Parameters Influencing the Accuracy of a Wrist Photoplethysmography Heart-Rate Monitor (Polar Unite) During Exercise

PURPOSE

The accuracy of heart rate measured with a wrist photoplethysmography monitor can be influenced by the tightening of the wristband, movement of arms, or kinetics of the signal (eg, steady-state exercise vs on- and off-transients). To test these hypotheses, photoplethysmographic and electrocardiographic (ECG) signals were compared.

METHODS

Thirty participants (50% female) randomly performed two 13' sequences (3' rest, 5' submaximal-intensity exercise, and 5' passive recovery) on a motorized treadmill and a bicycle ergometer. Heart rate was measured concomitantly with a 10-lead ECG, a chest-strap monitor, and 2 wrist photoplethysmography monitors (Polar Unite) with different tightening (free vs imposed at the maximum tolerable).

RESULTS

The level of association (r) and coefficient of variation (CV; ie, the error of measurement) of the Polar Unite versus the 10-lead ECG is affected by the tightness of the wristband (normal vs high; r = .83 and .96, CV = 16.1 and 8.1% for the treadmill, respectively; r = .71 and .97, CV = 20.3% and 6.2% for the bicycle, respectively) by the phase of the signal (transition vs steady state; r = .90 and .97, CV = 9.0% and 7.6% for the treadmill, respectively; r = .93 and .99, CV = 7.5% and 3.1% for the bicycle, respectively) and movement of arms (treadmill vs bicycle; r = .90 and .93, CV = 9.0% and 7.5% during the transition phase, respectively; r = .97 and .99, CV = 7.6% and 3.1% during the steady-state phase, respectively).

CONCLUSION

The accuracy of heart rate measured with a wrist photoplethysmography monitor is affected by the tightness of the wristband and the phase of the signal. A high tightening is required when high accuracy is expected.

Motorized Treadmill and Optical Recording System for Gait Analysis of Grasshoppers

(1) Background: Insects, which serve as model systems for many disciplines with their unique advantages, have not been extensively studied in gait research because of the lack of appropriate tools and insect models to properly study the insect gaits. (2) Methods: In this study, we present a gait analysis of grasshoppers with a closed-loop custom-designed motorized insect treadmill with an optical recording system for quantitative gait analysis. We used the eastern lubber grasshopper, a flightless and large-bodied species, as our insect model. Gait kinematics were recorded and analyzed by making three grasshoppers walk on the treadmill with various speeds from 0.1 to 1.5 m/s. (3) Results: Stance duty factor was measured as 70–95% and decreased as walking speed increased. As the walking speed increased, the number of contact legs decreased, and diagonal arrangement of contact was observed at walking speed of 1.1 cm/s. (4) Conclusions: This pilot study of gait analysis of grasshoppers using the custom-designed motorized insect treadmill with the optical recording system demonstrates the feasibility of quantitative, repeatable, and real-time insect gait analysis.

Etiology of Neuromuscular Fatigue After Repeated Sprints Depends on Exercise Modality

PURPOSE

To compare neuromuscular fatigue induced by repeated-sprint running vs cycling.

METHODS

Eleven active male participants performed 2 repeated-maximal-sprint protocols (5×6 s, 24-s rest periods, 4 sets, 3 min between sets), 1 in running (treadmill) and 1 in cycling (cycle ergometer). Neuromuscular function, evaluated before (PRE); 30 s after the first (S1), the second (S2), and the last set (LAST); and 5 min after the last set (POST5) determined the knee-extensor maximal voluntary torque (MVC); voluntary activation (VA); single-twitch (Tw), high- (Db100), and low- (Db10) frequency torque; and maximal muscle compound action potential (M-wave) amplitude and duration of vastus lateralis.

RESULTS

Peak power output decreased from 14.6 ± 2.2 to 12.4 ± 2.5 W/kg in cycling (P < .01) and from 21.4 ± 2.6 to 15.2 ± 2.6 W/kg in running (P < .001). MVC declined significantly from S1 in running but only from LAST in cycling. VA decreased after S2 (~-7%, P < .05) and LAST (~-9%, P < .01) set in repeated-sprint running and did not change in cycling. Tw, Db100, and Db10/Db100 decreased to a similar extent in both protocols (all P < .001 post-LAST). Both protocols induced a similar level of peripheral fatigue (ie, low-frequency peripheral fatigue, no changes in M-wave characteristics), while underlying mechanisms probably differed. Central fatigue was found only after running.

CONCLUSION

Findings about neuromuscular fatigue resulting from RS cycling cannot be transferred to RS running.