolescents (GEN). ATH scored higher than GEN for all scores except physical and social functioning (P < .017). Error bars represent 95% confidence intervals.

Male adolescent athletes reported higher scores than female adolescent athletes for TS (P < .001), PSF (P < .001), PF (P < .001), EF (P < .001), and SCF (P = .001; Table 4). Effect sizes for these scores ranged from trace (0.15) to small (0.34), with the largest effect size reported for EF (d = 0.34; Table 4). No sex differences were noted for SOF (P = 0.229), and no differences were evident between ATH age groups (P ≥ .05).

Table 4.

Pediatric Quality of Life Generic Core Scale: Summary and Subscale Scores of Male and Female Adolescent Athletes

Pediatric Quality of Life Generic Core Scale: Summary and Subscale Scores of Male and Female Adolescent Athletes
Pediatric Quality of Life Generic Core Scale: Summary and Subscale Scores of Male and Female Adolescent Athletes

DISCUSSION

The purpose of our study was to compare HRQOL differences, as measured by the PedsQL GCS, between ATH and GEN. Our primary finding indicates that ATH between the ages of 14 and 16 years reported higher scores than GEN, suggesting that the former experienced better overall HRQOL and enhanced health across several domains, particularly in emotional and social functioning. We also found that, within the ATH, males tended to report higher HRQOL scores than did females, but no differences were observed across age groups.

Our findings further support the notion that ATH constitute their own distinct patient population and require their own set of normative values with regard to PRO instruments. The issue of normative values for instrument interpretation is not new and, in fact, it is considered regularly with our traditional clinician-rated measures. The importance of normative values is clear when clinician-rated and patient-rated measures of function are compared. Like clinician-rated measures, normative values for PRO instruments can vary across different patient populations.11  This, in turn, can affect the clinical usefulness of a PRO instrument for patient care. Just as clinicians would not use the normal limits of active glenohumeral joint external-rotation range of motion for the general healthy population (normal range: approximately 0°–90°) as their benchmark when treating baseball pitchers (normal range: approximately 0°–135°), clinicians should not use the normative values of a PRO instrument from 1 distinct patient population (eg, general, healthy population) when providing care for another distinct patient population (eg, patients with asthma) because the values may not provide an accurate frame of reference on which to base clinical decisions.11 

Other investigators who studied the normative values in other PROs have found similar results. Using the SF-36 and PODCI, Snyder et al26  noted that adolescent athletes reported better overall HRQOL as well as better social functioning and mental functioning and more happiness than their nonathlete counterparts. Our results support those reported by Snyder et al in that our ATH scored higher than GEN on the total summary score, psychosocial-functioning summary score, and emotional, social, and school subscales. These findings suggest that, in general, ATH tend to be healthier emotionally (eg, less frequently afraid, sad, angry, or worried), socially (eg, more likely to get along with peers and less likely to have trouble making friends or being teased by others), and academically (eg, fewer problems paying attention in class, forgetting things, keeping up with schoolwork, or missing school) than GEN. These findings are not surprising given that regular physical activity has been associated with higher self-esteem,14  improved emotional well-being,14  and better academic performance.13 

Similar to Snyder et al,26  we found the largest differences between ATH and GEN for the EF subscale: ATH scored 7.8 to 9.1 points higher than GEN across all age groups (Table 3). In addition, the largest effect sizes were noted for the EF subscale (0.51–0.57). In conjunction with those reported by Snyder et al,26  our findings indicate that ATH tend to experience greater emotional well-being than GEN and suggest that routine physical activity has the most influence on emotional health for adolescents between 14 and 16 years old. Additionally, although we did not compare instruments in our investigation, the similarity of findings between the instruments may provide clinicians with an opportunity to select a generic PRO measure that best fits their purpose. For example, the PedsQL may be desirable in busy athletic training rooms and for routine implementation in patient care due to the small number of questions and quick completion time when compared with other instruments, such as the PODCI (83 questions).

Furthermore, the emotional and mental benefits of regular physical activity may persist across age groups. In an investigation of college-aged individuals, Huffman et al24  found that collegiate athletes reported higher scores for vitality, emotional, and mental health subscales on the SF-36 when compared with the general, healthy population. Similarly, McAllister et al25  observed that, even when injured, elite collegiate athletes reported higher emotional- and mental-functioning scores than general, healthy individuals. This finding highlights the importance of establishing normative values for distinct patient populations. Clinically, using normative values from the general, healthy population as a frame of reference may suggest that an injured athlete is within normal limits for emotional and mental health when, in fact, he or she is experiencing deficits. Thus, use of normative values from the general, healthy population in athletes could result in missed deficits, simply because the reference point was incorrect. Although we did not compare injured adolescent athletes with general, healthy adolescent individuals, it would not be surprising to detect similar findings in the adolescent population based on the large between-groups differences on the EF subscale we report. However, further research is needed.

Snyder et al26  found that adolescent athletes reported higher physical functioning than nonathletes. Interestingly, ATH reported higher physical functioning scores than GEN across all age groups in our study, but we found group differences only for the 15-year olds. This discrepancy may be due to the different PRO instruments used in each study. Like Snyder et al, Huffman et al24  and McAllister et al25  used the SF-36 and reported differences in physical-functioning scores between athletes and the general, healthy population. These results suggest that the PedsQL GCS PF subscale may not be as sensitive as the SF-36 physical-functioning subscale. These instruments differ in the number of questions in each subscale (PedsQL GCS physical subscale = 8 items, SF-36 physical-functioning subscale = 10 items), which may explain the difference.

Between sexes, differences occurred across all PedsQL GCS scores except for SOF. The largest sex differences were noted for the EF subscale, which agrees with a previous investigation by Tanabe et al,38  who found that female adolescent athletes had lower scores for the vitality and mental composite score of the SF-36 and the happiness subscale of the PODCI when compared with their male counterparts. The authors suggested that emotional status in both females and males should be considered and managed on an individual basis due to potential differences between the sexes.38  Females, with a lower emotional HRQOL at the start, may be affected more than males by a negative event such as an injury. Males, in contrast, may have a less accurate perception of their health39  and, therefore, underreport their emotional HRQOL.

Our findings highlight current initiatives in health care, namely providing patient-centered, whole-person health care and assessing PROs during patient care.9,40,41  Patient-centered, whole-person health care encourages health care professionals to shift the weight of assessment from a strong focus on a disease or condition to an approach that considers the greater influence of a health condition on a person's overall HRQOL.26  In conjunction with previous investigations,2426  our study suggests that changes in the emotional well-being of athlete-patients may warrant attention. Athlete-patients generally experience a higher level of emotional functioning than do general, healthy individuals. Subsequently, a decrease in emotional well-being in an athlete may be significant and yet appear normal if the reference values are based on values from a general population and not a population of athletes. Thus, by recognizing that athletic individuals may not present in the same manner as typical patients, health care providers will be more prepared to identify potential emotional deficits efficiently and, in turn, provide better quality of care. Care may include referral to other health professionals, such as sports psychologists, should significant or long-term decrements occur. Research is needed in other athlete populations to further elucidate the differences between athlete and general population patients.

Unlike clinician-rated outcomes, which are evaluated from the clinician's point of view and focus on impairment-related changes (eg, range of motion, strength), PROs are evaluated from the patient's point of view and capture function- and disability-related changes, such as the patient's ability to perform functional tasks or to fulfill societal roles.4244  Because these types of changes are thought to be more meaningful and relevant to the patient, these types of outcomes may be more appropriate to guide patient care and answer questions related to best clinical practices.4246 

Although PRO instruments should be used when caring for athletic patients, health care providers should consider that these individuals likely constitute their own unique patient population. Our findings agree with those of previous studies that have shown that athletes score differently than their nonathlete counterparts on generic PRO instruments (eg, SF-36, PODCI).2426  Furthermore, recent evidence suggests that athletes also tend to score differently on region-specific PRO measures, such as the Oswestry Low Back Pain Disability Questionnaire47  and the Disabilities of the Arm, Shoulder, and Hand measure.48  Normative values often serve as a frame of reference to guide clinical decisions during patient care and act as a basis for investigating the effectiveness of treatment interventions, so future authors should focus on establishing normative values for the adolescent athletic population for commonly used PRO measures. Due to several limitations within our dataset (eg, restricted geographic diversity), the PedsQL GCS values we report are likely insufficient to establish normative values for an adolescent athletic population. However, until normative values are established, these PedsQL GCS values likely provide a better frame of reference for health care providers than previously published values for the general healthy population and may be helpful to health care providers who commonly use the PedsQL for the care of adolescent athletes.

Another limitation of this study is our use of a previously published dataset32  to represent GEN. Although this type of method has been used in similar investigations24,25  and we used the best available published dataset, our analysis was constrained by the data presented by Varni et al.32  For instance, we were unable to include analyses related to 17- and 18-year-old athletes or to sex differences because these data were not reported by Varni et al.32  In addition, the data presented by Varni et al32  and by our group were collected within the same general geographic region of the United States. Therefore, the generalizability of our findings to other regions of the country or to other countries could be affected. Findings from our study and previous studies2426  indicate that individuals participating in interscholastic sports (eg, elite, subelite) experience better HRQOL than the general, healthy population, but whether recreational athletes or individuals who exercise routinely would report similar levels of improved HRQOL is unclear. Still, it is reasonable to hypothesize that these individuals' scores would trend similarly to those of athletes due to the association of regular physical activity with decreased levels of anxiety, depression, and stress.1217 

CONCLUSIONS

In general, ATH experienced better HRQOL than GEN. These results suggest that adolescent individuals should be encouraged to participate in sport and physical activities as a means of enhancing overall HRQOL. Additionally, health care providers should consider whether a patient participates in sports or regular physical activities as part of the overall evaluation because these patients may present differently than a general, healthy individual, particularly in emotional well-being. Lastly, in conjunction with previous findings,2426  our study adds to the current evidence that athletes are a unique patient population and that normative values related to PRO instruments should be established for these individuals in order to provide the best clinical care.

Figure 2.

Pediatric Quality of Life Generic Core Scale summary and subscale scores for 15-year-old adolescent athletes (ATH) and general, healthy adolescents (GEN). ATH scored higher than GEN for all scores (P < .017). Error bars represent 95% confidence intervals.

Figure 2.

Pediatric Quality of Life Generic Core Scale summary and subscale scores for 15-year-old adolescent athletes (ATH) and general, healthy adolescents (GEN). ATH scored higher than GEN for all scores (P < .017). Error bars represent 95% confidence intervals.

ACKNOWLEDGMENTS

We thank the Headache Foundation (Chicago, IL) for funding this investigation.

REFERENCES

REFERENCES
1
Spilker
B
.
Quality of Life and Pharmacoeconomics in Clinical Trials. 2nd ed
.
Philadelphia, PA
:
Lippincott-Raven;
1996
.
2
Guyatt
G
,
Feeny
D
,
Patrick
D
.
Measuring health-related quality of life
.
Ann Intern Med
.
1993
;
118
(
8
) :
622
629
.
3
Guyatt
GH
,
Ferrans
CE
,
Halyard
MY
,
et al .
Exploration of the value of health-related quality-of-life information from clinical research and into clinical practice
.
Mayo Clin Proc
.
2007
;
82
(
10
) :
1229
1239
.
4
Irrgang
JJ
,
Anderson
AF
.
Development and validation of health-related quality of life measures for the knee
.
Clin Orthop Relat Res
.
2002
;
402
:
95
109
.
5
Main
DS
,
Quintela
J
,
Araya-Guerra
R
,
Holcomb
S
,
Pace
WD
.
Exploring patient reactions to pen-tablet computers: a report from CaReNet
.
Ann Fam Med
.
2004
;
2
(
5
) :
421
424
.
6
Testa
MA
,
Simonson
DC
.
Assessment of quality-of-life outcomes
.
N Engl J Med
.
1996
;
334
(
13
) :
835
840
.
7
Fayers
PM
,
Machin
D
.
Quality of Life: The Assessment, Analysis and Interpretation of Patient-Reported Outcomes
.
2nd ed
.
West Sussex, UK
:
Wiley
;
2007
.
8
Parsons
JT
,
Snyder
AR
.
Health-related quality of life as a primary clinical outcome in sport rehabilitation
.
J Sport Rehabil
.
2011
;
20
(
1
) :
17
36
.
9
Evans
TA
,
Lam
KC
.
Clinical outcomes assessment in sport rehabilitation
.
J Sport Rehabil
.
2011
;
20
(
1
) :
8
16
.
10
Varni
JW
,
Burwinkle
TM
,
Seid
M
.
The PedsQL as a pediatric patient-reported outcome: reliability and validity of the PedsQL Measurement Model in 25,000 children
.
Expert Rev Pharmacoecon Outcomes Res
.
2005
;
5
(
6
) :
705
719
.
11
Hunsaker
FG
,
Cioffi
DA
,
Amadio
PC
,
Wright
JG
,
Caughlin
B
.
The American Academy of Orthopaedic Surgeons outcomes instruments: normative values from the general population
.
J Bone Joint Surg Am
.
2002
;
84-A
(
2
) :
208
215
.
12
Donaldson
S
,
Ronan
K
.
The effects of sports participation on young adolescents' emotional well-being
.
Adolescence
.
2006
;
41
(
162
) :
369
389
.
13
Field
T
,
Diego
M
,
Sanders
C
.
Exercise is positively related to adolescents' relationships and academics
.
Adolescence
.
2001
;
36
(
141
) :
105
110
.
14
Hallal
PC
,
Victora
CG
,
Azevedo
MR
,
Wells
JC
.
Adolescent physical activity and health: a systematic review
.
Sport Med
.
2006
;
36
(
12
) :
1019
1030
.
15
Piko
BF
,
Keresztes
N
.
Physical activity, psychosocial health, and life goals among youth
.
J Community Health
.
2006
;
31
(
2
) :
136
145
.
16
Sanders
C
,
Field
T
,
Diego
M
,
Kaplan
M
.
Moderate involvement in sports is related to lower depression levels among adolescents
.
Adolescence
.
2000
;
35
(
140
) :
793
797
.
17
Steptoe
A
,
Butler
N
.
Sports participation and emotional wellbeing in adolescents
.
Lancet
.
1996
;
347
(
9018
) :
1789
1792
.
18
U. S. Department of Health and Human Services
.
Physical Activity and Health: A Report of the Surgeon General
.
Atlanta, GA
:
U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion;
1996
.
19
U.S. Department of Health and Human Services
.
Physical Activity Fundamental to Preventing Disease
.
Washington, DC
:
U.S. Department of Health and Human Services;
2002
.
20
Valois
RF
,
Zullig
KJ
,
Huebner
ES
,
Drane
JW
.
Physical activity behaviors and perceived life satisfaction among public high school adolescents
.
J Sch Health
.
2004
;
74
(
2
) :
59
65
.
21
Varni
JW
,
Limbers
CA
.
The PedsQL Multidimensional Fatigue Scale in young adults: feasibility, reliability and validity in a University student population
.
Qual Life Res
.
2008
;
17
(
1
) :
105
114
.
22
Warburton
DE
,
Nicol
CW
,
Bredin
SS
.
Health benefits of physical activity: the evidence
.
CMAJ
.
2006
;
174
(
6
) :
801
809
.
23
Warburton
DE
,
Nicol
CW
,
Bredin
SS
.
Prescribing exercise as preventive therapy
.
CMAJ
.
2006
;
174
(
7
) :
961
974
.
24
Huffman
GR
,
Park
J
,
Roser-Jones
C
,
Sennett
BJ
,
Yagnik
G
,
Webner
D
.
Normative SF-36 values in competing NCAA intercollegiate athletes differ from values in the general population
.
J Bone Joint Surg Am
.
2008
;
90
(
3
) :
471
476
.
25
McAllister
DR
,
Motamedi
AR
,
Hame
SL
,
Shapiro
MS
,
Dorey
FJ
.
Quality of life assessment in elite collegiate athletes
.
Am J Sports Med
.
2001
;
29
(
6
) :
806
810
.
26
Snyder
AR
,
Martinez
JC
,
Bay
RC
,
Parsons
JT
,
Sauers
EL
,
Valovich McLeod TC. Health-related quality of life differs between adolescent athletes and adolescent nonathletes
.
J Sport Rehabil
.
2010
;
19
(
3
) :
237
248
.
27
Varni
JW
,
Limbers
CA
.
The PedsQL 4.0 Generic Core Scales Young Adult Version: feasibility, reliability and validity in a university student population
.
J Health Psychol
.
2009
;
14
(
4
) :
611
622
.
28
Varni
JW
,
Burwinkle
TM
,
Katz
ER
,
Meeske
K
,
Dickinson
P
.
The PedsQL in pediatric cancer: reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales, Multidimensional Fatigue Scale, and Cancer Module
.
Cancer
.
2002
;
94
(
7
) :
2090
2106
.
29
Varni
JW
,
Burwinkle
TM
,
Limbers
CA
,
Szer
IS
.
The PedsQL as a patient-reported outcome in children and adolescents with fibromyalgia: an analysis of OMERACT domains
.
Health Qual Life Outcomes
.
2007
;
5
:
9
.
30
Varni
JW
,
Burwinkle
TM
,
Rapoff
MA
,
Kamps
JL
,
Olson
N
.
The PedsQL in pediatric asthma: reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales and Asthma Module
.
J Behav Med
.
2004
;
27
(
3
) :
297
318
.
31
Varni
JW
,
Seid
M
,
Smith Knight
T
,
Burwinkle
T
,
Brown
J
,
Szer
IS
.
The PedsQL in pediatric rheumatology: reliability, validity, and responsiveness of the Pediatric Quality of Life Inventory Generic Core Scales and Rheumatology Module
.
Arthritis Rheum
.
2002
;
46
(
3
) :
714
725
.
32
Varni
JW
,
Limbers
CA
,
Burwinkle
TM
.
How young can children reliably and validly self-report their health-related quality of life? An analysis of 8,591 children across age subgroups with the PedsQL 4.0 Generic Core Scales
.
Health Qual Life Outcomes
.
2007
;
5
:
1
.
33
Varni
JW
,
Burwinkle
TM
,
Seid
M
.
The PedsQL 4.0 as a school population health measure: feasibility, reliability, and validity
.
Qual Life Res
.
2006
;
15
(
2
) :
203
215
.
34
Varni
JW
,
Burwinkle
TM
,
Seid
M
,
Skarr
D
.
The PedsQL 4.0 as a pediatric population health measure: feasibility, reliability, and validity
.
Ambul Pediatr
.
2003
;
3
(
6
) :
329
341
.
35
Varni
JW
,
Seid
M
,
Kurtin
PS
.
PedsQL 4.0: reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations
.
Med Care
.
2001
;
39
(
8
) :
800
812
.
36
Varni
JW
,
Seid
M
,
Rode
CA
.
The PedsQL: measurement model for the pediatric quality of life inventory
.
Med Care
.
1999
;
37
(
2
) :
126
139
.
37
Cohen
J
.
Statistical Power Analysis for the Behavior Sciences. 2nd ed
.
Hillsdale, NJ
:
Lawrence Erlbaum Associates;
1988
.
38
Tanabe
T
,
Snyder
,
AR
,
Bay
,
RC
,
Valovich McLeod
,
TC
.
Representative values of health-related quality of life among female and male adolescent athletes and the impact of gender
.
Athl Train Sports Health Care
.
2010
;
2
(
3
) :
106
112
.
39
Wijnhoven
HA
,
Kriegsman
DM
,
Snoek
FJ
,
Hesselink
AE
,
de Haan
M
.
Gender differences in health-related quality of life among asthma patients
.
J Asthma
.
2003
;
40
(
2
) :
189
199
.
40
Institute of Medicine
.
Health Professions Education: A Bridge to Quality
.
Washington, DC
:
Institute of Medicine;
2003
.
41
Pew Health Professions Commission
.
Critical Challenges: Revitalizing the Health Professions for the Twenty-first Century
.
Philadelphia, PA
:
Pew Charitable Trust;
1995
.
42
Jette
AM
.
Physical disablement concepts for physical therapy research and practice
.
Phys Ther
.
1994
;
74
(
5
) :
380
386
.
43
Jette
AM
.
Outcomes research: shifting the dominant research paradigm in physical therapy
.
Phys Ther
.
1995
;
75
(
11
) :
965
970
.
44
Jette
AM
.
Toward a common language for function, disability, and health
.
Phys Ther
.
2006
;
86
(
5
) :
726
734
.
45
Snyder
AR
,
Parsons
JT
,
Valovich McLeod
TC
,
Bay
RC
,
Michener
LA
,
Sauers
EL
.
Using disablement models and clinical outcomes assessment to enable evidence-based athletic training practice, part I: disablement models
.
J Athl Train
.
2008
;
43
(
4
) :
428
436
.
46
Valovich McLeod
TC
,
Snyder
AR
,
Parsons
JT
,
Bay
RC
,
Michener
LA
,
Sauers
EL
.
Using disablement models and clinical outcomes assessment to enable evidence-based athletic training practice, part II: clinical outcomes assessment
.
J Athl Train
.
2008
;
43
(
4
) :
437
445
.
47
Fritz
JM
,
Clifford
SN
.
Low back pain in adolescents: a comparison of clinical outcomes in sports participants and nonparticipants
.
J Athl Train
.
2010
;
45
(
1
) :
61
66
.
48
Hsu
JE
,
Nacke
E
,
Park
MJ
,
Sennett
BJ
,
Huffman
GR
.
The disabilities of the arm, shoulder, and hand questionnaire in intercollegiate athletes: validity limited by ceiling effect
.
J Shoulder Elbow Surg
.
2010
;
19
(
3
) :
349
354
.