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Background Effects of Selective Serotonin Reuptake Inhibitors (SSRI) for symptoms of autism spectrum disorder (ASD) or comorbid conditions are uncertain. We conducted a systematic review and meta-analyses to determine the efficacy and safety of SSRIs in children with ASD on restricted repetitive behaviors, obsessive-compulsive, anxiety and depressive symptoms, disruptive behaviors, global functioning, and quality of life. Methods We searched MEDLINE, EMBASE, CENTRAL, CINAHL, PsycINFO, WHO ICTRP, Clinical.Trials.gov. to retrieve RCT of SSRIs versus placebo in children with ASD that evaluated the outcomes of interest with a validated measurement instrument. Two authors independently extracted data, assessed risk of bias and rated the certainty of evidence. Results Seven RCTs with a total of 606 participants were included. The evidence is very uncertain on the effect of SSRIs on restricted-repetitive behaviors and anxiety symptoms in children with ASD, there is low certainty of the evidence suggesting that SSRIs result in little to no effect on obsessive-compulsive symptoms and disruptive behaviors and there is moderate certainty of the evidence that SSRIs likely result in little to no difference in global functioning and a slight increase in adverse events. No studies evaluated depressive symptoms. Conclusions The number of studies on this population remains limited, and there is a lack of commonly accepted outcome measures able to identify relevant changes for patients and families. Protocol registration number:PROSPERO-CRD42020169836

BACKGROUND Given suboptimal performance of Boolean searching to identify methodologically sound and clinically relevant studies in large bibliographic databases such as MEDLINE, exploring the performance of machine learning (ML) tools is warranted. OBJECTIVE Using a large internationally recognized dataset of articles tagged for methodological rigor, we trained and tested binary classification models to predict the probability of clinical research articles being of high methodologic quality to support a literature surveillance program. METHODS Over 12,000 models were trained on a dataset of 97,805 articles indexed in PubMed from 2012-2018 which were manually appraised for rigor by highly trained research associates with expertise in research methods and critical appraisal. As the dataset is unbalanced, with more articles that do not meet criteria for rigor, we used the unbalanced dataset and over- and under-sampled datasets. Models that maintained sensitivity for high rigor at 99% and maximized specificity were selected and tested in a retrospective set of 30,424 articles from 2020 and validated prospectively in a blinded study of 5253 articles. RESULTS The final selected algorithm, combining a model trained in each dataset, maintained high sensitivity and achieved 57% specificity in the retrospective validation test and 53% in the prospective study. The number of articles needed to read to find one that met appraisal criteria was 3.68 (3.52 to 3.85) in the prospective study. CONCLUSIONS ML models improved the efficiency and precision of detecting high quality clinical research publications for literature surveillance and subsequent dissemination to clinicians and other evidence users.

Objective Our aim was to develop an efficient search strategy for prognostic studies and clinical prediction guides (CPGs), optimally balancing sensitivity and precision while independent of MeSH terms, as relying on them may miss the most current literature. Materials and Methods We combined 2 Hedges-based search strategies, modified to remove MeSH terms for overall prognostic studies and CPGs, and ran the search on 269 journals. We read abstracts from a random subset of retrieved references until ≥ 20 per journal were reviewed and classified them as positive when fulfilling standardized quality criteria, thereby assembling a standard dataset used to calibrate the search strategy. We determined performance characteristics of our new search strategy against the Hedges standard and performance characteristics of published search strategies against the standard dataset. Results Our search strategy retrieved 16 089 references from 269 journals during our study period. One hundred fifty-four journals yielded ≥ 20 references and ≥ 1 prognostic study or CPG. Against the Hedges standard, the new search strategy had sensitivity/specificity/precision/accuracy of 84%/80%/2%/80%, respectively. Existing published strategies tested against our standard dataset had sensitivities of 36%–94% and precision of 5%–10%. Discussion We developed a new search strategy to identify overall prognosis studies and CPGs independent of MeSH terms. These studies are important for medical decision-making, as they identify specific populations and individuals who may benefit from interventions. Conclusion Our results may benefit literature surveillance and clinical guideline efforts, as our search strategy performs as well as published search strategies while capturing literature at the time of publication.

Knowledge about the risk for bleeding in patients with hemophilia (PWH) would be relevant for patients, stakeholders, and policy makers.To perform a systematic review of the literature on risk assessment models (RAMs) and risk factors for bleeding in PWH on regular prophylaxis.We searched Medline, Embase, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews from inception through August 2019. In duplicate, reviewers screened the articles for inclusion, extracted data, and assessed the risk for bias using the Quality in Prognostic Studies (QUIPS) tool. A qualitative synthesis of the results was not performed due to high heterogeneity in risk factors, outcomes definition and measurement, and statistical analysis of the results.From 1843 search results, 10 studies met the inclusion criteria. No RAM for the risk for bleeding in PWH was found. Most studies included only PWH A or both PWH A and B and were conducted in North America or Europe. Only one study had a low risk for bias in all the domains. Eight categories of risk factors were identified. The risk for bleeding was increased when factor levels were lower and in people with a significant history of bleeding or who engaged in physical activities involving contact.Our findings suggest that plasma factor levels, history of bleeds, and physical activity should be considered for the derivation analysis when building a RAM for bleeding in PWH, and the role of other risk factors, including antithrombotic treatment and obesity, should be explored.

BACKGROUND Numerous wrist-wearable devices to measure physical activity are currently available, but there is a need to unify the evidence on how they compare in terms of acceptability and accuracy. OBJECTIVE The aim of this study is to perform a systematic review of the literature to assess the accuracy and acceptability (willingness to use the device for the task it is designed to support) of wrist-wearable activity trackers. METHODS We searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, and SPORTDiscus for studies measuring physical activity in the general population using wrist-wearable activity trackers. We screened articles for inclusion and, for the included studies, reported data on the studies’ setting and population, outcome measured, and risk of bias. RESULTS A total of 65 articles were included in our review. Accuracy was assessed for 14 different outcomes, which can be classified in the following categories: count of specific activities (including step counts), time spent being active, intensity of physical activity (including energy expenditure), heart rate, distance, and speed. Substantial clinical heterogeneity did not allow us to perform a meta-analysis of the results. The outcomes assessed most frequently were step counts, heart rate, and energy expenditure. For step counts, the Fitbit Charge (or the Fitbit Charge HR) had a mean absolute percentage error (MAPE) 30% for all the brands, showing poor accuracy across devices. Acceptability was most frequently measured through data availability and wearing time. Data availability was ≥75% for the Fitbit Charge HR, Fitbit Flex 2, and Garmin Vivofit. The wearing time was 89% for both the GENEActiv and Nike FuelBand. CONCLUSIONS The Fitbit Charge and Fitbit Charge HR were consistently shown to have a good accuracy for step counts and the Apple Watch for measuring heart rate. None of the tested devices proved to be accurate in measuring energy expenditure. Efforts should be made to reduce the heterogeneity among studies.

Background The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. Objective The goal of the research was to summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. Methods We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. Results From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%. Conclusions Machine learning approaches perform well in retrieving high-quality clinical studies. Performance may improve by applying more sophisticated approaches such as active learning and unsupervised machine learning approaches.

BACKGROUND The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. OBJECTIVE The goal of the research was to summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. METHODS We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. RESULTS From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%. CONCLUSIONS Machine learning approaches perform well in retrieving high-quality clinical studies. Performance may improve by applying more sophisticated approaches such as active learning and unsupervised machine learning approaches.

Background A barrier to practicing evidence-based medicine is the rapidly increasing body of biomedical literature. Use of method terms to limit the search can help reduce the burden of screening articles for clinical relevance; however, such terms are limited by their partial dependence on indexing terms and usually produce low precision, especially when high sensitivity is required. Machine learning has been applied to the identification of high-quality literature with the potential to achieve high precision without sacrificing sensitivity. The use of artificial intelligence has shown promise to improve the efficiency of identifying sound evidence. Objective The primary objective of this research is to derive and validate deep learning machine models using iterations of Bidirectional Encoder Representations from Transformers (BERT) to retrieve high-quality, high-relevance evidence for clinical consideration from the biomedical literature. Methods Using the HuggingFace Transformers library, we will experiment with variations of BERT models, including BERT, BioBERT, BlueBERT, and PubMedBERT, to determine which have the best performance in article identification based on quality criteria. Our experiments will utilize a large data set of over 150,000 PubMed citations from 2012 to 2020 that have been manually labeled based on their methodological rigor for clinical use. We will evaluate and report on the performance of the classifiers in categorizing articles based on their likelihood of meeting quality criteria. We will report fine-tuning hyperparameters for each model, as well as their performance metrics, including recall (sensitivity), specificity, precision, accuracy, F-score, the number of articles that need to be read before finding one that is positive (meets criteria), and classification probability scores. Results Initial model development is underway, with further development planned for early 2022. Performance testing is expected to star in February 2022. Results will be published in 2022. Conclusions The experiments will aim to improve the precision of retrieving high-quality articles by applying a machine learning classifier to PubMed searching. International Registered Report Identifier (IRRID) DERR1-10.2196/29398

BACKGROUND A barrier to practicing evidence-based medicine is the rapidly increasing body of biomedical literature. Use of method terms to limit the search can help reduce the burden of screening articles for clinical relevance; however, such terms are limited by their partial dependence on indexing terms and usually produce low precision, especially when high sensitivity is required. Machine learning has been applied to the identification of high-quality literature with the potential to achieve high precision without sacrificing sensitivity. The use of artificial intelligence has shown promise to improve the efficiency of identifying sound evidence. OBJECTIVE The primary objective of this research is to derive and validate deep learning machine models using iterations of Bidirectional Encoder Representations from Transformers (BERT) to retrieve high-quality, high-relevance evidence for clinical consideration from the biomedical literature. METHODS Using the HuggingFace Transformers library, we will experiment with variations of BERT models, including BERT, BioBERT, BlueBERT, and PubMedBERT, to determine which have the best performance in article identification based on quality criteria. Our experiments will utilize a large data set of over 150,000 PubMed citations from 2012 to 2020 that have been manually labeled based on their methodological rigor for clinical use. We will evaluate and report on the performance of the classifiers in categorizing articles based on their likelihood of meeting quality criteria. We will report fine-tuning hyperparameters for each model, as well as their performance metrics, including recall (sensitivity), specificity, precision, accuracy, F-score, the number of articles that need to be read before finding one that is positive (meets criteria), and classification probability scores. RESULTS Initial model development is underway, with further development planned for early 2022. Performance testing is expected to star in February 2022. Results will be published in 2022. CONCLUSIONS The experiments will aim to improve the precision of retrieving high-quality articles by applying a machine learning classifier to PubMed searching. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/29398

Background: The rapid outbreak of Coronavirus diseases (COVID-19) originating in Wuhan, China, and the subsequent declaration of a state of “pandemic” on March 11, 2020 has necessitated a widespread global response to manage and control the transmission, spread and impact of COVID-19. Mobile technology has been leveraged in a number of ways to control the spread of COVID-19, including to support knowledge translation. Mobile applications are accessible, acceptable, easily adopted, and have the ability to support social distancing efforts. The following review assesses the mobile applications currently available to address COVID-19 and seeks existing studies in the literature that evaluate such applications. Methods: 3 databases (Embase, Web of Science and PubMed), 2 application stores (GooglePlay and Apple’s App Store) and google search engine were searched from inception until April 05, 2020 using key words and search strings to identify relevant apps and/or literature. Reviews of the obtained results were examined by two reviewers in double-blind nature and assessed for inclusion or exclusion. Results: 36 studies, 72 websites and 312 mobile-based applications were identified through searches. 14 applications met the inclusion criteria and were analyzed. Additional eHealth tools identified through the search strategy were considered for supplemental analysis (including online dashboards and applications not available through application stores).Conclusion: This review provides a brief assessment of the goals of applications addressing COVID-19, the types of approaches being used by these applications, and draws conclusions on the needs not being met by such application-based interventions. Innovation and collaboration between government, healthcare organizations and application developers is needed to address the identified gaps and facilitate the successful harnessing of mobile applications in the management of COVID-19.

Background The rapid outbreak of Coronavirus diseases (COVID-19) originating in Wuhan, China, and the subsequent declaration of a state of “pandemic” on March 11, 2020 has necessitated a widespread global response to manage and control the transmission, spread and impact of COVID-19. Mobile technology has been leveraged in a number of ways to control the spread of COVID-19, including to support knowledge translation. Mobile applications are accessible, acceptable, easily adopted, and have the ability to support social distancing efforts. The following review assesses the mobile applications currently available to address COVID-19 and seeks existing studies in the literature that evaluate such applications. Methods 3 databases (Embase, Web of Science and PubMed), 2 application stores (GooglePlay and Apple’s App Store) and google search engine were searched from inception until April 05th 2020 using key words and search strings to identify relevant apps and/or literature. Reviews of the obtained results were examined by two reviewers in double-blind nature and assessed for inclusion or exclusion. Results 36 studies, 72 websites and 312 mobile-based applications were identified through searches. 14 applications met the inclusion criteria and were analyzed. Additional eHealth tools identified through the search strategy were considered for supplemental analysis (including online dashboards and applications not available through application stores). Conclusion This review provides a brief assessment of the goals of applications addressing COVID-19, the types of approaches being used by these applications, and draws conclusions on the needs not being met by such application-based interventions. Innovation and collaboration between government, healthcare organizations and application developers is needed to address the identified gaps and facilitate the successful harnessing of mobile applications in the management of COVID-19.

ObjectiveTo evaluate and compare the effect of interventions for improving adherence to medications for atherosclerotic cardiovascular disease (ASCVD) secondary prevention.MethodsWe extracted eligible trials from a 2014 Cochrane systematic review on adherence for any condition. We updated the search from CENTRAL, Medline, Embase, PsycINFO, CINAHL, Sociological Abstracts and trial registers through November 2016. Study reports needed to be from a randomised controlled trial, incorporate participants identified as having ASCVD and interventions aimed at improving adherence to medicines for secondary prevention of ASCVD and measure both adherence and a clinical outcome. Two reviewers independently determined the eligibility of studies, extracted data and conducted a narrative synthesis.ResultsWe identified 17 trials (n=17 448 participants). Most trials had high risk of bias in at least one domain. The intervention group adherence rates ranged from 44%to99% and the comparator group adherence rates ranged from 13% to 96%. Three distinct interventions reported improvements in both adherence and clinical outcomes: short message service (65% vs 13% of participants with high adherence in the intervention vs control group), a fixed-dose combination pill (86% vs 65% adherence, risk ratio of being adherent, 1.33; 95% CI 1.26 to 1.41) and a community health worker-based intervention (97% in the intervention group compared with 92% in the control group; OR=2.62, 95% CI 1.32 to 5.19).ConclusionsWe identified three interventions that demonstrated improvements in adherence and clinical outcomes. Ongoing, longer-term trials will help determine whether short-term changes in adherence can be maintained and lead to differences in clinical events.

Due to the negative outcomes of medication nonadherence, interventions to improve adherence have been the focus of countless studies. The congruence between adherence-related patient characteristics and interventions may partly explain the variability of effectiveness in medication adherence studies. In their latest update of a Cochrane review reporting inconsistent effects of adherence interventions, the authors offered access to their database for subanalysis. We aimed to use this database to assess congruence between adherence-related patient characteristics and interventions and its association with intervention effects.We developed a congruence score consisting of six features related to inclusion criteria, patient characteristics at baseline, and intervention design. Two independent raters extracted and scored items from the 190 studies available in the Cochrane database. We correlated overall congruence score and individual features with intervention effects regarding adherence and clinical outcomes using Kruskal-Wallis rank sum test and Fisher's exact test.Interrater reliability for newly extracted data was almost perfect with a Cohen's Kappa of 0.92 [95% confidence interval (CI) = 0.89-0.94; P 0.001]. Although present in only six studies, the inclusion of nonadherent patients was the single feature significantly associated with effective adherence interventions (P = 0.003). Moreover, effective adherence interventions were significantly associated with improved clinical outcomes (odds ratio = 6.0; 95% CI = 3.1-12.0; P 0.0001). However, neither the overall congruence score nor any other individual feature (i.e., "determinants of nonadherence as inclusion criteria," "tailoring of interventions to the inclusion criteria," "reasons for nonadherence assessed at baseline," "adjustment of intervention to individual patient needs," and "theory-based interventions") was significantly associated with intervention effects.The presence of only six studies that included nonadherent patients and the interdependency of this feature with the remaining five might preclude a conclusive assessment of congruence between patient characteristics and adherence interventions. In order to obtain clinical benefits from effective adherence interventions, we encourage researchers to focus on the inclusion of nonadherent patients.

A retrospective analysis published by the German Institute for Quality and Efficiency in Health Care (IQWiG) in 2018 concluded that no filter for non-randomized studies (NRS) achieved sufficient sensitivity (≥92%), a precondition for comprehensive information retrieval. New NRS filters are therefore required, taking into account the challenges related to this study type. Our evaluation focused on the development of study filters for NRS with a control group ("controlled NRS"), as this study type allows the calculation of an effect size. In addition, we assumed that due to the more explicit search syntax, controlled NRS are easier to identify than non-controlled ones, potentially resulting in better performance measures of study filters for controlled NRS. Our aim was to develop study filters for identifying controlled NRS in PubMed and Ovid MEDLINE. We developed two new search filters that can assist clinicians and researchers in identifying controlled NRS in PubMed and Ovid MEDLINE. The reference set was based on 2110 publications in Medline extracted from 271 Cochrane reviews and on 4333 irrelevant references. The first filter maximizes sensitivity (92.42%; specificity 79.67%, precision 68.49%) and should be used when a comprehensive search is needed. The second filter maximizes specificity (92.06%; precision 82.98%, sensitivity 80.94%) and should be used when a more focused search is sufficient.

Patient non-adherence to prescribed anti-epileptic drugs (AEDs) remains a challenge to successful treatment of patients with epilepsy. However, the literature on epilepsy does not document a comprehensive review of interventions to improve adherence as a means to improve clinical outcomes. This study systematically reviews existing literature on interventions to enhance AED adherence and clinical outcomes, and the measures of adherence included in these studies. We selected randomized controlled trials (RCTs) of interventions to enhance adherence with AEDs, which also measured clinical outcomes, with at least 80% follow-up of participants for at least 6 months, from a comprehensive Cochrane review of adherence interventions for medications, complete to January 2013, and updated searches for additional AED studies in multiple bibliographic databases to January 2016. Two review authors independently extracted all data and a third author resolved disagreements. The present update included one trial from the Cochrane review and three RCTs published since, bringing the total number of RCTs on this topic to four. Two types of intervention were tested: educational (e.g., providing information to the patient or carer about treatment characteristics, duration, dosage regime, and how to use the AED) and behavioral (activity in order to remind the patient to take a medicine). Methods of measuring adherence included a combination of direct (plasma AED levels) and indirect measures (prescription refill frequency and appointment keeping) or use alone of self-report adherence on standardized scales. Despite the importance of the problem, evidence is limited concerning enhancement of adherence among people with epilepsy. However, the trials available to date show that medication adherence in epilepsy can be improved, leading to better seizure control.

Objectives To compare Clinical Queries (CQs) for randomized trials of therapy ‘methods’ and ‘NOT’ limits search filters with Cochrane methods filters. Study Design and Setting Analytic survey of Cochrane reviews as the reference standard for retrieving studies included in the reviews (“included studies [ISs]”). The sensitivity and precision of Cochrane content terms + Cochrane methods terms were compared in MEDLINE and Embase with Cochrane content terms + CQs maximally sensitive filter for therapy studies, without and with additional ‘NOT’ limits (CQ-S [CQ sensitive]; CQ-S + limits) and a balanced filter without and with additional NOT limits (CQ-B [CQ balanced]; CQ-B + limits). Results Cochrane or CQ methods terms reduced, by 64–96%, the overall retrieval of articles with minimal loss of ISs. Sensitivity was high and similar for the 4 filters. However, CQ-B + limits had the highest precision (2.64%, number needed to be read to find one eligible study [NNR] 38) followed by the CQ-B (1.05%, NNR 95), Cochrane search (0.51%, NNR 198), CQ-S + limits (0.34%, NNR 296), and CQ-S filters (0.31%, NNR 325). Conclusion For systematic reviews of therapeutic interventions, the efficiency of searches in MEDLINE and Embase was better served by the CQs for therapy studies with balanced methods filter and NOT limits.

Hypertension control through pharmacological treatment has led to substantial benefits in the prevention of morbidity and mortality from cardiovascular diseases. However, evidence from a number of studies suggests that as many as 50 to 80 % of patients treated for hypertension have low adherence to their treatment regimen. The objective of this systematic review is to evaluate the effectiveness of medication adherence interventions for hypertension. In addition, we aim to explore what barriers and facilitators in the interventions may have been targeted and how these might be related to the effect size on blood pressure (BP).This review is a hypertension-specific update to the previous Cochrane Review by Nieuwlaat et al. (2014) on interventions to enhance medication adherence. A systematic literature search will be carried out, and two authors will independently screen titles and abstracts for their eligibility for inclusion and independently extract data from the selected studies and assess the methodological quality using the Cochrane Collaboration Risk of Bias Tool. A meta-analysis will be conducted, and additionally, theoretical factors in interventions will be identified using the Theoretical Domains Framework.This review will generate new information by quantitatively evaluating the effectiveness of adherence interventions for hypertension and potentially identify which theoretical domains are associated with more effective interventions and which domains have not been the subject of intervention development.PROSPERO CRD42016033358.

The objective of this systematic review is to evaluate the effectiveness of medication adherence interventions on blood pressure control in hypertensive patients. In addition, we aim to explore what barriers and facilitators in the interventions may have been targeted and how these might be related to the effect size on blood pressure (BP).This review is a hypertension-specific update to the previous Cochrane Review by Nieuwlaat et al. ( 2014 ) on interventions to enhance medication adherence. A systematic literature search was carried out and two authors independently screened titles and abstracts for their eligibility for inclusion and independently extracted data from the selected studies and assessed the methodological quality using the Cochrane Collaboration Risk of Bias Tool. A meta-analysis was conducted and additionally, theoretical factors in interventions were identified using the Theoretical Domains Framework.The meta-analysis found a modest main effect of adherence interventions on SBP (MD -2.71 mm Hg, 95% CI -4.17 to -1.26) and DBP (MD -1.25 mm Hg, 95% CI -1.72 to -.79). However, there was substantial significant heterogeneity across both outcomes. A narrative review on adherence outcomes was conducted. In terms of the theoretical analysis, the relationship between the total number of times the domains were coded within an intervention and change of SBP (r = -.234, p = .335) and DBP was not significant (r = -.080, p = .732). Similarly, the relationship between the total number of times different domains were coded within an intervention and change of SBP (r = .080, p = .746) and DBP was not significant (r = -.188, p = .415).This review and meta-analysis of interventions documented significant but modest post-intervention improvements in BP outcomes among hypertensive patients. However, this is a tentative finding as substantial heterogeneity and potential biases were present. One of the greatest challenges of this review was assessing risk of bias, extracting sufficient data to calculate effect size and coding interventions with the amount of information provided in papers. It is imperative that future adherence research comprehensively reports methodology.

OBJECTIVES: To develop a scale and survey the measurement of patient adherence and patient recruitment and to explore how these methods impact the results in randomized controlled trials of interventions to improve patient adherence to medications. STUDY DESIGN: Analytic survey of a purposively selected sample of patient adherence intervention trials from a systematic review assessing the quality of adherence measurement and patient recruitment methods. RESULTS: We identified 44 different measures of adherence with qualities ranging from valid and objective to unreliable and subjective. The median overall quality of measures of adherence was 5 (interquartile range [IQR] 3; range 0 9 9 is high quality). The quality of the measures was associated with variation in the estimate of adherence (Spearman r = 0.66; 95 confidence interval: 0.39 0.83). The median overall quality of patient recruitment methods was 2 (IQR 1; maximum score 6 higher is better). There was no significant correlation between the power of the trial to detect an effect and the quality of the patient recruitment methods. CONCLUSION: Measurement and recruitment methods in adherence trials varied considerably and most methods were of low quality. Adherence research could be advanced by using higher quality measures of adherence and better selection and baseline assessment of study participants.

Background Identifying scientific literature addressing patients’ views and preferences is complex due to the wide range of studies that can be informative and the poor indexing of this evidence. Given the lack of guidance we developed a search strategy to retrieve this type of evidence. Methods We assembled an initial list of terms from several sources, including the revision of the terms and indexing of topic-related studies and, methods research literature, and other relevant projects and systematic reviews. We used the relative recall approach, evaluating the capacity of the designed search strategy for retrieving studies included in relevant systematic reviews for the topic. We implemented in practice the final version of the search strategy for conducting systematic reviews and guidelines, and calculated search’s precision and the number of references needed to read (NNR). Results We assembled an initial version of the search strategy, which had a relative recall of 87.4% (yield of 132/out of 151 studies). We then added some additional terms from the studies not initially identified, and re-tested this improved version against the studies included in a new set of systematic reviews, reaching a relative recall of 85.8% (151/out of 176 studies, 95% CI 79.9 to 90.2). This final version of the strategy includes two sets of terms related with two domains: “Patient Preferences and Decision Making” and “Health State Utilities Values”. When we used the search strategy for the development of systematic reviews and clinical guidelines we obtained low precision values (ranging from 2% to 5%), and the NNR from 20 to 50. Conclusions This search strategy fills an important research gap in this field. It will help systematic reviewers, clinical guideline developers, and policy-makers to retrieve published research on patients’ views and preferences. In turn, this will facilitate the inclusion of this critical aspect when formulating heath care decisions, including recommendations. Electronic supplementary material The online version of this article (doi:10.1186/s12955-017-0698-5) contains supplementary material, which is available to authorized users.

Background Haemophilia care is commonly provided via multidisciplinary specialized management. To date, there has been no systematic assessment of the impact of haemophilia care delivery models on patient-important outcomes. Objective To conduct a systematic review of published studies assessing the effects of the integrated care model for persons with haemophilia (PWH). Search methods We searched MEDLINE, EMBASE and CINAHL up to April 22, 2015, contacted experts in the field, and reviewed reference lists. Selection criteria Randomized and non-randomized studies of PWH or carriers, focusing mainly on the assessment of care models on delivery. Data collection and analysis Two investigators independently screened title, abstract, and full text of retrieved articles for inclusion. Risk of bias and overall quality of evidence was assessed using Cochrane's ACROBAT-NRSI tool and GRADE respectively. Relative risks, mean differences, proportions, and means and their variability were calculated as appropriate. Results 27 non-randomized studies were included: eight comparative and 19 non-comparative studies. We found low- to very low-quality evidence that in comparison to other models of care, integrated care may reduce mortality, hospitalizations and emergency room visits, may lead to fewer missed days of school and work, and may increase knowledge seeking. Conclusion Our comprehensive review found low- to very low-quality evidence from a limited number of non-randomized studies assessing the impact of haemophilia care models on some patient-important outcomes. While the available evidence suggests that adoption of the integrated care model may provide benefit to PWH, further high-quality research in the field is needed.

Background: The consistency of treatment recommendations of evidence-based medical textbooks with more recently published evidence has not been investigated to date. Inconsistencies could affect the quality of medical care. Objective: To determine the frequency with which topics in leading online evidence-based medical textbooks report treatment recommendations consistent with more recently published research evidence. Methods: Summarized treatment recommendations in 200 clinical topics (ie, disease states) covered in four evidence-based textbooks–UpToDate, Physicians’ Information Education Resource (PIER), DynaMed, and Best Practice–were compared with articles identified in an evidence rating service (McMaster Premium Literature Service, PLUS) since the date of the most recent topic updates in each textbook. Textbook treatment recommendations were compared with article results to determine if the articles provided different, new conclusions. From these findings, the proportion of topics which potentially require updating in each textbook was calculated. Results: 478 clinical topics were assessed for inclusion to find 200 topics that were addressed by all four textbooks. The proportion of topics for which there was 1 or more recently published articles found in PLUS with evidence that differed from the textbooks’ treatment recommendations was 23% (95% CI 17-29%) for DynaMed, 52% (95% CI 45-59%) for UpToDate, 55% (95% CI 48-61%) for PIER, and 60% (95% CI 53-66%) for Best Practice ( χ 2 3 =65.3, P

Background Optimization of the return on investments in information technology innovations requires that current best evidence be considered for an effect on care processes and health outcomes. Computerized clinical decisions support systems (CCDSSs) might improve therapeutic drug monitoring and dosing (TDMD) by providing patient-tailored clinical recommendations. We summarized current evidence from randomized controlled trials (RCTs) for the effect of CCDSSs on TDMD. Methods A decision-maker - researcher partnership systematic review was performed to optimize the practical implementation of results. Studies from a previous review on the effect of CCDSSs (Garg AX, 2005) were included if they addressed TDMD and were RCTs. Additional RCTs were sought until January 2010 in MEDLINE, EMBASE, Evidence-Based Medicine Reviews and Inspec databases. RCTs assessing the effect of a CCDSS on process of care or patient outcomes were selected by pairs of independent reviewers. Results In total, 33 RCTs were identified that assessed the effect of a CCDSS on management of vitamin K antagonists (14), insulin (6), theophylline/aminophylline (4), aminoglycosides (3), digoxin (2), lidocaine (1), or as part of a multifaceted approach (3). All studies combined enrolled 24,627 patients; 13,219 were in the largest study, and only 6 other studies enrolled over 500 patients. Most studies were performed in one center (63%) and cluster randomization, of either clinics or physicians, was rarely used (18%). CCDSSs were usually stand-alone systems (76%) primarily used by physicians (85%). Overall, 18 of 30 studies (60%) showed an improvement in the process of care and 4 of 19 (21%) an improvement in patient outcomes. All evaluable studies assessing insulin dosing for glycemic control showed an improvement. In meta-analysis, CCDSSs for vitamin K antagonist dosing improved the time that patients spent in the therapeutic range by 6.1% (95% confidence interval: 0.46-11.83; p=0.03). Conclusions CCDSSs have potential for improving process of care for TDMD, specifically insulin and vitamin K antagonist dosing, but effects on patient outcomes were uncertain. More potent CCDSSs are needed and should be evaluated using cluster randomization, primarily assessing patient outcomes.

Background Acute medical care often demands timely, accurate decisions in complex situations. Computerized clinical decision support systems (CCDSSs) have many features that could help. However, as for any medical intervention, claims that CCDSSs improve care processes and patient outcomes need to be rigorously assessed. The objective of this review was to systematically review the effects of CCDSSs on process of care and patient outcomes for acute medical care. Methods We conducted a decision-maker-researcher partnership systematic review. MEDLINE, EMBASE, Evidence-Based Medicine Reviews databases (Cochrane Database of Systematic Reviews, DARE, ACP Journal Club, and others), and the Inspec bibliographic database were searched to January 2010, in all languages, for randomized controlled trials (RCTs) of CCDSSs in all clinical areas. We included RCTs that evaluated the effect on process of care or patient outcomes of a CCDSS used for acute medical care compared with care provided without a CCDSS. A study was considered to have a positive effect (i.e., CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive. Results Thirty-six studies met our inclusion criteria for acute medical care. The CCDSS improved process of care in 63% (22/35) of studies, including 64% (9/14) of medication dosing assistants, 82% (9/11) of management assistants using alerts/reminders, 38% (3/8) of management assistants using guidelines/algorithms, and 67% (2/3) of diagnostic assistants. Twenty studies evaluated patient outcomes, of which three (15%) reported improvements, all of which were medication dosing assistants. Conclusion The majority of CCDSSs demonstrated improvements in process of care, but patient outcomes were less likely to be evaluated and far less likely to show positive results.

Background The use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations). Methods We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes. Results Of 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported. Conclusions A small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes.

Background Underuse and overuse of diagnostic tests have important implications for health outcomes and costs. Decision support technology purports to optimize the use of diagnostic tests in clinical practice. The objective of this review was to assess whether computerized clinical decision support systems (CCDSSs) are effective at improving ordering of tests for diagnosis, monitoring of disease, or monitoring of treatment. The outcome of interest was effect on the diagnostic test-ordering behavior of practitioners. Methods We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for eligible articles published up to January 2010. We included randomized controlled trials comparing the use of CCDSSs to usual practice or non-CCDSS controls in clinical care settings. Trials were eligible if at least one component of the CCDSS gave suggestions for ordering or performing a diagnostic procedure. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of test ordering outcomes. Results Thirty-five studies were identified, with significantly higher methodological quality in those published after the year 2000 (p = 0.002). Thirty-three trials reported evaluable data on diagnostic test ordering, and 55% (18/33) of CCDSSs improved testing behavior overall, including 83% (5/6) for diagnosis, 63% (5/8) for treatment monitoring, 35% (6/17) for disease monitoring, and 100% (3/3) for other purposes. Four of the systems explicitly attempted to reduce test ordering rates and all succeeded. Factors of particular interest to decision makers include costs, user satisfaction, and impact on workflow but were rarely investigated or reported. Conclusions Some CCDSSs can modify practitioner test-ordering behavior. To better inform development and implementation efforts, studies should describe in more detail potentially important factors such as system design, user interface, local context, implementation strategy, and evaluate impact on user satisfaction and workflow, costs, and unintended consequences.

Background Some drugs have a narrow therapeutic range and require monitoring and dose adjustments to optimize their efficacy and safety. Computerized clinical decision support systems (CCDSSs) may improve the net benefit of these drugs. The objective of this review was to determine if CCDSSs improve processes of care or patient outcomes for therapeutic drug monitoring and dosing. Methods We conducted a decision-maker-researcher partnership systematic review. Studies from our previous review were included, and new studies were sought until January 2010 in MEDLINE, EMBASE, Evidence-Based Medicine Reviews, and Inspec databases. Randomized controlled trials assessing the effect of a CCDSS on process of care or patient outcomes were selected by pairs of independent reviewers. A study was considered to have a positive effect (i.e., CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive. Results Thirty-three randomized controlled trials were identified, assessing the effect of a CCDSS on management of vitamin K antagonists (14), insulin (6), theophylline/aminophylline (4), aminoglycosides (3), digoxin (2), lidocaine (1), or as part of a multifaceted approach (3). Cluster randomization was rarely used (18%) and CCDSSs were usually stand-alone systems (76%) primarily used by physicians (85%). Overall, 18 of 30 studies (60%) showed an improvement in the process of care and 4 of 19 (21%) an improvement in patient outcomes. All evaluable studies assessing insulin dosing for glycaemic control showed an improvement. In meta-analysis, CCDSSs for vitamin K antagonist dosing significantly improved time in therapeutic range. Conclusions CCDSSs have potential for improving process of care for therapeutic drug monitoring and dosing, specifically insulin and vitamin K antagonist dosing. However, studies were small and generally of modest quality, and effects on patient outcomes were uncertain, with no convincing benefit in the largest studies. At present, no firm recommendation for specific systems can be given. More potent CCDSSs need to be developed and should be evaluated by independent researchers using cluster randomization and primarily assess patient outcomes related to drug efficacy and safety.

BACKGROUND: People who are prescribed self administered medications typically take only about half their prescribed doses. Efforts to assist patients with adherence to medications might improve the benefits of prescribed medications. OBJECTIVES: The primary objective of this review is to assess the effects of interventions intended to enhance patient adherence to prescribed medications for medical conditions, on both medication adherence and clinical outcomes. SEARCH METHODS: We updated searches of The Cochrane Library, including CENTRAL (via http://onlinelibrary.wiley.com/cochranelibrary/search/), MEDLINE, EMBASE, PsycINFO (all via Ovid), CINAHL (via EBSCO), and Sociological Abstracts (via ProQuest) on 11 January 2013 with no language restriction. We also reviewed bibliographies in articles on patient adherence, and contacted authors of relevant original and review articles. SELECTION CRITERIA: We included unconfounded RCTs of interventions to improve adherence with prescribed medications, measuring both medication adherence and clinical outcome, with at least 80% follow‐up of each group studied and, for long‐term treatments, at least six months follow‐up for studies with positive findings at earlier time points. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted all data and a third author resolved disagreements. The studies differed widely according to medical condition, patient population, intervention, measures of adherence, and clinical outcomes. Pooling results according to one of these characteristics still leaves highly heterogeneous groups, and we could not justify meta‐analysis. Instead, we conducted a qualitative analysis with a focus on the RCTs with the lowest risk of bias for study design and the primary clinical outcome. MAIN RESULTS: The present update included 109 new RCTs published since the previous update in January 2007, bringing the total number of RCTs to 182; we found five RCTs from the previous update to be ineligible and excluded them. Studies were heterogeneous for patients, medical problems, treatment regimens, adherence interventions, and adherence and clinical outcome measurements, and most had high risk of bias. The main changes in comparison with the previous update include that we now: 1) report a lack of convincing evidence also specifically among the studies with the lowest risk of bias; 2) do not try to classify studies according to intervention type any more, due to the large heterogeneity; 3) make our database available for collaboration on sub‐analyses, in acknowledgement of the need to make collective advancement in this difficult field of research. Of all 182 RCTs, 17 had the lowest risk of bias for study design features and their primary clinical outcome, 11 from the present update and six from the previous update. The RCTs at lowest risk of bias generally involved complex interventions with multiple components, trying to overcome barriers to adherence by means of tailored ongoing support from allied health professionals such as pharmacists, who often delivered intense education, counseling (including motivational interviewing or cognitive behavioral therapy by professionals) or daily treatment support (or both), and sometimes additional support from family or peers. Only five of these RCTs reported improvements in both adherence and clinical outcomes, and no common intervention characteristics were apparent. Even the most effective interventions did not lead to large improvements in adherence or clinical outcomes. AUTHORS' CONCLUSIONS: Across the body of evidence, effects were inconsistent from study to study, and only a minority of lowest risk of bias RCTs improved both adherence and clinical outcomes. Current methods of improving medication adherence for chronic health problems are mostly complex and not very effective, so that the full benefits of treatment cannot be realized. The research in this field needs advances, including improved design of feasible long‐term interventions, objective adherence measures, and sufficient study power to detect improvements in patient‐important clinical outcomes. By making our comprehensive database available for sharing we hope to contribute to achieving these advances.

Introduction: The efficacy of factor VIII and IX concentrates administered to prevent bleeding episodes in patients with hemophilia A and B is correlated with the plasma levels measured over time after the infusion. The inter-patient variability of pharmacokinetic (PK) parameters is large, and it is difficult to assess individual PK profiles due to the need for multiple time points. This is often not feasible, particularly for pediatric patients. Population PK modeling potentially provides a practical solution to this problem. The successful modelling of PK parameters at the population level requires knowledge of disposal characteristics and relevant covariates. We performed a systematic review of the available evidence in order to identify available PK data for factor VIII and IX concentrates to facilitate the implementation of a population PK approach. Methods: We conducted a literature search in MEDLINE and EMBASE from January 1997 to May 2014, using the keywords "hemophilia" and "pharmacokinetic". We included only articles that published original PK data for factor VIII and IX concentrates in humans and published in English. Two authors independently screened the studies and extracted the relevant data. Results: We retrieved 237 unique articles published between 1998 and 2013. We excluded 185 articles that did not meet our research criteria. We included 52 articles, with a total of 1365 patients included in PK analyses. 26 articles reported PK data on factor VIII concentrates, 18 articles report PK data on factor IX concentrates, and one article reported on both factor VIII and IX concentrates. Seven articles reported pharmacokinetic data on both factor VIII and Von Willebrand factor concentrates. We extracted the following data: number of patients, type and severity of hemophilia, patient age, factor concentrate infused, dose infused, sampling data points, half-life, clearance, recovery and the model used for pharmacokinetics, and inclusion of patients undergoing surgery or with inhibitors. The main results are summarized in table 1. Conclusions: This review provides the first systematic appraisal of the methods and results of published papers in the field. The data gathered confirms the intra-patient variability of factor concentrate PK and provides useful information on which to build population based PK models. *3 FIX articles and 2 FVIII articles did not report lab test; one article reported PK data for both FIX and FVIII †11 articles reported FVIII PK data for both one-stage clotting and chromogenic assays ǂPapers reporting on long-acting FVIII and FIX were included in the review, but not summarized in the table. For this reason, not all 1365 patients are accounted for in the table §Estimate of the range of the means found in the papers Disclosures Xi: Baxter: Research Funding. Navarro-Ruan:Baxter: Research Funding. Mammen:Baxter: Research Funding. Collins:Baxter: Consultancy, Honoraria, Research Funding, Speakers Bureau; CSL: Consultancy, Honoraria, Research Funding, Speakers Bureau; NovoNordisk: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bayer: Consultancy, Honoraria, Research Funding, Speakers Bureau. Neufeld:Baxter: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: data safety monitoring board, data safety monitoring board Other; Biogen IDEC: Membership on an entity's Board of Directors or advisory committees; NovoNordisk: Membership on an entity's Board of Directors or advisory committees; Pfiser: consultancy, data and safety monitoring board Other; Octapharma: Research Funding. Dunn:CSL Behring,: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees; Baxter: Membership on an entity's Board of Directors or advisory committees; Biogen: Membership on an entity's Board of Directors or advisory committees; Pfiser: Membership on an entity's Board of Directors or advisory committees. Iorio:Baxter: Honoraria, Research Funding; Bayer: Honoraria, Research Funding; NovoNordisk: Honoraria, Research Funding; Biogen: Honoraria, Research Funding; Pfiser: Honoraria, Research Funding.

Background Exhaustive search strategies (ESS) for updating clinical practice guidelines (CPGs) recommendations are laborious and expensive. Highly sensitive and specific alternative search strategies are necessary to improve the efficiency in recommendations updating. Objectives To compare alternative search strategies against ESS Methods We ran three different search strategies in a convenience sample of four CPGs from the CPGs National Programme in Spain: 1) Original ESS (gold standard); 2) Search strategy in the McMaster Premium LiteratUre Service (PLUS) database; and 3) Restrictive strategy with the least number of MeSH terms and text words from the original ESS. We retrieved the key references (which triggered an update) from the original ESS and evaluated their presence in the PLUS and restrictive strategies results. We calculated the sensitivity, specificity, precision, and accuracy for the PLUS and restrictive strategies compared to the ESS. Results The overall number of references in the PLUS strategy was lower than in the ESS (39,133 versus 2,635). The PLUS strategy retrieved a range of 1.12% to 12.1% of the total number of references retrieved by the ESS per guideline. Discussion Our project assessed two novel restrictive search strategies for the updating of CPGs, which could reduce the workload while displaying similar results. Full final findings of this project will be presented at the GIN meeting. Implications for Guideline Developers/Users Our project has important implications for updating CPGs, informing on the feasibility and efficiency of two novel search strategies.

Background Computerized clinical decision support systems (CCDSSs) are claimed to improve processes and outcomes of primary preventive care (PPC), but their effects, safety, and acceptance must be confirmed. We updated our previous systematic reviews of CCDSSs and integrated a knowledge translation approach in the process. The objective was to review randomized controlled trials (RCTs) assessing the effects of CCDSSs for PPC on process of care, patient outcomes, harms, and costs. Methods We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews Database, Inspec, and other databases, as well as reference lists through January 2010. We contacted authors to confirm data or provide additional information. We included RCTs that assessed the effect of a CCDSS for PPC on process of care and patient outcomes compared to care provided without a CCDSS. A study was considered to have a positive effect (i.e., CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive. Results We added 17 new RCTs to our 2005 review for a total of 41 studies. RCT quality improved over time. CCDSSs improved process of care in 25 of 40 (63%) RCTs. Cumulative scientifically strong evidence supports the effectiveness of CCDSSs for screening and management of dyslipidaemia in primary care. There is mixed evidence for effectiveness in screening for cancer and mental health conditions, multiple preventive care activities, vaccination, and other preventive care interventions. Fourteen (34%) trials assessed patient outcomes, and four (29%) reported improvements with the CCDSS. Most trials were not powered to evaluate patient-important outcomes. CCDSS costs and adverse events were reported in only six (15%) and two (5%) trials, respectively. Information on study duration was often missing, limiting our ability to assess sustainability of CCDSS effects. Conclusions Evidence supports the effectiveness of CCDSSs for screening and treatment of dyslipidaemia in primary care with less consistent evidence for CCDSSs used in screening for cancer and mental health-related conditions, vaccinations, and other preventive care. CCDSS effects on patient outcomes, safety, costs of care, and provider satisfaction remain poorly supported.

Background Computerized clinical decision support systems (CCDSSs) for drug therapy management are designed to promote safe and effective medication use. Evidence documenting the effectiveness of CCDSSs for improving drug therapy is necessary for informed adoption decisions. The objective of this review was to systematically review randomized controlled trials assessing the effects of CCDSSs for drug therapy management on process of care and patient outcomes. We also sought to identify system and study characteristics that predicted benefit. Methods We conducted a decision-maker-researcher partnership systematic review. We updated our earlier reviews (1998, 2005) by searching MEDLINE, EMBASE, EBM Reviews, Inspec, and other databases, and consulting reference lists through January 2010. Authors of 82% of included studies confirmed or supplemented extracted data. We included only randomized controlled trials that evaluated the effect on process of care or patient outcomes of a CCDSS for drug therapy management compared to care provided without a CCDSS. A study was considered to have a positive effect (i.e., CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive. Results Sixty-five studies met our inclusion criteria, including 41 new studies since our previous review. Methodological quality was generally high and unchanged with time. CCDSSs improved process of care performance in 37 of the 59 studies assessing this type of outcome (64%, 57% of all studies). Twenty-nine trials assessed patient outcomes, of which six trials (21%, 9% of all trials) reported improvements. Conclusions CCDSSs inconsistently improved process of care measures and seldomly improved patient outcomes. Lack of clear patient benefit and lack of data on harms and costs preclude a recommendation to adopt CCDSSs for drug therapy management.

Background Hypertension control through pharmacological treatment has led to substantial benefits in the prevention of morbidity and mortality from cardiovascular diseases. However, evidence from a number of studies suggests that as many as 50 to 80 % of patients treated for hypertension have low adherence to their treatment regimen. The objective of this systematic review is to evaluate the effectiveness of medication adherence interventions for hypertension. In addition, we aim to explore what barriers and facilitators in the interventions may have been targeted and how these might be related to the effect size on blood pressure (BP). Methods This review is a hypertension-specific update to the previous Cochrane Review by Nieuwlaat et al. (2014) on interventions to enhance medication adherence. A systematic literature search will be carried out, and two authors will independently screen titles and abstracts for their eligibility for inclusion and independently extract data from the selected studies and assess the methodological quality using the Cochrane Collaboration Risk of Bias Tool. A meta-analysis will be conducted, and additionally, theoretical factors in interventions will be identified using the Theoretical Domains Framework. Discussion This review will generate new information by quantitatively evaluating the effectiveness of adherence interventions for hypertension and potentially identify which theoretical domains are associated with more effective interventions and which domains have not been the subject of intervention development. Systematic review registration PROSPERO CRD42016033358 Electronic supplementary material The online version of this article (doi:10.1186/s13643-016-0278-5) contains supplementary material, which is available to authorized users.