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. Author manuscript; available in PMC: 2016 Nov 1.
Published in final edited form as: Med Sci Sports Exerc. 2015 Nov;47(11):2431–2440. doi: 10.1249/MSS.0000000000000678

Pain Response after Maximal Aerobic Exercise in Adolescents across Weight Status

Stacy Stolzman 1, Michael Danduran 2,3, Sandra K Hunter 1,2, Marie Hoeger Bement 1
PMCID: PMC4598265  NIHMSID: NIHMS678036  PMID: 25856681

Abstract

Introduction

Pain reports are greater with increasing weight status, and exercise can reduce pain perception. It is unknown however, whether exercise can relieve pain in adolescents of varying weight status. The purpose of this study was to determine if adolescents across weight status report pain relief following high intensity aerobic exercise (exercise-induced hypoalgesia [EIH]).

Methods

62 adolescents (15.1±1.8 years, 29 males) participated in three sessions: 1) Pressure pain thresholds (PPTs) before and after quiet rest, clinical pain (McGill Pain Questionnaire), and physical activity levels (self-report and ActiSleep Plus Monitors) were measured; 2) PPTs were measured with a computerized algometer at the 4th finger nailbed, middle deltoid muscle, and quadriceps muscle before and after maximal oxygen uptake test (VO2 max Bruce Treadmill Protocol); and 3) Body composition was measured with Dual-energy X-ray absorptiometry.

Results

All adolescents met criteria for VO2 max. Based on body mass index z-score, adolescents were categorized as normal weight (n=33) or overweight/obese (n=29). PPTs increased following exercise (EIH) and were unchanged with quiet rest (trial × session: p=0.02). EIH was similar across the 3 sites and between normal weight and overweight/obese adolescents. Physical activity and clinical pain were not correlated with EIH. Overweight/obese adolescents had similar absolute VO2 max (L·min-1) but lower relative VO2 max (ml·kg-1·min-1) compared with normal weight adolescents. When adolescents were categorized using FitnessGram standards as unfit (n=15) and fit (n=46), the EIH response was similar between fitness levels.

Conclusion

This study is the first to establish that adolescents experience EIH in both overweight and normal weight youth. EIH after high intensity aerobic exercise was robust in adolescents regardless of weight status and not influenced by physical fitness.

Keywords: pain relief, children, body mass index, VO2 max, physical activity, physical fitness

Introduction

Exercise can decrease pain (i.e. exercise-induced hypoalgesia [EIH]) in adults and is dependent on both the intensity and duration of the exercise stimulus (19). To maximize EIH, aerobic exercise should be performed at a moderate/high intensity and longer duration than 10 minutes (19, 39). EIH is systemic in that pain relief is not localized to the exercising muscle; although some studies show that EIH is more robust in the exercising body part compared with non-exercising body parts (20, 39). All of these studies have been conducted on adults. To our knowledge, there are no studies investigating the impact of exercise on pain perception in adolescents. Identifying the EIH response in pediatric populations will help establish the potential benefits of exercise as a non-pharmacological pain management tool.

Weight status is an understudied factor in pain perception. Of the few studies, obese adults have lower pain sensitivity to a noxious stimulus (higher pain thresholds) compared with non-obese adults (7, 30). In contrast, clinical pain reports tend to increase as the weight status of both adults and adolescents increases (27, 33). Unfortunately, pain is commonly overlooked as a health outcome despite the majority of obese youth reporting that they currently feel pain (14). Physical and psychosocial consequences of obesity include development of musculoskeletal dysfunction, poor quality of life, missed school, and social withdrawal (13, 17). Thus, strategies to manage pain in obese adolescents is important for quality of life and health.

Physical inactivity is a major contributor to the obesity epidemic (22) and associated with the increase in pain that is reported in this population (33). Pediatric barriers to participation in physical activity include age (adolescents at greater risk than younger children) (10), weight status (obese youth at greater risk than normal weight) (10), and the presence of pain (24). Thus, overweight adolescents with pain may be at high risk for sedentary behavior resulting in poor physical fitness levels. Finally, self-reported physical activity is associated with endogenous pain modulation (23); those individuals that report higher levels of physical activity have more efficient descending pain modulatory function. Furthermore, several chronic pain conditions (e.g., fibromyalgia, irritable bowel syndrome, headache, etc.) often exhibit decreased endogenous pain modulation (31). Because endogenous pain modulation is one of the mechanisms for pain relief associated with exercise, individuals that are more physically active may experience greater reduction in response to a pain stimulus with exercise (i.e. EIH) whereas individuals that report current pain may experience less EIH.

Understanding how pain changes with exercise will provide a scientific rationale to better utilize exercise in the management of pain in pediatric populations. The purpose of this study was to compare the magnitude of EIH in normal weight and overweight/obese adolescents following a single session of intense aerobic exercise. To determine the possible modifying effect of baseline fitness, we also compared EIH in adolescents of varying levels of physical fitness in both normal weight and overweight/obese adolescents. We hypothesized that normal weight and overweight/obese adolescents would experience similar levels of EIH but those who were more physically fit would experience greater EIH. Because physical activity levels, the presence of pain, and psychosocial influences (i.e. pain catastrophizing & quality of life) have been implicated in pain modulation, we examined their influence on EIH in the adolescents.

Methods

Subjects

Sixty-two adolescents (15.1 ± 1.8 years [12.0 – 17.9 years]; 29 boys and 33 girls) and their parent/legal guardian were recruited from a Midwestern US metropolitan area (Milwaukee, Wisconsin) through community flyers, Marquette University electronic newsletter, monthly parenting magazine advertisement, and a Facebook advertisement. The adolescents were enrolled as part of a larger study that investigated the association between inflammatory markers, physical fitness, and pain in adolescents of varying weight status. All adolescents (12- 17 years) were screened via phone conversation with a parent/legal guardian about the study components and exclusionary criteria. Adolescents in good health were eligible for participation in the study with the following exclusions: 1) body mass index below the 10th percentile for age and sex, 2) inability to report pain threshold (i.e. tissue trauma or neurological condition that would affect sensory perception), 3) unable to tolerate ice water submersion (e.g. Raynaud's Disease or cold urticaria), 4) exercise contraindications, 5) non-English speaking participants, 6) cognitive delays, 7) pregnancy, 8) claustrophobia, or 9) history of mental health disorder. The protocol was approved by the Institutional Review Board at Marquette University.

Experimental Protocol

Adolescents participated in three experimental sessions with approximately 1 week between sessions. The respective parent/legal guardian completed questionnaires related to the adolescent's health and wellness. At the start of the first session, the adolescent and parent completed assent and consent, respectively. During this session, resting vitals, weight status, experimental pain (i.e., pressure pain thresholds) before and after 20 minutes of quiet rest, clinical pain, and self-report physical activity levels were measured. Adolescents were also instructed on wearing the physical activity monitors. Following the first session, adolescents participated in either the treadmill or Dual-energy X-ray absorptiometry (DXA) sessions in a counterbalanced manner. The treadmill session involved the measurement of experimental pain before and after a maximal aerobic treadmill test (maximal oxygen uptake test [VO2 max]) along with psychosocial assessments (i.e., quality of life and pain catastrophizing). The DXA session measured body composition.

Weight Status & Body Composition

From height (cm) and mass (kg) measurements, BMI was calculated and plotted for percentiles and z scores. Based on BMI z scores, 33 adolescents were classified as normal weight (BMI z score < 1.00) and 29 as overweight/obese (BMI z score ≥ 1.00) (29).

Total body DXA scans were performed with a Lunar GE Prodigy (GE, Madison, WI) bone densitometer to determine body composition. Prior to each scan, a quality assurance protocol was completed, a phantom was scanned for calibration purposes, and female adolescents completed a pregnancy test. Scans were analyzed using the Lunar GE Prodigy pediatric software to quantify fat body mass (kg), lean body mass (kg), total body fat (%), android fat (%), gynoid fat (%), and android:gynoid (A:G) ratio. Adolescents were classified as either android (A:G ratio ≥ 1.0) or gynoid (A:G ratio < 1.0). Total body fat z scores for age and sex were determined using the online Baylor College of Medicine Body Composition Laboratory Pediatric Body Composition Reference Charts (18).

Experimental Pain -- Pressure Pain Threshold Testing

Pressure pain thresholds were measured during the first experimental session (pre/post 20 minutes quiet rest) and pre/post maximal aerobic exercise (treadmill session) at three sites: left 4th finger nailbed, left middle deltoid muscle (one quarter the distance from the acromion to the lateral epicondyle), and right quadriceps muscle (half the distance from the anterior superior iliac crest to the superior patella) (2). Three trials were completed at each site with a 10 second inter-stimulus interval, and the site order were randomized at each session. A battery-operated pressure algometer (Algomed) with a 1 cm2 probe was placed on each site at a rate of 50 kilopascals/second (16). The adolescents were seated and instructed to push the Patient Response Unit when they first felt pain (i.e., pain threshold), which was electronically recorded in kilopascals (kPa).

Pain and Quality of Life Assessments

McGill Questionnaire (MPQ)

Participants completed the MPQ during the first session. This questionnaire evaluates the multi-dimensional aspect of pain (sensory, affective, and cognitive) related to current pain. Higher scores represent more pain.

Pain Catastrophizing Scale – Child & Parent (PCS-C & PCS-P)

To address pain catastrophizing (i.e., negative mental response to anticipated or actual pain), the adolescent and respective parent completed the PCS-C and PCS-P during the treadmill session. The questionnaire has 13 statements that are scored in a Likert Scale (0-5) with three subcategories: rumination, magnification, and helplessness. Higher PCS scores are indicative of higher pain catastrophizing with clinical reference points for the PCS-C as low (0-14), moderate (15-25), and high (> 26) (28).

Pediatric Quality of Life Inventory (PedsQL)

All adolescents and respective parents completed the PedsQL for Child (8-12 years of age) or Teen (13-18 years of age) with the corresponding parent version. The PedsQL is a valid and reliable measurement of pediatric health-related quality of life and has been used in youth of varying weight status (40). The PedsQL was completed during the treadmill session and was scored for Total, Physical, Social, Emotional, and School domains. Higher PedsQL scores represent higher quality of life. Total PedsQL cut-off scores for impaired quality of life are 69.7 for the child/teen and 65.4 for the parent (40).

Physical Activity Assessments

Physical activity was quantified with accelerometers and self-reported physical activity levels. Adolescents were instructed to wear the Actigraph monitor (ActiSleep Plus Monitor, Pensacola, FL) (9) on the non-dominant wrist for seven consecutive days, complete daily logs (awake/sleep times, physical activity participation, and removal/reapplication of the device), and return the device at session two. Data were downloaded via the ActiLife 6 Data Analysis Software (Actigraph, Pensacola, FL) at 60 second epochs. Wear-time validation was done (4), and adolescents were included in data scoring if they met the youth wearing recommendations for Actigraph monitors (at least 1 weekend day and 4 weekdays for > 950 minutes per day) (3). Next, the data were scored using pediatric cut-offs (11) to quantify length of time (minutes) in sedentary activity, light activity, and moderate/vigorous activity along with vector magnitude counts per minute and step counts. Additional sedentary analysis was completed to determine the average length of sedentary bouts (minutes), as defined by ≥ 10 minutes with ≤ 99 counts per minute (37). Wear time of the Actigraph monitor was compared with the written activity logs completed by each adolescent.

Self-reported physical activity was quantified using the Physical Activity Questionnaire--Elementary School & High School Versions (PAQ). The PAQ is a reliable and valid instrument which provides a general measure of physical activity for youth from grades 4-12 (approximate age of 8-20 years). Higher PAQ scores represent higher general physical activity. Cut-off points have been proposed to categorize youth as “at risk” or “no risk” for metabolic syndrome. Cut-off points for “at risk” are < 2.9/5 for boys and < 2.7/5 for girls (41).

Maximal Aerobic Treadmill Test (VO2 max)

Adolescents completed a maximal aerobic treadmill test (T-2100 Treadmill, GE Healthcare, El Paso, TX) with a VO2 max Bruce protocol which involved an increase in the grade and speed of the treadmill every 3 minutes (8). Twelve-lead EKG (CASE Cardiosoft V6.61, GE Healthcare, El Paso, TX) was obtained and metabolic monitoring (Encore 29c, VMAX, Palm Springs, CA) of expired gases (O2 and CO2) and volumes were measured continuously online with 20 second averaging. Variables assessed from the VO2 max protocol include peak respiratory rate, relative VO2 max (ml·total body mass kg-1·min-1), and absolute VO2 max (L·min-1). Lean VO2 max (ml·lean body mass kg-1·min-1) was also calculated by dividing absolute VO2 max by the lean body mass (kg) (21); the lean body mass was obtained from the DXA scan.

Adolescents reported rate of perceived exertion (RPE, 0-10) at the end of each three minute stage and at termination. Verbal encouragement was given throughout the test until the subject signaled they wanted to terminate the test. Criteria for establishing VO2 max was based on meeting at least 2 out of the 4 following criteria: 1) volitional fatigue (RPE > 8), 2) respiratory quotient (RQ) > 1.0, 3) heart rate > 85% age-predicted heart rate max (42), and 4) plateau in O2 consumption. Upon completion of the treadmill test, recovery included walking (2 minutes) followed by sitting (2 minutes). Immediately following the 4 minute recovery, measurement of post-exercise experimental pain (pressure pain thresholds) was completed.

Adolescents were categorized as “fit” or “unfit” according to their relative VO2 max (ml· kg-1·min-1) and FitnessGram standards. Specifically, the FitnessGram has established criteria (age and sex specific) to classify a youth's aerobic capacity (ml· kg-1·min-1) as being in the healthy fitness zone. This threshold for aerobic capacity which varies according to age and sex, represents the minimum fitness level to offer protection against diseases that result from sedentary living (34). For example, the healthy fitness zone for a 12 year old female is ≥ 40.1 ml· kg-1·min-1, whereas the healthy zone for a 17 year old male is ≥ 44.2 ml· kg-1·min-1. Using these VO2 max criteria, each adolescent was categorized into either the ‘fit’ group (within the healthy fitness zone) or the ‘unfit’ group (below the healthy fitness zone).

Statistical Analysis

Data were analyzed using Statistical Package for the Social Sciences (SPSS, version 21, IBM, Chicago, IL) and reported as mean ± standard deviation within the text and table and the mean ± standard error in the figure. A p-value of ≤ 0.05 was used for statistical significance.

Pressure pain threshold was calculated by averaging the three thresholds at each site (nailbed, deltoid muscle, and quadriceps muscle). Mixed-design multivariate repeated-measures ANOVAs were used to assess for change in pressure pain thresholds (trial [pre and post] × session [baseline and treadmill] × site [nailbed, deltoid muscle, and quadriceps muscle]). Weight status (normal weight or overweight/obese), fitness level (fit or unfit), and body composition (android or gynoid) were between subject factors. When a significant effect was found, Bonferroni corrected t-tests for post hoc multiple comparisons were used to identify differences. Pearson correlations were calculated to determine associations between EIH (post-pre PPTs at combined sites) and dependent variables.

Rate of perceived exertion during the treadmill test was analyzed using repeated measures ANOVA (time: end of stage 1, midpoint, and termination) using between subject factors of weight status (time x weight status [normal weight or overweight/obese]) and fitness levels (time × fitness levels [fit or unfit]). Post-hoc independent t-tests were used when appropriate. Independent t-tests were completed between the weight (normal weight or overweight/obese) and fitness (fit or unfit) groups to identify potential differences in demographics, weight and body composition, physical activity, physical fitness, health, pain (MPQ, present pain intensity, and baseline [pre-exercise] PPTs), and psychosocial measures. Additional independent t-tests were used to compare adolescents at risk and no risk for metabolic syndrome.

Physical activity monitor data was analyzed via the ActiLife 6 Data Analysis Software (Actigraph, Pensacola, FL) (Figure 1). Twelve subjects were not included in wear time analysis due to the following: 1) lost monitor (n=4; 1 normal weight fit, 1 overweight/obese fit, and 2 overweight/obese unfit adolescents); 2) refused to wear the monitor for the full duration (n=4; 2 normal weight fit and 2 overweight/obese fit adolescents); and 3) chose not to wear the monitor due to participation in organized swimming or baseball regulations (n=4; all normal weight fit adolescents). Data from fifty adolescents underwent ActiLife pediatric wear time validation (4), and four adolescents were excluded because they did not wear the device for the minimum time (3). Final data scoring from 46 adolescents (n=27 normal weight; 23 fit and 2 unfit, and n=19 overweight/obese; 12 fit and 7 unfit) was completed to quantify levels of physical activity (11). Of the 46 adolescents, 11 adolescents (n=6 normal weight; 5 fit and 1 unfit, and n=5 overweight/obese; 4 fit and 1 unfit) were required per coach/referee rules to remove the device during practices and/or competitive sporting events.

Figure 1. Actigraph Data Analysis Flow Chart.

Figure 1

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Results

Subjects

Of the 62 adolescents (and respective parent/legal guardian) that participated in this study, one adolescent was excluded from each of the EIH, DXA, and VO2 max analyses due to technical difficulties with the software, positive pregnancy test, and operational error, respectively.

Baseline Measures in Normal Weight vs. Overweight/Obese Adolescents

Based on the BMI z score classification, 33 adolescents were normal weight and 29 overweight/obese (Table 1). Overweight/obese adolescents had higher total body fat percentage, total body fat z score, and fat mass (all p<0.0001) but similar lean body mass (p>0.05) compared with normal weight adolescents. All adolescents were classified as either gynoid (n=41; 18 males, 23 females; 31 normal weight, 10 overweight/obese) or android (n=20; 11 males, 9 females; 2 normal weight, 18 overweight/obese) based on the DXA scan.

Table 1.

Descriptives of Adolescent Subjects by Weight Status and Physical Fitness Levels. Data are represented as mean ± SD.

Normal Weight
(n=33)		 Overweight/Obese
(n=29)		 Fit (n=46) Unfit (n=15)
Demographics
Sex (males) 17 12 23 6
Age (years) 15.5 ± 1.8 14.6 ± 1.8 15.3 ± 1.8 14.7 ± 1.8
Ethnicity
 Caucasian 31 16 40 6
 African American 0 9 3 6
 Hispanic 2 4 3 3
Vitals n=33 n=29 n=46 n=15
Resting HR (bpm) 72.5 ± 11.4 78.0 ± 11.9 71.7 ± 11.3 85.5 ± 7.0**
Resting Systolic BP (mm Hg) 107.1 ± 10.7 115.7 ± 11.3 # 109.4 ± 11.7 116.8 ± 10.6 *
Resting Diastolic BP (mm Hg) 71.3 ± 6.8 74.1 ± 8.7 71.6 ± 6.9 76.5 ± 8.8 *
Resting MAP (mm Hg) 83.2 ± 6.4 87.9 ± 8.6 * 84.2 ± 7.0 90.0 ± 8.6 *
Resting Pulse Ox (%) 97.3 ± 1.1 97.5 ± 1.1 97.4 ± 1.2 97.3 ± 0.9
Weight Status & Body Composition n=33 n=28 n=46 n=14
BMI (kg/m2) 21.1 ± 1.9 30.5 ± 6.8 ** 23.2 ± 3.4 33.4 ± 8.4 **
BMI z score 0.27 ± 0.47 1.87 ± 0.50 ** 0.72 ± 0.75 2.04 ± 0.64 **
Total Body Fat (%) 20.9 ± 9.2 40.8 ± 7.6 ** 26.7 ± 11.9 41.8 ± 10.2 **
Total Body Fat z score (Age & Sex) -0.80 ± 1.00 1.60 ± 0.47 ** -0.05 ± 1.34 1.58 ± 0.88 **
Android Fat (%) 22.3 ± 11.5 47.1 ± 8.6 ** 29.6 ± 14.6 48.3 ± 12.2 **
Gynoid Fat (%) 29.0 ± 10.7 46.6 ± 6.7 ** 34.4 ± 12.4 46.3 ± 8.8 **
A:G Ratio 0.74 ± 0.19 1.0 ± 0.11 ** 0.82 ± 0.19 1.03 ± 0.16 **
Lean Body Mass (kg) 45.40 ± 10.55 47.61 ± 12.23 45.22 ± 10.30 51.48 ± 13.05
Fat Body Mass (kg) 11.91 ± 5.52 33.90 ± 13.39 ** 16.98 ± 9.16 39.56 ± 16.81 **
Physical Activity
Self-Reported Physical Activity n=33 n=29 n=46 n=15
 PAQ Score (0-5) 2.7 ± 0.6 2.5 ± 0.7 2.7 ± 0.6 2.3 ± 0.7 *
Actigraph Accelerometry n=26 n=20 n=35 n=10
 Average Length of Sedentary Bouts (min) 22.9 ± 2.7 25.1 ± 2.0 # 23.3 ± 2.7 25.6 ± 1.7 *
 % in Sedentary 53.7 ± 5.6 54.15 ± 5.7 53.4 ± 5.6 54.9 ± 5.6
 % in Light Activity 11.6 ± 1.6 11.9 ± 1.4 11.6 ± 1.5 12.3 ± 1.6
 % in Moderate/vigorous Activity 34.7 ± 5.3 34.0 ± 5.7 35.0 ± 5.4 32.8 ± 5.4
 Steps Count 72100.2 ± 17599.5 72527.3 ± 15938.1 73781.1 ± 17015.0 68003.7 ± 16250.2
 Vector Magnitude Counts Per Minute 1988.5 ± 459.6 1852.2 ± 421.9 2002.6 ± 444.6 1695.8 ± 389.4
Normal Weight
(n=33)		 Overweight/Obese
(n=29)		 Fit (n=46) Unfit (n=15)
Physical Fitness n=31 n=29 n=46 n=15
Absolute VO2 max (L·min-1) 3.48 ± 1.01 3.20 ± 0.90 3.51 ± 0.94 2.86 ± 0.86 *
Relative VO2 max (mL·TBW kg-1·min-1) 57.4 ± 12.7 38.9 ± 11.3 ** 54.4 ± 12.1 30.9 ± 8.3 **
Lean VO2 max (mL·LBM kg-1·min-1) 75.5 ± 10.9 69.1 ± 14.3 77.4 ± 9.9 56.5 ± 7.6 **
RR Peak (breaths/min) 49.3 ± 5.9 46.8 ± 9.1 49.7 ± 6.5 43.0 ± 8.9#
HR Exercise Slope 8.42 ± 1.56 10.12 ± 2.46 # 8.68 ± 1.54 10.65 ± 3.09 *
HR Recovery Slope -18.87 ± 8.48 -15.43 ± 7.49 -18.08 ± 7.55 -14.49 ± 9.65
RQ 1.08 ± 0.07 1.05 ± 0.06 1.08 ± 0.07 1.03 ± 0.1 *
O2 Pulse (mL/beat) 17.5 ± 5.2 17.2 ± 5.1 17.8 ± 5.0 16.0 ± 5.5
Time to Exhaustion (min) 13.4 ± 2.4 10.2 ± 2.4 ** 13.0 ± 2.4 9.1 ± 2.4 **
Exercise Tolerance n=31 n=29 n=46 n=15
Peak RPE during VO2 max (0-10) 9.2 ± 1.1 9.0 ± 1.7 9.0 ± 1.5 9.1 ± 1.2
HR Max (%) 97.3 ± 8.2 95.6 ± 13.7 98.0 ± 9.2 92.0 ± 15.2
Experimental Pain n=32 n=29 n=45 n=15
Pre-Exercise PPTs (kPa, all sites combined) 380.3 ± 201.3 425.7 ± 273.9 379.9 ± 207.7 468.5 ± 311.3
Post-Exercise PPTs (kPa, all sites combined) 411.4 ± 235.7 462.3 ± 314.3 414.6 ± 238.8 502.2 ± 363.4
Clinical Pain & Quality of Life n=33 n=29 n=46 n=15
McGill Pain Questionnaire
 MPQ (Total, 0-78) 4.48 ± 6.20 3.76 ± 8.72 4.74 ± 8.29 2.27 ± 3.81
 MPQ (PPI, 0-5) 0.82 ± 0.98 0.48 ± 0.74 0.67 ± 0.92 0.53 ± 0.74
 MPQ (VAS, 0-10 cm) 1.13 ± 1.42 0.48 ± 1.11 * 0.89 ± 1.4 0.44 ± 0.67
Pain Catastrophizing Scale
 PCS-Child (Total. 0-52) 16.39 ± 10.22 17.07 ± 10.04 16.20 ± 10.11 18.67 ± 10.21
 PCS-Parent (Total, 0-52) 13.52 ± 9.05 18.00 ± 10.85 14.78 ± 9.74 18.87 ± 10.88
 PCS-Parent (Magnification, 0-12) 2.70 ± 2.08 3.34 ± 2.48 2.83 ± 2.18 3.67 ± 2.50
 PCS-Parent (Rumination, 0-16) 6.52 ± 3.51 8.72 ± 4.11 * 7.24 ± 3.97 8.80 ± 3.65
 PCS-Parent (Helplessness, 0-24) 4.30 ± 4.20 5.93 ± 5.23 4.72 ± 4.47 6.40 ± 5.50
Pediatric Quality of Life Inventory (all 0-100)
 PedsQL Child (Total) 84.3 ± 8.3 84.3 ± 11.8 86.1 ± 8.8 78.9 ± 12.2 *
 PedsQL Child (Physical) 86.1 ± 10.1 84.8 ± 11.9 87.4 ± 10.4 80.0 ± 11.4 *
 PedsQL Child (Social) 91.5 ± 10.3 88.8 ± 13.3 92.1 ± 10.0 84.0 ± 15.0 *
 PedsQL Child (Emotional) 80.8 ± 13.4 81.6 ± 18.1 82.5 ± 14.1 76.3 ± 19.8
 PedsQL Child (School) 77.7 ± 12.9 81.7 ± 15.3 81.6 ± 12.2 74.7 ± 17.9
 PedsQL Child (PsychoSocial Health Summary) 83.3 ± 9.7 84.0 ± 14.0 85.4 ± 10.0 78.3 ± 15.7
 PedsQL Parent (Total) 88.8 ± 10.3 82.7 ± 18.5 89.2 ± 10.3 75.4 ± 21.6 *
 PedsQL Parent (Physical) 92.3 ± 8.2 83.4 ± 21.0 * 92.3 ± 8.7 74.8 ± 25.0 *
 PedsQL Parent (Social) 94.1 ± 9.7 86.21 ± 20.2 93.5 ± 10.3 80.3 ± 24.8
 PedsQL Parent (Emotional) 82.0 ±17.1 81.4 ± 20.2 83.8 ± 16.4 74.7 ± 23.5
 PedsQL Parent (School) 84.8 ± 16.7 79.5 ± 20.0 85.7 ± 15.9 72.0 ± 22.5 *
 PedsQL Parent (PsychoSocial Health Summary) 87.0 ± 12.5 82.4 ± 18.2 87.6 ± 12.0 75.7 ± 21.5
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Data are represented as mean ± SD

*

Significance: p < 0.05,

#

p < 0.01,

**

p < 0.0001

Abbreviations: A:G, android:gynoid

BMI, body mass index

BP, blood pressure

bpm, beats per minute

HR, heart rate

LBM, lean body mass

MAP, mean arterial pressure

PAQ, Physical Activity Questionnaire

PCS, Pain Catastrophizing Scale

PedsQL, Pediatric Quality of Life Inventory

PPI, present pain intensity

PPT, pressure pain threshold

RPE, rate of perceived exertion

RQ, respiratory quotient

RR, respiratory rate

SD, standard deviation

TBW, total body weight

VAS, Visual Analogue Scale

Self-reported physical activity (PAQ) was similar between the normal weight and overweight/obese adolescents (Table 1), however PAQ was inversely correlated with total body fat z score (r=-0.263, p=0.04) so that adolescents with more body fat reported less physical activity. Based on accelerometry (n = 46), normal weight and overweight/obese adolescents had similar physical activity levels with the exception of average length of sedentary bouts (min) where overweight/obese adolescents had longer sedentary time (p=0.005).

Overweight/obese adolescents had lower relative VO2 max (ml·kg-1·min-1) (p<0.0001) and decreased time to exhaustion during the aerobic capacity test (p<0.0001) compared with the normal weight adolescents (Table 1). Absolute VO2 max and lean VO2 max were the same between the weight groups.

For the pain measures, baseline (pre-exercise) pressure pain thresholds were similar between the two weight groups. Furthermore, clinical pain and quality of life were similar between the weight groups with the exception of current pain intensity (MPQ VAS, p=0.05) where normal weight adolescents reported higher pain than the overweight/obese adolescents. Additionally, parent perspective for physical quality of life (p=0.04) was higher in the normal weight group and pain catastrophizing rumination (p=0.03) was lower in the normal weight group in contrast to the overweight/obese group (Table 1).

Baseline Measures in Fit vs. Unfit Adolescents

Based on the FitnessGram performance standards using relative VO2 max, adolescents were classified as fit (n=46; 30 normal weight, 16 overweight/obese) or unfit (n=15; 2 normal weight, 13 overweight/obese) (Table 1). The two groups differed in that fit adolescents had higher absolute VO2 max (p=0.02), lean VO2 max (p<0.0001), peak respiratory rate (p=0.002), and time to exhaustion (p<0.0001 (Table 1) than unfit adolescents. The fit group also had higher self-reported physical activity (p=0.03), lower resting heart rate (p<0.0001), and lower resting systolic pressure (p=0.03) and diastolic pressure (p=0.03). Based on Actigraph monitoring, fit and unfit adolescents had similar physical activity levels with the exception of average length of sedentary bouts (min) where fit adolescents had shorter sedentary time (p=0.018). In respect to body composition and weight status, fit adolescents compared with unfit adolescents had lower BMI z scores, body fat percentage, total body fat z scores, fat mass, android fat, and gynoid fat (all p<0.0001) but similar lean body mass.

Specific to pain measures, baseline (pre-exercise) pressure pain thresholds were similar between the fit and unfit adolescents (Table 1). There were no differences between the fit and unfit group for the adolescent's clinical pain (MPQ) and the child and parent perspectives for pain catastrophizing (p>0.05). Quality of life from the child perspective (total quality of life [p=0.02], physical functioning [p=0.02], and social functioning [p=0.02]) were significantly higher in the fit adolescents compared with the unfit. Quality of life assessment from the parent perspective (total quality of life [p=0.02], physical functioning [p=0.02], and school functioning [p=0.01]) were significantly higher in the fit adolescents (Table 1).

Exercise Response

All adolescents, regardless of weight status, met the ACSM criteria for the completion of a VO2 max test: peak RPE (9.1 ± 1.4), RQ (1.1 ± 0.1), and Heart Rate Max (96.5 ± 11.1%). There was no difference in peak RPE or percent heart rate max by weight status or physical fitness levels (Table 1). RQ differed between fitness levels in that unfit adolescents had lower RQ values than fit adolescents (p=0.02) but did not differ with weight status, (Table 1).

Perceived exertion increased during the aerobic capacity treadmill test (time: p<0.0001). This increase was similar between fitness groups (time x fitness levels: p>0.05). In contrast, the increase in perceived exertion differed by weight status (time x weight status: p=0.04). Post-hoc analyses showed that perceived exertion at the end of stage 1 was greater for the overweight/obese adolescents (1.8 ± 2.0) compared with the normal weight adolescents (0.8 ± 0.9, p=0.04). There were no differences in perceived exertion at midpoint or termination of the treadmill.

Experimental Pain Following Quiet Rest and Exercise Sessions

Pain thresholds increased following exercise (i.e., EIH) but were unchanged following quiet rest (trial x session: p=0.02, Figure 2). This response was similar across the sites assessed for pain (trial × session × site: p>0.05).

Figure 2.

Figure 2

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Exercise Induced Hypoalgesia versus Quiet Rest. Pressure pain thresholds increased following maximal aerobic exercise and were unchanged following quiet rest (trial × session, p=0.02). Data are represented as mean ± SEM.

EIH: Weight Status & Body Composition

EIH was similar between the normal weight and overweight/obese adolescents (p>0.05). Regardless of weight status, gynoid and android adolescents reported similar EIH (trial × android/gynoid: p>0.05). Lean body mass (kg) was weakly correlated with EIH (r=0.146, p=0.05, Figure 3); adolescents with higher total body lean mass experienced greater EIH; yet fat mass (kg) was not correlated (p>0.05).

Figure 3.

Figure 3

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EIH and Lean Mass by Weight Status. EIH is positively correlated with lean mass (r=0.146, p=0.05). Normal weight adolescents are shown as filled circles and overweight/obese adolescents as open circles. The distribution of normal weight and overweight/obese adolescents demonstrate that they have similar lean mass and EIH.

EIH: Physical Fitness and Activity

Fit and unfit adolescents (based on relative VO2 max), reported similar EIH (trial x fitness: p>0.05). EIH was not correlated with relative VO2 max, absolute VO2 max, lean VO2 max, and peak respiratory rate. Self-reported physical activity (PAQ) was not correlated with EIH but total sedentary bouts (r=-0.189, p=0.03) from the Actigraph monitors were weakly inversely correlated with EIH; adolescents with greater sedentary time experienced less EIH. Furthermore, adolescents at risk for metabolic syndrome (n=38; 19 normal weight, 19 overweight/obese) reported similar EIH compared with those not at risk for metabolic syndrome (n=24; 14 normal weight, 10 overweight/obese).

EIH: Clinical Pain & Quality of Life

Clinical pain measured with the MPQ was not correlated with EIH. PedsQL Child/Teen physical functioning was weakly correlated with EIH (r=0.149, p=0.05); adolescents with greater physical functioning experienced greater EIH. PCS-Child and PedsQL Parent were not associated with EIH (p>0.05).

Discussion

This study is the first to demonstrate overweight/obese adolescents report reduced response to a pain stimulus following intense aerobic exercise, and this response was analogous to normal weight adolescents. The increase in pain threshold was similar across body sites (nailbed, deltoid muscle, quadriceps muscle) demonstrating a systemic response, which has important clinical implications and possibilities as a strategy for pain relief in adolescents. The decrease in pain perception throughout the body indicates that pain relief following exercise is not isolated to the exercising muscle. Thus, aerobic exercise has strong potential as a non-pharmacological pain management tool in adolescents regardless of their weight status.

Few studies have investigated the impact of weight status on EIH. Comstock and colleagues showed that following a single exercise session of moderately heavy resistance training, lean and obese men reported similar levels of soreness and fatigue (5); pain perception was not specifically addressed in this study. The majority of studies that focus on overweight/obese individuals tend to focus on changes in health-related quality of life in conjunction with weight loss. Our results specifically address the role of exercise in decreasing pain perception in overweight/obese adolescents and parallels other findings that have shown exercise gains extend beyond weight loss (35). For individuals with knee osteoarthritis, decreasing body fat and increasing physical activity were more important than weight loss for symptomatic relief (35). Similarly, in the current study, EIH was weakly associated with lean body mass but not total body mass, and lean mass was similar between the weight groups. For individuals who are overweight/obese, the benefits of exercise are manifold and significant improvements in health may occur independent of weight loss (32).

When the adolescents were categorized based on physical fitness, EIH was similar between the fit and unfit groups. Additionally, self-reported physical activity was not associated with EIH. Previously we have shown that self-reported physical activity was not associated with pain relief following isometric contractions in young and older healthy adults (23). In contrast, there are some reports that inactive individuals may not experience the same benefits with exercise and may even report an increase in pain (38); our data concurs that inactive adolescents with more sedentary bouts experience less pain reduction after exercise. However, when adolescents were identified as being “at risk” for metabolic syndrome using published cut-off scores for self-reported physical activity (PAQ) (41), similar EIH occurred between the adolescent groups “at risk” and “no risk.”

Weight Status

Adolescents with higher BMI levels were categorized as overweight/obese category with the majority of these adolescents designated as obese. Of the 29 overweight/obese adolescents, 16 were classified as fit and 13 as unfit. All of the weight status and body composition values, except lean body mass, were significantly different between the normal weight and overweight/obese groups. In relation to physical fitness, relative VO2 max (ml· kg-1·min-1) was lower for the overweight/obese adolescents. There was no difference between the weight groups when lean VO2 max (relative to lean body mass) was compared. Thus, the lower relative VO2 max in overweight/obese adolescents was due to their elevated fat mass. Several studies have reported comparable results between obese and normal weight participants; obese individuals tend to have lower relative VO2 max when calculated relative to total body mass and similar levels as normal weight individuals when computed in relation to lean body mass (6, 12, 36). Overall, these results indicate that when total body mass is used as part of the physical fitness calculation, adolescents with greater mass have lower oxygen uptake. From a functional standpoint, they may have lower aerobic capacity when performing activities that necessitate movement of their body mass (12). In contrast, based on the lean VO2 max, overweight/obese adolescents and normal weight adolescents have similar maximal aerobic capacity.

Several investigators have shown that pain reports increase and quality of life decreases with increasing weight status (14, 15, 43). The adolescents in this study reported minimal to no current pain intensity. Surprisingly, the normal weight adolescents reported slightly higher current pain intensity (VAS) compared with the overweight/obese adolescents; both pain intensity reports, however were not clinically relevant. In contrast, Hainsworth and colleagues found that 50% of obese youth (class 2 & 3 obesity) in a clinical setting reported current pain (14). In the current study, the adolescents had a lower overall obese classification and were not participating in a clinical weight management program, which may help explain the differences in pain reports. Quality of life was also similar between the two weight groups and within normal limits for both the child and parent perspectives (40). The only difference between the two weight groups was from the parent perspective. Parents of overweight/obese adolescents reported lower physical functioning of their adolescent compared with parents of normal weight adolescents. While several studies have shown lower health-related quality of life with increasing weight status (43, 44); this relation between quality of life and weight status tends to be more distinct in clinical populations than community-based sampling (43). Using community-based sampling, our results demonstrate normal weight and overweight/obese adolescents have minimal pain reports and comparable quality of life.

Physical Fitness

When the adolescents were categorized based on physical fitness levels, the majority of adolescents (75%) were determined to be in the healthy fitness zone (i.e. fit). All of the weight status and body composition measures, except lean body mass, were different between the fit and unfit groups. In relation to quality of life, fitness levels appeared to have a much bigger impact, both from the child and parent perspectives, than weight status. Only one quality of life measure was different when comparing weight groups (parent-physical domain). When comparing fitness groups, however six quality of life measures differed (three child and three parent). This finding emphasizes the need to view weight status and physical fitness as separate entities when addressing health outcomes (12, 21). Despite the differences in quality of life, experimental and clinical pain reports were similar for the fit and unfit adolescents.

As anticipated, self-reported physical activity and sedentary time (Actigraph monitoring) were lower in the unfit than the fit adolescents. Self-reported physical activity level was then used to identify adolescents at risk for metabolic syndrome (boys < 2.9 and girls < 2.7) (41). The average physical activity for the unfit adolescents indicates that this group was at risk for metabolic syndrome. Surprisingly, the average for the fit adolescents (2.7) is borderline for metabolic syndrome. These results highlight the lack of physical activity in adolescents; only 8% of adolescents (12-19 years) meet the U.S. Department of Health and Human Services recommendations of 60 minutes of moderate-to-vigorous daily physical activity (26).

Objective physical activity measurement is considered more valid and reliable than self-report. Unfortunately, this study demonstrates that considerable data can be lost in adolescent participants that compete in sporting activities because referees and/or coaches require the adolescents to remove the device. The missing data no doubt impacts the results for moderate/vigorous physical activity levels with a greater impact between the fitness levels than weight groups. For example, of the 11 adolescent athletes that were told to remove the device, the missing data was evenly distributed between normal weight (n=6) and overweight/obese (n=5) adolescents. In contrast, nine of the eleven adolescents were “fit” vs. two “unfit.” As a result, this may have contributed to the lack of difference in physical activity specifically for moderate/vigorous activity between fitness levels. These results highlight the challenges in capturing wrist-based accelerometry in an active pediatric population.

Exercise Response

Although maximal aerobic exercise is not typically prescribed for pain management in adolescents, we chose this exercise dose because it is a measure of physical fitness and allowed us to determine the EIH at maximal dose. It also allowed us to investigate whether adolescents could tolerate maximal aerobic exercise. One reason for the lack of evidence on whether obese individuals experience EIH is the concern that they cannot tolerate regular exercise and are at a higher risk for injury (25). In this study, perceived exertion was similar between the normal weight and overweight adolescents except at the initiation of the treadmill test when obese adolescents reported slightly higher perceived exertion. Furthermore, all of the adolescents tolerated and met the ACSM criteria for termination of the VO2 max test, and none of the adolescents experienced any contraindications for early termination. This exercise protocol is in line with Expert Committee Recommendations for the prevention and treatment of child and adolescent overweight/obesity by promoting moderate to vigorous physical activity for at least 60 minutes each day (1). While maximal VO2 testing is not typically used as an exercise stimulus in the clinic, our results indicate that adolescents of different weight status tolerate and experience pain relief after maximal aerobic exercise.

Taking into account the differences in the VO2 max in relation to total body mass and lean mass, exercise tolerance could be impacted based on the degree of body mass movement (e.g., running vs. cycling). Due to the shorter time to exhaustion and lower VO2 max relative to total body mass, obese adolescents may have more difficulty in the performance of weight bearing activities (12); although, this was not reflected in their perceived exertion at the midpoint or termination of the treadmill protocol. Thus, different VO2 max calculations (total body mass vs. lean mass) may be used to calculate physical fitness and help establish performance levels during weight-bearing vs. non-weight-bearing activities.

Limitations

While this study helps to lay the foundation for the prescription of maximal aerobic exercise in the management of pain, there are some limitations. First, the population sample should be expanded to include more distinct weight groups (overweight through class III obesity). These distinctions would help to identify if any changes in pain at rest and following exercise occur similarly at each level of weigh status. Second, physical activity measured by self-report has the potential for inaccuracies as both over- and under- reporting have been described in youth. However, objective physical activity monitoring may also be limited when adolescents participate in organized competitive sports. Third, an order effect may be present in this study's design with the quiet rest condition always occurring before the exercise condition. Finally, we categorized the adolescents as fit or unfit based on their relative VO2 max (ml· kg-1·min-1). While this is the primary calculation used in determining physical fitness levels, excess adiposity in obese individuals may result in lower perceived physical fitness levels than if the VO2 max was based on the lean mass. Despite this equivocality, there are no standardized data to use VO2 max per lean mass as a marker for physical fitness.

Conclusions

These results significantly add to the literature by providing much needed evidence in the prescription of therapeutic exercise as a pain management tool for adolescents of varying weight and fitness levels. Both normal weight and overweight/obese adolescents experienced similar levels of EIH after maximal aerobic exercise. Additionally, physical fitness levels did not influence the magnitude of EIH but sedentary time was associated with EIH. Nevertheless, physical fitness levels may be more important than weight status when determining quality of life in adolescents. When measuring physical fitness, the influence of total body mass and lean mass on VO2 max should be assessed for a broader understanding of physical fitness levels and implications for weight-bearing and non-weight-bearing activities. Additional pediatric research is warranted in identifying the impact of weight status on pain perception at rest and following exercise with multiple stages of obesity as well as community- versus clinically- based populations.

Acknowledgments

The results of the present study do not constitute endorsement by ACSM.

This project was supported by the following:

  1. American Association of University Women – American Fellowship (SS)

  2. Foundation for Physical Therapy – Promotion of Doctoral Scholarships I & II (SS)

  3. Marquette University President's Council -- Raynor Fellowship (SS)

  4. National Center for Advancing Translational Sciences, National Institutes of Health, through Grant Number 8UL1TR000055 (SS, MD, SH, MHB)

  5. American Association of University Women – American Fellowship (SS)

  6. Foundation for Physical Therapy – Promotion of Doctoral Scholarships I & II (SS)

  7. Marquette University President's Council -- Raynor Fellowship (SS)

  8. National Center for Advancing Translational Sciences, National Institutes of Health, through Grant Number 8UL1TR000055 (SS, MD, SH, MHB)

Footnotes

Conflicts of Interest: None.

References

  • 1.Barlow SE Expert Committee. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007;120(Suppl 4):S164–92. doi: 10.1542/peds.2007-2329C. [DOI] [PubMed] [Google Scholar]
  • 2.Birnie KA, Caes L, Wilson AC, Williams SE, Chambers CT. A practical guide and perspectives on the use of experimental pain modalities with children and adolescents. Pain Manag. 2014;4(2):97–111. doi: 10.2217/pmt.13.72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Cain KL, Sallis JF, Conway TL, Van Dyck D, Calhoon L. Using accelerometers in youth physical activity studies: a review of methods. J Phys Act Health. 2013;10(3):437–50. doi: 10.1123/jpah.10.3.437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43(2):357–64. doi: 10.1249/MSS.0b013e3181ed61a3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Comstock BA, Thomas GA, Dunn-Lewis C, et al. Effects of acute resistance exercise on muscle damage and perceptual measures between men who are lean and obese. J Strength Cond Res. 2013;27(12):3488–94. doi: 10.1519/JSC.0b013e31828f8202. [DOI] [PubMed] [Google Scholar]
  • 6.Cooper DM, Poage J, Barstow TJ, Springer C. Are obese children truly unfit? Minimizing the confounding effect of body size on the exercise response. J Pediatr. 1990;116(2):223–30. doi: 10.1016/s0022-3476(05)82878-1. [DOI] [PubMed] [Google Scholar]
  • 7.Dodet P, Perrot S, Auvergne L, et al. Sensory Impairment in Obese Patients? Sensitivity and Pain Detection Thresholds for Electrical Stimulation After Surgery-induced Weight Loss, and Comparison With a Nonobese Population. Clin J Pain. 2013 Jan;29(1):43–49. doi: 10.1097/AJP.0b013e31824786ad. [DOI] [PubMed] [Google Scholar]
  • 8.Ehrman JK American College of Sports Medicine. ACSM's Guidelines for Exercise Testing and Prescription. 9th. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2014. pp. 112–115. [Google Scholar]
  • 9.Ekblom O, Nyberg G, Bak EE, Ekelund U, Marcus C. Validity and comparability of a wrist-worn accelerometer in children. J Phys Act Health. 2012;9(3):389–93. [PubMed] [Google Scholar]
  • 10.Fakhouri TH, Hughes JP, Brody DJ, Kit BK, Ogden CL. Physical activity and screen-time viewing among elementary school-aged children in the United States from 2009 to 2010. JAMA Pediatr. 2013;167(3):223–9. doi: 10.1001/2013.jamapediatrics.122. [DOI] [PubMed] [Google Scholar]
  • 11.Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sports Exerc. 2005;37(11 Suppl):S523–30. doi: 10.1249/01.mss.0000185658.28284.ba. [DOI] [PubMed] [Google Scholar]
  • 12.Goran M, Fields DA, Hunter GR, Herd SL, Weinsier RL. Total body fat does not influence maximal aerobic capacity. Int J Obes Relat Metab Disord. 2000;24(7):841–8. doi: 10.1038/sj.ijo.0801241. [DOI] [PubMed] [Google Scholar]
  • 13.Groenewald CB, Essner BS, Wright D, Fesinmeyer MD, Palermo TM. The economic costs of chronic pain among a cohort of treatment-seeking adolescents in the United States. J Pain. 2014;15(9):925–33. doi: 10.1016/j.jpain.2014.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Hainsworth KR, Miller LA, Stolzman SC, et al. Pain as a Comorbidity of Pediatric Obesity. ICAN. 2012;4(5):315–20. doi: 10.1177/1941406412458315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Heo M, Allison DB, Faith MS, Zhu S, Fontaine KR. Obesity and quality of life: mediating effects of pain and comorbidities. Obes Res. 2003;11(2):209–16. doi: 10.1038/oby.2003.33. [DOI] [PubMed] [Google Scholar]
  • 16.Hogeweg JA, Kuis W, Oostendorp RA, Helders PJ. The influence of site of stimulation, age, and gender on pain threshold in healthy children. Phys Ther. 1996;76(12):1331–9. doi: 10.1093/ptj/76.12.1331. [DOI] [PubMed] [Google Scholar]
  • 17.Huguet A, Miro J. The severity of chronic pediatric pain: an epidemiological study. J Pain. 2008;9(3):226–36. doi: 10.1016/j.jpain.2007.10.015. [DOI] [PubMed] [Google Scholar]
  • 18.Kelly TL, Wilson KE, Heymsfield SB. Dual energy X-Ray absorptiometry body composition reference values from NHANES. PLoS One. 2009;4(9):e7038. doi: 10.1371/journal.pone.0007038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Koltyn KF. Exercise-induced hypoalgesia and intensity of exercise. Sports Med. 2002;32(8):477–87. doi: 10.2165/00007256-200232080-00001. [DOI] [PubMed] [Google Scholar]
  • 20.Kosek E, Lundberg L. Segmental and plurisegmental modulation of pressure pain thresholds during static muscle contractions in healthy individuals. Eur J Pain. 2003;7(3):251–8. doi: 10.1016/S1090-3801(02)00124-6. [DOI] [PubMed] [Google Scholar]
  • 21.Krachler B, Savonen K, Komulainen P, Hassinen M, Lakka TA, Rauramaa R. Cardiopulmonary fitness is a function of lean mass, not total body weight: The DR's EXTRA study. Eur J Prev Cardiol. 2014 Nov 7; doi: 10.1177/2047487314557962. Epub ahead of print. [DOI] [PubMed] [Google Scholar]
  • 22.Ladabaum U, Mannalithara A, Myer PA, Singh G. Obesity, Abdominal Obesity, Physical Activity, and Caloric Intake in US Adults: 1988 to 2010. Am J Med. 2014;127(8):717. doi: 10.1016/j.amjmed.2014.02.026. 727.e12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Lemley KJ, Hunter SK, Bement MK. Conditioned pain modulation predicts exercise-induced hypoalgesia in healthy adults. Med Sci Sports Exerc. 2015;47(1):176–84. doi: 10.1249/MSS.0000000000000381. [DOI] [PubMed] [Google Scholar]
  • 24.Lim CS, Mayer-Brown SJ, Clifford LM, Janicke DM. Pain is Associated with Physical Activity and Health-Related Quality of Life in Overweight and Obese Children. Child Health Care. 2014;43(3):186–202. doi: 10.1080/02739615.2013.837825. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.McHugh MP. Oversized young athletes: a weighty concern. Br J Sports Med. 2010;44(1):45–9. doi: 10.1136/bjsm.2009.068106. [DOI] [PubMed] [Google Scholar]
  • 26.Office of Disease Prevention and Health Promotion Web site [Internet} Washington DC, USA: Physical Activity Guidelines for Americans; Chapter 3: Active Children and Adolescents. [cited 2015 01/21]. Available from: http://www.health.gov/paguidelines/guidelines/chapter3.aspx. [Google Scholar]
  • 27.Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA. 2014;311(8):806–14. doi: 10.1001/jama.2014.732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Pielech M, Ryan M, Logan D, Kaczynski K, White MT, Simons LE. Pain catastrophizing in children with chronic pain and their parents: Proposed clinical reference points and re-examination of the PCS measure. Pain. 2014;155(11):2360–7. doi: 10.1016/j.pain.2014.08.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Preedy V. Handbook of Anthropometry: Physical Measures of Human Form in Health and Disease. 2012:29–28. [Google Scholar]
  • 30.Price RC, Asenjo JF, Christou NV, Backman SB, Schweinhardt P. The role of excess subcutaneous fat in pain and sensory sensitivity in obesity. Eur J Pain. 2013;17(9):1316–26. doi: 10.1002/j.1532-2149.2013.00315.x. [DOI] [PubMed] [Google Scholar]
  • 31.Staud R. Abnormal endogenous pain modulation is a shared characteristic of many chronic pain conditions. Expert Rev Neurother. 2012;12(5):577–85. doi: 10.1586/ern.12.41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Stolzman S, Hoeger Bement M. Inflammatory Markers in Pediatric Obesity: Health and Physical Activity Implication. ICAN. 2012;4(5):297–302. [Google Scholar]
  • 33.Stone AA, Broderick JE. Obesity and Pain Are Associated in the United States. Obesity. 2012;20(7):1491–1495. doi: 10.1038/oby.2011.397. [DOI] [PubMed] [Google Scholar]
  • 34.The Cooper Institute. Fitnessgram/Activitygram Reference Guide. 4th. Dallas, TX: Internet Resource; 2013. pp. 63–71. [Google Scholar]
  • 35.Toda Y, Toda T, Takemura S, Wada T, Morimoto T, Ogawa R. Change in body fat, but not body weight or metabolic correlates of obesity, is related to symptomatic relief of obese patients with knee osteoarthritis after a weight control program. J Rheumatol. 1998;25(11):2181–6. [PubMed] [Google Scholar]
  • 36.Treuth MS, Figueroa-Colon R, Hunter GR, Weinsier RL, Butte NF, Goran MI. Energy expenditure and physical fitness in overweight vs non-overweight prepubertal girls. Int J Obes Relat Metab Disord. 1998;22(5):440–7. doi: 10.1038/sj.ijo.0800605. [DOI] [PubMed] [Google Scholar]
  • 37.Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc. 2011;43(7):1360–8. doi: 10.1249/MSS.0b013e318206476e. [DOI] [PubMed] [Google Scholar]
  • 38.Umeda M, Corbin LW, Maluf KS. Examination of contraction-induced muscle pain as a behavioral correlate of physical activity in women with and without fibromyalgia. Disabil Rehabil. 2014 Nov 20;:1–6. doi: 10.3109/09638288.2014.984878. Epub ahead of print. [DOI] [PubMed] [Google Scholar]
  • 39.Vaegter HB, Handberg G, Graven-Nielsen T. Similarities between exercise-induced hypoalgesia and conditioned pain modulation in humans. Pain. 2014;155(1):158–67. doi: 10.1016/j.pain.2013.09.023. [DOI] [PubMed] [Google Scholar]
  • 40.Varni JW, Limbers CA. The pediatric quality of life inventory: measuring pediatric health-related quality of life from the perspective of children and their parents. Pediatr Clin North Am. 2009;56(4):843–63. doi: 10.1016/j.pcl.2009.05.016. [DOI] [PubMed] [Google Scholar]
  • 41.Voss C, Ogunleye AA, Sandercock GR. Physical Activity Questionnaire for children and adolescents: English norms and cut-off points. Pediatr Int. 2013;55(4):498–507. doi: 10.1111/ped.12092. [DOI] [PubMed] [Google Scholar]
  • 42.Washington RL, Bricker JT, Alpert BS, et al. Guidelines for exercise testing in the pediatric age group. From the Committee on Atherosclerosis and Hypertension in Children, Council on Cardiovascular Disease in the Young, the American Heart Association. Circulation. 1994;90(4):2166–79. doi: 10.1161/01.cir.90.4.2166. [DOI] [PubMed] [Google Scholar]
  • 43.Williams J, Wake M, Hesketh K, Maher E, Waters E. Health-related quality of life of overweight and obese children. JAMA. 2005;293(1):70–6. doi: 10.1001/jama.293.1.70. [DOI] [PubMed] [Google Scholar]
  • 44.Zeller MH, Modi AC. Predictors of health-related quality of life in obese youth. Obesity (Silver Spring) 2006;14(1):122–30. doi: 10.1038/oby.2006.15. [DOI] [PubMed] [Google Scholar]

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