Longitudinal monitoring of power output and heart rate profiles in elite cyclists

Impact of Menstrual Cycles or Combined Oral Contraception on Training Loads Assessed Using Latent Effort States in Female Elite Cyclists

To quantify the impact of the regular or irregular menstrual cycle (MC) or combined oral contraception (OC) on the time spent at the maximum effort exertion per training, assessed using latent effort states through a Hidden Markov chain Model (HMM). 6303 training sessions with heart rate (HR) and power output (PO) recorded every second were used to train HMM in order to determine latent effort states of 12 elite French cyclists followed up over 30 months. A total of 101 MC/OCs full cycles were analyzed. A calendar method was used to estimate regular MC phases (menstruation, estimated follicular phase, estimated luteal phase). Irregular MC was divided into menstruations/no menstruations and OC into break/active pill taking. Four latent effort states were identified: high, medium+, medium-, and low. Focused on high intensity-oriented training sessions, the proportion of time spent in high intensity effort state was significantly lower during menstruation (34.5%) compared to estimated follicular (65.2%, p = 0.0009) and luteal (55.4% p = 0.024) phases for regular MC, and during pills' break (43.7%) compared to active pill taking phase (62.6% p = 0.031) for OC cycles. During the high intensity-oriented training sessions, the proportion of time spent in high effort state is almost twice higher in mid-regular cycles whereas is lower during menstruation or pill's break in elite cyclists. These findings rely on repeated assessment of training loads in a real-world context, analyzed using novel machine learning techniques that objectively quantify both internal and external training loads in elite female cyclists.

Durability and Underlying Physiological Factors: How Do They Change Throughout a Cycling Season in Semiprofessional Cyclists?

PURPOSE

To investigate how cycling time-trial (TT) performance changes over a cycling season, both in a "fresh" state and in a "fatigued" state (durability). Additionally, the aim was to explore whether these changes are related to changes in underlying physiological factors such as gross efficiency, energy expenditure (EE), and substrate oxidation (fat oxidation [FatOx] and carbohydrate oxidation [CarbOx]).

METHODS

Sixteen male semiprofessional cyclists visited the laboratory on 3 occasions during a cycling season (PRE, START, and IN) and underwent a performance test in both fresh and fatigued states (after 38.1 [4.9] kJ/kg), containing a submaximal warm-up for the measurement of gross efficiency, EE, FatOx, and CarbOx and a maximal TT of 1 (TT1min) and 10 minutes (TT10min). Results were compared across states (fresh vs fatigued) and periods (PRE, START, and IN).

RESULTS

The average power output (PO) in TT1min decreased (P < .05) from fresh to fatigued state across all observed periods, whereas there was no change in the PO in TT10min. Over the course of the season, the PO in TT1min in the fatigued state improved more compared with the PO in TT1min in the fresh state. Furthermore, while EE did not significantly change, there was an increase in FatOx and a decrease in CarbOx toward the fatigued state. These changes diminished during the cycling season (IN), indicating a greater contribution of CarbOx in the fatigued state.

CONCLUSIONS

TT1min performance is more sensitive to fatigue compared with TT10min. Also, during a cycling season, durability improves more when compared with fresh maximal POs, which is also observed in the changes in substrate oxidation.

Assessing the Use of Heart-Rate Monitoring for Competitive Swimmers

PURPOSE

Quantifying training intensity provides a comprehensive understanding of the training stimulus. Recent technological advances may have improved the feasibility of using heart-rate (HR) monitoring in swimming. However, the implementation of HR monitoring is yet to be assessed longitudinally in the daily training environment of swimmers. This study aimed to assess the implementation of HR by comparing the training-intensity distribution from an external measure, planned volume at set intensities (PVSI), with the internal training-intensity distribution measured using time in HR zones.

METHODS

Using a longitudinal observational design, 10 competitive swimmers (8 male and 2 female, age: 22.0 [2.3] y, Fédération Internationale de Natation point score: 842.9 [58.5], mean [SD]) were monitored daily for 6 months. Each session, HR data, and coached-planned and athlete-reported session rating of perceived exertion (Modified Category Ratio 10 scale) were recorded. Based on previously determined training zones from an incremental step test, PVSI was calculated using the planned distance and planned intensity of each swim bout. Training-intensity distributions were analyzed using a linear mixed model (lme4).

RESULTS

The model revealed a small to moderate relationship between PVSI and time in HR zone, based on the Nakagawa R-squared value (range .14-.42).

CONCLUSIONS

Training-intensity distribution differed between the internal measure (ie, HR) and the external measure of intensity (ie, PVSI). This demonstrates that internal and planned external measures of intensity cannot be used interchangeably to monitor training. Further research should explore how to best integrate these measures to better understand training in swimming.

Conventional methods to prescribe exercise intensity are ineffective for exhaustive interval training

PURPOSE

To compare methods of relative intensity prescription for their ability to normalise performance (i.e., time to exhaustion), physiological, and perceptual responses to high-intensity interval training (HIIT) between individuals.

METHODS

Sixteen male and two female cyclists (age: 38 ± 11 years, height: 177 ± 7 cm, body mass: 71.6 ± 7.9 kg, maximal oxygen uptake ([Formula: see text]O2max): 54.3 ± 8.9 ml·kg-1 min-1) initially undertook an incremental test to exhaustion, a 3 min all-out test, and a 20 min time-trial to determine prescription benchmarks. Then, four HIIT sessions (4 min on, 2 min off) were each performed to exhaustion at: the work rate associated with the gas exchange threshold ([Formula: see text]GET) plus 70% of the difference between [Formula: see text]GET and the work rate associated with [Formula: see text]O2max; 85% of the maximal work rate of the incremental test (85%[Formula: see text]max); 120% of the mean work rate of the 20 min time-trial (120%TT); and the work rate predicted to expend, in 4 min, 80% of the work capacity above critical power. Acute HIIT responses were modelled with participant as a random effect to provide estimates of inter-individual variability.

RESULTS

For all dependent variables, the magnitude of inter-individual variability was high, and confidence intervals overlapped substantially, indicating that the relative intensity normalisation methods were similarly poor. Inter-individual coefficients of variation for time to exhaustion varied from 44.2% (85%[Formula: see text]max) to 59.1% (120%TT), making it difficult to predict acute HIIT responses for an individual.

CONCLUSION

The present study suggests that the methods of intensity prescription investigated do not normalise acute responses to HIIT between individuals.

What Does It Take to Complete the Cape Epic?

ABSTRACT

Reinpõld, K, Bossi, AH, and Hopker, JG. What does it take to complete the cape epic? J Strength Cond Res 36(12): 3513-3520, 2022-This study aimed to describe the racing and training demands of the Cape Epic. Six male mountain bike riders (age: 39 ± 7 years, height: 181 ± 3 cm, and body mass: 78.7 ± 8.1 kg) trained for 4.5 months and took part in the Cape Epic. Training and racing data (prologue, stage 1, and 2) were analyzed, and riders were tested in the laboratory on 3 distinct occasions for maximal oxygen uptake (V̇O 2 max), maximal work rate (Ẇmax), and power output associated with the respiratory compensation point (RCP PO ). Statistical significance was set at p ≤ 0.05. With race durations of 1.5 ± 0.2, 6.5 ± 1.2, and 6.4 ± 1.4 hours for, respectively, prologue, stage 1, and 2, normalized power was higher in prologue (3.73 ± 0.72 W·kg -1 ) compared with stages 1 (3.06 ± 0.59 W·kg -1 , p < 0.001) and 2 (2.94 ± 0.69 W·kg -1 , p < 0.001). Riders spent more time in power zones 1 and 2 (as %RCP PO ) and less time in zones 4 and 5, during stage 2 compared with prologue (all zones p ≤ 0.028). Despite no changes in V̇O 2 max or Ẇmax, RCP PO increased from midtraining (3.89 ± 0.61 W·kg -1 ) to prerace testing (4.08 ± 0.64 W·kg -1 , p = 0.048). No differences were found between base and build training phases for time in power zones. In conclusion, the Cape Epic requires an ability to sustain high submaximal power outputs for several hours as well as an ability to repeat high-intensity efforts throughout the race. A well-balanced program, incorporating a pyramidal intensity distribution, may be used as a starting point for the design of optimal training approaches.

Case Report: Training Monitoring and Performance Development of a Triathlete With Spinal Cord Injury and Chronic Myeloid Leukemia During a Paralympic Cycle

Introduction

Paratriathlon allows competition for athletes with various physical impairments. The wheelchair category stands out from other paratriathlon categories, since competing in swimming, handcycling, and wheelchair racing entails substantial demands on the upper extremity. Therefore, knowledge about exercise testing and training is needed to improve performance and avoid overuse injuries. We described the training monitoring and performance development throughout a Paralympic cycle of an elite triathlete with spinal cord injury (SCI) and a recent diagnosis of chronic myeloid leukemia (CML).

Case Presentation/Methods

A 30-year-old wheelchair athlete with 10-years experience in wheelchair basketball contacted us for guidance regarding testing and training in paratriathlon. Laboratory and field tests were modified from protocols used for testing non-disabled athletes to examine their physical abilities. In handcycling, incremental tests were used to monitor performance development by means of lactate threshold (P_OBLA) and define heart rate-based training zones. All-out sprint tests were applied to calculate maximal lactate accumulation rate (V˙Lamax) as a measure of glycolytic capabilities in all disciplines. From 2017 to 2020, training was monitored to quantify training load (TL) and training intensity distribution (TID).

Results

From 2016 to 2019, the athlete was ranked within the top ten at the European and World Championships. From 2017 to 2019, annual TL increased from 414 to 604 h and demonstrated a shift in TID from 77-17-6% to 88-8-4%. In this period, P_OBLA increased from 101 to 158 W and V˙Lamax decreased from 0.56 to 0.36 mmol·l⁻¹·s⁻¹. TL was highest during training camps. In 2020, after he received his CML diagnosis, TL, TID, and P_OBLA were 317 h, 94-5-1%, and 108 W, respectively.

Discussion

TL and TID demonstrated similar values when compared with previous studies in para-swimming and long-distance paratriathlon, respectively. In contrast, relative TL during training camps exceeded those described in the literature and was accompanied by physical stress. Increased volumes at low intensity are assumed to increase P_OBLA and decrease V˙Lamax over time. CML treatment and side effects drastically decreased TL, intensity, and performance, which ultimately hindered a qualification for Tokyo 2020/21. In conclusion, there is a need for careful training prescription and monitoring in wheelchair triathletes to improve performance and avoid non-functional overreaching.

The Relationship Between the Distribution of Training Intensity and Performance of Kayak and Canoe Sprinters: A Retrospective Observational Analysis of One Season of Competition

Purpose: To evaluate retrospectively the training intensity distribution (TID) among highly trained canoe sprinters during a single season and to relate TID to changes in performance.

Methods: The heart rates during on-water training by 11 German sprint kayakers (7 women, 4 men) and one male canoeist were monitored during preparation periods (PP) 1 and 2, as well as during the period of competition (CP) (total monitoring period: 37 weeks). The zones of training intensity (Z) were defined as Z1 [&lt;80% of peak oxygen consumption (VO2peak)], Z2 (81–87% VO2peak) and Z3 (&gt;87% VO2peak), as determined by 4 × 1,500-m incremental testing on-water. Prior to and after each period, the time required to complete the last 1,500-m stage (all-out) of the incremental test (1,500-m time-trial), velocities associated with 2 and 4 mmol·L−1 blood lactate (v2[BLa], v4[BLa]) and VO2peak were determined.

Results: During each period, the mean TID for the entire group was pyramidal (PP1: 84/12/4%, PP2: 80/12/8% and CP: 91/5/4% for Z1, Z2, Z3) and total training time on-water increased from 5.0 ± 0.9 h (PP1) to 6.1 ± 0.9 h (PP2) and 6.5 ± 1.0 h (CP). The individual ranges for Z1, Z2 and Z3 were 61–96, 2–26 and 0–19%. During PP2 VO2peak (25.5 ± 11.4%) markedly increased compared to PP1 and CP and during PP1 v2[bla] (3.6 ± 3.4%) showed greater improvement compared to PP2, but not to CP. All variables related to performance improved as the season progressed, but no other effects were observed. With respect to time-trial performance, the time spent in Z1 (r = 0.66, p = 0.01) and total time in all three zones (r = 0.66, p = 0.01) showed positive correlations, while the time spent in Z2 (r = −0.57, p = 0.04) was negatively correlated.

Conclusions: This seasonal analysis of the effects of training revealed extensive inter-individual variability. Overall, TID was pyramidal during the entire period of observation, with a tendency toward improvement in VO2peak, v2[bla], v4[bla] and time-trial performance. During PP2, when the COVID-19 lockdown was in place, the proportion of time spent in Z3 doubled, while that spent in Z1 was lowered; the total time spent training on water increased; these changes may have accentuated the improvement in performance during this period. A further increase in total on-water training time during CP was made possible by reductions in the proportions of time spent in Z2 and Z3, so that more fractions of time was spent in Z1.

Caveats and Recommendations to Assess the Validity and Reliability of Cycling Power Meters: A Systematic Scoping Review

A large number of power meters have become commercially available during the last decades to provide power output (PO) measurement. Some of these power meters were evaluated for validity in the literature. This study aimed to perform a review of the available literature on the validity of cycling power meters. PubMed, SPORTDiscus, and Google Scholar have been explored with PRISMA methodology. A total of 74 studies have been extracted for the reviewing process. Validity is a general quality of the measurement determined by the assessment of different metrological properties: Accuracy, sensitivity, repeatability, reproducibility, and robustness. Accuracy was most often studied from the metrological property (74 studies). Reproducibility was the second most studied (40 studies) property. Finally, repeatability, sensitivity, and robustness were considerably less studied with only 7, 5, and 5 studies, respectively. The SRM power meter is the most used as a gold standard in the studies. Moreover, the number of participants was very different among them, from 0 (when using a calibration rig) to 56 participants. The PO tested was up to 1700 W, whereas the pedalling cadence ranged between 40 and 180 rpm, including submaximal and maximal exercises. Other exercise conditions were tested, such as torque, position, temperature, and vibrations. This review provides some caveats and recommendations when testing the validity of a cycling power meter, including all of the metrological properties (accuracy, sensitivity, repeatability, reproducibility, and robustness) and some exercise conditions (PO range, sprint, pedalling cadence, torque, position, participant, temperature, vibration, and field test).

Concurrent Validity of Power From Three On-Water Rowing Instrumentation Systems and a Concept2 Ergometer

Purpose: Instrumentation systems are increasingly used in rowing to measure training intensity and performance but have not been validated for measures of power. In this study, the concurrent validity of Peach PowerLine (six units), Nielsen-Kellerman EmPower (five units), Weba OarPowerMeter (three units), Concept2 model D ergometer (one unit), and a custom-built reference instrumentation system (Reference System; one unit) were investigated.

Methods: Eight female and seven male rowers [age, 21 ± 2.5 years; rowing experience, 7.1 ± 2.6 years, mean ± standard deviation (SD)] performed a 30-s maximal test and a 7 × 4-min incremental test once per week for 5 weeks. Power per stroke was extracted concurrently from the Reference System (via chain force and velocity), the Concept2 itself, Weba (oar shaft-based), and either Peach or EmPower (oarlock-based). Differences from the Reference System in the mean (representing potential error) and the stroke-to-stroke variability (represented by its SD) of power per stroke for each stage and device, and between-unit differences, were estimated using general linear mixed modeling and interpreted using rejection of non-substantial and substantial hypotheses.

Results: Potential error in mean power was decisively substantial for all devices (Concept2, –11 to –15%; Peach, −7.9 to −17%; EmPower, −32 to −48%; and Weba, −7.9 to −16%). Between-unit differences (as SD) in mean power lacked statistical precision but were substantial and consistent across stages (Peach, ∼5%; EmPower, ∼7%; and Weba, ∼2%). Most differences from the Reference System in stroke-to-stroke variability of power were possibly or likely trivial or small for Peach (−3.0 to −16%), and likely or decisively substantial for EmPower (9.7–57%), and mostly decisively substantial for Weba (61–139%) and the Concept2 (−28 to 177%).

Conclusion: Potential negative error in mean power was evident for all devices and units, particularly EmPower. Stroke-to-stroke variation in power showed a lack of measurement sensitivity (apparent smoothing) that was minor for Peach but larger for the Concept2, whereas EmPower and Weba added random error. Peach is therefore recommended for measurement of mean and stroke power.

The Application of Critical Power, the Work Capacity above Critical Power (W'), and its Reconstitution: A Narrative Review of Current Evidence and Implications for Cycling Training Prescription

The two-parameter critical power (CP) model is a robust mathematical interpretation of the power–duration relationship, with CP being the rate associated with the maximal aerobic steady state, and W′ the fixed amount of tolerable work above CP available without any recovery. The aim of this narrative review is to describe the CP concept and the methodologies used to assess it, and to summarize the research applying it to intermittent cycle training techniques. CP and W′ are traditionally assessed using a number of constant work rate cycling tests spread over several days. Alternatively, both the 3-min all-out and ramp all-out protocols provide valid measurements of CP and W′ from a single test, thereby enhancing their suitability to athletes and likely reducing errors associated with the assumptions of the CP model. As CP represents the physiological landmark that is the boundary between heavy and severe intensity domains, it presents several advantages over the de facto arbitrarily defined functional threshold power as the basis for cycle training prescription at intensities up to CP. For intensities above CP, precise prescription is not possible based solely on aerobic measures; however, the addition of the W′ parameter does facilitate the prescription of individualized training intensities and durations within the severe intensity domain. Modelling of W′ reconstitution extends this application, although more research is needed to identify the individual parameters that govern W′ reconstitution rates and their kinetics.

Heart rate and stroke rate misrepresent supramaximal sprint kayak training as quantified by power

This study examined the utility of novel measures of power output (PO) compared to traditional measures of heart rate (HR) and stroke rate (SR) for quantifying high-intensity sprint kayak training. Twelve well-trained, male and female sprint kayakers (21.3 ± 6.8 y) completed an on-water graded exercise test (GXT) and a 200-, 500- and 1000-m time-trial for the delineation of individualised training zones (T) for HR (5-zone model, T1-T5), SR and PO (8-zone model, T1-T8). Subsequently, athletes completed two repeat trials of a high-intensity interval (HIIT) and a sprint interval (SIT) training session, where intensity was prescribed using individualised PO-zones. Time-in-zone (minutes) using PO, SR and HR was then compared for both HIIT and SIT. Compared to PO, time-in-zone using HR was higher for T1 in HIIT and SIT (P < 0.001, d ≥ 0.90) and lower for T5 in HIIT (P < 0.001, d = 1.76). Average and peak HR were not different between HIIT (160 ± 9 and 173 ± 11 bpm, respectively) and SIT (157 ± 13 and 174 ± 10 bpm, respectively) (P ≥ 0.274). In HIIT, time-in-zone using SR was higher for T4 (P < 0.001, d = 0.85) and was lower for T5 (P = 0.005, d = 0.43) and T6 (P < 0.001, d = 0.94) compared to PO. In SIT, time-in-zone using SR was lower for T7 (P = 0.001, d = 0.66) and was higher for T8 (P = 0.004, d = 0.70), compared to PO. Heart rate measures were unable to differentiate training demands across different high-intensity sessions, and could therefore misrepresent the training load in such instances. Furthermore, SR may not provide a sensitive measure for detecting changes in intensity due to fatigue, whereas PO may be more suitable.

Comparison of Training Monitoring and Prescription Methods in Sprint Kayaking

Purpose: To compare methods of monitoring and prescribing on-water exercise intensity (heart rate [HR], stroke rate [SR], and power output [PO]) during sprint kayak training. Methods: Twelve well-trained flat-water sprint kayak athletes completed a preliminary on-water 7 × 4-min graded exercise test and a 1000-m time trial to delineate individual training zones for PO, HR, and SR into a 5-zone model (T1–T5). Subsequently, athletes completed 2 repeated trials of an on-water training session, where intensity was prescribed based on individual PO zones. Times quantified for T1–T5 during the training session were then compared between PO, HR, and SR. Results: Total time spent in T1 was higher for HR (P < .01) compared with PO. Time spent in T2 was lower for HR (P < .001) and SR (P < .001) compared with PO. Time spent in T3 was not different between PO, SR, and HR (P > .05). Time spent in T4 was higher for HR (P < .001) and SR (P < .001) compared with PO. Time spent in T5 was higher for SR (P = .03) compared with PO. Differences were found between the prescribed and actual time spent in T1–T5 when using PO (P < .001). Conclusions: The measures of HR and SR misrepresented time quantified for T1–T5 as prescribed by PO. The stochastic nature of PO during on-water training may explain the discrepancies between prescribed and actual time quantified for power across these zones. For optimized prescription and monitoring of athlete training loads, coaches should consider the discrepancies between different measures of intensity and how they may influence intensity distribution.

Development of an On-Water Graded Exercise Test for Flat-Water Sprint Kayak Athletes

PURPOSE

To determine the reliability and validity of a power-prescribed on-water (OW) graded exercise test (GXT) for flat-water sprint kayak athletes.

METHODS

Nine well-trained sprint kayak athletes performed 3 GXTs in a repeated-measures design. The initial GXT was performed on a stationary kayak ergometer in the laboratory (LAB). The subsequent 2 GXTs were performed OW (OW1 and OW2) in an individual kayak. Power output (PWR), stroke rate, blood lactate, heart rate, oxygen consumption, and rating of perceived exertion were measured throughout each test.

RESULTS

Both PWR and oxygen consumption showed excellent test-retest reliability between OW1 and OW2 for all 7 stages (intraclass correlation coefficient > .90). The mean results from the 2 OW GXTs (OWAVE) were then compared with LAB, and no differences in oxygen consumption across stages were evident (P ≥ .159). PWR was higher for OWAVE than for LAB in all stages (P ≤ .021) except stage 7 (P = .070). Conversely, stroke rate was lower for OWAVE than for LAB in all stages (P < .010) except stage 2 (P = .120).

CONCLUSIONS

The OW GXT appears to be a reliable test in well-trained sprint kayak athletes. Given the differences in PWR and stroke rate between the LAB and OW tests, an OW GXT may provide more specific outcomes for OW training.

Comparison of power output during ergometer and track cycling in adolescent cyclists

The aim of this study is to establish the level of agreement between test performance of young elite cyclists in a laboratory and a track field-based trial. Fourteen adolescent cyclists (age: 14.8 ± 1.1 years; (Equation is included in full-text article.): 63.5 ± 5.6 ml·min(-1)·kg(-1)) performed 3 tests of 10 seconds, 1 minute, and 3 minutes on an air-braked ergometer (Wattbike) and on a 250-m track using their own bikes mounted with mobile power meters (SRM). The agreement between the maximum and mean power output (Pmax and Pmean) measured on the Wattbike and SRM was assessed with the 95% limits of agreement (LoA). Power output was strongly correlated between Wattbike and SRM for all tests (r = 0.94-0.96; p < 0.001). However, power output was significantly higher on the Wattbike compared with track cycling during all tests. The bias and 95% LoA were 76 ± 78 W (8.8 ± 9.5%; p = 0.003, d = 0.38) for Pmax10s and 82 ± 55 W (10.9 ± 7.9%; p < 0.001, d = 0.46) for Pmean10s. During the 1- and 3-minute test, the bias and 95% LoA were 72 ± 30 W (17.9 ± 7.1%; p < 0.001, d = 0.84) and 28 ± 20 W (9.6 ± 6.1%; p < 0.001, d = 0.51), respectively. Laboratory tests, as assessed using a stationary ergometer, resulted in maximal and mean power output scores that were consistently higher than a track field-based test using a mobile ergometer. These results might be attributed to the technical ability of the riders and their experience to optimize gearing and cadence to maximize performance. Prediction of field-based testing on the track from laboratory tests should be used with caution.

Validity and reliability of critical power field testing

PURPOSE

To test the validity and reliability of field critical power (CP).

METHOD

Laboratory CP tests comprised three exhaustive trials at intensities of 80, 100 and 105 % maximal aerobic power and CP results were compared with those determined from the field. Experiment 1: cyclists performed three CP field tests which comprised maximal efforts of 12, 7 and 3 min with a 30 min recovery between efforts. Experiment 2: cyclists performed 3 × 3, 3 × 7 and 3 × 12 min individual maximal efforts in a randomised order in the field. Experiment 3: the highest 3, 7 and 12 min power outputs were extracted from field training and racing data.

RESULTS

Standard error of the estimate of CP was 4.5, 5.8 and 5.2 % for experiments 1-3, respectively. Limits of agreement for CP were -26 to 29, 26 to 53 and -34 to 44 W for experiments 1-3, respectively. Mean coefficient of variation in field CP was 2.4, 6.5 and 3.5 % for experiments 1-3, respectively. Intraclass correlation coefficients of the three repeated trials for CP were 0.99, 0.96 and 0.99 for experiments 1-3, respectively.

CONCLUSIONS

Results suggest field-testing using the different protocols from this research study, produce both valid and reliable CP values.