A NEW study from CSIRO has shown the potential benefits of using an artificial intelligence (AI) algorithm to improve engagement and health outcomes from digital health programs.

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, the study says supporting health intervention and lifestyle change is more important than ever.

The study was conducted with more than 59,000 participants from CSIRO’s Total Wellbeing Diet, and is a world-first in analysing 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 recognise 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,” 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 focussed on other factors of engagement with digital health programs will lead to improved experience for users, including tailored content at critical time points,” Hendrie said.

“Past habits really do predict future behaviour. But if we can predict when that behaviour 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.”