Using Natural Language Processing (NLP) to understand self-reported barriers and enablers to treatment adherence in type 2 diabetes (T2D) using data from the International Diabetes Management Practices Study (IDMPS)

Introduction

IDMPS is an international, observational study to assess care practices and clinical profiles of people with diabetes in developing countries. Treatment non-adherence is a major barrier to treatment effectiveness. Open-ended self-reported reasons for omitting or adhering to glucose-lowering drugs in people with T2D were analyzed.

Methods

NLP and clustering methods were used to analyze responses on omitting medications (Q1) , not taking medications as prescribed (Q2) , and methods to improve adherence (Q3) , with Q1+2 responses merged as reasons for ‘omission’.

Results

Of 2475 participants from 13 countries (mean ± SD age 58 ± 12 years, 49% male, diabetes duration ± 8 years, 43% on insulin) , 85%, 79%, and 37% had responses for Q1-3 respectively.

Of these, 36% had responses indicative of non-adherence, 62% of adherence, and 2% uncertain responses (Q1+2).

Non-adherent patients were younger (55 ± 12 vs. 60 ± 12 years) , had shorter diabetes duration (± 8 vs. 12 ± 9 years) , and higher HbA_1c (8.3 ± 1.9 vs. 7.9 ± 1.8 %) vs. adherent patients (all p<0.001).

Patient responses for adherence/non-adherence are shown in Figure.

Conclusions

Conflicts with daily routine are the main barrier to therapy adherence, while planning and habit formation can improve adherence.