METHODS: This study utilized the scoping review methodology of the Joanna Briggs Institute Reviewers' Manual 2015. Articles on pharmacist-led diabetes management focusing on the service content, delivery methods, settings, frequency of appointments, collaborative work with other healthcare providers, and reported outcomes were searched and identified from four electronic databases: Ovid Medline, PubMed, Scopus, and Web of Science from 1990 to October 2020. Relevant medical subject headings and keywords, such as "diabetes," "medication adherence," "blood glucose," "HbA1c," and "pharmacist," were used to identify published articles.
RESULTS: The systematic search retrieved 4,370 articles, of which 61 articles met the inclusion criteria. The types of intervention strategies and delivery methods were identified from the studies based on the description of activities reported in the articles and were tabulated in a summary table.
CONCLUSION: There were variations in the descriptions of intervention strategies, which could be classified into diabetes education, medication review, drug consultation/counseling, clinical intervention, lifestyle adjustment, self-care, peer support, and behavioral intervention. In addition, most studies used a combination of two or more intervention strategy categories when providing services, with no specific pattern between the service model and patient outcomes.
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
METHODS: Study within a trial of an international parallel group randomized controlled trial (RCT) that compares spironolactone to placebo. Adults receiving dialysis enter an 8-week active run-in period with spironolactone. Adherence was assessed by both self-report and pill counts in a subgroup of participants at both 3 weeks and 7 weeks.
RESULTS: 332 participants entered the run-in period of which 166 had complete data. By self-report, 146/166 (94.0%) and 153/166 (92.2%) had at least 80% adherence at 3 and 7 weeks respectively (kappa = 0.27 (95% C.I. 0.16 to 0.38). By pill counts, the mean (SD) adherence was 96.5% (16.1%) and 92.4% (18.2%) at 3 and 7 weeks respectively (r = 0.32) with a mean (SD) difference of 3.1% (17.8%) and a 95% limit of agreement from -31.7% to +37.9%. The proportion of adherent participants by self-report and pill counts at 3 weeks agreed in 87.4% of participants (McNemar's p-value 0.58, kappa 0.11, p = 0.02) and at 7 weeks agreed in 92.2% (McNemar's p-value 0.82, kappa 0.47, p
METHODS: 71 patients from 18 facilities participated in the 8-week single-arm intervention study. GRVOTS mobile apps were installed in their mobile apps, and patients were expected to fulfill tasks such as providing Video Direct Observe Therapy (VDOTS) daily as well as side effect reporting. At 3-time intervals of baseline,1-month, and 2-month intervals, the number of VDOT taken, the Malaysian Medication Adherence Assessment Tool (MyMAAT), and the Intrinsic Motivation Inventory (IMI) questionnaire were collected. One-sample t-test was conducted comparing the VDOT video adherence to the standard rate of 80%. RM ANOVA was used to analyze any significant differences in MyMAAT and IMI scores across three-time intervals.
RESULTS: This study involved 71 numbers of patients from 18 healthcare facilities who showed a significantly higher treatment adherence score of 90.87% than a standard score of 80% with a mean difference of 10.87(95% CI: 7.29,14.46; p
METHODS: Pubmed, Cochrane Central, PsycINFO, EMBASE, and WoS were searched from inception to April 2023. Randomised controlled trials (RCTs) that evaluated effects of mHealth apps on primary outcomes OAM adherence and symptom burden were included. Two reviewers independently assessed risk-of-bias using Cochrane Risk-of-Bias version 2 and extracted the data. Quality of evidence was assessed using GRADE. The protocol was registered in PROSPERO (CRD42023406024).
RESULTS: Four RCTs involving 806 patients with cancer met the eligibility criteria. mHealth apps features included a combinations of symptom reporting, medication reminder, automated alert to care team, OAM and side effect information, one study implemented structured follow-up by a nurse. The intervention group showed no significant difference in OAM adherence (relative ratio 1.20; 95% CI 1.00 to 1.43), but significantly improved symptoms to OAMs with a lower standardised mean symptom burden score of 0.49 (SMD - 0.49; 95% CI - 0.93 to - 0.06), and a 25% lower risk of grade 3 or 4 toxicity (risk ratio 0.75; 95% CI 0.58 to 0.95) compared to usual care.
CONCLUSION: These findings suggest a potential role for mHealth app in managing OAM side effect. Further research should explore the role of AI-guided algorithmic pathways on the interactive features of mHealth apps.
DESIGN: Systematic review and regression analysis.
ELIGIBILITY: Medication adherence levels studied at primary, secondary and tertiary care settings. Self-reported measures with scoring methods were included. Studies without proxy measures were excluded.
DATA SOURCES: Using detailed searches with key concepts including questionnaires, reliability and validity, and restricted to English, MEDLINE, EMBASE, CINAHL, International Pharmaceutical Abstracts, and Cochrane Library were searched until 01 March 2022. Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA-2020) checklist was used.
DATA ANALYSIS: Risk of bias was assessed via COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN-2018) guidelines. Narrative synthesis aided by graphical figures and statistical analyses.
OUTCOME MEASURES: Process domains [behaviour (e.g., self-efficacy), barrier (e.g., impaired dexterity) or belief (e.g., perception)], and overall outcome domains of either intentional (I), unintentional (UI), or mixed non-adherence.
RESULTS: Paper summarises evidence from 59 studies of PROMs, validated among patients aged 18-88 years in America, the United Kingdom, Europe, Middle East, and Australasia. PROMs detected outcome domains: intentional non-adherence, n=44 (I=491 criterion items), mixed intentionality, n=13 (I=79/UI=50), and unintentional, n=2 (UI=5). Process domains detected include belief (383 criterion items), barrier (192) and behaviour (165). Criterion validity assessment used proxy measures (biomarkers, e-monitors), and scoring was ordinal, dichotomised, or used Visual Analogue Scale. Heterogeneity was revealed across psychometric properties (consistency, construct, reliability, discrimination ability). Intentionality correlated positively with negative beliefs (r(57)=0.88) and barriers (r(57)=0.59). For every belief or barrier criterion-item, PROMs' aptitude to detect intentional non-adherence increased by β=0.79 and β=0.34 units, respectively (R2=0.94). Primary care versus specialised care predicted intentional non-adherence (OR 1.9; CI 1.01 to 2.66).
CONCLUSIONS: Ten PROMs had adequate psychometric properties. Of the ten, eight PROMs were able to detect total, and two PROMs were able to detect partial intentionality to medication default. Fortification of patients' knowledge and illness perception, as opposed to daily reminders alone, is most imperative at primary care levels.
DESIGN: Randomized clinical trial with parallel-group design guided by the CONSORT checklist.
METHODS: In this study, sixty cardiovascular inpatients were selected through convenience sampling and then randomly assigned to control and intervention groups, in 2018, Iran. The intervention group took responsibility for consuming their prescribed medication according to the self-administration of medication programme and the control group took medications routinely. Medication adherence was measured one and two weeks after the discharge via telephonic follow-up by Morisky Medication Adherence Scale MMAS-8-item and nurses' satisfaction by researcher-made questioner.
RESULT: There was a higher medication adherence level in the intervention group rather than the usual care group at the follow-up. Most nurses in the study environment were very satisfied.
CONCLUSION: The self-administration of medication programme can effectively increase patients' medication adherence and nurses' satisfaction.