The effect of electronic monitoring feedback on medication adherence and clinical outcomes: A systematic review.
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ABSTRACT:
SUBMITTER: van Heuckelum M
PROVIDER: S-EPMC5633170 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
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The effect of electronic monitoring feedback on medication adherence and clinical outcomes: A systematic review.
van Heuckelum Milou M van den Ende Cornelia H M CHM Houterman Anne E J AEJ Heemskerk Charlotte P M CPM van Dulmen Sandra S van den Bemt Bart J F BJF
PloS one 20171009 10
<h4>Objective</h4>This study aims to assess the efficacy of Electronic Monitoring Feedback (EMF) as an intervention to improve medication adherence (i.e. dose- or full adherence) and clinical outcomes in adult patients.<h4>Methods</h4>A systematic search was performed in Medline, EMBASE, PsycINFO and Web of Science and reported according to the PRISMA guidelines. Randomised controlled trials (RCTs) comparing EMF with usual care were identified to systematically summarise the evidence for use of ...[more]
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Project description:IntroductionElectronic adherence monitoring (EAM) is increasingly used to improve adherence. However, there is limited evidence on the effect of EAM in across chronic conditions and on patient acceptability. We aimed to assess the effect of EAM on adherence and clinical outcomes, across all ages and all chronic conditions, and examine acceptability in this systematic review and meta-analysis.MethodsA systematic search of Ovid MEDLINE, EMBASE, Social Work Abstracts, PsycINFO, International Pharmaceutical Abstracts and CINAHL databases was performed from database inception to December 31, 2020. Randomised controlled trials (RCTs) that evaluated the effect of EAM on medication adherence as part of an adherence intervention in chronic conditions were included. Study characteristics, differences in adherence and clinical outcomes between intervention and control were extracted from each study. Estimates were pooled using random-effects meta-analysis, and presented as mean differences, standardised mean differences (SMD) or risk ratios depending on the data. Differences by study-level characteristics were estimated using subgroup meta-analysis to identify intervention characteristics associated with improved adherence. Effects on adherence and clinical outcomes which could not be meta-analysed, and patient acceptability, were synthesised narratively. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline was followed, and Risk of bias (RoB) assessed using the Cochrane Collaboration's RoB tool for RCTs. The review is registered with PROSPERO CRD42017084231.FindingsOur search identified 365 studies, of which 47 studies involving 6194 patients were included. Data from 27 studies (n = 2584) were extracted for the adherence outcome. The intervention group (n = 1267) had significantly better adherence compared to control (n = 1317), (SMD = 0.93, CI:0.69 to 1.17, p<0.0001) with high heterogeneity across studies (I2 = 86%). There was a significant difference in effect according to intervention complexity (p = 0.01); EAM only improved adherence when used with a reminder and/or health provider support. Clinical outcomes were measured in 38/47 (81%) of studies; of these data from 14 studies were included in a meta-analysis of clinical outcomes for HIV, hypertension and asthma. In total, 13/47 (28%) studies assessed acceptability; patient perceptions were mixed.InterpretationPatients receiving an EAM intervention had significantly better adherence than those who did not, but improved adherence did not consistently translate into clinical benefits. Acceptability data were mixed. Further research measuring effects on clinical outcomes and patient acceptability are needed.
| S-EPMC8936478 | biostudies-literature
Project description:Background: In pharmacotherapy, the achievement of a target clinical outcome requires a certain level of medication intake or adherence. Based on Haynes's early empirical definition of sufficient adherence to antihypertensive medications as taking ≥80% of medication, many researchers used this threshold to distinguish adherent from non-adherent patients. However, we propose that different diseases, medications and patient's characteristics influence the cut-off point of the adherence rate above which the clinical outcome is satisfactory (thereafter medication adherence threshold). Moreover, the assessment of adherence and clinical outcomes may differ greatly and should be taken into consideration. To our knowledge, very few studies have defined adherence rates linked to clinical outcomes. We aimed at investigating medication adherence thresholds in relation to clinical outcomes. Method: We searched for studies that determined the relationship between adherence rates and clinical outcomes in the databases PubMed, EmbaseⓇ and Web of Science™ until December 2017, limited to English-language. Our outcome measure was any threshold value of adherence. The inclusion criteria of the retrieved studies were (1) any measurement of medication adherence, (2) any assessment of clinical outcomes, and (3) any method to define medication adherence thresholds in relation to clinical outcomes. We excluded articles considered as a tutorial. Two authors (PB and IA) independently screened titles and abstracts for relevance, reviewed full-texts, and extracted items. The results of the included studies are presented qualitatively. Result: We analyzed 6 articles that assessed clinical outcomes linked to adherence rates in 7 chronic disease states. Medication adherence was measured with Medication Possession Ratio (MPR, n = 3), Proportion of Days Covered (PDC, n = 1), both (n = 1), or Medication Event Monitoring System (MEMS). Clinical outcomes were event free episodes, hospitalization, cortisone use, reported symptoms and reduction of lipid levels. To find the relationship between the targeted clinical outcome and adherence rates, three studies applied logistic regression and three used survival analysis. Five studies defined adherence thresholds between 46 and 92%. One study confirmed the 80% threshold as valid to distinguish adherent from non-adherent patients. Conclusion: The analyzed studies were highly heterogeneous, predominantly concerning methods of calculating adherence. We could not compare studies quantitatively, mostly because adherence rates could not be standardized. Therefore, we cannot reject or confirm the validity of the historical 80% threshold. Nevertheless, the 80% threshold was clearly questioned as a general standard.
| S-EPMC6256123 | biostudies-literature
Project description:Glaucoma, the leading cause of irreversible blindness, affects >70 million people worldwide. Lowering intraocular pressure via topical administration of eye drops is the most common first-line therapy for glaucoma. This treatment paradigm has notoriously high non-adherence rates: ranging from 30% to 80%. The advent of smart phone enabled technologies creates promise for improving eyedrop adherence. However, previous eyedrop electronic monitoring solutions had awkward medication bottle adjuncts and crude software for monitoring the administration of a drop that adversely affected their ability to foster sustainable improvements in adherence. The current work begins to address this unmet need for wireless technology by creating a "smart drop" bottle. This medication bottle is instrumented with sensing electronics that enable detection of each eyedrop administered while maintaining the shape and size of the bottle. This is achieved by a thin electronic force sensor wrapped around the bottle and underneath the label, interfaced with a thin electronic circuit underneath the bottle that allows for detection and wireless transmission to a smart-phone application. We demonstrate 100% success rate of wireless communication over 75 feet with <1% false positive and false negative rates of single drop deliveries, thus providing a viable solution for eyedrop monitoring for glaucoma patients.
| S-EPMC7248824 | biostudies-literature
Project description:This systematic review applied meta-analytic procedures to integrate primary research that examined blood pressure outcomes of medication adherence interventions. Random-effects model analysis calculated standardized mean difference effect sizes. Exploratory dichotomous and continuous moderator analyses using meta-analytic analogues of ANOVA and regression were performed. Codable data were extracted from 156 reports with 60,876 participants. The overall weighted mean difference systolic effect size was 0.235 across 161 treatment versus control comparisons. The diastolic effect size was 0.189 from 181 comparisons. Effect sizes were significantly heterogeneous. Common risks of bias included lack of allocation concealment, unmasked data collectors, and absent intention-to-treat analyses. Exploratory moderator analyses suggested that habit-based interventions may be most effective. The largest effect sizes were for interventions delivered by pharmacists. The modest magnitude effect sizes suggest future research should explore novel higher dose interventions that might address multiple levels of influence on adherence behavior.
| S-EPMC5018410 | biostudies-literature
Project description:ObjectiveUsing meta-analytic procedures to synthesize changes in patient-centered outcomes after medication adherence interventions.MethodsStrategies to locate studies included online searches of 13 databases and 19 research registries, hand searches of 57 journals, and author and ancestry searches of all eligible studies. Search terms included patient compliance, medication adherence, and related terms. Searches were conducted for all studies published since 1960. Eligible published or unpublished primary studies tested medication adherence interventions and reported medication knowledge, quality of life, physical function, and symptom outcomes. Primary study attributes and outcome data were reliably coded. Overall standardized mean differences (SMDs) were analyzed using random-effects models. Dichotomous and continuous moderator analyses and funnel plots were used to explore risks of bias.ResultsThorough searching located 141 eligible reports. The reports included 176 eligible comparisons between treatment and control subjects across 23,318 subjects. Synthesis across all comparisons yielded statistically significant SMDs for medication knowledge (d = 0.449), quality of life (d = 0.127), physical function (d = 0.142), and symptoms (d = 0.182). The overall SMDs for studies focusing on subsamples of patients with specific illnesses were more modest but also statistically significant. Of specific symptoms analyzed (depression, anxiety, pain, energy/vitality, cardiovascular, and respiratory), only anxiety failed to show a significant improvement after medication adherence interventions. Most SMDs were significantly heterogeneous, and risk of bias analyses suggested links between study quality and SMDs.ConclusionsModest but significant improvements in patient-centered outcomes were observed after medication adherence interventions.
| S-EPMC4812829 | biostudies-literature
Project description:Excellent medication adherence contributes to decreases in morbidity, mortality, and health care costs. Although researchers have tested many interventions to increase adherence, results are sometimes conflicting and often unclear. This systematic review applied meta-analytic procedures to integrate primary research that tested medication adherence interventions. Comprehensive searching completed in 2015 located 771 published and unpublished intervention studies with adherence behavior outcomes. Random-effects model analysis calculated standardized mean difference effect sizes. Meta-analytic moderator analyses examined the association between adherence effect sizes and sample, design, and intervention characteristics. Analyses were conducted in 2016. A standardized mean difference effect size of 0.290 comparing treatment and control groups was calculated. Moderator analyses revealed larger effect sizes for habit-based and behavioral-targeted (vs. cognitive-focused) interventions. The most effective interventions were delivered face-to-face, by pharmacists, and administered directly to patients. Effect sizes were smaller in studies with older and homeless participants. Risks of bias were common; effect sizes were significantly lower among studies with masked data collectors and intention-to-treat analyses. The largest effect sizes were reported by studies using medication electronic event monitoring and pill count medication adherence measures. Publication bias was present. This most comprehensive review to date documented that, although interventions can increase adherence, much room remains for improvement. Findings suggest health care providers should focus intervention content on behavioral strategies, especially habit-based interventions, more so than cognitive strategies designed to change knowledge and beliefs.
| S-EPMC5467216 | biostudies-literature
Project description:BackgroundElectronic prescriptions are often created and delivered electronically to the pharmacy while paper-based/handwritten prescriptions may be delivered to the pharmacy by the patients. These differences in the mode of creation and transmission of the two types of prescription could influence the rate at which outpatients fill new prescriptions of previously untried medications.ObjectivesThis study aimed to evaluate literatures to determine the impact of electronic prescribing compared with paper-based/handwritten prescribing on primary medication adherence in an outpatient setting.MethodsThe keywords and phrases "outpatients," "e-prescriptions," "paper-based prescriptions," and "primary medication adherence" were combined with their relevant synonyms and medical subject headings. A comprehensive literature search was conducted on EMBASE, CINAHL, and MEDLINE databases, and Google Scholar. The results of the search were screened and selected using predefined inclusion and exclusion criteria. The Critical Appraisal Skills Program (CASP) was used for quality appraisal of included studies. Data relevant to the objective of the review were extracted and analyzed through narrative synthesis.ResultsA total of 10 original studies were included in the final review, including 1 prospective randomized study and 9 observational studies. Nine of the 10 studies were performed in the United States. Four of the studies indicated that electronic prescribing significantly increases initial medication adherence, while four of the studies suggested the opposite. The remaining two studies found no significant difference in primary medication adherence between the two methods of prescribing. The variations in the studies did not allow the homogeneity required for meta-analysis to be achieved.ConclusionThe conflicting findings relating to the efficacy of primary medication adherence across both systems demonstrate the need for a standardized measure of medication adherence. This would help further determine the respective benefits of both approaches. Future research should also be conducted in different countries to give a more accurate representation of adherence.
| S-EPMC8387129 | biostudies-literature
Project description:BackgroundElectronic health (eHealth) tools are becoming increasingly popular for helping patients' self-manage chronic conditions. Little research, however, has examined the effect of patients using eHealth tools to self-report their medication management and use. Similarly, there is little evidence showing how eHealth tools might prompt patients and health care providers to make appropriate changes to medication use.ObjectiveThe objective of this systematic review was to determine the impact of patients' use of eHealth tools on self-reporting adverse effects and symptoms that promote changes to medication use. Related secondary outcomes were also evaluated.MethodsMEDLINE, EMBASE, and CINAHL were searched from January 1, 2000, to April 25, 2018. Reference lists of relevant systematic reviews and included articles from the literature search were also screened to identify relevant studies. Title, abstract, and full-text review as well as data extraction and risk of bias assessment were performed independently by 2 reviewers. Due to high heterogeneity, results were not meta-analyzed and instead presented as a narrative synthesis.ResultsA total of 14 studies, including 13 randomized controlled trials (RCTs) and 1 open-label intervention, were included, from which 11 unique eHealth tools were identified. In addition, 14 RCTs found statistically significant increases in positive medication changes as a result of using eHealth tools, as did the single open-label study. Moreover, 8 RCTs found improvement in patient symptoms following eHealth tool use, especially in adolescent asthma patients. Furthermore, 3 RCTs showed that eHealth tools might improve patient self-efficacy and self-management of chronic disease. Little or no evidence was found to support the effectiveness of eHealth tools at improving medication recommendations and reconciliation by clinicians, medication-use behavior, health service utilization, adverse effects, quality of life, or patient satisfaction. eHealth tools with multifaceted functionalities and those allowing direct patient-provider communication may be more effective at improving patient self-management and self-efficacy.ConclusionsEvidence suggests that the use of eHealth tools may improve patient symptoms and lead to medication changes. Patients generally found eHealth tools useful in improving communication with health care providers. Moreover, health-related outcomes among frequent eHealth tool users improved in comparison with individuals who did not use eHealth tools frequently. Implementation issues such as poor patient engagement and poor clinician workflow integration were identified. More high-quality research is needed to explore how eHealth tools can be used to effectively manage use of medications to improve medication management and patient outcomes.
| S-EPMC6315271 | biostudies-literature
Project description:BackgroundMedication nonadherence leads to suboptimal treatment outcomes, making it a major priority in health care. eHealth provides an opportunity to offer medication adherence interventions with minimal effort from health care providers whose time and resources are limited.ObjectiveThe aim of this systematic review is twofold: (1) to evaluate effectiveness of recently developed and tested interactive eHealth (including mHealth) interventions on medication adherence in adult patients using long-term medication and (2) to describe strategies among effective interventions.MethodsMEDLINE, EMBASE, Cochrane Library, PsycINFO, and Web of Science were systematically searched from January 2014 to July 2019 as well as reference lists and citations of included articles. Eligible studies fulfilled the following inclusion criteria: (1) randomized controlled trial with a usual care control group; (2) a total sample size of at least 50 adult patients using long-term medication; (3) applying an interactive eHealth intervention aimed at the patient or patient's caregiver; and (4) medication adherence as primary outcome. Methodologic quality was assessed using the Cochrane risk of bias tool. Selection and quality assessment of studies were performed by 2 researchers (BP and BvdB or JV) independently. A best evidence synthesis was performed according to the Cochrane Back Review Group.ResultsOf the 9047 records screened, 22 randomized clinical trials were included reporting on 29 interventions. Most (21/29, 72%) interventions specified using a (mobile) phone for calling, SMS text messaging, or mobile apps. A majority of all interactive interventions (17/29) had a statistically significant effect on medication adherence (P<.05). Of these interventions, 9 had at least a small effect size (Cohen d ≥ 0.2) and 3 showed strong odds for becoming adherent in the intervention group (odds ratio > 2.0). Our best evidence synthesis provided strong evidence for a positive effect of interventions using SMS text messages or interactive voice response, mobile app, and calls as mode of providing adherence tele-feedback. Intervention strategies "to teach medication management skills," "to improve health care quality by coordinating medication adherence care between professionals," and "to facilitate communication or decision making between patients and health care providers" also showed strong evidence for a positive effect.ConclusionsOverall, this review supports the hypothesis that interactive eHealth interventions can be effective in improving medication adherence. Intervention strategies that improve patients' treatment involvement and their medication management skills are most promising and should be considered for implementation in practice.
| S-EPMC7822716 | biostudies-literature
Project description:BackgroundDiabetes is one of the leading noncommunicable chronic diseases globally. In people with diabetes, blood glucose levels need to be monitored regularly and managed adequately through healthy lifestyles and medications. However, various factors contribute to poor medication adherence. Smartphone apps can improve medication adherence in people with diabetes, but it is not clear which app features are most beneficial.ObjectiveThis study aims to systematically review and evaluate high-quality apps for diabetes medication adherence, which are freely available to the public in Android and Apple app stores and present the technical features of the apps.MethodsWe systematically searched Apple App Store and Google Play for apps that assist in diabetes medication adherence, using predefined selection criteria. We assessed apps using the Mobile App Rating Scale (MARS) and calculated the mean app-specific score (MASS) by taking the average of app-specific scores on 6 dimensions, namely, awareness, knowledge, attitudes, intention to change, help-seeking, and behavior change rated on a 5-point scale (1=strongly disagree and 5=strongly agree). We used the mean of the app's performance on these 6 dimensions to calculate the MASS. Apps that achieved a total MASS mean quality score greater than 4 out of 5 were considered to be of high quality in our study. We formulated a task-technology fit matrix to evaluate the apps for diabetes medication adherence.ResultsWe identified 8 high-quality apps (MASS score≥4) and presented the findings under 3 main categories: characteristics of the included apps, app features, and diabetes medication adherence. Our framework to evaluate smartphone apps in promoting diabetes medication adherence considered physiological factors influencing diabetes and app features. On evaluation, we observed that 25% of the apps promoted high adherence and another 25% of the apps promoted moderate adherence. Finally, we found that 50% of the apps provided low adherence to diabetes medication.ConclusionsOur findings show that almost half of the high-quality apps publicly available for free did not achieve high to moderate medication adherence. Our framework could have positive implications for the future design and development of apps for patients with diabetes. Additionally, apps need to be evaluated using a standardized framework, and only those promoting higher medication adherence should be prescribed for better health outcomes.
| S-EPMC9257622 | biostudies-literature