Validity and Reliability of the Garmin Instinct in Measuring Heart Rate during Pickleball

Playing a racquet sport called pickleball is increasing in popularity every day in the United States. Garmin is a popular brand that manufactures wearable fitness trackers capable of measuring heart rate (HR). Because HR is a common way to determe exercise intensity, the validity and reliability of wearables to provide accurate HR during pickleball is important. PURPOSE: This study aimed to analyze and assess the validity and reliability of HR from the Garmin Instinct vs. Polar H10 (criterion) during pickleball. METHODS: Eleven adults (2 female, 8 male, 1 prefer not to disclose; age = 28.1 ± 9.2 years; height = 176.0 ± 8.0 cm; mass = 73.2 ± 13.4 kg) were recruited to participate via convenience sampling. Participants were asked to play alternating intervals of five minutes of pickleball interspersed with five minutes of rest while wearing two Garmin Instinct watches on the same wrist and a Polar H10 chest strap. Outcome measures were average and maximum HR, recorded in beats per minute (bpm). Mean Absolute Percent Error (MAPE) and Lin’s Concordance Correlation Coefficient (CCC) were used to assess validity; MAPE ≤ 5% and CCC ≥ 0.90. Coefficient of Variation (CV) were used to assess reliability; CV ≤ 10% and ICC ≥ 0.70. RESULTS: The Garmin Instinct did not meet the CCC threshold for validity of average or maximum HR but met the thresholds for both reliability tests for average and maximum HR (see Table 1). CONCLUSION: These results indicate that, in the present study, the Garmin Instinct was only reliable for measuring average and maximum HR. This is challenging for those who wish to track their HR while playing racquet sports such as pickleball because the Garmin Instinct did not provide accurate average or maximum HR

Does Hand Use Affect Metabolic Measures During Pickleball

Pickleball is one of the fastest up and coming sports in the United States today. This low impact sport has the combined elements of Ping-Pong, tennis, and badminton. Pickleball can be played with the dominant hand (DH) or non-dominant (NDH). Though many people enjoy the sport, it is under-researched. The metabolic demands of pickleball are not clear, nor is whether the demands differ by the hand used. PURPOSE: The aim of this study was to determine the differences in metabolic measures while playing pickleball with the DH and NDH. METHODS: Eleven people were recruited via convenience sampling and participated (2 female, 8 males, 1 prefer not to disclose; age = 28.1 ± 9.2 years; height = 176.0 ± 8.0 cm; mass = 73.2 ± 13.4 kg). Participants were connected to a COSMED K5 portable metabolic analysis system. Outcome measures were VO2 (ml/kg/min), Metabolic Equivalents (METS), Percent of Calories from Fat (FAT%), Percent of Calories from Carbohydrate (CHO%), and Respiratory Quotient (RQ). Participants played for five minutes with one hand, rested, and played for five minutes with the other hand. The hand order was counterbalanced. Data were analyzed using a paired t-test with significance accepted at p ≤ 0.05. RESULTS: A significant difference was observed for VO2 (DH = 27.3 ± 4.2, NDH = 24.7± 4.4, p = 0.03) and METS (DH = 7.8 ± 1.2, NDH = 7.1 ± 1.3, p = 0.03). No difference was noted for RQ (DH = 0.84 ± 0.07, NDH = 0.82 ± 0.07, p = 0.2), FAT% (DH = 54.9 ± 22.1%, NDH = 62.4 ± 20.9%, p = 0.2), or CHO% (DH = 45.1 ± 22.1%, NDH = 37.6 ± 20.9% p = 0.2) CONCLUSION: Pickleball players consume more oxygen while playing with their dominant hand, but the difference is not reflected in other metabolic measures associated with substrate utilization. While playing pickleball with the dominant hand may confer an advantage from a skill and intensity perspective, there is no statistical advantage when considering the fuels used during the activity. The practical implications, however, should be further explored

Validity and Reliability of the Polar OH1 biceps-band Heart Rate Monitor during Pickleball

Pickleball is making a name for itself as one of the fastest growing sports in the United States. According to the Association of Pickleball Professionals (APP), 14% of Americans 18 years and over (~36.5 million people) played pickleball at least once in 12 months. With almost half of the total players planning to play more often in the upcoming months, pickleball is certain to continue its ascension. It is unclear if paying intensity can be gauged accurately and reliably with heart rate (HR) measurements from biceps-band monitors. PURPOSE: This study aimed to determine the validity and reliability of the Polar OH1 heart rate during one, 15-minute round of pickleball. METHODS: Participants (2 Female, 8 Male, and 1 identifying as Other) wore two Polar OH1 bands on their left arm, and completed one 15-minute round of pickleball, alternating playing dominant and non-dominant hand every 5-minutes with a 5-minute rest in between. The Polar OH1s collected average and maximum HR, as did the criterion device: Polar H9/H10 chest-strap HR monitor. Validity was measured using mean absolute percent error (MAPE), and Lin’s Concordance Correlation Coefficient (CCC). Reliability was measured using the coefficient of variation (CV), and intraclass correlation coefficient (ICC) between the two OH1s. The threshold for validity was MAPE ≤ 10% and CCC ≥ 0.9. The threshold for reliability was CV ≤ 10% and ICC ≥ 0.7. RESULTS: The Polar OH1 biceps-band HR monitor met the threshold for both validity tests for average and maximum HR (see table). The Polar OH1 met the threshold for validity and reliability for average and maximum HR(see table).. CONCLUSION: People who want an accurate and consistent monitoring of their average and maximum HR during pickleball can trust in the feedback from wearing a Polar OH1. Biceps-band technology may be a great option when participating in any racquet-based sports (tennis, pickleball, ping-pong, etc)

Evaluation of Caloric Expenditure Metrics of Garmin Instinct Wearable Technology Devices During Pickleball

While tennis and badminton may present a moderate learning curve for beginners, pickleball, a similar racquet sport that has become increasingly popular in recent years, offers a notably simpler play-style that can be more easily adapted for new players. This sport has emerged during a time when wearable technology devices, such as the Garmin Instinct, have become commonly used to estimate physiological measures among individuals engaging in physical activity. Yet, there is little research that examines the validity and reliability of this technology during pickleball. PURPOSE: The purpose of this study was to assess the reliability and validity of caloric expenditure estimates generated by Garmin Instinct devices during pickleball. METHODS: Eleven participants (2 females, 8 males, and 1 prefer not to disclose) with an average age of 28.1 ± 9.2 years, average height of 176 ± 8.0 cm, and average mass of 73 ± 13.4 kg consented and were selected for this study through convenience sampling. Participants were equipped with two Garmin Instinct devices on their right wrists. A COSMED K5 wearable metabolic system was secured to their backs and provided the criterion measure. Participants played pickleball for a total of 10 minutes and switched hands after 5 minutes of play. The starting hand was counterbalanced. Total Kcals were measured during each trial. Data were analyzed for validity (Lin\u27s Concordance [CCC] and Mean Absolute Percent Error [MAPE]) and reliability (Coefficient of Variation [CV]). Predetermined thresholds were: MAPE0.90, CVRESULTS: The Garmin Instinct did not meet the threshold for either validity test (CCC=0.375, MAPE=31.08%). The Garmin Instinct did not meet the threshold for the reliability test (CV=12.90%).CONCLUSION: These results suggest that the Garmin Instinct is not valid nor reliable for measuring caloric expenditure during pickleball. Caloric metrics were statistically different between the two devices and between the devices and the K5. Players cannot be confident that the Garmin Instinct provides an accurate measure of caloric expenditure during pickleball

Assessing the Reliability of Stryd 27 for Variable Speed Running

Wearable technology is beneficial when it comes to tracking and optimizing performance. The Stryd 27 is a wearable footpod marketed as being more responsive in measuring power during running than the previous version (Stryd 25). However, the reliability of this newer device to return consistent values has not been determined. PURPOSE: This study aimed to observe whether Stryd 27 gives reliable metrics during variable speed running. METHODS: Sixteen participants (N = 16; 50% female; height = 174.1 ± 8.1 cm; mass = 73.0 ± 12.4 kg) were recruited, each equipped with two Stryd 27 footpods (updated to the same software version) affixed to the shoelaces of their running shoes. The researchers recorded data using the Stryd app on a mobile device that was connected to the Stryd 27 via Bluetooth. Recording on both devices were started and stopped at the same time. Each participant completed two, 10-minute runs on an indoor track. The initial run was used to establish a baseline. Following a 5-minute rest period, participants proceeded with the second run, during which they alternated between faster and slower intervals. The pace for these intervals was set to be 20% faster and 20% slower than what each participant’s average pace was during the first run. Reliability of power, cadence, form power, ground contact time (GCT), vertical oscillation (VO), leg spring stiffness (LSS), and stride length during the interval run was determined using coefficient of variation (CV) and intraclass correlation coefficient (ICC), with CV0.70 (p \u3c 0.05) being considered evidence of reliability. RESULTS: Reliability data are shown in Table 1. The following measures were found to be reliable: power, cadence, form power, GCC, and VO. The measures of LSS and stride length were not found to be reliable. CONCLUSION: Runners using the new Stryd 27 can have confidence that most measures return reliable values (power, cadence, form power, GCT, and VO). Unfortunately, two measures were observed to not meet the threshold for reliability (LSS and stride length). Athletes interested in these measures should be cautious when interpreting their data

Does Handedness Impact Pulmonary Measures during Pickleball?

Pickleball is a racquet sport that originated in the 1960s. Due to its beginner-friendly nature, it attracts players of all ages and fitness levels. Despite becoming the most quickly growing sport in the nation, it is still underresearched. The sport’s physiological demands based on pulmonary measures, and whether the demands differ by handedness, are not fully understood. PURPOSE: The purpose of this study was to analyze and assess whether differences were evident in performance and physiological responses in players when using the dominant (DH) and nondominant hand (NDH) during pickleball. METHODS: Participants were selected through convenience sampling and consisted of 11 (2 female, 8 male, 1 prefer not to disclose; age = 28.1 ± 9.2 years; height = 176 ± 8.0 cm; mass = 73 ± 13.4 kg). Participants were all equipped with a COSMED K5 wearable metabolic system attached through a harness securely worn on their back. Outcome measures included Ventilation (VE [L/min]), Ventilatory Equivalent for Oxygen (VE/VO2), Ventilatory Equivalent for Carbon Dioxide (VE/VCO2), Tidal Volume (VT), and Respiratory Frequency (Rf). Alternating intervals of five minutes of play followed by five minutes of rest were consistent throughout. The order of using the DH or NDH was counterbalanced. Data were analyzed using a paired t-test with significance accepted at p £ 0.05. RESULTS: Significant differences were observed for VT (DH = 1.4 ± 0.3 vs. NDH = 1.3 ± 0.2, p = 0.05) and VE/VCO2 (DH = 30.9 ± 2.5 vs. NDH = 32 ± 2.9, p = 0.04). However, there were no significant differences found for VE in L/min (DH = 57.5 ± 9.7 ; NDH = 52.5 ± 11.6, p = 0.08), VE/VO2 (DH = 25.9 ± 2.1 vs. NDH = 26 ± 2.9, p = 0.43), or Rf (DH = 40.9 ± 4.1 vs. NDH = 41.3 ± 5.4, p = 0.36). CONCLUSION: The greater mean VE/VCO2 during NDH play compared to DH play suggests that the use of the NDH presents more difficulty performing pickleball-related tasks. Switching to the NDH is reflected in expiring more CO₂, indicating that players exert more effort when using their NDH. Although the respiration measures were similar in terms of exhalation, the use of DH caused a greater mean VT than NDH

The Effectiveness of Running Power as a Metric of Exercise Intensity During Running Interval Training

Wearable power meters are increasingly popular among runners with Coros and Stryd offering running power as a real-time, trackable of a metric. PURPOSE: This study compared running power (RP) to physiological measures, heart rate (HR) and oxygen consumption (VO2), across high and low intensity running intervals. METHODS: Thirteen adult participants (n = 6 male; height = 174.9 ± 6.9 cm; mass = 72.5 ± 12.0 kg) were equipped with a Stryd 27 RP meter, a Polar H10 HR monitor, and a Cosmed K5 portable metabolic unit. Participants’ self-selected RP was obtained during a 10-min run on an indoor track (10 laps/mile). After resting for five minutes, participants ran another 10 min, alternating between equal intervals of RP 20% higher and 20% lower than self-selected RP: 120 s × 2, 60 s × 2, 30 s × 4, and 15 s × 8. All devices were started simultaneously before each run. RP (W/kg) was sampled at 1 Hz. HR (bpm) and VO2 (mL/kg/min) were sampled at 0.1 Hz throughout the interval run. Data were analyzed from the 60 s mark through the end of the run. HR and VO2 data were interpolated to 1 Hz, and cross correlations (max lag = 60 s) were used to compare RP, HR, and VO2 (mean values in Table 1). RESULTS: There were weak to moderate correlations between RP and VO2 (r = 0.351; lag = -29.1 s), RP and HR (r = 0.475; lag = 9.38 s), and HR and VO2 (r = 0.572; lag = -29.1 s; Table 2). CONCLUSION: HR showed the strongest correlation and smallest time delay with RP. This may be practically useful because HR data is more readily available to runners than VO2. However, the correlation is only moderate. While related, the three metrics of running intensity are fundamentally different. When exercising at a moderate intensity, changes in HR or VO2, which take seconds to minutes to stabilize, may be less evident than changes in mechanical power, which are immediate. Thus, it is possible that HR and VO2 would show a stronger relationship with RP across intervals longer than the 120 s maximum observed here. While RP can be a useful metric, it may not be informative about physiological responses to running especially over short intervals or when running at high intensity

Stryd 25 vs. Stryd 27: Comparing Running Metrics Between a Predecessor and “The Next Gen Stryd”

Wearable technology has claimed the top spot in the Worldwide Survey of Fitness Trends in all but two years since 2016. A popular wearable among runners is the Stryd power meter. The company markets its latest model, the Stryd 27, as 5x more responsive in measuring running power. Yet, it is unclear whether the new model performs differently than its predecessor. PURPOSE: This study aimed to compare running metrics of the Stryd 25 and Stryd 27 in self-paced and interval runs. METHODS: Participants consented (N = 16; 50% female; height = 174.1 ± 8.1 centimeters [cm]; mass = 73.0 ± 12.4 kilograms) and were equipped with the Stryd 25 and Stryd 27, attached randomly to the left and right shoelaces. Each Stryd was paired with a separate mobile device using the Stryd app. Researchers started and stopped recording on each Stryd simultaneously. Participants ran for 10 minutes at a self-selected pace counterclockwise around an indoor track (10 laps/mile) before resting for five minutes. Then participants ran 10 more minutes, alternating between fast and slow intervals: 120 seconds (s) × 2, 60 s × 2, 30 s × 4, and 15 s × 8. Fast and slow intervals were 20% faster and 20% slower, respectively, than the participant’s mean pace of the first run. The Stryd app recorded power in watts (W), cadence in steps per minute (spm), vertical oscillation (VO) in cm, and stride length in meters (m). Four independent t-tests were run to compare these measurements between the two Stryd models for the self-paced and interval runs. The alpha level was .05, and the effect size was Cohen’s d (0.2 small, 0.5 medium, 0.8 large). RESULTS: See Table 1. CONCLUSION: Four running metrics were statistically similar between the Stryd 25 and Stryd 27 during two indoor runs. Runners using the predecessor indoors can be confident it returns similar data to the newest model