Effectiveness of a pharmacist-led structured group-based intervention in improving medication adherence and glycaemic control among type 2 diabetes mellitus patients: A randomized controlled trial

Ting, Chuo Yew; Adruce, Shahren Ahmad Zaidi; Lim, Chien Joo; Jabar, Abu Hassan Alshaari Abd; Ting, Rachel Sing-Kiat; Ting, Hiram; Osman, Nor Anizah; Ngau, Esther; Talin, Benodict Apok; Muhammad, Munira Taj; Loo, Shing Chyi; Lim, Su Ee; Hassali, Mohamed Azmi

doi:10.1016/j.sapharm.2020.03.026

Cited by 13 publications

(12 citation statements)

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“…Adherence to therapy is defined as the extent to which a person’s behavior in taking medication, monitoring of indicators, and/or following a diet corresponds with agreed recommendations from a health-care provider ( García-Pérez et al, 2013 ). Adherence to the recommended therapy is associated with better glycemic control, fewer complications, risk reduction, and lower medical costs ( Egede et al, 2012 ; McAdam-Marx et al, 2014 ; Kennedy-Martin et al, 2017 ; Ting et al, 2021 ). It is reported that nonadherence to medication among patients is common ( Ting et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

et al. 2021

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Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D).Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People’s Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set.Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A ≥7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 ± 0.040, 0.859 ± 0.050, 0.889 ± 0.059, 0.832 ± 0.086, and 0.825 ± 0.092, respectively.Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

et al. 2021

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“…Typically, in adherence trials in people with diabetes, the consent rate is higher than we report [49][50][51][52].…”

Section: Trial Feasibilitymentioning

confidence: 52%

Adherence Therapy for adults with type 2 diabetes: a feasibility study of a randomised controlled trial

Alenazi

Peddle

Bressington

et al. 2022

Preprint

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BackgroundAdherence Therapy is a candidate intervention to promote consistent medication taking in people with type 2 diabetes. The aim of this study was to establish the feasibility of conducting a randomised controlled trial of Adherence Therapy in people with type 2 diabetes who were non-adherent with medication.MethodsAn open-label, single-centre, randomised controlled feasibility trial. Participants were randomly allocated to receive either eight sessions of telephone delivered Adherence Therapy or treatment as usual. Recruitment occurred during the COVID-19 pandemic. Outcome measures – adherence, beliefs about medication and HbA1c – were administered at baseline and after eight weeks or at the completion of treatment. Feasibility outcomes included the number of people initiated to participate in the trial that consented, completed study measures, finished treatment with adherence therapy, and dropped out of the trial.ResultsSeventy-eight people were screened of which 39 met eligibility criteria and were invited to take part in the trial. Thirteen people consented to participate, were enrolled in the trial and randomised. Five (71%) of the seven participants in the Adherence Therapy arm completed treatment. Baseline measures were completed by all participants. Week 8 (post-treatment) measures were completed by eight (62%) participants. Drop out may have been linked with a poor understanding of what was involved in taking part in the trial.ConclusionsIt may be feasible to conduct a full RCT of Adherence Therapy, but careful consideration should be given to developing effective recruitment strategies, consent procedures, rigorous field testing and clear support materials.Trial registrationThe trial was prospectively registered with the Australian New Zealand Clinical Trials Registry (ANZCTR), ACTRN12619000827134, on the 7th June 2019.

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“…In a study using TPB to investigate medication adherence in patients with chronic diseases, SN was the weakest predictor ( 39 ). In 2021, a randomized, controlled trial in Malaysia using the TPB to study medication compliance in patients with diabetes obtained similar results: SN were not significantly correlated with behavioral intentions ( 40 ). This study supported our findings that SN were not significantly correlated with intentions.…”

Section: Discussionmentioning

confidence: 90%

Applying the theory of planned behavior to investigate type 2 diabetes patients' intention to receive injection therapy

Hsu

Tang²,

Lin³

et al. 2023

Front. Public Health

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ObjectivesThis study applied the theory of planned behavior (TPB) in shared decision making (SDM) to understand behavioral intention in patients with type 2 diabetes with regard to injection therapy for blood sugar control.MethodsA cross sectional study was conducted. Two hundred and fifty-four patients with type 2 diabetes participated this study and were interviewed by pharmacists in different clinics. A patient decision aid (PDA) entitled “Should I receive injection therapy regarding my type 2 diabetes condition?” was developed for this study and served as interview agenda which comprised 18 items to inquire their willingness to use injection therapy and related considerations during the SDM process.ResultsThe questionnaires were revised using item analysis, exploratory factor analysis, and a criteria of Cronbach's α > 0.7. This resulted in three constructs for all questionnaires that fit the TPB model. Attitude (β = 0.432; P < 0.001) and PBC (β = 0.258; P < 0.001) were directly correlated with intention. TPB explained 35.2% of the variance in intention toward the use of injection therapy.ConclusionsAttitude and PBC toward injection therapy positively and significantly influence the patients' intention to use injection therapy.Practical implicationsThese findings identify a key association for understanding behavioral intention in patients with type 2 diabetes with regard to blood sugar control during SDM.

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Effectiveness of a pharmacist-led structured group-based intervention in improving medication adherence and glycaemic control among type 2 diabetes mellitus patients: A randomized controlled trial

Cited by 13 publications

References 47 publications

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

Adherence Therapy for adults with type 2 diabetes: a feasibility study of a randomised controlled trial

Applying the theory of planned behavior to investigate type 2 diabetes patients' intention to receive injection therapy

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