Class III Obesity May Benefit Most From AI-CGM Engagement: Stephanie Kim, MD, MPH

For patients with class III obesity, who are facing the greatest cardiometabolic burden and the fewest tolerable treatment options, highly engaged use of the Signos System was associated with more than 7% total body weight loss at 6 months, a finding that principal investigator Stephanie Kim, MD, MPH, described as "especially encouraging."

The result was part of a broader pattern: across body mass index (BMI) categories, sex, and most age groups studied, higher engagement consistently tracked with greater weight loss, and highly engaged users logged meals roughly 4 times more frequently and exercised more often than their less-engaged counterparts, suggesting that app engagement reflected genuine behavioral change rather than passive tracking.

In the second part of an email interview with The American Journal of Managed Care^® (AJMC^®), Kim, assistant professor of clinical practice in the Department of Metabolism, Endocrinology & Nutrition and medical director of the Diabetes Institute Clinical Research Unit at the University of Washington, explores what the subgroup data reveal about who stands to benefit most from a personalized, feedback-driven approach; how Signos fits alongside glucagon-like peptide-1 (GLP-1) receptor agonists as a complement or nonpharmacologic alternative for patients who cannot access or tolerate medication; and what a prospective follow-up study would need to include to move from association to causation.

This interview has been lightly edited for clarity.

AJMC: The study found that higher engagement was associated with greater weight loss fairly consistently across BMI categories, sex, and most age groups. Were there any subgroup findings that surprised you or that you think warrant further investigation?

Kim: The result among participants with class III obesity was especially encouraging. Highly engaged participants in that group achieved approximately 7.1% total body weight loss at 6 months. This suggests that people facing the greatest obesity-related burden may have substantial potential to benefit from a personalized, feedback-driven approach.

What stood out wasn't necessarily a single subgroup but rather how strongly engagement showed up as concrete behavior—highly engaged users logged meals about 4 times as often (roughly 10 vs 2 times per week) and exercised more frequently.

AJMC: How do you see a platform like the Signos System fitting into the broader obesity treatment landscape alongside pharmacotherapy options like GLP-1 receptor agonists?

Kim: Signos and GLP-1 therapies can play complementary roles. Medication addresses important biological drivers of obesity, while Signos supports the daily behavioral decisions that remain essential to long-term weight management—helping people understand how food, activity, sleep, and other choices affect their individual metabolic responses.

Signos supports people before medication, alongside pharmacotherapy, or as a nonpharmacologic option for those who do not want, cannot access, or cannot tolerate medication. Lastly, Signos is a valuable complement to GLP-1 therapy and an effective solution for those looking to transition off a GLP-1 while maintaining their progress.

AJMC: Given the retrospective design and the limitations you note, what would you want to see in a prospective follow-up study to strengthen the causal case for engagement driving weight loss?

Kim: A particularly informative extension would include medication-only, Signos-only, and combination arms. That would help define where Signos delivers the greatest value across the full obesity-care treatment continuum. This would provide a stronger opportunity to evaluate the independent and additive effects of engagement on weight-loss outcomes.

Reference

Grossi G. AI-CGM platform engagement linked to clinically meaningful weight loss. AJMC. June 23, 2026. Accessed June 23, 2026. https://www.ajmc.com/view/ai-cgm-platform-engagement-linked-to-clinically-meaningful-weight-loss