Diet tracking essential element for effective weight loss: Study

Keeping track of everything you consume in a day is difficult and harder to keep up with over time. Sadly, a recent study suggests that diligent tracking is an essential element of effective weight loss. The findings of the study, published in Obesity, suggest that accurate tracking is not required for significant weight loss. Users of a commercial digital weight loss program who self-reported their food intake were followed for six months by researchers from UConn, the University of Florida and the University of Pennsylvania.

The goal of the study was to determine ideal diet tracking levels for predicting 3%, 5% and 10% weight loss after six months. “We partnered with WeightWatchers, who were planning to release a new personal points program, and they wanted to obtain empirical data through our clinical trial,” co-author and Department of Allied Health Sciences says Professor Sherry Pagoto.

Pagoto explains that the new program takes a personalized approach to assigning points, including a list of zero-point foods to eliminate the need to calculate calories for everything. “Diet tracking is the cornerstone of all weight loss interventions, and it is the biggest predictor of outcomes. This program eases the burden of that work by allowing for zero-point foods that don’t need to be tracked.”

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Researchers and developers are looking for ways to make the tracking process less cumbersome, because for too many programs, says Pagoto, users can feel like they need to count calories for the rest of their lives: “It’s just Not sustainable. Do users need it.” To track everything every single day or not necessary?”

With six months of data, Ran Xu, assistant professor in the Department of Allied Health Sciences, was interested to see if there was any way to predict outcomes based on how much diet-tracking participants ate. Ran Xu and Allied Health Sciences Ph.D. student Richard Baynor analyzed the data to see if there were patterns associated with weight loss success from a data science perspective. Using a method called receiver operating characteristics (ROC) curve analysis, they found how many days people needed to track their food to reach clinically significant weight loss.

“It turns out, you don’t need to be on track 100% each day to be successful,” says Xu. “Specifically in this trial, we find that people only need to track about 30% of the days to lose more than 3% and 40% of the days to lose more than 5% or so.” 70% of days require more weight loss than 10% more weight. The key point here is that you don’t need to track every day to lose a clinically significant amount of weight.

This is promising because Pagoto points out that the target for a six-month weight loss program is typically 5% to 10%, a range where health benefits have been seen in clinical trials.

“Many times people feel they need to lose 50 pounds to be healthy, but in reality we start to see changes in things like blood pressure, lipids, cardiovascular disease risk and diabetes risk when people are about 5 to 10 % of their weight,” says Pagoto. “This can be accomplished if participants lose approximately one to two pounds a week, which is considered a healthy pace of weight loss.”

Xu then looked at the trajectories of diet tracking over the six months of the program. The researchers found three distinct trajectories. One they call high trackers, or super users, who tracked food most days of the week over a full six months, lost about 10% of their weight on average.

However, many participants belonged to a second group who began tracking regularly, before their tracking gradually decreased over time past the four-month mark, to just one day per week. He still lost about 5% of his weight.

A third group, called low trackers, began tracking only three days a week, and dropped to month three, where they stayed for the rest of the intervention. On average, this group lost only 2% of their weight.

“One thing that is interesting about this data is that, often in the literature, researchers only look at whether there is a correlation between tracking and overall weight loss outcomes. Ran took a data science approach to the data and found that the story There’s a lot more.” says Pagoto. “We’re seeing different patterns of tracking now. This will help us identify when to provide additional support and who will need it most.”

The patterns can help inform future programs that can be tailored to help improve user tracking based on which group they fall into. Future studies will dig deeper into these patterns to understand why they arise and hopefully develop interventions to improve outcomes.

“To me, the exciting thing about these digital programs is that we have a digital footprint of participant behavior,” says Xu.

“We can drill down to the micro-gritty of what people do during these events. The data can inform precision medicine approaches, where we can take this data science perspective, identify patterns of behavior and formulate a targeted approach.”

Digitally delivered health programs give researchers a lot of data they never had before from which to gain new insights, but this science requires a multidisciplinary approach.

“Previously, it felt like we were flying in the dark or just going by anecdotes or self-reported measures, but it’s different now that we have so much user data. We need data to make sense of all this data. science is needed. This is where team science is so important because clinical and data scientists think about the problem from very different perspectives, but together, we can generate insights that none of us can’t even do it on its own. This should be the future of work,” says Pagoto.

Xu agrees: “From a data science perspective, machine learning is exciting, but if we just have machine learning, we only know what people do, but we don’t know why or what to do with this information.” .. That’s where we need clinical scientists.” Like Sherry for understanding these results. That’s why team science is so important.”