1365-P: Key Characteristics and Predictive Ability for HbA1c Improvement—A Latent Class Analysis of 912 Patients

Introduction & Objectives: Type 2 Diabetes Mellitus (T2DM) strains healthcare. As mobile health (mHealth) interventions gain prominence in T2DM self-management, understanding the HbA1c response of diverse patient subgroups is crucial. This study aimed to classify T2DM patients into distinct latent classes and assess their predictive ability for 12-month HbA1c reduction. Methods: Latent class analysis was applied to 912 T2DM patients using Fitbit-measured step tracker, aged ≥ 40 years old with HbA1c ≥ 7%. Exclusion criteria involved insulin treatment and cognitive impairment. Indicators comprised patients’ use of mHealth interventions, age, education, living arrangement, baseline levels of HbA1c, step count, and motivation (Patient Activation Measure). 12-Month HbA1c reduction was assessed with regression models. Results: Within cohort (mean [SD] age 55.5 [7.6] years old; 55.9% male; 61.2% Chinese, 25.3% Malay, 9.8% Indian), Class 3 had the most significant HbA1c improvement, while lower baseline HbA1c and motivation suggest the contrary. Class 4, despite high baseline HbA1c, exhibited a significantly lower HbA1c improvement (Table 1). Conclusion: Younger patients who are motivated and educated are most likely to be tech savvy to use and benefit from mHealth interventions. Patients with poorer control can make a more significant improvement in HbA1c through behavioural change. Disclosure Y. Gan: None. L. Low: None. Y. Kwan: None. Funding AI Singapore Programme (AISG-GC-2019-001-2A); Singapore Ministry of Health's National Medical Research Council (HCSAINV21jun-0004)