Concussions are among the most common neurological conditions, with emergency departments and sports injury clinics seeing hundreds of patients each year. The consideration of risk factors such as age, sex, and comorbid conditions are very important when looking at individual physiological and psychological outcomes after a concussion. The purpose of this study was to look at four comorbid conditions (depression, anxiety, behavioural disorder, or learning disability) and identify any interactions with age and sex in symptom presentation after suffering a concussion. A total of 4,865 participants from the CCMI (Complete Concussion Management Inc.) dataset were used with 1,577 self-identified with a diagnosis of anxiety, depression, a behavioural disorder, or a learning disability. Fixed-factor analyses of variance were used with age and sex as fixed, grouping factors and symptom total and severity as dependent measures. For the individuals who did not have one of the 4 mental health conditions (3,288 control participants), symptom total and symptom severity increased with age (<span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.42938pt" id="M1" height="11.7782pt" version="1.1" viewBox="-0.0498162 -8.34882 44.5261 11.7782" width="44.5261pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-113" d="M570 304C570 398 525 448 414 448C385 448 343 445 312 434L329 511L321 518C297 504 262 482 244 460L233 411C195 397 159 381 128 358L135 332C160 347 189 360 224 373L111 -147C97 -210 84 -218 17 -231L13 -257L254 -247L259 -218L233 -216C183 -212 177 -202 189 -142L218 -1C238 -10 266 -12 283 -12C351 3 429 48 483 105C543 168 570 242 570 304ZM482 289C482 161 380 33 304 33C278 33 248 51 233 69L303 396C326 400 352 403 369 403C428 403 482 380 482 289Z"/></g><g transform="matrix(.013,0,0,-0.013,11.342,0)"><path id="g117-91" d="M512 -3V55L134 254V256L512 456V514L75 281V230L512 -3Z"/></g><g transform="matrix(.013,0,0,-0.013,22.605,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,28.845,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,31.809,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,38.049,0)"><path id="g113-54" d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"/></g></svg>),</span> and females showed more symptoms and a higher symptom severity than males across all ages (<span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.42938pt" id="M2" height="11.7782pt" version="1.1" viewBox="-0.0498162 -8.34882 44.5261 11.7782" width="44.5261pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-113"/></g><g transform="matrix(.013,0,0,-0.013,11.342,0)"><use xlink:href="#g117-91"/></g><g transform="matrix(.013,0,0,-0.013,22.605,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,28.845,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,31.809,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,38.049,0)"><use xlink:href="#g113-54"/></g></svg>).</span> A diagnosis of anxiety or depression exacerbated total symptoms and symptom severity from 25–50% above control levels in the 19 and under age groups, while depression or anxiety exacerbated total symptoms and severity by 10–15% in males more than females over 20. A diagnosis of a behavioural disorder or a learning disability exacerbated symptom severity by approximately 50% above control levels in 13–19–year-old females and in males of 30 years and older. This study highlights how the presence of a mental health condition may alter concussion symptom presentation dependent on age and sex. The identification of risk factors and how they may interact can be of great value to health care providers who manage concussion symptoms and recovery.

Katie Lariviere

Samantha Bureau

Cameron Marshall

Matthew R. Holahan

Interaction between Age, Sex, and Mental Health Status as Precipitating Factors for Symptom Presentation in Concussed Individuals

<i>Introduction</i>. A significant step towards sport-related injury prevention is the introduction of easily accessible smartphone applications (apps). However, it is unknown whether this type of app-based instruction facilitates similar acute neuromuscular and biomechanical characteristics of the preventive exercises as achieved when instructed on-site by an expert. Thus, the aim was to evaluate acute neuromuscular characteristics observed during a single bout of selected lower extremity injury preventive exercises instructed by an on-screen app versus on-site individual instruction provided by a physiotherapist. <i>Methods</i>. In a cross-sectional study design, 47 female football and handball players were randomly assigned to receive app instruction (APP group) or on-site instruction provided by a physiotherapist (PHY group) while performing five lower extremity injury preventive exercises. The exercises performed comprised (1) one-legged balance on Airex, (2) vertical drop jump, (3) one-legged horizontal jump onto floor, (4) one-legged horizontal jump onto Airex, and (5) two-hand Kettlebell Swing. Primary outcome was hamstring (biceps femoris and semitendinosus) muscle activity. Secondary outcomes were quadriceps (vastus lateralis and medialis) muscle activity, as well as hip and knee joint angles. Muscle activity was monitored by surface electromyography (EMG) and normalized to the peak amplitude obtained during a maximal voluntary isometric contraction (MVC). Hip and knee joint angles were recorded by a 3D motion analysis system. A linear mixed model was used to evaluate the differences between experimental conditions for each outcome variable. <i>Results</i>. Medial hamstring (semitendinosus) muscle activity was significantly higher during one-legged jump onto Airex (17 percentage points (95&#x0025; CI 7 to 27)) and Kettlebell Swing (19 percentage points (95&#x0025; CI 2 to 36)) in the PHY group than the APP group. Likewise, the PHY group demonstrated 18 percentage points (95&#x0025; CI 1 to 35) and 19 percentage points (95&#x0025; CI 0 to 38), greater lateral quadriceps muscle (vastus lateralis) activity during one-legged jump onto floor and one-legged jump onto Airex, respectively, compared with that of the APP group. <i>Conclusions</i>. Complex exercises, i.e., Kettlebell Swing and one-legged jump onto Airex, are characterized by lower neuromuscular activity when using app-based instructions compared with on-site instruction provided by a physiotherapist. However, the effectiveness of app-based instruction versus on-site individual instruction in injury prevention interventions remains to be investigated in future longitudinally studies.

M. K. Zebis

C. Sanderhoff

L. L. Andersen

L. Fernandes

M. M&#xf8;ller

E. Ageberg

G. Myklebust

P. Aagaard

J. Bencke

Acute Neuromuscular Activity in Selected Injury Prevention Exercises with App-Based versus Personal On-Site Instruction: A Randomized Cross-Sectional Study

<i>Introduction</i>. The purpose of this study was to evaluate the application of the Dmax method on heart rate variability (HRV) to estimate the lactate thresholds (LT), during a maximal incremental running test (MIRT). <i>Methods</i>. Nineteen male runners performed two MIRTs, with the initial speed at 8 km·h<sup>−1</sup> and increments of 1 km·h<sup>−1</sup> every 3 minutes, until exhaustion. Measures of HRV and blood lactate concentrations were obtained, and lactate (LT<sub>1</sub> and LT<sub>2</sub>) and HRV (HRVT<sub>DMAX1</sub> and HRVT<sub>DMAX2</sub>) thresholds were identified. ANOVA with Scheffe’s post hoc test, effect sizes (<i>d</i>), the bias ± 95% limits of agreement (LoA), standard error of the estimate (SEE), Pearson’s (<i>r</i>), and intraclass correlation coefficient (ICC) were calculated to assess validity. <i>Results</i>. No significant differences were observed between HRVT<sub>DMAX1</sub> and LT<sub>1</sub> when expressed for speed (12.1 ± 1.4 km·h<sup>−1</sup> and 11.2 ± 2.1 km·h<sup>−1</sup>; <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.42938pt" id="M1" height="11.7782pt" version="1.1" viewBox="-0.0498162 -8.34882 44.5261 11.7782" width="44.5261pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-113" d="M570 304C570 398 525 448 414 448C385 448 343 445 312 434L329 511L321 518C297 504 262 482 244 460L233 411C195 397 159 381 128 358L135 332C160 347 189 360 224 373L111 -147C97 -210 84 -218 17 -231L13 -257L254 -247L259 -218L233 -216C183 -212 177 -202 189 -142L218 -1C238 -10 266 -12 283 -12C351 3 429 48 483 105C543 168 570 242 570 304ZM482 289C482 161 380 33 304 33C278 33 248 51 233 69L303 396C326 400 352 403 369 403C428 403 482 380 482 289Z"/></g><g transform="matrix(.013,0,0,-0.013,11.342,0)"><path id="g117-34" d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"/></g><g transform="matrix(.013,0,0,-0.013,22.605,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,28.845,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,31.809,0)"><path id="g113-54" d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"/></g><g transform="matrix(.013,0,0,-0.013,38.049,0)"><use xlink:href="#g113-54"/></g></svg>;</span> <i>d</i> = 0.45; <i>r</i> = 0.46; bias ± LoA = 0.8 ± 3.7 km·h<sup>−1</sup>; SEE = 1.2 km·h<sup>−1</sup> (95% CI, 0.9–1.9)). Significant differences were observed between HRVT<sub>DMAX2</sub> and LT<sub>2</sub> when expressed for speed (12.0 ± 1.2 km·h<sup>−1</sup> and 14.1 ± 2.5 km·h<sup>−1</sup>; <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.42938pt" id="M2" height="11.7782pt" version="1.1" viewBox="-0.0498162 -8.34882 44.5261 11.7782" width="44.5261pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-113"/></g><g transform="matrix(.013,0,0,-0.013,11.342,0)"><use xlink:href="#g117-34"/></g><g transform="matrix(.013,0,0,-0.013,22.605,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,28.845,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,31.809,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,38.049,0)"><use xlink:href="#g113-49"/></g></svg>;</span> <i>d</i> = 1.21; <i>r</i> = 0.48; bias ± LoA = −1.0 ± 1.8 km·h<sup>−1</sup>; SEE = 1.1 km·h<sup>−1</sup> (95% CI, 0.8–1.6)), respectively. Reproducibility values were found for the LT<sub>1</sub> (ICC = 0.90; bias ± LoA = −0.7 ± 2.0 km·h<sup>−1</sup>), LT<sub>2</sub> (ICC = 0.97; bias ± LoA = −0.1 ± 1.1 km·h<sup>−1</sup>), HRVT<sub>DMAX1</sub> (ICC = 0.48; bias ± LoA = −0.2 ± 3.4 km·h<sup>−1</sup>), and HRVT<sub>DMAX2</sub> (ICC = 0.30; bias ± LoA = 0.3 ± 3.5 km·h<sup>−1</sup>). <i>Conclusions</i>. The Dmax method applied over a HRV dataset allowed the identification of LT<sub>1</sub> that is close to aerobic threshold, during a MIRT.

Eduardo Marcel Fernandes Nascimento

Diego Antunes

Paulo Cesar do Nascimento Salvador

Fernando Klitzke Borszcz

Ricardo Dantas de Lucas

Applicability of Dmax Method on Heart Rate Variability to Estimate the Lactate Thresholds in Male Runners

<i>Background</i>. Good cardiorespiratory fitness (high <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.2957pt" id="M1" height="12.0621pt" version="1.1" viewBox="-0.0498162 -8.7664 39.5782 12.0621" width="39.5782pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g121-84" d="M687 650H462V622C543 612 549 605 530 547C498 447 422 252 372 126H370C302 298 229 492 204 563C188 607 191 615 262 622V650H17V622C77 616 93 608 122 534C180 389 262 172 329 -11H360C436 196 541 450 568 516C606 605 619 614 687 622V650Z"/></g><g transform="matrix(.013,0,0,-0.013,8.656,0)"><path id="g121-77" d="M381 665C170 665 44 498 44 318C44 125 188 -15 369 -15C552 -15 703 117 703 333C703 534 550 665 381 665ZM359 629C491 629 601 517 601 306C601 114 502 21 390 21C249 21 146 158 146 346S248 629 359 629Z"/></g><g transform="matrix(.0091,0,0,-0.0091,18.438,3.132)"><path id="g50-51" d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z"/></g><g transform="matrix(.0091,0,0,-0.0091,22.87,3.132)"><path id="g58-107" d="M810 0V30C751 35 744 40 744 106V296C744 396 694 451 614 451C583 451 558 438 537 425C512 409 485 391 454 368C434 420 390 451 342 451C309 451 285 439 260 424C228 405 206 387 185 372V453C141 434 90 421 43 413V386C102 378 105 373 105 309V106C105 41 96 35 29 30V0H243V30C194 35 185 41 185 106V335C216 360 256 389 297 389C357 389 384 348 384 275V106C384 41 374 35 312 30V0H530V30C475 35 465 41 465 104V294C465 313 464 324 463 336C502 367 539 389 576 389C637 389 663 346 663 274V109C663 41 653 35 593 30V0H810Z"/></g><g transform="matrix(.0091,0,0,-0.0091,30.432,3.132)"><path id="g58-95" d="M442 39L431 68C420 63 406 57 394 57C377 57 359 71 359 118V301C359 353 349 393 314 424C291 443 261 451 230 451C171 438 107 401 79 381C59 366 46 355 46 340C46 316 72 295 91 295C106 295 117 303 122 319C131 350 139 371 152 384C163 397 178 404 197 404C246 404 281 365 281 293V278C260 262 186 232 124 213C69 194 41 161 41 112C41 47 92 -12 163 -12C195 -12 243 25 283 51C288 35 293 22 308 7C319 -4 339 -12 354 -12L442 39ZM281 87C262 68 226 51 199 51C169 51 129 74 129 126C129 162 151 181 190 198C211 208 259 229 281 240V87Z"/></g><g transform="matrix(.0091,0,0,-0.0091,34.517,3.132)"><path id="g58-118" d="M482 0V30C420 38 407 48 370 103L273 248C308 299 331 332 352 357C387 397 401 404 463 409V439H277V409C318 405 324 400 307 369C290 337 270 307 251 279L194 367C175 396 180 402 220 409V439H18V409C74 401 87 395 117 349L204 213C173 168 147 130 119 95C78 46 67 38 5 30V0H195V30C144 38 143 47 162 79C181 112 202 149 225 181L297 70C314 43 307 35 264 30V0H482Z"/></g></svg>)</span> has beneficial effects on morbidity and mortality. Therefore, a tool to estimate <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.2957pt" id="M2" height="12.0621pt" version="1.1" viewBox="-0.0498162 -8.7664 39.5782 12.0621" width="39.5782pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g121-84"/></g><g transform="matrix(.013,0,0,-0.013,8.656,0)"><use xlink:href="#g121-77"/></g><g transform="matrix(.0091,0,0,-0.0091,18.438,3.132)"><use xlink:href="#g50-51"/></g><g transform="matrix(.0091,0,0,-0.0091,22.87,3.132)"><use xlink:href="#g58-107"/></g><g transform="matrix(.0091,0,0,-0.0091,30.432,3.132)"><use xlink:href="#g58-95"/></g><g transform="matrix(.0091,0,0,-0.0091,34.517,3.132)"><use xlink:href="#g58-118"/></g></svg> in daily clinical practice is of great value for preventing chronic diseases in healthy adults. This study aimed at exploring the cardiometabolic profile in a representative Swiss working population. Based on these insights, a regression model was derived revealing factors associated with <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.2957pt" id="M3" height="12.0621pt" version="1.1" viewBox="-0.0498162 -8.7664 39.5782 12.0621" width="39.5782pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g121-84"/></g><g transform="matrix(.013,0,0,-0.013,8.656,0)"><use xlink:href="#g121-77"/></g><g transform="matrix(.0091,0,0,-0.0091,18.438,3.132)"><use xlink:href="#g50-51"/></g><g transform="matrix(.0091,0,0,-0.0091,22.87,3.132)"><use xlink:href="#g58-107"/></g><g transform="matrix(.0091,0,0,-0.0091,30.432,3.132)"><use xlink:href="#g58-95"/></g><g transform="matrix(.0091,0,0,-0.0091,34.517,3.132)"><use xlink:href="#g58-118"/></g></svg>.</span><i> Methods</i>. Cross-sectional data of 337 healthy and full-time employed adults recruited in the Basel region, Switzerland, were collected. Anthropometric measurements to compute body mass index (BMI) and waist circumference (WC) were performed. A 20-meter shuttle run test was conducted to determine individual <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.2957pt" id="M4" height="12.0621pt" version="1.1" viewBox="-0.0498162 -8.7664 39.5782 12.0621" width="39.5782pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g121-84"/></g><g transform="matrix(.013,0,0,-0.013,8.656,0)"><use xlink:href="#g121-77"/></g><g transform="matrix(.0091,0,0,-0.0091,18.438,3.132)"><use xlink:href="#g50-51"/></g><g transform="matrix(.0091,0,0,-0.0091,22.87,3.132)"><use xlink:href="#g58-107"/></g><g transform="matrix(.0091,0,0,-0.0091,30.432,3.132)"><use xlink:href="#g58-95"/></g><g transform="matrix(.0091,0,0,-0.0091,34.517,3.132)"><use xlink:href="#g58-118"/></g></svg>.</span> Heart rate (HR) was measured at rest, during maximal exertion, and two minutes after exercise. Systolic (SBP) and diastolic blood pressure (DBP) were assessed at rest and after exercise. A multiple linear regression model was built to identify a set of nonexercise predictor variables of <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.2957pt" id="M5" height="12.0621pt" version="1.1" viewBox="-0.0498162 -8.7664 39.5782 12.0621" width="39.5782pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g121-84"/></g><g transform="matrix(.013,0,0,-0.013,8.656,0)"><use xlink:href="#g121-77"/></g><g transform="matrix(.0091,0,0,-0.0091,18.438,3.132)"><use xlink:href="#g50-51"/></g><g transform="matrix(.0091,0,0,-0.0091,22.87,3.132)"><use xlink:href="#g58-107"/></g><g transform="matrix(.0091,0,0,-0.0091,30.432,3.132)"><use xlink:href="#g58-95"/></g><g transform="matrix(.0091,0,0,-0.0091,34.517,3.132)"><use xlink:href="#g58-118"/></g></svg>.</span><i> Results</i>. Complete data of 303 individuals (63% male) aged 18 to 61 years (mean 33 ± 12 years) were considered for analysis. The regression model (adjusted R<sup>2</sup> = 0.647, SE = 5.3) identified sex (<i>β</i> = -0.699, p &lt; 0.001), WC (<i>β</i> = -0.403, p &lt; 0.001), difference of maximal to resting HR (<i>β</i> = 0.234, p &lt; 0.001), smoking (<i>β</i> = -0.171, p &lt; 0.001), and age (<i>β</i> = -0.131, p &lt; 0.01) as the most important factors associated with <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.2957pt" id="M6" height="12.0621pt" version="1.1" viewBox="-0.0498162 -8.7664 39.5782 12.0621" width="39.5782pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g121-84"/></g><g transform="matrix(.013,0,0,-0.013,8.656,0)"><use xlink:href="#g121-77"/></g><g transform="matrix(.0091,0,0,-0.0091,18.438,3.132)"><use xlink:href="#g50-51"/></g><g transform="matrix(.0091,0,0,-0.0091,22.87,3.132)"><use xlink:href="#g58-107"/></g><g transform="matrix(.0091,0,0,-0.0091,30.432,3.132)"><use xlink:href="#g58-95"/></g><g transform="matrix(.0091,0,0,-0.0091,34.517,3.132)"><use xlink:href="#g58-118"/></g></svg>,</span> while BMI, SBP, and DBP did not contribute to the regression model.<i> Conclusions</i>. This study introduced a simple model to evaluate <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.2957pt" id="M7" height="12.0621pt" version="1.1" viewBox="-0.0498162 -8.7664 39.5782 12.0621" width="39.5782pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g121-84"/></g><g transform="matrix(.013,0,0,-0.013,8.656,0)"><use xlink:href="#g121-77"/></g><g transform="matrix(.0091,0,0,-0.0091,18.438,3.132)"><use xlink:href="#g50-51"/></g><g transform="matrix(.0091,0,0,-0.0091,22.87,3.132)"><use xlink:href="#g58-107"/></g><g transform="matrix(.0091,0,0,-0.0091,30.432,3.132)"><use xlink:href="#g58-95"/></g><g transform="matrix(.0091,0,0,-0.0091,34.517,3.132)"><use xlink:href="#g58-118"/></g></svg> based on nonexercise parameters as part of daily clinical routine without needing a time-consuming, cost-intense, and physically demanding direct assessment of <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.2957pt" id="M8" height="12.0621pt" version="1.1" viewBox="-0.0498162 -8.7664 39.5782 12.0621" width="39.5782pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g121-84"/></g><g transform="matrix(.013,0,0,-0.013,8.656,0)"><use xlink:href="#g121-77"/></g><g transform="matrix(.0091,0,0,-0.0091,18.438,3.132)"><use xlink:href="#g50-51"/></g><g transform="matrix(.0091,0,0,-0.0091,22.87,3.132)"><use xlink:href="#g58-107"/></g><g transform="matrix(.0091,0,0,-0.0091,30.432,3.132)"><use xlink:href="#g58-95"/></g><g transform="matrix(.0091,0,0,-0.0091,34.517,3.132)"><use xlink:href="#g58-118"/></g></svg>.</span> Knowledge about <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.2957pt" id="M9" height="12.0621pt" version="1.1" viewBox="-0.0498162 -8.7664 39.5782 12.0621" width="39.5782pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g121-84"/></g><g transform="matrix(.013,0,0,-0.013,8.656,0)"><use xlink:href="#g121-77"/></g><g transform="matrix(.0091,0,0,-0.0091,18.438,3.132)"><use xlink:href="#g50-51"/></g><g transform="matrix(.0091,0,0,-0.0091,22.87,3.132)"><use xlink:href="#g58-107"/></g><g transform="matrix(.0091,0,0,-0.0091,30.432,3.132)"><use xlink:href="#g58-95"/></g><g transform="matrix(.0091,0,0,-0.0091,34.517,3.132)"><use xlink:href="#g58-118"/></g></svg> may help identifying individuals at increased cardiovascular risk and may provide the basis for health counselling and tailoring preventive measures.

Sara Kind

Stefanie Brighenti-Zogg

Jonas Mundwiler

Ulla Sch&#xfc;pbach

J&#xf6;rg D. Leuppi

David Miedinger

Thomas Dieterle

Factors Associated with Cardiorespiratory Fitness in a Swiss Working Population

Many studies have validated the use of bioimpedance analysis (BIA) to quantify body fat percentage (BF%). However, it is unknown if some model types (i.e., hand to hand, foot to foot, and hand to foot) are differing in their validity depending on hip and waist circumferences. The purpose of this study was to compare the difference in BF% between three BIA models (i.e., hand to hand, foot to foot, and hand to foot) against the Bod Pod across different hip and waist circumferences. A total of 92 people aged 19-72 years were recruited in this study. After following the pretesting procedures recommended for BIA measures, BF% was estimated using three BIA models and the Bod Pod. Hip and waist circumferences were obtained using standard procedures and tertiles were computed. The Bland-Altman was plotted and 1-sample T-test as well as correlation between the average measure and the difference between the two measures was tested. Within the entire sample, across all BIA models, the Bland-Altman analysis showed significant difference compared to 0 and a significant difference for the proportional. However, when stratified by tertiles, the two measurements were only significant for the highest tertiles of hip and waist for all BIA apparatus (all p <0.01) and the proportional bias was nonsignificant for all tertiles and across all BIA apparatus. For the highest tertile of waist and hip, the average difference was between 1.67% and 3.29% compared with the Bod Pod estimation. In conclusion, the three BIA models offer a BF% measurement agreeing with the estimation obtained with the Bod Pod with the exception of people having a greater waist or greater hip.

Viseth Long

Matthew Short

Spencer Smith

Martin Sénéchal

Danielle R. Bouchard

Testing Bioimpedance to Estimate Body Fat Percentage across Different Hip and Waist Circumferences

<i>Purpose</i>. This study compared gender differences in eating disorder risk among NCAA Division I cross country and track distance running student-athletes.<i> Methods</i>. Six hundred thirty-eight male and female student-athletes competing at distances of 800m or greater completed the Eating Disorder Screen for Primary Care (ESP). Scores on the ESP were used as the risk of eating disorders.<i> Results</i>. Females screened positive at higher rates for risk of eating disorders than males on the ESP at a cutoff of 2 (sensitivity 90-100%, specificity 71%) with rates of <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="48.327pt" style="vertical-align:-0.4673605pt" id="M1" height="8.99234pt" version="1.1" viewBox="-0.0498162 -8.52498 48.327 8.99234"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-53" d="M456 178V225H360V632H320C217 496 115 347 20 206V178H280V106C280 40 276 34 189 27V0H445V27C364 34 360 39 360 106V178H456ZM280 225H82C149 335 214 431 278 520H280V225Z"/></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><path id="g113-54" d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 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465 -12 566 -12S710 90 710 170S667 351 567 351ZM566 322C628 322 637 221 637 169C637 121 628 18 567 18S494 119 494 170C494 223 506 322 566 322Z"/></g><g transform="matrix(.013,0,0,-0.013,40.696,0)"><path id="g117-37" d="M535 290V340H323V533H265V340H52V290H265V84H323V290H535ZM535 -22V28H52V-22H535Z"/></g></svg><span class="ibiop"/><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="21.996pt" style="vertical-align:-0.4673605pt" height="8.99234pt" version="1.1" viewBox="51.183183799999995 -8.52498 21.996 8.99234"><g transform="matrix(.013,0,0,-0.013,51.233,0)"><path id="g113-52" d="M285 378C315 398 338 416 353 432C373 451 384 474 384 503C384 579 325 635 236 635H235C182 635 136 610 108 579L65 516L85 496C110 533 150 575 205 575C258 575 300 543 300 481C300 407 232 369 141 339L147 310C163 315 188 321 211 321C268 321 338 284 338 192C338 94 288 40 217 40C160 40 119 68 93 91C85 98 77 97 69 91C60 84 47 71 46 58C44 46 48 35 62 22C75 10 116 -12 162 -12C234 -12 424 62 424 224C424 297 373 359 285 376V378Z"/></g><g transform="matrix(.013,0,0,-0.013,57.473,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,60.437,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,66.677,0)"><use xlink:href="#g113-52"/></g></svg></span> and <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="48.326pt" style="vertical-align:-0.4673605pt" id="M2" height="8.99234pt" version="1.1" viewBox="-0.0498162 -8.52498 48.326 8.99234"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-50" d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"/></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><use xlink:href="#g113-52"/></g><g transform="matrix(.013,0,0,-0.013,12.48,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,15.444,0)"><path id="g113-55" d="M137 343C167 482 260 545 321 574C357 591 397 603 429 609L423 641C382 634 335 622 295 608C189 570 37 457 37 238C37 84 125 -12 242 -12C362 -12 447 89 447 209C447 311 374 393 267 393C247 393 226 386 204 376L137 343ZM227 337C318 337 361 256 361 173C361 105 336 22 258 22C176 22 126 120 126 240C126 266 127 291 132 310C155 323 189 337 227 337Z"/></g><g transform="matrix(.013,0,0,-0.013,21.684,0)"><use xlink:href="#g113-55"/></g><g transform="matrix(.013,0,0,-0.013,27.925,0)"><use xlink:href="#g190-38"/></g><g transform="matrix(.013,0,0,-0.013,40.695,0)"><use xlink:href="#g117-37"/></g></svg><span class="ibiop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="21.997pt" style="vertical-align:-0.4673605pt" height="8.99234pt" version="1.1" viewBox="51.1821838 -8.52498 21.997 8.99234"><g transform="matrix(.013,0,0,-0.013,51.232,0)"><use xlink:href="#g113-50"/></g><g transform="matrix(.013,0,0,-0.013,57.472,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,60.436,0)"><path id="g113-57" d="M249 635C141 635 70 555 70 471C70 401 114 353 179 316C143 294 106 267 90 252C68 231 45 202 45 157C45 50 130 -12 237 -12C322 -12 435 52 435 169C435 256 372 304 303 343C349 374 375 398 383 407C401 429 411 458 411 487C411 569 344 635 249 635ZM238 603C285 603 337 567 337 482C337 422 310 385 276 358C205 393 145 426 145 500C145 552 179 603 238 603ZM248 20C183 20 125 70 125 163C125 218 158 268 206 300C284 261 355 217 355 143C355 66 308 20 248 20Z"/></g><g transform="matrix(.013,0,0,-0.013,66.676,0)"><use xlink:href="#g113-49"/></g></svg>,</span></span> respectively. Females were also screened positive at higher rates than males at a stricter cutoff of 3 (sensitivity 81%, specificity 92%), with rates of <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="48.327pt" style="vertical-align:-0.4673605pt" id="M3" height="8.99234pt" version="1.1" viewBox="-0.0498162 -8.52498 48.327 8.99234"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-51" d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z"/></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><use xlink:href="#g113-50"/></g><g transform="matrix(.013,0,0,-0.013,12.48,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,15.444,0)"><use xlink:href="#g113-55"/></g><g transform="matrix(.013,0,0,-0.013,21.684,0)"><use xlink:href="#g113-58"/></g><g transform="matrix(.013,0,0,-0.013,27.926,0)"><use xlink:href="#g190-38"/></g><g transform="matrix(.013,0,0,-0.013,40.696,0)"><use xlink:href="#g117-37"/></g></svg><span class="ibiop"/><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="21.997pt" style="vertical-align:-0.4673605pt" height="8.99234pt" version="1.1" viewBox="51.1821838 -8.52498 21.997 8.99234"><g transform="matrix(.013,0,0,-0.013,51.232,0)"><use xlink:href="#g113-51"/></g><g transform="matrix(.013,0,0,-0.013,57.472,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,60.436,0)"><use xlink:href="#g113-54"/></g><g transform="matrix(.013,0,0,-0.013,66.677,0)"><use xlink:href="#g113-49"/></g></svg></span> compared to <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="42.086pt" style="vertical-align:-0.4673605pt" id="M4" height="8.99234pt" version="1.1" viewBox="-0.0498162 -8.52498 42.086 8.99234"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-53"/></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,9.204,0)"><use xlink:href="#g113-55"/></g><g transform="matrix(.013,0,0,-0.013,15.444,0)"><use xlink:href="#g113-53"/></g><g transform="matrix(.013,0,0,-0.013,21.685,0)"><use xlink:href="#g190-38"/></g><g transform="matrix(.013,0,0,-0.013,34.455,0)"><use xlink:href="#g117-37"/></g></svg><span class="ibiop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="21.974pt" style="vertical-align:-0.4673605pt" height="8.99234pt" version="1.1" viewBox="44.942183799999995 -8.52498 21.974 8.99234"><g transform="matrix(.013,0,0,-0.013,44.992,0)"><use xlink:href="#g113-50"/></g><g transform="matrix(.013,0,0,-0.013,51.232,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,54.196,0)"><use xlink:href="#g113-50"/></g><g transform="matrix(.013,0,0,-0.013,60.436,0)"><use xlink:href="#g113-49"/></g></svg>,</span></span> respectively.<i> Conclusion</i>. This study highlights that, among distance runners, both males and females are at risk of eating disorders, with females being at higher risk. It also emphasizes the need for screening for risk of eating disorders in this population.

Paul A. Krebs

Christopher R. Dennison

Lisa Kellar

Jeff Lucas

Gender Differences in Eating Disorder Risk among NCAA Division I Cross Country and Track Student-Athletes

<i>Background</i>. Several anthropometric equations that estimate skeletal muscle mass (SMM) have been published, but their applicability and accuracy among athletes are still uncertain.<i> Purpose</i>. To assess the accuracy of different anthropometric equations that estimate SMM in professional male soccer players, as compared to dual-energy X-ray absorptiometry (DXA) as the reference method.<i> Methods</i>. In this cross-sectional study, we evaluated 179 professional male soccer players aged between 18 and 37 years. Anthropometric measurements (height, body weight, skinfold thicknesses, and girths) and a DXA whole body scan were performed the same day for each participant, and SMM was estimated with nine anthropometric equations (Heymsfield, Martin, Doupe, Kerr, Drinkwater, Lee, De Rose, and two equations published by Kuriyan). To determine differences between SMM estimated with anthropometric equations and SMM evaluated with DXA, a Kruskal-Wallis test was performed using Dunn&#x2019;s test as post hoc. The significance level was set at<i> p</i> &#x3c; 0.05. We calculated the mean difference and 95&#x25; limits of agreement for the analyzed equations (Equation &#x2013; DXA).<i> Results</i>. Only Heymsfield&#x2019;s and Lee&#x2019;s equations showed no significant differences with DXA. Heymsfield&#x2019;s equation had the smallest mean difference (-0.17 kg), but wider limits of agreement with DXA (-6.61 to 6.94 kg). Lee&#x2019;s equation had a small mean difference (1.10 kg) but narrower limits of agreement with DXA (-1.83 to 4.03 kg).<i> Conclusions</i>. In this study, the prediction equation published by Lee et al. showed the best agreement with DXA and is able to estimate SMM accurately in professional male soccer players.

Roberto Gabriel Gonz&#xe1;lez-Mendoza

Alejandro Gayt&#xe1;n-Gonz&#xe1;lez

Juan Antonio Jim&#xe9;nez-Alvarado

Marisol Villegas-Balc&#xe1;zar

Edtna E. J&#xe1;uregui-Ulloa

Francisco Torres-Naranjo

Juan R. L&#xf3;pez-Taylor

Accuracy of Anthropometric Equations to Estimate DXA-Derived Skeletal Muscle Mass in Professional Male Soccer Players

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<p><em>Journal of Sports Medicine</em> publishes original research articles and&nbsp;review articles in all areas of sports medicine.</p>


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Journal of Sports Medicine publishes original research articles and review articles in all areas of sports medicine.

Journal of Sports Medicine