So let\u2019s do what that Reddit post did\u2014but in coach language, with real literature, and with a clear answer to the question:\u00a0which wearable makes the most sense to pair with Spleeft App, for which metric?<\/strong><\/p>\r\n\r\n Before crowning a “most accurate” wearable, we need to ask\u00a0accurate for what?<\/strong><\/p>\r\n Different metrics have different gold standards:<\/p>\r\n\r\n Sleep stages \u2192 polysomnography (PSG) with EEG<\/p>\r\n<\/li>\r\n \t Heart rate and HRV \u2192 ECG chest strap or multi\u2011lead ECG<\/p>\r\n<\/li>\r\n \t VO\u2082 max \u2192 lab gas-exchange\u00a0VO\u2082 Max Test<\/a><\/strong><\/p>\r\n<\/li>\r\n \t Steps \u2192 manual counting or video + force plates<\/p>\r\n<\/li>\r\n \t SpO\u2082 \u2192 medical\u2011grade pulse oximeter<\/p>\r\n<\/li>\r\n<\/ul>\r\n Most validation papers compare a wearable to one of those references and report:<\/p>\r\n\r\n Bias (does it systematically over\u2011 or underestimate?)<\/p>\r\n<\/li>\r\n \t Error (MAPE, MAE, RMSE)<\/p>\r\n<\/li>\r\n \t Agreement (Cohen\u2019s \u03ba for sleep, concordance correlation for HR\/HRV)<\/p>\r\n<\/li>\r\n<\/ul>\r\n So when you see rankings like “Apple Watch<\/a> 86% for active heart rate” or “Oura Gen 4 MAPE 5.96% for nocturnal HRV,” that\u2019s what they\u2019re doing: comparing to a gold standard in a specific context.<\/p>\r\n Key idea:\u00a0no single device wins everything<\/strong>. The smart move is to pick your wearable based on the metric you care about most, then integrate that metric intelligently with Spleeft\u2019s\u00a0velocity<\/strong>\u00a0and training data.<\/p>\r\n\r\n A 2025 validation at the University of Antwerp tested six popular wearables (Apple Watch Series 8, Fitbit Sense, Fitbit Charge 5, WHOOP 4.0, Withings, Garmin Vivosmart 4) against one night of in\u2011lab PSG in 62 adults.\u00b9<\/p>\r\n Headline findings:<\/p>\r\n\r\n All devices detected >90% of sleep epochs correctly (good at sleep vs wake).<\/p>\r\n<\/li>\r\n \t Specificity for wake was much lower (roughly 29\u201352%).<\/p>\r\n<\/li>\r\n \t Cohen\u2019s \u03ba for 4\u2011stage sleep (wake, light, deep, REM) ranged from\u00a00.21 to 0.53<\/strong>, meaning\u00a0fair to moderate<\/strong>\u00a0agreement.\u00b9<\/p>\r\n<\/li>\r\n<\/ul>\r\n In that independent study:<\/p>\r\n\r\n Apple Watch Series 8 had \u03ba\u22480.53 (highest of the bunch) with decent REM detection but still underestimated wake and deep sleep.\u00b9<\/p>\r\n<\/li>\r\n \t Fitbit Sense and Charge 5 were moderate (\u03ba\u22480.41\u20130.42).<\/p>\r\n<\/li>\r\n \t WHOOP, Withings, and Garmin sat in the “fair” range (\u03ba\u22480.21\u20130.37).\u00b9<\/p>\r\n<\/li>\r\n<\/ul>\r\n Another lab study comparing six devices (Apple Watch S6, Garmin, Polar, Oura Gen 2, WHOOP 3.0, Somfit) found similar themes: good at total sleep time, weaker at staging, with Oura and WHOOP slightly ahead on multi\u2011stage classification (\u03ba\u22480.43\u20130.52 vs Apple\u2019s \u03ba\u22480.20).\u00b2<\/p>\r\n What this means for you:<\/p>\r\n\r\n For\u00a0broad sleep trends<\/strong>\u00a0(time in bed, total sleep time, rough architecture), Apple Watch, Oura, and WHOOP are all serviceable.\u00b9\u00b2<\/p>\r\n<\/li>\r\n \t For\u00a0precise stage counts<\/strong>\u00a0(“you had 54 minutes of deep sleep”), none are clinical\u2011grade, but Oura and WHOOP tend to hold up slightly better in multi\u2011stage classification; Apple often does well on wake detection.\u00b9\u00b2<\/p>\r\n<\/li>\r\n<\/ul>\r\n From a Spleeft perspective, you don\u2019t need perfect staging\u2014you need a\u00a0reliable trend signal<\/strong>\u00a0to pair with your\u00a0velocity<\/strong>\u00a0and training\u2011load data:<\/p>\r\n\r\n Night\u2011to\u2011night changes in total sleep time<\/p>\r\n<\/li>\r\n \t Big drops in efficiency or big spikes in wake after sleep onset<\/p>\r\n<\/li>\r\n \t Shifts in REM\/”deep” proportions over weeks<\/p>\r\n<\/li>\r\n<\/ul>\r\n Apple Watch, Oura, and WHOOP can all do this reasonably well; Oura and WHOOP are slightly more oriented toward long\u2011term recovery narratives, while Apple integrates more tightly with the broader Apple ecosystem.<\/p>\r\n HRV is where the gap between devices really starts to matter for training.<\/p>\r\n A 2025 study compared nocturnal resting heart rate (RHR) and HRV from Oura Gen 3 & 4, WHOOP 4.0, Garmin Fenix 6, and Polar Grit X Pro against a Polar H10 ECG chest strap over 536 nights in 13 participants.\u00b3<\/p>\r\n Results for nocturnal HRV:<\/p>\r\n\r\n Oura Gen 4: CCC ~0.99, MAPE \u2248 6% \u2192\u00a0highest agreement<\/strong><\/p>\r\n<\/li>\r\n \t Oura Gen 3: CCC ~0.97, MAPE \u2248 7% \u2192 substantial<\/p>\r\n<\/li>\r\n \t WHOOP 4.0: CCC ~0.94, MAPE \u2248 8% \u2192 moderate<\/p>\r\n<\/li>\r\n \t Garmin Fenix 6: CCC ~0.87, MAPE \u2248 10.5% \u2192 poor<\/p>\r\n<\/li>\r\n \t Polar Grit X Pro: CCC ~0.82, MAPE \u2248 16% \u2192 poor\u00b3<\/p>\r\n<\/li>\r\n<\/ul>\r\n Takeaways:<\/p>\r\n\r\n For\u00a0nocturnal HRV and RHR<\/strong>, Oura Gen 3\/4 are currently the most accurate consumer options, with WHOOP a close, acceptable second.\u00b3<\/p>\r\n<\/li>\r\n \t Garmin and Polar trail behind in this particular study (and this was on older Fenix hardware).\u00b3<\/p>\r\n<\/li>\r\n<\/ul>\r\n If you want to use HRV\u2011based readiness\u00a0with<\/strong>\u00a0Spleeft:<\/p>\r\n\r\n Pair\u00a0Oura or WHOOP<\/strong>\u00a0for nocturnal HRV trends<\/p>\r\n<\/li>\r\n \t Combine their readiness\/HRV curves with your Spleeft\u00a0velocity<\/strong>\u00a0data (jump\u00a0velocity<\/strong>, bar\u00a0velocity<\/strong>, sprint\u00a0velocity<\/strong>) to build a much richer readiness model<\/p>\r\n<\/li>\r\n<\/ul>\r\n Spleeft gives you\u00a0how they\u2019re actually moving<\/strong>\u00a0under load. The wearable gives you\u00a0how their system recovered overnight<\/strong>. That combo is far more robust than leaning on HRV alone.<\/p>\r\n\r\n For heart rate during exercise:<\/p>\r\n\r\n Meta\u2011analysis data tend to show\u00a0Apple Watch<\/strong>\u00a0near the top for active HR accuracy (~mid\u201180% agreement), with Fitbit and Garmin in the 70s and high 60s.\u2074<\/p>\r\n<\/li>\r\n \t Chest straps (Polar H10, etc.) are still the gold standard (correlation r\u22480.99 vs ECG), with Apple Watch around r\u22480.80 and some Garmin models around r\u22480.5\u20130.6 in more challenging conditions.\u2074<\/p>\r\n<\/li>\r\n<\/ul>\r\n So if you care about\u00a0in\u2011session HR data<\/strong>\u00a0to structure intervals alongside\u00a0velocity<\/strong>\u00a0(e.g., Spleeft\u2011tracked running\u00a0velocity<\/strong>\u00a0plus HR zones):<\/p>\r\n\r\n Apple Watch + chest strap (for the hardest sessions) is still a very strong combo.\u00b2\u2074<\/p>\r\n<\/li>\r\n<\/ul>\r\n For VO\u2082 max estimates, a 2024\u20132025 set of validations is instructive:<\/p>\r\n\r\n Garmin Forerunner 245 and Fenix 6 show VO\u2082 max estimation errors around 5.7\u20137.0% vs lab values in runners, which is pretty solid for field use.\u2075<\/p>\r\n<\/li>\r\n \t Apple Watch Series 7 underestimates VO\u2082 max with mean absolute errors around 6\u20137 ml\/kg\/min and MAPE in the low\u2011 to mid\u2011teens (13\u201316%).\u2075<\/p>\r\n<\/li>\r\n<\/ul>\r\n Practically:<\/p>\r\n\r\n For\u00a0VO\u2082 max \/ maximal aerobic velocity<\/strong>\u00a0estimates, Garmin is currently ahead. Apple Watch can still track trends, but the absolute value may be off, especially in very fit or very unfit populations.\u2075<\/p>\r\n<\/li>\r\n<\/ul>\r\n In a Spleeft context:<\/p>\r\n\r\n If you\u2019re building VO\u2082 or aerobic velocity\u2011based running sessions and you want a watch to set paces, Garmin\u2019s VO\u2082 module + Spleeft\u2019s\u00a0velocity<\/strong>\u00a0feedback is a strong pairing.<\/p>\r\n<\/li>\r\n \t If you\u2019re using Spleeft mostly for strength and power<\/a>, Apple Watch HR + Spleeft bar\u00a0velocity<\/strong>\u00a0is often more than enough.<\/p>\r\n<\/li>\r\n<\/ul>\r\n On step counts:<\/p>\r\n\r\n Several large comparisons show\u00a0Garmin and Apple Watch<\/strong>\u00a0clustering around 80\u201383% accuracy vs manual counts, with Fitbit slightly lower and ring\u2011style devices like Oura struggling more\u2014especially in free\u2011living settings.\u2074<\/p>\r\n<\/li>\r\n<\/ul>\r\n For energy expenditure:<\/p>\r\n\r\n All consumer devices are mediocre. Apple Watch often “wins” with ~70% accuracy in structured protocols, Fitbit lands mid\u201160s, and others perform worse.\u2074<\/p>\r\n<\/li>\r\n \t Error grows during high\u2011intensity, multi\u2011modal, or resistance\u2011based sessions\u2014exactly what you care about with Spleeft and velocity\u2011based lifting.\u2074<\/p>\r\n<\/li>\r\n<\/ul>\r\n Rule of thumb:<\/p>\r\n\r\n Use steps as a\u00a0rough volume proxy<\/strong>\u00a0(non\u2011training activity, general load).<\/p>\r\n<\/li>\r\n \t Treat calories as a\u00a0very fuzzy estimate<\/strong>; never build nutrition or performance decisions on those numbers alone.<\/p>\r\n<\/li>\r\n<\/ul>\r\n If we summarize independent and semi\u2011independent research to date, a pragmatic ranking by\u00a0primary metric<\/strong>\u00a0looks like this (focusing on Apple Watch, Oura, WHOOP, Garmin):<\/p>\r\n\r\n Sleep architecture (multi\u2011stage) & nocturnal HRV<\/strong>\u00a0\u2192<\/p>\r\n\r\n Oura Ring Gen 3\/4 and WHOOP perform strongly, with Oura slightly ahead for HRV accuracy and reasonably strong staging; Apple Watch 8 also performs well on staging, especially in Antwerp\u2019s independent validation.\u00b9\u00b3<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n \t Active heart rate & everyday usability<\/strong>\u00a0\u2192<\/p>\r\n\r\n Apple Watch consistently lands near the top for in\u2011session HR accuracy, while integrating extremely well into daily life.\u00b2\u2074<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n \t VO\u2082 max estimation for runners<\/strong>\u00a0\u2192<\/p>\r\n\r\n Garmin Forerunner\/Fenix families currently lead, with lower VO\u2082 estimation error and closer alignment to lab values.\u2075<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n \t Long\u2011term recovery and readiness narratives<\/strong>\u00a0\u2192<\/p>\r\n\r\n WHOOP and Oura focus heavily on strain\/recovery\/HRV framing; Apple and Garmin are catching up but are still more generalist.\u00b3<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n The honest answer:\u00a0the best wearable to pair with Spleeft depends on which data you actually plan to use in your coaching decisions.<\/strong><\/p>\r\n\r\n Here\u2019s how I\u2019d think about it as a Spleeft coach.<\/p>\r\n\r\n Your priority: bar\u00a0velocity<\/strong>, jump\u00a0velocity<\/strong>, readiness for high\u2011intensity lifting.<\/p>\r\n\r\n Wearable target:<\/p>\r\n\r\n Apple Watch (for HR, ecosystem) or Oura\/WHOOP (for nocturnal HRV and sleep trends).<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n \t Spleeft use:<\/p>\r\n\r\n Daily bar\/jump\u00a0velocity<\/strong>\u00a0\u2192 see if outputs match expected zones.<\/p>\r\n<\/li>\r\n \t Use wearable only as a\u00a0context layer<\/strong>: “HRV tanked and bar\u00a0velocity<\/strong>\u00a0dropped? Possibly pull volume.”<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n You don\u2019t need perfect VO\u2082 or steps; you need consistent\u00a0velocity<\/strong>\u00a0plus a clean readiness signal.<\/p>\r\n\r\n Your priority: blend\u00a0velocity\u2011based lifting<\/strong>, sprint\u00a0velocity<\/strong>, and readiness from sleep\/HRV.<\/p>\r\n\r\n Wearable target:<\/p>\r\n\r\n Oura or WHOOP for HRV + sleep; or Apple Watch if you want stronger on\u2011wrist ecosystem and good HR data.\u00b3<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n \t Spleeft use:<\/p>\r\n\r\n Track jump and bar\u00a0velocity<\/strong>\u00a0pre\u2011 and in\u2011session.<\/p>\r\n<\/li>\r\n \t Compare to overnight HRV\/sleep to understand who is under\u2011recovering.<\/p>\r\n<\/li>\r\n \t Adjust load or\u00a0velocity<\/strong>\u00a0targets (e.g., lower loads at same\u00a0velocity<\/strong>\u00a0zone) on days where both wearable and Spleeft data show reduced readiness.<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n Your priority: VO\u2082, pace\/velocity precision, and long\u2011term aerobic development.<\/p>\r\n\r\n Wearable target:<\/p>\r\n\r\n Garmin (Forerunner\/Fenix) for VO\u2082 and running\u00a0velocity<\/strong>\u00a0metrics; optionally Oura for HRV if they want ring\u2011based readiness.\u2075<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n \t Spleeft use:<\/p>\r\n\r\n Use Garmin\u2019s VO\u2082 estimates to set initial aerobic\u00a0velocity<\/strong>\u00a0zones.<\/p>\r\n<\/li>\r\n \t Use Spleeft to track running\u00a0velocity<\/strong>\u00a0in key intervals and to keep lifting in the right\u00a0velocity<\/strong>\u00a0band so that strength work supports, rather than fights, endurance.<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n A few reality checks, echoing what the better reviews and meta\u2011analyses emphasize:\u00b9\u00b2\u00b3\u2074<\/p>\r\n\r\n No device wins everything.<\/strong>\r\nYou pick based on primary use: sleep + HRV vs daily usability vs VO\u2082 vs sport features.<\/p>\r\n<\/li>\r\n \t Funding bias is real.<\/strong>\r\nSome sleep studies are manufacturer\u2011funded (e.g., Oura) and rank their device highest; independent work sometimes reshuffles the deck. Read the funding statement.<\/p>\r\n<\/li>\r\n \t Hardware generations matter.<\/strong>\r\nMany papers still test Fenix 6, Watch S6, WHOOP 3.0, etc. Fenix 8 or Watch Series 10 may behave differently.<\/p>\r\n<\/li>\r\n \t Sample sizes are often small.<\/strong>\r\nThat HRV study? 13 participants, though 536 nights of data.\u00b3 Great depth, limited breadth.<\/p>\r\n<\/li>\r\n \t Skin tone, tattoos, BMI, and strap fit all affect accuracy.<\/strong>\r\nMost validation cohorts are still skewed lighter\u2011skinned and relatively lean\u2014this is a known gap.\u2074<\/p>\r\n<\/li>\r\n \t PSG and lab tests aren\u2019t perfect either.<\/strong>\r\nInter\u2011scorer reliability for sleep staging is \u03ba\u22480.7\u20130.8 even among human experts. Devices are compared against an imperfect gold standard.<\/p>\r\n<\/li>\r\n<\/ul>\r\n The job of Spleeft and your coaching isn\u2019t to pretend a wearable is a clinic. It\u2019s to use imperfect but useful data\u00a0together<\/strong>\u00a0with precise\u00a0velocity<\/strong>\u00a0and performance metrics to make better\u2011than\u2011yesterday decisions.<\/p>\r\n\r\n You\u00a0can<\/em>, but you\u2019ll mostly see the limits of multi\u2011device agreement. For sanity, pick\u00a0one primary sleep\/HRV source<\/strong>\u00a0and\u00a0one training watch<\/strong>\u00a0if needed. Then anchor everything in Spleeft via\u00a0velocity<\/strong>\u00a0and performance: whichever wearable you choose, treat it as “one lens” on the athlete, not the final verdict.<\/p>\r\n\r\n Any time there\u2019s a major hardware or firmware update. Accuracy can change with new algorithms. Check whether new validation studies exist for your device generation once or twice a year, especially if you\u2019re making high\u2011stakes decisions based on HRV or VO\u2082 estimates.<\/p>\r\n\r\n They\u2019re usually\u00a0better<\/strong>\u00a0for nocturnal HRV and comfort, but worse for in\u2011session HR and steps, because hands move differently than wrists and rings aren\u2019t ideal for high\u2011impact work.\u00b3 If Spleeft is your main training environment, a ring + watch combo can work great: ring for recovery, watch for in\u2011session\u00a0velocity<\/strong>\/HR.<\/p>\r\n\r\n You can trust it as a\u00a0starting point<\/strong>\u00a0and especially as a\u00a0trend indicator<\/strong>, but not as a replacement for a proper VO\u2082 Max Test in high\u2011performance contexts. Garmin tends to be closer to lab values; Apple Watch is usable but more error\u2011prone.\u2075 Use Spleeft\u2019s\u00a0velocity<\/strong>\u00a0tracking in key intervals to refine those zones based on actual performance.<\/p>\r\n\r\n Start with one readiness metric and one performance metric:<\/p>\r\n\r\n Readiness: nocturnal HRV\/sleep score from Oura\/WHOOP\/Apple<\/p>\r\n<\/li>\r\n \t Performance: jump or bar\u00a0velocity<\/strong>\u00a0check in Spleeft at the start of key sessions<\/p>\r\n<\/li>\r\n<\/ul>\r\n If both are down, that\u2019s a strong case to adjust load or\u00a0velocity<\/strong>\u00a0zones that day. If only one is off, dig deeper (travel, life stress, local muscle fatigue). Over time, you can layer in VO\u2082, step volume, or more advanced metrics\u2014but even this basic combo is a big upgrade over going by feel alone.<\/p>\r\n\r\n MSC Physical Performance & CEO SpleeftApp<\/p>\r\n Dedicated to improving athletic performance and cycling training, combining science and technology to drive results.<\/p>\r\n\r\n Schyvens AM et al. “A performance validation of six commercial wrist-worn wearable sleep-tracking devices for sleep stage scoring compared to polysomnography.” Sleep Advances. 2025.<\/p>\r\n<\/li>\r\n \t Miller DJ et al. “A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults.” Sensors. 2022.<\/p>\r\n<\/li>\r\n \t Dial MB et al. “Validation of nocturnal resting heart rate and heart rate variability in consumer wearables.” Physiol Rep. 2025.<\/p>\r\n<\/li>\r\n \t Vora & similar summaries compiling step, HR, and sleep accuracy across Apple Watch, Oura, WHOOP, Garmin, and Fitbit from multiple validation studies.<\/p>\r\n<\/li>\r\n \t Apple Watch and Garmin VO\u2082 max validation work: Lambe RF et al., Caserman P et al., and related trials comparing watch estimates vs lab VO\u2082 Max Test.<\/p>\r\n<\/li>\r\n \t General reviews on consumer wearables for cardiovascular monitoring and clinical decision support.<\/p>\r\n<\/li>\r\n<\/ol>","protected":false},"excerpt":{"rendered":" If you coach in 2026, you\u2019re basically living inside a wearable marketing war. Apple Watch promises health, ECG, and VO\u2082, WHOOP promises strain and recovery, Oura promises deep sleep and readiness, Garmin promises endurance metrics down to the last meter. All of them claim to be “accurate”\u2014but almost nobody reads the actual validation studies behind … <\/p>\nDOWNLOAD SPLEEFT APP NOW FOR iOS, ANDROID AND APPLE WATCH!<\/a><\/h2>\r\n
What \u201caccuracy\u201d really means for wearables<\/h2>\r\n
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Sleep tracking: useful trends, imperfect stages<\/h2>\r\n
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\r\nNocturnal HR and HRV: Oura and WHOOP lead, Garmin lags<\/h2>\r\n
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Active heart rate and VO\u2082 max: Apple\/Polar for HR, Garmin for VO\u2082<\/h2>\r\n
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\r\nSteps and calories: good enough for steps, weak for calories<\/h2>\r\n
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Putting it together: \u201cbest wearable\u201d by primary metric<\/h2>\r\n
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How to pair wearables with Spleeft App in the real world<\/h2>\r\n
Scenario 1: Strength & power athlete (velocity-first)<\/h3>\r\n
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Scenario 2: Field sport squad (readiness + running + gym)<\/h3>\r\n
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Scenario 3: Endurance athlete (VO\u2082, thresholds, long\u2011term trends)<\/h3>\r\n
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Important caveats before you marry a wearable<\/h2>\r\n
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FAQs<\/h2>\r\n
1. Should I use multiple wearables at once (e.g., Oura + Apple Watch + WHOOP)?<\/h3>\r\n
2. How often should I re\u2011evaluate whether my wearable is still accurate?<\/h3>\r\n
3. Are ring devices (like Oura) worse for training than wrist wearables?<\/h3>\r\n
4. Can I trust watch VO\u2082 max to build serious interval sessions?<\/h3>\r\n
5. What\u2019s the simplest way to combine a wearable with Spleeft for better decisions?<\/h3>\r\n
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<\/div>\r\n\r\nIv\u00e1n de Lucas Rogero<\/a><\/h3>\r\n
References<\/h4>\r\n
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