Every number has an origin.

A quantitative systematic review of normal values for short-term heart rate variability in healthy adults

Nunan, Sandercock, Brodie · Pacing and Clinical Electrophysiology 33(11):1407–1417

44 studies with 21,438 participants: RMSSD ≈ 42 ± 15 ms, SDNN ≈ 50 ± 16 ms over 5 minutes. RMSSD and SDNN are different metrics on different scales. Important for how this is read: the ± is the spread BETWEEN studies, not the distribution within a population — sound as a normative reference, not as an individual assessment.

2 sites · HRV baselines and deviations

The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance

Billman · Frontiers in Physiology 4:26

“Parasympathetic nerve activation contributes to at least 50 % of the LF variability while sympathetic activity, at best, only contributes 25 % to this variability.” That makes the LF/HF ratio unusable as a sympathovagal index — which is why Aellic deliberately does not use it.

1 site · HRV baselines and deviations

The effect of acute sleep deprivation on skeletal muscle protein synthesis and the hormonal environment

Lamon, Morabito, Arentson-Lantz, Knowles, Vincent, Condo, Alexander, Garnham, Paddon-Jones, Aisbett · Physiological Reports 9(1):e14660

One night of total sleep deprivation (N = 13, randomised crossover): muscle protein synthesis −18 %, cortisol +21 %, testosterone −24 %. Acute anabolic resistance — the basis for how heavily sleep weighs in the recovery score.

1 site · Recovery score weighting and age references

Validation of nocturnal resting heart rate and heart rate variability in consumer wearables

Dial, Hollander, Vatne, Emerson, Edwards, Hagen · Physiological Reports 13(16):e70527

13 healthy adults, 536 nights against an ECG reference. Resting heart rate reached Lin's concordance correlation of 0.86 (Polar Grit X Pro, which the authors themselves grade “poor”) to 0.98 (Oura Gen 4), with mean absolute percentage errors of 1.67–3.00 %. The corresponding HRV errors were three to five times larger on the same nights and devices (MAPE 5.96–16.32 %). That error ratio — not the concordance coefficient — is what supports treating nocturnal resting heart rate as the steadier of the two signals.

1 site · Recovery score weighting and age references corrected 26 July 2026

Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring

Plews, Laursen, Stanley, Kilding, Buchheit · Sports Medicine 43(9):773–781

ln(RMSSD) is the most practical measure for day-to-day monitoring; the log transform normalises the right-skewed RMSSD distribution, and the paper establishes that averaging is required at all, rather than reading single measurements. NOT supported are the specific window lengths (14 nights minimum, 7 days acute, 30 days chronic) — those are design decisions.

3 sites · HRV baselines and deviations corrected 25 July 2026

Twenty-four hour time domain heart rate variability and heart rate: relations to age and gender over nine decades

Umetani, Singer, McCraty, Atkinson · Journal of the American College of Cardiology 31(3):593–601

24-hour norms for 260 healthy people (112 m / 148 f) aged 10 to 99, by decade. The actual primary source for the age dependence of HRV, which Aellic previously misattributed to Shaffer & Ginsberg. The decline is NOT linear: RMSSD falls to 47 % of its starting value by the sixth decade (pNN50 to 24 %) “and then stabilized”. A constant rate of “ms per year of life” cannot be derived from it.

1 site · HRV baselines and deviations

National Sleep Foundation's sleep time duration recommendations: methodology and results summary

Hirshkowitz, Whiton, Albert, Alessi, Bruni, DonCarlos, Hazen, Herman, Katz, Kheirandish-Gozal, Neubauer, O'Donnell, Ohayon, Peever, Rawding, Sachdeva, Setters, Vitiello, Ware, Adams Hillard — National Sleep Foundation · Sleep Health 1(1):40–43

Age-dependent sleep duration recommendations from an expert panel: 7–9 h for young adults and adults, 7–8 h from age 65. A population default, sound as a cold start, not as an individual target. Full report: Sleep Health 1(4):233–243, doi 10.1016/j.sleh.2015.10.004.

1 site · Recovery score weighting and age references

Prevention, diagnosis, and treatment of the overtraining syndrome: joint consensus statement of the European College of Sport Science and the American College of Sports Medicine

Meeusen, Duclos, Foster, Fry, Gleeson, Nieman, Raglin, Rietjens, Steinacker, Urhausen — ECSS/ACSM Consensus Statement · Medicine & Science in Sports & Exercise 45(1):186–205

There is no reliable single biomarker for overtraining: “none of them meet all the criteria to make their use generally accepted”. A multi-signal approach beats any individual marker — hence the corroboration through resting heart rate and respiratory rate. The statement names NO threshold values, however; the specific limits (+5 bpm, +1.5 brpm) are Aellic's own. Second publication: European Journal of Sport Science 13(1):1–24, doi 10.1080/17461391.2012.730061 — different pagination, do not mix the two.

1 site · HRV baselines and deviations

Heart rate variability: Standards of measurement, physiological interpretation, and clinical use

Task Force of the European Society of Cardiology and NASPE · Circulation 93(5):1043–1065

The reference definition of the time- and frequency-domain HRV measures. Basis for the separate RMSSD and SDNN tables. Deliberately recorded WITHOUT a PMID: the frequently co-cited PMID 8737210 belongs to the second publication in the European Heart Journal 17(3):354–381 (doi 10.1093/oxfordjournals.eurheartj.a014868), not to this Circulation version — the combination would be wrong in itself.

1 site · HRV baselines and deviations

Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan

Ohayon, Carskadon, Guilleminault, Vitiello · Sleep 27(7):1255–1273

Meta-analysis across 65 studies / 3,577 people aged 5 to 102. WASO is the largest age effect in sleep architecture — rising by roughly 10 min per decade of life between 30 and 60, largely stable thereafter. The peer-reviewed basis for normative bands of sleep architecture; the N3 and REM bands used in Aellic have not yet been reconciled line by line against this paper's tables.

2 sites · Sleep stages and bedtime

A two process model of sleep regulation

Borbély · Human Neurobiology 1(3):195–204

The two-process model: sleep propensity is the interaction of homeostatic sleep pressure (Process S, rising exponentially during wake and falling exponentially during sleep) and a circadian rhythm (Process C). Its consequence for any sleep-debt feature: Process S SATURATES — sleep debt is not a bank account, it neither accumulates linearly forever nor is it repaid 1:1. The model supports only the qualitative shape (bounded, recency-weighted); every concrete window, decay or cap constant is construction, not literature.

2 sites · Sleep stages and bedtime

The effect of acute sleep deprivation on skeletal muscle protein synthesis and the hormonal environment

Lamon, Morabito, Arentson-Lantz, Knowles, Vincent, Condo, Alexander, Garnham, Paddon-Jones, Aisbett · Physiological Reports 9(1):e14660

One night of total sleep deprivation (N = 13, randomised crossover): muscle protein synthesis −18 %, cortisol +21 %, testosterone −24 %. Acute anabolic resistance — the basis for how heavily sleep weighs in the recovery score.

1 site · Sleep stages and bedtime

Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults

Robbins, Weaver, Sullivan, Quan, Gilmore, Shaw, Benz, Qadri, Barger, Czeisler, Duffy · Sensors 24(20):6532

n = 35 against laboratory polysomnography: Oura performs best among the consumer devices (κ 0.65 on the four-stage classification, ahead of Apple Watch 0.60 and Fitbit Sense 0.55) — but remains far from polysomnography.

1 site · Sleep stages and bedtime corrected 25 July 2026

Sleep regularity is a stronger predictor of mortality risk than sleep duration: A prospective cohort study

Windred, Burns, Lane, Saxena, Rutter, Cain, Phillips · Sleep 47(1):zsad253

UK Biobank (n = 60,977, 1,859 deaths): sleep regularity is a STRONGER predictor of mortality than sleep duration — 20–48 % lower all-cause mortality in the upper four SRI quintiles compared with the lowest.

1 site · Sleep stages and bedtime

Sleep regularity and mortality: a prospective analysis in the UK Biobank

Cribb, Sha, Yiallourou, Grima, Cavuoto, Baril, Pase · eLife 12:RP88359

n = 88,975: hazard ratio 1.53 (95 % CI 1.41–1.66) at the 5th SRI percentile.

1 site · Sleep regularity corrected 25 July 2026

A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults

Miller, Sargent, Roach · Sensors 22(16):6317

n = 53 across 6 devices against polysomnography: agreement on multi-class sleep stage only 50–65 %, κ 0.20–0.52 (Apple 0.20 up to Somfit 0.52). Sleep sensitivity 90–98 %. This is why the stage distribution now weighs only 0.10 in Aellic instead of 0.25.

1 site · Sleep stages and bedtime corrected 25 July 2026

Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing

Phillips, Clerx, O'Brien, Sano, Barger, Picard, Lockley, Klerman, Czeisler · Scientific Reports 7(1):3216

Definition of the Sleep Regularity Index: SRI = 100 × (2 × P(same state at t and t+24 h) − 1), on a scale of −100 to 100. The paper does not ask for “at least 7 days” but for whole MULTIPLES of 7 days — a weekly grid, so that the distribution of weekdays cannot skew the result.

2 sites · Sleep regularity, Sleep stages and bedtime

Social jetlag: misalignment of biological and social time

Wittmann, Dinich, Merrow, Roenneberg · Chronobiology International 23(1-2):497–509

Definition of social jetlag: SJL = |MSF − MSW|, the difference in mid-sleep between free days and work days (n = 501, Munich ChronoType Questionnaire). Limit of our verification: the full text is paywalled; the formula itself was confirmed only through the citing literature, not read in the original.

1 site · Sleep regularity

The diagnostic accuracy of wearable digital technology in detecting fertility window and menstrual cycles: a systematic review and Bayesian network meta-analysis

Shi et al. · npj Digital Medicine 9(1):139

Network meta-analysis of wearable-based ovulation detection: exact-day accuracy pooled across devices is 0.56, and only ~68 % land within ±1 day. This is the justification for Aellic ALWAYS shipping the estimated ovulation with a confidence band and never presenting it as “measured”.

1 site · Phases and cycle-aware baselines

The Effects of Menstrual Cycle Phase on Exercise Performance in Eumenorrheic Women: A Systematic Review and Meta-Analysis

McNulty, Elliott-Sale, Dolan, Swinton, Ansdell, Goodall, Thomas, Hicks · Sports Medicine 50(10):1813–1827

78 studies, n = 1,193: ES −0.04, CrI [−0.11, 0.08], GRADE low. There is NO appreciable luteal performance decrement. The popular advice to “take it easier in the luteal phase” inverts a finding that is trivial to begin with — which is why Aellic builds no cycle-phase-driven training recommendation.

1 site · Phases and cycle-aware baselines

A Systematic Review and Meta-Analysis of Within-Person Changes in Cardiac Vagal Activity across the Menstrual Cycle: Implications for Female Health and Future Studies

Schmalenberger, Eisenlohr-Moul, Würth, Schneider, Thayer, Ditzen, Jarczok · Journal of Clinical Medicine 8(11):1946

37 studies: meta-analytic d = −0.39, 95 % CI [−0.67, −0.11]; the primary literature is “not only inconsistent but contradictory”, two studies found the opposite direction, and only 42 % verified the phase hormonally. Too weak for a fixed constant — hence no cold-start offset for HRV.

1 site · Phases and cycle-aware baselines

Current evidence shows no influence of women's menstrual cycle phase on acute strength performance or adaptations to resistance exercise training

Colenso-Semple, D'Souza, Elliott-Sale, Phillips · Frontiers in Sports and Active Living 5:1054542

Umbrella review of meta-analyses and systematic reviews on cycle-phase-driven training: “it is premature to conclude that short-term fluctuations in reproductive hormones appreciably influence acute exercise performance or longer-term strength or hypertrophic adaptations”.

1 site · Phases and cycle-aware baselines corrected 25 July 2026

The menstrual cycle through the lens of a wearable device: insights into physiology, sleep, and cycle variability

Gonzalez, O'Day, Johnson, Kim, Jasinski, Holmes, Delp, Hicks (Stanford × WHOOP) · npj Digital Medicine (online 2026-05-25; volume and article number not yet assigned)

42,759 cycles from 2,596 women across 1.2 million days: the amplitude of the cyclical swing scales with cycle length; measured across respiratory rate, resting heart rate, HRV, temperature and SpO₂. Preprint of the same work: bioRxiv, doi 10.1101/2025.09.11.675620 — the peer-reviewed version is deliberately the one cited here.

1 site · Phases and cycle-aware baselines

Oura Ring as a Tool for Ovulation Detection: Validation Analysis

Thigpen, Patel, Zhang, Fitzgerald, Kim, Symul, Wang · Journal of Medical Internet Research 27:e60667

Validation of ovulation detection through a temperature wearable; confirms the order of magnitude of MAE 1.2–1.3 days from wang2025 in a second data set and on a second device.

1 site · Phases and cycle-aware baselines

Performance of algorithms using wrist temperature for retrospective ovulation day estimate and next menses start day prediction: a prospective cohort study

Wang, Park, Zhang, Jukic, Baird, Coull, Hauser, Mahalingaiah, Zhang · Human Reproduction 40(3):469–478

Retrospective ovulation estimation from wrist temperature: MAE 1.2–1.3 days, 89 % of estimates within ±2 days (PAE2). Carries three constants of the temperature-based ovulation estimate: the signal threshold of +0.2 °C, the confidence band of ±2 days, and the honesty rule that a non-detection is not a finding — only ~80.8 % of cycles reach the threshold at all, and ~19 % produce no usable signal whatsoever. A non-detection therefore explicitly does NOT mean “anovulatory”.

3 sites · Phases and cycle-aware baselines

A Novel method for quantifying fluctuations in wearable derived daily cardiovascular parameters across the menstrual cycle

Jasinski, Rowan, Presby, Stevens, Chapman, Hanes, Capodilupo · npj Digital Medicine 7(1):373

n = 11,590: only 80.6 % of women show the expected RMSSD amplitude across the cycle. A fixed offset would impose a pattern on one woman in five that she does not have — THE load-bearing argument for a per-person learned offset instead of a constant from the literature. It also supplies the shape that the two-strata model deliberately simplifies: resting heart rate has its minimum on cycle day 5 and its maximum only on day 26, so it rises during the fertile window already, rather than jumping after ovulation.

4 sites · Phases and cycle-aware baselines

Nocturnal Heart Rate Variability in Women Discordant for Hormonal Contraceptive Use

Ahokas, Hanstock, Löfberg, Nyman, Wenning, Kyröläinen, Laaksonen, Ihalainen · Medicine & Science in Sports & Exercise 55(7):1342–1349

Users of combined oral contraceptives are NOT signal-free: nocturnal HRV follows a real rhythm driven by the pill pack (higher during the pill-free interval). Aellic's strata sit at estimated ovulation, not at the pack boundary — so part of this is captured, but not all of it. A genuine pill-pack mode would be a development stage of its own; until then the position is: partially correct, never wrong, and documented.

1 site · Phases and cycle-aware baselines

The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study

Zhu, Rothenbühler, Hamvas, Hofmann, Leeners, Kyburz, Yang · Journal of Medical Internet Research 23(6):e20710

Wrist skin temperature against oral basal temperature for ovulation detection; supplies the upper end of the luteal temperature rise (up to +0.50 °C) and confirms that skin carries the larger amplitude. Aellic deliberately does NOT take this value but the lower edge from maijala2019 — too large an offset would mask genuine strain.

1 site · Phases and cycle-aware baselines

Menstrual Cycle Changes in Vagally-Mediated Heart Rate Variability are Associated with Progesterone: Evidence from Two Within-Person Studies

Schmalenberger, Eisenlohr-Moul, Surez Vasquez, Momm, Girdler, Kiesner, Ditzen, Jarczok · Journal of Clinical Medicine 9(3):617

Progesterone, not estradiol, predicts the lower luteal HRV. That makes it an endocrine signal, not exhaustion — penalising it as a recovery deficit was a category error, and that is the justification for the entire seasonal adjustment.

2 sites · Phases and cycle-aware baselines

Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles

Bull, Rowland, Berglund Scherwitzl, Scherwitzl, Danielsson, Harper · npj Digital Medicine 2:83

612,613 cycles with ovulation confirmed by basal temperature and LH: cycle length 29.3 ± 5.2 d, follicular phase 16.9 ± 5.3 d, luteal phase 12.4 ± 2.4 d. Variance ratio follicular:luteal ≈ 4.9:1 — all of the spread sits BEFORE ovulation. This is why ovulation is estimated backwards from the next expected period: going backwards inherits the luteal SD of 2.4 instead of the follicular one of 5.3. It also carries `DEFAULT_CYCLE_LENGTH = 29` (only 13 % of all cycles really run 28 days; the mean is 29.3).

4 sites · Phases and cycle-aware baselines

Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study

Goodale, Shilaih, Falco, Dammeier, Hamvas, Leeners · Journal of Medical Internet Research 21(4):e13404

708 cycles: resting heart rate +3.96 bpm from the follicular to the late luteal phase. The luteal RESPIRATORY RATE swing, by contrast, is only +0.2 to +0.6 br/min and therefore BELOW the night-to-night precision of commercial sensors (~1.0 br/min) — which is why there is deliberately no cold-start offset for `respiratory_rate`, only the learned one.

2 sites · Phases and cycle-aware baselines

Nocturnal finger skin temperature in menstrual cycle tracking: ambulatory pilot study using a wearable Oura ring

Maijala, Kinnunen, Koskela, Jämsä, Kangas · BMC Women's Health 19(1):150

Nocturnal FINGER skin temperature across the cycle, measured with an Oura ring: luteal rise +0.30 ± 0.12 °C. That is the lower edge of the range Aellic uses and therefore the cold-start value for `body_temp`. Important for how this is read: skin, not core — distal skin temperature has the LARGER amplitude compared with an oral basal temperature (~0.20–0.23 °C), and skin is exactly what a wearable measures.

1 site · Phases and cycle-aware baselines corrected 26 July 2026

Assessment of menstrual health status and evolution through mobile apps for fertility awareness

Symul, Wac, Hillard, Salathé · npj Digital Medicine 2:64

2.7 million cycles: only ~24 % of ovulations fall on cycle day 14–15, and 90 % are spread across days 10–24. Anyone who simply assumes “day 14” is wrong in a good three cases out of four.

1 site · Phases and cycle-aware baselines

Modern fertility awareness methods: wrist wearables capture the changes in temperature associated with the menstrual cycle

Shilaih, Goodale, Falco, Kübler, De Clerck, Leeners · Bioscience Reports 38(6):BSR20171279

Wrist skin temperature: luteal rise +0.33 °C; a sustained 3-day shift occurs in 82 % of cycles, and 86 % of shifts begin on the day of ovulation or later. Carries three constants of the ovulation estimate at once: the 3-day rule (`TEMP_SHIFT_SUSTAIN_DAYS`), the choice of the day BEFORE the shift begins as ovulation, and the stratum boundary `> ovu_day`.

2 sites · Phases and cycle-aware baselines corrected 26 July 2026

Pulse Rate Measurement During Sleep Using Wearable Sensors, and its Correlation with the Menstrual Cycle Phases, A Prospective Observational Study

Shilaih, Clerck, Falco, Kübler, Leeners · Scientific Reports 7(1):1294

274 cycles: pulse rate during sleep rises mid-luteally by +3.8 bpm compared with menstruation. One of three independent data sets behind the range of +2 to +4 bpm (the others: goodale2019 +3.96, jasinski2024 ~3.5 peak-to-trough). Aellic sets the cold start to +2.0 — the lower edge.

1 site · Phases and cycle-aware baselines

Phasic menstrual cycle effects on the control of breathing in healthy women

Slatkovska, Jensen, Davies, Wolfe · Respiratory Physiology & Neurobiology 154(3):379–388

Progesterone acts as a respiratory drive; minute ventilation rises luteally by about 1 L/min. The effect is real — but it runs almost entirely through TIDAL VOLUME, not through rate. Which is why, despite demonstrable physiology, all that remains in respiratory rate is a swing below device precision (see goodale2019), and why there is no cold-start constant for it. Listed in PubMed without a DOI.

1 site · Phases and cycle-aware baselines

Sleep and 24 hour body temperatures: a comparison in young men, naturally cycling women and women taking hormonal contraceptives

Baker, Waner, Vieira, Taylor, Driver, Mitchell · The Journal of Physiology 530(Pt 3):565–574

Comparison of 24-hour body temperature between men, naturally cycling women and women using hormonal contraceptives. Establishes the honest limit of the cycle engine: on combined pills the temperature stays chronically raised and flat — it does not even fall back during the pill-free interval. The learned temperature offset therefore correctly tends towards zero there. Listed in PubMed without a DOI. The addition “n = 8, rectal” found in the code prose has not been verified against the full text.

1 site · Phases and cycle-aware baselines

Normal variation in the length of the luteal phase of the menstrual cycle: identification of the short luteal phase

Lenton, Landgren, Sexton · British Journal of Obstetrics and Gynaecology 91(7):685–689

Luteal length 14.13 ± 1.41 d — measured from the LH PEAK. Aellic has no LH test and must therefore use bull2019 (12.4 d from estimated ovulation); mixing the two anchors would shift every phase boundary by ~2 days. This anchor trap is exactly why `LUTEAL_LENGTH_DAYS = 12` rather than the reflexive 14. No DOI: published in 1984, before this journal assigned them.

1 site · Phases and cycle-aware baselines

The Resistance Training Dose Response: Meta-Regressions Exploring the Effects of Weekly Volume and Frequency on Muscle Hypertrophy and Strength Gains

Pelland, Remmert, Robinson, Hinson, Zourdos · Sports Medicine 56(2):481–505

Meta-regression: “a 0.24 % increase in hypertrophy (95 % CrI: 0.15, 0.33) per additional set at the average ‘fractional’ weekly volume of 12.25 sets”.

1 site · Muscle growth — sets per muscle corrected 25 July 2026

Influence of Resistance Training Proximity-to-Failure on Skeletal Muscle Hypertrophy: A Systematic Review with Meta-analysis

Refalo, Helms, Trexler, Hamilton, Fyfe · Sports Medicine 53(3):649–665

15 studies. Training to set failure held a “trivial advantage” over non-failure for hypertrophy (ES 0.19; 95 % CI 0.00–0.37; p = 0.045), and none at all for momentary muscular failure specifically (ES 0.12; p = 0.343). Proximity was graded by velocity-loss thresholds, where high (>25 %) versus moderate (20–25 %) also came out level (ES 0.08; p = 0.529). Supports the direction — closer to failure is not worse — and nothing more precise than that.

1 site · Muscle growth — sets per muscle corrected 26 July 2026

Effects of resistance training performed to repetition failure or non-failure on muscular strength and hypertrophy: A systematic review and meta-analysis

Grgic, Schoenfeld, Orazem, Sabol · Journal of Sport and Health Science 11(2):202–211

15 studies in young adults. No significant difference between failure and non-failure training, for strength (ES −0.09; 95 % CI −0.22 to 0.05) or for hypertrophy (ES 0.22; 95 % CI −0.11 to 0.55). One subgroup runs the other way and matters for Aellic's audience: among already resistance-trained participants, training to failure WAS significantly better for hypertrophy (ES 0.15; 95 % CI 0.03–0.26).

1 site · Muscle growth — sets per muscle corrected 26 July 2026

The Effects of Concurrent Aerobic and Strength Training on Muscle Fiber Hypertrophy: A Systematic Review and Meta-Analysis

Lundberg, Feuerbacher, Sünkeler, Schumann · Sports Medicine 52(10):2391–2403

The fibre-level follow-up to schumann2022, and the one place where a small interference effect does show up: 15 studies, SMD −0.23 for overall muscle fibre hypertrophy (95 % CI −0.46 to −0.00; p = 0.050), −0.34 for type I (p = 0.078) and −0.13 for type II (p = 0.315). This is also the source for the running-versus-cycling difference: type I fibres were negatively affected when the aerobic work was running (SMD −0.81; 95 % CI −1.26 to −0.36) but not cycling. The authors call the contrast with whole-muscle findings “intriguing” rather than resolved.

1 site · Muscle growth — sets per muscle corrected 26 July 2026

Compatibility of Concurrent Aerobic and Strength Training for Skeletal Muscle Size and Function: An Updated Systematic Review and Meta-Analysis

Schumann, Feuerbacher, Sünkeler, Freitag, Rønnestad, Doma, Lundberg · Sports Medicine 52(3):601–612

43 studies, 1,090 participants — the whole-muscle picture, and the reason Aellic does not warn about interference. Hypertrophy SMD −0.01 (p = 0.919) and maximal strength −0.06 (p = 0.446) are untouched; only explosive strength is attenuated (−0.28; p = 0.007), and more so when both sessions fall in the same session than when they are separated by at least three hours (p = 0.043). Type of aerobic training, training frequency, training status and age were all non-significant moderators.

1 site · Muscle growth — sets per muscle corrected 26 July 2026

How many times per week should a muscle be trained to maximize muscle hypertrophy? A systematic review and meta-analysis of studies examining the effects of resistance training frequency

Schoenfeld, Grgic, Krieger · Journal of Sports Sciences 37(11):1286–1295

With weekly volume equated, training frequency has no meaningful influence on hypertrophy. THIS is the source for Aellic's decision not to score frequency separately — not the 2016 predecessor, which was wrongly cited for it until 2026-07-25.

1 site · Muscle growth — sets per muscle

Dose-response relationship between weekly resistance training volume and increases in muscle mass: A systematic review and meta-analysis

Schoenfeld, Ogborn, Krieger · Journal of Sports Sciences 35(11):1073–1082

34 treatment groups from 15 studies: “Each additional set was associated with an increase in effect size (ES) of 0.023 corresponding to an increase in the percentage gain by 0.37%” (p = 0.002). The dose is coded as total sets per muscle group per week.

1 site · Muscle growth — sets per muscle corrected 26 July 2026

Age-predicted maximal heart rate revisited

Tanaka, Monahan, Seals · Journal of the American College of Cardiology 37(1):153–156

HRmax = 208 − 0.7 × age. Meta-analysis across 351 studies / 492 groups / 18,712 people (r = −0.90), cross-validated in a laboratory cohort of 514 healthy people whose own regression, 209 − 0.7 × age, the authors call “virtually identical”. Individual values scatter about ±10 bpm around the line, so the formula sets a population expectation and not a personal ceiling. The widespread 220 − age formula has no comparable derivation.

2 sites · Training load — zones, TRIMP, ACWR corrected 26 July 2026

A Systematic Review of The Effects of Different Resistance Training Volumes on Muscle Hypertrophy

Baz-Valle, Balsalobre-Fernández, Alix-Fages, Santos-Concejero · Journal of Human Kinetics 81:199–210

Six studies in trained men aged 18–35, binned a priori into “low” (<12 weekly sets), “moderate” (12–20) and “high” (>20). Moderate and high volumes did not differ for the quadriceps (p = 0.19) or the biceps brachii (p = 0.59); for the triceps brachii the HIGHER volume was significantly better (p = 0.01). The authors' recommendation is “a range of 12–20 weekly sets per muscle group may be an optimum standard recommendation”, offered as pragmatic advice for that population.

1 site · Muscle growth — sets per muscle corrected 26 July 2026

Electromyographic activity in deadlift exercise and its variants. A systematic review

Martín-Fuentes, Oliva-Lozano, Muyor · PLoS ONE 15(2):e0229507

EMG in the deadlift: erector spinae and quadriceps show higher activation than gluteus maximus and biceps femoris. Confirms the mapping to the erector spinae. For the Romanian variant the relationship reverses.

1 site · Which muscles an exercise loads

Acute:Chronic Workload Ratio: Conceptual Issues and Fundamental Pitfalls

Impellizzeri, Tenan, Kempton, Novak, Coutts · International Journal of Sports Physiology and Performance 15(6):907–913

Severe methodological criticism of the ACWR: “there are known issues with the use of ratio data”, it is “an inaccurate metric (failing to normalize the numerator by the denominator even when uncoupled)”, “not consistently and unidirectionally related to injury risk”, and “there is no evidence supporting the use of ACWR in training-load-management systems”. This is why Aellic reports the ACWR only with the label “heuristic”.

2 sites · Training load — zones, TRIMP, ACWR corrected 25 July 2026

Interpreting Signal Amplitudes in Surface Electromyography Studies in Sport and Rehabilitation Sciences

Vigotsky, Halperin, Lehman, Trajano, Vieira · Frontiers in Physiology 8:985

Acute EMG amplitude is NOT a validated predictor of hypertrophy; conclusions about long-term adaptation are “frequently unsubstantiated and unwarranted”. The muscle mapping may therefore only say which muscles a movement loads — not which one grows most. Note: year 2018 with volume 8 and a 2017 DOI stem is how this was actually published (eCollection 2017), not a typo.

2 sites · Muscle growth — sets per muscle, Which muscles an exercise loads

Has the athlete trained enough to return to play safely? The acute:chronic workload ratio permits clinicians to quantify a player's risk of subsequent injury

Blanch, Gabbett · British Journal of Sports Medicine 50(8):471–475

The actual origin of the four-week chronic window Aellic computes against: injury likelihood is modelled against “acute load spikes above what they have been doing on average over the past 4 weeks (chronic load)”, with a polynomial fit of R² = 0.53 across three sports. Also the source of the sweet-spot figure that gabbett2016 reproduces.

3 sites · Training load — zones, TRIMP, ACWR

The training—injury prevention paradox: should athletes be training smarter and harder?

Gabbett · British Journal of Sports Medicine 50(5):273–280

Source of the corridor Aellic ships, twice stated verbatim: “acute:chronic workload ratios within the range of 0.8–1.3 could be considered the training ‘sweet spot’, while acute:chronic workload ratios ≥1.5 represent the ‘danger zone’”. Acute load is one week of training, “a logical and convenient unit”.

3 sites · Training load — zones, TRIMP, ACWR corrected 26 July 2026

Muscle strength testing: use of normalisation for body size

Jaric · Sports Medicine 32(10):615–631

Strength scales with body mass^(2/3) under geometric similarity. Linear division systematically penalises heavier athletes. Important limitation: the exponent 0.67 applies to muscle STRENGTH (dynamometer); for torque (isokinetic) the paper explicitly recommends b = 1, so linear division is the correct choice there. Scaling a torque-derived measure by ^(2/3) would be a misapplication of this source.

1 site · Strength standards and ranks

Modeling elite athletic performance

Banister · Book chapter in: MacDougall, Wenger, Green (eds.), Physiological Testing of the High-Performance Athlete, 2nd edition, Human Kinetics, pp. 403–424

TRIMP weights intensity exponentially (lactate-weighted) and computes on heart rate reserve — not in linear steps on raw %HRmax. A book chapter, hence no DOI. Two caveats: the chapter's content is confirmed only through the citing literature, not read in the original; and the frequently cited book title “Physiological Testing of Elite Athletes” belongs to the first edition of 1982 — the source here is the second edition of 1991.

2 sites · Training load — zones, TRIMP, ACWR

Muscle Failure Promotes Greater Muscle Hypertrophy in Low-Load but Not in High-Load Resistance Training

Lasevicius, Schoenfeld, Silva-Batista, Barros, Aihara, Brendon, Longo, Tricoli, Peres, Teixeira · Journal of Strength and Conditioning Research 36(2):346–351

25 untrained men, 8 weeks, unilateral leg extension, volume equated. At 30 % 1RM failure IS required (quadriceps CSA +7.8 % to failure versus +2.8 % not to failure); at 80 % it is not (+8.1 % versus +7.7 %). Strength followed load rather than failure (~33 % at 80 % 1RM versus ~17 % at 30 %).

1 site · Muscle growth — sets per muscle corrected 26 July 2026

Effects of different intensities of resistance training with equated volume load on muscle strength and hypertrophy

Lasevicius, Ugrinowitsch, Schoenfeld, Roschel, Tavares, De Souza, Laurentino, Tricoli · European Journal of Sport Science 18(6):772–780

With volume load equated, 40, 60 and 80 % 1RM were equivalent, “however, 20 % 1RM was suboptimal” (vastus lateralis cross-sectional area +8.9 % at 20 % versus ~20 % for all the others).

1 site · Muscle growth — sets per muscle corrected 25 July 2026

Neither load nor systemic hormones determine resistance training-mediated hypertrophy or strength gains in resistance-trained young men

Morton, Oikawa, Wavell, Mazara, McGlory, Quadrilatero, Baechler, Baker, Phillips · Journal of Applied Physiology 121(1):129–138

Confirms Mitchell 2012 in already-trained men over 12 weeks (~30–50 % versus ~75–90 % 1RM, all sets to failure): load is secondary for hypertrophy, proximity to failure is what counts.

1 site · Muscle growth — sets per muscle

Resistance exercise load does not determine training-mediated hypertrophic gains in young men

Mitchell, Churchward-Venne, West, Burd, Breen, Baker, Phillips · Journal of Applied Physiology 113(1):71–77

Hypertrophy is comparable across 30–80 % 1RM provided the work is taken close to muscular failure (muscle volume by MRI +6.8 % at 30 %, +7.2 % at 80 %, p = 0.18). Only the two end points were tested, no intermediate loads.

1 site · Muscle growth — sets per muscle

Normative data for the squat, bench press and deadlift exercises in powerlifting: Data from 809,986 competition entries

van den Hoek, Beaumont, van den Hoek, Owen, Garrett, Buhmann, Latella · Journal of Science and Medicine in Sport 27(10):734–742

809,986 drug-tested raw competition entries (571,650 male, 238,336 female). For male lifters aged 18–35 the 90th percentile lands almost exactly on Aellic's elite anchor: squat 2.83 versus our 2.75, bench press 1.95 versus 2.00, deadlift 3.25 versus 3.25 (all as 1RM ÷ body mass). Peer-reviewed backing for three of the twelve lifts, on the male side. The female elite anchors rest on strengthlevel alone; this paper's female 90th percentiles are lower (squat 2.26, bench press 1.35, deadlift 2.66) and were never used to set them.

1 site · Strength standards and ranks corrected 26 July 2026

Front vs Back and Barbell vs Machine Overhead Press: An Electromyographic Analysis and Implications For Resistance Training

Coratella, Tornatore, Longo, Esposito, Cè · Frontiers in Physiology 13:825880

Overhead pressing involves the upper trapezius across all four variants tested (its task: stabilising scapular elevation) — which justifies adding `traps` to the vertical push family. Limitation: the paper publishes effect sizes only, no absolute %MVIC values; HOW strongly the upper trapezius contributes cannot be quantified from it.

1 site · Which muscles an exercise loads

An electromyographic and kinetic comparison of conventional and Romanian deadlifts

Lee, Schultz, Timgren, Staelgraeve, Miller, Liu · Journal of Exercise Science & Fitness 16(3):87–93

EMG in the Romanian deadlift: biceps femoris is the most active of the muscles measured (56.66 % peak versus gluteus maximus 46.88 % and rectus femoris 25.26 %). Confirms the hamstring mapping.

1 site · Which muscles an exercise loads corrected 25 July 2026

A Comparison of Gluteus Maximus, Biceps Femoris, and Vastus Lateralis Electromyographic Activity in the Back Squat and Barbell Hip Thrust Exercises

Contreras, Vigotsky, Schoenfeld, Beardsley, Cronin · Journal of Applied Biomechanics 31(6):452–458

EMG of the hip thrust versus the squat: upper gluteus maximus 69.5 % versus 29.4 %, lower 86.8 % versus 45.4 %. Confirms the gluteus mapping.

1 site · Which muscles an exercise loads

The relationship between the number of repetitions performed at given intensities is different in endurance and strength trained athletes

Richens, Cleather · Biology of Sport 31(2):157–161

Why a repetition-based estimate degrades at higher rep counts for reasons no formula can fix: at 70 % 1RM endurance runners managed 39.9 ± 17.6 repetitions against weightlifters' 17.9 ± 2.8, and at 80 % 19.8 ± 6.4 against 11.8 ± 2.7 (both p < 0.05); at 90 % the difference vanished. Above roughly 10 repetitions the estimate depends more on training history than on which equation is used. Small sample — 8 athletes per group.

1 site · Workout logging — 1RM estimation

Prediction of one repetition maximum strength from multiple repetition maximum testing and anthropometry

Reynolds, Gordon, Robergs · Journal of Strength and Conditioning Research 20(3):584–592

70 subjects aged 18–69, bench press and leg press at 1, 5, 10 and 20RM. The load-repetition relationship is curvilinear, not linear (bench press linear Sy.x = 2.6 kg versus non-linear 0.2 kg; leg press 11.0 versus 2.6 kg), and accuracy falls as the repetition count rises (R² for 5/10/20RM: bench 0.993/0.976/0.955, leg press 0.974/0.933/0.915). Concludes verbatim: “no more than 10 repetitions should be used in linear equations to estimate 1RM”. This is the source of Aellic's 10-repetition cap.

1 site · Workout logging — 1RM estimation

A new approach to monitoring exercise training

Foster, Florhaug, Franklin, Gottschall, Hrovatin, Parker, Doleshal, Dodge · Journal of Strength and Conditioning Research 15(1):109–115

sRPE = RPE (0–10) × duration in minutes, deliberately collected only 30 minutes after the session, “so that particularly difficult or particularly easy segments toward the end of the exercise bout would not dominate the subject's rating”. Works entirely without a heart rate signal and also yields monotony and strain (Table 5).

3 sites · Training load — zones, TRIMP, ACWR corrected 25 July 2026

Maximal and ventilatory threshold responses to treadmill and water immersion running

Frangolias, Rhodes · Medicine and Science in Sports and Exercise 27(7):1007–1013

In water the heart rate runs lower than on land: 190 versus 175 bpm maximal (15 bpm) and 165 versus 152 bpm at the ventilatory threshold (13 bpm). Without an offset, every HR zone systematically overestimates intensity in water.

1 site · Training load — zones, TRIMP, ACWR corrected 25 July 2026

The effects of training on heart rate; a longitudinal study

Karvonen, Kentala, Mustala · Annales Medicinae Experimentalis et Biologiae Fenniae 35(3):307–315

The paper from which the heart rate reserve method emerged: personalise zones through the reserve, that is, take resting heart rate into account rather than maximum heart rate alone. Assessment: the closed formula THR = HRrest + f × (HRmax − HRrest) is a later textbook formalisation, not a formula this small longitudinal study puts forward itself. PubMed carries no abstract; the content is evidenced only through secondary literature.

1 site · Training load — zones, TRIMP, ACWR

ACE-Sponsored Research: What Is the Best Back Exercise?

Edelburg, Porcari, Camic, Kovacs, Foster — American Council on Exercise

EMG comparison of eight back exercises in 19 trained participants: rowing movements activate the middle trapezius most strongly, pull-up and chin-up the latissimus.

1 site · Which muscles an exercise loads corrected 25 July 2026

Exercise Directory (muscle involvement classification)

ExRx.net

Target / synergist / stabiliser framework, translated into primary and secondary. Convention, not measurement — the mapping was cross-checked against EMG for each movement pattern.

1 site · Muscle growth — sets per muscle

Strength Standards

strengthlevel.com

Percentile bands from roughly 195 million reported lifts (beginner 5th to elite 95th percentile). Crowd-sourced self-report with an upward bias — for 9 of the 12 lifts it is the only source, and it is labelled as exactly that. For the male squat, bench press and deadlift anchors vandenhoek2024 provides peer-reviewed agreement; the female anchors rest on this source alone.

1 site · Strength standards and ranks

Poundage Chart, in: Boyd Epley Workout (Lincoln, NE: Body Enterprises, p. 86)

Epley

1RM estimate w × (1 + reps/30). Grey literature, and worth stating plainly: the source is a self-published training chart, not a study — the formula is an algebraic reconstruction of that table, with no sample and no derivation data behind it. Richens & Cleather 2014 describe it in their own comparison table as “a poundage chart, not based on scientific research”. Aellic uses it because it is the established convention and performs acceptably in validation work, not because it is well founded. The cap at 10 repetitions is carried by reynolds2006.

2 sites · Strength standards and ranks, Workout logging — 1RM estimation corrected 26 July 2026

Update of the Nutri-Score algorithm (main report 2022, foods) and Update of the Nutri-Score algorithm for beverages (second report V2-2023, beverages)

Scientific Committee of the Nutri-Score — published by Santé publique France

The published Nutri-Score algorithm in its current version. The thresholds and the fruit and vegetable bonus of the Aellic Health Score follow this specification. Note that there are TWO documents — foods adopted on 2022-06-29, beverages on 2023-02-01 (beverage report: https://www.santepubliquefrance.fr/sites/default/files/rdd/document/Update%20report%20beverages_31%2001%202023-VOTED.pdf).

1 site · Food scoring corrected 25 July 2026

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Benjamini, Hochberg · Journal of the Royal Statistical Society: Series B (Methodological) 57(1):289–300

Control of the false discovery rate across a family of tests. Necessary because the insight engine computes ~10 habits × ~12 metrics ≈ 120 tests — of which roughly 6 would break the p < 0.05 threshold by chance alone and be shown to the user as advice. A note on grading: `strength = review` is a makeshift here — this is an original methodological work with proof and simulation, for which the registry's vocabulary holds no value of its own. No PMID; the paper predates PubMed coverage of this journal.

1 site · Statistical safeguards

Alcohol Ingestion Impairs Maximal Post-Exercise Rates of Myofibrillar Protein Synthesis following a Single Bout of Concurrent Training

Parr, Camera, Areta, Burke, Phillips, Hawley, Coffey · PLoS ONE 9(2):e88384

Muscle protein synthesis −24 % (alcohol with protein) and −37 % (alcohol with carbohydrate) — but at 1.5 g/kg, which the authors themselves put at 12 ± 2 standard drinks. Outside anything that gets logged in practice, so no penalty is applied.

1 site · Alcohol and caffeine

Caffeine Effects on Sleep Taken 0, 3, or 6 Hours before Going to Bed

Drake, Roehrs, Shambroom, Roth · Journal of Clinical Sleep Medicine 9(11):1195–1200

400 mg of caffeine six hours before bedtime costs more than an hour of sleep: “Even at 6 hours, caffeine reduced sleep by more than 1 hour” (n = 12, double-blind, placebo-controlled). Carries the mechanism — but not the dose, since a marker knows only a time of day.

2 sites · Alcohol and caffeine

The influence of a CYP1A2 polymorphism on the ergogenic effects of caffeine

Womack, Saunders, Bechtel, Bolton, Martin, Luden, Dunham, Hancock · Journal of the International Society of Sports Nutrition 9(1):7

CYP1A2 genotype determines the strength of the caffeine response (n = 35 trained cyclists, 6 mg/kg, 40 km time trial). Aellic will never have this genotype; the learned effect captures the phenotype implicitly. Note: an erratum appeared in 2015 (J Int Soc Sports Nutr 12:24, doi 10.1186/s12970-015-0079-6) — two participants had been assigned to the wrong genotype group. The core claim holds, the percentages shift slightly; anyone citing them must use the corrected ones.

1 site · Alcohol and caffeine

Real-world effects of alcohol on heart rate, sleep, and physical activity by age and sex

Grosicki, Robinson, Joyner, Carter, von Hippel, Presby, Fielding, Bigalke, Kim, Chapman, Holmes · PLOS Digital Health 5(3):e0001284

5,109,185 person-days from 20,968 participants: women are affected more strongly (resting heart rate +2.8 bpm versus +2.4 bpm in men) — but the paper publishes NO multiplier, only absolute sex-separated differences. Hence no sex multiplier in Aellic; the per-person learned effect absorbs it in any case.

1 site · Alcohol and caffeine corrected 25 July 2026

Acute Effect of Alcohol Intake on Cardiovascular Autonomic Regulation During the First Hours of Sleep in a Large Real-World Sample of Finnish Employees: Observational Study

Pietilä, Helander, Korhonen, Myllymäki, Kujala, Lindholm · JMIR Mental Health 5(1):e23

Dose-resolved resting heart rate response to alcohol: “HR was increased by 1.4 bpm with low, 4.0 bpm with moderate, and 8.7 bpm with high alcohol intake” (bands: ≤0.25 / >0.25–0.75 / >0.75 g/kg pure alcohol), n = 4,098 across 111,025 measurement days. Preferred over WHOOP's linear extrapolation because it is dose-resolved rather than projected. Firstbeat is the data provider and the employer of three co-authors; the company's own pages do not carry these bpm values at all.

3 sites · Alcohol and caffeine corrected 25 July 2026

Effects of Alcohol on Sleep, HRV, and Recovery

WHOOP

Publishes only “1 drink → −7 ms HRV” together with a persistence of 74 / 29 / 19 % over three days. The HRV figure is NOT used as a cold start in Aellic — extrapolating it to 5 drinks would be invented linearity.

2 sites · Alcohol and caffeine

The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 3

American Academy of Sleep Medicine

The binding scoring standard for sleep stages. It prescribes that the share of each stage in total sleep time be REPORTED — but it names no normal ranges for those shares.

corrected 25 July 2026

Gaining more from doing less? The effects of a one-week deload period during supervised resistance training on muscular adaptations

Coleman, Burke, Augustin, Piñero, Maldonado, Fisher, Israetel, Androulakis Korakakis, Swinton, Oberlin, Schoenfeld · PeerJ 12:e16777

DOES NOT SUPPORT THE RECOMMENDATION ATTRIBUTED TO IT — and contradicts it. It is not a review but a randomised trial, and its result reads: a deload week in the middle of a 9-week programme “appears to negatively influence measures of lower body muscle strength but has no effect on lower body hypertrophy, power or local muscular endurance”. A recommendation of “every 3–6 weeks” appears nowhere in it. Until 2026-07-25 this source stood in Aellic for the opposite of what it shows; the observed intervals are now carried by rogerson2024.

Modeling the association between HR variability and illness in elite swimmers

Hellard, Guimaraes, Avalos, Houel, Hausswirth, Toussaint · Medicine & Science in Sports & Exercise 43(6):1063–1070

DOES NOT SUPPORT THE RULE ATTRIBUTED TO IT. The paper measures SD1, SD2, HF and LF — RMSSD does not appear at all — samples weekly over two years in n = 18, and finds the OPPOSITE direction: a RISE in the parasympathetic indices (HF, SD1) measured supine one week earlier went together with a HIGHER risk of illness. A threshold of “≥20 % drop over 3–5 days” appears nowhere in it; a 3–5 day window is not even part of the study design. Until 2026-07-25 this source carried Aellic's deviation rule — that rule is a practitioner heuristic without a clean primary source and now stands as an openly unsourced design decision.

Reference Data for Polysomnography-Measured and Subjective Sleep in Healthy Adults

Hertenstein, Gabryelska, Spiegelhalder, Nissen, Johann, Umarova, Riemann, Baglioni, Feige · Journal of Clinical Sleep Medicine 14(4):523–532

Reference data for healthy sleepers — and an explicit objection to hard cut-offs: “values often classified as pathological by clinicians, such as a SOL longer than 30 minutes and a SE below 80 %, are well within the bounds of the healthy sample”. Until 2026-07-25 this paper sat under the id “mitterling” by mistake, because its DOI had been entered there.

Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers

Hulin, Gabbett, Blanch, Chapman, Bailey, Orchard · British Journal of Sports Medicine 48(8):708–712

The primary data behind the “two- to fourfold risk” that gabbett2016 quotes. A negative training-stress balance raised injury risk in the following week (RR 2.2 for internal, 2.1 for external workload), and a balance above 200 % raised it to RR 4.5 (internal) and 3.3 (external) versus a balance of 50–99 %. One sport, one role — elite cricket fast bowlers — which is the ceiling on how far it generalises to Aellic's users.

How are habits formed: Modelling habit formation in the real world

Lally, van Jaarsveld, Potts, Wardle · European Journal of Social Psychology 40(6):998–1009

“The median time to reach 95 % of asymptote was 66 days, with a range from 18 to 254 days.” A habit takes a median of 66 days to become automatic, with considerable spread — not 21 days. To be cited before any statement of the kind “you have built a habit”. The curve was fitted for 39 of 96 participants.

Sleep and Respiration in 100 Healthy Caucasian Sleepers — A Polysomnographic Study According to American Academy of Sleep Medicine Standards

Mitterling, Högl, Schönwald, Hackner, Gabelia, Biermayr, Frauscher · Sleep 38(6):867–875

Polysomnography in 100 healthy adults to AASM standard; reports sleep onset latency as age percentile curves and finds no age effect.

corrected 25 July 2026

Social jetlag and obesity

Roenneberg, Allebrandt, Merrow, Vetter · Current Biology 22(10):939–943

Social jetlag is associated with a higher BMI — and independently of sleep duration.

Deloading Practices in Strength and Physique Sports: A Cross-sectional Survey

Rogerson, Nolan, Androulakis Korakakis, Immonen, Wolf, Bell · Sports Medicine — Open 10(1):26

A survey of practice as it is actually lived: deloads are “integrated into the training programme every 5.6 ± 2.3 weeks”, triggered predominantly when performance stagnates or when soreness and joint complaints increase. Describes what trainees DO — it makes no claim about efficacy.

Effects of Resistance Training Frequency on Measures of Muscle Hypertrophy: A Systematic Review and Meta-Analysis

Schoenfeld, Ogborn, Krieger · Sports Medicine 46(11):1689–1697

“When comparing studies that investigated training muscle groups between 1 to 3 days per week on a volume-equated basis, the current body of evidence indicates that frequencies of training twice a week promote superior hypertrophic outcomes to once a week” (ES 0.49 versus 0.30).

corrected 25 July 2026

Longer Interset Rest Periods Enhance Muscle Strength and Hypertrophy in Resistance-Trained Men

Schoenfeld, Pope, Benik, Hester, Sellers, Nooner, Schnaiter, Bond-Williams, Carter, Ross, Just, Henselmans, Krieger · Journal of Strength and Conditioning Research 30(7):1805–1812

21 trained men, 8 weeks, 3 full-body sessions per week at 3 × 8–12 RM. Three minutes of rest beat one for strength (1RM squat and bench press both significantly higher) and for hypertrophy — with the caveat that the size advantage was site-dependent: significant in the anterior thigh, only a trend at the triceps brachii (p = 0.06). Overturned the old dogma of “short rests for hypertrophy”.

corrected 26 July 2026

An Overview of Heart Rate Variability Metrics and Norms

Shaffer & Ginsberg · Frontiers in Public Health 5:258

The most-cited practitioner reference for HRV metrics and norms; its Table 6 carries the short-term norms from Nunan 2010. Usable only as a cold-start fallback — the spread among healthy adults is too wide to decide anything about an individual.

corrected 25 July 2026

Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability

Stone, Ulman, Tran, Thompson, Halter, Ramadan, Stephenson, Finomore, Galster, Rezai, Hagen · Frontiers in Sports and Active Living 3:585870

Consumer devices deviate from a multi-channel ECG reference to differing degrees; an absolute RMSSD comparison across device boundaries is therefore invalid.

corrected 25 July 2026