# 12-Month Weight Loss and Adherence Predictors in a Real-World UK Tirzepatide-Supported Digital Obesity Service: A Retrospective Cohort Analysis

**Authors:** Louis Talay, Jason Hom, Tamara Scott, Neera Ahuja

PMC · DOI: 10.3390/healthcare14010060 · Healthcare · 2025-12-26

## TL;DR

A digital obesity service using Tirzepatide achieved significant weight loss, but only 27% of patients adhered for 12 months, with consistent weekly engagement being key to success.

## Contribution

Identifies behavioral predictors of adherence and weight loss in a real-world Tirzepatide-supported digital obesity service.

## Key findings

- Adherent patients lost 22.60% of their weight on average, significantly more than the full cohort.
- Consistent weekly tracking and coaching were the strongest predictors of long-term adherence.
- Hyper-engagement in the first month predicted lower 12-month adherence.

## Abstract

Background: Obesity management is evolving with the integration of dual GIP/GLP-1 receptor agonists (Tirzepatide) into comprehensive Digital Weight-Loss Services (DWLSs). This model leverages virtual, app-based multidisciplinary care (MDT) to deliver continuous, supervised treatment, distinguishing it from traditional, intermittent clinic-based care. While clinical trials demonstrate high efficacy, real-world data are necessary to evaluate long-term adherence and identify predictive markers for patient persistence in these scalable care models. Specifically, there is a knowledge gap regarding the specific behavioral factors that govern 12-month persistence in these comprehensive, medicated DWLS settings. This study retrospectively assessed the 12-month effectiveness and adherence of a Tirzepatide-supported DWLS and identified demographic, clinical, and behavioral predictors of weight loss and program attrition. Methods: Data from 19,693 patients enrolled in the Juniper UK DWLS were analyzed. Adherence was defined by a minimum of 10 medication orders and 12-month weight submission. Weight loss in the full cohort was evaluated using the Last Observation Carried Forward (LOCF) method. Binary logistic and multiple linear regression models identified predictors of adherence and weight loss, respectively, using a comprehensive set of demographic, clinical (e.g., BMI, comorbidities), and behavioral variables. Results: The 12-month adherence rate was 27%. The adherent sub-cohort (n = 5322) achieved a mean weight loss of 22.60 (±7.46) percent, compared to 13.62 (±10.85) percent in the full cohort (LOCF). This difference in 12-month mean weight loss was statistically significant (p < 0.001). Consistent weekly weight tracking and health coach communication were the strongest positive predictors of long-term adherence and weight loss. Conversely, hyper-engagement, specifically intensive tracking frequency and high weight loss velocity in the first month, was a significant inverse predictor of 12-month adherence. Reporting side effects was positively correlated with adherence, suggesting a reporting bias among engaged patients. Conclusions: The DWLS model facilitates the maximum therapeutic effectiveness for adherent patients. However, patient persistence remains the primary translational challenge. As consistent weekly engagement (tracking, coaching) is the strongest predictor of success, clinical strategies should prioritize promoting sustainable, moderate behavioral pacing (i.e., emphasizing consistent weekly engagement over intensive daily tracking and rapid early weight loss) to mitigate attrition risk and optimize the public health effectiveness of medicated DWLSs.

## Linked entities

- **Chemicals:** Tirzepatide (PubChem CID 163285897)
- **Diseases:** obesity (MONDO:0011122)

## Full-text entities

- **Genes:** GLP1R (glucagon like peptide 1 receptor) [NCBI Gene 2740] {aka GLP-1, GLP-1-R, GLP-1R}, GIP (gastric inhibitory polypeptide) [NCBI Gene 2695]
- **Diseases:** Weight Loss (MESH:D015431), Obesity (MESH:D009765)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## References

34 references — full list in the complete paper: https://tomesphere.com/paper/PMC12786109/full.md

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Source: https://tomesphere.com/paper/PMC12786109