The Effectiveness of Health Information Technology on Promoting Medication Adherence in Adults with Diabetes Mellitus Type 2: A Systematic Review Protocol

The objective of this review is to identify the effectiveness of health information technology on promoting medication adherence in adults with diabetes mellitus type 2. More specifically, the review questions are: What is the effectiveness of health information technology on promoting medication adherence in adults with diabetes mellitus type 2? What is the effectiveness of health information technology for enhancing medication adherence in adults with diabetes mellitus type 2 on quality of life? What is the effectiveness of health information technology for enhancing medication adherence in adults with diabetes mellitus type 2 on hospital readmissions?


Introduction
Chronic diseases are by far, the leading cause of morbidity and mortality in the world, representing 60% of all deaths [1]. Diabetes mellitus, particularly type 2, is one of the ten most significant public health care challenges, in terms of prevalence, impact on those living with the disease and costs [2][3]. In other words, diabetes is a worsening epidemic in the worldwide that affected, in 2014, 422 million people in the world, which represent a prevalence of 8.5% among the adult population [1]. Diabetes is a group metabolic disease characterized by hyperglycemia resulting from defects in insulin secretion, action, or both [4].
According to the Centers for Disease Control and Prevention, in 2012, over 29 million people or 9.3% of the population in the United States aged 20 or older had diabetes [5]. More than 80% of Diabetes related deaths occur in developing countries [6]. Diabetes is a chronic disease requiring lifestyle adjustment, medication regimen and nursing intervention for people can live with quality of life and less complications [7]. In this systematic review our population target are adult people aged of 19 years or older with diabetes. We selected diabetes for two reasons. First, diabetes is a complex illness that requires self-care and self-management with focus on medication adherence to achieve good glycemic control and minimize mortality and morbidity [8]. Secondly, since diabetes is the leading cause of kidney failure, lower-limb amputations and new cases of blindness among adults [5], it is important to promote medication adherence among this population through the comparison of interventions with the common goal [8]. As we know chronic diseases tend to become more common with age, but, nowadays there is an increase in the prevalence rates of this condition among young adults. So, this research aims to synthesize the best scientific knowledge on this field.
In this sense, several chronic disorders including diabetes mellitus are associated with an often-complex medication regimen [9][10]. Medication self-management can be challenging for individuals and the society [11]. Non-adherence is observed, in all groups of patients, where the self-management of treatment is required [12][13], however, the adherence in patients with arthritis, cancer, human immunodeficiency virus infections and gastrointestinal disorders have the highest levels, whereas it is the lowest in patients with diabetes mellitus, pulmonary disease, and sleep disorders [14][15]. The burden of medication non-adherence is a real problem and is recognize as one of the leading public health challenges facing the whole world [16]. It has been estimated that half of chronic disease medications are not taken as prescribed on developed countries, the rates in countries in developing is less than 50% [17][18][19]. In the United States, half of the 3.2 billion prescriptions dispensed annually are not followed by patients as prescribed [20][21]; this is even lower among patients with chronic disorders [16,[22][23].
Medications adherence has been defined as the extent to which patients take medications as prescribed by their health care providers and decide on the treatment plan [12,16,24], although the lack of adherence to prescribed therapies is termed medication non-adherence [23]. Medication non-adherence is an important health problem which is related with sub-optimal clinical outcomes and increased health-care costs [10,12,25]. Even after a prescription has been filled, many patients do not take their medication as prescribed. Up to 1 year after the onset of the drug regimen, more than 50% of patients prematurely stop their medications [26][27][28]. This widespread non-adherence has serious consequences al individual and global level, with an increase of complications and costs at the health care systems that serve them [8]. As the result of deteriorating health and adverse events, non-adherence is also correlated with increased hospital admissions and health services utilization [29][30][31]. Furthermore, the archetypal non-adherent patient needs three additional medical visits per year, yielding an average increase in treatment costs of $2.000 annually [32]. It has been estimated that medication non-adherence costs US health care systems between $100-290 billion annually [21,33].
Several reasons are documented for the non-adherence to medication, and they may be intentional or unintentional. The causes of non-adherence to medication can be manifold including those which are patient related and health-care system related [30,34]. The most common reasons delivered by patients for not taking their medications are forgetfulness, other priorities, deciding to omit a dose, lack of information, and emotional reasons, as forgetfulness assumes the highest rates associated with non-adherence to medication [35].
Health information technology (HIT) presents an opportunity to increase medication adherence rates, through electronic reminders systems. The term HIT refers to the electronic systems healthcare professionals that can be used to store, share and analyze health information. The use of HITs seems to be an aid particularly for patients who are unintentionally non-adherent, like those suffering from forgetfulness [35][36]. Access to mobile technology has grown rapidly in the last two decades and is increasingly being used in the healthcare sector for a variety of purposes [37][38]. Mobile technology has seen many applications in the clinical settings including promoting medication adherence. Among examples are voice reminders, special applications ("apps"), short messaging serve or reminder messages that are automatically sent to a patient's mobile phone in the form of text messaging accessible on every model of mobile phone [39][40][41][42].
This reality is intensified because several studies have clearly demonstrated that medication non-adherence can lead patients to unwanted results and adverse events, such as increase morbidity and mortality, increase hospital admissions, decrease quality of life e increase cost [8][9][10]. The HIT can be a possibility to increase the rates of medication adherence and help patients who lives with Diabetes mellitus type 2 [36].
A preliminary search, on July 2017, of the JBI Database of Systematic Reviews and Implementation Reports, the Cochrane Database of Systematic Reviews, Prospero, CINAHL and Medline revealed that there are currently no systematic reviews (neither published nor in progress) in the effects of HIT on promoting medication adherence among patients with Diabetes.
Therefore, it is necessary to perform a systematic review to determine the effects of HIT on promoting medication adherence among patient with Diabetes mellitus type 2, which involves an intense critical analysis based on scientific evidence. However, it is consensual the need of doing more research in this field.
It is essential to understand the reason of non-adherence to achieve the change of behavior. Non-adherence to medication is a health phenomenon with a huge impact on the practice, especially in primary care, which affects a large number of the elderly [2,15]. Nowadays, we have an increase in the older age group with all their needs and, every day, nurses are face to face with them. So, it is imperative that nurses are acquainted with the best available evidence to fight this worldwide problem.

Types of Participants
This review will consider studies that include people aged 19 years and over, of any race, ethnicity, or gender with a diagnosis of Diabetes mellitus type 2, living at home.

Types of Intervention(s)
This review will consider studies that evaluate the effectiveness of health information technology on promoting medication adherence among older people with Diabetes mellitus type 2.

Types of Comparator
This review will consider studies that compare the use of health information technology versus no use of health information technology on promoting medication adherence among older people with Diabetes mellitus type 2.  et al., 1984). According to each instrument it will be possible to refer the percentage of medication adherence and then a statistical analysis. Each instrument will enable the categorization of the patient as adherent or non-adherent.  Secondary outcomes:

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Quality of life, assessed by, but not limited to, WHOQoL BREF, and Hospital readmissions;  Disease complication that can be a bias to HIT and ratio of medication adherence.

Types of Studies
The review will consider experimental study designs including randomized controlled trials, non-randomized controlled trials, quasi-experimental studies, before and after studies.

Search Strategy
The search strategy aims to find both published and unpublished studies. A three-step search strategy will be utilized in this review. An initial limited search of MEDLINE and CINAHL will be undertaken followed by an analysis of the text words contained in the title and abstract, and of the index terms used to describe article. A second search using all identified keywords and index terms will then be undertaken across all included databases. Thirdly, the reference list of all identified reports and articles will be searched for additional studies. Studies published in English, Spanish and Portuguese language from 2000 to the present will be considered for inclusion in this review. MEDLINE trend indicates that there are very few studies of health information technology in the medication adherence field prior to 2000, and the first decade of the new century has been characterized by a strong focus on improving health information technology.
The databases to be searched include: PubMed, CINAHL, SCOPUS, Cochrane Central Register of Controlled Trials and Lilacs.
The trial registers to be searched include: ClinicalTrials.gov and ISRCTN registry.
The search for unpublished studies will include: OpenGrey and RCAAP.
Initial keywords to be used will be:

Assessment of Methodological Quality
Quantitative papers selected for retrieval will be assessed by two independent reviewers for methodological validity prior to inclusion in the review using standardized critical appraisal instruments from JBI (JBI Critical Appraisal Checklist for Randomized Controlled Trials and JBI Critical Appraisal Checklist for Quasi-Experimental Studies) [43][44]. Any disagreements that arise between the reviewers will be resolved through discussion, or with a third reviewer.
All studies, regardless of their methodological quality, will undergo data extraction and synthesis (where possible).

Data Extraction
Quantitative data will be extracted from papers included in the review by two independent reviewers using the standardized data extraction tool available in JBI-SUMARI [45]. The data extracted will include specific details about the interventions, populations, study methods and outcomes of significance to the review question and specific objectives. The authors of the primary studies will be contacted to provide missing or additional data. Any disagreements that arise between the reviewers will be resolved through discussion or with a third reviewer.

Data Synthesis
Quantitative studies will, where possible be pooled in statistical meta-analysis using JBI-MAStARI to provide a statistical summary of the effectiveness of HIT on promoting medication adherence. All results will be subject to double data entry into JBI-SUMARI. Effect sizes expressed as odds ratio (for categorical data) and weighted mean differences (for continuous data) and their 95% confidence intervals will be calculated for analysis. A t-test might be useful to assess the variability and distribution of assessment scores. Heterogeneity will be assessed statistically using the standard Chi-square test and, also explored using subgroup analyses based on the age of the people enrolled in the different selected studies and type of studies. Where statistical pooling is not possible the findings will be presented in narrative form including tables and figures to aid in data presentation where appropriate.

Conflicts of Interest
There are no conflicts of interest.