[{"data":1,"prerenderedAt":1666},["ShallowReactive",2],{"blog-\u002Fblog\u002Fsub-4-marathon":3,"blog-related-candidates":1526},{"id":4,"title":5,"author":6,"body":7,"date":1505,"dateModified":1505,"description":1506,"extension":1507,"image":1508,"imageHeight":1509,"imageWidth":1510,"meta":1511,"navigation":1512,"path":1513,"qa":1514,"seo":1517,"sitemap":1518,"stem":1519,"tags":1520,"__hash__":1525},"blog\u002Fblog\u002Fsub-4-marathon.md","Sub-4 Marathon: It's Volume, Not Talent","Runima Team",{"type":8,"value":9,"toc":1489},"minimark",[10,36,50,55,58,145,148,152,204,231,248,252,306,310,328,354,360,434,437,454,458,552,556,568,602,605,609,612,677,711,715,721,731,786,806,828,832,838,844,850,856,868,872,1037,1041,1044,1079,1082,1086,1484],[11,12,15],"callout",{"color":13,"icon":14},"primary","i-ph-flag-checkered",[16,17,18,19,23,24,27,28,31,32,35],"p",{},"Here's the split that makes the sub-4 marathon strange: at the ",[20,21,22],"strong",{},"Berlin Marathon",", across 873,334 finishers from 1999 to 2025, the men's ",[20,25,26],{},"median"," finish time is ",[20,29,30],{},"3:57:46"," — meaning more than half the male field already runs sub-4 without it being anyone's stretch goal. The women's median is ",[20,33,34],{},"4:26:02",". For men, four hours is basically the middle of the pack. For women, it's a genuinely fast, top-quartile time. Same barrier, two very different climbs — and neither one is really about talent.",[16,37,38,39,44,45,49],{},"That asymmetry is the tell. ",[40,41,43],"a",{"href":42},"\u002Fblog\u002Fsub-3-marathon","Sub-3"," is a sub-elite physiological standard — VO",[46,47,48],"sub",{},"2","max, lactate threshold, and running economy all have to clear a real bar on the same day. Sub-4 is a different kind of problem. The research keeps landing on the same answer: the single best predictor of whether you break four hours isn't your engine size, your threshold, or your economy — it's how many easy kilometers you've banked in the months before race day. This is a research-backed answer for what that actually looks like, with every claim rated for how solid the evidence is.",[51,52,54],"h2",{"id":53},"why-volume-beats-talent-at-this-pace","Why volume beats talent at this pace",[16,56,57],{},"The case for volume-as-destiny doesn't rest on one study — three independent lines of evidence converge on it.",[59,60,61,89,117],"card-group",{},[62,63,66],"card",{"icon":64,"title":65},"i-ph-chart-line-up","The training-only model",[16,67,68,74,75,78,79,82,83,88],{},[40,69,73],{"href":70,"rel":71},"https:\u002F\u002Fdoi.org\u002F10.4100\u002Fjhse.2011.63.05",[72],"nofollow","Giovanni Tanda's model"," predicts marathon finish time from ",[20,76,77],{},"8 weeks of training data alone — no race required",": Pace = 17.1 + 140.0·exp(−0.0053·weekly km) + 0.55·average training pace. Built from 22 runners across 46 marathons, weekly mileage and training pace together explained ",[20,80,81],{},"~77% of the variance"," in finish time, independent of body composition. It's held up well enough that ",[40,84,87],{"href":85,"rel":86},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F24379719\u002F",[72],"a companion analysis"," later confirmed body-fat percentage only matters as a secondary factor once volume and pace are accounted for.",[62,90,93],{"icon":91,"title":92},"i-ph-database","The 119,452-runner reality check",[16,94,95,100,101,104,105,108,109,112,113,116],{},[40,96,99],{"href":97,"rel":98},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F39616560\u002F",[72],"Muniz-Pumares et al. (2025)"," analyzed 16 weeks of Strava training data preceding ",[20,102,103],{},"151,813 marathons",". Average volume across the whole population was ",[20,106,107],{},"45.1 ± 26.4 km\u002Fweek"," — but runners finishing 2:00-2:30 averaged ",[20,110,111],{},"~107 km\u002Fweek",", while runners finishing over 4:00 averaged just ",[20,114,115],{},"~35 km\u002Fweek",". The gap wasn't harder workouts. It was mostly easy-paced mileage, and the fastest runners built it primarily by adding more Zone 1 running, not more intensity.",[62,118,121],{"icon":119,"title":120},"i-ph-ruler","The Dutch cohort's hard numbers",[16,122,123,128,129,132,133,136,137,140,141,144],{},[40,124,127],{"href":125,"rel":126},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F32421886\u002F",[72],"Fokkema et al. (2020)"," tracked 441 marathon and 556 half-marathon runners and found volume ",[20,130,131],{},"under 40 km\u002Fweek predicted a slower finish"," (β 6.33), ",[20,134,135],{},"over 65 km\u002Fweek predicted a faster one"," (β −14.09), and a longest run ",[20,138,139],{},"under 25 km predicted a slower finish"," (β 13.44) — all independent of each other. Just as notably: higher volume was ",[20,142,143],{},"not"," associated with more injuries in this cohort.",[16,146,147],{},"The volume story holds at every scale researchers have checked it — a 22-runner physiological model, a 997-runner Dutch cohort, and a 119,452-runner Strava dataset all point the same direction.",[51,149,151],{"id":150},"what-does-a-recent-race-actually-tell-you","What does a recent race actually tell you?",[153,154,155,168],"table",{},[156,157,158],"thead",{},[159,160,161,165],"tr",{},[162,163,164],"th",{},"Distance",[162,166,167],{},"Approx. equivalent for sub-4 (3:59:xx)",[169,170,171,180,188,196],"tbody",{},[159,172,173,177],{},[174,175,176],"td",{},"Marathon pace",[174,178,179],{},"5:41\u002Fkm (9:09\u002Fmile)",[159,181,182,185],{},[174,183,184],{},"Half marathon",[174,186,187],{},"~1:52-1:55",[159,189,190,193],{},[174,191,192],{},"10K",[174,194,195],{},"~50-52 min",[159,197,198,201],{},[174,199,200],{},"VDOT",[174,202,203],{},"~40-42",[16,205,206,207,212,213,216,217,222,223,226,227,230],{},"Those numbers are a floor, not a target. ",[40,208,211],{"href":209,"rel":210},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F7235349\u002F",[72],"Riegel's classic exponent"," (1.06) and VDOT tables are accurate to within 2-5% between adjacent distances, but they systematically ",[20,214,215],{},"over-predict"," marathon capability, because they don't model glycogen depletion, pacing inexperience, or the \"wall.\" ",[40,218,221],{"href":219,"rel":220},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC5000509\u002F",[72],"Vickers & Vertosick's analysis"," of roughly 2.5 million NYC Marathon finishes found the real population-average half-to-marathon multiplier is closer to ",[20,224,225],{},"2.14",", not Riegel's 2.11 — and traditional formulas are only about 80% accurate, meaning roughly 1 in 5 runners miss their predicted time by a meaningful margin. Build in a ",[20,228,229],{},"5-10% buffer"," if you're a first-timer, more if your longest run sits under 25-30 km.",[232,233,235],"tip",{"icon":234},"i-ph-calculator",[16,236,237,238,242,243,247],{},"Plug a recent result into the ",[40,239,241],{"href":240},"\u002Ftools\u002Frace-time-predictor","Race Time Predictor"," for your own equivalents, and use the ",[40,244,246],{"href":245},"\u002Fcheatsheets\u002Frace-time-prediction","Race Time Prediction cheatsheet"," for the one-screen version of how these formulas actually work.",[51,249,251],{"id":250},"the-vdot-40-trap","The VDOT-40 trap",[253,254,256],"note",{"icon":255},"i-ph-eye-slash",[16,257,258,259,262,263,266,267,271,272,277,278,280,281,286,287,292,293,298,299,301,302,305],{},"Here's the detail that explains why so many fit runners still miss sub-4: at ",[20,260,261],{},"VDOT 40, your lactate-threshold pace is already ~5:41\u002Fkm"," — the exact pace a sub-4 marathon demands, for 42.2 km, not a 20-minute tempo. A raw VDOT-40 runner is essentially being asked to race a marathon at threshold effort, which isn't sustainable. That's why the safer readiness band is ",[20,264,265],{},"VDOT 42-44"," (10K ~48-50 min, half ~1:48-1:52), where marathon pace sits comfortably ",[268,269,270],"em",{},"below"," threshold rather than right on top of it. Fitness on paper and fitness that survives 42 km aren't the same thing — which is exactly why the ",[40,273,276],{"href":274,"rel":275},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F2022559\u002F",[72],"big-three physiology model"," (VO",[46,279,48],{},"max × threshold utilization × running economy — ",[40,282,285],{"href":283,"rel":284},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F3732253\u002F",[72],"72.1% of marathon variance",") now has a fourth term. ",[40,288,291],{"href":289,"rel":290},"https:\u002F\u002Fphysoc.onlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1113\u002FJP284205",[72],"Andrew Jones's 2024 \"fourth dimension\" paper"," and a ",[40,294,297],{"href":295,"rel":296},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC12082016\u002F",[72],"2025 study"," running well-trained marathoners for 90 and 120 minutes both found VO",[46,300,48],{},"max, threshold utilization, and running economy all ",[20,303,304],{},"deteriorate"," as the race goes long — \"durability\" now sits alongside the classic three, and it's exactly why short-race predictors flatter you.",[51,307,309],{"id":308},"the-training-that-gets-you-there","The training that gets you there",[16,311,312,313,317,318,323,324,327],{},"No single branded plan — ",[40,314,316],{"href":315},"\u002Fblog\u002Ftraining-paces","Daniels",", Pfitzinger, Hansons, Higdon — has been crowned by a head-to-head trial. A ",[40,319,322],{"href":320,"rel":321},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC11065819\u002F",[72],"quantitative analysis of 92 sub-elite 12-week plans"," found peak weekly volumes cluster at 108 km (high-volume tier), 59 km (middle tier), and 43 km (low tier) — sub-4 runners belong in that middle band, building toward ",[20,325,326],{},"55-70 km\u002Fweek"," at peak.",[16,329,330,331,334,335,340,341,346,347,350,351],{},"On intensity distribution, the field mostly agrees on ",[20,332,333],{},"~80% easy, ~20% at or above threshold"," (",[40,336,339],{"href":337,"rel":338},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC3912323\u002F",[72],"Stöggl & Sperlich, 2014","). But it's worth knowing the honest counter-evidence: ",[40,342,345],{"href":343,"rel":344},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F33344993\u002F",[72],"Festa et al. (2020)"," put 38 recreational runners through 8 weeks of either an 77\u002F3\u002F20 polarized split or a 40\u002F50\u002F10 \"focused endurance\" split matched for total load, and found ",[20,348,349],{},"statistically similar gains"," — with the focused-endurance group saving 17% of training time. For a time-limited recreational runner, the takeaway isn't \"hit 80\u002F20 exactly.\" It's simpler: ",[20,352,353],{},"run easy often, run enough of it, and don't stress the precise ratio.",[16,355,356,359],{},[20,357,358],{},"Sample sessions",", anchored to VDOT ~42 paces:",[153,361,362,375],{},[156,363,364],{},[159,365,366,369,372],{},[162,367,368],{},"Session type",[162,370,371],{},"Pace",[162,373,374],{},"Example",[169,376,377,388,399,410,420],{},[159,378,379,382,385],{},[174,380,381],{},"Easy",[174,383,384],{},"~6:45-7:15\u002Fkm",[174,386,387],{},"The bulk of weekly volume, conversational",[159,389,390,393,396],{},[174,391,392],{},"Marathon-pace long run",[174,394,395],{},"5:41\u002Fkm",[174,397,398],{},"Progress from 24 km toward 32-35 km; every 2-3 weeks, finish 12-16 km at goal pace",[159,400,401,404,407],{},[174,402,403],{},"Threshold",[174,405,406],{},"~5:20-5:35\u002Fkm",[174,408,409],{},"2×20 min with 3 min jog, or 5×2 km cruise intervals",[159,411,412,415,417],{},[174,413,414],{},"Marathon-specific",[174,416,395],{},[174,418,419],{},"16-20 km within a medium-long run, or 3×5 km at MP with 1 km float",[159,421,422,428,431],{},[174,423,424,425,427],{},"VO",[46,426,48],{},"max (sharpening)",[174,429,430],{},"~4:50-5:05\u002Fkm",[174,432,433],{},"5-6×1000 m with equal jog recovery",[16,435,436],{},"A peak week (~60 km) runs 5-6 days: one threshold session, one marathon-pace or medium-long run, easy days between, long run on the weekend.",[16,438,439,440,443,444,449,450,453],{},"On progression: the popular ",[20,441,442],{},"10% rule has no RCT support",". The ",[40,445,448],{"href":446,"rel":447},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F17940147\u002F",[72],"GRONORUN trial"," put 532 novice runners through either an 8-week standard program or a 13-week graded program built explicitly on the 10% rule, and found ",[20,451,452],{},"no reduction in injuries"," (HR 0.8, 95% CI 0.6-1.3). Treat it as a conservative heuristic for avoiding sudden spikes, not an evidence-based safety net — build volume gradually with a deload week roughly every 3-4 weeks (cut ~20-30%).",[51,455,457],{"id":456},"taper-strength-and-fuel","Taper, strength, and fuel",[59,459,460,483,521],{},[62,461,464],{"icon":462,"title":463},"i-ph-timer","The taper",[16,465,466,467,472,473,476,477,482],{},"A ",[40,468,471],{"href":469,"rel":470},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F17762369\u002F",[72],"27-study meta-analysis"," found the optimal taper is ",[20,474,475],{},"2 weeks, cutting volume 41-60% exponentially while holding intensity and frequency steady"," — worth roughly a 2-3% performance gain. A ",[40,478,481],{"href":479,"rel":480},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC10171681\u002F",[72],"follow-up meta-analysis"," of 14 studies confirmed it, with the largest effects at 8-14 days. A 5-day taper is not enough; don't cut intensity, only volume.",[62,484,487],{"icon":485,"title":486},"i-ph-barbell","Strength training",[16,488,489,494,495,500,501,506,507,512,513,516,517,520],{},[40,490,493],{"href":491,"rel":492},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F27249636\u002F",[72],"Balsalobre-Fernández's meta-analysis"," and ",[40,496,499],{"href":497,"rel":498},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F29249083\u002F",[72],"Blagrove's systematic review"," both confirm real economy gains from heavy lower-body work; a ",[40,502,505],{"href":503,"rel":504},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F38165636\u002F",[72],"2024 update"," found combined heavy-load-plus-plyometric methods work best, with plyometrics especially useful below 12 km\u002Fh — squarely in sub-4 territory. A ",[40,508,511],{"href":509,"rel":510},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F40016936\u002F",[72],"2025 RCT"," found 10 weeks of strength work also improved economy ",[268,514,515],{},"durability"," across 90 minutes of running — directly relevant to how you feel at kilometer 35. Aim for ",[20,518,519],{},"2×\u002Fweek",", ~40-70% 1RM plus plyometrics.",[62,522,525],{"icon":523,"title":524},"i-ph-drop","Fueling",[16,526,527,528,531,532,537,538,541,542,545,546,551],{},"Consensus is ",[20,529,530],{},"60-90 g\u002Fhour"," of carbohydrate for events over 2.5 hours. A ",[40,533,536],{"href":534,"rel":535},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F40089940\u002F",[72],"2025 study of 160 Seville Marathon finishers"," found runners who hit that range were significantly more likely to finish under 3 hours (p=0.035) — yet actual mean intake was just 35 ± 17 g\u002Fh. Most runners badly under-fuel; target ",[20,539,540],{},"60-80 g\u002Fh"," for a sub-4 pace. Carb-load at ",[20,543,544],{},"8-12 g\u002Fkg\u002Fday for 36-48 hours"," beforehand — a ",[40,547,550],{"href":548,"rel":549},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F12111292\u002F",[72],"classic protocol study"," found a single day at ~10 g\u002Fkg plus rest raised muscle glycogen from 95 to 180 mmol\u002Fkg wet weight and held it there. No depletion phase needed.",[51,553,555],{"id":554},"the-injury-trap-watch-the-signal-not-the-slogan","The injury trap: watch the signal, not the slogan",[16,557,466,558,563,564,567],{},[40,559,562],{"href":560,"rel":561},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC9528699\u002F",[72],"systematic review of 23,047 runners across 36 studies"," puts overall running-injury incidence at ",[20,565,566],{},"26.2%"," — 14.9% in novices, 26.1% in recreational runners, a striking 62.6% in competitive runners. Prospective studies flag weekly distances above roughly 30 km, 64 km, or 60-70 km as raising risk, which is an uncomfortable fact: the same volume that gets you to sub-4 also raises your exposure, so it has to be built patiently, not accumulated in a rush.",[569,570,572],"caution",{"icon":571},"i-ph-warning-octagon",[16,573,574,575,578,579,583,584,589,590,595,596,601],{},"Two popular interventions that ",[20,576,577],{},"failed in RCTs",": the ",[40,580,582],{"href":446,"rel":581},[72],"10% rule"," (no injury reduction), and a ",[40,585,588],{"href":586,"rel":587},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F30954948\u002F",[72],"multifactorial online injury-prevention program tested on 2,378 runners",", which showed identical injury rates in the intervention and control groups (37.5% vs 36.7%, OR 1.08). What did work: ",[40,591,594],{"href":592,"rel":593},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F24100287\u002F",[72],"strength training",", reducing acute injuries by 35% and overuse injuries by nearly half (RR 0.527, 95% CI 0.373-0.746) across 26,610 people. Smaller but real: ",[40,597,600],{"href":598,"rel":599},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F20581720\u002F",[72],"increasing cadence 5-10%"," cut knee-joint energy absorption 20-34% in a controlled biomechanics study.",[16,603,604],{},"Practical translation: manage total load, prioritize consistency over heroic long-run weekends, address old injury sites specifically, lift twice a week, and respect pain rather than run through it.",[51,606,608],{"id":607},"how-long-will-this-actually-take-you","How long will this actually take you?",[16,610,611],{},"Large single-race datasets put the overall average marathon finish somewhere between 4:20 and 4:45. Sub-4 sits faster than that average for both sexes and roughly in the top third of a big-city field — even while, per the Berlin numbers above, it's below the male median.",[153,613,614,627],{},[156,615,616],{},[159,617,618,621,624],{},[162,619,620],{},"Starting point",[162,622,623],{},"Realistic timeline",[162,625,626],{},"Why",[169,628,629,644,655,666],{},[159,630,631,634,637],{},[174,632,633],{},"Complete beginner (couch)",[174,635,636],{},"~18-24 months",[174,638,639,640,643],{},"Finish one marathon first (typical first-timer: 4:30-5:30), build a durable base, ",[268,641,642],{},"then"," target sub-4 in a later cycle",[159,645,646,649,652],{},[174,647,648],{},"Base fitness, no marathon yet (half ~1:50-1:55)",[174,650,651],{},"16-20 weeks, sometimes two cycles",[174,653,654],{},"Often sub-4-ready off a single well-structured build",[159,656,657,660,663],{},[174,658,659],{},"Already finishing 4:05-4:25",[174,661,662],{},"Single 16-20-week cycle",[174,664,665],{},"The highest-probability group — add volume to ~55-65 km\u002Fweek plus marathon-pace work",[159,667,668,671,674],{},[174,669,670],{},"Masters (50+)",[174,672,673],{},"Achievable, longer build-up",[174,675,676],{},"Recreational performance declines meaningfully from ~50; needs more recovery, not less ambition",[253,678,680],{"icon":679},"i-ph-hourglass",[16,681,682,683,688,689,692,693,696,697,700,701,706,707,710],{},"Age is a real headwind but not a wall. ",[40,684,687],{"href":685,"rel":686},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F28187185\u002F",[72],"Zavorsky, Tomko & Smoliga (2017)"," analyzed NYC, Boston, and Chicago age-group winners from 2001-2016 and found peak performance at ",[20,690,691],{},"28.3 years (men) and 30.8 years (women)"," — but also that men's age-group decline runs a gentler ",[20,694,695],{},"~2:06\u002Fyear"," after 35, versus ",[20,698,699],{},"~2:33\u002Fyear"," for women. The same ",[40,702,705],{"href":703,"rel":704},"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-56334-7",[72],"873,334-runner Berlin dataset"," that gave us the median split above also found men are about ",[20,708,709],{},"twice as likely to \"hit the wall\""," — a ≥20% second-half slowdown — which matters more to a sub-4 attempt than most people assume.",[51,712,714],{"id":713},"tracking-readiness-over-the-cycle","Tracking readiness over the cycle",[16,716,717,720],{},[20,718,719],{},"Field tests."," Retest a 5K\u002F10K or threshold benchmark every 4-8 weeks and recompute VDOT or the Tanda prediction — it should climb 1-3 points per cycle if training is working. Treat any short-race prediction as an upper bound, not a guarantee: a controlled 16-20 km run at 5:41\u002Fkm is a better readiness signal than any 10K calculator.",[16,722,723,726,727,730],{},[20,724,725],{},"Training-load models."," CTL\u002FATL\u002FTSB — rooted in Banister's fitness-fatigue model — are useful trend tools: build CTL ~3-5 points\u002Fweek, and aim to arrive at the start line with ",[20,728,729],{},"TSB around +15 to +25",". Treat the exact thresholds as platform convention, not physiology; they ignore sleep, nutrition, and life stress.",[16,732,733,736,737,742,743,746,747,334,752,757,758,763,764,766,767,494,772,777,778,781,782,785],{},[20,734,735],{},"HRV-guided training"," has real evidence behind it. ",[40,738,741],{"href":739,"rel":740},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F26909534\u002F",[72],"Vesterinen et al. (2016)"," found an HRV-guided group improved 3000 m speed significantly (+2.1%, p=0.004) while a matched predefined-training group's gain (+1.1%) fell short of significance — and the HRV group did it with ",[268,744,745],{},"fewer"," hard sessions. Cyclist trials by ",[40,748,751],{"href":749,"rel":750},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F29809080\u002F",[72],"Javaloyes et al.",[40,753,756],{"href":754,"rel":755},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F31490431\u002F",[72],"and a follow-up",") found similar advantages in peak power and time-trial performance. Meta-analyses are more measured: ",[40,759,762],{"href":760,"rel":761},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC7663087\u002F",[72],"Granero-Gallegos et al."," found a small but real VO",[46,765,48],{},"max edge, while ",[40,768,771],{"href":769,"rel":770},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F34639599\u002F",[72],"Manresa-Rocamora et al.",[40,773,776],{"href":774,"rel":775},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F34489178\u002F",[72],"Düking et al."," found the ",[268,779,780],{},"performance"," advantage often falls short of significance — but HRV-guided training reliably produces ",[20,783,784],{},"fewer negative responders",". Net read: at least equivalent, probably more efficient.",[232,787,789],{"icon":788},"i-ph-trend-down",[16,790,791,795,796,800,801,805],{},[40,792,794],{"href":793},"\u002Fblog\u002Fhow-to-increase-hrv","Learn to increase your HRV"," and set your zones with the ",[40,797,799],{"href":798},"\u002Ftools\u002Fheart-rate-zone-calculator","Heart Rate Zone Calculator",", then let the ",[40,802,804],{"href":803},"\u002F","Runima app"," surface the trend that matters — pace at a given effort, improving month over month.",[16,807,808,811,812,817,818,827],{},[20,809,810],{},"Overtraining warning signs",", per the ",[40,813,816],{"href":814,"rel":815},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F23247672\u002F",[72],"ECSS\u002FACSM consensus statement",": resting heart rate elevated more than 5 bpm above baseline, a suppressed or declining HRV trend, loss of motivation (one of the most reliable signals), unusually prolonged soreness beyond 72 hours, and performance stagnating at normal effort. Counterintuitively, ",[40,819,822,823,826],{"href":820,"rel":821},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F23195630\u002F",[72],"functional overreaching has been linked to ",[268,824,825],{},"faster"," heart-rate recovery",", not slower — a reminder that these signals need to be read as a pattern, not a single number.",[51,829,831],{"id":830},"the-build-stage-by-stage","The build, stage by stage",[16,833,834,837],{},[20,835,836],{},"Stage 0 — Confirm readiness."," A recent half marathon at 1:52-1:55 or better, or a Tanda-model prediction under 4:00 from current training. You should be able to complete a 28+ km long run and hold 5:41\u002Fkm for 2×20 minutes without it feeling desperate. If you can't yet, spend a block building base first rather than forcing the cycle.",[16,839,840,843],{},[20,841,842],{},"Stage 1 — Base (6-8 weeks)."," Build easy volume toward 45-55 km\u002Fweek at ~6:45-7:15\u002Fkm, roughly 80\u002F20 distribution, long run progressing to 26-28 km, strength training twice a week.",[16,845,846,849],{},[20,847,848],{},"Stage 2 — Build (6-8 weeks)."," Peak at 55-70 km\u002Fweek. One threshold session weekly, one marathon-pace session progressing to 16-20 km at 5:41\u002Fkm, long run to 32-35 km. Start practicing race fueling (60-80 g carbs\u002Fh) on the long runs — don't save it for race day.",[16,851,852,855],{},[20,853,854],{},"Stage 3 — Peak and taper (final 2-3 weeks)."," A short burst of sharpening (5×1000 m at ~5:00\u002Fkm), then the 2-week, 41-60% exponential taper. Carb-load 8-12 g\u002Fkg\u002Fday for the final 36-48 hours. Target TSB around +15 to +25.",[857,858,860],"warning",{"icon":859},"i-ph-warning",[16,861,862,863,867],{},"Start at or just slightly slower than 5:41\u002Fkm — even or slightly negative splits, not positive ones. Men especially should guard against that ~20% wall slowdown the Berlin data flags. Fuel early, before you feel like you need it; by the time hunger or fatigue shows up, you're already behind on glycogen. Rehearse the split plan with the ",[40,864,866],{"href":865},"\u002Ftools\u002Frace-strategy-calculator","Race Strategy Calculator"," before race week, not during it.",[51,869,871],{"id":870},"how-solid-is-each-claim-really","How solid is each claim, really?",[153,873,874,887],{},[156,875,876],{},[159,877,878,881,884],{},[162,879,880],{},"Topic",[162,882,883],{},"Evidence quality",[162,885,886],{},"Notes",[169,888,889,902,915,928,940,957,973,986,999,1012,1024],{},[159,890,891,894,899],{},[174,892,893],{},"Volume → performance",[174,895,896],{},[20,897,898],{},"Strong",[174,900,901],{},"Converges across a physiological model, a 997-runner cohort, and a 119,452-runner dataset",[159,903,904,907,912],{},[174,905,906],{},"Taper protocol",[174,908,909,911],{},[20,910,898],{}," (two meta-analyses)",[174,913,914],{},"2 weeks, 41-60% volume cut, hold intensity",[159,916,917,920,925],{},[174,918,919],{},"Strength training → economy & injury",[174,921,922],{},[20,923,924],{},"Strong-moderate",[174,926,927],{},"Economy gains are consistent; injury reduction is one of the best-evidenced interventions available",[159,929,930,933,937],{},[174,931,932],{},"Carbohydrate strategy",[174,934,935],{},[20,936,898],{},[174,938,939],{},"Well-established sports-nutrition science",[159,941,942,945,954],{},[174,943,944],{},"Race-time prediction (Riegel\u002FVDOT)",[174,946,947,949,950,953],{},[20,948,898],{}," for method, ",[20,951,952],{},"moderate"," for marathon accuracy",[174,955,956],{},"Systematically over-predicts marathon time; ~1 in 5 miss significantly",[159,958,959,962,970],{},[174,960,961],{},"Big-three physiology + durability",[174,963,964,966,967],{},[20,965,898],{},", durability construct ",[20,968,969],{},"newer",[174,971,972],{},"2023-2025 literature on fatigue-related deterioration is still developing",[159,974,975,978,983],{},[174,976,977],{},"10% rule \u002F online injury programs",[174,979,980],{},[20,981,982],{},"Debunked in RCTs",[174,984,985],{},"Popular but not supported; strength training outperforms both",[159,987,988,991,996],{},[174,989,990],{},"Intensity distribution optimum",[174,992,993],{},[20,994,995],{},"Moderate, contested",[174,997,998],{},"\"Mostly easy\" is solid; the exact 80\u002F20 split is not load-bearing for recreational runners",[159,1000,1001,1004,1009],{},[174,1002,1003],{},"Timeline by starting point",[174,1005,1006],{},[20,1007,1008],{},"Weak \u002F indirect",[174,1010,1011],{},"No direct cohort tracks time-to-sub-4; synthesized from adaptation science and coaching consensus",[159,1013,1014,1016,1021],{},[174,1015,735],{},[174,1017,1018],{},[20,1019,1020],{},"Moderate",[174,1022,1023],{},"Reliable for trends and reducing negative responders; performance edge is small and inconsistent",[159,1025,1026,1029,1034],{},[174,1027,1028],{},"Age & sex differences",[174,1030,1031,1033],{},[20,1032,898],{}," (large datasets)",[174,1035,1036],{},"Berlin and NYC\u002FBoston\u002FChicago cohorts are about as close to population-level truth as this field gets",[51,1038,1040],{"id":1039},"the-takeaway","The takeaway",[16,1042,1043],{},"Sub-3 asks three separate physiological systems to peak on the same day. Sub-4 asks something more mundane and, in a way, more democratic: show up consistently enough, for long enough, at mostly-easy paces, and the fitness accumulates on schedule. The data backs this up almost embarrassingly well — a formula built on nothing but your training log predicts your finish time about as well as a lab test would. That's not a knock on the goal. It means sub-4 is one of the most controllable big outcomes in distance running, and the runners who miss it are usually the ones who tried to out-clever the mileage rather than just log it.",[11,1045,1048],{"color":1046,"icon":1047},"neutral","i-ph-link",[16,1049,1050,1051,1054,1055,1061,1062,1066,1067,1071,1072,1066,1075,1078],{},"Once four hours is behind you, ",[40,1052,1053],{"href":42},"sub-3"," is where the physiology trilogy — ",[40,1056,424,1058,1060],{"href":1057},"\u002Fblog\u002Fthe-vo2max-trap",[46,1059,48],{},"max",", ",[40,1063,1065],{"href":1064},"\u002Fblog\u002Flactate-threshold","lactate threshold",", and ",[40,1068,1070],{"href":1069},"\u002Fblog\u002Frunning-economy","running economy"," — starts to matter far more than volume alone. For now: build the base, ",[40,1073,1074],{"href":315},"nail your training paces",[40,1076,1077],{"href":803},"track the trend"," that tells you whether this cycle is actually working.",[1080,1081],"hr",{},[51,1083,1085],{"id":1084},"references","References",[1087,1088,1089,1101,1111,1122,1133,1143,1153,1163,1173,1183,1193,1203,1213,1224,1234,1244,1254,1264,1274,1284,1294,1304,1314,1324,1334,1344,1354,1364,1374,1384,1394,1404,1414,1424,1434,1444,1454,1464,1474],"ol",{},[1090,1091,1092,1096,1097,1100],"li",{},[40,1093,1095],{"href":274,"rel":1094},[72],"Joyner MJ (1991)",". ",[268,1098,1099],{},"Modeling: optimal marathon performance on the basis of physiological factors."," J Appl Physiol. 70(2):683-687.",[1090,1102,1103,1096,1107,1110],{},[40,1104,1106],{"href":283,"rel":1105},[72],"di Prampero PE et al. (1986)",[268,1108,1109],{},"The energetics of endurance running."," Eur J Appl Physiol. 55(3):259-266.",[1090,1112,1113,1096,1118,1121],{},[40,1114,1117],{"href":1115,"rel":1116},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC2375555\u002F",[72],"Joyner MJ, Coyle EF (2008)",[268,1119,1120],{},"Endurance exercise performance: the physiology of champions."," J Physiol. 586(1):35-44.",[1090,1123,1124,1096,1129,1132],{},[40,1125,1128],{"href":1126,"rel":1127},"https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.2165\u002F00007256-200737040-00009",[72],"Coyle EF (2007)",[268,1130,1131],{},"Physiological regulation of marathon performance."," Sports Med. 37(4-5):306-311.",[1090,1134,1135,1096,1139,1142],{},[40,1136,1138],{"href":289,"rel":1137},[72],"Jones AM (2024)",[268,1140,1141],{},"The fourth dimension: physiological resilience as an independent determinant of endurance exercise performance."," J Physiol. 602(17):4113-4128.",[1090,1144,1145,1096,1149,1152],{},[40,1146,1148],{"href":295,"rel":1147},[72],"Zanini M, Folland JP, Blagrove RC (2025)",[268,1150,1151],{},"The Effect of 90 and 120 Min of Running on the Determinants of Endurance Performance in Well-Trained Male Marathon Runners."," Scand J Med Sci Sports. 35:e70076.",[1090,1154,1155,1096,1159,1162],{},[40,1156,1158],{"href":209,"rel":1157},[72],"Riegel PS (1981)",[268,1160,1161],{},"Athletic records and human endurance."," American Scientist. 69(3):285-290.",[1090,1164,1165,1096,1169,1172],{},[40,1166,1168],{"href":219,"rel":1167},[72],"Vickers AJ, Vertosick EA (2016)",[268,1170,1171],{},"An empirical study of race times in recreational endurance runners."," BMC Sports Sci Med Rehabil. 8:26.",[1090,1174,1175,1096,1179,1182],{},[40,1176,1178],{"href":70,"rel":1177},[72],"Tanda G (2011)",[268,1180,1181],{},"Prediction of marathon performance time on the basis of training indices."," J Hum Sport Exerc. 6(3):511-520.",[1090,1184,1185,1096,1189,1192],{},[40,1186,1188],{"href":85,"rel":1187},[72],"Tanda G, Knechtle B (2013)",[268,1190,1191],{},"Marathon performance in relation to body fat percentage and training indices in recreational male runners."," Open Access J Sports Med. 4:141-149.",[1090,1194,1195,1096,1199,1202],{},[40,1196,1198],{"href":97,"rel":1197},[72],"Muniz-Pumares D, Hunter B, Meyler S, Maunder ED (2025)",[268,1200,1201],{},"The Training Intensity Distribution of Marathon Runners Across Performance Levels."," Sports Med. 55(4):1023-1035.",[1090,1204,1205,1096,1209,1212],{},[40,1206,1208],{"href":125,"rel":1207},[72],"Fokkema T et al. (2020)",[268,1210,1211],{},"Training for a (half-)marathon: Training volume and longest endurance run related to performance and running injuries."," Scand J Med Sci Sports. 30(9):1692-1704.",[1090,1214,1215,1096,1220,1223],{},[40,1216,1219],{"href":1217,"rel":1218},"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F40913707\u002F",[72],"DeJong Lempke AF et al. (2025)",[268,1221,1222],{},"Training Volume and Training Frequency Changes Associated with Boston Marathon Race Performance."," Sports Med.",[1090,1225,1226,1096,1230,1233],{},[40,1227,1229],{"href":320,"rel":1228},[72],"Knopp M et al. (2024)",[268,1231,1232],{},"Quantitative Analysis of 92 12-Week Sub-elite Marathon Training Plans."," Sports Med Open. 10:50.",[1090,1235,1236,1096,1240,1243],{},[40,1237,1239],{"href":337,"rel":1238},[72],"Stöggl T, Sperlich B (2014)",[268,1241,1242],{},"Polarized training has greater impact on key endurance variables than threshold, high intensity, or high volume training."," Front Physiol. 5:33.",[1090,1245,1246,1096,1250,1253],{},[40,1247,1249],{"href":343,"rel":1248},[72],"Festa L et al. (2020)",[268,1251,1252],{},"Effects of Different Training Intensity Distribution in Recreational Runners."," Front Sports Act Living. 2:70.",[1090,1255,1256,1096,1260,1263],{},[40,1257,1259],{"href":469,"rel":1258},[72],"Bosquet L et al. (2007)",[268,1261,1262],{},"Effects of tapering on performance: a meta-analysis."," Med Sci Sports Exerc. 39(8):1358-1365.",[1090,1265,1266,1096,1270,1273],{},[40,1267,1269],{"href":479,"rel":1268},[72],"Wang Z et al. (2023)",[268,1271,1272],{},"Effects of tapering on performance in endurance athletes: a systematic review and meta-analysis."," PLoS ONE. 18(5):e0282838.",[1090,1275,1276,1096,1280,1283],{},[40,1277,1279],{"href":491,"rel":1278},[72],"Balsalobre-Fernández C, Santos-Concejero J, Grivas GV (2016)",[268,1281,1282],{},"Effects of strength training on running economy in highly trained runners: a meta-analysis."," J Strength Cond Res. 30(8):2361-2368.",[1090,1285,1286,1096,1290,1293],{},[40,1287,1289],{"href":497,"rel":1288},[72],"Blagrove RC, Howatson G, Hayes PR (2018)",[268,1291,1292],{},"Effects of strength training on the physiological determinants of middle- and long-distance running performance: a systematic review."," Sports Med. 48(5):1117-1149.",[1090,1295,1296,1096,1300,1303],{},[40,1297,1299],{"href":503,"rel":1298},[72],"Llanos-Lagos C et al. (2024)",[268,1301,1302],{},"The effect of strength training methods on middle- and long-distance running economy: a systematic review with meta-analysis."," Sports Med. 54(4):895-932.",[1090,1305,1306,1096,1310,1313],{},[40,1307,1309],{"href":509,"rel":1308},[72],"Zanini M, Folland JP, Wu C, Blagrove RC (2025)",[268,1311,1312],{},"Strength Training Improves Running Economy Durability and Fatigued High-Intensity Performance in Well-Trained Male Runners: A Randomized Control Trial."," Med Sci Sports Exerc.",[1090,1315,1316,1096,1320,1323],{},[40,1317,1319],{"href":534,"rel":1318},[72],"Jiménez-Alfageme R et al. (2025)",[268,1321,1322],{},"Nutritional Intake and Timing of Marathon Runners: Influence of Athlete's Characteristics and Fueling Practices on Finishing Time."," Sports Med Open. 11:26.",[1090,1325,1326,1096,1330,1333],{},[40,1327,1329],{"href":548,"rel":1328},[72],"Bussau VA, Fairchild TJ, Rao A, Steele P, Fournier PA (2002)",[268,1331,1332],{},"Carbohydrate loading in human muscle: an improved 1 day protocol."," Eur J Appl Physiol. 87(3):290-295.",[1090,1335,1336,1096,1340,1343],{},[40,1337,1339],{"href":560,"rel":1338},[72],"Fredette A, Roy JS, Perreault K, Dupuis F, Napier C, Esculier JF (2022)",[268,1341,1342],{},"The Association Between Running Injuries and Training Parameters: A Systematic Review."," J Athl Train.",[1090,1345,1346,1096,1350,1353],{},[40,1347,1349],{"href":446,"rel":1348},[72],"Buist I et al. (2008)",[268,1351,1352],{},"No effect of a graded training program on the number of running-related injuries in novice runners: a randomized controlled trial."," Am J Sports Med. 36(1):33-39.",[1090,1355,1356,1096,1360,1363],{},[40,1357,1359],{"href":586,"rel":1358},[72],"Fokkema T et al. (2019)",[268,1361,1362],{},"Online multifactorial prevention programme has no effect on the number of running-related injuries: a randomised controlled trial."," Br J Sports Med. 53(23):1479.",[1090,1365,1366,1096,1370,1373],{},[40,1367,1369],{"href":592,"rel":1368},[72],"Lauersen JB, Bertelsen DM, Andersen LB (2014)",[268,1371,1372],{},"The effectiveness of exercise interventions to prevent sports injuries: a systematic review and meta-analysis of randomised controlled trials."," Br J Sports Med. 48(11):871-877.",[1090,1375,1376,1096,1380,1383],{},[40,1377,1379],{"href":598,"rel":1378},[72],"Heiderscheit BC et al. (2011)",[268,1381,1382],{},"Effects of step rate manipulation on joint mechanics during running."," Med Sci Sports Exerc. 43(2):296-302.",[1090,1385,1386,1096,1390,1393],{},[40,1387,1389],{"href":703,"rel":1388},[72],"Seffrin A, Villiger E, Andrade MS et al. (2026)",[268,1391,1392],{},"Sex differences in marathon pacing: analysis of 873,000 Berlin marathon runners reveals men are twice as likely to \"hit the wall.\""," Sci Rep. 16:19529.",[1090,1395,1396,1096,1400,1403],{},[40,1397,1399],{"href":685,"rel":1398},[72],"Zavorsky GS, Tomko KA, Smoliga JM (2017)",[268,1401,1402],{},"Declines in marathon performance: sex differences in elite and recreational athletes."," PLoS ONE. 12(2):e0172121.",[1090,1405,1406,1096,1410,1413],{},[40,1407,1409],{"href":739,"rel":1408},[72],"Vesterinen V et al. (2016)",[268,1411,1412],{},"Individual Endurance Training Prescription with Heart Rate Variability."," Med Sci Sports Exerc. 48(7):1347-1354.",[1090,1415,1416,1096,1420,1423],{},[40,1417,1419],{"href":749,"rel":1418},[72],"Javaloyes A et al. (2019)",[268,1421,1422],{},"Training Prescription Guided by Heart-Rate Variability in Cycling."," Int J Sports Physiol Perform. 14(1):23-32.",[1090,1425,1426,1096,1430,1433],{},[40,1427,1429],{"href":754,"rel":1428},[72],"Javaloyes A, Sarabia JM, Lamberts RP, Plews D, Moya-Ramon M (2020)",[268,1431,1432],{},"Training Prescription Guided by Heart Rate Variability Vs. Block Periodization in Well-Trained Cyclists."," J Strength Cond Res. 34(6):1511-1518.",[1090,1435,1436,1096,1440,1443],{},[40,1437,1439],{"href":760,"rel":1438},[72],"Granero-Gallegos A et al. (2020)",[268,1441,1442],{},"HRV-based training for improving VO2max in endurance athletes: a systematic review with meta-analysis."," Int J Environ Res Public Health. 17(21):7999.",[1090,1445,1446,1096,1450,1453],{},[40,1447,1449],{"href":769,"rel":1448},[72],"Manresa-Rocamora A, Sarabia JM, Javaloyes A, Flatt AA, Moya-Ramon M (2021)",[268,1451,1452],{},"Heart Rate Variability-Guided Training for Enhancing Cardiac-Vagal Modulation, Aerobic Fitness, and Endurance Performance: A Methodological Systematic Review with Meta-Analysis."," Int J Environ Res Public Health. 18(19):10299.",[1090,1455,1456,1096,1460,1463],{},[40,1457,1459],{"href":774,"rel":1458},[72],"Düking P et al. (2021)",[268,1461,1462],{},"Monitoring and adapting endurance training on the basis of heart rate variability monitored by wearable technologies: a systematic review with meta-analysis."," J Sci Med Sport. 24(11):1180-1192.",[1090,1465,1466,1096,1470,1473],{},[40,1467,1469],{"href":814,"rel":1468},[72],"Meeusen R et al. (2013)",[268,1471,1472],{},"Prevention, diagnosis and treatment of the overtraining syndrome: Joint consensus statement of the ECSS and ACSM."," Eur J Sport Sci. 13(1):1-24.",[1090,1475,1476,1096,1480,1483],{},[40,1477,1479],{"href":820,"rel":1478},[72],"Le Meur Y et al. (2013)",[268,1481,1482],{},"A multidisciplinary approach to overreaching detection in endurance trained athletes."," J Appl Physiol. 114(3):411-420.",[16,1485,1486],{},[268,1487,1488],{},"This article is for general education and isn't medical advice. If you're new to distance running, returning from injury, or managing a health condition, clear a marathon build-up with your clinician before you increase volume or intensity.",{"title":1490,"searchDepth":1491,"depth":1491,"links":1492},"",2,[1493,1494,1495,1496,1497,1498,1499,1500,1501,1502,1503,1504],{"id":53,"depth":1491,"text":54},{"id":150,"depth":1491,"text":151},{"id":250,"depth":1491,"text":251},{"id":308,"depth":1491,"text":309},{"id":456,"depth":1491,"text":457},{"id":554,"depth":1491,"text":555},{"id":607,"depth":1491,"text":608},{"id":713,"depth":1491,"text":714},{"id":830,"depth":1491,"text":831},{"id":870,"depth":1491,"text":871},{"id":1039,"depth":1491,"text":1040},{"id":1084,"depth":1491,"text":1085},"2026-07-19T12:00:00","What research on 150,000+ marathons says it takes to break 4 hours: why mileage beats genetics, a timeline by starting point, and how to build the cycle.","md","\u002Fimages\u002Fblog\u002Fsub-4-marathon.png",752,1424,{},true,"\u002Fblog\u002Fsub-4-marathon",{"question":1515,"answer":1516},"What does it actually take to break four hours in the marathon?","Sub-4 (5:41\u002Fkm, 9:09\u002Fmile) is faster than the average finisher of both sexes and sits in roughly the top third of big-city fields. Unlike sub-3, it isn't primarily a physiology ceiling — it's a training-volume threshold. The strongest predictor is weekly mileage: Tanda's model shows volume and training pace explain ~77% of finish-time variance, and a 119,452-runner Strava study confirms it. The clearest race-based readiness gate is a half marathon around 1:52-1:55. Build to 55-70 km\u002Fweek, mostly easy, with one threshold and one marathon-pace session weekly, a real 2-week taper, and 60-80 g\u002Fhour of race-day carbohydrate. Timelines vary from a single 16-20-week cycle (if you're already running 4:05-4:25) to 18-24 months (starting from the couch).",{"title":5,"description":1506},{"loc":1513},"blog\u002Fsub-4-marathon",[1521,1522,1523,1524],"running","marathon","training-science","endurance","fhS4yKomLj5sIn6FG5EPGV1d3kGZHxipYRvDBE8XZ50",[1527,1534,1536,1547,1559,1563,1569,1577,1584,1591,1596,1604,1614,1618,1626,1635,1640,1645,1651,1657],{"path":1528,"title":1529,"date":1530,"image":1531,"imageWidth":1510,"imageHeight":1509,"tags":1532},"\u002Fblog\u002Ffirst-marathon","Your First Marathon: It's Tendons, Not Fitness","2026-07-20T11:15:00","\u002Fimages\u002Fblog\u002Ffirst-marathon.png",[1521,1522,1523,1533],"injury-prevention",{"path":1513,"title":5,"date":1505,"image":1508,"imageWidth":1510,"imageHeight":1509,"tags":1535},[1521,1522,1523,1524],{"path":1537,"title":1538,"date":1539,"image":1540,"imageWidth":1541,"imageHeight":1541,"tags":1542},"\u002Fblog\u002Fdfa-ddfa-hrv-threshold","DFA a1: How Accurate 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