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Suunto athletes Iris Pessey, Abby Hall, and Dakota Jones reveal their pacing strategies during the UTMB Mont Blanc in Chamonix. Race tactics! How should you approach a trail race to get the best possible outcome? How do you pace a race right? We turned to three of our ambassadors – Abby Hall, Iris Pessey, and Dakota Jones – to get their tips. They each ran different distances at the UTMB Mont Blanc this year: Iris flew through the MCC, Dakota tackled the CCC, and Abby took the UTMB by its horns. In the video below, Iris, Abby and Dakota share their unique insights on balancing effort, conserving energy, and navigating the mental and physical challenges of ultra-distance racing.  Abby uses her Suunto watch's Climb Guidance to pace climbs and descents evenly, while Dakota, a strong climber, pushes harder on uphills, saying, "Racing is about taking risks." Iris breaks her race into sections, allowing her to mentally reset along the way, finding motivation in in each checkpoint.  Curious about how the pros pace themselves in tough trail races? Watch the video now to gain valuable tips on skill, strategy, and resilience that could make all the difference on your next trail race!

Last week, Suunto users around the world came together for the Suunto Commuting Day in an effort to do good for the planet. Here’s a snapshot of what the global community achieved! Small, everyday choices are the beginning of change. One single commute may not seem like much, but over time, it all adds up. And when we look at the entire Suunto community, we can see that together we can make a bigger impact! The combined CO₂e savings for the participants on Thursday, October 24 were over 65,000 kg. This collective effort shows that even small changes in our daily routines can have a real impact. 65,000 kg of CO₂e is equal to about 260 one-way flights from Paris to Berlin, where each passenger's emissions are around 0.25 metric tons for the 1.5-hour trip. It is also similar to the CO₂ absorbed by around 1,300 trees over ten years (based on Tree-Nation’s Eden Reforestation Projects in Mozambique). 65,000 kg is also equal to the emissions of 5,500 Finland-made Suunto Ocean watches generate during their whole lifespan. Half on bikes, half on foot Our data revealed that 49% of participants chose to cycle, 31% to run, and 20% to walk their commutes — each step and pedal turn contributing to both personal health benefits and a greener future. Whether on wheels, on foot, or even other creative methods, our Suunto users took meaningful action to reduce their carbon footprint. Distances covered and time invested Cyclists typically commuted around 7.9 km per trip, spending 28 minutes on average. Runners tackled 6.1 km per commute, taking 40 minutes on average. Walkers covered 2.7 km on average, enjoying 35 minutes outdoors. The bike commute distances are distributed quite evenly over different distances: roughly a third is less than 5 km, a third is between 5–10 km, and a third is over 10 km. The longest commuting rides last Thursday were over 100 km. Top commuting champions by country Our Swiss users led in cycling distances with an impressive 10.3 km being the most typical commuting distance, while runners in China topped the charts, typically covering 7.5 km per commute. Globally, France took first place for the total distance covered by its participants, followed closely by Finland, Germany, Spain, and China. Suunto’s Commuting Day is a testament to what’s possible when people come together for a common goal. These statistics show that, collectively, we’re reducing emissions, embracing healthier lifestyles, and setting a precedent for sustainable commuting worldwide. Thank you everyone for participating in the Suunto Commuting Day and also the photo contest. Winners of the Suunto Wing open-ear headphones have been contacted directly. Please remember that our small everyday choices make a difference over a longer period of time and together with the community. Keep up the good work! Learn more about tracking human-powered commutes and your CO2e savings Learn more about Suunto sustainability work

Brave the weather (or simply enjoy it if the conditions are good) and join us for Suunto Commuting Day on October 24, 2024! Small, everyday choices are the beginning of change. One single commute may not seem like much, but over time, it all adds up. And when we look at the entire Suunto community, we can see that together we can make a bigger impact!   On Commuting Day, track your human-powered commute with your Suunto device and tag it as a commute. After the event, we'll calculate how much CO2e we saved together!   Tracking CO2e savings with the Suunto app is simple: ride, run, or walk from point A to point B and save your activity. The Suunto app can automatically tag one-way trips that start and end at least 500 meters (0.3 miles) apart as commutes. If you don’t have auto-tagging on, you can enable it in the Suunto app settings ('Settings' > 'Tags'). To manually tag your commutes, go to the activity, select 'Edit', 'Add tags', and choose 'Commute'.   The reduced CO2e emissions for a single activity will be shown in the workout summary. Your monthly total for CO2e savings can be viewed in the commuting widget on the app’s home page.    You don’t even need a Suunto watch or a Suunto-compatible Hammerhead bike computer to start tracking your human-powered commutes – you can also use the Suunto app for free. Download the Suunto app for iOS here or for Android here.   Learn more about tracking human-powered commutes and your CO2e savings Share your Commuting Day activity with Suunto app for a chance to win Suunto Wing open-ear headphones! Share your Suunto Commuting Day activity on Instagram via the Suunto app and tag @suunto for a chance to win Suunto Wing open-ear headphones! Open-ear headphones are ideal for urban commutes, allowing you to listen to audio while still hearing your surroundings.   To share your commute, add a photo to your activity in the Suunto app and include the CO2e savings data for your commute. Three of the most inspiring posts shared between October 24 and October 27 will win Suunto Wing open-ear headphones. (Terms and conditions apply. Learn more here.)   Learn more about sharing with Suunto app     Calculating saved CO2e emissions CO2e demonstrates the global warming potential (GWP) of all six greenhouse gases in one number. We calculate your emission reduction by comparing cycling, walking or running to driving your car.   About the calculation CO2e (Carbon Dioxide Equivalent) emissions of travel by car (average of a petrol/diesel powered car). Emission factor: 0.166867 kg CO2e/km. CO2e demonstrates the global warming potential (GWP) of all six greenhouse gases: CO2, CH4, N2O, HFCs, PFCs, SF6, NF3 in one number. Data source: Govt of UK, Dept. of Business, Energy & Industrial Strategy, 2023.   Committed to be better Learn more about our sustainability efforts here. We know we are not perfect, but we are committed to be better. 

After winning the Lavaredo Ultra Trail (for the third time!), ultra runner Hannes Namberger shares some of his activity data from the race – and his tips how you can learn from your races, too! Hannes Namberger has once again demonstrated why he's one of the world's top ultrarunners, clinching his third victory at the Lavaredo Ultra Trail by the UTMB back in June. Throughout the challenging 120 km course, Hannes relied on his deep understanding of race strategy and some valuable data that helped him be prepared for the race and ultimately stay ahead of the competition.  For a closer look at how Hannes leverages his data and insights, and to get a detailed breakdown of his race day performance, watch the full video below. And, as a bonus, you might learn a thing or two that will benefit you as well!     Learn more about Hannes's watch of choice, the Suunto Race here

A Technological Breakthrough from Tampere University MoniCardi, a medical technology and software company originating from Tampere University, has been diligently developing novel and heart rate variability (HRV) methods to decode the intricate phenomena of the human body. The MoniCardi team aims to unveil the various physiological characteristics influenced by the heart's behavior, opening new frontiers in health and performance measurement. The Foundation: Validation with Massive Datasets MoniCardi's groundbreaking research is rooted in statistical and time-series analysis methods originally developed in computational physics. These methods have surprising, yet highly impactful applications in electrocardiography, including HRV analysis. MoniCardi's novel methods and their usefulness have been validated in various scientific studies [1-9] and they have been featured in the leading conferences of cardiology such as the Scientific Sessions of American Heart Association. The studies include exploitation of massive datasets such as the extensive Finnish Cardiovascular Study (FINCAVAS), which contains comprehensive measurement data from 4386 participants of a clinical stress test. In a recent breakthrough study [1], it was found that MoniCardi's HRV analysis of a one-minute rest phase prior to the test predicts sudden cardiac death significantly better than the conventional analysis of the complete 20-minute stress test (hazard ratios of ~2.5 and ~1.5, respectively). The superiority of MoniCardi increases further when considering all the other risk factors in the analysis. Outside clinical studies, MoniCardi's patented methodology allows accurate estimation of metabolic thresholds in sport applications. This was confirmed in a ground-breaking study published by the team in the leading physiological journal in 2023 [2]. The study was featured in several national and international news sites, including a full-page article in the main Finnish newspaper Helsingin Sanomat (link below). The results are currently under validation in academic collaboration between Tampere University and the Finnish Institute of High-Performance Sport (KIHU). Through a partnership with Suunto launched in 2024, MoniCardi's novel technology is now entering in the use of professional athletes, sport enthusiasts and all the consumers interested in these novel features that push the HRV analysis into a completely new level and ensure practical and actionable results. Understanding Heart Rate Variability (HRV) Heart rate variability (HRV) measures the variation in the time intervals between consecutive heartbeats. By analyzing the fluctuations in these intervals, it is possible to gain insights into the body's state, particularly the autonomic nervous system's influence on the heart. Conventionally, HRV has been used to gauge recovery states during sleep through RMSSD (Root Mean Square of Successive Differences), which observes nightly changes in HRV to detect stress levels. At rest, the body shows significant variability between heartbeats, known as HRV. However, as the body encounters increased stress, the autonomic nervous system shifts into the fight-or-flight mode, resulting in minimal heart rate variability. This reduction in HRV can be used as an indicator to assess stress levels. Introducing DDFA: A Revolutionary Measurement Technology HRV methods are conventionally split into time-domain, frequency-domain and nonlinear methods. One of the most common nonlinear methods is Detrended Fluctuation Analysis (DFA) developed in the early 1990s. The key information provided by DFA is the overall long-term characteristics of HRV in terms of correlations, in particular, how changes in heartbeat intervals at some time affect the changes at another time. This information has powerful predictive value, but the practical usefulness of this information was unleashed only recently, when Dynamical DFA (DDFA) was developed [8,9] and further refined to assess changes in HRV correlations in a time-sensitive manner [10]. In brief, DDFA utilizes a multitude of "measure sticks" from 4 up to >50 consecutive heartbeats. At every instant of time, DDFA then gives a so-called scaling exponent - a characteristic feature of correlations in heartbeat intervals - for all these measure sticks simultaneously. This information can be precisely mapped to the physiological state during physical exercise. Real-Time Intensity Monitoring DDFA excels in assessing real-time changes in the heartbeat correlations during exercise. Training intensity directly correlates with time- and scale-dependent variations in the DDFA scaling exponent. Research indicates that increasing intensity in physical exercise decreases the scaling exponents. Eventually, at very high intensities, the beat-to-beat intervals may show so-called anticorrelations, where large and small beat-to-beat intervals alternate in a specific manner depending on the time scale. This information allows for precise monitoring of exercise intensity and physiological thresholds. Visualizing DDFA in Action A pivotal study, "Estimation of Physiological Exercise Thresholds Based on Dynamical Correlation Properties of Heart Rate Variability," published in Frontiers in Physiology in 2023 [2] illustrates DDFA's capabilities. The research paper presents an exercise scenario where intensity increases over time. The cyan lines denote the two metabolic thresholds: LT1 (aerobic threshold) and LT2 (anaerobic threshold), with the black dotted set on locations where these thresholds would be based blood lactate levels. This illustrates an ideal scenario where the DDFA-based analysis yields threshold levels nearly identical to those obtained using lactate-based threshold definitions. While this represents the optimal case, variations are expected in real applications. The DDFA analysis and lactate-based thresholds may differ from case to case, with heart rate measurements typically matching within +-5 beats per minute. There are also uncertainties inherent in lactate thresholds, which are subject to interpretation. Validity up to clinical accuracy MoniCardi methodology has been used to predict the overall cardiac risk and sudden cardiac death [1], and several cardiac diseases such as long QT syndrome [4,5], atrial fibrillation and congestive heart failure [in preparation]. The methods have also been applied to estimate stress and sleep stages [6,7]. The prediction of sudden cardiac death [1] has gained traction and it was featured in all the big news sites in Finland (YLE, Helsingin Sanomat, Ilta-Sanomat, Aamulehti) and on several international news sites (list below). In medical technology, MoniCardi is currently collaborating with Cardiolex Medical, a Swedish MedTech company developing modern ECG devices and systems. MoniCardi is also seeking partners in wearable technologies to bring cardiac risk assessment to mass markets. References: [1] Jussi Hernesniemi, Teemu Pukkila, Matti Molkkari, Kjell Nikus, Leo-Pekka Lyytikäinen, Terho Lehtimäki, Jari Viik, Mika Kähönen, Esa Räsänen, Prediction of sudden cardiac death with ultra-short-term heart rate fluctuations, JACC: Clinical Electrophysiology, 2024 [2] Matias Kanniainen, Teemu Pukkila, Joonas Kuisma, Matti Molkkari, Kimmo Lajunen, and Esa Räsänen, Estimation of Physiological Exercise Thresholds Based on Dynamical Correlation Properties of Heart Rate Variability, Front. Physiol. 14 (2023). [3] Teemu Pukkila, Matti Molkkari, Matias Kanniainen, Jussi Hernesniemi, Kjell Nikus, Leo- Pekka Lyytikäinen, Terho Lehtimäki, Jari Viik, Mika Kähönen, and Esa Räsänen, Effects of Beta Blocker Therapy on RR Interval Correlations During Exercise, Computing in Cardiology 50 (2023) 10.22489/CinC.2023.104 [4] Matias Kanniainen, Teemu Pukkila, Matti Molkkari, and Esa Räsänen, Effect of Diurnal Rhythm on RR Interval Correlations of Long QT Syndrome, Computing in Cardiology 50 (2023) 10.22489/CinC.2023.287           [5] T. Pukkila, M. Molkkari, J. Kim, and E. Räsänen, Reduced RR Interval Correlations of Long QT Syndrome Patients, Computing in Cardiology 49 (2022) 10.22489/CinC.2022.284 [6] Teemu Pukkila, Matti Molkkari and Esa Räsänen, Dynamical Heartbeat Correlations During Complex Tasks – A Case Study in Automobile Driving, Computing in Cardiology 48 (2021) 10.23919/CinC53138.2021.9662676 [7] M. Molkkari, M. Tenhunen, A. Tarniceriu, A. Vehkaoja, S.-L. Himanen, and E. Räsänen, Non-Linear Heart Rate Variability Measures in Sleep Stage Analysis with Photoplethysmography, Computing in Cardiology 46 (2019); 10.22489/cinc.2019.287 [8] M. Molkkari, G. Angelotti, T. Emig, and E. Räsänen, Dynamical Heartbeat Correlations During Running, Sci. Rep. 10, 13627 (2020) [9] M. Molkkari and E. Räsänen, Robust Estimation of the Scaling Exponent in Detrended Fluctuation Analysis of Beat Rate Variability, Computing in Cardiology 45 (2018); 10.22489/CinC.2018.219 [10] M. Molkkari and E. Räsänen, Inter-beat interval of heart for estimating condition of subject, Patent pending.   Latest news of MoniCardi International news:Science Daily: https://www.sciencedaily.com/releases/2024/06/240613140808.htmScience Alert: https://www.sciencealert.com/new-algorithm-can-predict-and-help- prevent-sudden-cardiac-deathMirage News: https://www.miragenews.com/tampere-university-researchers- predict-sudden-1255528/Medical XPress News: https://medicalxpress.com/news/2024-01-method-based-series-analysis-thresholds.html Finnish news: YLE: https://yle.fi/a/74-20093771Helsingin Sanomat: https://www.hs.fi/tiede/art-2000009847625.htmlIlta-Sanomat: https://www.is.fi/terveys/art-2000010505400.htmlAamulehti: https://www.aamulehti.fi/tiedejateknologia/art-2000010497986.html https://www.aamulehti.fi/tiedejateknologia/art-2000009863997.htmlSTT: https://www.sttinfo.fi/tiedote/70082024/aikasarja-analyysiin-perustuva-uusi-menetelma-helpottaa-urheilun-kynnysarvojen- maarittamista?publisherId=69818730&lang=fi

ZoneSense offers insights into whether your body's physiology is working aerobically with lower stress levels or if it has transitioned into higher stress, anaerobic efforts. Different sports impose varying demands on your cardiovascular system. Measuring intensity with heart rate can be difficult as the heart rate changes across different activities such as cycling and cross-country skiing, where same heart rate can be aerobic in one sport and an-aerobic in another. Additionally, daily performance fluctuations up to 5-10% can make it challenging to use predefined intensity levels. ZoneSense addresses these challenges by measuring the heart's response to workout intensity in real-time. This innovative technology empowers athletes to monitor their exertion levels daily across various sports disciplines. The following examples show how the ZoneSense measures your heart in different activities. Aerobic sessions Aerobic long run To keep the long runs purely aerobic, athlete should keep the ZoneSense state green most of the time. Its normal to see constant fluctuation of the DDFA index value in easy efforts such as in this example mainly around +0 - +0.4. On outdoor runs where athlete might need go over some hills, the Zonesense can illustrate this by turning to an-aerobic time to time. This type of few spikes that goes bit over aerobic threshold, is normal aerobic workouts.   Long ride with 3x45min an-aerobic effort The example illustrates a long bike ride of 5.5 hours, where the last part of the ride includes three 45minute sections with “Ironman race pace effort”. The three 45min work sections illustrate the effort at and above aerobic threshold as DDFA index turn mostly to yellow. As the temperature is close to 30 degrees, one can observe a raised heartrate after 3.5 hours, where the workout intensity is kept the same, but heart rate has now elevated by 15-20 beats per minute. ZoneSense however shows the steady intensity level. Interval sessions Long intervals - Cycling The harder interval sessions where athlete is working at an-aerobic threshold or even above can be hard to quantify on everyday sessions, do I go too hard or is it too easy. The ZoneSense helps to illustrate if you were reaching the threshold or did you go even beyond. The example here illustrates 6 x 7 min intervals above an-aerobic threshold. The ZoneSense gradually increases during the first 2-3 minutes in interval to get above threshold level.     Interval workouts are a common way to do the harder training efforts. ZoneSense DDFA index requires a longer period of consistent intensity due to time it takes the body to reach the homeostasis. The ZoneSense requires the effort to be consistent at least 2-3 minutes  to accurately represent the intensity level, with shortchanges this is not achieved. In the above example if the interval would have been stopped after 2 minutes, even with high effort the ZoneSense would have not reached the an-aerobic state. This can also be seen from the heart rate in the background, during the first 2 minutes the heart rate is rising despite constant effort. This is caused by the heart’s slow adaptation to the changed intensity level.   Long intervals - Open Water Swimming ZoneSense is not limited only to most common endurance sports such as running and cycling. Here is an example of an open water swim session where an athlete has done a 12 x 300m repetitions in lake with a short 30-45s rest between. The goal of the session has been to swim in good sustainable tempo pace. The ZoneSense illustrates this by reaching the an-aerobic level at end of each repetition. The pace is kept the same except with the last one being harder. The cumulative load of the work is illustrated by the last half of the repetitions always reaching an-aerobic state where the first ones don’t all reach this.   Long intervals - Rowing Following is a rowing workout 4x6min hard efforts and 8x 40s with 20s recovery. ZoneSense shows effort reaching to over an-aerobic threshold. The shorts repetitions also reach red, as recovery is short.   Short intervals with short recovery - Cycling With high volume of short intervals done with short recovery, the ZoneSense is able to illustrate the cumulative intensity. The example here illustrates the large volume of intervals with short recovery, 13x30s with 15second recovery. This effort can be shown as a black line illustrating cycling power. The ZoneSense DDFA index grows during the first five repetitions into an-aerobic range and then with next few following reps will reach the vo2max range represented in red color. This illustrates nicely the load for higher volume reps where the effort in power can be argued to be on correct level. Here the 15 seconds recovery is so short that the body’s homeostasis doesn’t recover into low intensity between the repetitions and rather describes the cumulative intensity of the constant intervals.   Short intervals with long recovery - Running The ZoneSense is not able to monitor short bursts with longer recovery sessions in a meaningful manner. ZoneSense requires to reach a homeostasis with the intensity. The running interval example with 20x400m with 40 s recovery illustrates that the measurement does not indicate the harder running pace as interval duration is short with relatively long recovery. If the intervals would have been longer each interval would have reached an-aerobic intensity or if recovery would have been shorter the cumulative intensity impact would have been shown as in the previous example.   Higher Intensity Near Anaerobic Threshold Half marathon The following examples illustrate the ZoneSense in session where intensity is an-aerobic and reaches regularly an-aerobic threshold level. This example is from a half marathon race where recreational runner is pushing their own limits close to 1.5hours. The racecourse has some turns and hills, which impact on the intensity effort that athlete is working. The ZoneSense shows the intensity at an-aerobic effort in yellow with several sections shown at red in vo2 range. When comparing the black line illustrating the pace, one can observe a slight gradual decline of the pace. Similarly, one can see slight decline in heartrate. However the ZoneSense illustrate the yellow & red sections as athlete has tried to keep the intensity as high as possible.   Ice hockey with various effort spikes. Its common method for endurance sports athletes to do a performance test in laboratory to establish the aerobic and an-aerobic levels as heart rate, pace or power. But for many sports this is not possible as the laboratory protocols do not represent sport in real life. Here is an example of an ice hockey game where players have been using ZoneSense to measure their effort during the game. Each work effort is shown as an-aerobic work where spikes in VO2max area. For team sports such as ice hockey, football, basketball its very difficult to understand the real intensity levels as laboratory test done in treadmill or exercise bike doesn’t truly correlate to the effort in the field. ZoneSense could be a solution for some of these sports or training sessions for these athletes. The challenge with ZoneSense is the requirement for constant work. Effort that leads to homeostasis a.k.a. balance needs to be long enough to DDFA index truly represent the intensity of the work. With many team sports and racquet sports, this can be a limitation with very short work durations and long rest periods. However, even if the games themselves couldn’t be measured with ZoneSense the training sessions with enough cumulative work could potentially be applicable for ZoneSense. This would be the long waited intensity measurement in team sports.   Muscle fatigue impact ZoneSense is measuring stress with heart rate variability. This stress effect correlates to metabolic state of the body, where less stress is aerobic, and more is an-aerobic. However, the metabolic state is not the only effect but can be induced with other stress factors. Here is a test example where an athlete has done two ramp up protocols with ergometer in one workout. The first part can be seen from 8min-16min and the second at 1h 15min – 1h 23min. The middle part of the session was strength training heavy leg exercises. The impact of fatigue in leg muscles can be witnessed on the ramp up protocol where ZoneSense reaches a higher intensity range compared to the first version. The impact is not massive, but still illustrates the other stressful factors.     By measuring the actual response of the heart to workout intensity, ZoneSense offers athletes a reliable and daily tool to optimize their training across different sports. This technology is set to revolutionize how athletes understand and manage their physical stress levels, enhancing performance and recovery. A similar impact of muscle load and potential different intensity levels can be found in the hiking session. The steep uphill and downhill make the workout become an-aerobic. Where the heartrate itself especially in downhill wouldn’t indicate this. The muscle fatigue and stress associated raises the ZoneSense intensity.