Purpose : To describe the heart-rate (HR) response during a prolonged, submaximal, multirepetition swimming bout (ie, typical early-season swimming training), as there is currently little or no literature on this topic. Methods : A total of 12 collegiate swimmers were instructed to complete sixty 91.4-m (100-yd) freestyle repetitions at their fastest sustainable pace, allowing between 5 and 10 seconds of rest between repetitions. Each swimmer was outfitted with a cardiotachometer, which monitored HR throughout the trial. Completion time (CT) was also recorded for each repetition. Individual means of HR and CT were calculated, and linear mixed models were used to determine the trend across repetitions and between- and within-subject SD for HR and CT. Results : The mean (SD) value for HR was 167.8 (10.8) beats per minute (bpm), for CT was 68.7 (4.1) seconds, and for percentage of best time was 71.2% (4.5%). There was no change (Δ rep 55–6) in HR (−0.1 bpm; 95% confidence interval, −6.8 to 6.6 bpm; P = .97), whereas CT increased (3.0 s; 95% confidence interval, 1.5–4.4 s; P = .001). The between-subjects SD (95% confidence interval) for HR was 12.6 (8.4–19.3 bpm) and for CT was 4.6 (3.1–7.0 s). The within-subject SDs for HR and CT were 4.0 (3.8–4.3 bpm) and 0.9 (0.8–0.95 s), respectively. Conclusions : The inherent individual variability between swimmers in HR during training suggests that coaches carefully consider the common practice of prescribing workout intensity using rigid HR zones.

Data from the Centers for Disease Control and Prevention show that 53% of adults age 18 and older met the Physical Activity Guidelines of the Health and Human Services. Previously published research has established a decreasing level of physical fitness in college students. While VO□ max is an accurate predictor of aerobic fitness, due to the relative difficulty of performing the VO□ max, the data collected on student physical activity is often comprised of indirect measurements of fitness including BMI, self‐reported activity level, and estimates of aerobic fitness using VO□ max estimations. We hypothesize that fitness of college students reflects that of general population. Therefore, a comprehensive database was developed that comprised of VO□ max values of college students in a small Midwestern liberal arts institution to assess the physical fitness level of college students. VO□ max were measured in over 500 student‐athletes and non‐athletes over two decades. In addition, other measures of fitness such BM, maximum heart rate were collected. For our studies, we extracted data for analysis using a Python script. Our preliminary observations on a small sample subset suggest that fitness level remained constant over the two decades contrary to general population trends. Further analysis accounting for athletes versus non athlete may explain the apparent steadiness of fitness level. Our study suggests that VO□ Max of college students can provide insight into longitudinal changes in physical fitness.Support or Funding InformationDePauw University

Recently, a commercially available starting ‘ledge’ designed to reduce foot slippage during the execution of the backstroke start was introduced in competitive swimming. For the purpose of identifying potential safety consequences, the present study investigated the effect of ledge use on head depths, speeds, and distances in backstroke starts of athletes with no prior or only novice familiarity of the ledge. Competitive backstroke starts were performed with and without ledges by high school-aged (14.5 to 19.2 yr, N = 61) swimmers in 1.52 m of water during a closed testing session. A SIMI Reality Motion System in a calibrated space using three cameras was employed for filming starts. Dependent measures were initial head height (Yset), distance from wall at entry (Xentry), entry angle (Angleentry), horizontal velocity at head entry (XVelentry), resultant velocity at entry (ResVelentry), maximum depth of the center of the head (Ymhd), resultant velocity at maximum head depth (ResVelmhd), and distance from the wall at maximum head depth (Xmhd). The ledge (L) condition showed significant increases compared to the non-ledge (NL) condition in Xentry (L 1.61 ± 0.59 m, NL 1.50 ± 0.53 m, p < .001), ResVelentry (L 3.44 ± 0.97 m·s-1, NL 3.08 ± 1.00 m·s-1, p < .001), Angleentry (L 43.13 ± 16.97°, NL 39.66 ± 18.11°, p = .030), Xmhd (L 4.18 ± 0.58 m, NL 4.09 ± 0.63 m, p = .008), and Ymhd (L 0.54 ± 0.21 m, NL 0.49 ± 0.18, p

Adherence to prescribed training intensity (i.e., swim speed) has been conveyed as a key parameter regarding improvements in seasonal performance. Evidence has also suggested swimmers have difficulty complying with coach-prescribed training. How this compliance is affected by a coaching presence and oversight has yet to be examined. PURPOSE:The purpose of this study was to determine swimmers' compliance to prescribed training intensity during 3 observational conditions using activity monitors. METHOD:Individualized prediction equations were created via linear regression analyses for intensity using arm-stroke and leg-kick activity counts during a series of seven 91.4-m swim bouts in a group of 17 collegiate swimmers. Equations were used to calculate intensity performed during a standardized training session during which only the observational condition varied (e.g., a coach present on deck, no coach present on deck, and an appointed observer in addition to the coach present on deck). Compliance was calculated from the difference between prescribed training intensity and performed swim intensity. Comparisons were made between observed coaching conditions for compliance using a repeated-measures analysis of variance. RESULTS:Swimmers' compliance to prescribed training intensity during the no-coach condition was less compared with the other conditions, including (a) when a coach was present and (b) when a coach and an additional observer were present (η2 = .58). CONCLUSION:The presence of a coach or lack thereof appeared to be critical in terms of swimmers' compliance to prescribed training intensity. Additional observation by nonsupervisory individuals appeared to have no significant effect on swimmers' compliance.

Beunen et al. (1978) found that the early maturers in their sample of non-athletic girls performed better on simple motor tasks than the late maturers early in adolescence, but that the late maturers performed better than the early maturers late in adolescence. PURPOSE: To determine if the same relationship between maturational timing and performance exists when high-level athletes execute complex motor tasks. METHODS: NCAA women swimmers (N = 254) completed an online questionnaire in which they provided age, height, weight, swimming history, and age at menarche (AaM). We divided the sample into early-, average-, and late-maturing groups using AaM. We utilized the USA Swimming (USAS) performance database to identify individual performances for each swimmer at three adolescent phases: (1) early adolescence (12 years old), (2) middle adolescence (15 years old), and (3) late adolescence (18 years old). Each performance in the USAS database equates to a standardized score called a Power Point Score (PPS). We selected the highest PPS for each swimmer at the three adolescent phases. We analyzed the data using a Two-way Mixed Design ANOVA. RESULTS: Mean AaM values for the early-, average-, and late-maturing groups were 12.0 years (95% CI, 11.8 to 12.2), 13.4 years (95% CI, 13.3 to 13.5), and 15.4 years (95% CI, 15.2 to 15.6). We identified performances for 173 of the 254 respondents (68.1%) in the USAS database at all three adolescent phases. We detected a significant two-way interaction (F4,334 = 5.8, P < 0.001), which indicated that the effect of maturational timing on swim performance differed by adolescent phase. Mean PPS for the early, average, and late maturers during early adolescence was 496.4, 494.8, and 480.0, whereas mean PPS during middle adolescence was 664.4, 683.1, and 721.3. Thus, the late maturers improved more (62.6%) from early to middle adolescence than the average (47.0%) and early (45.2%) maturers. In contrast, swim performance improved to a similar extent for the three groups from middle to late adolescence. CONCLUSION: Our results extend Beunen et al.’s findings by showing that early-maturing swimmers have a performance advantage over late-maturing swimmers during early adolescence. But by middle adolescence, the late maturers have a performance advantage that is maintained into late adolescence.

PURPOSE: Cardiovascular (CV) factors undergo progressive time-dependent changes beginning approximately 10 min into a bout of prolonged, submaximal exercise. One expected response is a gradual rise in heart rate (HR) throughout a prolonged sustained exercise bout. The purpose of this study was to examine whether or not CV drift occurs in trained swimmers during a typical early season repetition training session. METHODS: Thirteen swimmers (n=2 women; n=11 men) were asked to perform 60 repetitions of 91.4 m of freestyle swimming in 26.6 C water with a 5-10 sec rest interval between each repetition. HR was collected in 15 second epochs using a commercially available cardio-tachometer (Actiheart) for the entire bout. During the 60 min and after each swimmer achieved “steady state” (10 min after the beginning of the bout), the HR data were averaged each minute. A series of 6 one-tailed paired t-tests were used to compare the group mean HR at 10 min to each of the subsequent 10 min time interval of group mean HR. Standard deviations, and the subsequent average of those standard deviations, were calculated for each subject’s HR data. RESULTS: Significant differences were found between the group mean HR at 10 min (165.2 ± 8.2 bpm; baseline) and minutes 11-20, 21-30, 31-40, and 41-50 (166.1 ± 9.0 bpm, 169.1 ± 7.9 bpm, 169.8 ± 8.2 bpm, 169.4 ± 7.0 bpm, respectively; p<0.05). No significant differences were found between the group mean HR at 10 min and minutes 51-60 and 61-70 (167.8 ± 6.3 bpm, 166.4 ± 6.1 bpm, respectively). The range of standard deviations for each subject’s HR was 2.7-9.5 bpm (mean = 5.9 ± 1.8 bpm). CONCLUSIONS: The swimmers in this study experienced a small increase in HR from their first 10 minute value through minute 50 of the prolonged bout. However, HR then returned to baseline suggesting either CV drift did not occur or the results were related more to athlete pacing than any CV response per se.

INTRODUCTION: Since FINA’s initial approval for use of the backstroke starting device (ledge) in competition, these devices are now readily available in the marketplace. However, the use of these devices in collegiate, high school, and age-group competitions has yet to be legislated or implemented. Most importantly, no data exist for novice or inexperienced swimmers from the perspective of racing start safety. PURPOSE: To determine whether or not maximum head depth (MHD), velocity at max head depth (VMHD), distance at max head depth (DMHD), and entry angle (EAngle) attained when executing backstroke starts vary as a function of using the backstroke starting device in less experienced swimmers (i.e. novice backstroke starting device users). METHODS: 26 swimmers (8 collegiate, age: 21.5 ± 1.2 yr and 18 high school, age: 16.2 ± 1.5 yr) were filmed in a water depth of 1.59 m performing two backstroke starts (1st trial no device; NDEV, 2nd trial with the device; WDEV) in the sagittal plane at a sampling frequency of 120 Hz with cameras positioned at three points; 1m (above water), 1m (below water), and 3m (below water) from the starting end wall. Data for MHD, VMHD, DMHD, entry angle were tracked using Simi Reality Motion Systems software. Independent t-tests were used to compare between ability level and within each starting condition. Paired t-tests were used to compare between starting conditions within each ability level. RESULTS: MHD, VMHD, DMHD, and entry angle were significantly (p < 0.05) greater in collegiate swimmers when compared to high school swimmers in both starting conditions (NDEV: MHD; 1.14 ± 0.29 vs. 0.48 ± 0.17 m, VMHD; 1.98 ± 0.75 vs. 1.04 ± 0.41 m·sec-1, DMHD; 5.14 ± 0.34 vs. 4.06 ± 0.50 m, EAngle: 3.78 ± 6.1° vs. 30.7 ± 6.4° respectively, and WDEV: MHD; 1.02 ± 0.18 vs. 0.53 ± 0.20 m, VMHD; 1.63 ± 0.46 vs. 1.04 ± 0.38 m·sec-1, DMHD; 5.04 ± 0.31 vs. 4.28 ± 0.55 m, EAngle: 9.9 ± 10.2° vs. 31.2 ± 7.0° College vs Novice respectively). Only EAngle significantly (p < 0.05) increased in high school swimmers when using the backstroke starting device (3.78 ± 6.1° vs. 9.9 ± 10.2°). CONCLUSION: It appears that the recently introduced backstroke device tested causes few changes in common parameters that allow stratification of risk for swimmers executing racing starts. This appears true for the expert as well as the novice swimmer.

INTRODUCTION: Competitive swim coaches commonly use swim time observed from pace clocks and stopwatches and or direct verbal feedback from athletes as the primary means of gauging intensity during training. However, technological advances now permit the use of accelerometer-based monitors during aquatic activity. Recent studies suggest the use of activity monitors can provide an alternative, unobtrusive means of quantifying competitive swim activity (i.e. swim bout distance, speed, and energy expenditure; Wright & Stager, 2013, Wright, Brammer, & Stager, 2015). PURPOSE: The purpose of this study was to further examine the relationships between physiological measures (i.e. heart rate and blood lactate) and activity counts (from arm stroke and leg kick movement) during a progressive series of swim bouts. METHODS: Actical activity counts (from arm stroke and leg kick movement), swim speed, heart rate (HR), and blood lactate were collected during a series of seven progressive front crawl swim bouts each one 182.8m (200 yard) in distance. Subjects consisted of ten collegiate competitive swimmers (5 men & 5 women, Age 20.8 ± 1.1 years). The relationship between activity counts and physiological measures were modeled using linear and 2nd order polynomial fits for each subject. RESULTS: Linear regression analyses were significant in all models examining blood lactate (p < 0.05) and for eight of the ten subjects in models examining HR (p < 0.05). Polynomial regression analyses were significant for all subjects in models examining blood lactate (p < 0.05) and five of the ten subjects in models examining HR (p < 0.05). Mean values for individualized regression analyses R2 values ranged from 0.71-0.95 and 0.76-0.98 for linear and polynomial models respectively. Both linear and polynomial models examining the relationship between activity counts and swim speed were significant all in subjects (p < 0.05; R2 values ranged from 0.94-0.99). CONCLUSIONS : This study demonstrates that regression techniques using accelerometer-based activity counts recorded from arm stroke and leg kick movement may provide a non-invasive means of quantifying swim bout intensity within a group of collegiate swimmers.

Elite-level female athletes consistently show later ages at menarche (AaM) than their non-athletic peers. One possible explanation for this is that the stress from the intensive physical training required to reach the elite level acts to delay sexual maturation. Another possible explanation is that later maturers are simply more likely to reach elite status. This could occur if the presence - or absence - of certain traits associated with a slower maturational pace were coincident with athletic success. PURPOSE: To determine whether or not there are differences in the physical traits of early- and late-maturing women that could contribute to a greater proportion of women with a later AaM in the elite-level female athletic cohort. METHODS: College-age females were asked to complete questionnaires regarding the age at which they started menstruating as well as their past and present sports participation and physical training regimens. Then, the 25% of survey respondents with the earliest AaM and the 25% with the latest AaM were recruited for the second part of the study. Those agreeing to participate further underwent somatotype assessment and a dual-energy x-ray absorptiometry scan. Independent samples t tests were used to test for group differences between the early and late maturation groups. RESULTS: The mean AaM of the 206 Caucasian women that completed the questionnaire was 12.80 years (95% CI, 12.60 to 13.00 years). Seven of the earliest maturers (Mean AaM = 10.98 years, 95% CI, 10.54 to 11.42 years) and eight of the latest maturers (Mean AaM = 14.98 years, 95% CI, 14.37 to 15.59 years) participated in the second part of the study. Endomorph score was significantly less for late maturers (6.58; 95% CI, 5.44 to 7.72) than for early maturers (8.3; 95% CI, 6.71 to 9.89), and ectomorph score was significantly greater for late maturers (2.26; 95% CI, 1.12 to 3.40) than for early maturers (0.56; 95% CI, -0.39 to 1.51) (P < 0.05). No differences were found between the early and late maturers for BMI (P = 0.09) or percent body fat (P = 0.07). CONCLUSION: This provides evidence that late-maturing women have more linear body shapes than early-maturing women. And, since a linear body shape is also associated with athletic success, the older AaM observed in elite female athletes is likely due, in part, to the selection of this trait.

INTRODUCTION: Traditional methods utilized to estimate energy expenditure (i.e. backward extrapolation of expired gases) during swimming are limited due to the aquatic environment. Recent investigations have demonstrated the use of miniaturized accelerometer-based activity monitors as an alternative means of estimating swimming energy expenditure utilizing sample-based regression techniques (Johnston & Stager, 2005). However, previous research has documented large inter individual variations in energy expenditure at any given swim velocity (Faulkner, 1968). PURPOSE: The purpose of this project was to first determine the relationship between accelerometer activity counts recorded from arm stroke (AS), leg kick (LK), and the sum of arm stroke and leg kick (TOT) motion during swimming and swimming energy expenditure. An additional goal was to examine the use of the accelerometer-based activity counts as a means of estimating swimming energy expenditure via individually-based regression techniques. METHODS: Actical activity counts (from arm stroke and leg kick), heart rate, and expired gases were collected from a progressive series of front crawl swim bouts performed by six collegiate competitive swimmers (3 men & 3 women, Age 20.3 ± 1.0 years) in an Endless Pool. Water velocity during the progressive swims ranged from 1.00-1.65 m•s-1. The relationship between oxygen consumption and activity counts were modeled using linear and 2nd order polynomial fits for each subject. RESULTS: Linear and quadratic regression analyses were significant (p < 0.05) for all subjects. Individualized regression analyses R2 values ranged from 0.86-0.96 and 0.98-0.99 for linear and quadratic models respectively (n = 6). There were significant correlations between AS and heart rate (ranged from 0.91-0.96; p < 0.05), LK and heart rate (ranged from 0.79-0.97; p < 0.05), and TOT and heart rate (ranged from 0.93-0.99; p < 0.05) for each subject. CONCLUSIONS: This study demonstrates that individualized regression techniques using accelerometer-based activity counts recorded from AS, LK, and TOT may provide a valid non-invasive means of estimating swimming energy in collegiate competitive swimmers.

Although it’s well documented that top-performing swimmers are relatively late maturers, it’s not well understood why this is so. One explanation is that there are certain physical traits common to later maturers that contribute to better swim performance. And as a result, later maturers are more likely to be ‘selected’ for continued sport participation. PURPOSE: To determine if: (1) top-performing swimmers are later maturers than lower-performing swimmers; (2) later-maturing swimmers perform better than earlier-maturing swimmers; and (3) there are physical traits common to both top performers and later maturers. METHODS: Maturational timing was estimated using age at menarche (AaM), which was determined retrospectively in collegiate swimmers (N = 273). Each swimmer’s best performance during the 2015-2016 NCAA season was obtained from the USA Swimming database and selected based on Power Point Score (PPS), a standardized score given to all performances in the database. Independent samples t tests were used to compare (1) AaM and BMI (from self-reported height and weight) between bottom-performing (lowest 25% of PPS) and top-performing (highest 25% of PPS) swimmers and (2) PPS and BMI between earlier-maturing (youngest 25% of AaM) and later-maturing (oldest 25% of AaM) swimmers. RESULTS: The top performers were later maturers than the bottom performers (AaM 14.0 vs. 13.4 years, t = 2.48, P = 0.02, d = 0.46) and had lower BMIs (22.5 vs. 23.5 kg/m2, t = 2.30, P = 0.02, d = 0.41). The later maturers performed better than the earlier maturers (PPS 802.6 vs. 753.4, t = 2.11, P = 0.04, d = 0.39) and had lower BMIs (22.5 vs. 23.4 kg/m2, t = 2.29, P = 0.02, d = 0.40). CONCLUSION: Previous research has shown that top-performing swimmers and later-maturing women are more linear in body shape than their low-performing and earlier-maturing counterparts. Our results pertaining to weight per height (i.e., BMI) are consistent with these reports. And taken together, they provide evidence that there are physical traits common to top-performing swimmers and later-maturing women. So it’s certainly possible that later maturers are being selected (by themselves or others) for continued swim participation on the basis of these traits. But additional longitudinal research is required to determine the extent to which this is the case.

It could be argued that the only component of competitive swimming that is associated with any appreciable risk to the swimmer is the execution of the racing start from a starting block into shallow water.Recently, the Centers for Disease Control and Prevention (CDC) collected and considered input as a means to formulate guidelines for minimum water depths for the installation of starting blocks.Because there are only limited data on the depths and the velocities swimmers attain while executing starts, the data that are available need careful consideration.Insight into the central question, "how deep is deep enough?" involves consideration of values for maximum head depths, maximum head velocities, and the ability to control trajectory during racing starts.This review considers the literature pertinent to the key variables that, in general, stratify risk and determine successful, safe start outcomes.

Recent advances in miniaturized waterproof accelerometers have allowed their use as a tool in examining swim stroke characteristics (Ohgi et al. JPSE Int J 45:960–966, 2002; Sports Eng 6:113–123, 2003). A better use for this technology, however, might be to quantify characteristics of competitive swim training. The purpose of this study was thus to examine commercial accelerometers’ ability to track and quantify swim training variables common to all swim training programs: speed and distance. Swimmers (n = 43) were fitted with two accelerometer monitors on their right wrist and ankle. From this output, regression analyses were performed as a means to describe swim distance and speed. Ten additional swimmers (experimental group, n = 10) were then utilized to cross validate these equations as being useful to predict swim distance and speed. The results demonstrated a positive, significant relationship between activity counts and actual swim distance (r = 0.90, p < 0.05), actual swim speed (r = 0.80; p < 0.05), and cross validation confirmed the accuracy of the prediction equations. The findings of the present study suggest that commercial accelerometer-based activity monitors have the ability to quantify important characteristics of competitive swim training.

To expand upon previous studies showing inexperienced high school swimmers can complete significantly shallower racing starts when asked to start “shallow,” 42 age group swimmers (6-14 years old) were filmed underwater during completion of competitive starts. Two starts (1 normal and 1 “requested shallow”) were executed from a 0.76 m block into 1.83 m of water. Dependent measures were maximum depth of the center of the head, head speed at maximum head depth, and distance from the starting wall at maximum head depth. Statistical analyses yielded significant main effects (p < 0.05) for start type and age. The oldest swimmers’ starts were deeper and faster than the youngest swimmers’ starts. When asked to dive shallowly, maximum head depth decreased (0.10 m) and head speed increased (0.32 ms-1) regardless of age group. The ability of all age groups to modify start depth implies that spinal cord injuries during competitive swimming starts are not necessarily due to age-related deficits in basic motor skills.