METHODS: We conducted a questionnaire-based survey of consecutive out-patients with no diagnosed mental health illness (n = 289) and their primary caregivers (n = 247) from 10 centers across eight countries (Bangladesh, India, Iran, Malaysia, Myanmar, Nepal, Pakistan, Thailand) of IBD-Emerging Nations' Consortium (ENC). Patients were assessed for anxiety (PHQ-9), depression (GAD-7), quality of life (SIBDQ, IBDCOPE) and medication adherence (MMAS-8). Caregiver burden was assessed by Zarit-Burden Interview (ZBI), Ferrans and Power Quality of Life (QOL) scores and coping strategies (BRIEF-COPE). Multivariate logistic regression and correlation analyses were performed to identify risk factors and the impact on QOL in patients and caregivers.
RESULTS: Moderate to severe depression and anxiety were noted in 33% (severe 3.5%) and 24% (severe 3.8%) patients, respectively. The risk factor for depression was active disease (p
METHODS: A simulated patient method was used to evaluate pharmacist counseling practices in Sydney, Australia. Twenty community pharmacists received three simulated patient visits concerning antidepressant adherence-related scenarios at different phases of treatment: 1) patient receiving a first-time antidepressant prescription and hesitant to begin treatment; 2) patient perceiving lack of treatment efficacy for antidepressant after starting treatment for 2 weeks; and 3) patient wanting to discontinue antidepressant treatment after 3 months due to perceived symptom improvement. The interactions were recorded and analyzed to evaluate the content of consultations in terms of information gathering, information provision including key educational messages, and treatment recommendations.
RESULTS: There was variability among community pharmacists in terms of the extent and content of information gathered and provided. In scenario 1, while some key educational messages such as possible side effects and expected benefits from antidepressants were mentioned frequently, others such as the recommended length of treatment and adherence-related messages were rarely addressed. In all scenarios, about two thirds of pharmacists explored patients' concerns about antidepressant treatment. In scenarios 2 and 3, only half of all pharmacists' consultations involved questions to assess the patient's medication use. The pharmacists' main recommendation in response to the patient query was to refer the patient back to the prescribing physician.
CONCLUSION: The majority of pharmacists provided information about the risks and benefits of antidepressant treatment. However, there remains scope for improvement in community pharmacists' counseling practice for patients on antidepressant treatment, particularly in providing key educational messages including adherence-related messages, exploring patients' concerns, and monitoring medication adherence.
Method: We used pharmacy dispensing data of 1461 eligible T2DM patients from public primary care clinics in Malaysia treated with oral antidiabetic drugs between January 2018 and May 2019. Adherence rates were calculated during the period preceding the HbA1c measurement. Adherence cut-off values for the following conditions were compared: adherence measure (MPR versus PDC), assessment period (90-day versus 180-day), and HbA1c target (⩽7.0% versus ⩽8.0%).
Results: The optimal adherence cut-offs for MPR and PDC in predicting HbA1c ⩽7.0% ranged between 86.1% and 98.3% across the two assessment periods. In predicting HbA1c ⩽8.0%, the optimal adherence cut-offs ranged from 86.1% to 92.8%. The cut-off value was notably higher with PDC as the adherence measure, shorter assessment period, and a stricter HbA1c target (⩽7.0%) as outcome.
Conclusion: We found that optimal adherence cut-off appeared to be slightly higher than the conventional value of 80%. The adherence thresholds may vary depending on the length of assessment period and outcome definition but a reasonably wise cut-off to distinguish good versus poor medication adherence to be clinically meaningful should be at 90%.
OBJECTIVES: The aim of this study was to cross-culturally adapt and validate the Malay MALMAS (M-MALMAS) in Malaysia.
METHODS: Adults with type 2 diabetes, who could understand Malay, were recruited between May 2016 and February 2017 from a primary care clinic in Kuala Lumpur, Malaysia. The M-MALMAS and the Malay version of the Morisky Medication Adherence Scale (MMAS-8) were administered at baseline to test for convergent validity. Four weeks later, the M-MALMAS was re-administered. Predictive validity of the M-MALMAS was assessed by correlating the medication adherence scores with levels of glycated haemoglobin (HbA1c).
RESULTS: In total, 100 of 104 people agreed to participate (response rate = 96.2%). The overall Cronbach's α and McDonald's Ω for the M-MALMAS was 0.654 and 0.676, respectively (mean = 0.665). At test-retest, no significant difference was found for all items. The median total score interquartile range (IQR) of the M-MALMAS was 7.0 (6.0-8.0) and this was significantly correlated to the median total score of the Malay MMAS-8 [median (IQR) = 7.0 (5.8-8.0), p
METHODS: A qualitative approach using focus group discussions was conducted to get in-depth information about medicines use pattern and practice from the general public. Adult people who reported using medicines at the time of study or in the previous month were approached. Two focus group discussions were audio-recorded and transcribed verbatim. The obtained data were analysed using thematic content analysis.
RESULTS: This study found that there are some misunderstanding about the appropriate use of medicines. The majority of the participants reported that they were complying with their medication regimen. However, forgetting to take medicines was stated by 4 participants while 2 participants stopped taking medicines when they felt better. In addition, 10 participants reporting using medicines according to their own knowledge and past experience. Whereas 4 participants took medicines according to other informal resources such as family, friends or the media. Seven participants have experienced side effects with using medicines, 4 of them informed their doctor while 3 participants stopped taking medicines without informing their doctor.
CONCLUSION: There was a misunderstanding about medicines use in terms of medication compliance, self-management of the illness and the resources of information about using medicines. Many efforts are still needed from health care professionals to provide sufficient information about medicines use in order to decrease the risk of inappropriate use of medicines and to achieve better therapeutic outcome.
Method: Potential studies were identified through a systematic search of Scopus, Science Direct, Google Scholar, and PubMed. The keywords used to identify relevant articles were "adherence," "AED," "epilepsy," "non-adherence," and "complementary and alternative medicine." An article was included in the review if the study met the following criteria: 1) conducted in epilepsy patients, 2) conducted in patients aged 18 years and above, 3) conducted in patients prescribed AEDs, and 4) patients' adherence to AEDs.
Results: A total of 3,330 studies were identified and 30 were included in the final analysis. The review found that the AED non-adherence rate reported in the studies was between 25% and 66%. The percentage of CAM use was found to be between 7.5% and 73.3%. The most common reason for inadequate AED therapy and higher dependence on CAM was the patients' belief that epilepsy had a spiritual or psychological cause, rather than primarily being a disease of the brain. Other factors for AED non-adherence were forgetfulness, specific beliefs about medications, depression, uncontrolled recent seizures, and frequent medication dosage.
Conclusion: The review found a high prevalence of CAM use and non-adherence to AEDs among epilepsy patients. However, a limited number of studies have investigated the association between CAM usage and AED adherence. Future studies may wish to explore the influence of CAM use on AED medication adherence.
Methods: Data from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.
Result: Machine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.
Conclusion: This study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.
OBJECTIVE: With the growing body of evidence supporting the use of eHealth interventions, the intention is to conduct a meta-analysis on various health outcomes of eHealth interventions among ischaemic heart disease (IHD) patients.
METHODS: Based on PRISMA guidelines, eligible studies were searched through databases of Web of Science, Scopus, PubMed, EBSCOHost, and SAGE (PROSPERO registration CRD42021290091). Inclusion criteria were English language and randomised controlled trials published between 2011 to 2021 exploring health outcomes that empower IHD patients with eHealth interventions. RevMan 5.4 was utilised for meta-analysis, sensitivity analysis, and risk of bias (RoB) assessment while GRADE software for generating findings of physical health outcomes. Non-physical health outcomes were analysed using SWiM (synthesis without meta-analysis) method.
RESULTS: This review included 10 studies, whereby, six studies with 895 participants' data were pooled for physical health outcomes. Overall, the RoB varied significantly across domains, with the majority was low risks, a substantial proportion of high risks and a sizeable proportion of unclear. With GRADE evidence of moderate to high quality, eHealth interventions improved low density lipoprotien (LDL) levels in IHD patients when compared to usual care after 12 months of interventions (SMD -0.26, 95% CI [-0.45, -0.06], I2 = 0%, p = 0.01). Significance appraisal in each domain of the non-physical health outcomes found significant findings for medication adherence, physical activity and dietary behaviour, while half of the non-significant findings were found for other behavioural outcomes, psychological and quality of life.
CONCLUSIONS: Electronic Health interventions are found effective at lowering LDL cholesterol in long-term but benefits remain inconclusive for other physical and non-physical health outcomes for IHD patients. Integrating sustainable patient empowerment strategies with the advancement of eHealth interventions by utilising appropriate frameworks is recommended for future research.
METHODS: A two-phase mixed-methods approach was used. Phase 1 involved qualitative interviews with hypertensive patients from two health clinics in Kuala Lumpur, Malaysia. The themes extracted from these interviews were used to generate items for the MAANS. In Phase 2, data from 213 participants were analysed using exploratory factor analysis (EFA) to establish the scale's factor structure, thereby created the modified version of the MAANS. Confirmatory factor analysis (CFA) was then conducted on a separate dataset of 205 participants to confirm the factor structure, resulted in the final version of the MAANS. The reliability of the final MAANS version was assessed using Cronbach's alpha coefficient. The MAANS scores were used to predict subscales of the Malay version of the WHO Quality-of-Life (QOL) BREF, demonstrating the scale's predictive validity.
RESULTS: Ten qualitative interviews yielded 73 items. The EFA produced a modified MAANS with 21 items grouped into five factors. However, the CFA retained three factors in the final scale: Perceived Non-Susceptibility, Poor Doctor-Patient Relationship, and Unhealthy Lifestyle. The final 14-item, 3-factor MAANS demonstrated moderate reliability (Cronbach's alpha coefficient = 0.64) and exhibited partial predictive validity, with the Poor Doctor-Patient Relationship and Unhealthy Lifestyle subscales significantly predicting Social QOL and Environmental QOL.
CONCLUSION: The MAANS is a reliable, valid, and multidimensional scale specifically developed to evaluate non-adherence to anti-hypertensive medications in local clinical settings with the potential to further the advancement of research and practice in sociomedical and preventive medicine.
OBJECTIVE: To assess the effects of pharmacist-led interventions within DMTAC on the outcomes of patients with type 2 diabetes mellitus in two distinct hospitals in Kedah, Malaysia.
METHODS: Patients with type 2 diabetes were randomly selected from the two hospitals included in this study. The study population was divided into two equal groups. The control group consisted of 200 patients receiving routine care from the hospitals. On the other hand, the intervention group included those patients with type 2 diabetes (200), who received separate counseling sessions from pharmacists in the DMTAC departments along with the usual treatment. The study lasted 1 year, during which both study groups participated in two distinct visits.
RESULTS: Parametric data were analyzed by a paired t-test and one-way ANOVA, while non-parametric data were analyzed by a Chi-squared test using SPSS v24. A p
METHODS: Participants in the HOPE 4 intervention group with baseline and 12 months of follow-up were included for analysis. They were divided into Every Visit (n=339) and
PURPOSE: This study aims to compare the cost, medication adherence and glycaemic control of utilizing POMs versus usual dispensing.
METHODS: Prospective randomized controlled study was conducted among diabetic patients that required monthly medication refill in the Outpatient Pharmacy in 2017. Patients who consented were equally divided into POMs and control groups. Both groups brought excess medications from home at week-0 and week-12. Patients in the POMs group brought excess medications monthly and sufficient amount of drugs were added until the next refill date. Drugs were dispensed as usual in the control group. Total cost consisting of the cost of drugs, staff and building was calculated. Glycosylated haemoglobin (HbA1c) was measured at baseline and week-12. Adherence was measured based on pill counting.
RESULTS: Thirty patients aged 56.77 ± 14.67 years with 13.37 ± 7.36 years of diabetes participated. Baseline characteristics were similar between the groups. POMs minimized the total cost by 38.96% which translated to a cost saving of USD 42.76 ± 6.98, significantly different versus USD 0.02 ± 0.52 in the control group, p = 0.025. Mean HbA1c reduced significantly (-0.79%, p = 0.016) in the POMs group but not significant in the control group (-0.11%, p = 0.740). Medication adherence improved significantly in both groups at week-12 (p