METHODS: In this cross-sectional study, the 8-item Morisky Medication Adherence Scale (MMAS) was the main independent variable and the World Health Organization Quality of Life-Brief the dependent variable. Besides socio-demographic data, diabetes-related distress (DRD) and depression (DS) were included as covariates. Independent association between the MMAS score and HRQoL was done using multiple linear regression.
RESULTS: The participants' response rate was 93.1 % (700/752). Majorities were female (52.8 %), Malay (52.9 %) and married (79.1 %). The mean (SD) for age and the MMAS score was 56.9 (10.18) and 5.6 (1.42), respectively. MMAS total score correlated significantly with all HRQoL domains: overall QoL (OQoL) (r = 0.17), physical QoL (r = 0.11), psychological QoL (r = 0.10), social relationship QoL (r = 0.15) and environmental QoL (EQoL) (r = 0.18). After adjustment for covariates (age, gender, ethnicity, religion, education, income, exercise, macrovascular complications, DRD and DS), MA had persistent effects on OQoL (B = 0.53, 95 % CI 0.012-1.048) and EQoL (B = 0.95, 95 % CI 0.235-1.667).
CONCLUSION: MA showed prevalent correlation and positive effects on the domains of HRQoL. Despite the small effects of MA on HRQoL, the sheer presence of the independent effects provides healthcare providers good reason for initiative and intervention to improve MA, which would improve quality of life.
OBJECTIVE: This paper synthesizes ethical, technical, and epidemiological considerations for all involved in artificial intelligence tool production (ETEPAI), based on established guidelines, checklists, and frameworks.
METHODS: Relevant guidelines, checklists, frameworks, and expert recommendations were systematically identified and synthesized into ETEPAI, an ethical, technical, and epidemiological framework for AI tool development in health care.
RESULTS: From 30 reviewed frameworks, ETEPAI integrates critical considerations across 4 stages (design, development, deployment, and postdeployment) and 3 domains (ethics, technical, and epidemiological), providing a compact yet comprehensive guide. It includes probing questions, key indicators, and common pitfalls to support high-quality, ethically sound, and clinically relevant AI tools. ETEPAI aligns with European Union trustworthiness standards and is supported by a research proposal template and supplementary references to aid implementation and adoption. We present probing questions and critical pointers across 4 stages from the design, development, deployment, and postdeployment, highlighting their relevance in health care settings. The designing stage aligns with epidemiologic research methodologies, while the development stage emphasizes transparent project execution. Deployment and postdeployment stages focus on real-world implementation. Additionally included are common pitfalls and challenges to emphasize the importance of due attention to the importance of ETEPAI considerations to avoid serious consequences.
CONCLUSIONS: Applying ETEPAI ensures comprehensive, complete, compact, and crisp consideration from conception to execution, promoting high-quality, ethically sound, and clinically relevant AI tools. The brevity and conciseness of ETEPAI might be adequate for trained personnel and serve as clear signposts to unprepared stakeholders.
METHODS: The index prescriptions were those that when the annual blood tests were reviewed. Prescriptions of medication were verified, compared to the preceding prescriptions and classified as 1) no change, 2) stepping up and 3) stepping down. The treatment targets were HbA1c