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Adherence to antiretroviral therapy (ART) is essential in determining successful treatment of human immunodeficiency virus (HIV). The adoption of digital health is suggested to improve ART adherence among people living with HIV (PLHIV). This study aimed to systematically determine the effect of digital health in enhancing ART adherence among PLHIV from published studies. The systematic search was conducted on Scopus, Web of Science (WoS), PubMed, Ovid, EBSCOHost, and Google Scholar databases up to June 2022. Studies utilized any digital health as an intervention for ART adherence enhancement and ART adherence status as study's outcome was included. Digital health refers to the use of information and communication technologies to improve health. Quality assessment and data analysis were carried out using Review Manager (RevMan) version 5.4. A random-effects model computed the pooled odds ratio between intervention and control groups. The search produced a total of 1864 articles. Eleven articles were eligible for analysis. Digital health was used as follows: six studies used short message service or text message alone, three studies used mobile applications, and two studies used combination method. Four studies showed statistically significant impacts of digital health on ART adherence, while seven studies reported insignificant results. Results showed studies conducted using combination approach of digital health produced more promising outcome in ART adherence compared to single approach. New innovative in combination ways is required to address potential benefits of digital health in promoting ART adherence among PLHIV. [ABSTRACT FROM AUTHOR]

Adherence to iron chelation therapy (ICT) is fundamental for preventing iron overload complications and maintaining health-related quality of life (HRQoL) in transfusion-dependent thalassemia (TDT) patients. This study aimed to evaluate adherence, HRQoL, and their association among Malaysian TDT patients. Cross-sectional research was performed among TDT patients aged 18 years and above in Hospital Ampang, Selangor. The adherence and the HRQoL (SF-36) were evaluated using validated instruments. Correlation analyses were carried out to determine the association between ICT adherence and HRQoL. The study recruited 162 patients, with 46.91% (N=76) of respondents reporting being adherent. The mean SF-36 score for TDT patients is 74.58. Remarkably, adherent patients exhibit significantly higher HRQoL (mean SF-36 score 79.21) assessed by the SF-36 questionnaire versus nonadherent patients (mean SF-36 score 69.47) (p=0.00). The findings also revealed a significant positive correlation between female (p=0.032), employed (p=0.003), and age (p=0.050) factors with the patient's HRQoL. The rate of ICT adherence for adult TDT patients is suboptimal and non-adherent to ICT has significantly compromised HRQoL. Enhancing the rate of ICT adherence through interventions such as patient counseling and support programs may lead to enhanced HRQoL outcomes for the TDT population. [ABSTRACT FROM AUTHOR]

Background: An adherence model is required to optimise medication management among chronic kidney disease (CKD) patients, as current assessment methods overestimate the true adherence of CKD patients with complex regimens. An approach to assess adherence to individual medications is required to assist pharmacists in addressing non-adherence.Objective: To develop an adherence prediction model for CKD patients.Methods: This multi-centre, cross-sectional study was conducted in 10 tertiary hospitals in Malaysia using simple random sampling of CKD patients with ≥1 medication (sample size = 1012). A questionnaire-based collection of patient characteristics, adherence (defined as ≥80% consumption of each medication for the past one month), and knowledge of each medication (dose, frequency, indication, and administration) was performed. Continuous data were converted to categorical data, based on the median values, and then stratified and analysed. An adherence prediction model was developed through multiple logistic regression in the development group (n = 677) and validated on the remaining one-third of the sample (n = 335). Beta-coefficient values were then used to determine adherence scores (ranging from 0 to 7) based on the predictors identified, with lower scores indicating poorer medication adherence.Results: Most of the 1012 patients had poor medication adherence (n = 715, 70.6%) and half had good medication knowledge (n = 506, 50%). Multiple logistic regression analysis determined 4 significant predictors of adherence: ≤7 medications (constructed score = 2, p < 0.001), ≤3 co-morbidities (constructed score = 1, p = 0.015), absence of complementary/alternative medicine use (constructed score = 1, p = 0.003), and knowledge score ≥80% (constructed score = 3, p < 0.001). A higher total constructed score from the prediction model indicated a higher likelihood of adherence (odds ratio [OR]: 2.41; 95% confidence interval [CI]: 2.112-2.744; p < 0.001). The area under the receiver operating characteristic (ROC) curve of the developed model (n = 677) had good accuracy (ROC: 0.867, 95% CI: 0.840-0.896; p < 0.001). The validated model (n = 335) also had good accuracy (ROC: 0.812, 95% CI: 0.765-0.859; p < 0.001). There was no significant difference between the development and validation groups (p = 0.11, Z-value:1.62, standard error: 0.034).Conclusion: The score constructed from the medication adherence prediction model for CKD patients had good accuracy and could be useful for identifying patients with a higher risk of non-adherence, to ensure optimised adherence management. [ABSTRACT FROM AUTHOR]

Purpose: Diabetes mellitus is a growing global health problem that affects patients of all ages. Even though diabetes mellitus is recognized as a major chronic illness, adherence to antidiabetic medicines has often been found to be unsatisfactory. This study was conducted to assess adherence to medications and to identify factors that are associated with nonadherence in type 2 diabetes mellitus (T2DM) patients at Primary Health Clinics of the Ministry of Health in Malaysia. Materials and methods: The cross-sectional survey was carried out among T2DM patients to assess adherence to medication in primary health clinics. Adherence was measured by using the Medication Compliance Questionnaire that consists of a total of seven questions. Other data, such as patient demographics, treatment, outcome, and comorbidities were also collected from patient medical records. Results: A total of 557 patients were recruited in the study. Approximately 53% of patients in the study population were nonadherent. Logistic regression analysis was performed to predict the factors associated with nonadherence. Variables associated with nonadherence were age, odds ratio 0.967 (95% confidence interval [CI]: 0.948-0.986); medication knowledge, odds ratio 0.965 (95% CI: 0.946-0.984); and comorbidities, odds ratio 1.781 (95% CI: 1.064-2.981). Conclusion: Adherence to medication in T2DM patients in the primary health clinics was found to be poor. This is a cause of concern, because nonadherence could lead to a worsening of disease. Improving medication knowledge by paying particular attention to different age groups and patients with comorbidities could help improve adherence. [ABSTRACT FROM AUTHOR]