Jem Arnold

@jemarnold.bsky.social

665 Followers 381 Following

PhD candidate & physiotherapist | 🩸Iliac artery endofibrosis / FLIA | Endurance testing & NIRS. Treat declarative statements as questions?

ARE YOU AN EXPERT IN FLOW LIMITATIONS IN THE ILIAC ARTERY (FLIA)? HELP IMPROVE TREATMENT AND FOLLOW-UP CARE FOR ATHLETES We want to hear from you for an international research study. WHO CAN PARTICIPATE You can participate if you are: professionally involved in the diagnosis, treatment and/or follow-up of FLIA WHAT DOES PARTICIPATION INVOLVE Online questionnaire Approximately 15–20 minutes Completely voluntary JOIN THE STUDY Scan the QR code or use the link in the description. PLEASE SHARE Do you know other experts who have broad experience with patients with FLIA? Please send them this study information. STUDY INFORMATION The study is conducted by the INSITE group: INternational Study group for Identification and Treatment of Endofibrosis Questions: flia@mmc.nl
HAVE YOU HAD SURGERY FOR FLOW LIMITATIONS IN THE ILIAC ARTERY? HELP IMPROVE FUTURE CARE FOR ATHLETES We want to hear from you for an international research study WHO CAN PARTICIPATE You can participate if you: are 18 years or older have had surgery for flow limitations in the iliac artery (FLIA/endofibrosis) can complete an English questionnaire WHAT DOES PARTICIPATION INVOLVE Online questionnaire Approximately 15–20 minutes Completely voluntary JOIN THE STUDY contact email: flia@mmc.nl PLEASE SHARE Do you know other athletes who have had iliac artery surgery? Please send them this study information STUDY INFORMATION The study is conducted by the INSITE group: International Study group for Identification and Treatment of Endofibrosis Questions: flia@mmc.nl
Screenshot of warning message from Anthropic Fable during routine R coding assistance: "Fable 5's safeguards flagged this message. The safeguards are intentionally broad right now and may flag safe and routine coding, cybersecurity, or biology work. These measures let us bring you Mythos-level capabilities sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more: https://support.claude.com/en/articles/15363606"
Individual data table and representative plot of tracer dye concentration curves in 23 “normal young men” and 11 “normal young women” during rest and exercise (treadmill walking), with per-participant values reported for age, cardiac output, cardiac index, and mean circulation time (highlighted for this post). Plot shows a sample dye tracer accumulation curve in colorimeter density over time, peaking 10-sec after (presumably) injection and recirculation appearing after approx 10-sec during exercise. “TABLE 2.—Cardiac Output, Cardiac Index, and Mean Circulation Time at Rest and during Exercise in 23 Normal Young Men”. “FIG. 1.—Sample dye curves, at rest and during exercise, showing the extrapolated values after the beginning of recirculation.” “TABLE 3.—Cardiac Output, Cardiac Index, and Mean Circulation Time at Rest and during Exercise in 11 Normal Young Women”.
A figure showing near-infrared spectroscopy (NIRS) tracing over the vastus lateralis quadriceps muscle during a cycling VO2max interval, with indocyanine green (ICG) dye tracer injected into the median cubital vein at time 0:00. As the dye appears with blood flow in the VL under the probe, the optical signal rapidly reaches a peak at ~10-seconds after injection. The concentration briefly decays as the dye tracer flows out of the insonation site, then rises to a second and third peak at ~10-sec intervals, showing net transit time around the entire systemic circulation (from quadriceps, via venous blood back through lungs to heart, and out again via arterial circulation to the quadriceps). Figure title: “NIRS-ICG dye tracer monitored at vastus lateralis during cycling intervals. Systemic blood recirculation time ≈ 10 seconds”. Text label: “Dye concentration peaks under the NIRS probe every ~10-sec”. Caption: “Data: (manuscript in preparation) | Visuals: @jem_arnold”
m_rel <- lmer(smo2 ~ load * strength + (load | id), data = df) m_abs <- lmer(smo2 ~ load * strength + (load | id), data = df_abs) - all loads paired within participant - smo2 continuous from Train.Red normalised individual physiological range - load continuous abs (kg) or relative (%1RM) at prescribed workloads seq(0.2, 0.6, 0.2) and c(10, 15, 10) for each participant - strength continuous 1RM in kg
Picture of whiteboard and screen. Whiteboard shows hand-drawn hypothesised relationship between muscle oxygen saturation and resistance exercise load, for individuals with "high" and "low" strength. Load is plotted as absolute (kg) and relative to one-rep max (%). Screen shows resulting plots from modelling the same relationships with linear mixed effects models. Modelled plots mirror the hypothesised relationships on the whiteboard. A satisfying result!
Screenshot of code demonstrating how to use `mnirs::filter_mnirs()` on metabolic data. Only one of EITHER `width` OR `span` should be specified for respective breath- or time-averaging: mnirs::filter_mnirs( cpet_data, nirs_channels = VO2, ## not NIRS data 🤫 time_channel = TIME, width = 5, ## EITHER width alone is 5-breath averaging span = 5, ## OR span alone is 5-sec time-averaging partial = FALSE, ## exclude partial-length windows na.rm = TRUE ## ignore `NA`s )
Screenshot of code and plot showing how to use mnirs::resample_mnirs() to resample breath-by-breath metabolic data to 1 Hz sample rate. Plot shows BxB data points overlaid on the same interpolated data, showing how the time gaps between samples are filled. library(mnirs) library(ggplot2) ## breath-by-breath metabolic data cpet_data ## resample to 1 Hz and linearly interpolate cpet_resampled <- resample_mnirs( cpet_data, time_channel = TIME, resample_rate = 1, method = "linear" ) cpet_resampled ggplot(cpet_data, aes(TIME, VO2)) + theme_mnirs() + scale_colour_mnirs() + geom_point(data = cpet_resampled, aes(colour = "1 Hz")) + geom_point(aes(colour = "BxB"))
Screenshot of figure for representative trial with cycling workload showing incremental 5-min stages, with muscle oxygen saturation (SmO₂), heart rate (HR), volume of oxygen uptake (V̇O₂), blood lactate ([BLa]), and rating of perceived exertion (RPE) responses as a function of progressive graded intensity accross workloads from 100 to over 300 Watts. RPE, HR, and VO2 mostly respond linearly. BLa rises pseudo-exponentially. SmO2 falls curvilinearly.
Screenshot of Abstract: The intensity that elicits maximal fat oxidation (Fat max ) is recommended for training fat metabolism. However, it remains unclear whether Fat max leads to the highest fat oxidation rates during prolonged exercise. It was hypothesized that there are no differences in fat oxidation rates among 3 different exercise intensities. Therefore, fat metabolism was compared among 1-h constant load tests at Fat max , a higher and a lower intensity. A cohort of 16 male cyclists (28 ± 6 yrs, BMI: 22.5 ± 1.2 kg/m 2 ; n = 8 with maximal oxygen uptake [VO 2max ] of 50–60 ml/min/kg [ET]; n = 8 with VO 2max > 60 ml/min/kg [HET]) completed a maximal incremental cycling test, a submaximal incremental Fat max -test and, thereafter, three 1-h constant-load tests in randomized order at Fat max , one exercise stage below (LOW) and one above (HIGH). LOW, Fat max and HIGH were performed at 52 ± 13, 60 ± 13 and 70 ± 12 % VO 2max . Heart rate and blood lactate were significantly different (p < 0.001). However, the fat oxidation rate showed no difference (p = 0.61). This was also true within each subgroup (ET: p = 0.69, HET p = 0.61). In conclusion, the fat oxidation rate of endurance trained cyclists shows no difference between 1-h constant load exercise bouts at about 50–70 % VO 2max . The precision and necessity of Fat max -tests for controlling the training of fat oxidation are therefore debatable.
Screenshot of figure: Nine scatterplots showing relationships between respiratory exchange ratio (RER) and nutritional, exercise, and physiological variables. RER shows negative associations with fat intake, fitness level, and type I muscle fibre percentage, and positive associations with carbohydrate intake, glycogen, exercise intensity, and pre-exercise carbohydrate intake. Polynomial or linear trend lines with 95% confidence intervals and reported R2 values are overlaid. Figure description: Fig. 1 Relationships between respiratory exchange ratio (RER) and factors influencing RER. Best-fit regression lines based on univariable mixed effect models are shown, with fit indicated as R 2 . Best-fit lines using linear regression are shown in green, best-fit lines using polynomial regression are shown in red. Panel (g) is separated by the natural gap in the data of mean age > or <50 years. Shaded areas represent 95% confidence intervals. CHO carbohydrate, DM dry mass.
Screenshot of text: 5 | CONCLUSIONS Based on the findings of this systematic review, we suggest that researches measuring MFO and Fatmax using graded exercise protocols by indirect calorimetry should consider some key methodological issues that can considerably affect the accuracy, validity, and reliability of the measurement: (a) ergometer type, (b) metabolic cart used, (c) warm-up protocol (duration and intensity), (d) graded exercise protocol (stage duration and intensities imposed), (e) time interval selected for data analysis, (f) stoichiometric equation selected to estimate fat oxidation, (g) data analysis approach, (h) time of the day when the test was performed, (i) acute nutritional status (fasting time and standard meal conditions established before the incremental graded exercise protocol), and (j) testing days for MFO/Fatmax and VO2max assessment. Likewise, when comparing different studies, it is important to check whether the above-mentioned key methodological issues are similar in such studies to avoid ambiguous and unacceptable comparisons. Further studies are needed to develop a detailed guidelines for MFO and Fatmax assessment using graded exercise protocols by indirect calorimetry.
Two-panel figure comparing test-retest values for Trial A and Trial B of (a) peak fat oxidation (g·min⁻¹) and (b) FATMAX (%V̇O₂peak). Individual participant data show wide variability between trials, connected between trials with lines. Blue and red coloured example lines highlight substantial inter-individual variability in changes between trials, where values might substantially increase or decrease between trials A and B.
A screenshot of code excerpt from {mnirs} README https://github.com/jemarnold/mnirs/ demonstrating how to extract intervals by known time values to a list of data frames, then easily plot those intervals. Figure shows two facets with two muscle oxygen saturation channels each during rapid recover after exercise intervals for reoxygenation kinetics. Code excerpt below: extract_intervals(): detect events and extract intervals ## return each interval independently with `event_groups = "distinct"` distinct <- extract_intervals( nirs_data, ## channels blank for "distinct" grouping start = by_time(348, 1064), ## manually identified interval start times end = by_time(458, 1174), ## interval end time (start + 150 sec) event_groups = "distinct", ## return a list of data frames for each (2) event span = c(0, 0), ## no boundary modification zero_time = FALSE ## return original time values ) plot(distinct, time_labels = TRUE)
Screenshot of .ahk shortcuts: ; symbol, description, and literal execute string in quotations :*?:/=::{U+2260} ; ≠ not equal to "/=" :*?:~=::{U+2248} ; ≈ approx equal to "~=" :*C?:p=::{U+221D} ; ∝ proportional to "p=" :o?:<=::{U+2264} ; ≤ less than or equal to "<=" :o?:>=::{U+2265} ; ≥ greater than or equal to ">=" :*?:+-::{U+00B1} ; ± plus minus "+-" :o?:*::{U+00B7} ; · multiplication dot "*`" :o?:%::{U+00F7} ; ÷ division sign "%`" :*?:$$::{U+20AC} ; € euro "$$" :o?:**::{U+00B0} ; ° degree "**`" :oC?:alpha::{U+03B1} ; α alpha "alpha`" :oC?:beta::{U+03B2} ; β beta "beta`" :oC?:D::{U+0394} ; Δ delta "D`" :oC:u::{U+03BC} ; μ mu "u`" :oC?:tau::{U+1D70F} ; 𝜏 tau "tau`" :*?::``::{U+2234} ; ∴ therefore ":`" :*C?:V`*::V{U+0307} ; dot(V) "V*" :*C?:Q`*::Q{U+0307} ; dot(Q) "Q*" :o?:1::{U+2082} ; subscript[1] "1`" :o?:2::{U+2082} ; subscript[2] "2`" :o?:-1::⁻¹ ; superscript[-1]
Zoomed-in tracing showing distortion at the transition event of an ischaemic occlusion (release/reoxygenation), caused by over-parameterising a Butterworth digital filter order. This distortion may or may not be relevant, depending on our signal of interest. For example, if we care more about Oxy[Haeme] absolute values, or more about the kinetics of the response, we might use different filtering parameters.
Zoomed in to a 60-sec portion of data during cycling, showing regular oscillations in Oxy[Haeme] (O2Hb) associated with cadence.
A screen recording demonstrating new interactive hovertext and zoom functionality using Plotly in the {mnirs} app plot display.
Changelog mnirs 0.6.2 CRAN release: 2026-04-18
Screenshot: Preview AGENTS.md # `{mnirs}` Agent Reference **v0.6.2 | R | MIT** — workflow/dependency map. See `README.md` + vignettes for examples. | | | |---|---| | **Author** | Jem Arnold | | **Website** | https://jemarnold.github.io/mnirs/ | | **Citation** | \<coming soon\> | --- ## 1. `"mnirs"` — data frame/{tibble} subclass ### Metadata | Attribute | Type | Description | |---|---|---| | `nirs_channels` | character vector | NIRS signal column names | | `time_channel` | character(1) | time column name | | `event_channel` | character(1) | event/lap column name | | `nirs_device` | character(1) | device name (auto-detected) | | `sample_rate` | numeric(1) | Hz | | `start_timestamp` | POSIXct | absolute start datetime | | `interval_times` | numeric | set by `extract_intervals()` | | `interval_span` | numeric(2) | span used in `extract_intervals()` | `verbose` read from `getOption("mnirs.verbose", TRUE)` or pass explicitly. Access: `attr(data, "nirs_channels")`. --- ## 2. Pipeline ``` read_mnirs() └── resample_mnirs() # regularise time grid └── replace_mnirs() # clean invalid/outliers/NA └── filter_mnirs() # smooth ├── shift_mnirs() # optional: shift baseline ├── rescale_mnirs() # optional: normalise range └── extract_intervals() └── analyse_kinetics() └── plot() / print() ```
screenshot of excel spreadsheet and the same spreadsheet read into R. `hh:mm:ss` column in excel shows timestamp values e.g.: "13:52:59", but these are actually fractional numeric values behind the scenes: "0.578". When read by `mnirs::read_mnirs()` this returns an info message declaring "Estimated `sample_rate` = 50000 Hz" which is a few orders of magnitude wrong. Next {mnirs} update will fix this issue!
Screenshot of manuscript title and subheadings: "mnirs: An R Package for Reading, Processing, and Analysing Muscle Near-Infrared Spectroscopy Data Jem Arnold 2026-03-31"
screenshot of email text reading "Thanks, on its way to CRAN"
A code chunk and network visualisation diagram demonstrating a project pipeline for monitoring files and iterating function calls using R package {_targets}. Annotations on the networkvis diagram are "monitor unique participant files -> read and process -> synchronise & join -> analyse & plot -> summarise & export tables to file"
A raincloud plot showing the distribution of 11 metabolic threshold estimation methods (lactate and ventilatory thresholds) across relative workload (% Wpeak) from two trials each in 21 female and male competitive cyclists. Data from Yogev et al., 2023: https://doi.org/10.3389/fspor.2023.1143393. Thresholds are stacked vertically, colour-coded from red (FatMax; occuring at the lowest intensity) to pink (RER = 1.00, at the highest intensity), each displaying a kernel density curve, individual data points (n ≈ 42), and a white median marker. The intent is to visualise how various threshold estimates return values all along the range of relative intensity, thus suggesting that there is no single true intensity breakpoint, but rather that different methods find threshold-like behaviours at different intensities, depending on which metabolic variable and analyses methods are used.
a gif showing a use case for reading mNIRS data from file and performing standardised pre-processing steps, including resampling, replacing outliers and missing values, filtering and smoothing, shifting and rescaling dynamic range, inserting event markers, and downloading the processed data to an excel file for further analysis
Now I have the full picture. The problem was I was trying to use positron_editFile_internal with // ...existing code... markers, which the tool was applying at the top of the file. The correct approach is to use copilot_replaceString with exact literal context. Let me also check create_mock_interval to know what attributes it sets, then write the tests precisely. test-extract_intervals.R Now I have a precise picture. The problem with previous attempts was that the tool was trying to match oldString on content that appears at the top of the file, not at the actual target lines. Now I understand exactly:
Screenshot of .R script in Positron IDE with Positron assistant copilot edits suggested at the very top of the script, corrupting the true structure of the script, rather than at the right line location.
## pre-allocate row search counter apply_count <- 0L find_count <- 0L which_apply_count <- which(apply(data, 1L, \(.row_vec) { apply_count <<- apply_count + 1L all(nirs_channels %in% .row_vec) })) Find_result <- Find(\(.i) { find_count <<- find_count + 1L all(nirs_channels %in% data[.i, ]) }, seq_len(nrow(data))) ## compare rows checked data.frame( method = c("which(apply())", "Find()"), rows_checked = c(apply_count, find_count), result = c(which_apply_count, Find_result) ) #> method rows_checked result #> 1 which(apply()) 12042 41 #> 2 Find() 41 41
library(mnirs) ## development package library(bench) ## benchmarking ## read file to a data frame (internal fn) data <- mnirs:::read_file(example_mnirs("train.red")) ## column name strings to match all nirs_channels <- c("SmO2 unfiltered", "HBDiff unfiltered") ## return bench::mark results bench::mark( ## previous code searched through all rows which_apply = { which(apply(data, 1L, \(.row_vec) { all(nirs_channels %in% .row_vec) })) }, ## TIL about `Find()` which returns the first match and stops Find = { Find(\(.i) { all(nirs_channels %in% data[.i, ]) }, seq_len(nrow(data))) }, check = TRUE, iterations = 100 ) #> # A tibble: 2 × 6 #> expression min median `itr/sec` mem_alloc `gc/sec` #> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> #> 1 which_apply 31.5ms 34.89ms 27.1 2.56MB 86.0 #> 2 Find 2.73ms 3.03ms 304. 63.2KB 26.4
A cartoon meme template with a person looking at a computer screen and saying "show me a breakpoint". The computer answers "here is a breakpoint" and the screen shows the classic Jamnick et al, 2018 image of multiple lactate breakpoints on a lactate curve, with obviously no real "breakpoint" per se. The person answers "oh my god", as a commentary that when we ask a computational algorithm to find a breakpoint on a partial circular line, it will give us a breakpoint, whether or not it is physiologically meaningful. The corner of a circle, if you will.
library(mnirs) read_mnirs( example_mnirs("moxy_ramp"), ## example data file in {mnirs} package nirs_channels = c( smo2_left = "SmO2 Live", ## define nirs channel (column) names smo2_right = "SmO2 Live(2)" ) ) |> plot()
Scatterplot showing relationship between estimated VO₂peak (x-axis, 60-110 ml/kg/min) and cycling economy (y-axis, 60-110 W/L/min). Black dots represent observed values in 11 World Tour cyclists from Lucía et al. 2002 (DOI: 10.1249/01.MSS.0000039306.92778.DF), clustered between 90-105 ml/kg/min and 90-110 W/L/min. Black triangles represent observed values in a single World Junior TT champ from Rønnestad et al, 2019 (DOI: 10.1152/japplphysiol.00798.2018) clustered from 70-85 W/L/min. A blue diagonal line shows estimated cycling economy for Kristian at estimated peak power of 440 W. A blue dot at approximately 101 ml/kg/min and 57 W/L/min represents the observed value in the video. Title: "A super-human VO₂ after prolonged fatigue results in very poor cycling economy compared to fresh World Tour Pros". Subtitle: "A big caveat: the Lucía, 2002 ramp was much shorter and so peak powers and economy will tend to be higher". Caption: "Data: Lucia et al, 2002; Ronnestad et al, 2019; and made up. Visuals: @jemarnold".
A video frame capture at low resolution showing the TrainerRoad interface running the test protocol. Discernable is 7 intervals at what appears to be set as Kristian's threshold in the interface, which from another image we know is 340 W. A white vertical line demarcs the current workload around half way through the ramp. This video appears to be taken immediately after Kristian has reached maximal task tolerance during the session, so this seems reasonable to estimate as his peak power.
A line graph showing the relationship between breath-by-breath coefficient of variation (x-axis, range = 2-5%) and estimated 30-second mean VO₂peak (y-axis, 80-105 ml·kg⁻¹·min⁻¹). A blue dot at 101 ml·kg⁻¹·min⁻¹ represents a single observed VO₂ value as the possible upper bound of breath-by-breath variation. A downward-sloping black line shows that as CV (breath-by-breath variation) increases from 2% to 5%, the estimated 30-sec mean VO₂peak decreases from approximately 97 to 92 ml·kg⁻¹·min⁻¹. Additional points representing simulated breaths start tightly grouped around the mean where CV = 2%, and become more scattered at the higher CV values, according to the estimated distribution of VO2 breath measurements. Figure title: "A super-human single-breath VO₂ of 101 ml·kg⁻¹·min⁻¹ can represent a superb-but-believable 30-sec VO₂peak". The line is labelled "Estimated 30 sec mean VO2peak decreases under assumptions of higher breath by breath variation". "Visuals: @jemarnold".
Metabolic cart data displayed briefly at the end of Kristian's test protocol, showing a VO2 value of 101.1 ml/kg/min (circled), and 7797 ml/min. VCO2 reads 7250 ml/min and RER is 0.93. Test time appears to be 1:14:15. No other data is legible on screen.
image text extracted by LLM: Case 2 This patient, a man aged 23, gave no history of any definite traumatic incident, but explained that his hobby was sprint cycling. In August, 1944, after walking a distance estimated at... Histology.—The histological report on the excised segment of artery read as follows: “The artery is much decreased in size, and in consequence the internal and external elastic lamellae are tortuous and thickened. There is a slight increase in the amount of medial elastic tissue. The lumen is filled with fibrous tissue which has incorporated the intima. In this fibrous tissue are a few blood spaces which are lined by a simple epithelium. There is no evidence of any inflammatory reaction. There is nothing to suggest the cause for this now quiescent thrombosis” (Plate, Figs. 1, 2, and 3). A similar lesion or an intimal tear may be due to a “stretch-strain” or may be the result of traction on a muscular branch. The external iliac artery has been shown to be fixed to the ilio-psoas muscle at the level of Poupart’s ligament by the fusion of the iliac and transversalis fascia with the adventitia of the artery (Panton, 1923). The causes of the thrombosis in the above two cases would seem to be direct trauma in Case 1, who was struck by the falling tree, and indirect trauma due to stretch-strain from repeated flexion of the hip-joint in Case 2, the sprint cyclist. Prognosis In both cases, at the post-operative review, exercise caused the limb pulses to become palpably stronger, whereas pre-operatively the converse had been observed. Subjectively, both patients considered their condition to be much improved. The follow-up period cannot, however, be taken as sufficient for judgment of the long-term results.
This table presents key recommendations for macronutrients, hydration, and supplements across daily intake, pre‑exercise, during exercise, and post‑exercise periods for endurance exercise of varying durations. Carbohydrate needs range from 5–12 g/kg/day depending on daily training volume, with 6 g/kg in the 90 minutes before exercise or 10–12 g/kg over longer pre‑exercise windows, 30–90 g/h during exercise depending on duration, and 8–10 g/kg/day or 1.0–1.2 g/kg/h plus protein or caffeine in the first hours of recovery. Protein is recommended at 1.4 g/kg/day and 0.3 g/kg every 3–5 hours, with a 0.3 g/kg bolus immediately before or after exercise, 0.25 g/kg/h during very intense or eccentric sessions, and 0.3 g/kg within 0–2 hours post‑exercise. Fat intake should generally not fall below 20% of total energy, with possible temporary restriction only around carbohydrate loading or pre‑race if gastrointestinal comfort is a concern. For water, the table suggests an initial hydration plan of roughly 400–800 mL per hour, adjusted for individual sweat rate, sweat sodium, exercise intensity, body temperature, environmental conditions, bodyweight, and kidney function, and recommends replacing 150% of fluid losses after exercise. Sodium guidance includes an initial plan of 300–600 mg/h in heavy sweaters, self‑described “salty sweaters,” or prolonged exercise over 2 hours, with individual adjustment and an indication that post‑exercise water repletion is improved when drinks contain more than 60 mmol/L sodium (about 1380 mg/L). Nitrate recommendations are 300–600 mg nitrate (up to 10 mg/kg or 0.1 mmol/kg) from sources such as beetroot juice or whole beets within 90 minutes before exercise, with an option for multi‑day loading (for example, 6 days of high‑nitrate diet). Antioxidant supplements are discouraged immediately prior to exercise to avoid blunting training adaptation, with use reserved for situations where rapid recovery within 24 hours is requir
text extracted from image using LLM, not checked for errors: (a) What are the event/sport specific physiological, structural/neuromuscular and psychological determinants of success? (b) What are the individual athletes gaps to these event/sport-specific success determinants? (c) What are the exercise, training and recovery periodization decisions that are long (macro; months), medium (meso; weeks), and short term (micro; days/within day) used to address the individual athletes gaps in the context of the event/sport determinants? (d) What are the associated macro, meso, and micro purposefully periodized nutrition interventions that can support the periodized training and recovery stimuli or circumvent the event-specific performance determinants? Examples Macro (months) CHO: Daily CHO adjustments to match several month training phase (Inc. in general prep / high volume; Dec. in competition / low volume). PRO: Adjusting daily protein intake to training demands of a given phase (e.g., “1.5 g·kg⁻¹·day⁻¹” vs. “0.8 g·kg⁻¹·day⁻¹” in hard vs. recovery blocks). Iron: Adequate dietary iron intake linked to the stress/demands of training and individual athlete iron history and profile. Creatine: Use of creatine during a 2-month hypertrophy training block. Meso (weeks) CHO: Focus of CHO based fueling during race intensity endurance sessions to adapt the GI over several weeks for race day fueling. PRO: Increasing daily protein (~2 g·kg⁻¹·day⁻¹) intake over a body composition optimization phase to minimize skeletal muscle mass losses. Iron: Increase in iron intake via a supplement while at several week altitude camp. Creatine: Acute high-dose creatine loading phase to enhance a several week anaerobic short interval training block. Micro (days/within day) CHO: Acute manipulation of CHO availability to enhance recovery (increase) or induce aerobic adaptations (decrease). PRO: Optimizing within day protein type, amount and timi
Figure showing metabolic energy expenditure (kcal) as a function of power output (W) and external work output (kJ) from 100-400 W (400-1400 kJ), for a range of isolines of GE (gross metabolic efficiency). Figure titles: What is the plausible range of internal metabolic energy expenditure (kcal) for a given external work output (kJ), as a function of power output (W) over 60-minutes. GE is proportional to power output and intrinsic mechanical and metabolic factors, typically around 20%. The effect of intensity (RER; respiratory exchange ratio) on energy expenditure is minimal when compared to influence of external work output and GE. A 1:1 ratio of external work to internal energy expenditure is only accurate at the highest GE of ~25%.
Figure shows carbohydrate intake equivalent (grams per hour) as a function of power output (watts) from 100-400 W, for a range of isolines of RER (respiratory exchange ratio) from 0.7 to 1.00 (equivalent to the range of metabolic intensity). Each RER also has a band for gross metabolic efficiency (GE), which results in greater uncertainty for energetic output and therefore caloric and g/hr carb intake requirement. Figure titles: What is the plausible range of carbohydrate intake required to match 100% glycogen expenditure per hour? THIS IS NOT A RECOMMENDATION RER will be lowest at rest, somewhere in the middle in “zone 2”, and highest at and above FTP functional threshold power. Uncertainty ranges for GE derived from Ettema & Lorås, 2009. Efficiency in Cycling. A Review. https://www.researchgate.net/publication/24027428_Efficiency_in_cycling_A_review.
VO2max_rel <- 71 ## ml/kg/min weight <- 71 ## kg VO2max <- VO2max_rel / 1000 * weight ## ≈ 5.0 L/min power <- seq(300, 400, by = 10) ## W RER <- seq(0.85, 0.95, 0.02) ## RER at threshold GE.power <- (0.04824536 * log(250) - 0.05657161) + 0.025 ## % derived from Ettema & Lorås, 2009 GE <- round(seq(min(GE.power) - 0.02, max(GE.power) + 0.03, 0.01), 2) GE <- seq(0.2, 0.25, 0.01) ## use pretty GE range df <- expand_grid(power, RER, GE) |> mutate( O2kJ = 4.83535 * RER + 16.88348, ## kJ/L derived from Péronnet & Massicotte, 1991 VO2 = (power * 60 / 1000) / (GE * O2kJ), ## L/min VO2_rel = VO2 / VO2max, ## % )
Figure & table from Benítez-Muñoz et al, 2024. Differences in the ventilatory thresholds in treadmill according to training status in 971 males and 301 females: a cross-sectional study. https://pmc.ncbi.nlm.nih.gov/articles/PMC11829848/. Showing percent VO2max at ventilatory threshold 2 (close equivalence to critical power or functional threshold power) across a range of fitness levels in runners, from 85-92%. Running threshold will tend to be slightly higher %VO2max compared to cycling threshold, hence the low-end fitness estimate is higher than typically observed in cyclists. But the high-end estimate is probably similarly bounded around 90-92%.
Figure reproduced from Ettema & Lorås, 2009. Efficiency in Cycling. A Review. https://www.researchgate.net/publication/24027428_Efficiency_in_cycling_A_review. Figure description reads: Fig. 2 Overview of literature data explored in this review and used in the quantification of efficiency… d Same data as in b, but depicting a possible error of measurement of 5%. Thick curve is the average curve, based on the regression line in b. Thin curves indicate ranges if both metabolic rate and external power have deviation (error) of 5%, but in opposite directions. A thick vertical error bar indicates the same range if only one of the measures has a 5% deviation; the thin horizontal arrows indicate the efficiency difference following from this error.
Figure plotting individually observed gross efficiency (GE) values for cyclists across power output from 50 to 450 W, taken from published cross-sectional data in Ettema & Lorås, 2009. Efficiency in Cycling. A Review. https://www.researchgate.net/publication/24027428_Efficiency_in_cycling_A_review. A logarithmic model predicts GE as a function of PO, with 90% prediction intervals equivalent to ± 2.5% around the marginal estimate. e.g. at 250 W estimated GE = 21%, 90% PI = [18.5, 23.5].
Figure visualising the percent of a VO2max of 5.0 L/min required to produce functional threshold power (FTP) between 300 and 400 W, with isolines for gross metabolic efficiency (GE, %) from 20-25% and RER at each GE value between 0.85-0.95. The question was “is it possible to achieve an FTP of 380 W at a VO2max of 5.0 L/min (71 ml/kg/min at 71kg bodyweight)?” Based on these calculations, that would require somewhere around 24% GE and ~90% VO2max, which is just about physiologically plausible for an elite male cyclist… but would certainly take some work to achieve.
VS Code The package relies on the rstudioapi::viewer() function to display plot previews. By default it does not work in VS Code, but installing the R extension fixes it. It tricks the rstudioapi package into believing it is running in RStudio, and everything works.
A video showing simple usage of ggview::canvas() function to print a plot to the Viewer window at true scale. Plot parameters can be adjusted to suit text and image size/proportions, and save the final figure once, rather than faff about saving, checking, and re-saving figures until you find the combination of text size and image scale that works!