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It’s time to share our results. We’ve developed a revolutionary approach to eating for weight loss and metabolic health. It’s like GLP-1 drugs (e.g., Ozempic), but with food. We call it Satiety Per Calorie (SPC). Using data from 67,000 days of eating logged by thousands of users in our Hava app, we’ve now validated that our SPC method appears to work remarkably well. Today, for the first time, we are publicly releasing these results. Why SPC matters The SPC method offers a potential new and innovative way to treat or prevent obesity, metabolic syndrome, and related disorders. It may also help improve body composition. Background Our goal is to guide people toward foods, meals, and eating patterns that lead them to naturally eat fewer calories—without consciously restricting portion sizes—while still getting plenty of protein and other essential nutrients. This significant reduction in caloric intake occurs while allowing people to eat as much or as little as they desire. The dramatic reduction in calorie intake we’re demonstrating could yield results similar to those documented with GLP-1 drugs, including weight loss, improved type 2 diabetes management, a decreased risk of several diseases, and even extended lifespan. While GLP-1s are satiety drugs, we focus on satiety foods. Because our approach is non-pharmaceutical, it may avoid some of the negative side effects of GLP-1 drugs, such as nausea and potentially excessive muscle loss. It also bypasses the high cost associated with these drugs. For the past year, our Hava Eat nutrition app has made this approach accessible. Users can log their meals simply by taking a photo. Our state-of-the-art AI estimates the ingredients and amounts within seconds, instantly providing feedback. Our approach predicts which foods will lead to higher or lower overall calorie consumption. The key factors include protein percentage, energy density, fiber content, and estimated hyper-palatability. These components are well-supported by existing research. Our proprietary composite SPC score ranges from 0 to 100. We designed it to predict outcomes observed in human trials. Although we’ve seen encouraging results and have heard positive reports from our customers, we have not until now validated the scoring directly with user data. Data and results We now have 67,000 days of eating logged by thousands of users over the past 11 months. We used this data to examine the relationship between the average SPC score of a day’s meals and the total number of calories consumed that day. We excluded days that appeared incomplete or unreasonably large (below 420 or above 8,000 calories). Here, we show all days with an SPC score between 20 (extremely low) and 80 (extremely high). The vast majority of days fall within this range. Results Comments As you can see, there is a remarkably strong correlation between the SPC score of the foods consumed and the total calories eaten. At lower SPC scores, people eat nearly twice as many calories as they do when consuming high-SPC foods. At a balanced midpoint (SPC of 50), daily caloric intake falls somewhere between these two extremes. The relationship is roughly linear. This suggests that no matter where someone starts, increasing their SPC score by even a modest amount is likely to be associated with a noticeable decrease in caloric intake, potentially resulting in relative weight loss. Our average daily caloric intakes may seem low, likely due to including incomplete days (where users didn’t log all meals). We set a minimum cutoff of 420 calories to include a day’s data, but some people may log only one or two meals. This skews the average downward. If we increase the cutoff to a minimum of 1,000 calories to account for more complete days, the low-SPC range shows average intakes of around 2,500 calories—more in line with typical consumption. However, this higher cutoff skews the data at high SPC levels, where many days fall below 1,000 calories. It’s important to note that our app does not pressure customers to restrict their calories. Instead, it promotes two daily goals: A) Maintaining a good SPC range for the day (50-70 is a reasonable target). B) Eating more protein (with at least 1.6 g/kg of ideal body weight as a minimum recommendation). Additional analyses We also examined correlations between each of the SPC components and total calories consumed. These components include protein percentage, fiber (grams per 1,000 calories), energy density, and hyper-palatability (based on combinations of nutrients like fat and sugar, fat and salt, and carbs and salt). We plan to publish these graphs later, but preliminary results show that while each factor individually correlates with caloric intake, none is as consistently strong as the composite SPC score. Hyper-palatability shows the strongest individual correlation, and fiber the weakest. Out of curiosity, we also examined net carbohydrate percentage relative to total intake. While there is a correlation, it’s less consistent than with SPC. The relationship for carbohydrates peaks around 40% and shows lower caloric intake at both lower and higher ranges, but even zero carbs don’t correlate with the same low-calorie levels as a high SPC score. Implications These findings have important implications. Our SPC scoring, which combines several well-researched factors, appears significantly stronger and more consistent than any individual factor from other dietary approaches. Another advantage is that SPC is diet-agnostic. It can be applied to any dietary preference, making it highly flexible. The demonstrated effect—halving caloric intake when moving from low- to high-SPC foods—is large, but such a drastic dietary change might be challenging for most people. However, even a more modest shift, say from an SPC of 30 to an SPC of 55, corresponds to about a 20% reduction in caloric intake, which could have very meaningful impacts on weight and health. If these initial findings hold up through further testing, SPC could become a more effective and more flexible approach than current options. We intend to continue our research and gather more data as more people use the Hava Eat app. We will also follow up with results on body weight and metabolic health factors as they become available. Summary Users of our Hava Eat app consume significantly fewer calories when they choose foods with higher SPC scores—despite there being no explicit goal within the app to reduce calorie intake. We are reporting this strong correlation for the first time today. This likely results from higher SPC scores encouraging people to eat foods that are higher in protein percentage, lower in energy density, richer in fiber, and less hyper-palatable. The combination appears more powerful than any single factor alone. With this approach, the Hava app offers a flexible, simple way to guide better eating habits. Our goal is to make it simple to eat better, no matter what dietary style you prefer. If you’re interested, you’re welcome to download the Hava Eat app from the app stores. We’re continually improving it, often releasing significant updates every week. A big thank you to our hard-working and talented team, and sincere gratitude to our customers. We couldn’t do this without you. We also appreciate all the feedback we’ve received about SPC and the Hava app over the past few years. We welcome your continued input!

Allow me to interject that I am literally prescribing tirzepatide and semaglutide all day, every day, and I work at a major bariatric surgery site as well. These are lifesaving interventions that allow people to get halfway to their dream physique and state of health. AND you’re only getting the other half with 1) exercise, and 2) the type of permanent protein prioritization that is central to the SPC approach. These things should not be—and in fact definitely are not—mutually exclusive. SPC and these interventions dovetail so incredibly well — that the diets prescribed before, during, and after bariatric surgery are the highest SPC diets you can possibly find. The same protein-forward diet approach is given [or at least should be given] to anyone on a GLP1RA. You already know all of this, which I think is why you know that all of these interventions can peacefully coexist. ✌🏼😁