Highlight
1. Both adaptive telephone-based weight maintenance programs and static monthly support can maintain clinically meaningful weight loss with about 8% average reduction sustained at 24 months.
2. Personalized adaptive interventions triggered by algorithmic risk assessment did not outperform scheduled static support in sustaining weight loss.
3. Biological adaptations promote weight regain post-weight loss, demanding ongoing chronic care approaches rather than solely willpower.
4. Future weight maintenance models may benefit from improved patient engagement, user-centered design, and integration with pharmacotherapies such as GLP-1 receptor agonists.
Study Background and Disease Burden
Obesity is a major global health burden linked to type 2 diabetes, cardiovascular disease, and other chronic conditions. While initial weight loss achieved through evidence-based lifestyle interventions can reduce these risks, the challenge lies in sustaining this weight loss long term. Biological mechanisms such as increased hunger signals, decreased satiety hormones, and lowered resting metabolic rate create a strong physiological drive for weight regain. This chronic, biologically defended state renders weight maintenance exceptionally difficult, making relapse common within 1 to 2 years despite continued lifestyle efforts.
Extended care models offering ongoing support after the initial weight loss phase are considered current best practices for addressing maintenance. However, variability in efficacy and challenges in scalable delivery persists. It is crucial to develop more personalized, efficient interventions to enhance sustained benefits and reduce the burden of obesity-related disease.
Study Design
Ross and colleagues conducted a randomized clinical trial enrolling 255 adults who had achieved at least 5% weight loss after a 16-week intensive lifestyle-based program modeled on the Diabetes Prevention Program. The study compared two 20-month telephone-based weight maintenance strategies:
- An adaptive intervention approach where weekly self-monitoring questionnaires on hunger, adherence, and motivation triggered support calls via a risk algorithm. Calls were initiated only when the participant showed signs of risk for weight regain or missed reporting.
- A static support model delivering scheduled monthly coaching calls regardless of participant status.
The primary endpoint was the difference in weight maintenance at 24 months post-baseline, with secondary outcomes including proportion maintaining ≥5% weight loss and program adherence metrics. The trial was pragmatic, leveraging telephone delivery to replicate real-world feasibility and scalability.
Key Findings
At 24 months, both groups maintained substantial weight loss, averaging approximately 8% reduction from baseline weight, with no statistically significant difference between the adaptive and static groups. Nearly 60% of participants in each retained clinically meaningful weight loss (≥5%), associated with improved cardiometabolic health outcomes.
Participants randomized to the adaptive group received significantly more attempted calls (mean 56.2 vs. 17.8) and had longer total contact time than the static group. However, the adaptive group had a markedly lower call completion rate (56%) compared to 96% in the static model, suggesting engagement challenges.
The lack of superiority in the adaptive group may relate to implementation aspects: the algorithm triggered calls both for “risk” and missing data, potentially resulting in unexpected outreach that participants found disruptive or burdensome. Meanwhile, predictable scheduled contacts in the static group likely fostered greater participant engagement and adherence.
These data underscore the importance of user-centered design and suggest that personalized interventions must balance responsiveness with participant capacity and expectations to maximize effectiveness.
Safety data were not specifically highlighted, but no adverse effects related to the interventions were reported, affirming telephone coaching as a low-risk adjunct in weight management.
Expert Commentary
While the trial demonstrated that both adaptive and static telephone coaching can sustain meaningful long-term weight loss, it challenges assumptions that algorithm-driven personalized support inherently outperforms traditional fixed schedules. The findings emphasize the complex, multifactorial nature of weight maintenance and the critical role of human factors like engagement, experience, and intervention design.
Ross et al’s work adds to the evolving landscape of obesity management, particularly important in the context of increasing use of pharmacotherapies such as glucagon-like peptide-1 receptor agonists (GLP-1 RAs). Despite pharmaceutical advances, behavioral support remains essential, especially given weight regain often occurs when medications are stopped. Integrating scalable, effective behavioral models with medication adherence will be a key priority.
Methodological strengths include the diverse, pragmatic setting and extended follow-up; limitations center on the homogenous socio-economic and highly adherent participant profile, which may limit generalizability. Future research integrating qualitative assessments may illuminate participant perspectives and optimize intervention algorithms.
Conclusion
This study demonstrates that both static monthly and adaptive telephone-based support models can achieve substantial long-term weight loss maintenance following an intensive lifestyle intervention. The adaptive, risk-triggered model did not enhance outcomes compared to a more traditional static approach but highlighted important considerations for tailoring weight maintenance interventions.
Given the biological drive for weight regain and the chronic nature of obesity, refining adaptive models to improve engagement and reduce unwanted outreach will be central. Additionally, embedding these interventions within chronic care frameworks, potentially augmenting pharmacotherapy, offers promise for more effective large-scale obesity management. Overall, these results guide clinicians and researchers toward implementing sustainable, user-friendly, personalized weight maintenance strategies in real-world settings.
References
- Ross KM, Shankar MN, Qui P, Tian Z. Adaptive vs monthly support for weight-loss maintenance: a randomized clinical trial. JAMA Netw Open. 2025;8(9):e2532681. doi:10.1001/jamanetworkopen.2025.32681
- Aziz Z, Absetz P, Oldroyd J, Pronk NP, Oldenburg B. A systematic review of real-world diabetes prevention programs: learnings from the last 15 years. Implement Sci. 2015;10(1):172. doi:10.1186/s13012-015-0354-6
- Elmaleh-Sachs A, Schwartz JL, Bramante CT, Nicklas JM, Gudzune KA, Jay M. Obesity management in adults: a review. JAMA. 2023;330(20):2000-2015. doi:10.1001/jama.2023.19897
- Mozaffarian D, Agarwal M, Aggarwal M, et al. Nutritional priorities to support GLP-1 therapy for obesity: a joint advisory from the American College of Lifestyle Medicine, the American Society for Nutrition, the Obesity Medicine Association, and the Obesity Society. Obesity (Silver Spring). 2025;33(8):1475-1503.
