Quantifying Self Experiments
Damien Blenkinsopp
Damien Blenkinsopp (IRL)
Citizen Scientist, Entrepreneur, Host @ Quantified Body Podcast
Damien Blenkinsopp is an ex-strategy consultant / analyst, who turned his analytical skills towards optimizing the body and mind over 10 years ago. He is a frequent Quantified Self speaker and explores the practical value of using new data, lab tests and sensors to optimize body and mind in the Quantified Body podcast. He is a founder of several companies and currently manages two internet and health related businesses. Damien is a London Business School MBA, speaks 4 languages, and has lived in a wide variety of countries across 5 continents over the last 20 years, much of that time living as a digital nomad.
Damien Blenkinsopp discusses the challenges of self-experimentation,
If it’s not a habit in your life, it’s not going to change your life. That is the way you have to think about these things.
Summary
- Use "tool stacking" by combining multiple interventions simultaneously to create a strong, measurable signal that overcomes environmental noise and potential placebo effects. - Focus eighty percent of effort on selecting biomarkers that are accurate, stable, and well-benchmarked to ensure tracking leads to valid, actionable health insights. - Establish a solid baseline before starting any experiment and use checklists to control for variables such as sleep, stress, and human error that can disrupt data. - Repeat self-experiments at least two or three times to verify results and train biological mechanisms, as single data points are often skewed by inherent measurement errors. - Prioritize high-impact, high-certainty tactics to maximize the return on effort while avoiding low-impact interventions that function as "time wastes."
Article
Quantifying Self Experiments: Damien Blenkinsopp’s Search For Signal In A World Of Noise
At Biohacker Summit 2016 London, A Citizen Scientist Argued That The Hardest Part Of Self-Tracking Was Not Collecting Data, But Trusting It
On 21 May 2016, at Biohacker Summit 2016 London, Damien Blenkinsopp took the stage with a message that cut through the futurist glow of wearable sensors and wellness dashboards. The promise of self-quantification, he suggested, was real. But so were its distortions.
Blenkinsopp, a citizen scientist, entrepreneur and host of the *Quantified Body Podcast*, framed self-experimentation not as a glamorous frontier of personal optimization, but as a methodological struggle against noise. Anyone, he implied, could collect numbers. The true challenge was knowing whether those numbers meant anything at all.
“I spend eighty percent of my time figuring out the biomarker and the tool before I do any experiment to ensure I am investing my effort in the right place,” he said.
It was a deceptively simple line, but it captured the philosophy underpinning his talk, *Quantifying Self Experiments: Finding Signal Through Tool Stacking & Biomarkers*. In a culture often intoxicated by novelty, Blenkinsopp’s emphasis was almost austere: choose carefully, measure cautiously, repeat relentlessly.
The N=1 Dilemma
Self-experimentation has always carried an alluring logic. If medicine deals in averages, then the individual can become a laboratory of one, testing diets, fasts, supplements and routines against the body’s own feedback. But Blenkinsopp insisted that this ideal often collided with biological complexity and technological limitation.
Sleep changes. Stress intrudes. Circadian rhythms shift. Human error creeps in. Sensors fail to detect subtle changes or generate misleading variation. A single reading, he warned, could easily become a false story.
That is why, he said, most experiments had to be repeated two or three times before they yielded anything worth believing. One result was rarely enough. Too many variables, visible and invisible, sat inside the chain between intervention and interpretation.
His talk became, in effect, an argument for scientific humility in the age of personal data.
Tool Stacking As A Strategy For Forcing Clarity
Blenkinsopp’s answer to the problem of weak signals was what he called “tool stacking”. Rather than testing one intervention at a time and risking results too faint to detect, he preferred to combine multiple tactics that were all intended to move the same biomarker in the same direction.
“If I stack different tools together, I create one big result that is easy to track, allowing me to find a clear signal that gets past the noise,” he said.
The logic was pragmatic. If one tactic produced only a marginal shift, it might disappear into the static of daily life. But several aligned interventions could create a larger, more measurable effect. In the language of biohacking, this was less about purity of design than about overcoming the real-world messiness of living systems.
He was equally clear that not every tool deserved attention. He screened tactics according to certainty, impact and effort, rejecting low-value interventions as “time wastes” and treating high-impact, low-certainty approaches as risky “big bets”. The ideal was a tactic with a strong evidence base, a large potential upside and a feasible way to measure results.
The Gut Blitz And The Trouble With Microbiome Data
One of the most vivid examples in the presentation was what Blenkinsopp called the “gut blitz”, a stacked protocol aimed at improving gut microbiome diversity. Drawing on ideas from functional medicine practitioners and gut health specialists, he assembled an aggressive combination of interventions, including probiotics and other digestive supports, in search of a measurable rise in diversity scores.
The ambition was clear enough. Greater microbiome diversity is often associated with better health, while antibiotic use and chronic illness can reduce it. Blenkinsopp had data showing a plunge in his own diversity after antibiotics, followed by improvement during the gut-focused protocol.
But the story refused to settle into clean triumph. Side-by-side tests taken on consecutive days produced notably different scores. What appeared at first glance to be progress was complicated by inconsistency in the testing itself.
This was, in many ways, the beating heart of the talk. Biomarkers can look authoritative while remaining unstable. A graph can suggest momentum while concealing methodological weakness. The question was never simply whether a protocol worked, but whether the chosen metric was robust enough to reveal the truth.
Fasting, Metabolic Adaptation, And Multiple Returns On Effort
If the gut blitz exposed the fragility of some measures, Blenkinsopp’s discussion of fasting showed why certain interventions remained especially attractive. He described cyclic fasting and Fast Mimicking Diets, or FMDs, as unusually powerful because they acted on multiple systems at once.
“Whenever you can, look for a tool or tactic where you will get multiple benefits at the same time; that is how you get a better return on your effort,” he said.
In his account, fasting was not just a weight-management tool. It could affect ketone production, body composition, metabolic adaptation and cellular repair pathways. He described using body composition measurements, ketone tracking and IGF-1 levels to mirror published studies and compare his own responses with research findings.
One striking result was a reported gain in lean mass following an FMD cycle, alongside a drop in IGF-1 that he treated as a proxy for deeper regenerative processes. He acknowledged the limits of proxy measurement, noting that the ideal biomarker, such as direct stem cell assessment, was often inaccessible or prohibitively expensive. But in the absence of perfect data, he argued for using markers that were at least directionally meaningful and tied to existing evidence.
Why Biomarkers Matter More Than Gadgets
For Blenkinsopp, the real work of self-quantification lay in biomarker selection. Early in his tracking life, he said, he monitored almost everything, only to realise that much of it was useless for decision-making. More data did not necessarily create more clarity. Sometimes it only multiplied distraction.
“It is vital to understand the accuracy and sensitivity of the biomarkers you use, as they tell you whether to keep doing something, drop it, or change it,” he said.
That meant asking difficult questions. Was the biomarker accurate? Was it consistent? Was it stable enough over time to be meaningful? Was there a credible benchmark for what “good” looked like? Was it close enough to the end goal to act as a trustworthy proxy?
These questions pushed against the consumer fantasy that all health metrics are equally valuable. Blenkinsopp argued instead for prioritising high-impact indicators such as heart rate variability, high-sensitivity C-reactive protein and mitochondrial functional profiles, metrics more likely to generate useful longitudinal insight into inflammation, resilience and longevity.
When Devices Mislead
Among the more sobering moments in the presentation was his discussion of blood glucose monitors. Using readings taken within seconds of each other, Blenkinsopp showed how variance in a commonly used device could produce results that implied very different health interpretations.
A small discrepancy on paper could mean the difference between reassurance and alarm. To compensate, he had begun taking multiple readings and averaging them before making decisions.
The problem, as he framed it, was not merely technical. It was philosophical. People were increasingly making lifestyle and clinical judgments based on tools they did not fully understand. Sensor limitations, algorithm changes, lab handling issues and sample collection errors could all reshape the final number. In some cases, the user was not measuring their biology so much as measuring the weaknesses of the system around it.
Baselines, Checklists, And The Discipline Of Repetition
Blenkinsopp urged his audience to begin with baselines. Before changing anything, know what “normal” looks like. Ketones, blood glucose and other biomarkers vary across the day, across stress states and across sleep quality. Without a baseline, an experiment has no real point of comparison.
He also stressed the value of checklists and journaling. If a protocol involved many moving parts, memory alone could not be trusted. A missed supplement, a bad night of sleep, a flight, unusual stress, all of it could contaminate a result. Better to document the deviations than pretend they did not happen.
“When you find something that works, keep repeating it. You are training a mechanism in your body to get better, much like going to the gym to grow muscle,” he said.
The metaphor was apt. For Blenkinsopp, self-experimentation was not a hunt for magic bullets but a process of repeated, disciplined exposure. Biological systems adapted through iteration, not revelation.
Beyond Average: The Politics Of Benchmarks
Perhaps the most quietly radical thread in the talk was his critique of reference ranges. A lab result, he noted, often tells you where you stand relative to a sampled population, not relative to an optimal state of health. In some cases, the comparison group may itself be highly skewed, composed of unusually ill or unusually health-obsessed people.
A number can therefore appear normal while masking dysfunction, or appear alarming when compared with an elite subset. Without understanding the benchmark, interpretation becomes shaky.
That warning extended beyond technical caution. It touched something larger in contemporary health culture: the ease with which data can project certainty while obscuring context. Metrics do not speak for themselves. They inherit the assumptions of the systems that generate them.
A More Mature Vision Of Biohacking
At Biohacker Summit 2016 London, Damien Blenkinsopp offered something more durable than a list of hacks. He offered a framework for skepticism, one built on pre-screening, replication, strategic selection and a refusal to confuse quantity of data with quality of insight.
In an era obsessed with monitoring every heartbeat and fluctuation, his message felt unexpectedly disciplined. Self-knowledge, he suggested, did not come from piling up dashboards. It came from finding the rare signals that survived scrutiny.
The quantified self, in his telling, was not a machine to be endlessly instrumented. It was a living system, noisy, adaptive and resistant to easy conclusions. That was not a reason to stop experimenting. It was a reason to experiment better.
Part of Biohacker Summit 2016 London