Sex and Age Differences in Trail Half Marathon Running

International Journal of Exercise Science 11(6): 281-289, 2018. Female participation is growing in trail running races. The purpose was to evaluate sex and age differences in top finishers of a trail running half marathon. Velocity differences between males (M) and females (F) were determined for the top 10 finishers of the Moab Trail Half Marathon from 2012 - 2015 across age, and by finishing place. Differences between age category and between sexes were determined through ANOVA with significance accepted at P \u3c 0.05. A significant difference for running velocity was present between sexes at each age category (20-29 yr F = 2.9±0.3, M = 3.4±0.4 m·sec-1; 30-39 yr F = 2.8±0.3, M = 3.3±0.3; 40-49 yr F = 2.7±0.3, M = 3.0±0.5; 50-59 yr F = 2.3±0.2, M = 2.8±0.3; 60-69 yr F = 1.6±0.3, M = 2.2±0.4; P \u3c 0.0001). Sex difference in trail running velocity was consistent (~13%) among all age categories with exception of the oldest group (33%, P = 0.0001). There were significantly greater female finishers in every age category (20 - 29 yr F = 107±18, M = 56±1;, 30 - 39 yr F = 150±34, M = 84±21; 40 - 49 yr F = 112±17, M = 64±16; P \u3c 0.01) until 50 - 59 yr (F = 48±13, M = 41±14; P = 0.50). These data indicate that the widening gap in sex differences observed in road races are ameliorated in a trail running environment that has a larger number of female participants

The Effects of Sitting and Walking in Green Space on State Mindfulness and Connectedness to Nature

People report feeling connected to nature while spending time in green space. The modulators of this relationship are unclear. One modulator may be state mindfulness, which is how mindful someone is in a specific moment. The first step of studying state mindfulness as a potential modulator is describing how state mindfulness and connectedness to nature respond to acute exposure to green space. PURPOSE: This study aimed to determine whether sitting and walking in green space change state mindfulness and connectedness to nature in tandem. METHODS: Participants arrived at one of two green spaces: the Thunderbird Gardens Trailhead in Cedar City, UT, or the Clark County Wetlands Park in Las Vegas, NV. After giving verbal and written consent, the participants completed the State Mindfulness Scale (SMS) and Love and Care of Nature Scale (LCN). The participants then sat alone and undisturbed for 10 minutes near the trailhead and completed the SMS and LCN again. Next, the participants walked alone for 10 minutes on the trail and completed the SMS and LCN once more. The SMS and LCN scores were compared among pre-sit, post-sit, and post-walk via two separate one-way repeated-measures ANOVAs. Population effect sizes were estimated as partial omega squared (ωp2; large effect \u3e 0.14). After each ANOVA, the post hoc pairwise comparisons were dependent-samples t-tests with Bonferroni adjustments. The α-level was 0.05 for all the statistical analyses. RESULTS: Forty-two participants completed the study (22 females, 20 males, 0 intersex; 4 African American/Black, 4 Asian, 19 Caucasian/White, 9 Hispanic/Latino, 1 Mediterranean, 1 Middle Eastern, 3 Multi-Racial, 1 Polynesian; 26 ± 9 years, 170 ± 9 cm, 69 ± 16 kg, 24 ± 4 kg/m2). The SMS scores significantly increased from pre-sit to post-sit (+29 arbitrary units [AU], 95% CI: 20, 38; p \u3c 0.001) but not from post-sit to post-walk (p = 0.23). The LCN scores significantly increased from pre-sit to post-sit (+5 AU, 95% CI: 2, 8; p = 0.003) and from post-sit to post-walk (+4 AU, 95% CI: 1, 6; p = 0.002). CONCLUSION: Sitting for 10 minutes in green space increases state mindfulness and connectedness to nature. Walking for 10 minutes further increases connectedness to nature but not state mindfulness. The next step is determining whether state mindfulness predicts connectedness to nature while in green space

Concurrent Heart Rate Validity of Wearable Technology Devices During Trail Running

Validation of heart rate responses in wearable technology devices is generally composed of laboratory-based protocols that are steady state in nature and as a result, high accuracy measures are returned. However, there is a need to understand device validity in applied settings that include varied intensities of exercise. The purpose was to determine concurrent heart rate validity during trail running. Twenty-one healthy participants volunteered (female n = 10, [mean (SD)]: age = 31 [11] years, height = 173.0 [7] cm, mass = 75.6 [13] kg). Participants were outfitted with wearable technology devices (Garmin Fenix 5 wristwatch, Jabra Elite Sport earbuds, Motiv ring, Scosche Rhythm+ forearm band, Suunto Spartan Sport watch with accompanying chest strap) and completed a self-paced 3.22 km trail run while concurrently wearing a criterion heart rate strap (Polar H7 heart rate monitor). The trail runs were out-and-back with the first 1.61 km in an uphill direction, and the 1.61 return being downhill in nature. Validity was determined through three methods: Mean Absolute Percent Error (MAPE), Bland-Altman Limits of Agreement (LOA), and Lin’s Concordance Coefficient (rC). Validity measures overall are as follows: Garmin Fenix 5 (MAPE = 13%, LOA = -32 to 162, rC = 0.32), Jabra Elite Sport (MAPE = 23%, LOA = -464 to 503, rC = 0.38), Motiv ring (MAPE = 16%, LOA = -52 to 96, rC = 0.29), Scosche Rhythm+ (MAPE = 6%, LOA = -114 to 120, rC = 0.79), Suunto Spartan Sport (MAPE = 2%, LOA = -62 to 61, rC = 0.96). All photoplethysmography-based (PPG) devices displayed poor heart rate agreement during variable intensity trail running. Until technological advances occur in PPG-based devices allowing for acceptable agreement, heart rate in outdoor environments should be obtained using an ECG-based chest strap that can be connected to a wristwatch or other comparable receiver

Repetition Count Concurrent Validity of Various Garmin Wrist Watches During Light Circuit Resistance Training

Wearable technology and strength training with free weights are two of the top 5 fitness trends worldwide. However, minimal physiological research has been conducted on the two together and none have measured the accuracy of devices measuring repetition counts across exercises. PURPOSE: The purpose of this study was to determine the concurrent validity of four wrist-worn Garmin devices, Instinct (x2), Fenix 6 Pro, and Vivoactive 3, to record repetition counts while performing 4 different exercises during circuit resistance training. METHODS: Twenty participants (n=10 female, n=10 male; age: 23.2 ± 7.7 years) completed this study. Participants completed 4 circuits of 4 exercises (front squat, reverse lunge, push-ups, and shoulder press) using dumbbells at a light intensity with 1 set of 10 repetitions per exercise and 30 seconds rest between exercises and 1-1.5 min rest between circuits. Mean absolute percent error (MAPE, ≤10%) and Lin’s Concordance Coefficient (CCC, ρ≥0.7) were used to validate the device’s repetitions counts in all exercises compared to the criterion reference manual count. Dependent T-tests determined differences (p≤0.05). RESULTS: No devices were considered valid (meeting both the threshold for MAPE and CCC) for measuring repetition counts during front squats (MAPE range: 3.0-18.5% and CCC range: 0.27-0.68, p value range: 0.00-0.94), reverse lunge (MAPE range: 44.5-67.0% and CCC range: 0.19-0.31, p value range: 0.00-0.28), push-ups (MAPE range: 12.5-67.5% and CCC range: 0.10-0.34, p value range: 0.07-0.83), and shoulder press (MAPE range: 18.0-51.0% and CCC range: 0.11-0.43, p value range: 0.00-0.79) exercises. CONCLUSION: The wearable wrist-worn devices were not considered accurate for repetition counts and thus manual counting should be utilized. People who strength train using free weights will need to wait for either improved repetition counting algorithms or increased sensitivity of devices before this measure can be obtained with confidence

Evaluation of Average and Maximum Heart Rate of Wrist-worn Wearable Technology Devices During Trail Running

It has been estimated that there are 20 million people who participate in trail running, and these numbers are expected to increase by 15% each year. Our laboratory group has conducted studies on the validity of wearable technology watches and heart rate (HR) during trail running. The previous generation devices were mostly inaccurate, and a limitation was that reliability was not measured. PURPOSE: To determine both validity and reliability in newer models of wearable devices during trail running. METHODS: Seventeen participants (F = 7) ran on the Thunderbird Gardens Lightning Switch trail in Cedar City, UT. Demographic characteristics: Age = 25 (9) years (mean [standard deviation]), ht = 168 (9) cm, mass = 72 (14) kg. Two Garmin Instincts and two Polar Vantage M2s were evaluated, along with the Polar H10 chest strap as the criterion measure. Participants ran out on the trail for 10-minutes, and then returned to the trailhead. Maximum HR and average HR were measured during the run. Data were analyzed for validity (Mean Absolute Percent Error [MAPE] and Lin’s Concordance [CCC]) and reliability (Coefficient of Variation [CV] and Intraclass Correlation Coefficient [ICC]). Predetermined thresholds were: MAPE0.70, CV0.70. RESULTS: The Garmin Instinct met the threshold for both reliability tests for average and maximum HR (see table). The Garmin Instinct and Polar Vantage met the threshold for both validity tests for maximum HR. CONCLUSION: In order for a device to be considered valid, it must meet the predetermined thresholds for both validity and reliability. These results indicate that only the Garmin Instinct is valid and reliable, but only for measuring maximum HR. This is challenging for those who wish to track their HR while trail running, because neither of the studied devices were valid and reliable for maximum and average HR

Concurrent Validity and Reliability of Average Heart Rate and Energy Expenditure of Identical Garmin Instinct Watches During Low Intensity Resistance Training

ABSTRACT Wearable technology and resistance training are two of the top five worldwide fitness trends for 2022 as determined by ACSM. Many devices, such as Garmin’s Instinct, have functions to track various physiological aspects during resistance training. However, to our knowledge, independent verification of the validity and reliability of these devices for estimating average heart rate (HR) and energy expenditure (EE) during resistance training are nonexistent. PURPOSE: To determine the concurrent validity and reliability of identical Garmin Instinct watches during resistance training. METHODS: Twenty subjects (n=10 female and male; age: 23.2±7.7 years; height: 169.7±11.1; weight: 76.3±15.7 kg) completed this study. Two Garmin Instinct watches were evaluated, along with the Polar H10 chest strap and Cosmed K5 portable metabolic unit as the criterion devices for average HR and EE, respectively. Subjects completed 4 circuits of 4 exercises (front squat, reverse lunge, push-ups, and shoulder press) using dumbbells at a light intensity with 1 set of 10 repetitions per exercise, 30 seconds rest between exercises, and 1-1.5 min. rest between circuits. Data were analyzed for validity (Mean Absolute Percent Error [MAPE] and Lin’s Concordance Coefficient [CCC]) and reliability (Coefficient of Variation [CV]), with predetermined thresholds of MAPE0.70, and CVRESULTS: Garmin Instinct 1 and Instinct 2 were significantly (

Average Heart Rate and Energy Expenditure Validity of Garmin Vivoactive 3 and Fenix 6 Wrist Watches During Light Circuit Resistance Training

Our laboratory recently found wrist-worn wearable technology devices to be valid for measuring average heart rate (HR), but not valid for estimated energy expenditure (EE) compared to criterion devices, during steady state aerobic training (walking, running, biking). However, the validity of wrist-worn devices for HR and EE measures during resistance training is largely unknown. PURPOSE: The purpose of this study was to determine if two wrist-worn devices, Garmin Vivoactive 3 and Garmin Fenix 6 Pro, record valid measures of average HR and EE while performing circuit resistance training. METHODS: Twenty participants (n=10 female, n=10 male; age: 23.2 ± 7.7 years) completed this study. The Garmin Vivoactive 3 and Garmin Fenix 6 Pro were tested along with the Polar H10 chest strap and Cosmed K5 portable metabolic unit as the criterions for average HR and EE, respectively. Participants completed 4 circuits of 4 exercises (front squat, reverse lunge, push-ups, and shoulder press) using dumbbells at a light intensity with 1 set of 10 repetitions per exercise and 30 seconds rest between exercises and 1-1.5 min. rest between circuits. Mean absolute percent error (MAPE, ≤10%) and Lin’s Concordance (ρ≥0.7) were used to validate the device’s average HR (in bpm) and estimated EE (in kcals) compared to criterion reference devices. Dependent T-tests determined differences (p≤0.05). RESULTS: Average HR for Garmin Vivoactive 3 and Fenix 6 Pro were significantly different (p\u3c0.01) than the Polar H10 (115.0±23.9 and 124.5±15.4 vs 128.9±19.0 bpm, respectively), and were not considered valid (MAPE: 44.8% and 25.1%; Lin’s Concordance: 0.50 and 0.63, respectively). Estimated EE for Garmin Vivoactive 3 and Fenix 6 Pro were significantly different (p\u3c0.0001) than the Cosmed K5 (31.7±12.3 and 39.7±13.1 vs 20.3±5.5 kcals, respectively), and were not considered valid (MAPE: 309.7% and 322.1%; Lin’s Concordance: 0.04 and 0.15, respectively). CONCLUSION: Anyone involved in any resistance training aspect should be aware of the limitations of these wrist-worn devices in measuring average HR or EE