AI algorithm can predict when people will drop out of digital weight loss programs

A new study from CSIRO, Australia’s national science agency, has shown the potential benefits of using an artificial intelligence (AI) algorithm to improve engagement and health outcomes from digital health programs.

AI algorithm can predict when people will drop out of digital weight loss programs
CSIRO scientists found that by using machine learning, they could accurately predict from week three when a user would disengage from an online weight loss program. Image Credit: CSIRO

The study, published in The Journal of Medical Internet Research, used a CSIRO-developed algorithm that uses AI to predict when a person will drop out of an online weight loss program.

With 67 per cent of Australian adults overweight or obese, 8 per cent of the burden of disease in Australians due to obesity and about 5 per cent due to dietary risks, supporting that health intervention and lifestyle change is more important than ever.

Recently, there has been an increase in digital behavioral intervention programs to help reduce modifiable health risk factors such as obesity and lack of exercise.

Engagement in these programs is critical to interventions that achieve successful behavior change and improvements in health.

The study of over 59,000 participants from CSIRO’s Total Wellbeing Diet is a world-first in analyzing data from a large, online, commercial weight loss program.

It found by using machine learning, scientists can accurately predict from week three when a user is going to disengage from an online program.

Machine learning is a method of “teaching” a computer to recognize patterns by training it on data containing examples.

Dr Aida Brankovic, research scientist with CSIRO’s Australian e-Health Research Centre and lead author on the paper, said there is currently little evidence for how people engage with these programs, especially in terms of when and why they quit.

“Despite the growing application and adoption of technology in health interventions, one persistent challenge remains – engagement deterioration or non-usage attrition,” Dr Brankovic said.

“The successful machine learning model used in the study predicted disengagement from the program on a weekly basis, based on the user’s total activity on the platform, including weight entries from the weeks prior.

“With this information, digital health interventions can become more tailored, supportive and offer users a greater chance of making long-term lifestyle changes.

“Importantly, the machine learning model used in this study can also be adjusted and applied to large cohorts of data in other online programs requiring engagement.”

CSIRO research scientist and co-author of the paper, Dr Gilly Hendrie, said effective engagement in a digital health program is vital to success and yet also challenged by the fact that it is open to the user’s discretion.

It is hoped that the findings of this study and future work focused on other factors of engagement with digital health programs will lead to improved experience for users, including tailored content at critical time points. Past habits really do predict future behavior. But if we can predict when that behavior will kick in and adapt so we can re-engage someone – we’ve got a better change of helping them make long-lasting changes to their health and lifestyle.”

Dr Gilly Hendrie, Research Scientist, CSIRO

Journal reference:

Brankovic, A., et al. (2023). Predicting Disengagement to Better Support Outcomes in a Web-Based Weight Loss Program Using Machine Learning Models: Cross-Sectional Study. Journal of Medical Internet Research.


The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
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