Medication Adherence in Diabetes Mellitus Patients Based on the Health Belief Model Perception

Abstract : Diabetes Mellitus (DM) is a chronic disease that requires long-term treatment. Treatment adherence is an important factor in controlling blood glucose levels and preventing complications. Patient perception based on the Health Belief Model (HBM) is thought to play a role in shaping treatment adherence behavior. This study aims to analyze the influence of HBM perceptions on treatment adherence in DM patients at the Gadingrejo community health center, Pringsewu, Lampung province, Indonesia. This study used an observational analytical design with a case-control approach conducted in April–June 2026. A sample of 206 respondents consisting of 103 case groups and 103 control groups, selected using a stratified random sampling technique. Data were collected using the HBM and MMAS-8 questionnaires, then analyzed using the Chi-Square test with α = 0.05. There was a significant influence between perceived susceptibility (p<0.001; OR=3.051; 95%CI=1.730–5.383), perceived severity (p=0.002; OR=2.482; 95%CI=1.417–4.347), perceived benefit (p<0.001; OR=2.927; 95%CI=1.662–5.156), perceived barriers (p=0.012; OR=2.120; 95%CI=1.213–3.702), self-efficacy (p=0.005; OR=2.290; 95%CI=1.310–4.003), and cues to action (p<0.001; OR=4.531; 95%CI=2.492–8.239) on medication adherence. All constructs of the HBM influence medication adherence in DM patients. Increasing positive perceptions and reducing barriers can support better medication adherence.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-25
DOI
https://doi.org/10.5281/zenodo.20913968
Primary Topic
Medication Adherence and Compliance
Type
article
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article

Medication Adherence in Diabetes Mellitus Patients Based on the Health Belief Model Perception

Kurniawan Betta, Rahmawati Dwi, Suwandi Jhons Fatriyadi, Angraini Dian Isti et al.
Zenodo (CERN European Organization for Nuclear Research)
Medication Adherence and Compliance
article

Medication Adherence in Diabetes Mellitus Patients Based on the Health Belief Model Perception

Kurniawan Betta, Rahmawati Dwi, Suwandi Jhons Fatriyadi, Angraini Dian Isti, Berawi Khairun Nisa
article en

Abstract

Abstract : Diabetes Mellitus (DM) is a chronic disease that requires long-term treatment. Treatment adherence is an important factor in controlling blood glucose levels and preventing complications. Patient perception based on the Health Belief Model (HBM) is thought to play a role in shaping treatment adherence behavior. This study aims to analyze the influence of HBM perceptions on treatment adherence in DM patients at the Gadingrejo community health center, Pringsewu, Lampung province, Indonesia. This study used an observational analytical design with a case-control approach conducted in April–June 2026. A sample of 206 respondents consisting of 103 case groups and 103 control groups, selected using a stratified random sampling technique. Data were collected using the HBM and MMAS-8 questionnaires, then analyzed using the Chi-Square test with α = 0.05. There was a significant influence between perceived susceptibility (p<0.001; OR=3.051; 95%CI=1.730–5.383), perceived severity (p=0.002; OR=2.482; 95%CI=1.417–4.347), perceived benefit (p<0.001; OR=2.927; 95%CI=1.662–5.156), perceived barriers (p=0.012; OR=2.120; 95%CI=1.213–3.702), self-efficacy (p=0.005; OR=2.290; 95%CI=1.310–4.003), and cues to action (p<0.001; OR=4.531; 95%CI=2.492–8.239) on medication adherence. All constructs of the HBM influence medication adherence in DM patients. Increasing positive perceptions and reducing barriers can support better medication adherence.

Zenodo (CERN European Organization for Nuclear Research)
Lampung University (ID), Universitas Bandar Lampung (ID)
Good health and well-being
Openalex Percentile: Top 7%
Medication Adherence and Compliance
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Medication Adherence in Diabetes Mellitus Patients Based on the Health Belief Model Perception — Kurniawan Betta, Rahmawati Dwi, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS