Research
Every numbered claim on the sales page is listed here with the source behind it, what it found, and what it doesn’t show.
Walter B. Cannon, The Wisdom of the Body (W. W. Norton, 1932).
Read the study at Internet Archive — The Wisdom of the Body ↗
Cannon was a Harvard physiologist who named and described homeostasis. It’s the set of automatic processes that keep things like body temperature, blood sugar, and blood pressure stable.
What it doesn’t show: Cannon studied physical stability inside the body. Applying homeostasis to income is part of our method, not part of his research.
Ian A. Hatton, Eric D. Galbraith, Nono S. C. Merleau, Teemu P. Miettinen, Benjamin McDonald Smith, and Jeffery A. Shander, “The human cell count and size distribution,” PNAS, September 2023.
The authors compiled cell size and count data for every major cell type from more than 1,500 published sources, and estimated about 36 trillion cells in a man, 28 trillion in a woman, and 17 trillion in a 10-year-old child. Earlier estimates put the total at around 30 to 37 trillion. The 30 trillion on our page is a rounded figure within these estimates.
William Samuelson and Richard Zeckhauser, “Status quo bias in decision making,” Journal of Risk and Uncertainty, 1988.
Read the study at Journal of Risk and Uncertainty (Springer) ↗
Through a series of decision experiments and real-world data, including university employees’ health plan choices and retirement fund allocations, they showed that people stick with their current option much more often than they should.
What it doesn’t show: the study measured decisions in general, not income decisions specifically.
Archy O. de Berker, Robb B. Rutledge, Christoph Mathys, Louise Marshall, Gemma F. Cross, Raymond J. Dolan, and Sven Bestmann, “Computations of uncertainty mediate acute stress responses in humans,” Nature Communications, March 2016.
Read the study at Nature Communications ↗
Participants learned which images predicted an electric shock, and cortisol levels confirmed the task was stressful. Self-reported stress, pupil size, and skin conductance all rose and fell with how uncertain people were. A small chance of a shock caused significantly more stress than knowing a shock was certain.
What it doesn’t show: this is a lab study using shocks. It shows the body treats uncertainty as a threat. It doesn’t measure anything about money.
Bogdan Draganski, Christian Gaser, Volker Busch, Gerhard Schuierer, Ulrich Bogdahn, and Arne May, “Neuroplasticity: Changes in grey matter induced by training,” Nature, 2004.
People who learned to juggle over three months showed growth in brain areas that process movement. Those areas shrank back after they stopped practicing.
Background: Donald Hebb’s 1949 book, The Organization of Behavior, is the source of the idea that brain connections strengthen with repeated use.
What it doesn’t show: this study shows learning physically changes the brain. It doesn’t test beliefs specifically.
Brooks King-Casas, Damon Tomlin, Cedric Anen, Colin F. Camerer, Steven R. Quartz, and P. Read Montague, “Getting to know you: reputation and trust in a two-person economic exchange,” Science, 2005.
Pairs of people played a repeated money-trust game while their brains were scanned. As they learned whether their partner could be trusted, a reward area of the brain began predicting their trust decisions before the partner acted.
What it doesn’t show: this is a lab study over a few rounds of a game. It shows the brain learns who to trust from experience. It doesn’t track betrayal over years.
Rollin McCraty, Mike Atkinson, Dana Tomasino, and Raymond Trevor Bradley, “The coherent heart: Heart-brain interactions, psychophysiological coherence, and the emergence of system-wide order,” Integral Review, 2009.
Read the study at Integral Review (PDF) ↗
The paper defines coherence as heart rhythm, breathing, blood pressure, and brain activity working in sync.
Disclosure: the authors are HeartMath Institute researchers, and a HeartMath researcher consults on our current study. This paper supports our definition of coherence only.
Transformation Central LLC is running a two-year study of 28 people using the method in a group setting. Jackie Waterman, MT, a HeartMath researcher, is consulting on protocol setup, data collection, and preparing the recordings for analysis.
The study asks two questions. First, can a group using the method increase its combined income tenfold in two years? The group’s goal is $1 billion. Second, can every participant increase their income, their enterprise value, or both over those two years? In 1:1 work, every client has reported an increase in their income. This study tests whether that result holds in a group, with the results tracked independently.
The 28 participants include entrepreneurs, founders, W2 employees, and people who started with no income. All 28 started with an income under $500K. They were recruited from our email list and social media. Some had worked with us before, and some were new.
Revenue results are tracked by a third-party bookkeeper, Jay Woolvine of Quick Figures Bookkeeping. Enterprise value growth is based on two private valuations completed before fundraising. Participants who started with no income are measured the same way as everyone else, starting from zero.
Using HeartMath technology systems, stationary HRV and coherence levels are being tracked, and pre-post changes will be calculated. How often participants practice with the HeartMath app during the two-year target period is also being observed. EEG brainwave measurement is being added. Participants also self-report changes in mood, emotional reactivity, anxiety and depression levels, relationships, and sleep.
Through week 17 of the study (October 2026), the group has added $39,006,489. This includes $34,500,000 in enterprise appreciation (valuation) and $4,506,489 in revenue increases (cash). Final results will be published at the end of the two years. Clinical trials are planned to begin by March 2027.
Between 2019 and 2024, clients in our group programs and 1:1 work reported over $2 billion in combined increases in personal income and business revenue. Clients reported these figures as results came in, and they were added up at the time. They have not been independently verified.
Typical results in this period were 2 to 4 times starting income, and the lowest increases were around double.
The table below shows our 22 strongest results from the same period, with names removed. About 11 of these clients had results far above typical. All figures were reported by the clients.
| # | Starting income | Ending income | After finishing | Multiplier |
|---|---|---|---|---|
| 01 | $0 (laid off, 5 months prior) | $60K cash plus $1.4M over 3 years | — | 1.46M × |
| 02 | $0 (3 months prior) | Co-owner of two $20M businesses | — | 40M × |
| 03 | $0 (stay-at-home parent) | $200K+ | $500K a year | 500K × |
| 04 | $0 (6 months prior) | $100K a month | — | 1.2M × |
| 05 | $84K a year | $1.2M a year | $1.5M a year | 17.8 × |
| 06 | $12K a year | $80K a year | $200K a year | 16.6 × |
| 07 | $120K a year | $800K a year | $360K a year, part time | 6.6 × |
| 08 | $56K a year (employed) | $150K a year | $150K a year | 2.6 × |
| 09 | $250K a year | $47M | — | 188 × |
| 10 | $0 (fundraising) | $350K, Series A | — | 350 × |
| 11 | $48K a year | $1.2M a year | $1.2M a year | 20 × |
| 12 | $0 (6 months prior) | $1.2M a year | $1.3M | 1.3M × |
| 13 | $50K a year | $1.2M a year | $1.5M a year | 30 × |
| 14 | $250K a year take-home ($6M company) | $3M plus two $20M contracts | $1.5M a year | 12 × |
| 15 | $0 (fundraising) | $225K, Series A | — | 225 × |
| 16 | $240K a year | $1.2M a year | $15M | 62.5 × |
| 17 | $120K a year (employed) | $260K a year | $360K a year | 3 × |
| 18 | $12K a year | $1.5M a year, passive | — | 125 × |
| 19 | $0 (18 months prior) | $37M deal | — | 37M × |
| 20 | $2K a month | $204K a year | $325K a year | 162.5 × |
| 21 | $240K a year | $2M a year, $1M saved | $10M a year | 41.6 × |
| 22 | $800K a year | $5M a year | — | 6.25 × |
22 clients. Every one increased their income. Where the starting income was zero, the multiplier is shown against the ending figure. “—” means we have no later figure.
What those results look like from a $40K month
The share of our 22 top-performing clients in each range, and what that range means if you start at $40,000 a month.
An increase of 160% on $40K is $40K plus 1.6 × $40K, or $104K. Each figure is calculated the same way. These are our top 22 results, not typical results. Typical results are 2 to 4 times starting income.